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Logo of hhspaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Circulation. Author manuscript; available in PMC 2017 April 28.
Published in final edited form as:
PMCID: PMC5408159
NIHMSID: NIHMS846247

Heart Disease and Stroke Statistics—2014 Update

A Report From the American Heart Association
WRITING GROUP MEMBERS, Alan S. Go, MD, Dariush Mozaffarian, MD, DrPH, FAHA, Véronique L. Roger, MD, MPH, FAHA, Emelia J. Benjamin, MD, ScM, FAHA, Jarett D. Berry, MD, FAHA, Michael J. Blaha, MD, MPH, Shifan Dai, MD, PhD,* Earl S. Ford, MD, MPH, FAHA,* Caroline S. Fox, MD, MPH, FAHA, Sheila Franco, MS,* Heather J. Fullerton, MD, MAS, Cathleen Gillespie, MS,* Susan M. Hailpern, DPH, MS, John A. Heit, MD, FAHA, Virginia J. Howard, PhD, FAHA, Mark D. Huffman, MD, MPH, Suzanne E. Judd, PhD, Brett M. Kissela, MD, MS, FAHA, Steven J. Kittner, MD, MPH, FAHA, Daniel T. Lackland, DrPH, MSPH, FAHA, Judith H. Lichtman, PhD, MPH, Lynda D. Lisabeth, PhD, MPH, FAHA, Rachel H. Mackey, PhD, MPH, FAHA, David J. Magid, MD, Gregory M. Marcus, MD, MAS, FAHA, Ariane Marelli, MD, MPH, David B. Matchar, MD, FAHA, Darren K. McGuire, MD, MHSc, FAHA, Emile R. Mohler, III, MD, FAHA, Claudia S. Moy, PhD, MPH, Michael E. Mussolino, PhD, FAHA, Robert W. Neumar, MD, PhD, Graham Nichol, MD, MPH, FAHA, Dilip K. Pandey, MD, PhD, FAHA, Nina P. Paynter, PhD, MHSc, Matthew J. Reeves, PhD, FAHA, Paul D. Sorlie, PhD, Joel Stein, MD, Amytis Towfighi, MD, Tanya N. Turan, MD, MSCR, FAHA, Salim S. Virani, MD, PhD, Nathan D. Wong, PhD, MPH, FAHA, Daniel Woo, MD, MS, FAHA, and Melanie B. Turner, MPH, on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Summary

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a critical resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best available national data on heart disease, stroke, and other cardiovascular disease–related morbidity and mortality and the risks, quality of care, use of medical procedures and operations, and costs associated with the management of these diseases in a single document. Indeed, since 1999, the Statistical Update has been cited >10 500 times in the literature, based on citations of all annual versions. In 2012 alone, the various Statistical Updates were cited ≈3500 times (data from Google Scholar). In recent years, the Statistical Update has undergone some major changes with the addition of new chapters and major updates across multiple areas, as well as increasing the number of ways to access and use the information assembled.

For this year’s edition, the Statistics Committee, which produces the document for the AHA, updated all of the current chapters with the most recent nationally representative data and inclusion of relevant articles from the literature over the past year. This year’s edition includes a new chapter on peripheral artery disease, as well as new data on the monitoring and benefits of cardiovascular health in the population, with additional new focus on evidence-based approaches to changing behaviors, implementation strategies, and implications of the AHA’s 2020 Impact Goals. Below are a few highlights from this year’s Update.

The 2014 Update Expands Data Coverage of the Epidemic of Poor Cardiovascular Health Behaviors and Their Antecedents and Consequences

  • Adjusted estimated population attributable fractions for cardiovascular disease (CVD) mortality were as follows1: 40.6% (95% confidence interval [CI], 24.5%–54.6%) for high blood pressure; 13.7% (95% CI, 4.8%–22.3%) for smoking; 13.2% (95% CI, 3.5%–29.2%) for poor diet; 11.9% (95% CI, 1.3%–22.3%) for insufficient physical activity; and 8.8% (95% CI, 2.1%–15.4%) for abnormal blood glucose levels.
  • Although significant progress has been made over the past 4 decades, in 2012, among Americans ≥18 years of age, 20.5% of men and 15.9% of women continued to be cigarette smokers. In 2011, 18.1% of students in grades 9 through 12 reported current cigarette use.
  • The percentage of the nonsmoking population with exposure to secondhand smoke (as measured by serum cotinine levels ≥0.05 ng/mL) declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008. More than half of children 3 to 11 years of age (53.6%) and almost half of those 12 to 19 years of age (46.5%) had detectable levels, compared with just over a third of adults 20 years of age and older (36.7%).
  • The proportion of youth (≤18 years of age) who report engaging in no regular physical activity is high, and the proportion increases with age.
  • In 2011, among adolescents in grades 9 through 12, 17.7% of girls and 10.0% of boys reported that they had not engaged in ≥60 minutes of moderate to vigorous physical activity (defined as any activity that increased heart rate or breathing rate) at least once in the previous 7 days, despite recommendations that children engage in such activity 7 days per week.
  • In 2012, 29.9% of adults reported engaging in no aerobic leisure-time physical activity.
  • In 2009 to 2010, <1% of Americans met at least 4 of 5 healthy dietary goals. Among adults aged ≥20 years, only 12.3% met recommended goals for fruits and vegetables; 18.3% met goals for fish; 0.6% met goals for sodium; 51.9% met goals for sugar-sweetened beverages; and 7.3% met goals for whole grains. These proportions were even lower in children, with only 29.4% of adolescents aged 12 to 19 years meeting goals for low sugar-sweetened beverage intake.
  • The estimated prevalence of overweight and obesity in US adults (≥20 years of age) is 154.7 million, which represented 68.2% of this group in 2010. Nearly 35% of US adults are obese (body mass index ≥30 kg/m2). Men and women of all race/ethnic groups in the population are affected by the epidemic of overweight and obesity.
  • Among children 2 to 19 years of age, 31.8% are overweight and obese (which represents 23.9 million children) and 16.9% are obese (12.7 million children). Mexican American boys and girls and African American girls are disproportionately affected. From 1971–1974 to 2007–2010, the prevalence of obesity in children 6 to 11 years of age has increased from 4.0% to 18.8%.
  • Obesity (body mass index ≥30 kg/m2) is associated with marked excess mortality in the US population. Even more notable is the excess morbidity associated with overweight and obesity in terms of risk factor development and incidence of diabetes mellitus, CVD end points (including coronary heart disease, stroke, and heart failure), and numerous other health conditions, including asthma, cancer, end-stage renal disease, degenerative joint disease, and many others.

Prevalence and Control of Cardiovascular Health Factors and Risks Remain an Issue for Many Americans

  • An estimated 31.9 million adults ≥20 years of age have total serum cholesterol levels ≥240 mg/dL, with a prevalence of 13.8%.
  • Based on 2007 to 2010 data, 33.0% of US adults ≥20 years of age have hypertension. This represents ≈78 million US adults with hypertension. The prevalence of hypertension is similar for men and women. African American adults have among the highest prevalence of hypertension (44%) in the world.
  • Among hypertensive Americans, ≈82% are aware of their condition and 75% are using antihypertensive medication, but only 53% of those with documented hypertension have their condition controlled to target levels.
  • In 2010, an estimated 19.7 million Americans had diagnosed diabetes mellitus, representing 8.3% of the adult population. An additional 8.2 million had undiagnosed diabetes mellitus, and 38.2% had prediabetes, with abnormal fasting glucose levels. African Americans, Mexican Americans, Hispanic/Latino individuals, and other ethnic minorities bear a strikingly disproportionate burden of diabetes mellitus in the United States.
  • The prevalence of diabetes mellitus is increasing dramatically over time, in parallel with the increases in prevalence of overweight and obesity.

Rates of Death Attributable to CVD Have Declined, but the Burden of Disease Remains High

  • The 2010 overall rate of death attributable to CVD was 235.5 per 100 000. The rates were 278.4 per 100 000 for white males, 369.2 per 100 000 for black males, 192.2 per 100 000 for white females, and 260.5 per 100 000 for black females.
  • From 2000 to 2010, death rates attributable to CVD declined 31.0%. In the same 10-year period, the actual number of CVD deaths per year declined by 16.7%. Yet in 2010, CVD (I00–I99; Q20–Q28) still accounted for 31.9% (787 650) of all 2 468 435 deaths, or ≈1 of every 3 deaths in the United States.
  • On the basis of 2010 death rate data, >2150 Americans die of CVD each day, an average of 1 death every 40 seconds. About 150 000 Americans who died of CVD in 2010 were <65 years of age. In 2010, 34% of deaths attributable to CVD occurred before the age of 75 years, which is before the current average life expectancy of 78.7 years.
  • Coronary heart disease alone caused ≈1 of every 6 deaths in the United States in 2010. In 2010, 379 559 Americans died of CHD. Each year, an estimated ≈620 000 Americans have a new coronary attack (defined as first hospitalized myocardial infarction or coronary heart disease death) and ≈295 000 have a recurrent attack. It is estimated that an additional 150 000 silent first myocardial infarctions occur each year. Approximately every 34 seconds, 1 American has a coronary event, and approximately every 1 minute 23 seconds, an American will die of one.
  • From 2000 to 2010, the relative rate of stroke death fell by 35.8% and the actual number of stroke deaths declined by 22.8%. Yet each year, ≈795 000 people continue to experience a new or recurrent stroke (ischemic or hemorrhagic). Approximately 610 000 of these are first events and 185 000 are recurrent stroke events. In 2010, stroke caused ≈1 of every 19 deaths in the United States. On average, every 40 seconds, someone in the United States has a stroke, and someone dies of one approximately every 4 minutes.
  • The decline in stroke mortality over the past decades, a major improvement in population health observed for both sexes and all race and age groups, has resulted from reduced stroke incidence and lower case fatality rates. The significant improvements in stroke outcomes are concurrent with cardiovascular risk factor control interventions. The hypertension control efforts initiated in the 1970s appear to have had the most substantial influence on the accelerated decline in stroke mortality, with lower blood pressure distributions in the population. Control of diabetes mellitus and high cholesterol and smoking cessation programs, particularly in combination with hypertension treatment, also appear to have contributed to the decline in stroke mortality.2
  • In 2010, 1 in 9 death certificates (279 098 deaths) in the United States mentioned heart failure. Heart failure was the underlying cause in 57 757 of those deaths in 2010. The number of any-mention deaths attributable to heart failure was approximately as high in 1995 (287 000) as it was in 2010 (279 000). Additionally, hospital discharges for heart failure remained stable from 2000 to 2010, with first-listed discharges of 1 008 000 and 1 023 000, respectively.

The 2014 Update Provides Critical Data About Cardiovascular Quality of Care, Procedure Utilization, and Costs

In light of the current national focus on healthcare utilization, costs, and quality, it is critical to monitor and understand the magnitude of healthcare delivery and costs, as well as the quality of healthcare delivery, related to CVD risk factors and conditions. The Statistical Update provides these critical data in several sections.

Quality-of-Care Metrics for CVDs

Quality data are available from the AHA’s Get With The Guidelines programs for coronary heart disease, heart failure, and resuscitation and from the American Stroke Association/AHA’s Get With The Guidelines program for acute stroke. Similar data from the Veterans Healthcare Administration, national Medicare and Medicaid data, and Acute Coronary Treatment and Intervention Outcomes Network (ACTION)–Get With The Guidelines Registry data are also reviewed. These data show impressive adherence to guideline recommendations for many, but not all, metrics of quality of care for these hospitalized patients. Data are also reviewed on screening for CVD risk factor levels and control.

Cardiovascular Procedure Use and Costs

  • The total number of inpatient cardiovascular operations and procedures increased 28%, from 5 939 000 in 2000 to 7 588 000 in 2010 (National Heart, Lung, and Blood Institute computation based on National Center for Health Statistics annual data).
  • The total direct and indirect cost of CVD and stroke in the United States for 2010 is estimated to be $315.4 billion. This figure includes health expenditures (direct costs, which include the cost of physicians and other professionals, hospital services, prescribed medications, home health care, and other medical durables) and lost productivity that results from premature mortality (indirect costs).
  • By comparison, in 2008, the estimated cost of all cancer and benign neoplasms was $201.5 billion ($77.4 billion in direct costs, and $124 billion in mortality indirect costs). CVD costs more than any other diagnostic group.

The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the Statistics Update.

This annual Statistical Update is the product of an entire year’s worth of effort by dedicated professionals, volunteer physicians and scientists, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.

  • Alan S. Go, MD
    Melanie B. Turner, MPH
    On behalf of the American Heart Association Statistics
    Committee and Stroke Statistics Subcommittee

Note: Population data used in the compilation of National Health and Nutrition Examination Survey (NHANES) prevalence estimates are for the latest year of the NHANES survey being used. Extrapolations for NHANES prevalence estimates are based on the census resident population for 2010 because this is the most recent year of NHANES data used in the Statistical Update.

1. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307:1273–1283. [PubMed]
2. Lackland DT, Roccella EJ, Deutsch A, Fornage M, George MG, Howard G, Kissela B, Kittner SJ, Lichtman JH, Lisabeth L, Schwamm LH, Smith EE, Towfighi A. on behalf of the American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Quality of Care and Outcomes and Research, and Council on Functional Genomics and Translational Biology. Factors influencing the decline in stroke mortality: a statement from the American Heart Association/ American Stroke Association. [Accessed December 5, 2013];Stroke. 2013 Dec 5; doi: 10.1161/01.str.0000437068.30550.cf. http://stroke.ahajournals.org/lookup/doi/10.1161/01.str.0000437068.30550.cf. [PubMed] [Cross Ref]

*Statistics 12.5% and 10.3% are statistically unreliable (relative standard error >30% and <50%). The statistic not shown has a relative standard error >50%.

The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.

Disclosures

Writing Group Disclosures

Writing
Group
Member
EmploymentResearch
Grant
Other
Research
Support
Speakers’ Bureau/
Honoraria
Expert
Witness
Ownership
Interest
Consultant/
Advisory Board
Other
Alan S. GoKaiser
Permanente
NoneNoneNoneNoneNoneNoneNone
Dariush
Mozaffarian
Brigham and Women’s Hospital, Harvard Medical School, and Harvard School of Public HealthNoneNoneAd hoc travel
reimbursement and/ or honoraria
for one-time scientific presentations or
reviews on diet and
cardiometabolic
diseases from Life
Sciences Research
Organization (10/12)
and Bunge (4/13) (each)*
NoneNoneAd hoc consulting
fees from
Amarin (9/13),
Omthera (9/13),
and Winston
and Strawn LLP
(9/13) (each)*;
Advisory board:
Unilever North America
Scientific Advisory Board*
Royalties from
UpToDate, for an
online chapter
on fish oil*;
Patent: Harvard
University
has filed a
provisional
patent
application
that has been
assigned to
Harvard University,
listing Dr. Mozaffarian
as a co-inventor
to the US Patent
and Trademark Office
for use of
trans-palmitoleic
acid to prevent and
treat insulin resistance,
type 2 diabetes, and
related conditions
(no compensation)*
Véronique L.
Roger
Mayo ClinicNIHNoneNoneNoneNoneNoneNone
Emelia J.
Benjamin
Boston University
School of Medicine
2R01HL092577-05;
1R01HL102214;
HHSN26820130047C
NoneNoneNoneNoneNIH, NHLBI
Outside Safety
& Monitoring
Board for the
Coronary Artery
Risk Development
in Young Adults
[CARDIA] Study*;
Honorarium,
American Heart
Association,
Associate Editor,
Circulation
None
Jarett D.
Berry
UT
Southwestern
NHLBI;
AHA
NoneMerckNoneNoneNoneNone
Michael J.
Blaha
Johns HopkinsNoneNoneNoneNoneNoneNoneNone
Shifan
Dai
Centers for
Disease Control
and Prevention
NoneNoneNoneNoneNoneNoneNone
Earl S.
Ford
Centers for
Disease Control
and Prevention
NoneNoneNoneNoneNoneNoneNone
Caroline S.
Fox
National Heart,
Lung, and Blood
Institute
NoneNoneNoneNoneNoneNoneNone
Sheila FrancoCenters for
Disease Control
and Prevention/
National Center
for Health
Statistics
NoneNoneNoneNoneNoneNoneNone
Heather J.
Fullerton
University of California,
San Francisco
NIH;
AHA
Private
Philanthropy
NoneNoneNoneNoneNone
Cathleen
Gillespie
Centers for
Disease Control a
nd Prevention
NoneNoneNoneNoneNoneNoneNone
Susan M.
Hailpern
Independent
Consultant
NoneNoneNoneNoneNoneNoneNone
John A.
Heit
Mayo ClinicNIH*NoneNoneNoneNoneDaiichi Sankyo*; Janssen Pharmaceutical*None
Virginia J.
Howard
University of Alabama
at Birmingham
NIHNoneNoneNoneNoneNoneNone
Mark D.
Huffman
Northwestern University
Feinberg School
of Medicine
National Heart, Lung,
and Blood Institute;
Eisenberg Foundation
Fogarty International
Center (travel)*;
World Heart Federation
(conference, travel,
and contract proposal
under development);
American Heart
Association (travel)*;
Cochrane Heart
Group (travel)*
NoneNoneNoneNoneNone
Suzanne E.
Judd
University of Alabama
at Birmingham
NIH; diaDexusNoneNoneNoneNonediaDexusNone
Brett M.
Kissela
University of
Cincinnati
NIHAbbVie
and Reata*
NoneNoneNoneAllergan*None
Steven J.
Kittner
University of Maryland
School of Medicine
and Veterans
Administration
Health Care System
NINDS Ischemic
Stroke Genetics
Consortium
(U01NS069208)
NoneNoneNoneNoneNoneNone
Daniel T.
Lackland
Medical University
of South Carolina
NoneNoneNoneNoneNoneNoneNone
Judith H.
Lichtman
Yale
University
AHA;
NIH
NoneNoneNoneNoneNoneNone
Lynda D.
Lisabeth
University of
Michigan
R01 NS38916;
R01 NS062675*;
R01 HL098065;
R01 NS070941
NoneNoneNoneNoneNoneNone
Rachel H.
Mackey
University of
Pittsburgh
LipoScience Inc.NoneNational Lipid
Association*
NoneNoneNoneNone
David J.
Magid
Colorado
Permanente
Medical Group
NHLBI;
NIMH*;
NIA*;
AHRQ;
PCORI;
Amgen*
NoneNoneNoneNoneNoneNone
Gregory M.
Marcus
University
of California,
San Francisco
American Heart
Association;
Gilead Sciences;
Medtronic;
SentreHeart
NoneNoneNoneNoneInCarda*None
Ariane MarelliMcGill University
Health Center
NoneNoneNoneNoneNoneNoneNone
David B. MatcharDuke University
Medical Center/
Duke-NUS Graduate
Medical School
Singapore National
Medical Research
Council (NMRC)
NoneNoneNoneNoneNoneNone
Darren K.
McGuire
UT South-western
Medical Center
NoneAstra Zeneca*;
Boehringer
Ingelheim*;
Bristol Myers Squibb*;
Daiichi Sankyo*;
Eli Lilly*;
Genentech*;
Glaxo Smith Kline*;
F. Hoffmann
LaRoche;
Merck*;
Orexigen
Therapeutic;
Takeda Pharmaceuticals
North America*
NoneTakeda
Pharmaceuticals
North America
NoneBoehringer
Ingelheim*;
Bristol
Myers Squibb*;
Genentech*;
Janssen;
F. Hoffmann
LaRoche*;
Merck*;
Sanofi
Aventis*
None
Emile R.
Mohler III
University of
Pennsylvania
GSK*;
NIH*;
Pluristem*
NoneNoneNoneCytovas;
Floxmedical
Pfizer*;
Takeda*
None
Claudia S.
Moy
National
Institutes
of Health
NoneNoneNoneNoneNoneNoneNone
Michael E.
Mussolino
National Heart,
Lung, and Blood
Institute
NoneNoneNoneNoneNoneNoneNone
Robert W. NeumarUniversity
of Michigan
Health System
NoneNoneNoneNoneNoneNoneNone
Graham NicholUniversity of
Washington
Resuscitation Outcomes
Consortium
(NIH U01 HL077863-06)
2010–2015, Co-PI;
Dynamic AED
Registry (Food and
Drug Administration,
Cardiac Science Corp.,
Philips Healthcare
Inc., Physio-Control
Inc., HealthSine Technologies
Inc., ZOLL Inc) 2012–2016,
PI*;
Velocity Pilot
Study of Ultrafast
Hypothermia in
Patients with
ST-elevation
Myocardial
Infarction
(Velomedix Inc.)
2012–2014,
National Co-PI
(Waived personal
compensation)*
Novel method
of tracking
location of
medical devices
in time and space.
(Patent pending,
assigned to
University of
Washington)*
NoneNoneNoneMedic One
Foundation Board
of Directors
(Money to Institution)*
None
Dilip K.
Pandey
University
of Illinois
at Chicago
NoneNoneNoneNoneNoneNoneNone
Nina P.
Paynter
Brigham
and Women’s
Hospital
Celera; National
Institutes
of Health
NoneNoneNoneNoneNoneNone
Matthew J.
Reeves
Michigan State
University
NoneNoneNoneNoneNoneNoneNone
Paul D.
Sorlie
National Heart
, Lung, and Blood
Institute, NIH
NoneNoneNoneNoneNoneNoneNone
Joel
Stein
Columbia
University
NoneMyomo*;
Tyromotion*
QuantiaMD*NoneNoneMyomo*None
Amytis
Towfighi
University
of Southern
California
AHA;
NIH/NINDS
NoneNoneNoneNoneNoneNone
Tanya N.
Turan
Medical University
of South
Carolina
NIH/NINDS K23 – CHIASM PINoneNoneExpert witness
in Stroke-related
medical malpractice
cases*
NoneBoehringer
Ingelheim,
BI1356/BI 10773
Trials – Clinical
Endpoint Adjudication
Committee;
Gore REDUCE
Trial-Clinical
Endpoint Adjudication
Committee*;
NIH/NINDS VERITAS
study – Clinical
Endpoint Adjudication
Committee*
None
Melanie B.
Turner
American Heart
Association
NoneNoneNoneNoneNoneNoneNone
Salim S.
Virani
Department of
Veterans Affairs,
Baylor College
of Medicine
Agency for
Health Care
Research and Quality*;
Department of
Veterans Affairs;
NIH*; Roderick D.
MacDonald Research
Foundation
NoneNoneNoneNoneNoneNone
Nathan D.
Wong
University of
California, Irvine
Bristol-Myers
Squibb;
Regeneron
NoneNoneNoneNoneGenzyme*None
Daniel
Woo
University of
Cincinnati
NoneNoneNoneNoneNoneNoneNone

This table represents the relationships of writing group members that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all members of the writing group are required to complete and submit. A relationship is considered to be “significant” if (1) the person receives $10 000 or more during any 12-month period, or 5% or more of the person’s gross income; or (2) the person owns 5% or more of the voting stock or share of the entity, or owns $10 000 or more of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.

*Modest.
Significant.

1. About These Statistics

The AHA works with the CDC’s NCHS, the NHLBI, the NINDS, and other government agencies to derive the annual statistics in this Heart Disease and Stroke Statistical Update. This chapter describes the most important sources and the types of data we use from them. For more details, see Chapter 26 of this document, the Glossary.

The surveys used are:

  • BRFSS—ongoing telephone health survey system
  • GCNKSS—stroke incidence rates and outcomes within a biracial population
  • MEPS—data on specific health services that Americans use, how frequently they use them, the cost of these services, and how the costs are paid
  • NHANES—disease and risk factor prevalence and nutrition statistics
  • NHIS—disease and risk factor prevalence
  • NHDS—hospital inpatient discharges and procedures (discharged alive, dead, or status unknown)
  • NAMCS—physician office visits
  • NHHCS—staff, services, and patients of home health and hospice agencies
  • NHAMCS—hospital outpatient and ED visits
  • Nationwide Inpatient Sample of the AHRQ—hospital inpatient discharges, procedures, and charges
  • NNHS—nursing home residents
  • National Vital Statistics System—national and state mortality data
  • WHO—mortality rates by country
  • YRBSS—health-risk behaviors in youth and young adults

Abbreviations Used in Chapter 1

AHAAmerican Heart Association
AHRQAgency for Healthcare Research and Quality
APangina pectoris
ARICAtherosclerosis Risk in Communities Study
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHSCardiovascular Health Study
CVDcardiovascular disease
DMdiabetes mellitus
EDemergency department
FHSFramingham Heart Study
GCNKSSGreater Cincinnati/Northern Kentucky Stroke Study
HDheart disease
HFheart failure
ICDInternational Classification of Diseases
ICD-9-CMInternational Classification of Diseases, Clinical Modification, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
MEPSMedical Expenditure Panel Survey
MImyocardial infarction
NAMCSNational Ambulatory Medical Care Survey
NCHSNational Center for Health Statistics
NHAMCSNational Hospital Ambulatory Medical Care Survey
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHHCSNational Home and Hospice Care Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NINDSNational Institute of Neurological Disorders and Stroke
NNHSNational Nursing Home Survey
PADperipheral artery disease
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

See Glossary (Chapter 26) for explanation of terms.

Disease Prevalence

Prevalence is an estimate of how many people have a disease at a given point or period in time. The NCHS conducts health examination and health interview surveys that provide estimates of the prevalence of diseases and risk factors. In this Update, the health interview part of the NHANES is used for the prevalence of CVDs. NHANES is used more than the NHIS because in NHANES, AP is based on the Rose Questionnaire; estimates are made regularly for HF; hypertension is based on BP measurements and interviews; and an estimate can be made for total CVD, including MI, AP, HF, stroke, and hypertension.

A major emphasis of this Statistical Update is to present the latest estimates of the number of people in the United States who have specific conditions to provide a realistic estimate of burden. Most estimates based on NHANES prevalence rates are based on data collected from 2007 to 2010 (in most cases, these are the latest published figures). These are applied to census population estimates for 2010. Differences in population estimates cannot be used to evaluate possible trends in prevalence because these estimates are based on extrapolations of rates beyond the data collection period by use of more recent census population estimates. Trends can only be evaluated by comparing prevalence rates estimated from surveys conducted in different years.

Risk Factor Prevalence

The NHANES 2007 to 2010 data are used in this Update to present estimates of the percentage of people with high lipid values, DM, overweight, and obesity. The NHIS is used for the prevalence of cigarette smoking and physical inactivity. Data for students in grades 9 through 12 are obtained from the YRBSS.

Incidence and Recurrent Attacks

An incidence rate refers to the number of new cases of a disease that develop in a population per unit of time. The unit of time for incidence is not necessarily 1 year, although we often discuss incidence in terms of 1 year. For some statistics, new and recurrent attacks or cases are combined. Our national incidence estimates for the various types of CVD are extrapolations to the US population from the FHS, the ARIC study, and the CHS, all conducted by the NHLBI, as well as the GCNKSS, which is funded by the NINDS. The rates change only when new data are available; they are not computed annually. Do not compare the incidence or the rates with those in past editions of the Heart Disease and Stroke Statistics Update (also known as the Heart and Stroke Statistical Update for editions before 2005). Doing so can lead to serious misinterpretation of time trends.

Mortality

Mortality data are generally presented according to the underlying cause of death. “Any-mention” mortality means that the condition was nominally selected as the underlying cause or was otherwise mentioned on the death certificate. For many deaths classified as attributable to CVD, selection of the single most likely underlying cause can be difficult when several major comorbidities are present, as is often the case in the elderly population. It is useful, therefore, to know the extent of mortality attributable to a given cause regardless of whether it is the underlying cause or a contributing cause (ie, its “any-mention” status). The number of deaths in 2010 with any mention of specific causes of death was tabulated by the NHLBI from the NCHS public-use electronic files on mortality.

The first set of statistics for each disease in this Update includes the number of deaths for which the disease is the underlying cause. Two exceptions are Chapter 9 (High Blood Pressure) and Chapter 19 (Cardiomyopathy and Heart Failure). High BP, or hypertension, increases the mortality risks of CVD and other diseases, and HF should be selected as an underlying cause only when the true underlying cause is not known. In this Update, hypertension and HF death rates are presented in 2 ways: (1) As nominally classified as the underlying cause and (2) as anymention mortality.

National and state mortality data presented according to the underlying cause of death were computed from the mortality tables of the NCHS World Wide Web site, the Health Data Interactive data system of the NCHS, or the CDC compressed mortality file. Any-mention numbers of deaths were tabulated from the electronic mortality files of the NCHS World Wide Web site and from Health Data Interactive.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 2010 in the computation of morbidity data. NCHS population estimates for 2010 were used in the computation of death rate data. The Census Bureau World Wide Web site1 contains these data, as well as information on the file layout.

Hospital Discharges and Ambulatory Care Visits

Estimates of the numbers of hospital discharges and numbers of procedures performed are for inpatients discharged from short-stay hospitals. Discharges include those discharged alive, dead, or with unknown status. Unless otherwise specified, discharges are listed according to the first-listed (primary) diagnosis, and procedures are listed according to all listed procedures (primary plus secondary). These estimates are from the NHDS of the NCHS unless otherwise noted. Ambulatory care visit data include patient visits to physician offices and hospital outpatient departments and EDs. Ambulatory care visit data reflect the first-listed (primary) diagnosis. These estimates are from NAMCS and NHAMCS of the NCHS.

International Classification of Diseases

Morbidity (illness) and mortality (death) data in the United States have a standard classification system: the ICD. Approximately every 10 to 20 years, the ICD codes are revised to reflect changes over time in medical technology, diagnosis, or terminology. Where necessary for comparability of mortality trends across the 9th and 10th ICD revisions, comparability ratios computed by the NCHS are applied as noted.2 Effective with mortality data for 1999, we are using the 10th revision (ICD-10). It will be a few more years before the 10th revision is systematically used for hospital discharge data and ambulatory care visit data, which are based on ICD-9-CM.3

Age Adjustment

Prevalence and mortality estimates for the United States or individual states comparing demographic groups or estimates over time either are age specific or are age adjusted to the 2000 standard population by the direct method.4 International mortality data are age adjusted to the European standard.5 Unless otherwise stated, all death rates in this publication are age adjusted and are deaths per 100 000 population.

Data Years for National Estimates

In this Update, we estimate the annual number of new (incidence) and recurrent cases of a disease in the United States by extrapolating to the US population in 2010 from rates reported in a community- or hospital-based study or multiple studies. Age-adjusted incidence rates by sex and race are also given in this report as observed in the study or studies. For US mortality, most numbers and rates are for 2010. For disease and risk factor prevalence, most rates in this report are calculated from the 2007 to 2010 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US population in 2010, recognizing that this probably underestimates the total prevalence, given the relatively high prevalence in the institutionalized population. The numbers and rates of hospital inpatient discharges for the United States are for 2010. Numbers of visits to physician offices, hospital EDs, and hospital outpatient departments are for 2010. Except as noted, economic cost estimates are for 2010.

Cardiovascular Disease

For data on hospitalizations, physician office visits, and mortality, CVD is defined according to ICD codes given in Chapter 26 of the present document. This definition includes all diseases of the circulatory system, as well as congenital CVD. Unless so specified, an estimate for total CVD does not include congenital CVD. Prevalence of CVD includes people with hypertension, HD, stroke, PAD, and diseases of the veins.

Race

Data published by governmental agencies for some racial groups are considered unreliable because of the small sample size in the studies. Because we try to provide data for as many racial groups as possible, we show these data for informational and comparative purposes.

Contacts

If you have questions about statistics or any points made in this Update, please contact the AHA National Center, Office of Science & Medicine at gro.traeh@scitsitats. Direct all media inquiries to News Media Relations at gro.traeh@seiriuqni or 214-706-1173.

We do our utmost to ensure that this Update is error free. If we discover errors after publication, we will provide corrections at our World Wide Web site, http://www.heart.org/statistics, and in the journal Circulation.

1. US Census Bureau population estimates. Historical data: 2000s. [Accessed October 29, 2012];US Census Bureau Web site. http://www.census.gov/popest/data/historical/2000s/index.html.
2. National Center for Health Statistics. Health, United States, 2009, With Special Feature on Medical Technology. Hyattsville, MD: National Center for Health Statistics; 2010. [Accessed October 29, 2012]. http://www.cdc.gov/nchs/data/hus/hus09.pdf.
3. National Center for Health Statistics, Centers for Medicare and Medicaid Services. [Accessed October 29, 2012];ICD-9-CM Official Guidelines for Coding and Reporting. 2011 http://www.cdc.gov/nchs/data/icd9/icd9cm_guidelines_2011.pdf.
4. Anderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep. 1998;47:1–16. 20. [PubMed]
5. World Health Organization. World Health Statistics Annual. Geneva, Switzerland: World Health Organization; 1998.

2. Cardiovascular Health

See Tables 2-1 through 2-8 and Charts 2-1 through 2-13.

Chart 2-1
Prevalence (unadjusted) estimates for poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US children aged 12 to 19 years, National Health and Nutrition ...
Chart 2-13
Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors. Reprinted from Folsom et al7 with permission from Elsevier. Copyright © 2011, American College of Cardiology Foundation.
Table 2-1
Definitions of Poor, Intermediate, and Ideal Cardiovascular Health for Each Metric in the AHA 2020 Goals
Table 2-8
AHA Advocacy and Policy Strategies Related to the 2020 Impact Goals for Ideal Cardiovascular Health

After achieving its major Impact Goals for 2010, the AHA created a new set of central organizational Impact Goals for the current decade1:

By 2020, to improve the cardiovascular health of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.1

These goals introduce a new concept, cardiovascular health, which is characterized by 7 health metrics. Ideal cardiovascular health is defined by the absence of clinically manifest CVD together with the simultaneous presence of optimal levels of all 7 metrics, including 4 health behaviors (not smoking and having sufficient PA, a healthy diet pattern, and appropriate energy balance as represented by normal body weight) and 3 health factors (optimal total cholesterol, BP, and fasting blood glucose, in the absence of drug treatment; Table 2-1). Because a spectrum of cardiovascular health can also be envisioned and the ideal cardiovascular health profile is known to be rare in the US population, a broader spectrum of cardiovascular health can also be represented as being “ideal,” “intermediate,” or “poor” for each of the health behaviors and health factors.1 Table 2-1 provides the specific definitions for ideal, intermediate, and poor cardiovascular health for each of the 7 metrics, both for adults (≥20 years of age) and children (age ranges for each metric depending on data availability).

Abbreviations Used in Chapter 2

AHAAmerican Heart Association
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CVDcardiovascular disease
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
FDAFood and Drug Administration
HbA1chemoglobin A1c
HBPhigh blood pressure
HDheart disease
HFheart failure
HRhazard ratio
ICDInternational Classification of Diseases
ICD-10International Classification of Diseases, 10th Revision
MImyocardial infarction
NHANESNational Health and Nutrition Examination Survey
NOMASNorthern Manhattan Study
PAphysical activity
PEphysical education
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEstandard error
UNUnited Nations
WHOWorld Health Organization

This concept of cardiovascular health represents a new focus for the AHA, with 3 central and novel emphases:

  • An expanded focus on CVD prevention and promotion of positive “cardiovascular health,” in addition to the treatment of established CVD.
  • Efforts to promote both healthy behaviors (healthy diet pattern, appropriate energy intake, PA, and nonsmoking) and healthy biomarker levels (optimal blood lipids, BP, glucose levels) throughout the lifespan.
  • Population-level health promotion strategies to shift the majority of the public towards greater cardiovascular health, in addition to targeting those individuals at greatest CVD risk, since healthy lifestyles in all domains are uncommon throughout the US population.

Beginning in 2011, and recognizing the time lag in the nationally representative US data sets, this chapter in the annual Statistical Update evaluates and publishes metrics and information to provide insights into both progress toward meeting the 2020 AHA goals and areas that require greater attention to meet these goals.

Cardiovascular Health: Current Prevalence

  • The most up-to-date data on national prevalence of ideal, intermediate, and poor levels of each of the 7 cardiovascular health metrics are shown for adolescents and teens 12 to 19 years of age (Chart 2-1) and for adults ≥20 years of age (Chart 2-2).
    Chart 2-2
    Age-standardized prevalence estimates for poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US adults aged ≥20 years, National Health and Nutrition ...
  • For most metrics, the prevalence of ideal levels of health behaviors and health factors is higher in US children than in US adults. Major exceptions are diet and PA, for which prevalence of ideal levels in children is similar to (for PA) or worse (for diet) than in adults.
  • Among children (Chart 2-1), the prevalence (unadjusted) of ideal levels of cardiovascular health behaviors and factors currently varies from <1% for the healthy diet pattern (ie, <1 in 100 US children meets at least 4 of the 5 dietary components) to >80% for the smoking, BP, and fasting glucose metrics.
  • Among US adults (Chart 2-2), the age-standardized prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from 0.5% for having at least 4 of 5 components of the healthy diet pattern to up to 76% for never having smoked or being a former smoker who has quit for >12 months.
  • Age-standardized and age-specific prevalence estimates for ideal cardiovascular health and for ideal levels of each of its components are shown for 2007 to 2008 (baseline) and 2009 to 2010 in Table 2-2.
    Table 2-2
    Prevalence of Ideal Cardiovascular Health and its Components in the US Population, Overall and in Selected Age Strata From NHANES 2007 to 2008 and 2009 to 2010
    • In 2009 to 2010, the prevalence of ideal levels across 7 health factors and health behaviors decreased dramatically from younger to older age groups. The same trend was seen in 2007 to 2008.
    • The prevalence of both children and adults meeting the dietary goals appeared to improve between 2007 to 2008 and 2009 to 2010, although this improvement should be viewed with caution given the challenges of accurately determining time trends across only 2 cycles of NHANES data collection. The improvement was attributable to the greater numbers of children and adults who met the whole grains goal, greater numbers of middle-aged and older adults who met the fruits and vegetables goal, and greater numbers of adults who met the fish goal.
  • Chart 2-3 displays the prevalence estimates for the population of US children (12–19 years of age) meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2009 to 2010.
    Chart 2-3
    Proportion (unadjusted) of US children aged 12 to 19 years meeting different numbers of criteria for ideal cardiovascular health, overall and by sex, National Health and Nutrition Examination Survey 2009 to 2010.
    • Few US children (<7%) meet only 0, 1, or 2 criteria for ideal cardiovascular health.
    • Nearly half of US children (45%) meet 3 or 4 criteria for ideal cardiovascular health, and about half meet 5 or 6 criteria (mostly 5 criteria).
    • Virtually no children meet all 7 criteria for ideal cardiovascular health.
    • Overall distributions are similar in boys and girls.
  • Charts 2-4 and 2-5 display the age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2009 to 2010, overall and stratified by age, sex, and race.
    Chart 2-4
    Age-standardized prevalence estimates of US adults aged ≥20 years meeting different numbers of criteria for ideal cardiovascular health, overall and by age and sex subgroups, National Health and Nutrition Examination Survey 2009 to 2010.
    Chart 2-5
    Age-standardized prevalence estimates of US adults aged ≥20 years meeting different numbers of criteria for ideal cardiovascular health, overall and in selected race subgroups from National Health and Nutrition Examination Survey 2009 to 2010. ...
    • Approximately 2% of US adults have 0 of the 7 criteria at ideal levels, and another 12% meet only 1 of 7 criteria. This is much worse than among children.
    • Most US adults (≥65%) have 2, 3, or 4 criteria at ideal cardiovascular health, with ≈1 in 5 adults within each of these categories.
    • Approximately 13% of US adults meet 5 criteria, 4% meet 6 criteria, and 0.1% meet 7 criteria at ideal levels.
    • Presence of ideal cardiovascular health is both age and sex related (Chart 2-4). Younger adults are more likely to meet greater numbers of ideal metrics than are older adults. More than 60% of Americans >60 years of age have ≤2 metrics at ideal levels. At any age, women tend to have more metrics at ideal levels than do men.
    • Race is also related to presence of ideal cardiovascular health (Chart 2-5). Blacks and Mexican Americans tend to have fewer metrics at ideal levels than whites or other races. Approximately 6 in 10 white adults and 7 in 10 black or Mexican American adults have no more than 3 of 7 metrics at ideal levels.
  • Chart 2-6 displays the age-standardized percentages of US adults and percentages of children who have ≥5 of the metrics (out of 7 possible) at ideal levels.
    Chart 2-6
    Prevalence estimates of meeting ≥5 criteria for ideal cardiovascular health among US adults aged ≥20 years (age standardized), overall and by sex and race, and US children aged 12 to 19 years (unadjusted), by sex, National Health and Nutrition ...
    • Approximately 50% of US children 12 to 19 years of age have ≥5 metrics at ideal levels, with lower prevalence in girls (46%) than in boys (51%).
    • In comparison, only 17% of US adults have ≥5 metrics with ideal levels, with lower prevalence in men (11%) than in women (24%).
    • Among adults, whites are more likely to have ≥5 metrics at ideal levels (19%) than are Mexican Americans (12%) or blacks (10%).
  • Chart 2-7 displays the age-standardized percentages of US adults meeting different numbers of criteria for both poor and ideal cardiovascular health. Meeting the AHA 2020 Strategic Impact Goals is predicated on reducing the relative percentage of those with poor levels while increasing the relative percentage of those with ideal levels for each of the 7 metrics.
    Chart 2-7
    Age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal and poor cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US adults aged ≥20 ...
    • Approximately 92% of US adults have ≥1 metric at poor levels.
    • Approximately 35% of US adults have ≥3 metrics at poor levels.
    • Few US adults (<3%) have ≥5 metrics at poor levels.
    • More US adults have 4 to 6 ideal metrics than 4 to 6 poor metrics.
  • Using data from the BRFSS, Fang and colleagues2 estimated the prevalence of ideal cardiovascular health by state, which ranged from 1.2% (Oklahoma) to 6.9% (District of Columbia). Southern states tended to have higher rates of poor cardiovascular health, lower rates of ideal cardiovascular health, and lower mean cardiovascular health scores than New England and Western states (Chart 2-8).
    Chart 2-8
    Age-standardized cardiovascular health status by US states, Behavioral Risk Factor Surveillance System, 2009. A, Age-standardized prevalence of population with ideal cardiovascular health by states. B, Age-standardized percentage of population with 0 ...
  • The prevalence of poor health behaviors and health factors and their awareness, treatment, and control are displayed in Table 2-3 separately for those with and without self-reported CVD.
    Table 2-3
    Selected Secondary Metrics for Monitoring CVD, NHANES 2009 to 2010
    • Americans with CVD are much more likely to be current or former smokers than Americans without CVD.
    • Approximately 20% of US adults are current smokers or have quit recently (<12 months ago).
    • As measured by self-reported data, Americans with CVD are very likely to have intermediate or poor levels of PA (74.1%), whereas Americans without CVD still commonly have such levels (58.4%). Furthermore, 64.5% of those with CVD and 47.3% of those without CVD report engaging in no moderate or vigorous activity at all.
    • Seventy percent of US adults with CVD and 79% of those without CVD meet 0 or only 1 of the 5 healthy diet metrics.
    • Two thirds of US adults are overweight, with little difference by prevalent CVD. Half of all US adults with CVD and one third without CVD are obese.
    • Hypertension is present in 28.5% of US adults without CVD and 51.0% of US adults with CVD. Of these, nearly all with CVD are aware of their hypertension (98.6%) and are receiving treatment (97.4%), but a much smaller proportion of those without CVD are aware (70.6%) or receiving treatment (61.4%).
    • Both presence of hypercholesterolemia (total cholesterol ≥240 mg/dL or receiving medication) and DM (fasting glucose ≥126 mg/dL or receiving medications) and awareness and treatment of these conditions are similarly higher among those with CVD than among those without CVD.

Cardiovascular Health: Trends Over Time

  • The trends over the past decade in each of the 7 cardiovascular health metrics (for diet, trends from 2005–2006 to 2009–2010) are shown in Chart 2-9 (for children 12–19 years of age) and Chart 2-10 (for adults ≥20 years of age).
    Chart 2-9
    Trends in prevalence (unadjusted) of meeting criteria for ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US children aged 12 to 19 years, National Health and Nutrition ...
    Chart 2-10
    Age-standardized trends in prevalence of meeting criteria for ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US adults aged ≥20 years, National Health and Nutrition ...
    • Fewer children over time are meeting the BMI metric, whereas more are meeting the smoking and total cholesterol metrics. Other metrics do not show consistent trends over time in children.
    • More adults over time are meeting the smoking metric, whereas fewer are meeting the BMI and glucose metrics. Trends for other metrics are not evident over time in adults.
  • On the basis of NHANES data from 1988 to 2008, if current trends continue, estimated cardiovascular health is projected to improve by 6% between 2010 and 2020, short of the AHA’s goal of 20% improvement (Chart 2-11).3 On the basis of current trends among individual metrics, anticipated declines in prevalence of smoking, high cholesterol, and high BP (in men) would be offset by substantial increases in the prevalence of obesity and DM and small changes in ideal dietary patterns or PA.3
    Chart 2-11
    Prevalence of ideal, intermediate, and poor cardiovascular health metrics in 2006 (American Heart Association 2020 Impact Goals baseline year) and 2020 projections assuming current trends continue. The 2020 targets for each cardiovascular health metric ...
  • On the basis of these projections in cardiovascular health factors and behaviors, CHD deaths are projected to decrease by 30% between 2010 and 2020 because of projected improvements in total cholesterol, SBP, smoking and PA (≈167 000 fewer deaths), offset by increases in DM and BMI (≈24 000 more deaths).4

Cardiovascular Diseases

  • In 2010, the age-standardized death rate attributable to all CVD was 236.6 per 100 000 (includes congenital CVD [ICD-10 I00-I99, Q20-Q28]; Chart 2-12), down 8.8% from 259.4 per 100 000 in 2007 (baseline data for the 2020 Impact Goals on CVD and stroke mortality).5
    Chart 2-12
    US age-standardized death rates* attributable to CVD, 2000 to 2010. *Directly standardized to the age distribution of the 2000 US standard population. †Total CVD: International Classification of Diseases, 10th Revision (ICD-10) I00 to I99 and ...
    • Death rates in 2010 attributable to stroke, CHD, and other CVDs were 39.1, 113.6, and 82.7 per 100 000, respectively.5
  • Data from NHANES 2009 to 2010 reveal that overall, 7.2% of Americans self-reported having some type of CVD (Table 2-3), including 3.2% with CHD, 2.7% with stroke, and 2.0% with CHF (some individuals reported >1 condition).

Relevance of Ideal Cardiovascular Health

Since the AHA announced its 2020 Impact Goals, multiple investigations have confirmed the importance of these metrics of cardiovascular health. Overall, these data demonstrate the relevance of the concept of cardiovascular health to the risk of future risk factors, disease, and mortality, including a strong inverse, stepwise association with all-cause, CVD, and ischemic HD mortality.

  • Bambs et al,6 Folsom et al,7 and Dong et al8 have all described the low prevalence (<1%) of ideal cardiovascular health, defined as being in the ideal category of all 7 AHA metrics in the Heart Strategies Concentrating on Risk Evaluation, ARIC, and NOMAS cohorts, respectively.
  • In ARIC and NOMAS, a stepwise inverse association was present between the number of ideal health metrics and incident CVD events (including CHD death, nonfatal MI, stroke, and HF) during 20 and 11 years of follow-up, respectively.7,8 For ARIC participants with 0, 1, 2, 3, 4, 5, 6, and 7 metrics at ideal levels, the age-, sex-, and race-adjusted rates of incident CVD incidence were 3.21, 2.19, 1.60, 1.20, 0.86, 0.64, 0.39, and 0 per 100 person-years, respectively.7 Findings were similar in the Aerobics Center Longitudinal Study, in which individuals with 6 to 7 ideal metrics had a 63% lower risk of CVD death (HR [95% CI], 0.37 [0.15, 0.95]) compared with individuals with 0 to 2 ideal metrics.9
  • A similar stepwise association was present between the number of ideal cardiovascular health metrics and risk of all-cause mortality, CVD mortality, and ischemic HD mortality after 14.5 years of follow-up based on NHANES 1988 to 2006 data.10 The HRs for individuals with 6 or 7 ideal health metrics compared with individuals with 0 ideal health metrics were 0.49 (95% CI, 0.33–0.74) for all-cause mortality, 0.24 (95% CI, 0.13–0.47) for CVD mortality, and 0.30 (95% CI, 0.13–0.68) for ischemic HD mortality.10 Ford et al11 demonstrated similar relationships.
  • The adjusted population attributable fractions for CVD mortality were as follows10:
    • 40.6% (95% CI, 24.5%–54.6%) for HBP
    • 13.7% (95% CI, 4.8%–22.3%) for smoking
    • 13.2% (95% CI, 3.5%–29.2%) for poor diet
    • 11.9% (95% CI, 1.3%–22.3%) for insufficient PA
    • 8.8% (95% CI, 2.1%–15.4%) for abnormal glucose levels
  • The adjusted population attributable fractions for ischemic HD mortality were as follows10:
    • 34.7% (95% CI, 6.6%–57.7%) for HBP
    • 16.7% (95% CI, 6.4%–26.6%) for smoking
    • 20.6% (95% CI, 1.2%–38.6%) for poor diet
    • 7.8% (95% CI, 0%–22.2%) for insufficient PA
    • 7.5% (95% CI, 3.0%–14.7%) for abnormal glucose levels
  • Data from the Cardiovascular Lifetime Risk Pooling Project indicate that adults with all-optimal risk factor levels (similar to having ideal cardiovascular health factor levels of cholesterol, blood sugar, and BP, as well as nonsmoking status) have substantially longer overall and CVD-free survival than those who have poor levels of ≥1 of these cardiovascular health factor metrics. For example, at an index age of 45 years, men with optimal risk factor profiles lived on average 14 years longer free of all CVD events, and ≈12 years longer overall, than individuals with ≥2 risk factors.12
  • Importantly, in many of these analyses, ideal health behaviors and ideal health factors were each independently associated with lower CVD risk in a stepwise fashion (Chart 2-13). Thus, across any levels of health behaviors, health factors were still associated with incident CVD, and across any levels of health factors, health behaviors were still associated with incident CVD.
  • Interestingly, based on NHANES 1999 to 2002, only modest intercorrelations are present between different cardiovascular health metrics. For example, these ranged from a correlation of −0.12 between PA and HbA1c to a correlation of 0.29 between BMI and HbA1c. Thus, although the 7 AHA cardiovascular health metrics appear modestly interrelated, substantial independent variation in each exists, and each is independently related to cardiovascular outcomes.11
  • The AHA metrics may also be related to risk of noncardiovascular conditions. Rasmussen-Torvik et al13 demonstrated a graded, inverse association between ideal cardiovascular health and cancer incidence, with 51% lower risk among individuals with 6 or 7 ideal cardiovascular health metrics than among those with 0 ideal metrics. These results were only partially attenuated (25% lower risk) when smoking was removed from the sum of metrics. In contrast, Artero et al9 did not find a significant association between ideal cardiovascular health and death attributable to cancer in the Aerobics Center Longitudinal Study. The AHA cardiovascular health metrics have also been cross-sectionally associated with lower prevalence of depressive symptoms in the REGARDS cohort.14
  • Recent analyses from the US Burden of Disease Collaborators demonstrated that each of the 7 health factors and behaviors causes substantial mortality and morbidity in the United States. The top risk factor related to overall disease burden was suboptimal diet, followed by tobacco smoking, high BMI, HBP, high fasting plasma glucose, and physical inactivity.14a

Achieving the 2020 Impact Goals

  • Taken together, these data continue to demonstrate both the tremendous relevance of the AHA 2020 Impact Goals for cardiovascular health and the substantial progress that will be needed to achieve these goals over the next decade.
  • A range of complementary strategies and approaches can lead to improvements in cardiovascular health. These include each of the following:
    • Individual-focused approaches, which target lifestyle and treatments at the individual level (Table 2-4)
      Table 2-4
      Evidence-Based Individual Approaches for Improving Health Behaviors and Health Factors in the Clinic Setting
    • Healthcare systems approaches, which encourage, facilitate, and reward efforts by providers to improve health behaviors and health factors (Table 2-5)
      Table 2-5
      Evidence-Based Healthcare Systems Approaches to Support and Facilitate Improvements in Health Behaviors and Health Factors1721
    • Population approaches, which target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation (Table 2-6)
      Table 2-6
      Summary of Evidence-Based Population Approaches for Improving Diet, Increasing Physical Activity, and Reducing Tobacco Use*
  • Such approaches can focus on both (1) improving cardiovascular health among those who currently have less than optimal levels and (2) preserving cardiovascular health among those who currently have ideal levels (in particular, children, adolescents, and young adults) as they age.
  • The metrics with the greatest potential for improvement are health behaviors, including diet quality, PA, and body weight. However, each of the cardiovascular health metrics can be improved and deserves major focus.
  • Continued emphasis is also needed on the treatment of acute CVD events and secondary prevention through treatment and control of health behaviors and risk factors.
  • For each cardiovascular health metric, modest shifts in the population distribution toward improved health would produce relatively large increases in the proportion of Americans in both ideal and intermediate categories. For example, on the basis of NHANES 2009 to 2010, the current prevalence of ideal levels of BP among US adults is 44.3%. To achieve the 2020 goals, a 20% relative improvement would require an increase in this proportion to 53.1% by 2020 (44.3% × 1.20). On the basis of NHANES data, a reduction in population mean BP of just 2 mm Hg would result in 56.1% of US adults having ideal levels of BP, which represents a 26.8% relative improvement in this metric (Table 2-7). Larger population reductions in BP would lead to even larger numbers of people with ideal levels. Such small reductions in population BP could result from small health behavior changes at a population level, such as increased PA, increased fruit and vegetable consumption, decreased sodium intake, decreased adiposity, or some combination of these and other lifestyle changes, with resulting substantial projected decreases in CVD rates in US adults.15
    Table 2-7
    Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 Years of Age; NHANES 2009 to 2010
  • The AHA has a broad range of policy initiatives to improve cardiovascular health and meet the 2020 Strategic Impact Goals (Table 2-8). Future Statistical Updates will update these initiatives and track progress toward the 2020 Impact Goals.
1. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD. on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613. [PubMed]
2. Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 states and the District of Columbia, 2009. J Am Heart Assoc. 2012;1:e005371. [PMC free article] [PubMed]
3. Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM. Cardiovascular health behavior and health factor changes (1988–2008) and projections to 2020: results from the National Health and Nutrition Examination Surveys. Circulation. 2012;125:2595–2602. [PMC free article] [PubMed]
4. Huffman MD, Lloyd-Jones DM, Ning H, Labarthe DR, Guzman Castillo M, O’Flaherty M, Ford ES, Capewell S. Quantifying options for reducing coronary heart disease mortality by 2020. Circulation. 2013;127:2477–2484. [PMC free article] [PubMed]
5. Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999–2010. Series 20 No. 2P. [Accessed July 21, 2013];CDC WONDER Online Database [database online] Released January 2013. http://wonder.cdc.gov/cmf-icd10.html.
6. Bambs C, Kip KE, Dinga A, Mulukutla SR, Aiyer AN, Reis SE. Low prevalence of “ideal cardiovascular health” in a community-based population: the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) study. Circulation. 2011;123:850–857. [PMC free article] [PubMed]
7. Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD. ARIC Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol. 2011;57:1690–1696. [PMC free article] [PubMed]
8. Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and Hispanics: the Northern Manhattan Study. Circulation. 2012;125:2975–2984. [PMC free article] [PubMed]
9. Artero EG, España-Romero V, Lee DC, Sui X, Church TS, Lavie CJ, Blair SN. Ideal cardiovascular health and mortality: Aerobics Center Longitudinal Study. Mayo Clin Proc. 2012;87:944–952. [PMC free article] [PubMed]
10. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307:1273–1283. [PubMed]
11. Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation. 2012;125:987–995. [PMC free article] [PubMed]
12. Wilkins JT, Ning H, Berry J, Zhao L, Dyer AR, Lloyd-Jones DM. Lifetime risk and years lived free of total cardiovascular disease. JAMA. 2012;308:1795–1801. [PMC free article] [PubMed]
13. Rasmussen-Torvik LJ, Shay CM, Abramson JG, Friedrich CA, Nettleton JA, Prizment AE, Folsom AR. Ideal cardiovascular health is inversely associated with incident cancer: the Atherosclerosis Risk In Communities study. Circulation. 2013;127:1270–1275. [PMC free article] [PubMed]
14. Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association’s definition in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PLoS ONE. 2012;7:e52771. [PMC free article] [PubMed]
14a. Murray CJ, Abraham J, Ali MK, Alvarado M, Atkinson C, Baddour LM, Bartels DH, Benjamin EJ, Bhalla K, Birbeck G, Bolliger I, Burstein R, Carnahan E, Chen H, Chou D, Chugh SS, Cohen A, Colson KE, Cooper LT, Couser W, Criqui MH, Dabhadkar KC, Dahodwala N, Danaei G, Dellavalle RP, Des Jarlais DC, Dicker D, Ding EL, Dorsey ER, Duber H, Ebel BE, Engell RE, Ezzati M, Felson DT, Finucane MM, Flaxman S, Flaxman AD, Fleming T, Forouzanfar MH, Freedman G, Freeman MK, Gabriel SE, Gakidou E, Gillum RF, Gonzalez-Medina D, Gosselin R, Grant B, Gutierrez HR, Hagan H, Havmoeller R, Hoffman H, Jacobsen KH, James SL, Jasrasaria R, Jayaraman S, Johns N, Kassebaum N, Khatibzadeh S, Knowlton LM, Lan Q, Leasher JL, Lim S, Lin JK, Lipshultz SE, London S, Lozano R, Lu Y, Macintyre MF, Mallinger L, McDermott MM, Meltzer M, Mensah GA, Michaud C, Miller TR, Mock C, Moffitt TE, Mokdad AA, Mokdad AH, Moran AE, Mozaffarian D, Murphy T, Naghavi M, Narayan KM, Nelson RG, Olives C, Omer SB, Ortblad K, Ostro B, Pelizzari PM, Phillips D, Pope CA, Raju M, Ranganathan D, Razavi H, Ritz B, Rivara FP, Roberts T, Sacco RL, Salomon JA, Sampson U, Sanman E, Sapkota A, Schwebel DC, Shahraz S, Shibuya K, Shivakoti R, Silberberg D, Singh GM, Singh D, Singh JA, Sleet DA, Steenland K, Tavakkoli M, Taylor JA, Thurston GD, Towbin JA, Vavilala MS, Vos T, Wagner GR, Weinstock MA, Weisskopf MG, Wilkinson JD, Wulf S, Zabetian A, Lopez AD. Collaborators USBoD. The State of US Health, 1990–2010: Burden of Diseases, Injuries, and Risk Factors. JAMA. 2013;310:591608. [PubMed]
15. Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease. N Engl J Med. 2010;362:590–599. [PMC free article] [PubMed]
16. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE. on behalf of the American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation. 2010;122:406–441. [PubMed]
17. Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, Franch HA, Jacobs DR, Jr, Kraus WE, Kris-Etherton PM, Krummel DA, Popkin BM, Whitsel LP, Zakai NA. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation. 2012;126:1514–1563. [PMC free article] [PubMed]
18. Bodenheimer T. Helping patients improve their health-related behaviors: what system changes do we need? Dis Manag. 2005;8:319–330. [PubMed]
19. Simpson LA, Cooper J. Paying for obesity: a changing landscape. Pediatrics. 2009;123(suppl 5):S301–S307. [PubMed]
20. Quist-Paulsen P. Cessation in the use of tobacco: pharmacologic and non-pharmacologic routines in patients. Clin Respir J. 2008;2:4–10. [PubMed]
21. Davis D, Galbraith R. American College of Chest Physicians Health and Science Policy Committee. Continuing medical education effect on practice performance: effectiveness of continuing medical education: American College of Chest Physicians Evidence-Based Educational Guidelines. Chest. 2009;135(suppl):42S–48S. [PubMed]

3. Smoking/Tobacco Use

See Table 3-1 and Charts 3-1 and 3-2.

Chart 3-1
Prevalence (%) of students in grades 9 to 12 reporting current cigarette use by sex and race/ethnicity (Youth Risk Behavior Surveillance System, 2011). NH indicates non-Hispanic. Data derived from MMWR: Morbidity and Mortality Weekly Report.3
Chart 3-2
Prevalence (%) of current smoking for adults >18 years of age by race/ethnicity and sex (National Health Interview Survey: 2009–2011). All percentages are age adjusted. AIAN indicates American Indian/Alaska Native; and NH, non-Hispanic. ...
Table 3-1
Cigarette Smoking

Smoking is a major risk factor for CVD and stroke.1 The AHA has identified never tried or never smoked a whole cigarette (for children) and never smoking or quitting >12 months ago (for adults) as 1 of the 7 components of ideal cardiovascular health.2 According to NHANES 2009 to 2010 data, 85.2% of children and 76.2% of adults met these criteria.

Prevalence

Youth

(See Chart 3-1.)

  • In 2011, in grades 9 through 12:
    • 18.1% of students reported current cigarette use (on ≥1 day during the 30 days before the survey), 13.1% of students reported current cigar use, and 7.7% of students reported current smokeless tobacco use. Overall, 23.4% of students reported any current tobacco use (YRBS; Chart 3-1).3
    • Male students were more likely than female students to report current cigarette use (19.9% compared with 16.1%). Male students were also more likely than female students to report current cigar use (17.8% compared with 8.0%) and current smokeless tobacco use (12.8% compared with 2.2%; YRBS).3
    • Non-Hispanic white students were more likely than Hispanic or non-Hispanic black students to report any current tobacco use, which includes cigarettes, cigars, or smokeless tobacco (26.5% compared with 20.5% for Hispanic students and 15.4% for non-Hispanic black students; YRBS).3
  • Among youths 12 to 17 years of age in 2011, 2.4 million (10.0%) used a tobacco product (cigarettes, cigars, or smokeless tobacco) in the past month, and 1.9 million (7.8%) used cigarettes. Cigarette use in the past month in this age group declined significantly from 13.0% in 2002 to 7.8% in 2011 (NSDUH).4
  • Data from the YRBS5 for students in grades 9 to 12 indicated the following:
    • The percentage of students who reported ever trying cigarettes remained stable from 1991 to 1999 and then declined from 70.4% in 1999 to 44.7% in 2011.
    • The percentage who reported current cigarette use (on at least 1 day in the 30 days before the survey) increased between 1991 and 1997 and then declined from 36.4% in 1997 to 18.1% in 2011.
    • The percentage who reported current frequent cigarette use (smoked on ≥20 of the 30 days before the survey) increased from 1991 to 1999 and then declined from 16.8% in 1999 to 6.4% in 2011.
  • In 2011, 49.9% of students in grades 9 to 12 who currently smoked cigarettes had tried to quit smoking cigarettes during the previous 12 months. The prevalence of trying to quit smoking was higher among female student smokers (53.9%) than among male student smokers (47.0%) and among white females (54.0%) and Hispanic females (55.9%) than among white males (46.3%) and Hispanic males (44.7%; YRBS).3

Abbreviations Used in Chapter 3

AHAAmerican Heart Association
AIANAmerican Indian or Alaska Native
AMIacute myocardial infarction
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
NSDUHNational Survey on Drug Use and Health
RRrelative risk
WHOWorld Health Organization
YRBSYouth Risk Behavior Survey

Adults

(See Table 3-1 and Chart 3-2.)

  • In 2012, among adults ≥18 years of age:
    • 20.5% of men and 15.9% of women were current cigarette smokers (NHIS).6
    • The percentage of current cigarette smokers (18.1%) declined 25% since 1998 (24.1%).6,7
    • The states with the highest percentage of current cigarette smokers were Kentucky (28.3%), West Virginia (28.2%), and Arkansas (25.0%). Utah had the lowest percentage of smokers (10.6%) (BRFSS).8
  • In 2011, an estimated 68.2 million Americans ≥12 years of age were current (past month) users of a tobacco product (cigarettes, cigars, smokeless tobacco, or tobacco in pipes). The rate of current use of any tobacco product in this age range declined from 2007 to 2011 (from 28.6% to 26.5%; NSDUH).4
  • From 1998 to 2007, cigarette smoking prevalence among adults ≥18 years of age decreased in 44 states and the District of Columbia. Six states had no substantial changes in prevalence after controlling for age, sex, and race/ethnicity (BRFSS).9
  • In 2009 to 2011, among people ≥65 years of age, 8.9% of men and 8.7% of women were current smokers. In this age group, men were more likely than women to be former smokers (53.0% compared with 30.6%) on the basis of age-adjusted estimates (NHIS).10
  • In 2009 to 2011, among adults ≥18 years of age, Asian men (15.1%) and Hispanic men (16.3%) were less likely to be current cigarette smokers than non-Hispanic black men (23.2%), non-Hispanic white men (23.6%), and American Indian or Alaska Native men (23.7%) on the basis of ageadjusted estimates (NHIS). Similarly, in 2009 to 2011, Asian women (5.7%) and Hispanic women (8.9%) were less likely to be current cigarette smokers than non-Hispanic black women (16.9%), non-Hispanic white women (20.3%), and American Indian or Alaska Native women (23.6%; NHIS).10
  • In 2010 to 2011, among women 15 to 44 years of age, past-month cigarette use was lower for those who were pregnant (17.6%) than among those who were not pregnant (25.4%). This pattern was found for women 18 to 25 years of age (22.4% versus 29.9% for pregnant and nonpregnant women, respectively) and for women 26 to 44 years of age (14.3% versus 25.7%, respectively; NSDUH).4

Incidence

  • In 2011:
    • Approximately 2.4 million people ≥12 years of age smoked cigarettes for the first time within the past 12 months, which was similar to the estimate in 2010. The 2011 estimate averages out to ≈6500 new cigarette smokers every day. Most new smokers (55.7%) in 2011 were <18 years of age when they first smoked cigarettes (NSDUH).4
    • The number of new smokers <18 years of age (1.3 million) was similar to that in 2002 (1.3 million); however, new smokers ≥18 years of age increased from ≈600 000 in 2002 to 1.1 million in 2011 (NSDUH).4
    • Among people 12 to 49 years of age who had started smoking within the past 12 months, the average age of first cigarette use was 17.2 years, similar to the average in 2010 (17.3 years).4
  • Data from 2002 to 2004 suggest that ≈1 in 5 nonsmokers 12 to 17 years of age is likely to start smoking. Youths in the Mexican subpopulations were significantly more susceptible (28.8%) to start smoking than those in non-Hispanic white (20.8%), non-Hispanic black (23.0%), Cuban (16.4%), Asian Indian (15.4%), Chinese (15.3%), and Vietnamese (13.8%) subpopulations. There was no significant difference in susceptibility to start smoking between boys and girls in any of the major populations or subpopulations (NSDUH).11

Morbidity

A 2010 report of the US Surgeon General on how tobacco causes disease summarizes an extensive body of literature on smoking and CVD and the mechanisms through which smoking is thought to cause CVD.12 Among its conclusions are the following:

  • There is a sharp increase in CVD risk with low levels of exposure to cigarette smoke, including secondhand smoke, and a less rapid further increase in risk as the number of cigarettes per day increases.
  • A meta-analysis comparing pooled data of ≈2.4 million smokers and nonsmokers found the RR ratio of smokers to nonsmokers for developing CHD was 25% higher in women than in men (95% CI, 1.12–1.39).13
  • Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.14,15
  • Recent analysis has found that tobacco exposure is a top risk factor for disability in the United States, second only to dietary risks.16
  • Worldwide, tobacco smoking (including secondhand smoke) was 1 of the top 3 leading risk factors for disease in 2010.17

Mortality

  • In 2005, tobacco smoking was the cause of ≈467 000 adult deaths (19.1%) in the United States. Approximately one third of these deaths were related to CVD.18
  • During 2000 to 2004, ≈49 000 (11.1%) of cigarette smoking–related deaths were attributable to secondhand smoke.19
  • Each year from 2000 to 2004, smoking caused 3.1 million years of potential life lost for males and 2.0 million years for females, excluding deaths attributable to smoking-attributable residential fires and adult deaths attributable to secondhand smoke.19
  • From 2000 to 2004, smoking during pregnancy resulted in an estimated 776 infant deaths annually.17
  • During 2000 to 2004, cigarette smoking resulted in an estimated 269 655 deaths annually among males and 173 940 deaths annually among females.19
  • On average, male smokers die 13.2 years earlier than male nonsmokers, and female smokers die 14.5 years earlier than female nonsmokers.1
  • In 2010, tobacco smoking was the second-leading risk factor for deaths in the United States, after dietary risks.16
  • Overall mortality among US smokers is 3 times higher than that for never-smokers.20
  • Worldwide, tobacco smoking (including secondhand smoke) was estimated to contribute to 6.2 million deaths in 2010.17

Smoking Cessation

  • Smoking cessation reduces the risk of cardiovascular morbidity and mortality for smokers with and without CHD.
    • There is no evidence to date that reducing the amount smoked by smoking fewer cigarettes per day reduces the risk of CVD.12
  • Smokers who quit smoking at 25 to 34 years of age gained 10 years of life compared with those who continued to smoke. Those aged 35 to 44 years gained 9 years and those aged 45 to 54 years gained 6 years of life, on average, compared with those who continued to smoke.20
  • In 2010, 48.3% of adult current smokers ≥18 years of age who had a health checkup during the preceding year reported that they had been advised to quit. Smokers between 18 and 24 (31%) and 24 to 44 (44%) years of age were less likely to be advised to quit than those at older ages (57%; NHIS).21
  • Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.22
  • In addition to medications, smoke-free policies, increases in tobacco prices, cessation advice from healthcare professionals, and quitlines and other counseling have contributed to smoking cessation.21
  • In 2010, 52.4% of adult smokers reported trying to quit smoking in the past year; 6.2% reported they recently quit smoking. Of those who tried to quit smoking, 30.0% used cessation medications.21
  • To help combat the global problem of tobacco exposure, in 2003 the WHO adopted the Framework Convention on Tobacco Control treaty. The WHO Framework Convention on Tobacco Control contains a set of universal standards to limit tobacco supply and demand worldwide. These standards include the use of tax policies to reduce tobacco consumption, a ban on the indoor use of tobacco products, implementation of educational programs about the dangers of tobacco use, and restrictions of the sale of tobacco products to international travelers. Since it came into force in 2005, >175 countries have ratified the WHO Framework Convention on Tobacco Control.23

Secondhand Smoke

  • Data from a 2006 report of the US Surgeon General on the consequences of involuntary exposure to tobacco smoke12 indicate the following:
    • Nonsmokers who are exposed to secondhand smoke at home or at work increase their risk of developing CHD by 25% to 30%.
    • Short exposures to secondhand smoke can cause blood platelets to become stickier, damage the lining of blood vessels, and decrease coronary flow velocity reserves, potentially increasing the risk of an AMI.
  • In 2008, data from 11 states showed that the majority of people surveyed in each state reported having smoke-free home rules, ranging from 68.8% in West Virginia to 85.6% in Arizona (BRFSS).24
  • As of December 31, 2010, 25 states and the District of Columbia had laws that prohibited smoking in indoor areas of worksites, restaurants, and bars; no states had such laws in 2000. As of December 31, 2010, an additional 10 states had laws that prohibited smoking in 1 or 2 but not all 3 venues.25
  • In 2012, 30 of the 50 largest US cities prohibited indoor smoking in private workplaces, either through state or local ordinances.26
  • Pooled data from 17 studies in North America, Europe, and Australasia suggest that smoke-free legislation can reduce the incidence of acute coronary events by 10%.27
  • The percentage of the US nonsmoking population with serum cotinine ≥0.05 ng/mL declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008, with declines occurring for both children and adults. During 2007 to 2008, the percentage of nonsmokers with detectable serum cotinine was 53.6% for those 3 to 11 years of age, 46.5% for those 12 to 19 years of age, and 36.7% for those ≥20 years of age. The percentage was also higher for non-Hispanic blacks (55.9%) than for non-Hispanic whites (40.1%) and Mexican Americans (28.5%; NHANES).28

Cost

  • Direct medical costs ($96 billion) and lost productivity costs ($97 billion) associated with smoking totaled an estimated $193 billion per year between 2000 and 2004.18
  • In 2008, $9.94 billion was spent on marketing cigarettes in the United States.29
  • Cigarette prices have increased 283% between the early 1980s and 2011, which contributed to decreased sales from ≈30 million packs sold in 1982 to ≈14 million packs sold in 2011.29
1. The 2004 United States Surgeon General’s Report: The Health Consequences of Smoking. N S W Public Health Bull. 2004;15:107. [PubMed]
2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD. American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613. [PubMed]
3. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H. Centers for Disease Control and Prevention (CDC) Youth risk behavior surveillance: United States, 2011. MMWR Surveill Summ. 2012;61:1–162. [PubMed]
4. Substance Abuse and Mental Health Services Administration. [Accessed June 3, 2013];Results from the 2011 National Survey on Drug Use and Health: national findings and detailed tables. http://www.samhsa.gov/data/NSDUH.aspx.
5. Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion. [Accessed July 25, 2012];Trends in the prevalence of tobacco use, national YRBS: 1991–2011. http://www.cdc.gov/healthyyouth/yrbs/pdf/us_tobacco_trend_yrbs.pdf.
6. Blackwell D, Lucas J, Clarke T. Summary health statistics for U.S. adults: National Health Interview Survey, 2012. National Center for Health Statistics. Vital Health Stat 10. In press. [PubMed]
7. Centers for Disease Control and Prevention (CDC) Cigarette smoking among adults and trends in smoking cessation: United States, 2008. MMWR Morb Mortal Wkly Rep. 2009;58:1227–1232. [PubMed]
8. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System Survey Data. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2012. [Accessed September 17, 2013]. Prevalence and trends data, tobacco use. http://apps.nccd.cdc.gov/brfss/index.asp.
9. Centers for Disease Control and Prevention (CDC) State-specific prevalence and trends in adult cigarette smoking: United States, 1998–2007. MMWR Morb Mortal Wkly Rep. 2009;58:221–226. [PubMed]
10. Centers for Disease Control and Prevention, National Center for Health Statistics. Health Data Interactive. [Accessed June 4, 2013];Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nchs/hdi.htm.
11. Centers for Disease Control and Prevention (CDC) Racial/ethnic differences among youths in cigarette smoking and susceptibility to start smoking: United States, 2002–2004. MMWR Morb Mortal Wkly Rep. 2006;55:1275–1277. [PubMed]
12. US Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2010. [PubMed]
13. Huxley RR, Woodward M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and meta-analysis of prospective cohort studies. Lancet. 2011;378:1297–1305. [PubMed]
14. Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, Creager MA, Culebras A, Eckel RH, Hart RG, Hinchey JA, Howard VJ, Jauch EC, Levine SR, Meschia JF, Moore WS, Nixon JV, Pearson TA. on behalf of the American Heart Association Stroke Council; Council on Cardiovascular Nursing; Council on Epidemiology and Prevention; Council for High Blood Pressure Research, Council on Peripheral Vascular Disease, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published correction appears in Stroke. 2011;42:e26] Stroke. 2011;42:517–584. [PubMed]
15. Shah RS, Cole JW. Smoking and stroke: the more you smoke the more you stroke. Expert Rev Cardiovasc Ther. 2010;8:917–932. [PMC free article] [PubMed]
16. US Burden of Disease Collaborators. The state of US health, 1990–2010: burden of diseases, injuries, and risk factors. JAMA. 2013;319:591–608. [PubMed]
17. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A, Bell ML, Blore JD, Blyth F, Bonner C, Borges G, Bourne R, Boussinesq M, Brauer M, Brooks P, Bruce NG, Brunekreef B, Bryan-Hancock C, Bucello C, Buchbinder R, Bull F, Burnett RT, Byers TE, Calabria B, Carapetis J, Carnahan E, Chafe Z, Charlson F, Chen H, Chen JS, Cheng AT, Child JC, Cohen A, Colson KE, Cowie BC, Darby S, Darling S, Davis A, Degenhardt L, Dentener F, Des Jarlais DC, Devries K, Dherani M, Ding EL, Dorsey ER, Driscoll T, Edmond K, Ali SE, Engell RE, Erwin PJ, Fahimi S, Falder G, Farzadfar F, Ferrari A, Finucane MM, Flaxman S, Fowkes FG, Freedman G, Freeman MK, Gakidou E, Ghosh S, Giovannucci E, Gmel G, Graham K, Grainger R, Grant B, Gunnell D, Gutierrez HR, Hall W, Hoek HW, Hogan A, Hosgood HD, 3rd, Hoy D, Hu H, Hubbell BJ, Hutchings SJ, Ibeanusi SE, Jacklyn GL, Jasrasaria R, Jonas JB, Kan H, Kanis JA, Kassebaum N, Kawakami N, Khang YH, Khatibzadeh S, Khoo JP, Kok C, Laden F, Lalloo R, Lan Q, Lathlean T, Leasher JL, Leigh J, Li Y, Lin JK, Lipshultz SE, London S, Lozano R, Lu Y, Mak J, Malekzadeh R, Mallinger L, Marcenes W, March L, Marks R, Martin R, McGale P, McGrath J, Mehta S, Mensah GA, Merriman TR, Micha R, Michaud C, Mishra V, Mohd Hanafiah K, Mokdad AA, Morawska L, Mozaffarian D, Murphy T, Naghavi M, Neal B, Nelson PK, Nolla JM, Norman R, Olives C, Omer SB, Orchard J, Osborne R, Ostro B, Page A, Pandey KD, Parry CD, Passmore E, Patra J, Pearce N, Pelizzari PM, Petzold M, Phillips MR, Pope D, Pope CA, 3rd, Powles J, Rao M, Razavi H, Rehfuess EA, Rehm JT, Ritz B, Rivara FP, Roberts T, Robinson C, Rodriguez-Portales JA, Romieu I, Room R, Rosenfeld LC, Roy A, Rushton L, Salomon JA, Sampson U, Sanchez-Riera L, Sanman E, Sapkota A, Seedat S, Shi P, Shield K, Shivakoti R, Singh GM, Sleet DA, Smith E, Smith KR, Stapelberg NJ, Steenland K, Stöckl H, Stovner LJ, Straif K, Straney L, Thurston GD, Tran JH, Van Dingenen R, van Donkelaar A, Veerman JL, Vijayakumar L, Weintraub R, Weissman MM, White RA, Whiteford H, Wiersma ST, Wilkinson JD, Williams HC, Williams W, Wilson N, Woolf AD, Yip P, Zielinski JM, Lopez AD, Murray CJ, Ezzati M, AlMazroa MA, Memish ZA. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 [published corrections appear in Lancet. 2013;381:1276 and Lancet. 2013;381:628] Lancet. 2012;380:2224–2260. [PMC free article] [PubMed]
18. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, Ezzati M. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors [published correction appears in PLoS Med. 2011;8. doi:10.1371/annotation/0ef47acd-9dcc-4296-a897-872d182cde57] PLoS Med. 2009;6:e1000058. [PMC free article] [PubMed]
19. Centers for Disease Control and Prevention (CDC) Smoking-attributable mortality, years of potential life lost, and productivity losses: United States, 2000–2004. MMWR Morb Mortal Wkly Rep. 2008;57:1226–1228. [PubMed]
20. Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, McAfee T, Peto R. 21st-Century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368:341–350. [PubMed]
21. Centers for Disease C, Prevention. Quitting smoking among adults: United States, 2001–2010. MMWR Morb Mortal Wkly Rep. 2011;60:1513–1519. [PubMed]
22. Clinical Practice Guideline Treating Tobacco Use and Dependence Update Panel, Liaisons, and Staff. A clinical practice guideline for treating tobacco use and dependence: 2008 update: a U.S. Public Health Service report. Am J Prev Med. 2008;35:158–176. [PMC free article] [PubMed]
23. World Health Organization. [Accessed July 18, 2013];About the WHO Framework Convention on Tobacco Control. http://www.who.int/fctc/about/en/index.html.
24. Centers for Disease Control and Prevention (CDC) State-specific secondhand smoke exposure and current cigarette smoking among adults: United States, 2008. MMWR Morb Mortal Wkly Rep. 2009;58:1232–1235. [PubMed]
25. Centers for Disease Control and Prevention (CDC) State smoke-free laws for worksites, restaurants, and bars: United States, 2000–2010. MMWR Morb Mortal Wkly Rep. 2011;60:472–475. [PubMed]
26. Centers for Disease Control and Prevention (CDC) Comprehensive smoke-free laws: 50 largest U.S. cities, 2000 and 2012. MMWR Morb Mortal Wkly Rep. 2012;61:914–917. [PubMed]
27. Mackay DF, Irfan MO, Haw S, Pell JP. Meta-analysis of the effect of comprehensive smoke-free legislation on acute coronary events. Heart. 2010;96:1525–1530. [PubMed]
28. Centers for Disease Control and Prevention (CDC) Vital signs: nonsmokers’ exposure to secondhand smoke: United States, 1999–2008. MMWR Morb Mortal Wkly Rep. 2010;59:1141–1146. [PubMed]
29. US Department of Health and Human Services. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012. [Accessed May 30, 2012]. http://www.surgeongeneral.gov/library/reports/preventing-youth-tobacco-use/prevent_youth_by_section.html.

4. Physical Inactivity

See Table 4-1 and Charts 4-1 through 4-5.

Chart 4-1
Prevalence of students in grades 9 to 12 who did not participate in ≥60 minutes of physical activity on any day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance ...
Chart 4-5
Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines among adults ≥18 years of age by race/ethnicity and sex (National Health Interview Survey: 2012). NH indicates non-Hispanic. Percentages are age adjusted. ...
Table 4-1
Met 2008 Federal PA Guidelines for Adults

Physical inactivity is a major risk factor for CVD and stroke.1 The AHA has identified ≥60 minutes of moderate- or vigorous-intensity activity every day (for children) and ≥150 min/wk of moderate-intensity activity or ≥75 min/wk of vigorous-intensity activity or a combination thereof (for adults) as 1 of the 7 components of ideal cardiovascular health.2 In 2009 to 2010, 36.5% of children and 41.1% of adults met these criteria.

Prevalence

Youth

Inactivity

(See Chart 4-1.)

In 20113:

  • Nationwide, 13.8% of adolescents were inactive during the previous 7 days, as indicated by their response that they did not participate in ≥60 minutes of any kind of PA that increased their heart rate and made them breathe hard on any 1 of the previous 7 days.
  • Girls were more likely than boys to report inactivity (17.7% versus 10.0%).
  • The prevalence of inactivity was highest among black (26.7%) and Hispanic (21.3%) girls, followed by white girls (13.7%), black boys (12.3%), Hispanic boys (10.7%), and white boys (8.5%).

Abbreviations Used in Chapter 4

AHAAmerican Heart Association
CARDIACoronary Artery Risk Development in Young Adults
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EFejection fraction
FMDflow-mediated dilation
HbA1chemoglobin AIc
HDLhigh-density lipoprotein
HFheart failure
HRhazard ratio
MImyocardial infarction
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
PAphysical activity
PADperipheral artery disease
RRrelative risk
SBPsystolic blood pressure
WHOWorld Health Organization

Television/Video/Computers

(See Chart 4-2.)

Chart 4-2
Percentage of students in grades 9 to 12 who used a computer for ≥3 hours a day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.3

In 20113:

  • Nationwide, 31.1% of adolescents used a computer for activities other than school work (eg, videogames or other computer games) for ≥3 hours per day on an average school day.
  • The prevalence of using computers or watching television ≥3 hours per day was highest among black (41.1%) and Hispanic (36.3%) boys, followed by white boys (33.3%), black girls (35.2%), Hispanic girls (28.3%), and white girls (22.6%).
  • 32.4% of adolescents watched television for ≥3 hours per day.
  • The prevalence of watching television ≥3 hours per day was highest among black girls (54.9%) and boys (54.4%), followed by Hispanic boys (38.4%) and girls (37.2%) and white boys (27.3%) and girls (23.9%).
  • Increased television time has significant nutritional associations with weight gain (refer to Chapter 5, Nutrition).

Activity Recommendations

(See Charts 4-3 and 4-4.)

Chart 4-3
Prevalence of students in grades 9 to 12 who met currently recommended levels of physical activity during the past 7 days by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). “Currently recommended levels” was defined as ...
Chart 4-4
Prevalence of children 6 to 19 years of age who attained sufficient moderate to vigorous physical activity to meet public health recommendations (≥60 minutes per day on 5 or more of the 7 days preceding the survey), by sex and age (National Health ...
  • In 2011, the proportion of students who met activity recommendations of ≥60 minutes of PA on 7 days of the week was 28.7% nationwide and declined from 9th (30.7%) to 12th (25.1%) grades. At each grade level, the proportion was higher in boys than in girls.3
  • In 2011, more high school boys (38.3%) than girls (18.5%) self-reported having been physically active ≥60 minutes per day on all 7 days; self-reported rates of activity were higher in white (30.4%) than in black (26.0%) or Hispanic (26.5%) adolescents.3
  • The 2010 National Youth Physical Activity and Nutrition Study showed that a total of 15.3% of high school students met the recommendations for aerobic activity, 51.0% met the recommendations for muscle-strengthening activity, and 12.2% met the recommendations for both aerobic and muscle-strengthening activities.4
  • There was a marked discrepancy between the proportion of youth (ages 6–11 years) who reported engaging in ≥60 minutes of moderate-to-vigorous PA on most days of the week and those who actually engaged in moderate-to-vigorous PA for ≥60 minutes when activity was measured objectively with accelerometers (ie, portable motion sensors that record and quantify the duration and intensity of movements) in the NHANES 2003 to 2004 survey.5
  • On the basis of accelerometer counts per minute >2020, 42% of 6- to 11-year-olds accumulated ≥260 minutes of moderate-to-vigorous PA on ≥5 days per week, whereas only 8% of 12-to 15-year-olds and 7.6% of 16- to 19-year-olds achieved similar counts.5
  • More boys than girls met PA recommendations (≥60 minutes of moderate to vigorous activity on most days of the week) as measured by accelerometry.5

Structured Activity Participation
  • Despite recommendations from the National Association for Sport and Physical Education that schools should require daily physical education for students in kindergarten through 12th grade,6 only 51.8% of students attended physical education classes in school daily (56.7% of boys and 46.7% of girls).3
  • Physical education class participation declined from the 9th through the 12th grades among boys and girls.3
  • Little more than half (58.4%) of high school students played on at least 1 school or community sports team in the previous year; however, the prevalence declined with increasing grade level, from 61.4% in the 9th grade to 52.5% in the 12th grade.3

Adults

Inactivity

According to 2012 data from the NHIS, in adults ≥18 years of age:

  • 29.9% do not engage in leisure-time PA (“no leisure-time PA/inactivity” refers to no sessions of light/moderate or vigorous PA of ≥10 minutes’ duration).7
  • Inactivity was higher among women than men (31.0% versus 28.6%, age adjusted) and increased with age from 24.5% to 31.8%, 35.7%, and 51.4% among adults 18 to 44, 45 to 64, 65 to 74, and ≥75 years of age, respectively.7
  • Non-Hispanic black and Hispanic adults were more likely to be inactive (39.4% and 39.8%, respectively) than were non-Hispanic white adults (26.2%) on the basis of age-adjusted estimates.7

Activity Recommendations

(See Table 4-1 and Chart 4-5.)

According to 2012 data from the NHIS, in adults ≥18 years of age:

  • 20.7% met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness.7
  • The age-adjusted proportion who reported engaging in moderate or vigorous PA that met the 2008 aerobic PA guidelines for Americans (≥150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination each week) was 50.1%; 53.9% of men and 46.5% of women met the recommendations. Age-adjusted prevalence was 53.6% for non-Hispanic whites, 40.9% for non-Hispanic blacks, and 42.5% for Hispanics.7
  • The proportion of respondents who did not meet the federal aerobic PA guidelines increased with age from 43.8% of 18- to 44-year-olds to 71.9% of adults ≥75 years of age.7
  • Non-Hispanic black adults (59.1%) and Hispanic/Latino adults (57.4%) were more likely not to meet the federal aerobic PA guidelines than non-Hispanic white (46.4%) adults, according to age-adjusted estimates.7
  • The percentage of adults ≥25 years of age not meeting the full (aerobic and muscle-strengthening) federal PA guidelines was inversely associated with education; 66.4% of participants with no high school diploma, 57.6% of those with a high school diploma or a high school equivalency credential, 46.8% of those with some college, and 33.2% of those with a bachelor’s degree or higher did not meet the full federal PA guidelines.7
  • The proportion of adults ≥25 years of age who met the 2008 federal PA guidelines for aerobic activity was positively associated with education level: 62.9% of those with a college degree or higher met the PA guidelines compared with 31.5% of adults with less than a high school diploma.7
  • The proportion of adults reporting levels of PA consistent with the 2008 Physical Activity Guidelines for Americans remains low and decreases with age.8,9 Thirty-three percent of respondents in a study examining awareness of current US PA guidelines had direct knowledge of the recommended dosage of PA (ie, frequency/duration).10
  • The percentage of adults reporting ≥150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination weekly decreased with age from 55.8% for adults 18 to 44 years of age to 27.4% for those ≥75 years of age, on the basis of the 2011 NHIS.9
  • The percentage of men who engaged in both leisure-time aerobic and strengthening activities decreased with age, from 39.8% at age 18 to 24 years to 11.1% at ≥75 years of age. The percentage of women who engaged in both leisure-time aerobic and strengthening activities also decreased with age, from 20.7% at age 18 to 24 years to 5.3% at ≥75 years of age, on the basis of the 2011 NHIS.9
  • Using PA recommendations that existed at the time of the survey, adherence to PA recommendations was much lower when based on PA measured by accelerometer in NHANES 2003 to 20045:
    • Among adults 20 to 59 years of age, 3.8% of men and 3.2% of women met recommendations to engage in moderate-to-vigorous PA (accelerometer counts >2020/min) for 30 minutes (in sessions of ≥10 minutes) on ≥5 of 7 days.
    • Among those ≥60 years of age, adherence was 2.5% in men and 2.3% in women.
  • Accelerometry data from NHANES 2003 to 2006 showed that men engaged in 35 minutes of moderate activity per day, whereas for women, it was 21 minutes. More than 75% of moderate activity was accumulated in 1-minute bouts. Levels of activity declined sharply after the age of 50 years in all groups.11
  • In a review examining self-reported versus actual measured PA (eg, accelerometers, pedometers, indirect calorimetry, doubly labeled water, heart rate monitor), 60% of respondents self-reported higher values of activity than what was measured by use of direct methods.12
  • Among men, self-reported PA was 44% greater than actual measured values; among women, self-reported activity was 138% greater than actual measured PA.12

Trends

Youth

In 20113:

  • Among adolescents, there was a significant decrease in the prevalence of watching television ≥3 hours per day, from 42.8% in 1999 to 32.4%, although there was no significant decrease from the 2009 prevalence of 32.8%.
  • Among students nationwide, there was a significant increase in the prevalence of having participated in muscle-strengthening activities on ≥3 days per week, from 47.8% in 1991 to 55.6%.
  • Nationwide, the prevalence of adolescents using computers ≥3 hours per day increased from 21.1% in 2005 to 24.9% in 2009 and 31.1% in 2011.
  • Among adolescents nationwide, the prevalence of attending physical education classes at least once per week did not increase significantly, from 48.9% in 1991 to 51.8%.
  • The prevalence of adolescents playing ≥1 team sport in the past year increased from 55.1% in 1999 to 58.4%.

Adults

  • Between NHANES III (1988–1994) and NHANES 2001 to 2006, the non–age-adjusted proportion of adults who engaged in >12 bouts of PA per month declined from 57.0% to 43.3% in men and from 49.0% to 43.3% in women.13
  • The proportion of US adults who meet criteria for muscle strength has improved between 1998 and 2011. Annual estimates of the percentage of US adults who met the muscle-strengthening criteria increased from 17.7% in 1998 to 24.5% in 2011, and estimates of the percentage who met both the muscle-strengthening and aerobic criteria increased from 14.4% in 1998 to 21.0% in 2011.8,14
  • A 2.3% decline in physical inactivity between 1980 and 2000 was estimated to have prevented or postponed ≈17 445 deaths (≈5%) attributable to CHD in the United States.15

CVD and Metabolic Risk Factors

Youth

  • More girls (67.9%) than boys (55.7%) reported having exercised to lose weight or to keep from gaining weight.3
  • White girls (72.2%) were more likely than black (54.2%) and Hispanic (66.3%) girls to report exercising to lose weight or to keep from gaining weight.3
  • Total and vigorous PA are inversely correlated with body fat and the prevalence of obesity.16
  • Among children 4 to 18 years of age, increased time in moderate to vigorous PA was associated with improvements in waist circumference, SBP, fasting triglycerides, HDL cholesterol, and insulin. These findings were significant regardless of the amount of the children’s sedentary time.17
  • Among children aged 4 to 18 years, both higher activity levels and lower sedentary time measured by accelerometry were associated with more favorable metabolic risk factor profiles.17

Adults

  • Participants in the Diabetes Prevention Project randomized trial who met the PA goal of 150 minutes of PA per week were 44% less likely to develop DM after 3.2 years of follow-up, even if they did not meet the weight-loss target.18
  • Exercise for weight loss, without dietary interventions, was associated with significant reductions in DBP (−2 mm Hg; 95% CI, −4 to −1 mm Hg), triglycerides (−0.2 mmol/L; 95% CI, −0.3 to −0.1 mmol/L), and fasting glucose (−0.2 mmol/L; 95% CI, −0.3 to −0.1 mmol/L).19
  • A total of 120 to 150 minutes per week of moderate-intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.20
  • In CARDIA, women who maintained high activity through young adulthood gained 6.1 fewer kilograms of weight and 3.8 fewer centimeters in waist circumference in middle age than those with lower activity. Highly active men gained 2.6 fewer kilograms and 3.1 fewer centimeters than their lower-activity counterparts.21
  • Self-reported low lifetime recreational activity has been associated with increased PAD.22
  • In 3 US cohort studies, men and women who increased their PA over time gained less weight in the long term, whereas those who decreased their PA over time gained more weight and those who maintained their current PA had intermediate weight gain.23
  • Among US men and women, every hour per day of increased television watching was associated with 0.3 lb of greater weight gain every 4 years, whereas every hour per day of decreased television watching was associated with a similar amount of relative weight loss.23

Morbidity and Mortality

  • Physical inactivity is responsible for 12.2% of the global burden of MI after accounting for other CVD risk factors such as cigarette smoking, DM, hypertension, abdominal obesity, lipid profile, no alcohol intake, and psychosocial factors.24
  • In a meta-analysis of longitudinal studies among women, RRs of incident CHD were 0.83 (95% CI, 0.69–0.99), 0.77 (95% CI, 0.64–0.92), 0.72 (95% CI, 0.59–0.87), and 0.57 (95% CI, 0.41–0.79) across increasing quintiles of PA compared with the lowest quintile.25
  • A 2003 meta-analysis of 23 studies on the association of PA with stroke indicated that compared with low levels of activity, high (RR, 0.79; 95% CI, 0.69–0.91) and moderate (RR, 0.91; 95% CI, 0.80–1.05) levels of activity were inversely associated with the likelihood of developing total stroke (ischemic and hemorrhagic).26
  • With television watching as a sedentary activity, 2 hours of television per day is associated with an RR for type 2 DM of 1.20 (95% CI, 1.14–1.27), an RR for fatal or nonfatal CVD of 1.15 (95% CI, 1.06–1.23), and an RR for all-cause mortality of 1.13 (95% CI, 1.07–1.18). The risk for all-cause mortality further increases with >3 hours of television daily.27
  • Longitudinal studies commonly report a graded, inverse association of PA amount and duration (ie, dose) with incident CHD and stroke.28
  • The PA guidelines for adults cite evidence that ≈150 minutes per week of moderate-intensity aerobic activity, compared with none, can reduce the risk of CVD.29
  • Adherence to PA guidelines for both aerobic and muscle-strengthening activities is associated with 27% lower all-cause mortality among adults without existing chronic conditions such as DM, cancer, MI, angina, CVD, stroke, or respiratory diseases and with 46% lower mortality among people with chronic comorbidities.29
  • In the Health Professionals Follow-Up Study, for every 3-hour-per-week increase in vigorous-intensity activity, the multivariate RR of MI was 0.78 (95% CI, 0.61–0.98) for men. This 22% reduction of risk can be explained in part by beneficial effects of PA on HDL cholesterol, vitamin D, apolipoprotein B, and HbA1c.30
  • In a 20-year study of older male veterans, an inverse, graded, and independent association between impaired exercise capacity and all-cause mortality risk was found. For each increase of 1 metabolic equivalent tasks in exercise capacity, mortality risk was 12% lower (HR, 0.88; 95% CI, 0.86–0.90). Unfit individuals who improved their fitness status had a 35% lower mortality risk (HR, 0.65; 95% CI, 0.46–0.93) than those who remained unfit.31

Secondary Prevention

  • PA improves inflammatory markers in people with existing stable CHD. After a 6-week training session, CRP levels declined by 23.7% (P<0.001), and plasma vascular cell adhesion molecule-1 levels declined by 10.23% (P<0.05); there was no difference in leukocyte count or levels of intercellular adhesion molecule-1.32
  • In a randomized trial of patients with PAD, supervised treadmill exercise training and lower-extremity resistance training were each associated with significant improvements in functional performance and quality of life compared with a usual-care control group. Exercise training was additionally associated with improved brachial artery FMD, whereas resistance training was associated with better stair-climbing ability versus control.33
  • On the basis of a meta-analysis of 34 randomized controlled trials, exercise-based cardiac rehabilitation after MI was associated with lower rates of reinfarction, cardiac mortality, and overall mortality.34
  • The benefit of intense exercise training for cardiac rehabilitation in people with HF was tested in a trial of 27 patients with stable, medically treated HF. Intense activity (an aerobic interval-training program 3 times per week for 12 weeks) was associated with a significant 35% improvement in left ventricular EF and decreases in pro-brain natriuretic peptide (40%), left ventricular end-diastolic volume (18%), and left ventricular end-systolic volume (25%) compared with control and endurance-training groups.35
  • Exercise training in patients with HF with preserved EF was associated with improved exercise capacity and favorable changes in diastolic function.36

Costs

  • The economic consequences of physical inactivity are substantial. In a summary of WHO data sources, the economic costs of physical inactivity were estimated to account for 1.5% to 3.0% of total direct healthcare expenditures in developed countries such as the United States.37
  • Interventions and community strategies to increase physical activity have been shown to be cost-effective in terms of reducing medical costs38:
    • Nearly $3 in medical cost savings is realized for every $1 invested in building bike and walking trails.
    • Incremental cost and incremental effectiveness ratios range from $14 000 to $69 000 per quality-adjusted life-year gained from interventions such as pedometer or walking programs compared with no intervention, especially in high-risk groups.
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12. Prince SA, Adamo KB, Hamel ME, Hardt J, Connor Gorber S, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5:56. [PMC free article] [PubMed]
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5. Nutrition

See Tables 5-1 and 5-2 and Charts 5-1 through 5-3.

Chart 5-1
Age-adjusted trends in macronutrients and total calories consumed by US adults (20–74 years of age), 1971 to 2008. Data derived from National Center for Health Statistics14 and Wright and Wang.55
Chart 5-3
Total US food expenditures away from home and at home, 1977 and 2007. Data derived from Davis and Saltos.66
Table 5-1
Dietary Consumption in 2009 to 2010 Among US Adults ≥20 Years of Age of Selected Foods and Nutrients Related to Cardiometabolic Health103106
Table 5-2
Dietary Consumption in 2009 to 2010 Among US Children and Teenagers of Selected Foods and Nutrients Related to Cardiometabolic Health

This chapter of the Update highlights national dietary consumption data, focusing on key foods, nutrients, dietary patterns, and other dietary factors related to cardiometabolic health. It is intended to examine current intakes, trends and changes in intakes, and estimated effects on disease to support and further stimulate efforts to monitor and improve dietary habits in relation to cardiovascular health.

Abbreviations Used in Chapter 5

ALAα-linoleic acid
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CVDcardiovascular disease
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DHAdocosahexaenoic acid
DMdiabetes mellitus
EPAeicosapentaenoic acid
GFRglomerular filtration rate
GISSIGruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico
HDheart disease
HDLhigh-density lipoprotein
HEIHealthy Eating Index
HFheart failure
LDLlow-density lipoprotein
MImyocardial infarction
n-6-PUFAω-6-polyunsaturated fatty acid
NAnot available
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
ORodds ratio
PAphysical activity
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
WHIWomen’s Health Initiative

Prevalence

Foods and Nutrients: Adults

(See Table 5-1; NHANES 2009–2010.)

The dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-1 according to sex and race or ethnic subgroups:

  • Average consumption of whole grains was 1.1 servings per day by white men and women and 0.8 servings per day by black men and women, with only between 7% and 10% of white and black adults meeting guidelines of ≥3 servings per day. Average whole grain consumption by Mexican Americans was ≈2 servings per day, with 27% to 29% consuming ≥3 servings per day.
  • Average fruit consumption ranged from 1.2 to 1.9 servings per day in these sex and race or ethnic subgroups: 11% to 13% of whites, 7% to 8% of blacks, and 14% of Mexican Americans met guidelines of ≥2 cups per day. When 100% fruit juices were included, the number of servings increased, and the proportions of adults consuming ≥2 cups per day approximately doubled in whites and Mexican Americans and nearly quadrupled in blacks.
  • Average vegetable consumption ranged from 1.3 to 2.2 servings per day; 6% to 8% of whites, 2% to 5% of blacks, and 2 to 4% of Mexican Americans consumed ≥2.5 cups per day. The inclusion of vegetable juices and sauces generally produced little change in these consumption patterns.
  • Average consumption of fish and shellfish was lowest among Mexican American and white women (1.2 and 1.4 servings per week, respectively) and highest among black women (2.1 servings per week); ≈72% to 78% of all adults in each sex and race or ethnic subgroup consumed <2 servings per week. Approximately 9% to 10% of whites, 10% to 12% of blacks, and 7% to 13% of Mexican Americans consumed ≥250 mg of eicosapentaenoic acid and docosahexaenoic acid per day.
  • Average consumption of nuts, legumes, and seeds was ≈2.5 servings per week among whites and blacks and 5 to 8 servings per week among Mexican Americans. Approximately 22% of whites, 18% of blacks, and 40% of Mexican Americans met guidelines of ≥4 servings per week.
  • Average consumption of processed meats was lowest among Mexican American women (1.2 servings per week) and highest among black men (3.3 servings per week). Between 49% (black men) and 75% (Mexican American women) of adults consumed 2 or fewer servings per week.
  • Average consumption of sugar-sweetened beverages ranged from ≈6 servings per week among white women to 12 servings per week among Mexican American men. Women generally consumed less than men. From 29% (Mexican American men) to 68% (white women) of adults consumed no more than 36 oz (4.5 8-oz servings) per week.
  • Average consumption of sweets and bakery desserts ranged from ≈4.5 servings per day (Mexican Americans) to 7 servings per day (white women). Approximately two thirds of white women and more than half of all other sex and race groups consumed >2.5 servings per week.
  • Between 35% and 58% of adults in each sex and race or ethnic subgroup consumed <10% of total calories from saturated fat, and between 56% and 72% consumed <300 mg of dietary cholesterol per day.
  • Only 6% to 12% of whites, 2% to 5% of blacks, and 13% of Mexican Americans consumed ≥28 g of dietary fiber per day.
  • Only 5% to 7% of whites, 6% to 12% of blacks, and 10% of Mexican Americans consumed <2.3 g of sodium per day.

Foods and Nutrients: Children and Teenagers

(See Table 5-2; NHANES 2009–2010.)

The dietary consumption by US children and teenagers of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-2:

  • Average whole grain consumption was low, <1 serving per day in all age and sex groups, with <7% of all children in different age and sex subgroups meeting guidelines of ≥3 servings per day.
  • Average fruit consumption was low and decreased with age: 1.6 to 1.7 servings per day in younger boys and girls (5–9 years of age), 1.3 servings per day in adolescent boys and girls (10–14 years of age), and 0.9 to 1.2 servings per day in teenage boys and girls (15–19 years of age). The proportion meeting guidelines of ≥2 cups per day was also low and decreased with age: ≈10% in those 5 to 9 years of age, 8% in those 10 to 14 years of age, and 5% in those 15 to 19 years of age. When 100% fruit juices were included, the number of servings consumed approximately doubled, and proportions consuming ≥2 cups per day increased to approximately one third of those 5 to 9 years of age and one fourth of those 10 to 14 years and 15 to 19 years of age.
  • Average vegetable consumption was low, ranging from 0.8 to 1.3 servings per day, with at most 3% of children in different age and sex subgroups meeting guidelines of ≥2.5 cups per day.
  • Average consumption of fish and shellfish was low, ranging between 0.3 and 0.9 servings per week in all age and sex groups. Among all ages, only 5% to 11% of youth consumed ≥2 servings per week.
  • Average consumption of nuts, legumes, and seeds ranged from 1.4 to 1.9 servings per week among different age and sex groups. Only between 11% and 14% of children in different age and sex subgroups consumed ≥4 servings per week.
  • Average consumption of processed meats ranged from ≈2 to 3 servings per week; was generally higher than the average consumption of nuts, legumes, and seeds; and was up to 8 times higher than the average consumption of fish and shellfish. Approximately 40% and 50% of children consumed >2 servings per week.
  • Average consumption of sugar-sweetened beverages was higher in boys than in girls and increased with age, from ≈7 to 8 servings per week in 5- to 9-year-olds, 9 to 10 servings per week in 10- to 14-year-olds, and 13 to 16 servings per week in 15- to 19-year-olds (each energy adjusted to 2000 kcal/d). This was generally considerably higher than the average consumption of whole grains, fruits, vegetables, fish and shellfish, or nuts, legumes, and seeds. Less than half of children 5 to 9 years of age and less than one quarter of boys 15 to 19 years of age consumed <4.5 servings per week.
  • Average consumption of sweets and bakery desserts was ≈9 to 10 servings per week in 5- to 9-year-olds, 7 to 8 servings per week in 10- to 14-year-olds, and 5 to 8 servings per week in 15- to 19-year-olds. From 61% (boys 15–19 years of age) to 79% (girls 5–9 years of age) of youths consumed >2.5 servings per week.
  • Average consumption of eicosapentaenoic acid and docosahexaenoic acid was low, ranging from 39 to 63 mg/d in boys and girls at all ages. Fewer than 6% of children and teenagers at any age consumed ≥250 mg/d.
  • Average consumption of saturated fat was ≈11% of calories, and average consumption of dietary cholesterol ranged from 225 to 250 mg/d. Approximately 30% to 40% of youth consumed <10% energy from saturated fat, and >75% consumed <300 mg of dietary cholesterol per day.
  • Average consumption of dietary fiber ranged from 14 to 15 g/d. Less than 2% of children in all age and sex subgroups consumed ≥28 g/d.
  • Average consumption of sodium ranged from 3.3 to 3.5 g/d. Only between 2% and 9% of children in different age and sex subgroups consumed <2.3 g/d.

Energy Balance

Energy balance, or consumption of total calories appropriate for needs, is determined by the balance of average calories consumed versus expended, with this balance depending on multiple factors, including calories consumed, PA, body size, age, sex, and underlying basal metabolic rate. Thus, one individual may consume relatively high calories but have negative energy balance (as a result of even greater calories expended), whereas another individual may consume relatively few calories but have positive energy balance (because of low calories expended). Given such variation, the most practical and reasonable method to assess energy balance in populations is to assess changes in weight over time (Trends section).

  • Average daily caloric intake in the United States is ≈2500 calories in adult men and 1800 calories in adult women (Table 5-1). In children and teenagers, average caloric intake is higher in boys than in girls and increases with age in boys (Table 5-2). Trends in energy balance are described below. The average US adult gains ≈1 lb per year. In an analysis of >120 000 US men and women in 3 separate US cohorts followed up for up to 20 years, changes in intakes of different foods and beverages were linked to long-term weight gain in different ways.1 Foods and beverages most positively linked to weight gain included refined grains, starches, and sugars, including potatoes, white bread, white rice, low-fiber breakfast cereals, sweets/desserts, and sugar-sweetened beverages, as well as red and processed meats. In contrast, increased consumption of several other foods, including nuts, whole grains, fruits, vegetables, and yogurt, was linked to relative weight loss over time. These findings indicate that attention to dietary quality, not simply counting total calories, is crucial for energy balance.1
  • Diet quality also appears to influence energy expenditure. After intentional weight loss, isocaloric diets higher in fat and lower in rapidly digestible carbohydrates produced significantly smaller declines in total energy expenditure than low-fat, high-carbohydrate diets.2 Similarly, isocaloric meals richer in rapidly digestible carbohydrate increased hunger and stimulated brain regions associated with reward and craving compared with isocaloric meals lower in rapidly digestible carbohydrate.3
  • Other nutritional determinants of positive energy balance (more calories consumed than expended), as determined by adiposity or weight gain, include larger portion sizes4,5 and greater consumption of fast food and commercially prepared meals.610
  • Preferences for portion size are associated with BMI, socioeconomic status, eating in fast food restaurants, and television watching.11,12 Portion sizes are larger at fast food restaurants than at home or at other restaurants.13
  • Between 1999 and 2004, 53% of Americans consumed an average of 1 to 3 restaurant meals per week, and 23% consumed ≥4 restaurant meals per week.14 Spending on food away from home, including restaurant meals, catered foods, and food eaten during out-of-town trips, increased from 26% of average annual food expenditures in 1970 to 42% in 2004.14
  • Macronutrient composition of the overall diet or of specific foods, such as percentage of calories from total fat, does not appear to be strongly associated with energy balance as ascertained by weight gain or loss.1,1517 In contrast, dietary quality as characterized by higher or lower intakes of specific foods and beverages is strongly linked to weight gain (see above).1
  • Emerging evidence suggests that consumption of trans fat may be associated with energy imbalance as assessed by changes in adiposity or weight, as well as more specific adverse effects on visceral adiposity.1820
  • Other individual factors associated with positive energy balance (weight gain) include greater television watching (with evidence that effects are mediated by diet, rather than physical inactivity, including greater snacking in front of the television and the influence of advertising on poor food choices)1,2125 and lower average sleep duration.1,26
  • Randomized controlled trials of weight loss in obese individuals generally show modestly greater weight loss with low-carbohydrate (high-fat) diets than with low-fat diets at 6 months, but at 1 year, such differences diminish, and a diet that focuses on dietary quality and whole foods may be most successful in the long term.2730
  • A comparison of BRFSS data in 1996 and 2003 suggested a shift in self-reported dietary strategies to lose weight, with the proportion focusing on calorie restriction increasing from 11.3% to 24.9% and the proportion focusing on restricting fat consumption decreasing from 41.6% to 29.1%.31
  • On the basis of BRFSS data from 2003, among all American adults who were overweight or obese, a higher proportion was trying to lose weight if also diagnosed with hypertension (58% trying to lose weight), DM (60%), or both diseases (72%) than adults with neither condition (50%).32
  • A 2007 to 2008 national survey of 1082 retail stores in 19 US cities found that energy-dense snack foods/beverages were present in 96% of pharmacies, 94% of gas stations, 22% of furniture stores, 16% of apparel stores, and 29% to 65% of other types of stores.33
  • Societal and environmental factors independently associated with energy imbalance (weight gain), via either increased caloric consumption or decreased expenditure, include education, income, race/ethnicity, and local conditions such as availability of grocery stores, types of restaurants, safety, parks and open spaces, and walking or biking paths.3436 PA is covered in Chapter 4 of this update.

Dietary Patterns

In addition to individual foods and nutrients, overall dietary patterns can be used to assess more global dietary quality. Different dietary patterns have been defined, including the HEI, Alternative HEI, Western versus prudent dietary patterns, Mediterranean dietary pattern, and DASH-type diet. The higher-monounsaturated-fat DASH-type diet is generally similar to a traditional Mediterranean dietary pattern.37

  • In 1999 to 2004, only 19.4% of hypertensive US adults were following a DASH-type diet (based on intake of fiber, magnesium, calcium, sodium, potassium, protein, total fat, saturated fat, and cholesterol). This represented a decrease from 26.7% of hypertensive US adults in 1988 to 1994.38
  • Among older US adults (≥60 years of age) in 1999 to 2002, 72% met guidelines for dietary cholesterol intake, but only between 18% and 32% met guidelines for the HEI food groups (meats, dairy, fruits, vegetables, and grains). On the basis of the HEI score, only 17% of older US adults consumed a good-quality diet. Higher HEI scores were seen in white adults and individuals with greater education; lower HEI scores were seen in black adults and smokers.39

Dietary Supplements

Use of dietary supplements is common in the United States among both adults and children:

  • Approximately half of US adults in 2007 to 2010 used ≥1 dietary supplement, with the most common supplement being multivitamin-multimineral products (32% of men and women reporting use).40 It has been shown that most supplements are taken daily and for ≥2 years.41 Supplement use is associated with older age, higher education, greater PA, moderate alcohol consumption, lower BMI, abstinence from smoking, having health insurance, and white race.40,41 Previous research also suggests that supplement users have higher intakes of most vitamins and minerals from their food choices alone than nonusers.42,43 The primary reasons US adults in 2007 to 2010 reported for using dietary supplements were to “improve overall health” (45%) and to “maintain health” (33%).40
  • One third (32%) of US children (birth to 18 years of age) used dietary supplements in 1999 to 2002, with the highest use (48.5%) occurring among 4- to 8-year-olds. The most common supplements were multivitamins and multiminerals (58% of supplement users). The primary nutrients supplemented (either by multivitamins or individual vitamins) included vitamin C (29% of US children), vitamin A (26%), vitamin D (26%), calcium (21%), and iron (19%). Supplement use was associated with higher family income, a smoke-free home environment, lower child BMI, and less screen time (television, video games, or computers).44
  • In a 2005 to 2006 telephone survey of US adults, 41.3% were making or had made in the past a serious weight-loss attempt. Of these, one third (33.9%) had used a dietary supplement for weight loss, with such use being more common in women (44.9%) than in men (19.8%) and in blacks (48.7%) or Hispanics (41.6%) than in whites (31.2%); in those with high school education or less (38.4%) than in those with some college or more (31.1%); and in those with household income <$40 000 per year (41.8%) than in those with higher incomes (30.3%).45
  • Multiple trials of most dietary supplements, including folate, vitamin C, and vitamin E, have generally shown no significant benefits for CVD risk, and even potential for harm.36 For example, a multicenter randomized trial in patients with diabetic nephropathy found that B vitamin supplementation (folic acid 2.5 mg/d, vitamin B6 25 mg/d, and vitamin B12 1 mg/d) decreased GFR and increased risk of MI and stroke compared with placebo.46
  • Fish oil supplements at doses of 1 to 2 g/d have shown CVD benefits in 2 large randomized, open-label trials and 1 large randomized, placebo-controlled trial (GISSI-Prevenzione, Japan Eicosapentaenoic Acid Lipid Intervention Study, and GISSI-HF),4749 but several other trials of fish oil have not shown significant effects on CVD risk.50 A meta-analysis of all randomized controlled clinical trials demonstrated a significant reduction for cardiac mortality but no statistically significant effects on other CVD end points.51

Trends

Energy Balance

(See Chart 5-1.)

Energy balance, or consumption of total calories appropriate for needs, has been steadily worsening in the United States over the past several decades, as evidenced by the dramatic increases over the past 30 years in overweight and obesity among both children and adults across broad cross sections of sex, race/ethnicity, geographic residence, and socioeconomic status. However, in more recent years, rates of obesity and overweight among both adults and children have begun to level off.5254

  • The US obesity epidemic began in approximately 1980, accelerated from 1990 to 2005, and may be slowing in more recent years. Examining trends in diet, activity, and other factors from 1980 to present is important to elucidate the drivers of this remarkably recent epidemic.
  • Although trends in total calories consumed are difficult to quantify exactly because of differing methods of serial national dietary surveys over time, multiple lines of evidence indicate that average total energy consumption has increased by ≥200 kcal/d per person in the past 3 decades.
  • Data from NHANES indicate that between 1971 and 2004, average total energy consumption among US adults increased by 22% in women (from 1542 to 1886 kcal/d) and by 10% in men (from 2450 to 2693 kcal/d).14 These increases are supported by data from 2 older surveys, the Nationwide Food Consumption Survey (1977–1978) and the Continuing Surveys of Food Intake (1989–1998).13 However, recent data show that energy intake appeared relatively stable among US adults during 1999 to 2008.55
  • The increases in calories consumed between 1971 and 2004 are attributable primarily to greater average carbohydrate intake, particularly of starches, refined grains, and sugars (Foods and Nutrients section). Other specific changes related to increased caloric intake in the United States include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, commercially prepared (especially fast-food) meals, and higher-energy-density foods.7,13,5660
  • Between 1977 and 1996, the average portion sizes for nearly all foods increased at fast-food outlets, other restaurants, and home. These included a 33% increase in the average portion of Mexican food (from 408 to 541 calories), a 34% increase in the average portion of cheeseburgers (from 397 to 533 calories), a 36% increase in the average portion of French fries (from 188 to 256 calories), and a 70% increase in the average portion of salty snacks such as crackers, potato chips, pretzels, puffed rice cakes, and popcorn (from 132 to 225 calories).13
  • Among US children 2 to 7 years of age, an estimated energy imbalance of only 110 to 165 kcal/d (the equivalent of one 12- to 16-oz bottle of soda/cola) was sufficient to account for the excess weight gain between 1988 and 1994 and 1999 and 2002.61
  • In a quantitative analysis using various US surveys between 1977 and 2006, the relations of changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.62 Decreases in energy density were actually linked to lower total energy intake over time, whereas increases in both portion size and number of eating occasions were linked to greater energy intake.
  • Among US children 2 to 18 years of age, increases in energy intake between 1977 and 2006 (179 kcal/d) were entirely attributable to substantial increases in energy eaten away from home (255 kcal/d).63 The percentage of energy eaten away from home increased from 23.4% to 33.9% during this time, with a shift toward energy from fast food as the largest contributor to foods away from home for all age groups.
  • A county-level investigation based on BRFSS and NHANES data found that prevalence of sufficient PA in the United States increased from 2001 to 2009 but that this was matched by increases in obesity in almost all counties during the same time period, with low correlation between level of PA and obesity in US counties.64

Foods and Nutrients

Several changes in foods and nutrients have occurred over time. Selected changes are highlighted below.

Macronutrients

(See Chart 5-1.)

  • Starting in 1977 and continuing until the most recent dietary guidelines revision in 2010, a major focus of US dietary guidelines was reduction of dietary fats.65 During this time, average total fat consumption declined as a percent of calories from 36.9% to 33.4% in men and from 36.1% to 33.8% in women.14 However, more recent analyses show that there were no significant trends in total fat intake among US adults from 1999 to 2008.55
  • Dietary guidelines during this time also emphasized carbohydrate consumption as the base of one’s dietary pattern66 and more recently specified the importance of complex rather than refined carbohydrates (eg, as the base of the Food Guide Pyramid).65 From 1971 to 2004, total carbohydrate intake increased from 42.4% to 48.2% of calories in men and from 45.4% to 50.6% of calories in women.14 Evaluated as absolute intakes, the increase in total calories consumed during this period was attributable primarily to the greater consumption of carbohydrates, both as foods (starches and grains) and as beverages.67,68 However, more recent analyses show that there has been a decrease in carbohydrate intake (expressed as percentage of energy) among US adults from 1999 to 2008.55

Sugar-Sweetened Beverages

(See Chart 5-2.)

Chart 5-2
Per capita calories consumed from different beverages by US adults (≥19 years of age), 1965 to 2010. Source: Nationwide Food Consumption Surveys (1965, 1977–1978) and National Health and Nutrition Examination Survey (1988–2010), ...
  • Between 1965 and 2002, the average percentage of total calories consumed from beverages in the United States increased from 11.8% to 21.0% of energy, which represents an overall absolute increase of 222 kcal/d per person.59 This increase was largely caused by increased consumption of sugar-sweetened beverages and alcohol: Average consumption of fruit juices went from 20 to 39 kcal/d; of milk, from 125 to 94 kcal/d; of alcohol, from 26 to 99 kcal/d; of sweetened fruit drinks, from 13 to 38 kcal/d; and of soda/cola, from 35 to 143 kcal/d.62
  • In contrast, between 1999 and 2010, sugar-sweetened beverage intake decreased among both youth and adults in the United States, consistent with increased attention to their importance as a cause of obesity. In 2009 to 2010, youth and adults consumed a daily average of 155 and 151 kcal/d from sugar-sweetened beverages, respectively, a decrease from 1999 to 2000 of 68 and 45 kcal/d, respectively.69
  • In addition to increased overall consumption, the average portion size of a single sugar-sweetened beverage increased by >50% between 1977 and 1996, from 13.1 to 19.9 fl oz.13
  • Among children and teenagers (2–19 years of age), the largest increases in consumption of sugar-sweetened beverages between 1988 to 1994 and 1999 to 2004 were seen among black and Mexican American youths compared with white youths.60

Fruits and Vegetables
  • Between 1994 and 2005, the average consumption of fruits and vegetables declined slightly, from a total of 3.4 to 3.2 servings per day. The proportions of men and women consuming combined fruits and vegetables ≥5 times per day were low (≈20% and 29%, respectively) and did not change during this period.70

Morbidity and Mortality

Effects on Cardiovascular Risk Factors

Dietary habits affect multiple cardiovascular risk factors, including both established risk factors (SBP, DBP, LDL cholesterol levels, HDL cholesterol levels, glucose levels, and obesity/weight gain) and novel risk factors (eg, inflammation, cardiac arrhythmias, endothelial cell function, triglyceride levels, lipoprotein[a] levels, and heart rate):

  • A DASH dietary pattern with low sodium reduced SBP by 7.1 mm Hg in adults without hypertension and by 11.5 mm Hg in adults with hypertension.71
  • Compared with the low-fat DASH diet, DASH-type diets that increased consumption of either protein or unsaturated fat had similar or greater beneficial effects on CVD risk factors. Compared with a baseline usual diet, each of the DASH-type diets, which included various percentages (27%–37%) of total fat and focused on whole foods such as fruits, vegetables, whole grains, and fish, as well as potassium and other minerals and low sodium, reduced SBP by 8 to 10 mm Hg, DBP by 4 to 5 mm Hg, and LDL cholesterol by 12 to 14 mg/dL. The diets that had higher levels of protein and unsaturated fat also lowered triglyceride levels by 16 and 9 mg/dL, respectively.72 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the low-fat/high-carbohydrate DASH diet.73
  • In a meta-analysis of randomized controlled trials, consumption of 1% of calories from trans fat in place of saturated fat, monounsaturated fat, or polyunsaturated fat, respectively, increased the ratio of total to HDL cholesterol by 0.031, 0.054, and 0.67; increased apolipoprotein B levels by 3, 10, and 11 mg/L; decreased apolipoprotein A-1 levels by 7, 5, and 3 mg/L; and increased lipoprotein(a) levels by 3.8, 1.4, and 1.1 mg/L.74
  • In meta-analyses of randomized controlled trials, consumption of eicosapentaenoic acid and docosahexaenoic acid for 212 weeks lowered SBP by 2.1 mm Hg75 and lowered resting heart rate by 2.5 beats per minute.76
  • In a pooled analysis of 25 randomized trials totaling 583 men and women both with and without hypercholesterolemia, nut consumption significantly improved blood lipid levels.77 For a mean consumption of 67 g of nuts per day, total cholesterol was reduced by 10.9 mg/dL (5.1%), LDL cholesterol by 10.2 mg/dL (7.4%), and the ratio of total cholesterol to HDL cholesterol by 0.24 (5.6% change; P<0.001 for each). Triglyceride levels were also reduced by 20.6 mg/dL (10.2%) in subjects with high triglycerides (2150 mg/dL). Different types of nuts had similar effects.77
  • A review of cross-sectional and prospective cohort studies suggests that higher intake of sugar-sweetened beverages is associated with greater visceral fat and higher risk of type 2 DM.78 Two randomized trials have confirmed that reducing intake of sugar-sweetened beverages reduces weight gain in children.79,80
  • In a randomized controlled trial, compared with a low-fat diet, 2 Mediterranean dietary patterns that included either virgin olive oil or mixed nuts lowered SBP by 5.9 and 7.1 mm Hg, plasma glucose by 7.0 and 5.4 mg/dL, fasting insulin by 16.7 and 20.4 pmol/L, the homeostasis model assessment index by 0.9 and 1.1, and the ratio of total to HDL cholesterol by 0.38 and 0.26 and raised HDL cholesterol by 2.9 and 1.6 mg/dL, respectively. The Mediterranean dietary patterns also lowered levels of C-reactive protein, interleukin-6, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1.81

Effects on Cardiovascular Outcomes

Because dietary habits affect a broad range of established and novel risk factors, estimation of the impact of nutritional factors on cardiovascular health by considering only a limited number of pathways (eg, only effects on lipids, BP, and obesity) will systematically underestimate or even misconstrue the actual total impact on cardiovascular health. Randomized controlled trials and prospective observational studies have been used to quantify the total effects of dietary habits on clinical outcomes.

Fats and Carbohydrates
  • In the WHI randomized clinical trial (n=48 835), reduction of total fat consumption from 37.8% energy (baseline) to 24.3% energy (at 1 year) and 28.8% energy (at 6 years) had no effect on incidence of CHD (RR, 0.98; 95% CI, 0.88–1.09), stroke (RR, 1.02; 95% CI, 0.90–1.15), or total CVD (RR, 0.98; 95% CI, 0.92–1.05) over a mean of 8.1 years.82 This was consistent with null results of 4 prior randomized clinical trials and multiple large prospective cohort studies that indicated little effect of total fat consumption on CVD risk.83
  • In 3 separate meta-analyses of prospective cohort studies, the largest of which included 21 studies with up to 2 decades of follow-up, saturated fat consumption overall had no significant association with incidence of CHD, stroke, or total CVD.8486 In comparison, in a pooled individual-level analysis of 11 prospective cohort studies, the specific exchange of polyunsaturated fat consumption in place of saturated fat was associated with lower CHD risk, with 13% lower risk for each 5% energy exchange (RR, 0.87; 95% CI, 0.70–0.97).87 These findings are consistent with a meta-analysis of randomized controlled trials in which increased polyunsaturated fat consumption in place of saturated fat reduced CHD events, with 10% lower risk for each 5% energy exchange (RR, 0.90; 95% CI, 0.83–0.97).88
  • In a pooled analysis of individual-level data from 11 prospective cohort studies in the United States, Europe, and Israel that included 344 696 participants, each 5% higher energy consumption of carbohydrate in place of saturated fat was associated with a 7% higher risk of CHD (RR, 1.07; 95% CI, 1.01–1.14).87 Each 5% higher energy consumption of monounsaturated fat in place of saturated fat was not significantly associated with CHD risk.87
  • Together these findings suggest that reducing saturated fat without specifying the replacement may have minimal effects on CHD risk, whereas increasing polyunsaturated fats from vegetable oils will reduce CHD.37
  • In a meta-analysis of prospective cohort studies, each 2% of calories from trans fat was associated with a 23% higher risk of CHD (RR, 1.23; 95% CI, 1.11–1.37).89
  • In meta-analyses of prospective cohort studies, greater consumption of refined complex carbohydrates, starches, and sugars, as assessed by glycemic index or load, was associated with significantly higher risk of CHD and DM. When the highest category was compared with the lowest category, risk of CHD was 36% greater (glycemic load: RR, 1.36; 95% CI, 1.13–1.63), and risk of DM was 40% greater (glycemic index: RR, 1.40; 95% CI, 1.23–1.59).90,91

Foods and Beverages
  • In meta-analyses of prospective cohort studies, each daily serving of fruits or vegetables was associated with a 4% lower risk of CHD (RR, 0.96; 95% CI, 0.93–0.99) and a 5% lower risk of stroke (RR, 0.95; 95% CI, 0.92–0.97).92,93
  • In a meta-analysis of prospective cohort studies, greater whole grain intake (2.5 compared with 0.2 servings per day) was associated with a 21% lower risk of CVD events (RR, 0.79; 95% CI, 0.73–0.85), with similar estimates in men and women and for various outcomes (CHD, stroke, and fatal CVD). In contrast, refined grain intake was not associated with lower risk of CVD (RR, 1.07; 95% CI, 0.94–1.22).94
  • In a meta-analysis of 16 prospective cohort studies that included 326 572 generally healthy individuals in Europe, the United States, China, and Japan, fish consumption was associated with significantly lower risk of CHD mortality.95 Compared with no consumption, an estimated 250 mg of long-chain omega-3 fatty acids per day was associated with 35% lower risk of CHD death (P<0.001).
  • In a meta-analysis of prospective cohort and case-control studies from multiple countries, consumption of unprocessed red meat was not significantly associated with incidence of CHD. In contrast, each 50-g serving per day of processed meats (eg, sausage, bacon, hot dogs, deli meats) was associated with a higher incidence of CHD (RR, 1.42; 95% CI, 1.07–1.89).96
  • In a meta-analysis of prospective cohort studies that included 442 101 participants and 28 228 DM cases, unprocessed red meat consumption was associated with a higher risk of DM (RR, 1.19; 95% CI, 1.04–1.37, per 100 g/d). On a per g/d basis, risk of DM was nearly 7-fold higher for processed meat consumption (RR, 1.51; 95% CI, 1.25–1.83, per 50 g/d).97
  • In a meta-analysis of 6 prospective observational studies, nut consumption was associated with significantly lower incidence of CHD (comparing higher to low intake: RR, 0.70; 95% CI, 0.57–0.82).85
  • Higher consumption of dairy or milk products is associated with lower incidence of DM and trends toward lower risk of stroke.77,90,91 Some limited evidence suggests that these associations are stronger for low-fat dairy or milk than for other dairy products. Dairy consumption is not significantly associated with higher or lower risk of CHD.85,98
  • Among 88 520 generally healthy women in the Nurses’ Health Study who were 34 to 59 years of age in 1980 and were followed up from 1980 to 2004, regular consumption of sugar-sweetened beverages was independently associated with higher incidence of CHD, with 23% and 35% higher risk with 1 and ≥2 servings per day, respectively, compared with <1 per month.99 Among the 15 745 participants in the ARIC study, the OR for developing CHD was 2.59 for participants who had a serum uric acid level >9.0 mg/dL and who drank >1 sugar-sweetened soda per day.100

Sodium and Potassium
  • Lower estimated consumption of dietary sodium was not associated with lower CVD mortality in NHANES,101 although such findings may be limited by changes in behaviors that result from underlying risk (reverse causation). In a post hoc analysis of the Trials of Hypertension Prevention, participants randomized to low-sodium interventions had a 25% lower risk of CVD (RR, 0.75; 95% CI, 0.57–0.99) after 10 to 15 years of follow-up after the original trials.102
  • In a meta-analysis of small randomized trials of sodium reduction of ≥6 months’ duration, nonsignificant trends were seen toward fewer CVD events in subjects with normal BP (RR, 0.71; 95% CI, 0.42–1.20; n=200 events) or hypertension (RR, 0.84; 95% CI, 0.57–1.23; n=93 events), but findings were not statistically significant, with relatively low statistical power because of the small numbers of events. Sodium restriction increased total mortality in trials of patients with CHF (RR, 2.59; 95% CI, 1.04–6.44), but these data were based on very few events (n=21 deaths).103
  • In a meta-analysis of 13 prospective cohorts that included 177 025 participants and >11 000 vascular events, higher sodium consumption was associated with greater risk of stroke (pooled RR, 1.23; 95% CI, 1.06–1.43; P=0.007) and a trend toward higher risk of CVD (1.14; 95% CI, 0.99–1.32; P=0.07). These associations were greater with larger differences in sodium intake and longer follow-up.104
  • In a meta-analysis of 15 prospective cohort samples that included 247 510 participants and 7066 strokes, 3058 CHD events, and 2497 total CVD events, each 1.64-g/d (42 mmol/d) higher potassium intake was associated with a 21% lower risk of stroke (RR, 0.79; 95% CI, 0.68–0.90) and trends toward lower risk of CHD and total CVD.105

Dietary Patterns
  • In a cohort of 380 296 US men and women, greater versus lower adherence to a Mediterranean dietary pattern, characterized by higher intakes of vegetables, legumes, nuts, fruits, whole grains, fish, and unsaturated fat and lower intakes of red and processed meat, was associated with a 22% lower cardiovascular mortality (RR, 0.78; 95% CI, 0.69–0.87).106 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke107 and for the DASH-type dietary pattern.108
  • In a cohort of 72 113 US female nurses, a dietary pattern characterized by higher intakes of vegetables, fruits, legumes, fish, poultry, and whole grains was associated with a 28% lower cardiovascular mortality (RR, 0.72; 95% CI, 0.60–0.87), whereas a dietary pattern characterized by higher intakes of processed meat, red meat, refined grains, French fries, and sweets/desserts was associated with a 22% higher cardiovascular mortality (RR, 1.22; 95% CI, 1.01–1.48).109 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.110116
  • The observational findings for benefits of a healthy food–based dietary pattern have been confirmed in 2 randomized clinical trials, including a small secondary prevention trial in France among patients with recent MI117 and a large primary prevention trial in Spain among patients with CVD risk factors.118 The latter trial demonstrated a 30% reduction in the risk of stroke, MI, and death attributable to cardiovascular causes in those patients randomized to Mediterranean-style diets.

Impact on US Mortality
  • One report used consistent and comparable risk assessment methods and nationally representative data to estimate the impact of all major modifiable risk factors on mortality and morbidity in the United States in 1990 and 2010.119 Suboptimal dietary habits were the leading cause of both mortality and disability-adjusted life-years lost, exceeding even tobacco. In 2010, a total of 678 000 deaths of all causes were attributable to suboptimal diet.
  • A previous investigation reported the estimated mortality effects of several specific dietary risk factors in 2005 in the United States. High dietary salt consumption was estimated to be responsible for 102 000 annual deaths, low dietary omega-3 fatty acids for 84 000 annual deaths, high dietary trans fatty acids for 82 000 annual deaths, and low consumption of fruits and vegetables for 55 000 annual deaths.120

Cost

(See Chart 5-3.)

The US Department of Agriculture forecast that the Consumer Price Index for all food would increase 3.0% to 4.0% in 2013 as retailers continued to pass on higher commodity and energy costs to consumers in the form of higher retail prices. The Consumer Price Index for food increased 3.7% in 2011. Prices for foods eaten at home increased 4.8% in 2011, whereas prices for foods eaten away from home increased by 1.9%.121

  • The proportion of total US food expenditures for meals outside the home, as a share of total food dollars, increased from 27% in 1961 to 40% in 1981 to 49% in 2011.66
  • The proportion of sales of meals and snacks from fast-food restaurants compared with total meals and snacks away from home increased from 5% in 1958 to 29% in 1982 to 36% in 2011.121
  • As a proportion of income, food has become less expensive over time in the United States. As a share of personal disposable income, average (mean) total food expenditures by families and individuals have decreased from 22.3% (1949) to 18.1% (1961) to 14.9% (1981) to 11.3% (2011). For any given year, the share of disposable income spent on food is inversely proportional to absolute income. The share increases as absolute income levels decline.121
  • Among 153 forms of fruits and vegetables priced with 2008 Nielsen Homescan data, price and calorie per portion of 20 fruits and vegetables were compared with 20 common snack foods such as cookies, chips, pastries, and crackers. Average price per portion of fruits and vegetables was 31 cents with an average of 57 calories per portion, compared with 33 cents and 183 calories per portion for snack foods.121
  • An overview of the costs of various strategies for primary prevention of CVD determined that the estimated costs per year of life gained were between $9800 and $18 000 for statin therapy, ≈$1500 for nurse screening and lifestyle advice, $500 to $1250 for smoking cessation, and $20 to $900 for population-based healthy eating.122
  • Each year, >$33 billion in medical costs and $9 billion in lost productivity resulting from HD, cancer, stroke, and DM are attributed to poor nutrition.123126
  • Two separate cost-effectiveness analyses estimated that population reductions in dietary salt would not only be cost-effective but actually cost-saving.127,128 In 1 analysis, a 1.2-g/d reduction in dietary sodium was projected to reduce US annual cases of incident CHD by 60 000 to 120 000, stroke by 32 000 to 66 000, and total mortality by 44 000 to 92 000.128 If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 to $24 billion annually.128 Such an intervention would be more cost-effective than using medications to lower BP in all people with hypertension.
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86. Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease. Am J Clin Nutr. 2010;91:535–546. [PubMed]
87. Jakobsen MU, O’Reilly EJ, Heitmann BL, Pereira MA, Bälter K, Fraser GE, Goldbourt U, Hallmans G, Knekt P, Liu S, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A. Major types of dietary fat and risk of coronary heart disease: a pooled analysis of 11 cohort studies. Am J Clin Nutr. 2009;89:1425–1432. [PubMed]
88. Mozaffarian D, Micha R, Wallace S. Effects on coronary heart disease of increasing polyunsaturated fat in place of saturated fat: a systematic review and meta-analysis of randomized controlled trials. PLoS Med. 2010;7:e1000252. [PMC free article] [PubMed]
89. Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans fatty acids and cardiovascular disease. N Engl J Med. 2006;354:1601–1613. [PubMed]
90. Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, Mitchell P, Brand-Miller JC. Glycemic index, glycemic load, and chronic disease risk: a meta-analysis of observational studies. Am J Clin Nutr. 2008;87:627–637. [PubMed]
91. Dong JY, Zhang YH, Wang P, Qin LQ. Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease. Am J Cardiol. 2012;109:1608–1613. [PubMed]
92. Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr. 2006;136:2588–2593. [PubMed]
93. Dauchet L, Amouyel P, Dallongeville J. Fruit and vegetable consumption and risk of stroke: a meta-analysis of cohort studies. Neurology. 2005;65:1193–1197. [PubMed]
94. Mellen PB, Walsh TF, Herrington DM. Whole grain intake and cardiovascular disease: a meta-analysis. Nutr Metab Cardiovasc Dis. 2008;18:283–290. [PubMed]
95. Harris WS, Mozaffarian D, Lefevre M, Toner CD, Colombo J, Cunnane SC, Holden JM, Klurfeld DM, Morris MC, Whelan J. Towards establishing dietary reference intakes for eicosapentaenoic and docosahexaenoic acids. J Nutr. 2009;139:804S–819S. [PubMed]
96. Micha R, Wallace SK, Mozaffarian D. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Circulation. 2010;121:2271–2283. [PMC free article] [PubMed]
97. Pan A, Sun Q, Bernstein AM, Schulze MB, Manson JE, Willett WC, Hu FB. Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am J Clin Nutr. 2011;94:1088–1096. [PubMed]
98. Soedamah-Muthu SS, Ding EL, Al-Delaimy WK, Hu FB, Engberink MF, Willett WC, Geleijnse JM. Milk and dairy consumption and incidence of cardiovascular diseases and all-cause mortality: dose-response meta-analysis of prospective cohort studies. Am J Clin Nutr. 2011;93:158–171. [PubMed]
99. Fung TT, Malik V, Rexrode KM, Manson JE, Willett WC, Hu FB. Sweetened beverage consumption and risk of coronary heart disease in women. Am J Clin Nutr. 2009;89:1037–1042. [PubMed]
100. Bomback AS, Derebail VK, Shoham DA, Anderson CA, Steffen LM, Rosamond WD, Kshirsagar AV. Sugar-sweetened soda consumption, hyperuricemia, and kidney disease. Kidney Int. 2010;77:609–616. [PMC free article] [PubMed]
101. Cohen HW, Hailpern SM, Alderman MH. Sodium intake and mortality follow-up in the Third National Health and Nutrition Examination Survey (NHANES III) J Gen Intern Med. 2008;23:1297–1302. [PMC free article] [PubMed]
102. Cook NR, Cutler JA, Obarzanek E, Buring JE, Rexrode KM, Kumanyika SK, Appel LJ, Whelton PK. Long term effects of dietary sodium reduction on cardiovascular disease outcomes: observational follow-up of the Trials of Hypertension Prevention (TOHP) BMJ. 2007;334:885–888. [PMC free article] [PubMed]
103. Taylor RS, Ashton KE, Moxham T, Hooper L, Ebrahim S. Reduced dietary salt for the prevention of cardiovascular disease: a meta-analysis of randomized controlled trials (Cochrane review) Am J Hypertens. 2011;24:843–853. [PubMed]
104. Strazzullo P, D’Elia L, Kandala NB, Cappuccio FP. Salt intake, stroke, and cardiovascular disease: meta-analysis of prospective studies. BMJ. 2009;339:b4567. [PubMed]
105. D’Elia L, Barba G, Cappuccio FP, Strazzullo P. Potassium intake, stroke, and cardiovascular disease a meta-analysis of prospective studies. J Am Coll Cardiol. 2011;57:1210–1219. [PubMed]
106. Mitrou PN, Kipnis V, Thiébaut AC, Reedy J, Subar AF, Wirfält E, Flood A, Mouw T, Hollenbeck AR, Leitzmann MF, Schatzkin A. Mediterranean dietary pattern and prediction of all-cause mortality in a US population: results from the NIH-AARP Diet and Health Study. Arch Intern Med. 2007;167:2461–2468. [PubMed]
107. Fung TT, Rexrode KM, Mantzoros CS, Manson JE, Willett WC, Hu FB. Mediterranean diet and incidence of and mortality from coronary heart disease and stroke in women. Circulation. 2009;119:1093–1100. [PMC free article] [PubMed]
108. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women [published correction appears in Arch Intern Med. 2008;168:1276] Arch Intern Med. 2008;168:713–720. [PubMed]
109. Heidemann C, Schulze MB, Franco OH, van Dam RM, Mantzoros CS, Hu FB. Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women. Circulation. 2008;118:230–237. [PMC free article] [PubMed]
110. Osler M, Heitmann BL, Gerdes LU, Jørgensen LM, Schroll M. Dietary patterns and mortality in Danish men and women: a prospective observational study. Br J Nutr. 2001;85:219–225. [PubMed]
111. van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in U.S. men. Ann Intern Med. 2002;136:201–209. [PubMed]
112. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer AF, Boeing H. European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study Cohort. A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study cohort. Diabetologia. 2005;48:1126–1134. [PubMed]
113. Brunner EJ, Mosdøl A, Witte DR, Martikainen P, Stafford M, Shipley MJ, Marmot MG. Dietary patterns and 15-y risks of major coronary events, diabetes, and mortality. Am J Clin Nutr. 2008;87:1414–1421. [PubMed]
114. Lutsey PL, Steffen LM, Stevens J. Dietary intake and the development of the metabolic syndrome: the Atherosclerosis Risk in Communities study. Circulation. 2008;117:754–761. [PubMed]
115. Fitzgerald KC, Chiuve SE, Buring JE, Ridker PM, Glynn RJ. Comparison of associations of adherence to a Dietary Approaches to Stop Hypertension (DASH)-style diet with risks of cardiovascular disease and venous thromboembolism. J Thromb Haemost. 2012;10:189–198. [PMC free article] [PubMed]
116. Joosten MM, Grobbee DE, van der ADL, Verschuren WM, Hendriks HF, Beulens JW. Combined effect of alcohol consumption and lifestyle behaviors on risk of type 2 diabetes. Am J Clin Nutr. 2010;91:1777–1783. [PubMed]
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118. Estruch R, Ros E, Salas-Salvadó J, Covas MI, Corella D, Arós F, Gómez-Gracia E, Ruiz-Gutiérrez V, Fiol M, Lapetra J, Lamuela-Raventos RM, Serra-Majem L, Pintó X, Basora J, Muñoz MA, Sorlí JV, Martínez JA. Martínez-González MA; PREDIMED Study Investigators. Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med. 2013;368:1279–1290. [PubMed]
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6. Overweight and Obesity

See Table 6-1 and Charts 6-1 through 6-3.

Chart 6-1
Prevalence of overweight and obesity among students in grades 9 through 12 by sex and race/ethnicity. NH indicates non-Hispanic. Data derived from Eaton et al (Table 101).72
Chart 6-3
Trends in the prevalence of obesity among US children and adolescents by age and survey year (National Health and Nutrition Examination Survey: 1971–1974, 1976–1980, 1988–1994, 1999–2002, 2003–2006, and 2007–2010). ...
Table 6-1
Overweight and Obesity

Overweight and obesity are major risk factors for CVD and stroke.1,2 The AHA has identified BMI <85th percentile (for children) and <25 kg/m2 (for adults aged ≥20 years) as 1 of the 7 components of ideal cardiovascular health.3 In 2009 to 2010, 64.2% of children and 31.1% of adults met these criteria (see Chapter 2, Cardiovascular Health).

Abbreviations Used in Chapter 6

AFatrial fibrillation
AFFIRMAtrial Fibrillation Follow-up Investigation of Rhythm Management
AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CADcoronary artery disease
CARDIACoronary Artery Risk Development in Young Adults
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CVDcardiovascular disease
DMdiabetes mellitus
FHSFramingham Heart Study
HbA1chemoglobin A1c
HDLhigh-density lipoprotein
HRhazard ratio
HUNT 2Nord-Trøndelag Health Study
MEPSMedical Expenditure Panel Survey
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
NCDRNational Cardiovascular Data Registry
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
PAphysical activity
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
STEMIST-segment–elevation myocardial infarction

Prevalence

Youth

(See Table 6-1 and Chart 6-1.)

  • The prevalence of overweight and obesity in children 2 to 5 years of age, based on a BMI-for-age value ≥85th percentile of the 2000 CDC growth charts, was 26% for non-Hispanic white boys and 21% for non-Hispanic white girls, 31% for non-Hispanic black boys and 27% for non-Hispanic black girls, and 34% for Mexican American boys and 33% for Mexican American girls according to 2009 to 2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 30% for non-Hispanic white boys and 25% for non-Hispanic white girls, 41% for non-Hispanic black boys and 44% for non-Hispanic black girls, and 39% for Mexican American boys and 40% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 32% for non-Hispanic white boys and 28% for non-Hispanic white girls, 37% for non-Hispanic black boys and 45% for non-Hispanic black girls, and 46% for Mexican American boys and 41% for Mexican American girls.4
  • The national prevalence of obesity in children 2 to 5 years of age, based on BMI-for-age values ≥95th percentile of the 2000 CDC growth charts, was 12% for non-Hispanic white boys and 6% for non-Hispanic white girls, 21% for non-Hispanic black boys and 17% for non-Hispanic black girls, and 19% for Mexican American boys and 12% for Mexican American girls according to 2009 to 2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 17% for non-Hispanic white boys and 11% for non-Hispanic white girls, 30% for non-Hispanic black boys and 28% for non-Hispanic black girls, and 22% for Mexican American boys and 22% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 18% for non-Hispanic white boys and 15% for non-Hispanic white girls, 23% for non-Hispanic black boys and 25% for non-Hispanic black girls, and 29% for Mexican American boys and 19% for Mexican American girls.4 Regional variation exists in these prevalences.
  • Overall, 18% of US children and adolescents 6 to 19 years of age have BMI-for-age values ≥95th percentile of the 2000 CDC growth charts for the United States (NHANES 2009–2010, NCHS).4
  • NHANES 2009 to 2010 found that 16.9% (95% CI, 15.4%–18.4%) of youth aged 2 to 19 years were obese, which was unchanged from NHANES 2007 to 2008. Rates of overweight and obesity (≥85th BMI percentile) were 39.1% for Hispanics, 39.4% for Mexican Americans, 27.9% for non-Hispanic whites, and 39.1% for non-Hispanic blacks.4
  • A study of >8500 4-year-olds in the Early Childhood Longitudinal Study, Birth Cohort (National Center for Education Statistics) found that 1 in 5 were obese. Almost 13% of Asian children, 16% of white children, nearly 21% of black children, 22% of Hispanic children, and 31% of American Indian children were obese. Children were considered obese if their BMI was ≥95th percentile on the basis of CDC BMI growth charts.5
  • Childhood sociodemographic factors may contribute to sex disparities in obesity prevalence. A study of data from the National Longitudinal Study of Adolescent Health (Add Health) found that parental education consistently modified sex disparity in blacks. The sex gap was largest in those with low parental education (16.7% of men compared with 45.4% of women were obese) and smallest in those with high parental education (28.5% of men compared with 31.4% of women were obese). In whites, there was little overall sex difference in obesity prevalence.6
  • The obesity epidemic is disproportionally more rampant among children living in low-income, low-education, and higher-unemployment households, according to data from the National Survey of Children’s Health.7
  • Data from 2011 show that among low-income preschool children, American Indians/Alaskan Natives have an obesity rate of 17.7%, whereas rates are 14.7% for Hispanics, 10.6% for non-Hispanic blacks, 10.3% for non-Hispanic whites, and 9.3% for Asian/Pacific Islanders.8
  • According to 1999 to 2008 NHANES survey data, lowest-income girls had an obesity prevalence of 17.9% compared with 13.1% among those with higher income; similar observations were observed for boys (20.6% versus 15.6%, respectively).9
  • According to the National Longitudinal Study of Adolescent Health, 1.0% of adolescents were severely obese in 1996 (defined as age <20 years and BMI ≥95th sex-specific BMI-for-age growth chart or BMI ≥30 kg/m2); the majority (70.5%) maintained this weight status into adulthood. Obese adolescents had a 16-fold increased risk of becoming severely obese adults compared with those with normal weight or those who were overweight.10
  • NHANES 2003 to 2004 and 2005 to 2006 data were used to determine overweight and obesity prevalence in rural versus urban youth; the results showed that 39% of rural versus 32% of urban children had BMI >85th percentile.11

Adults

(See Table 6-1 and Chart 6-2.)

Chart 6-2
Age-adjusted prevalence of obesity in adults 20 to 74 years of age by sex and survey year (National Health Examination Survey: 1960–1962; National Health and Nutrition Examination Survey: 1971–1974, 1976–1980, 1988–1994, ...
  • According to NHANES 2007 to 2010 (unpublished NHLBI tabulations):
    • Overall, 68% of US adults were overweight or obese (73% of men and 64% of women).
    • Among men, Mexican-Americans (81%) and non-Hispanic whites (73%) were more likely to be overweight or obese than non-Hispanic blacks (69%).
    • Among women, non-Hispanic blacks (80%) and Mexican-Americans (78%) were more likely to be overweight or obese than non-Hispanic whites (60%).
    • Among US adults, 35% were obese (35% of men and 36% of women).
    • Among men, non-Hispanic blacks (38%) and Mexican-Americans (36%) were more likely to be obese than non-Hispanic whites (34%).
    • Among women, non-Hispanic blacks (54%) and Mexican-Americans (45%) were more likely to be obese than non-Hispanic whites (33%).
  • When estimates were based on self-reported height and weight in the BRFSS/CDC survey in 2011, the prevalence of obesity ranged from 20.7% in Colorado to 34.9% in Mississippi. The median percentage by state was 27.8%.12 Additionally, no state met the Healthy People 2010 goal of reducing obesity to 15% of adults.13
  • On the basis of self-reported weights and heights from the 2012 NHIS14:
    • Blacks ≥18 years of age (27.9%), American Indians or Alaska Natives (26.6%), and whites (35.7%) were less likely than Asians (57.6%) to be at a healthy weight.
    • Blacks ≥18 years of age (36.2%) and American Indians or Alaska Natives (41.2%) were more likely to be obese than were whites (28.0%) and Asians (9.9%).
  • Most adults in Asian subgroups were in the healthy weight range, with rates ranging from 51% for Filipino adults to 68% for Chinese adults. Although the prevalence of obesity is low within the Asian adult population, Filipino adults (14%) were more than twice as likely to be obese (BMI ≥30 kg/m2) as Asian Indian (6%), Vietnamese (5%), or Chinese (4%) adults.15
  • According to the 2008 National Healthcare Disparities Report (based on NHANES 2003–2006)16:
    • Approximately 64.8% of obese adults were told by a doctor or health professional that they were overweight.
    • The proportion of obese adults told that they were overweight was significantly lower for non-Hispanic blacks (60.5%) and Mexican Americans (57.1%) than for non-Hispanic whites (66.4%), for middle-income people than for high-income people (62.4% versus 70.6%), and for adults with less than a high school education than for those with any college education (59.2% versus 70.3%).
  • As judged by an analysis of data from MESA, a large proportion of white, black, and Hispanic participants were overweight (60%–85%) or obese (30%–50%), whereas fewer Chinese American participants were overweight (33%) or obese (5%).17
  • According to NHANES 2007 to 2010 data, 35% of US adults >65 years of age were obese, which represents 13 million individuals.18

Trends

Youth

(See Chart 6-3.)

  • Among infants and children between 6 and 23 months of age, the prevalence of high weight for recumbent length was 7% in 1976 to 1980 and 12% in 2003 to 2006 (NHANES, NCHS).19
  • The obesity epidemic in children continues to grow on the basis of recent data from the Bogalusa Heart Study. Compared with 1973 to 1974, the proportion of children 5 to 17 years of age who were obese was 5 times higher in 2008 to 2009.20
  • A comparison of NHANES 2009 to 2010 data with 1999 to 2000 data demonstrates an increase in obesity prevalence in male youth of 5% (OR, 1.05; 95% CI, 1.01–1.10) but not in female youth (OR, 1.02; 95% CI, 0.98–1.07).4

Adults

  • On the basis of 2009 self-reported BRFSS data, overall obesity prevalence was 26.7% in the United States, with rates of 27.4% in men and 26.0% in women. By race/ethnicity, the prevalence of obesity among non-Hispanic whites was 25.2%, whereas it was 36.8% among non-Hispanic blacks and 30.7% among Hispanics. There was an inverse association by education level: College graduates had a 20.8% rate of obesity, whereas those who attained less than a high school education had an obesity prevalence of 32.9%.21
  • According to NHANES data, between 2009 and 2010, the prevalence of obesity remained steady among US adult men and women, with no significant change compared with 2003 to 2008.22 Among adults aged ≥65 years, the prevalence of obesity increased linearly for men between 1999 and 2010, but the increase among women was not statistically significant.18
  • Forecasts through 2030 using the BRFSS 1990 to 2008 data set suggest that by 2030, 51% of the population will be obese, with 11% with severe obesity, an increase of 33% for obesity and 130% for severe obesity.23

Morbidity

  • Overweight children and adolescents are at increased risk for future adverse health effects, including the following24:
    • Increased prevalence of traditional cardiovascular risk factors such as hypertension, hyperlipidemia, and DM.
    • Poor school performance, tobacco use, alcohol use, premature sexual behavior, and poor diet.
    • Other associated health conditions, such as asthma, hepatic steatosis, sleep apnea, stroke, some cancers (breast, colon, and kidney), renal insufficiency, musculoskeletal disorders, and gallbladder disease.
  • Data from 4 Finnish cohort studies examining childhood and adult BMI with a mean follow-up of 23 years found that overweight or obese children who remained obese in adulthood had increased risks of type 2 DM, hypertension, dyslipidemia, and carotid atherosclerosis. However, those who became normal weight by adulthood had risks comparable to individuals who were never obese.25
  • The CARDIA study showed that young adults who were overweight or obese had lower health-related quality of life than normal-weight participants 20 years later. On the basis of data from the Medical Outcomes Study 12-item short-form health survey, overweight and obese participants had lower multivariable-adjusted scores on the physical component summary score but not on the mental component summary score.25a
  • The increasing prevalence of obesity is driving an increased incidence of type 2 DM. Data from the FHS indicate a doubling in the incidence of DM over the past 30 years, most dramatically during the 1990s and primarily among individuals with a BMI >30 kg/m2.26
  • Among 68 070 participants across multiple NHANES surveys, the decline in BP in recent birth cohorts is slowing, mediated by BMI.27
  • In a meta-analysis from 58 cohorts, representing 221 934 people in 17 developed countries with 14 297 incident CVD outcomes, BMI, waist circumference, and waist-to-hip ratio were only minimally associated with cardiovascular outcomes after controlling for baseline SBP, DM, and total and HDL cholesterol in addition to age, sex, and smoking status. Measures of adiposity also did not improve risk discrimination or reclassification when risk factor data were included.28
  • The population attributable fraction for CHD associated with reducing current population mean BMI to 21 kg/m2 in the Asia-Pacific region ranged from 2% in India to 58% in American Samoa; the population attributable fraction for ischemic stroke ranged from 3% in India to 64% in American Samoa. These data from 15 countries show the proportion of CVD that would be prevented if the population mean BMI were reduced below the current overweight cut point.29
  • Obesity is also a strong predictor of sleep-disordered breathing, itself strongly associated with the development of CVD, as well as with myriad other health conditions, including numerous cancers, nonalcoholic fatty liver disease, gallbladder disease, musculoskeletal disorders, and reproductive abnormalities.30
  • A systematic review of prospective studies examining overweight and obesity as predictors of major stroke subtypes in >2 million participants over ≥4 years found an adjusted RR for ischemic stroke of 1.22 (95% CI, 1.05–1.41) in overweight individuals and an RR of 1.64 (95% CI, 1.36–1.99) for obese individuals relative to normal-weight individuals. RRs for hemorrhagic stroke were 1.01 (95% CI, 0.88–1.17) and 1.24 (95% CI, 0.99–1.54) for overweight and obese individuals, respectively. These risks were graded with increasing BMI and were independent of age, lifestyle, and other cardiovascular risk factors.31
  • A recent meta-analysis of 15 prospective studies demonstrated the increased risk for Alzheimer disease or vascular dementia and any dementia was 1.35 and 1.26 for overweight, respectively, and 2.04 and 1.64 for obesity, respectively.32 The inclusion of obesity in dementia forecast models increases the estimated prevalence of dementia through 2050 by 9% in the United States and 19% in China.33
  • Ten-year follow-up data from the Swedish Obese Subjects intervention study indicated that to maintain a favorable effect on cardiovascular risk factors, more than the short-term goal of 5% weight loss is needed to overcome secular trends and aging effects.34
  • A randomized clinical trial of 130 severely obese adult individuals randomized to either 12 months of diet and PA or only 6 months of PA resulted in 12.1 and 9.9 kg, respectively, of weight loss at 1 year, with improvements in waist circumference, visceral fat, BP, and insulin resistance.35

Mortality

  • Elevated childhood BMIs in the highest quartile were associated with premature death as an adult in a cohort of 4857 American Indian children during a median follow-up of 23.9 years.36
  • According to NHIS data, among young adults aged 18 to 39 years, the HR for all-cause mortality was 1.07 (95% CI, 0.91–1.26) for overweight individuals, 1.41 (95% CI, 1.16–1.73) for obese individuals, and 2.46 for extremely obese individuals (95% CI, 1.91–3.16).37
  • Among adults, obesity was associated with nearly 112 000 excess deaths (95% CI, 53 754–170 064) relative to normal weight in 2000. Grade 1 obesity (BMI 30 to <35 kg/m2) was associated with almost 30 000 of these excess deaths (95% CI, 8534–68 220) and grade 2 to 3 obesity (BMI ≥35 kg/m2) with >82 000 (95% CI, 44 843–119 289). Underweight was associated with nearly 34 000 excess deaths (95% CI, 15 726–51 766). As other studies have found,38 overweight (BMI 25 to <30 kg/m2) was not associated with excess deaths.39
  • A recent systematic review (2.88 million individuals and >270 000 deaths) showed that relative to normal BMI (18.5 to <25 kg/m2), all-cause mortality was lower for overweight (HR, 0.94; 95% CI, 0.91–0.96) but was not elevated for grade 1 obesity (HR, 0.95; 95% CI, 0.88–1.01). All-cause mortality was higher for obesity (all grades; HR, 1.18; 95% CI, 1.12–1.25) and grades 2 and 3 obesity (HR, 1.29; 95% CI, 1.18–1.41).40
  • In a collaborative analysis of data from almost 900 000 adults in 57 prospective studies, mostly in western Europe and North America, overall mortality was lowest at a BMI of ≈22.5 to 25 kg/m2 in both sexes and at all ages, after exclusion of early follow-up and adjustment for smoking status. Above this range, each 5-kg/m2-higher BMI was associated with ≈30% higher all-cause mortality, and no specific cause of death was inversely associated with BMI. Below 22.5 to 25 kg/m2, the overall inverse association with BMI was predominantly related to strong inverse associations for smoking-related respiratory disease, and the only clearly positive association was for ischemic heart disease.41
  • In a meta-analysis of 1.46 million white adults, over a mean follow-up period of 10 years, all-cause mortality was lowest at BMI levels of 20.0 to 24.9 kg/m2. Among women, compared with a BMI of 22.5 to 24.9 kg/m2, the HRs for death were as follows: BMI 15.0 to 18.4 kg/m2, 1.47; 18.5 to 19.9 kg/m2, 1.14; 20.0 to 22.4 kg/m2, 1.0; 25.0 to 29.9 kg/m2, 1.13; 30.0 to 34.9 kg/m2, 1.44; 35.0 to 39.9 kg/m2, 1.88; and 40.0 to 49.9 kg/m2, 2.51. Similar estimates were observed in men.42
  • Overweight was associated with significantly increased mortality resulting from DM or kidney disease and was not associated with increased mortality resulting from cancer or CVD in an analysis of 2004 data from NHANES. Obesity was associated with significantly increased mortality caused by CVD, some cancers, and DM or kidney disease. Obesity was associated with 13% of CVD deaths in 2004.43
  • A BMI paradox has been reported, with higher-BMI patients demonstrating favorable outcomes in CHF, hypertension, peripheral vascular disease, and CAD; similar findings have been seen for percent body fat. In AFFIRM, a multicenter trial of AF, obese patients had lower all-cause mortality (HR, 0.77; P=0.01) than normal-weight patients after multivariable adjustment over a 3-year follow-up period.44
  • Interestingly, among 2625 participants with new-onset DM, rates of total, CVD, and non-CVD mortality were higher among normal-weight people compared with overweight/obese participants, with adjusted HRs of 2.08 (95% CI, 1.52–2.85), 1.52 (95% CI, 0.89–2.58), and 2.32 (95% CI, 1.55–3.48), respectively.45
  • Calculations based on NHANES data from 1978 to 2006 suggest that the gains in life expectancy from smoking cessation are beginning to be outweighed by the loss of life expectancy related to obesity.46
  • Because of the increasing prevalence of obesity, the number of quality-adjusted life-years lost as a result of obesity is similar to or greater than that lost as a result of smoking, according to data from the BRFSS.47
  • Recent estimates suggest that reductions in smoking, cholesterol, BP, and physical inactivity levels resulted in a gain of 2 770 500 life-years; however, these gains were reduced by a loss of 715 000 life-years caused by the increased prevalence of obesity and DM.48
  • In a comparison of 5 different anthropometric variables (BMI, waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio) in 62 223 individuals from Norway with 12 years of follow-up from the HUNT 2 study, the risk of death per SD increase in each measure was 1.02 (95% CI, 0.99–1.06) for BMI, 1.10 (95% CI, 1.06–1.14) for waist circumference, 1.01 (95% CI, 0.97–1.05) for hip circumference, 1.15 (95% CI, 1.11–1.19) for waist-to-hip ratio, and 1.12 (95% CI, 1.08–1.16) for waist-to-height ratio. For CVD mortality, the risk of death per SD increase was 1.12 (95% CI, 1.06–1.20) for BMI, 1.19 (95% CI, 1.12–1.26) for waist circumference, 1.06 (95% CI, 1.00–1.13) for hip circumference, 1.23 (95% CI, 1.16–1.30) for waist-to-hip ratio, and 1.24 (95% CI, 1.16–1.31) for waist-to-height ratio.49
  • According to data from the NCDR, among patients presenting with STEMI and a BMI ≥40 kg/m2, in-hospital mortality rates were higher for patients with class III obesity (OR, 1.64; 95% CI, 1.32–2.03) when class I obesity was used as the referent.50
  • In a study of 22 203 women and men from England and Scotland, metabolically unhealthy obese individuals were at an increased risk of all-cause mortality compared with metabolically healthy obese individuals (HR, 1.72; 95% CI, 1.23–2.41).51

Cost

  • If current trends in the growth of obesity continue, total healthcare costs attributable to obesity could reach $861 to $957 billion by 2030, which would account for 16% to 18% of US health expenditures.52
  • According to NHANES I data linked to Medicare and mortality records, obese 45-year-olds had lifetime Medicare costs of $163 000 compared with $117 000 among those with normal weight by the time they reached 65 years of age.53
  • The total excess cost related to the current prevalence of adolescent overweight and obesity is estimated to be $254 billion ($208 billion in lost productivity secondary to premature morbidity and mortality and $46 billion in direct medical costs).54
  • According to 2006 MEPS and 2006 BRFSS data, annual medical expenditures would be 6.7% to 10.7% lower in the absence of obesity.55
  • According to data from the Medicare Current Beneficiary Survey from 1997 to 2006, in 1997, expenditures for a Part A and Part B services beneficiary were $6832 for a normal-weight individual, which was more than for overweight ($5473) or obese ($5790) individuals. However, over time, expenses increased more rapidly for overweight and obese individuals.56
  • The costs of obesity are high: Obese people pay on average $1429 (42%) more for healthcare costs than normal-weight individuals. For obese beneficiaries, Medicare pays $1723 more, Medicaid pays $1021 more, and private insurers pay $1140 more than for beneficiaries who are at normal weight. Similarly, obese people have 46% higher inpatient costs and 27% more outpatient visits and spend 80% more on prescription drugs.57

Bariatric Surgery

  • Patients with BMI >40 kg/m2 or >35 kg/m2 with an obesity-related comorbidity are eligible for gastric bypass surgery, which is typically performed as either a Roux-en-Y gastric bypass or a biliopancreatic diversion.
  • According to the 2006 NHDS, the incidence of bariatric surgery was estimated at 113 000 cases per year, with costs of nearly $1.5 billion annually.58
  • In a large bariatric surgery cohort, the prevalence of high 10-year predicted CVD risk was 36.5%,59 but 76% of those with low 10-year risk had high lifetime predicted CVD risk. The corresponding prevalence in US adults is 18% and 56%, respectively.60
  • Among obese Swedish patients undergoing bariatric surgery and followed up for up to 15 years, maximum weight loss was 32%. The risk of death was 0.76 among those who underwent bariatric surgery compared with matched control subjects.57 More recent data examining MI and stroke showed that bariatric surgery was associated with fewer CVD deaths (HR, 0.47; 95% CI, 0.29–0.76) and fewer strokes (HR, 0.67; 95% CI, 0.54–0.83) than in the control group. However, CVD risk was related to baseline CVD risk factors rather than to baseline BMI or 2-year weight change.61
  • Among 641 patients followed up for 10 years compared with 627 matched control subjects, after 2 years of follow-up, 72% of the surgically treated patients versus 21% of the control patients had remission of their DM; at 10 years of follow-up, results were 36% and 13%, respectively. Similar results have been observed for hypertension, elevated triglycerides, and low HDL cholesterol.62
  • According to retrospective data from the United States, among 9949 patients who underwent gastric bypass surgery, after a mean of 7 years, long-term mortality was 40% lower among the surgically treated patients than among obese control subjects. Specifically, cancer mortality was reduced by 60%, DM mortality by 92%, and CAD mortality by 56%. Nondisease death rates (eg, accidents, suicide) were 58% higher in the surgery group.63
  • A recent retrospective cohort from the Veterans Affairs medical system showed that in a propensity-matched analysis, bariatric surgery was not associated with reduced mortality compared with obese control subjects (time-adjusted HR, 0.94; 95% CI, 0.64–1.39).64
  • Two recent randomized controlled trials were performed that randomized bariatric surgery compared with intensive medical treatment among patients with type 2 DM. The first study randomized 150 patients and conducted 12-month follow-up; this study showed that glycemic control improved (6.4%) and weight loss was greater (29.4 versus 5.4 kg) in the surgical arm.65 The second trial randomized 60 patients to bariatric surgery versus medical therapy and conducted follow-up for 24 months. The results showed that DM remission occurred in 75% of the group that underwent gastric bypass surgery compared with 0% of those in the medical treatment arm, with HbA1c values of 6.35% in the surgical arm compared with 7.69% in the medical treatment arm.66
  • Of 120 patients with type 2 DM and a BMI between 30 and 39.9 kg/m2, 60 who were randomized to Roux-en-Y gastric bypass were almost 5-fold (OR, 4.8; 95% CI, 1.9–11.7) more likely to achieve an HbA1c <7.0% at 12-month follow-up. However, there were 22 serious adverse events in the intervention arm, including early and late perioperative complications and nutritional deficiencies.67
  • A recent cost-effectiveness study of laparoscopic adjustable gastric banding showed that after 5 years, $4970 was saved in medical expenses; if indirect costs were included (absenteeism and presenteeism), savings increased to $6180 and $10 960, respectively.68 However, when expressed per quality-adjusted life expectancy, only $6600 was gained for laparoscopic gastric bypass, $6200 for laparoscopic adjustable gastric band, and $17 300 for open Roux-en-Y gastric bypass, none of which exceeded the standard $50 000 per quality-adjusted life expectancy gained.69
  • Adolescents (aged 10–19 years old) underwent bariatric surgery at a rate of 0.8/100 000 procedures, which increased to 2.3/100 000 in 2003 and remained constant by 2009 at 2.4/100 000.70
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71. American Medical Association Expert Task Force on Childhood Obesity. [Accessed September 20, 2012];Appendix: Expert Committee recommendations on the assessment, prevention, and treatment of child and adolescent overweight and obesity. 2007 Jan 25; http://www.ama-assn.org/ama1/pub/upload/mm/433/ped_obesity_recs.pdf.
72. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H. Centers for Disease Control and Prevention (CDC) Youth Risk Behavior Surveillance: United States, 2011. MMWR Surveill Summ. 2012;61:1–162. [PubMed]
73. National Center for Health Statistics. Health, United States, 2011: With Special Feature on Socioeconomic Status and Health. Hyattsville, MD: National Center for Health Statistics; 2011. [Accessed August 1, 2012]. http://www.cdc.gov/nchs/data/hus/hus11.pdf. [PubMed]

7. Family History and Genetics

See Tables 7-1 through 7-3.

Table 7-1
OR for Combinations of Parental Heart Attack History
Table 7-3
Heritability of CVD Risk Factors From the FHS

Biologically related first-degree relatives (siblings, offspring and parents) share roughly 50% of their genetic variation with one another. This constitutes much greater sharing of genetic variation than with a randomly selected person from the population, and thus, when a trait aggregates within a family, this lends evidence for a genetic risk factor for the trait. Similarly, racial/ethnic minorities are more likely to share their genetic variation within their demographic than with other demographics. Familial aggregation of CVD may be related to aggregation of specific behaviors (eg, smoking, alcohol use) or risk factors (eg, hypertension, DM, obesity) that may themselves have environmental and genetic contributors. Unlike classic mendelian genetic risk factors, whereby usually 1 mutation directly causes 1 disease, a complex trait’s genetic contributors may increase risk without necessarily always causing the condition. The effect size of any specific contributor to risk may be small but widespread throughout a population, or may be large but affect only a small population, or may have an enhanced risk when an environmental contributor is present. Although the breadth of all genetic research into CVD is beyond the scope of this chapter, we present a summary of evidence that a genetic risk for CVD is likely, as well as a summary of evidence on the most consistently replicated genetic markers for HD and stroke identified to date.

Abbreviations Used in Chapter 7

AAAabdominal aortic aneurysm
ABIankle-brachial index
AFatrial fibrillation
BMIbody mass index
CACcoronary artery calcification
CADcoronary artery disease
CARDIoGRAMplusC4DCoronary Artery Disease Genome-wide Replication and Meta-Analysis (CARDIOGRAM) plus the Coronary Artery Disease (C4D) Genetics Consortium
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
FHSFramingham Heart Study
GFRglomerular filtration rate
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDheart disease
HDLhigh-density lipoprotein
HFheart failure
LDLlow-density lipoprotein
MImyocardial infarction
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
SBPsystolic blood pressure
SEstandard error
SNPsingle-nucleotide polymorphism

Family History

Prevalence

  • Among adults ≥20 years of age, 12.6% (SE 0.5%) reported having a parent or sibling with a heart attack or angina before the age of 50 years. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • For non-Hispanic whites, 12.4% (SE 0.7%) for men, 14.9% (SE 0.9%) for women
    • For non-Hispanic blacks, 8.1% (SE 0.8%) for men, 13.0% (SE 0.9%) for women
    • For Mexican Americans, 8.1% (SE 0.9%) for men, 10.0% (SE 1.1%) for women
    • For other Hispanics, 8.8% (SE 1.5%) for men, 12.0% (SE 1.2%) for women
    • For other races, 8.7% (SE 2.1%) for men, 10.7% (SE 2.6%) for women
  • HD occurs as people age, and those without a family history of HD may survive longer, so the prevalence of family history will vary depending on the age at which it is assessed. The breakdown of reported family history of heart attack by age in the US population as measured by NHANES is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • Age 20 to 39 years, 8.4% (SE 0.9%) for men, 10.3% (SE 0.7%) for women
    • Age 40 to 59 years, 12.8% (SE 0.9%) for men, 15.3% (SE 1.1%) for women
    • Age 60 to 79 years, 13.7% (SE 0.9%) for men, 17.5% (SE 1.2%) for women
    • Age ≥80 years, 9.8% (SE 1.5%) for men, 13.7% (SE 0.6%) for women
  • In the multigenerational FHS, only 75% of participants with a documented parental history of a heart attack before age 55 years reported that history when asked.1

Impact of Family History

  • Premature paternal history of a heart attack has been shown to approximately double the risk of a heart attack in men and increase the risk in women by ≈70%.2,3
  • History of a heart attack in both parents increases the risk of heart attack, especially when 1 parent had a premature heart attack4 (Table 7-1).
  • Sibling history of HD has been shown to increase the odds of HD in men and women by ≈50%.5
  • Premature family history of angina, MI, angioplasty, or bypass surgery increased the lifetime risk by ≈50% for both HD (from 8.9% to 13.7%) and CVD mortality (from 14.1% to 21%).6
  • Similarly, parental history of AF is associated with ≈80% increased odds of AF in men and women,7 and a history of stroke in a first-degree relative increases the odds of stroke in men and women by ≈50%.8

Genetics

Heart Disease

  • Genome-wide association is a robust technique to identify associations between genotypes and phenotypes. Table 7-2 presents results from the CARDIoGRAMplusC4D Consortium, which represents the largest genetic study of CAD to date. Although the ORs are modest, ranging from 1.06 to 1.51 per copy of the risk allele (individuals may harbor up to 2 copies of a risk allele), these are common alleles, which suggests that the attributable risk may be substantial. Additional analysis suggested that loci associated with CAD were involved in lipid metabolism and inflammation pathways.9
    Table 7-2
    Validated SNPs for CAD, the Nearest Gene, and the OR From the CARDIoGRAMplusC4D Consortium
  • The relationship between genetic variants associated with CHD and measured CHD risk factors is complex, with some genetic markers associated with multiple risk factors and other markers showing no association with risk factors.10
  • Genetic markers discovered thus far have not been shown to add to cardiovascular risk prediction tools beyond current models that incorporate family history.11 Genetic markers have also not been shown to improve prediction of subclinical atherosclerosis beyond traditional risk factors.12 However, an association between genetic markers and coronary calcification has been seen.13
  • The most consistently replicated genetic marker for HD in European-derived populations is located at 9p21.3. At this single-nucleotide polymorphism, ≈27% of the white population is estimated to have 0 risk alleles, 50% is estimated to have 1 risk allele, and the remaining 23% is estimated to have 2 risk alleles.14
  • The 10-year HD risk for a 65-year-old man with 2 risk alleles at 9p21.3 and no other traditional risk factors is ≈13.2%, whereas a similar man with 0 alleles would have a 10-year risk of ≈9.2%. The 10-year HD risk for a 40-year-old woman with 2 alleles and no other traditional risk factors is ≈2.4%, whereas a similar woman with 0 alleles would have a 10-year risk of ≈1.7%.14
  • Variation at the 9p21.3 region also increases the risk of HF15 and sudden death.16 Associations have also been observed between the 9p21.3 region and CAC.17,18 Additionally, stronger associations have been found between variation at 9p21.3 and earlier17,18 and more severe19 heart attacks. The biological mechanism underpinning the association of genetic variation in the 9p21 region with disease outcomes is still under investigation.

Stroke

  • The same 9p21.3 region has also been associated with intracranial aneurysm,20 AAA,21 and ischemic stroke.22
  • For large-vessel ischemic stroke, an association for large-vessel stroke with histone deacetylase 9 on chromosome 7p21.1 has been identified (>9000 subjects) and replicated (>12 000 subjects).22,23

CVD Risk Factors

  • Heritability is the ratio of genetically caused variation to the total variation of a trait or measure. Table 7-3 presents heritability estimates for standard CVD risk factors using data generated from the FHS. These data suggest that most CVD risk factors have at least moderate heritability.
1. Murabito JM, Nam BH, D’Agostino RB, Sr, Lloyd-Jones DM, O’Donnell CJ, Wilson PW. Accuracy of offspring reports of parental cardiovascular disease history: the Framingham Offspring Study. Ann Intern Med. 2004;140:434–440. [PubMed]
2. Lloyd-Jones DM, Nam BH, D’Agostino RB, Sr, Levy D, Murabito JM, Wang TJ, Wilson PW, O’Donnell CJ. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. 2004;291:2204–2211. [PubMed]
3. Sesso HD, Lee IM, Gaziano JM, Rexrode KM, Glynn RJ, Buring JE. Maternal and paternal history of myocardial infarction and risk of cardiovascular disease in men and women. Circulation. 2001;104:393–398. [PubMed]
4. Chow CK, Islam S, Bautista L, Rumboldt Z, Yusufali A, Xie C, Anand SS, Engert JC, Rangarajan S, Yusuf S. Parental history and myocardial infarction risk across the world: the INTERHEART Study. J Am Coll Cardiol. 2011;57:619–627. [PubMed]
5. Murabito JM, Pencina MJ, Nam BH, D’Agostino RB, Sr, Wang TJ, Lloyd-Jones D, Wilson PW, O’Donnell CJ. Sibling cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults. JAMA. 2005;294:3117–3123. [PubMed]
6. Bachmann JM, Willis BL, Ayers CR, Khera A, Berry JD. Association between family history and coronary heart disease death across long-term follow-up in men: the Cooper Center Longitudinal Study. Circulation. 2012;125:3092–3098. [PMC free article] [PubMed]
7. Fox CS, Parise H, D’Agostino RB, Sr, Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring. JAMA. 2004;291:2851–2855. [PubMed]
8. Liao D, Myers R, Hunt S, Shahar E, Paton C, Burke G, Province M, Heiss G. Familial history of stroke and stroke risk: the Family Heart Study. Stroke. 1997;28:1908–1912. [PubMed]
9. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, König IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikäinen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi-Boehm S, Cox D, Dimitriou M, Do R, Doney AS, El Mokhtari N, Eriksson P, Fischer K, Fontanillas P, Franco-Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Müller-Nurasyid M, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schäfer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, van der Schoot CE, Wagner PJ, Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrières J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kähönen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lee JY, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Trégouët DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvänen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimäki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O’Donnell C, Reilly MP, März W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ. Wellcome Trust Case Control Consortium, MuTHER Consortium, Diagram Consortium, CARDIOGENICS Consortium, CARDIoGRAMplusC4D Consortium. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33. [PMC free article] [PubMed]
10. Angelakopoulou A, Shah T, Sofat R, Shah S, Berry DJ, Cooper J, Palmen J, Tzoulaki I, Wong A, Jefferis BJ, Maniatis N, Drenos F, Gigante B, Hardy R, Laxton RC, Leander K, Motterle A, Simpson IA, Smeeth L, Thomson A, Verzilli C, Kuh D, Ireland H, Deanfield J, Caulfield M, Wallace C, Samani N, Munroe PB, Lathrop M, Fowkes FG, Marmot M, Whincup PH, Whittaker JC, de Faire U, Kivimaki M, Kumari M, Hypponen E, Power C, Humphries SE, Talmud PJ, Price J, Morris RW, Ye S, Casas JP, Hingorani AD. Comparative analysis of genome-wide association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration. Eur Heart J. 2012;33:393–407. [PMC free article] [PubMed]
11. Holmes MV, Harrison S, Talmud PJ, Hingorani AD, Humphries SE. Utility of genetic determinants of lipids and cardiovascular events in assessing risk. Nat Rev Cardiol. 2011;8:207–221. [PubMed]
12. Hernesniemi JA, Seppälä I, Lyytikäinen LP, Mononen N, Oksala N, Hutri-Kähönen N, Juonala M, Taittonen L, Smith EN, Schork NJ, Chen W, Srinivasan SR, Berenson GS, Murray SS, Laitinen T, Jula A, Kettunen J, Ripatti S, Laaksonen R, Viikari J, Kähönen M, Raitakari OT, Lehtimäki T. Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey: a meta-analysis of three independent studies. PLoS One. 2012;7:e28931. [PMC free article] [PubMed]
13. Thanassoulis G, Peloso GM, Pencina MJ, Hoffmann U, Fox CS, Cupples LA, Levy D, D’Agostino RB, Hwang SJ, O’Donnell CJ. A genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart Study. Circ Cardiovasc Genet. 2012;5:113–121. [PMC free article] [PubMed]
14. Palomaki GE, Melillo S, Bradley LA. Association between 9p21 genomic markers and heart disease: a meta-analysis. JAMA. 2010;303:648–656. [PubMed]
15. Yamagishi K, Folsom AR, Rosamond WD, Boerwinkle E. ARIC Investigators. A genetic variant on chromosome 9p21 and incident heart failure in the ARIC study. Eur Heart J. 2009;30:1222–1228. [PMC free article] [PubMed]
16. Newton-Cheh C, Cook NR, VanDenburgh M, Rimm EB, Ridker PM, Albert CM. A common variant at 9p21 is associated with sudden and arrhythmic cardiac death. Circulation. 2009;120:2062–2068. [PMC free article] [PubMed]
17. Assimes TL, Knowles JW, Basu A, Iribarren C, Southwick A, Tang H, Absher D, Li J, Fair JM, Rubin GD, Sidney S, Fortmann SP, Go AS, Hlatky MA, Myers RM, Risch N, Quertermous T. Susceptibility locus for clinical and subclinical coronary artery disease at chromosome 9p21 in the multi-ethnic ADVANCE study. Hum Mol Genet. 2008;17:2320–2328. [PMC free article] [PubMed]
18. O’Donnell CJ, Cupples LA, D’Agostino RB, Fox CS, Hoffmann U, Hwang SJ, Ingellson E, Liu C, Murabito JM, Polak JF, Wolf PA, Demissie S. Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study. BMC Med Genet. 2007;8(suppl 1):S4. [PMC free article] [PubMed]
19. Dandona S, Stewart AF, Chen L, Williams K, So D, O’Brien E, Glover C, Lemay M, Assogba O, Vo L, Wang YQ, Labinaz M, Wells GA, McPherson R, Roberts R. Gene dosage of the common variant 9p21 predicts severity of coronary artery disease. J Am Coll Cardiol. 2010;56:479–486. [PubMed]
20. Foroud T, Koller DL, Lai D, Sauerbeck L, Anderson C, Ko N, Deka R, Mosley TH, Fornage M, Woo D, Moomaw CJ, Hornung R, Huston J, Meissner I, Bailey-Wilson JE, Langefeld C, Rouleau G, Connolly ES, Worrall BB, Kleindorfer D, Flaherty ML, Martini S, Mackey J, De Los Rios La Rosa F, Brown RD, Jr, Broderick JP. FIA Study Investigators. Genome-wide association study of intracranial aneurysms confirms role of Anril and SOX17 in disease risk. Stroke. 2012;43:2846–2852. [PMC free article] [PubMed]
21. Helgadottir A, Thorleifsson G, Magnusson KP, Grétarsdottir S, Steinthorsdottir V, Manolescu A, Jones GT, Rinkel GJ, Blankensteijn JD, Ronkainen A, Jääskeläinen JE, Kyo Y, Lenk GM, Sakalihasan N, Kostulas K, Gottsäter A, Flex A, Stefansson H, Hansen T, Andersen G, Weinsheimer S, Borch-Johnsen K, Jorgensen T, Shah SH, Quyyumi AA, Granger CB, Reilly MP, Austin H, Levey AI, Vaccarino V, Palsdottir E, Walters GB, Jonsdottir T, Snorradottir S, Magnusdottir D, Gudmundsson G, Ferrell RE, Sveinbjornsdottir S, Hernesniemi J, Niemelä M, Limet R, Andersen K, Sigurdsson G, Benediktsson R, Verhoeven EL, Teijink JA, Grobbee DE, Rader DJ, Collier DA, Pedersen O, Pola R, Hillert J, Lindblad B, Valdimarsson EM, Magnadottir HB, Wijmenga C, Tromp G, Baas AF, Ruigrok YM, van Rij AM, Kuivaniemi H, Powell JT, Matthiasson SE, Gulcher JR, Thorgeirsson G, Kong A, Thorsteinsdottir U, Stefansson K. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet. 2008;40:217–224. [PubMed]
22. International Stroke Genetics Consortium (ISGC); Wellcome Trust Case Control Consortium 2 (WTCCC2) Bellenguez C, Bevan S, Gschwendtner A, Spencer CC, Burgess AI, Pirinen M, Jackson CA, Traylor M, Strange A, Su Z, Band G, Syme PD, Malik R, Pera J, Norrving B, Lemmens R, Freeman C, Schanz R, James T, Poole D, Murphy L, Segal H, Cortellini L, Cheng YC, Woo D, Nalls MA, Müller-Myhsok B, Meisinger C, Seedorf U, Ross-Adams H, Boonen S, Wloch-Kopec D, Valant V, Slark J, Furie K, Delavaran H, Langford C, Deloukas P, Edkins S, Hunt S, Gray E, Dronov S, Peltonen L, Gretarsdottir S, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Boncoraglio GB, Parati EA, Attia J, Holliday E, Levi C, Franzosi MG, Goel A, Helgadottir A, Blackwell JM, Bramon E, Brown MA, Casas JP, Corvin A, Duncanson A, Jankowski J, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Viswanathan AC, Wood NW, Worrall BB, Kittner SJ, Mitchell BD, Kissela B, Meschia JF, Thijs V, Lindgren A, Macleod MJ, Slowik A, Walters M, Rosand J, Sharma P, Farrall M, Sudlow CL, Rothwell PM, Dichgans M, Donnelly P, Markus HS. Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke. Nat Genet. 2012;44:328–333. [PMC free article] [PubMed]
23. Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng YC, Fornage M, Ikram MA, Malik R, Bevan S, Thorsteinsdottir U, Nalls MA, Longstreth W, Wiggins KL, Yadav S, Parati EA, Destefano AL, Worrall BB, Kittner SJ, Khan MS, Reiner AP, Helgadottir A, Achterberg S, Fernandez-Cadenas I, Abboud S, Schmidt R, Walters M, Chen WM, Ringelstein EB, O’Donnell M, Ho WK, Pera J, Lemmens R, Norrving B, Higgins P, Benn M, Sale M, Kuhlenbäumer G, Doney AS, Vicente AM, Delavaran H, Algra A, Davies G, Oliveira SA, Palmer CN, Deary I, Schmidt H, Pandolfo M, Montaner J, Carty C, de Bakker PI, Kostulas K, Ferro JM, van Zuydam NR, Valdimarsson E, Nordestgaard BG, Lindgren A, Thijs V, Slowik A, Saleheen D, Paré G, Berger K, Thorleifsson G, Hofman A, Mosley TH, Mitchell BD, Furie K, Clarke R, Levi C, Seshadri S, Gschwendtner A, Boncoraglio GB, Sharma P, Bis JC, Gretarsdottir S, Psaty BM, Rothwell PM, Rosand J, Meschia JF, Stefansson K, Dichgans M, Markus HS. Australian Stroke Genetics Collaborative, Wellcome Trust Case Control Consortium 2 (WTCCC2); International Stroke Genetics Consortium. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11:951–962. [PMC free article] [PubMed]

8. High Blood Cholesterol and Other Lipids

See Table 8-1 and Charts 8-1 through 8-3.

Chart 8-1
Trends in mean serum total cholesterol among adolescents 12 to 17 years of age by race, sex, and survey year (National Health and Nutrition Examination Survey: 1976–1980,* 1988–1994,* 1999–2004, and 2005–2010). Values are ...
Chart 8-3
Age-adjusted trends in the prevalence of serum total cholesterol ≥200 mg/dL in adults ≥20 years of age by sex, race/ethnicity, and survey year (National Health and Nutrition Examination Survey 2005–2006, 2007–2008, and ...
Table 8-1
High Total and LDL Cholesterol and Low HDL Cholesterol

High cholesterol is a major risk factor for CVD and stroke.1 The AHA has identified untreated total cholesterol <170 mg/dL (for children) and <200 mg/dL (for adults) as 1 of the 7 components of ideal cardiovascular health.2 In 2009 to 2010, 61.9% of children and 47.3% of adults met these criteria.

Prevalence

For information on dietary cholesterol, total fat, saturated fat, and other factors that affect blood cholesterol levels, see Chapter 5 (Nutrition).

Youth

(See Chart 8-1.)

  • Among children 6 to 11 years of age, the mean total cholesterol level is 161.9 mg/dL. For boys, it is 162.3 mg/dL; for girls, it is 161.5 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • For non-Hispanic whites, 160.9 mg/dL for boys and 161.6 mg/dL for girls
    • For non-Hispanic blacks, 165.2 mg/dL for boys and 157.9 mg/dL for girls
    • For Mexican Americans, 159.6 mg/dL for boys and 160.7 mg/dL for girls
  • Among adolescents 12 to 19 years of age, the mean total cholesterol level is 158.2 mg/dL. For boys, it is 156.1 mg/dL; for girls, it is 160.3 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • For non-Hispanic whites, 156.8 mg/dL for boys and 161.1 mg/dL for girls
    • For non-Hispanic blacks, 154.1 mg/dL for boys and 160.6 mg/dL for girls
    • For Mexican Americans, 157.8 mg/dL for boys and 158.0 mg/dL for girls
  • The prevalence of abnormal lipid levels among youths 12 to 19 years of age is 20.3%; 14.2% of normal-weight youths, 22.3% of overweight youths, and 42.9% of obese youths have ≥1 abnormal lipid level (NHANES 1999–2006, NCHS).3
  • Approximately 7.8% of adolescents 12 to 19 years of age have total cholesterol levels ≥200 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).
  • Fewer than 1% of adolescents are potentially eligible for pharmacological treatment on the basis of guidelines from the American Academy of Pediatrics.3,4

Abbreviations Used in Chapter 7

AHAAmerican Heart Association
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CVDcardiovascular diseases
DMdiabetes mellitus
HDheart disease
HDLhigh-density lipoprotein
LDLlow-density lipoprotein
Mex. Am.Mexican American
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute

Adults

(See Table 8-1 and Charts 8-2 and 8-3.)

Chart 8-2
Trends in mean serum total cholesterol among adults aged ≥20 years by race and survey year (National Health and Nutrition Examination Survey: 1988–1994, 1999–2002, 2003–2006, and 2007–2010). Values are in mg/dL. ...
  • An estimated 31.9 million adults ≥20 years of age have serum total cholesterol levels ≥240 mg/dL (extrapolated to 2010 by use of NCHS/NHANES 2007–2010 data), with a prevalence of 13.8% (Table 8-1; unpublished NHLBI tabulation).
  • Approximately 5.6% of adults ≥20 years of age have undiagnosed hypercholesterolemia, defined as a total cholesterol level ≥240 mg/dL and the participant having responded “no” to ever having been told by a doctor or other healthcare professional that the participant’s blood cholesterol level was high (NHANES 2007–2010, unpublished NHLBI tabulation).
  • Between the periods 1988 to 1994 and 1999 to 2002 (NHANES/NCHS), the age-adjusted mean serum total cholesterol level of adults ≥20 years of age decreased from 206 to 203 mg/dL, and LDL cholesterol levels decreased from 129 to 123 mg/dL.5
  • Data from NHANES 2003 to 2008 (NCHS) showed the serum total crude mean cholesterol level in adults ≥20 years of age was 195 mg/dL for men and 201 mg/dL for women.6
  • Data from the Minnesota Heart Survey (1980–1982 to 2000–2002) showed a decline in age-adjusted mean total cholesterol concentrations from 5.49 and 5.38 mmol/L (98.8 and 96.8 mg/dL) for men and women, respectively, in 1980 to 1982 to 5.16 and 5.09 mmol/L (92.8 and 91.6 mg/dL), respectively, in 2000 to 2002; however, the decline was not uniform across all age groups. Middle-aged to older people have shown substantial decreases, but younger people have shown little overall change and recently had increased total cholesterol values. Lipid-lowering drug use rose significantly for both sexes among those 35 to 74 years of age. Awareness, treatment, and control of hypercholesterolemia have increased; however, more than half of those at borderline-high risk remain unaware of their condition.7
  • According to data from NHANES 2005 to 2006, between the periods 1999 to 2000 and 2005 to 2006, mean serum total cholesterol levels in adults ≥20 years of age declined from 204 to 199 mg/dL. This decline was observed for men ≥40 years of age and for women ≥60 years of age. There was little change over this time period for other sex/age groups. In 2005 to 2006, ≈65% of men and 70% of women had been screened for high cholesterol in the past 5 years, and 16% of adults had serum total cholesterol levels ≥240 mg/dL.8
  • According to data from NHANES 2007 to 2008, mean serum total cholesterol levels in adults aged 20 to 74 years declined further to 197 mg/dL. Overall, the decline in cholesterol levels in recent years appears to reflect greater uptake of cholesterol-lowering medications rather than changes in dietary patterns.9
  • According to data from NHANES, from 1999 to 2006, the prevalence of elevated LDL cholesterol levels (as defined by levels higher than the specified Adult Treatment Panel III risk category) in adults ≥20 years of age has decreased by ≈33%.10
  • During the period from 1999 to 2006, 26.0% of adults had hypercholesterolemia, 9% of adults had both hypercholesterolemia and hypertension, 1.5% of adults had DM and hypercholesterolemia, and 3% of adults had all 3 conditions.11

Screening

  • Data from the BRFSS study of the CDC in 2011 showed that the percentage of adults who had been screened for high cholesterol in the preceding 5 years ranged from 66.3% in Utah to 83.7% in Massachusetts. The median percentage among all 50 states was 75.5%.12
  • The percentage of adults who reported having had a cholesterol check increased from 68.6% during 1999 to 2000 to 74.8% during 2005 to 2006.13

Awareness

  • Data from the BRFSS (CDC) survey in 2011 showed that among adults screened for high cholesterol, the percentage who had been told that they had high cholesterol ranged from 33.5% in Colorado to 42.3% in Mississippi. The median percentage among states was 38.4%.12
  • Among adults with hypercholesterolemia, the percentage who had been told that they had high cholesterol increased from 42.0% during 1999 to 2000 to 50.4% during 2005 to 2006.13

Treatment

  • NHANES data on the treatment of high LDL cholesterol showed an increase from 28.4% of individuals during 1999 to 2002 to 48.1% during 2005 to 2008.14
  • Self-reported use of cholesterol-lowering medications increased from 8.2% during 1999 to 2000 to 14.0% during 2005 to 2006.13

Adherence

Youth

The American Academy of Pediatrics recommends screening for dyslipidemia in children and adolescents who have a family history of dyslipidemia or premature CVD, those whose family history is unknown, and those youths with risk factors for CVD, such as being overweight or obese, having hypertension or DM, or being a smoker.3

Analysis of data from NHANES 1999 to 2006 showed that the overall prevalence of abnormal lipid levels among youths 12 to 19 years of age was 20.3%.3

Adults

  • On the basis of data from the Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults1:
    • Fewer than half of all people who qualify for any kind of lipid-modifying treatment for CHD risk reduction are receiving it.
    • Fewer than half of even the highest-risk people (those with symptomatic CHD) are receiving lipid-lowering treatment.
    • Only approximately one third of treated patients are achieving their LDL goal; <20% of patients with CHD are at their LDL goal.
  • Data from NHANES 2005 to 2006 indicate that among those with elevated LDL cholesterol levels, 35.5% had not been screened previously, 24.9% were screened but not told they had elevated cholesterol, and 39.6% were treated inadequately.10
  • There were 33.2% of adults overall during 2005 to 2008 in NHANES who achieved LDL cholesterol goals. Among adults without health insurance, only 22.6% achieved LDL cholesterol goals; however, 82.8% of those adults with uncontrolled LDL cholesterol did have some form of health insurance.14

Lipid Levels

LDL (Bad) Cholesterol

Youth
  • There are limited data available on LDL cholesterol for children 6 to 11 years of age.
  • Among adolescents 12 to 19 years of age, the mean LDL cholesterol level is 89.5 mg/dL. For boys, it is 88.6 mg/dL, and for girls, it is 90.5 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • Among non-Hispanic whites, 90.4 mg/dL for boys and 90.9 mg/dL for girls
    • Among non-Hispanic blacks, 85.8 mg/dL for boys and 91.8 mg/dL for girls
    • Among Mexican Americans, 90.6 mg/dL for boys and 87.1 mg/dL for girls
  • High levels of LDL cholesterol occurred in 7.3% of male adolescents and 7.6% of female adolescents during 2007 to 2010.3

Adults
  • The mean level of LDL cholesterol for American adults ≥20 years of age was 115.8 mg/dL in 2007 to 2010.8 Levels of 130 to 159 mg/dL are considered borderline high, levels of 160 to 189 mg/dL are classified as high, and levels of ≥190 mg/dL are considered very high according to Adult Treatment Panel III.
  • According to NHANES 2007 to 2010 (unpublished NHLBI tabulation):
    • Among non-Hispanic whites, mean LDL cholesterol levels were 115.1 mg/dL for men and 115.7 mg/dL for women.
    • Among non-Hispanic blacks, mean LDL cholesterol levels were 115.9 mg/dL for men and 114.2 mg/dL for women.
    • Among Mexican Americans, mean LDL cholesterol levels were 119.7 mg/dL for men and 115.0 mg/dL for women.
  • The age-adjusted prevalence of high LDL cholesterol in US adults was 26.6% in 1988 to 1994 and 25.3% in 1999 to 2004 (NHANES/NCHS). Between 1988 to 1994 and 1999 to 2004, awareness increased from 39.2% to 63.0%, and use of pharmacological lipid-lowering treatment increased from 11.7% to 40.8%. LDL cholesterol control increased from 4.0% to 25.1% among those with high LDL cholesterol. In 1999 to 2004, rates of LDL cholesterol control were lower among adults 20 to 49 years of age than among those ≥65 years of age (13.9% versus 30.3%, respectively), among non-Hispanic blacks and Mexican Americans than among non-Hispanic whites (17.2% and 16.5% versus 26.9%, respectively), and among men than among women (22.6% versus 26.9%, respectively).15
  • Mean levels of LDL cholesterol decreased from 126.1 mg/dL during 1999 to 2000 to 116.1 mg/dL during 2009 to 2010. The prevalence of high LDL cholesterol decreased from 31.5% during 1999 to 2000 to 28.2% during 2009 to 2010 (unpublished NHLBI tabulation).

HDL (Good) Cholesterol

Youth
  • Among children 6 to 11 years of age, the mean HDL cholesterol level is 53.6 mg/dL. For boys, it is 55.1 mg/dL, and for girls, it is 51.9 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • Among non-Hispanic whites, 53.9 mg/dL for boys and 51.4 mg/dL for girls
    • Among non-Hispanic blacks, 59.9 mg/dL for boys and 55.3 mg/dL for girls
    • Among Mexican Americans, 53.5 mg/dL for boys and 50.5 mg/dL for girls
  • Among adolescents 12 to 19 years of age, the mean HDL cholesterol level is 51.4 mg/dL. For boys, it is 49.2 mg/dL, and for girls, it is 53.6 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • Among non-Hispanic whites, 48.4 mg/dL for boys and 53.0 mg/dL for girls
    • Among non-Hispanic blacks, 53.9 mg/dL for boys and 55.4 mg/dL for girls
    • Among Mexican Americans, 47.5 mg/dL for boys and 53.3 mg/dL for girls
  • Low levels of HDL cholesterol occurred in 21.7% of male adolescents and 10.7% of female adolescents during 2007 to 2010 (NHANES 2007–2010, unpublished NHLBI tabulation).

Adults
  • An HDL cholesterol level <40 mg/dL in adult males and <50 mg/dL in adult females is considered low and is a risk factor for HD and stroke.1 The mean level of HDL cholesterol for American adults ≥20 years of age is 52.5 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).
  • According to NHANES 2007 to 2010 (unpublished NHLBI tabulation):
    • Among non-Hispanic whites, mean HDL cholesterol levels were 46.7 mg/dL for men and 58.1 mg/dL for women
    • Among non-Hispanic blacks, mean HDL cholesterol levels were 52.6 mg/dL for men and 58.7 mg/dL for women
    • Among Mexican Americans, mean HDL cholesterol levels were 45.4 mg/dL for men and 53.7 mg/dL for women

Triglycerides

Youth
  • There are limited data available on triglycerides for children 6 to 11 years of age.
  • Among adolescents 12 to 19 years of age, the geometric mean triglyceride level is 82.9 mg/dL. For boys, it is 85.6 mg/dL, and for girls, it is 80.1 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):
    • Among non-Hispanic whites, 89.6 mg/dL for boys and 83.5 mg/dL for girls
    • Among non-Hispanic blacks, 66.7 mg/dL for boys and 58.6 mg/dL for girls
    • Among Mexican Americans, 97.1 mg/dL for boys and 83.5 mg/dL for girls
  • High levels of triglycerides occurred in 9.4% of male adolescents and 6.7% of female adolescents during 2007 to 2010.3

Adults
  • A fasting triglyceride level ≥150 mg/dL in adults is considered elevated and is a risk factor for HD and stroke. The geometric mean level of triglycerides for American adults ≥20 years of age is 130.3 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).
    • Among men, the geometric mean triglyceride level is 141.7 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation). The racial/ethnic breakdown is as follows:
      • 140.0 mg/dL for non-Hispanic white men
      • 111.3 mg/dL for non-Hispanic black men
      • 161.4 mg/dL for Mexican American men
    • Among women, the geometric mean triglyceride level is 119.1 mg/dL, with the following racial/ethnic breakdown:
      • 121.5 mg/dL for non-Hispanic white women
      • 94.4 mg/dL for non-Hispanic black women
      • 134.1 mg/dL for Mexican American women
  • Approximately 27% of adults ≥20 years of age had a triglyceride level ≥150 mg/dL during 2007 to 2010 (NHANES 2007–2010, unpublished NHLBI tabulation).
  • Fewer than 3% of adults with a triglyceride level ≥150 mg/dL received pharmacological treatment during 1999 to 2004.16
1. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421. [PubMed]
2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD. American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613. [PubMed]
3. Centers for Disease Control and Prevention (CDC) Prevalence of abnormal lipid levels among youths: United States, 1999–2006 [published correction appears in MMWR Morb Mortal Wkly Rep. 2010;59:78] MMWR Morb Mortal Wkly Rep. 2010;59:29–33. [PubMed]
4. Ford ES, Li C, Zhao G, Mokdad AH. Concentrations of low-density lipoprotein cholesterol and total cholesterol among children and adolescents in the United States. Circulation. 2009;119:1108–1115. [PubMed]
5. Carroll MD, Lacher DA, Sorlie PD, Cleeman JI, Gordon DJ, Wolz M, Grundy SM, Johnson CL. Trends in serum lipids and lipoproteins of adults, 1960–2002. JAMA. 2005;294:1773–1781. [PubMed]
6. National Center for Health Statistics. Health, United States, 2010: With Special Feature on Death and Dying. Hyattsville, MD: National Center for Health Statistics; 2011. [Accessed July 5, 2011]. http://www.cdc.gov/nchs/data/hus/hus10.pdf.
7. Arnett DK, Jacobs DR, Jr, Luepker RV, Blackburn H, Armstrong C, Claas SA. Twenty-year trends in serum cholesterol, hypercholesterolemia, and cholesterol medication use: the Minnesota Heart Survey, 1980–1982 to 2000–2002. Circulation. 2005;112:3884–3891. [PubMed]
8. Schober S, Carroll M, Lacher D, Hirsch R. Division of Health and Nutrition Examination Surveys. High serum total cholesterol: an indicator for monitoring cholesterol lowering efforts: US adults, 2005–2006. NCHS Data Brief. 2007;(2):1–8. [PubMed]
9. Ford ES, Capewell S. Trends in total and low-density lipoprotein cholesterol among U.S. adults: contributions of changes in dietary fat intake and use of cholesterol-lowering medications. PLoS ONE. 2013;8:e65228. [PMC free article] [PubMed]
10. Kuklina EV, Yoon PW, Keenan NL. Trends in high levels of low-density lipoprotein cholesterol in the United States, 1999–2006. JAMA. 2009;302:2104–2110. [PubMed]
11. Fryar CD, Hirsch R, Eberhardt MS, Yoon SS, Wright JD. Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in U.S. adults, 1999–2006. NCHS Data Brief. 2010;(36):1–8. [PubMed]
12. Behavioral Risk Factor Surveillance System: prevalence and trends data. [Accessed July 5, 2011];Centers for Disease Control and Prevention Web site. http://apps.nccd.cdc.gov/brfss/index.asp.
13. Ford ES, Li C, Pearson WS, Zhao G, Mokdad AH. Trends in hypercholesterolemia, treatment and control among United States adults. Int J Cardiol. 2010;140:226–235. [PubMed]
14. Centers for Disease Control and Prevention (CDC) Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol: United States, 1999–2002 and 2005–2008. MMWR Morb Mortal Wkly Rep. 2011;60:109–114. [PubMed]
15. Hyre AD, Muntner P, Menke A, Raggi P, He J. Trends in ATP-III-defined high blood cholesterol prevalence, awareness, treatment and control among U.S. adults. Ann Epidemiol. 2007;17:548–555. [PubMed]
16. Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Hypertriglyceridemia and its pharmacologic treatment among US adults. Arch Intern Med. 2009;169:572–578. [PubMed]

9. High Blood Pressure

ICD-9 401 to 404, ICD-10 I10 to I15. See Tables 9-1 and 9-2 and Charts 9-1 through 9-5.

Chart 9-1
Prevalence of high blood pressure in adults ≥20 years of age by age and sex (National Health and Nutrition Examination Survey: 2007–2010). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ...
Chart 9-5
Extent of awareness, treatment, and control of high blood pressure by race/ethnicity and sex (National Health and Nutrition Examination Survey: 2007–2010). NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, ...
Table 9-1
High Blood Pressure
Table 9-2
Hypertension Awareness, Treatment, and Control: NHANES 1999 to 2004 and 2005 to 2010, by Race/Ethnicity and Sex

High blood pressure is a major risk factor for CVD and stroke.1 The AHA has identified untreated BP <90th percentile (for children) and <120/<80 mm Hg (for adults aged ≥20 years) as 1 of the 7 components of ideal cardiovascular health.2 In 2009 to 2010, 85.8% of children and 44.3% of adults met these criteria (Chapter 2, Cardiovascular Health).

Abbreviations Used in Chapter 9

AHAAmerican Heart Association
ARICAtherosclerosis Risk in Communities Study
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHFcongestive heart failure
CHSCardiovascular Health Study
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EDemergency department
FHSFramingham Heart Study
HBPhigh blood pressure
HDheart disease
ICD-9International Classification of Diseases, 9th Revision
ICD-9-CMInternational Classification of Diseases, Clinical Modification, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
LDLlow-density lipoprotein
MEPSMedical Expenditure Panel Survey
MESAMulti-Ethnic Study of Atherosclerosis
NAMCSNational Ambulatory Medical Care Survey
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHAMCSNational Hospital Ambulatory Medical Care Survey
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHESNational Health Examination Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NINDSNational Institute of Neurological Disorders and Stroke
NNHSNational Nursing Home Survey
PAphysical activity
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEARCHSearch for Diabetes in Youth Study
WHIWomen’s Health Initiative

Prevalence

(See Table 9-1 and Chart 9-1.)

  • HBP is defined as:
    • SBP ≥140 mm Hg or DBP ≥90 mm Hg or taking antihypertensive medicine, or
    • Having been told at least twice by a physician or other health professional that one has HBP.
  • One in 3 US adults has HBP (unpublished NHLBI tabulation).
  • Data from NHANES 2007 to 2010 found that ≈6% of US adults have undiagnosed hypertension. Data from the 2007 to 2008 BRFSS, NHIS, and NHANES surveys found 27.8%, 28.5%, and 30.7% US adults were told they had hypertension, respectively.3
  • Prevalence of hypertension (age adjusted) among US adults ≥18 years of age was estimated to be 28.6% in NHANES 2009 to 2010.
    • Among those 18 to 39 years of age, prevalence was 6.8%; among those 40 to 59 years of age, prevalence was 30.4%; and among those ≥60 years of age, prevalence was 66.7%. Furthermore, prevalence of hypertension among non-Hispanic blacks, non-Hispanic whites, and Hispanics was 40.4%, 27.4%, and 26.1%, respectively.4
  • An estimated 77.9 million adults ≥20 years of age have HBP, extrapolated to 2010 with NHANES 2007 to 2010 data (Table 9-1).
  • NHANES data show that a higher percentage of men than women have hypertension until 45 years of age. From 45 to 54 and from 55 to 64 years of age, the percentages of men and women with hypertension are similar. After that, a higher percentage of women have hypertension than men (Chart 9-1).
  • HBP is 2 to 3 times more common in women taking oral contraceptives than in women not taking them.1
  • Data from NHANES 1999 to 2008 and BRFSS 1997 to 2009 estimated the prevalence of hypertension in men and women ≥30 years of age to be 37.6% and 40.1%, respectively. Awareness, treatment, and control of hypertension varied across the country and were highest in the southeastern United States. Between 2001 and 2009, control of hypertension increased, as did prevalence of hypertension.5
  • Data from the 2011 BRFSS/CDC indicate that the percentage of adults ≥18 years of age who had been told that they had HBP ranged from 22.9% in Utah to 40.1% in Alabama. The median percentage was 30.8%.6
  • According to 2003 to 2008 NHANES data, among US adults with hypertension, 8.9% met the criteria for resistant hypertension (BP was ≥140/90 mm Hg, and they reported using antihypertensive medications from 3 different drug classes or drugs from ≥4 antihypertensive drug classes regardless of BP). This represents 12.8% of the population taking antihypertensive medication.7
  • According to data from NHANES 1988 to 1994 and 2007 to 2008, HBP control rates improved from 27.3% to 50.1%, treatment improved from 54.0% to 73.5%, and the control/treated rates improved from 50.6% to 72.3%.8
  • Projections show that by 2030, ≈41.4% of US adults will have hypertension, an increase of 8.4% from 2012 estimates (unpublished AHA computation, based on methodology described by Heidenreich et al9).

Older Adults

  • In 2009 to 2010, hypertension was among the diagnosed chronic conditions that were more prevalent among older (≥65 years of age) women than older men (57% for women, 54% for men). Ever-diagnosed conditions that were more prevalent among older men than older women included HD (37% for men, 26% for women) and DM (24% for men, 18% for women), on the basis of data from NHIS/NCHS.10
  • The age-adjusted prevalence of hypertension (both diagnosed and undiagnosed) in 2003 to 2006 was 75% for older women and 65% for older men on the basis of data from NHANES/NCHS.11
  • Data from the 2004 NNHS revealed the most frequent chronic medical condition among this nationally representative sample of long-term stay residents aged ≥65 years was hypertension (53% of men and 56% of women). In men, prevalence of hypertension decreased with increasing age.12
  • Among US adults ≥65 years of age (NHANES 1999–2004), prevalence of hypertension was 70.8%, awareness of hypertension was 75.9%, treatment for hypertension was 69.3%, and control of hypertension was 48.8%. Women had a slightly higher prevalence than men and a significantly lower rate of hypertension control.13

Children and Adolescents

  • Data from participants aged 12 to 19 years in the 2005 to 2010 NHANES found ideal blood pressure (<95th percentile) to be present in 78% of males and 90% of females; poor blood pressure (>95th percentile) was found in 2.9% of male and 3.7% of female participants.14
  • Analysis of data from participants aged 12 to 19 years in NHANES 1999 to 2008 found the prevalence of prehypertension/hypertension was 14%. Furthermore, there was no significant change in the prevalence of prehypertension/hypertension between 1999 to 2000 and 2007 to 2008 among this age group.15
  • Analysis of the NHES, the Hispanic Health and Nutrition Examination Survey, and the NHANES/NCHS surveys of the NCHS (1963–2002) found that the BP, pre-HBP, and HBP trends in children and adolescents 8 to 17 years of age moved downward from 1963 to 1988 and upward thereafter. Pre-HBP and HBP increased 2.3% and 1%, respectively, between 1988 and 1999. Increased obesity (abdominal obesity more so than general obesity) partially explained the HBP and pre-HBP rise from 1988 to 1999. BP and HBP reversed their downward trends 10 years after the increase in the prevalence of obesity. In addition, an ethnic and sex gap appeared in 1988 for pre-HBP and in 1999 for HBP: Non-Hispanic blacks and Mexican Americans had a greater prevalence of HBP and pre-HBP than non-Hispanic whites, and the prevalence was greater in boys than in girls. In that study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.16
  • A study in Ohio of >14 000 children and adolescents 3 to 18 years of age who were observed at least 3 times between 1999 and 2006 found that 507 children (3.6%) had hypertension. Of these, 131 (26%) had been diagnosed and 376 (74%) were undiagnosed. In addition, 3% of those with hypertension had stage 2 hypertension, and 41% of those with stage 2 hypertension were undiagnosed. Criteria for prehypertension were met by 485 children. Of these, 11% were diagnosed. In this study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.17
  • Analysis of data from the SEARCH study, which included children 3 to 17 years of age with type 1 and type 2 DM, found the prevalence of elevated BP to be 5.9% among those with type 1 DM and 23.7% among those with type 2 DM.18
  • A study of high school students in Houston, TX (mean age 15.4 years; 45.2% male, 49.3% Hispanic, 25.2% Caucasian, and 16.1% African American) found ≈30% of the students had ≥1 elevated BP measurement; elevated BP was significantly influenced by obesity.19
  • Longitudinal BP outcomes from the National Childhood Blood Pressure database (ages 13–15 years) were examined after a single BP measurement. Among those determined to have prehypertension, 14% of boys and 12% of girls had hypertension 2 years later; the overall rate of progression from prehypertension to hypertension was ≈7%.20

Race/Ethnicity and HBP

(See Table 9-1 and Chart 9-2.)

Chart 9-2
Age-adjusted prevalence trends for high blood pressure in adults ≥20 years of age by race/ethnicity, sex, and survey (National Health and Nutrition Examination Survey: 1988–1994, 1999–2004, and 2005–2010). NH indicates ...
  • The prevalence of hypertension in blacks in the United States is among the highest in the world, and it is increasing. From 1988 to 1994 through 1999 to 2002, the prevalence of HBP in adults increased from 35.8% to 41.4% among blacks, and it was particularly high among black women at 44.0%. Prevalence among whites also increased, from 24.3% to 28.1%.21
  • Compared with whites, blacks develop HBP earlier in life, and their average BPs are much higher. As a result, compared with whites, blacks have a 1.3-times greater rate of nonfatal stroke, a 1.8-times greater rate of fatal stroke, a 1.5-times greater rate of death attributable to HD, and a 4.2-times greater rate of end-stage kidney disease (fifth and sixth reports of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure).
  • Data from the 2012 NHIS showed that black adults 18 years of age were more likely (32.9%) to have been told on ≥2 occasions that they had hypertension than white adults (22.9%), American Indian/Alaska Native adults (24.8%), or Asian adults (21.2%).22
  • Trend analyses that used NHANES 1988 to 1994 and 1999 to 2004 data among non-Hispanic black and non-Hispanic white men and women found that non-Hispanic blacks had the highest prevalence of hypertension among both men and women during both time periods. The largest increase in prevalence of hypertension occurred among women (both non-Hispanic black and non-Hispanic white) compared with men. Racial/ethnic disparities did not change over time periods.23
  • Analysis from the REGARDS study of the NINDS suggests that efforts to raise awareness of prevalent hypertension among blacks apparently have been successful (31% greater odds in blacks relative to whites), and efforts to communicate the importance of receiving treatment for hypertension have been successful (69% greater odds among blacks relative to whites); however, substantial racial disparities remain with regard to the control of BP (SBP <140 mm Hg, DBP <90 mm Hg), with the odds of control being 27% lower in blacks than in whites. In contrast, geographic disparities in hypertension awareness, treatment, and control were minimal.24
  • The CDC analyzed death certificate data from 1995 to 2002 (any-mention mortality; ICD-9 codes 401–404 and ICD-10 codes I10–I13). The results indicated that Puerto Rican Americans had a consistently higher hypertension-related death rate than all other Hispanic subpopulations and non-Hispanic whites. The age-standardized hypertension-related mortality rate was 127.2 per 100 000 population for all Hispanics, similar to that of non-Hispanic whites (135.9). The age-standardized rate for Hispanic females (118.3) was substantially lower than that observed for Hispanic males (135.9). Hypertension-related mortality rates for males were higher than rates for females for all Hispanic subpopulations. Puerto Rican Americans had the highest hypertension-related death rate among all Hispanic subpopulations (154.0); Cuban Americans had the lowest (82.5).25
  • Some studies suggest that Hispanic Americans have rates of HBP similar to or lower than those of non-Hispanic white Americans. Findings from a new analysis of combined data from the NHIS of 2000 to 2002 point to a health disparity between black and white adults of Hispanic descent. Black Hispanics were at slightly greater risk than white Hispanics, although non-Hispanic black adults had by far the highest rate of HBP. The racial disparity among Hispanics also was evident in the fact that higher-income, better-educated black Hispanics still had a higher rate of HBP than lower-income, less-educated white Hispanics.26 Data from the NHLBI’s ARIC study found that hypertension was a particularly powerful risk factor for CHD in black people, especially black women.27
  • Data from MESA found that being born outside the United States, speaking a language other than English at home, and living fewer years in the United States were each associated with a decreased prevalence of hypertension.28
  • Filipino (27%) and Japanese (25%) adults were more likely than Chinese (17%) or Korean (17%) adults to have ever been told that they had hypertension.29

Mortality

(See Table 9-1.)

  • HBP mortality in 2010 was 63 119. Any-mention mortality in 2010 was 362 895. The 2010 death rate was 18.8.30
  • The 2010 overall death rate resulting from HBP was 18.8. Death rates were 17.2 for white males, 50.2 for black males, 15.0 for white females, and 37.1 for black females. When any-mention mortality for 2010 was used, the overall death rate was 108.9. Death rates were 112.5 for white males, 216.8 for black males, 90.6 for white females, and 161.9 for black females.30
  • From 2000 to 2010, the death rate attributable to HBP increased 16.0%, and the actual number of deaths rose 41.5% (AHA tabulation).31
  • A mathematical model was developed to estimate the number of deaths that potentially could be prevented annually by increasing the use of 9 clinical preventive services. The model predicted that a 10% increase in hypertension treatment would result in ≈14 000 deaths prevented.32
  • Analysis of NHANES I and II comparing hypertensive and nonhypertensive individuals found a reduction in age-adjusted mortality rate of 4.6 per 1000 person-years among people with hypertension compared with a reduction of 4.2 per 1000 person-years among those without hypertension.33
  • Assessment of 30-year follow-up of the Hypertension Detection and Follow-up Program identified the long-term benefit of stepped care, as well as the increased survival for hypertensive African Americans.34
  • Assessment of the Charleston Heart Study and Evans County Heart Study identified the excess burden of elevated BP for African Americans and its effect on long-term health outcomes.35
  • Data from the Harvard Alumni Health Study found that higher BP in early adulthood was associated several decades later with higher risk for all-cause mortality, CVD mortality, and CHD mortality but not stroke mortality.36

Risk Factors

  • Numerous risk factors and markers for development of hypertension have been identified, including age, ethnicity, family history of hypertension and genetic factors, lower education and socioeconomic status, greater weight, lower PA, tobacco use, psychosocial stressors, sleep apnea, and dietary factors (including dietary fats, higher sodium intake, lower potassium intake, and excessive alcohol intake).
  • A study of related individuals in the NHLBI’s FHS suggested that different sets of genes regulate BP at different ages.37
  • Recent data from the Nurses’ Health Study suggest that a large proportion of incident hypertension in women can be prevented by controlling dietary and lifestyle risk factors.38
  • A meta-analysis identified the benefit of a goal BP of 130/80 mm Hg for individuals with hypertension and type 2 DM but less evidence for treatment below this value.39

Aftermath

  • Approximately 69% of people who have a first heart attack, 77% of those who have a first stroke, and 74% of those who have CHF have BP >140/90 mm Hg (NHLBI unpublished estimates from ARIC, CHS, and FHS Cohort and Offspring studies).
  • Data from FHS/NHLBI indicate that recent (within the past 10 years) and remote antecedent BP levels may be an important determinant of risk over and above the current BP level.40
  • Data from the FHS/NHLBI indicate that hypertension is associated with shorter overall life expectancy, shorter life expectancy free of CVD, and more years lived with CVD.41
    • Total life expectancy was 5.1 years longer for normotensive men and 4.9 years longer for normotensive women than for hypertensive people of the same sex at 50 years of age.
    • Compared with hypertensive men at 50 years of age, men with untreated BP <140/90 mm Hg survived on average 7.2 years longer without CVD and spent 2.1 fewer years of life with CVD. Similar results were observed for women.

Hospital Discharges/Ambulatory Care Visits

(See Table 9-1.)

  • From 2000 to 2010, the number of inpatient discharges from short-stay hospitals with HBP as the first-listed diagnosis increased from 457 000 to 488 000 (no significant difference; NCHS, NHDS). The number of all-listed discharges increased from 8 034 000 to 11 282 000 (NHLBI, unpublished data from the NHDS, 2010; diagnoses in 2010 were truncated at 7 diagnoses for comparability with earlier year).
  • Data from the Nationwide Inpatient Sample from the years 2000 to 2007 found the frequency of hospitalizations for adults aged ≥18 years of age with a hypertensive emergency increased from 101 to 111 per 100 000 in 2007 (average increase of 1.11%). In contrast to the increased number of hospitalizations, the all-cause in-hospital mortality rate decreased during the same period from 2.8% to 2.6%.42
  • Data from ambulatory medical care use estimates for 2010 showed that the number of visits for essential hypertension was 43 436 000. Of these, 38 916 000 were physician office visits, 940 000 were ED visits, and 3 580 000 were outpatient department visits (NAMCS and NHAMCS, NHLBI tabulation).
  • In 2010, there were 280 000 hospitalizations with a first-listed diagnosis of essential hypertension (ICD-9-CM code 401), but essential hypertension was listed as either a primary or a secondary diagnosis on 11 048 000 hospitalized inpatient visits (unpublished data from the NHDS, NHLBI tabulation).

Awareness, Treatment, and Control

(See Table 9-2 and Charts 9-3 through 9-5.)

Chart 9-3
Extent of awareness, treatment, and control of high blood pressure by race/ethnicity (National Health and Nutrition Examination Survey: 2007–2010). NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, ...
  • Data from NHANES 2007 to 2010 showed that of those with hypertension who were ≥20 years of age, 81.5% were aware of their condition, 74.9% were under current treatment, 52.5% had their hypertension under control, and 47.5% did not have it controlled (NHLBI tabulation).
  • Data from NHANES 2009 to 2010 showed that 81.9% of adults were aware of their hypertension. Furthermore, 76.4% self-reported that they were currently taking prescribed medication to control hypertension. Awareness of hypertension was lower among those aged 18 to 39 years than among aged 40 to 59 years and those aged ≥60 years of age. Non-Hispanic black adults were more aware of their hypertension than Hispanics (87.0% and 77.7%, respectively).4
  • Analysis of NHANES 2007 to 2008 and 2009 to 2010 found the proportion of adults with controlled hypertension increased from 48.4% to 53.3%, respectively. Medication use to lower hypertension was lowest for those aged 18 to 39 years (46.0%) compared with those aged 40 to 59 years (77.1%) and those aged ≥60 years (80.7%). Non-Hispanic black adults were more likely to take antihypertensive medication than non-Hispanic whites or Hispanic adults (79.7%, 76.6%, and 69.6%, respectively).4
  • Data from the FHS of the NHLBI show that among those ≥80 years of age, only 38% of men and 23% of women had BPs that met targets set forth in the National High Blood Pressure Education Program’s clinical guidelines. Control rates in men <60, 60 to 79, and ≥80 years of age were 38%, 36%, and 38%, respectively; for women in the same age groups, they were 38%, 28%, and 23%, respectively.43
  • Data from the WHI observational study of nearly 100 000 postmenopausal women across the country enrolled between 1994 and 1998 indicate that although prevalence rates ranged from 27% of women 50 to 59 years of age to 41% of women 60 to 69 years of age to 53% of women 70 to 79 years of age, treatment rates were similar across age groups: 64%, 65%, and 63%, respectively. Despite similar treatment rates, hypertension control is especially poor in older women, with only 29% of hypertensive women 70 to 79 years of age having clinic BPs <140/90 mm Hg compared with 41% and 37% of those 50 to 59 and 60 to 69 years of age, respectively.44
  • Among a cohort of postmenopausal women taking hormone replacement, hypertension was the most common comorbidity, with a prevalence of 34%.45
  • A study of >300 women in Wisconsin showed a need for significant improvement in BP and LDL levels. Of the screened participants, 35% were not at BP goal, 32.4% were not at LDL goal, and 53.5% were not at both goals.46
  • In 2005, a survey of people in 20 states conducted by the BRFSS of the CDC found that 19.4% of respondents had been told on ≥2 visits to a health professional that they had HBP. Of these, 70.9% reported changing their eating habits; 79.5% reduced the use of or were not using salt; 79.2% reduced the use of or eliminated alcohol; 68.8% were exercising; and 73.4% were taking antihypertensive medication.47
  • Among 1509 NHANES 2005 to 2006 participants aged ≥30 years with hypertension, 24% were categorized as low risk, 21% as intermediate risk, and 23% as high risk according to Framingham global risk. Furthermore, an additional 32% had CVD. Treatment for hypertension varied by risk category and ranged from 58% to 75%; hypertension control was 80% for those in the low-risk category and <50% for those in the high-risk category.48
  • According to data from NHANES 2001 to 2006, non-Hispanic blacks had 90% higher odds of poorly controlled BP than non-Hispanic whites. Among those who were hypertensive, non-Hispanic blacks and Mexican Americans had 40% higher odds of uncontrolled BP than non-Hispanic whites.49
  • According to data from NHANES 1998 to 2008 for adults with DM, prevalence of hypertension increased, whereas awareness, treatment, and control improved during these time periods; however, for adults 20 to 44 years of age, there was no evidence of improvement.50
  • “Resistant hypertension” is a treatment and control issue for nearly 1 in 10 hypertensive adults. This category of HBP represents individuals with uncontrolled HBP despite the use of ≥3 antihypertensive medications or with BP controlled with the use of ≥4 medications.51,52

Cost

(See Table 9-1.)

  • The estimated direct and indirect cost of HBP for 2010 is $46.4 billion (MEPS, NHLBI tabulation).
  • Projections show that by 2030, the total cost of HBP could increase to an estimated $274 billion (unpublished AHA computation, based on methodology described in Heidenreich et al9).

Prehypertension

  • Prehypertension is untreated SBP of 120 to 139 mm Hg or untreated DBP of 80 to 89 mm Hg and not having been told on 2 occasions by a physician or other health professional that one has hypertension.
  • Among disease-free participants in NHANES 1999 to 2006, the prevalence of prehypertension was 36.3%. Prevalence was higher in men than in women. Furthermore, prehypertension was correlated with an adverse cardiometabolic risk profile.53
  • Follow-up of 9845 men and women in the FHS/NHLBI who attended examinations from 1978 to 1994 revealed that at 35 to 64 years of age, the 4-year incidence of hypertension was 5.3% for those with baseline BP <120/80 mm Hg, 17.6% for those with SBP of 120 to 129 mm Hg or DBP of 80 to 84 mm Hg, and 37.3% for those with SBP of 130 to 139 mm Hg or DBP of 85 to 89 mm Hg. At 65 to 94 years of age, the 4-year incidences of hypertension were 16.0%, 25.5%, and 49.5% for these BP categories, respectively.54
  • Data from FHS/NHLBI also reveal that prehypertension is associated with elevated relative and absolute risks for CVD outcomes across the age spectrum. Compared with normal BP (<120/80 mm Hg), prehypertension was associated with a 1.5- to 2-fold increased risk for major CVD events in those <60, 60 to 79, and ≥80 years of age. Absolute risks for major CVD associated with prehypertension increased markedly with age: 6-year event rates for major CVD were 1.5% in prehypertensive people <60 years of age, 4.9% in those 60 to 79 years of age, and 19.8% in those ≥80 years of age.43
  • In a study of NHANES 1999 to 2000 (NCHS), people with prehypertension were more likely than those with normal BP levels to have above-normal cholesterol levels (≥200 mg/dL) and to be overweight or obese, whereas the probability of current smoking was lower. People with prehypertension were 1.65 times more likely to have ≥1 of these adverse risk factors than were those with normal BP.55
  • Assessment of the REGARDS data identified high risk of prehypertension to be associated with increased age and black race.56
  • A meta-analysis of 12 prospective cohort studies (including 518 520 participants) found prehypertension was associated with incident stroke. The risk was particularly noted in nonelderly people and for those with BP values in the higher prehypertension range.57
  • Prehypertension was found to be significantly associated with stroke.57
  • Prehypertension was highest in blacks with other risk factors, including DM and elevated CRP.56
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28. Moran A, Diez Roux AV, Jackson SA, Kramer H, Manolio TA, Shrager S, Shea S. Acculturation is associated with hypertension in a multiethnic sample. Am J Hypertens. 2007;20:354–363. [PubMed]
29. Barnes PM, Adams PF, Powell-Griner E. Advance Data From Vital and Health Statistics; No. 394. Hyattsville, MD: National Center for Health Statistics; 2008. Health characteristics of the Asian adult population: United States, 2004–2006. [PubMed]
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31. Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999–2010. Series 20 No. 2P. [Accessed July 21, 2013];CDC WONDER Online Database [database online] Released January 2013. http://wonder.cdc.gov/cmf-icd10.html.
32. Farley TA, Dalal MA, Mostashari F, Frieden TR. Deaths preventable in the U.S. by improvements in use of clinical preventive services. Am J Prev Med. 2010;38:600–609. [PubMed]
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35. Gazes PC, Lackland DT, Mountford WK, Gilbert GE, Harley RA. Comparison of cardiovascular risk factors for high brachial pulse pressure in blacks versus whites (Charleston Heart Study, Evans County Study, NHANES I and II Studies) Am J Cardiol. 2008;102:1514–1517. [PMC free article] [PubMed]
36. Gray L, Lee IM, Sesso HD, Batty GD. Blood pressure in early adulthood, hypertension in middle age, and future cardiovascular disease mortality: HAHS (Harvard Alumni Health Study) J Am Coll Cardiol. 2011;58:2396–2403. [PMC free article] [PubMed]
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40. Vasan RS, Massaro JM, Wilson PW, Seshadri S, Wolf PA, Levy D, D’Agostino RB. Framingham Heart Study. Antecedent blood pressure and risk of cardiovascular disease: the Framingham Heart Study. Circulation. 2002;105:48–53. [PubMed]
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10. Diabetes Mellitus

ICD-9 250; ICD-10 E10 to E14. See Table 10-1 and Charts 10-1 through 10-4.

Chart 10-1
Age-adjusted prevalence of physician-diagnosed diabetes mellitus in adults ≥20 years of age by race/ethnicity and sex (National Health and Nutrition Examination Survey: 2007–2010). NH indicates non-Hispanic. Source: National Center for ...
Chart 10-4
Diabetes mellitus awareness, treatment, and control in adults ≥20 years of age (National Health and Nutrition Examination Survey: 2007–2010). Source: National Heart, Lung, and Blood Institute.
Table 10-1
Diabetes Mellitus

DM is a major risk factor for CVD and stroke.1 The AHA has identified untreated fasting blood glucose levels of <100 mg/dL for children and adults as 1 of the 7 components of ideal cardiovascular health.2 In 2009 to 2010, 88.2% of children and 57.4% of adults met these criteria.2

Prevalence

  • The prevalence of DM for all age groups worldwide was estimated to be 2.8% in 2000 and is projected to be 4.4% in 2030. The total number of people with DM is projected to rise from 171 million in 2000 to 366 million in 2030.3

Youths

  • Approximately 186 000 people <20 years of age have DM. Each year, ≈15 000 people <20 years of age are diagnosed with type 1 DM. Healthcare providers are finding more and more children with type 2 DM, a disease usually diagnosed in adults ≥40 years of age. Children who develop type 2 DM are typically overweight or obese and have a family history of the disease. Most are American Indian, black, Asian, or Hispanic/Latino.4
  • During the period from 2002 to 2005, 3600 youth (age <20 years) were diagnosed with type 2 DM annually.5
  • Among adolescents 10 to 19 years of age diagnosed with DM, 57.8% of blacks were diagnosed with type 2 versus type 1 DM compared with 46.1% of Hispanic youths and 14.9% of white youths.6
  • According to the Bogalusa Heart Study, a long-term follow-up study of youths aging into adulthood, youths who were prediabetic or who had DM were more likely to have a constellation of metabolic disorders in young adulthood (19–44 years of age), including obesity, hypertension, dyslipidemia, and metabolic syndrome, all of which predispose to CHD.7
  • Among youths with type 2 DM, 10.4% are overweight and 79.4% are obese.8
  • According to NHANES data from 1999 to 2007, among US adolescents aged 12 to 19 years, the prevalence of prediabetes and DM increased from 9% to 23%.9
  • The TODAY cohort comprised youths aged 10 to 17 years (41.1% Hispanic and 31.5% non-Hispanic black) participating in a randomized controlled study of new-onset type 2 DM; 41.5% of participants had household income <$25 000.10 The results of the clinical trial demonstrated that only half of the children maintained durable glycemic control with monotherapy,11 a higher rate of treatment failure than observed in adult cohorts.
  • In the TODAY cohort, youths who had type 2 DM were sedentary >56 minutes longer per day (via accelerometry) than obese youth from NHANES.12
  • Of 1514 SEARCH participants, 95% reported having undergone BP checks and 88% reported having had lipid-level checks, whereas slightly more than two thirds (68%) reported having had HbA1c testing or eye examinations (66%).13

Abbreviations Used in Chapter 10

ACCAmerican College of Cardiology
ACCORDAction to Control Cardiovascular Risk in Diabetes
ACSacute coronary syndrome
ADVANCEAction in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation
AFatrial fibrillation
AHAAmerican Heart Association
AHRQAgency for Healthcare Research and Quality
AMIacute myocardial infarction
ARICAtherosclerosis Risk in Communities study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHSCardiovascular Health Study
CIconfidence interval
CVDcardiovascular disease
DMdiabetes mellitus
EDemergency department
ESRDend-stage renal disease
EVERESTEfficacy of Vasopressin Antagonism in Heart Failure Outcome Study With Tolvaptan
FHSFramingham Heart Study
HbA1chemoglobin A1c
HDheart disease
HDLhigh-density lipoprotein
HFheart failure
HRhazard ratio
ICD-9International Classification of Diseases, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
IDDMinsulin-dependent diabetes mellitus
LDLlow-density lipoprotein
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NSTEMInon–ST-segment–elevation myocardial infarction
ORodds ratio
PAphysical activity
PARpopulation-attributable risk
RRrelative risk
SBPsystolic blood pressure
SEARCHSearch for Diabetes in Youth Study
STEMIST-segment–elevation myocardial infarction
TODAYTreatment Options for Type 2 Diabetes in Adolescents and Youth
UAunstable angina

Adults

(See Table 10-1 and Charts 10-1 through 10-3.)

Chart 10-3
Trends in diabetes mellitus prevalence in adults ≥20 years of age by sex (National Health and Nutrition Examination Survey: 1988–1994 and 2007–2010). Source: National Center for Health Statistics, National Heart, Lung, and Blood ...
  • On the basis of data from NHANES 2007 to 2010 (unpublished NHLBI tabulation), an estimated 19.7 million Americans ≥20 years of age have physician-diagnosed DM. An additional 8.2 million adults have undiagnosed DM, and 87.3 million adults have prediabetes (eg, fasting blood glucose of 100 to <126 mg/dL). The prevalence of prediabetes in the US adult population is 38%.
  • The prevalence of diagnosed DM in adults ≥65 years of age was 26.9% in 2010, and an additional 50% (>20 million) had prediabetes based on fasting glucose, oral glucose tolerance testing, or HbA1c. In addition, data from NHANES 2005 to 2006 show that 46% of DM cases remain undiagnosed in this group aged ≥65 years.14
  • According to the Bogalusa Heart Study, men >20 years of age have a slightly higher prevalence of DM (11.8%) than women (10.8%).6
  • After adjustment for population age differences, 2007 to 2009 national survey data for people >20 years of age indicate that 7.1% of non-Hispanic whites, 8.4% of Asian Americans, 11.8% of Hispanics, and 12.6% of non-Hispanic blacks had diagnosed DM.5
  • Compared with non-Hispanic white adults, the risk of diagnosed DM was 18% higher among Asian Americans, 66% higher among Hispanics/Latinos, and 77% higher among non-Hispanic blacks.5
  • In 2004 to 2006, the prevalence of diagnosed DM was more than twice as high for Asian Indian adults (14%) as for Chinese (6%) or Japanese (5%) adults.15
  • Type 2 DM accounts for 90% to 95% of all diagnosed cases of DM in adults.5
  • On the basis of 2012 BRFSS (CDC) data, the prevalence of adults in the United States who reported ever having been told by a physician that they had DM ranged from 6.9% in Alaska to 13.0% in West Virginia. The mean percentage among all states was 10.1%.16
  • The CDC analyzed data from 1994 to 2004 collected by the Indian Health Service that indicated that the age-adjusted prevalence of DM per 1000 population increased 101.2% among American Indian/Alaska Native adults <35 years of age (from 8.5% to 17.1%). During this time period, the prevalence of diagnosed DM was greater among females than males in all age groups.17
  • On the basis of projections from NHANES studies between 1984 and 2004, the total prevalence of DM in the United States is expected to more than double from 2005 to 2050 (from 5.6% to 12.0%) in all age, sex, and race/ethnicity groups. Increases are projected to be largest for the oldest age groups (for instance, projected to increase by 220% among those 65–74 years of age and by 449% among those ≥75 years of age). DM prevalence is projected to increase by 99% among non-Hispanic whites, by 107% among non-Hispanic blacks, and by 127% among Hispanics. The age/race/ethnicity group with the largest increase is expected to be blacks ≥75 years of age (projected increase of 606%).18
  • According to NHIS data from 1997 to 2008, the prevalence of DM was higher at both time points among Asian Americans (4.3%–8.2%) than among whites (3.8%–6.0%), with the Asian American group also having a greater proportional increase (1.9- versus 1.5-fold increase). This was observed despite lower BMI levels (23.6 versus 26.1 kg/m2 in the earliest time period) among Asians.19
  • According to international survey and epidemiologic data from 2.7 million participants, the prevalence of DM in adults increased from 8.3% in men and 7.5% in women in 1980 to 9.8% in men and 9.2% in women in 2008. The number of individuals affected with DM increased from 153 million in 1980 to 347 million in 2008.20

Incidence

Youths

  • In the SEARCH study, the incidence of DM in youths overall was 24.3 per 100 000 person-years. Among children <10 years of age, most had type 1 DM, regardless of race/ethnicity. The highest rates of incident type 1 DM were observed in non-Hispanic white youths (18.6, 28.1, and 32.9 per 100 000 person-years for age groups of 0–4, 5–9, and 10–14 years, respectively). Overall, type 2 DM was relatively infrequent, with the highest rates (17.0–49.4 per 100 000 person-years) seen among 15- to 19-year-old minority groups.6
  • Of 2291 individuals <20 years of age with newly diagnosed DM, slightly more than half (54.5%) had autoimmune, insulin-sensitive DM, and 15.9% had nonautoimmune, insulin-resistant DM.21
  • Projecting disease burden by 2050, the number of youths with type 1 DM will conservatively increase from 166 018 to 203 382, and the number with type 2 DM will increase from 20 203 to 30 111. Less conservative modeling projects the number of type 1 DM patients at 587 488 and of those with type 2 DM at 84 131 by 2050.22

Adults

(See Table 10-1.)

  • A total of 1.9 million new cases of DM (type 1 or type 2) were diagnosed in US adults ≥20 years of age in 2010.5
  • Data from the FHS indicate a doubling in the incidence of DM over the past 30 years, most dramatically during the 1990s. Among adults 40 to 55 years of age in each decade of the 1970s, 1980s, and 1990s, the age-adjusted 8-year incidence rates of DM were 2.0%, 3.0%, and 3.7% among women and 2.7%, 3.6%, and 5.8% among men, respectively. Compared with the 1970s, the age- and sex-adjusted OR for DM was 1.40 in the 1980s and 2.05 in the 1990s (P for trend=0.0006). Most of the increase in absolute incidence of DM occurred in individuals with a BMI ≥30 kg/m2 (P for trend=0.03).23
  • DM incidence in adults also varies markedly by race. Over 5 years of follow-up in 45- to 84-year-olds in MESA, 8.2% of the cohort developed DM. The cumulative incidence was highest in Hispanics (11.3%), followed by black (9.5%), Chinese (7.7%), and white (6.3%) participants.24
  • On the basis of meta-analyses of 4 longitudinal cohort studies comprising 175 938 individuals and 1.1 million person-years of follow-up, a statistically significant adjusted association was observed between net duration of television viewing and risk for incident type 2 DM, with a 20% increased risk per each 2-hour daily increment of exposure (adjusted RR, 1.20; 95% CI, 1.14–1.27).25
  • According to NHANES data from 1988 to 1994 compared with 2005 to 2010, the prevalence of DM increased from 8.4% to 12.1%. This increase was most pronounced among those ≥65 years of age (increase in prevalence from 18.6% to 28.5%).26
  • According to data from NHANES and BRFSS, up to 48.7% of individuals with self-reported DM did not meet glycemic, BP, and lipid targets, and only 14.3% met all 3 targets and did not smoke.27
  • Gestational DM complicates 2% to 10% of pregnancies and increases the risk of developing type 2 DM by 35% to 60%.5

Mortality

(See Table 10-1.)

DM mortality in 2010 was 69 071. Any-mention mortality in 2010 was 234 051.28

  • The 2010 overall underlying-cause death rate attributable to DM was 20.8. Death rates per 100 000 people were 23.1 for white males, 43.6 for black males, 15.6 for white females, and 35.1 for black females.28
  • According to data from the National Diabetes Information Clearinghouse, the National Institute of Diabetes and Digestive and Kidney Diseases, and the National Institutes of Health:
    • At least 68% of people >65 years of age with DM die of some form of HD; 16% die of stroke.
    • HD death rates among adults with DM are 2 to 4 times higher than the rates for adults without DM.5
  • In a collaborative meta-analysis of 820 900 individuals from 97 prospective studies, DM was associated with the following risks: all-cause mortality, HR 1.80 (95% CI, 1.71–1.90); cancer death, HR 1.25 (95% CI, 1.19–1.31); and vascular death, HR 2.32 (95% CI, 2.11–2.56). In particular, DM was associated with death attributable to the following cancers: liver, pancreas, ovary, colorectal, lung, bladder, and breast. A 50-year-old with DM died on average 6 years earlier than an individual without DM.29
  • FHS/NHLBI data show that having DM significantly increased the risk of developing CVD (HR 2.5 for women and 2.4 for men) and of dying when CVD was present (HR 2.2 for women and 1.7 for men). Diabetic men and women ≥50 years of age lived an average of 7.5 and 8.2 years less than their nondiabetic counterparts. The differences in life expectancy free of CVD were 7.8 and 8.4 years, respectively.30
  • Analysis of data from NHANES 1971 to 2000 found that men with DM experienced a 43% relative reduction in the age-adjusted mortality rate, which was similar to that of nondiabetic men. Among women with DM, however, mortality rates did not decrease, and the difference in mortality rates between diabetic and nondiabetic women doubled.31
  • During 1979 to 2004, DM death rates for black youths 1 to 19 years of age were approximately twice those for white youths. During 2003 to 2004, the annual average DM death rate per 1 million youths was 2.46 for black youths and 0.91 for white youths.32
  • Among individuals ≥65 years of age participating in the CHS, during follow-up for up to 16 years, adjusted CHD mortality risk was similar for those with prevalent CHD free of DM at study entry compared with participants with DM but free of CHD (HR, 1.04; 95% CI, 0.83–1.30).33
  • Analysis of data from the FHS from 1950 to 2005 found reductions in all-cause and CVD mortality among men and women with and without DM; however, all-cause and CVD mortality rates among individuals with DM remain ≈2-fold higher than for individuals without DM.34
  • According to NHIS data from 1997 to 2006, the rate of CVD death among adults with DM decreased by 40% (95% CI, 23%–54%). Similarly, all-cause mortality decreased by 23% (95% CI, 10%–35%). In contrast, over this same period among adults without DM, the CVD mortality rate decreased by 60%, and the all-cause mortality rate decreased by 44%.35

Awareness

(See Chart 10-4.)

  • Analysis of NHANES/NCHS data from 1988 to 1994 and from 2005 to 2006 in adults ≥20 years of age showed that 40% of those with DM did not know they had it.14 Although the prevalence of diagnosed DM has increased significantly over the past decade, the prevalence of undiagnosed DM and impaired fasting glucose has remained relatively stable. Minority groups remain disproportionately affected.36
  • Analysis of NHANES data collected during 2007 to 2010 indicated that the prevalence of DM was 8.3% among people ≥20 years of age. Prevalence of DM was defined as people who were told by a physician or other health professional that they had DM (NHANES 2007–2010, NHLBI tabulation).
  • Of the estimated 27.9 million adults with DM, 70.6% were told they had DM or were undergoing treatment, and 29.4% (8.2 million) were unaware of the diagnosis. Of 12.9 million people being treated (65.5% of the diagnosed diabetic population), 5.1 million (39.5%) had their hyperglycemia under control (ie, they were undergoing treatment and had fasting plasma glucose <126 mg/dL), and 7.8 million (60.5%) were being treated but did not have their hyperglycemia under control (fasting plasma glucose ≥126 mg/dL). An estimated 6.8 million individuals with diagnosed DM are not treated with glucose-lowering therapy (NHANES 2007–2010, NHLBI tabulation).

Aftermath

  • Although the exact date of DM onset can be difficult to determine, increasing duration of DM diagnosis is associated with increasing CVD risk. Longitudinal data from FHS suggest that the risk factor–adjusted RR of CHD is 1.38 (95% CI, 0.99–1.92) times higher and the risk for CHD death is 1.86 (95% CI, 1.17–2.93) times higher for each 10-year increase in duration of DM.37
  • On the basis of data from the NCHS/NHIS, 1997 to 200538
    • The estimated number of people ≥35 years of age with DM with a self-reported cardiovascular condition increased 36%, from 4.2 million in 1997 to 5.7 million in 2005; however, the respective age-adjusted prevalence decreased 11.2%, from 36.6% in 1997 to 32.5% in 2005, reflecting an increase in the number of patients diagnosed with DM that exceeded the increase in CVD prevalence.
    • Age-adjusted CVD prevalence was higher among men than women, among whites than blacks, and among non-Hispanics than Hispanics. Among women, the age-adjusted prevalence decreased by 11.2%; among men, it did not decrease significantly. Among blacks, the age-adjusted prevalence of self-reported CVD decreased by 25.3%; among whites, no significant decrease occurred; among non-Hispanics, the rate decreased by 12%. No clear trends were detected among Hispanics.
    • Because the total number of people with DM and self-reported CVD increased over this period but proportions with self-reported CVD declined, the data suggest that the mean age at which people are diagnosed with DM is decreasing, or the higher CVD mortality rate among older diabetic individuals is removing them from ability to self-report CVD. These and other data show a consistent increase over time in the United States of the number of people with DM and CVD.
  • Data from the FHS show that despite improvements in CVD morbidity and mortality over >4 decades of observation, DM continues to be associated with incremental CVD risk. Participants 45 to 64 years of age from the FHS original and offspring cohorts who attended examinations in 1950 to 1966 (“earlier” time period) and 1977 to 1995 (“later” time period) were followed up for incident MI, CHD death, and stroke. Among participants with DM, the age- and sex-adjusted CVD incidence rate was 286.4 per 10 000 person-years in the earlier period and 146.9 per 10 000 person-years in the later period, a 35.4% decline. HRs for DM as a predictor of incident CVD were not significantly different in the earlier (risk factor–adjusted HR, 2.68; 95% CI, 1.88–3.82) versus later (HR, 1.96; 95% CI, 1.44–2.66) period.39 Thus, although there was a 50% reduction in the rate of incident CVD events among adults with DM, the absolute risk of CVD remained 2-fold greater than among people without DM.39
    • Data from these earlier and later time periods in FHS also suggest that the increasing prevalence of DM is leading to an increasing rate of CVD, resulting in part from CVD risk factors that commonly accompany DM. The age- and sex-adjusted HR for DM as a CVD risk factor was 3.0 in the earlier time period and 2.5 in the later time period. Because the prevalence of DM has increased over time, the PAR for DM as a CVD risk factor increased from 5.4% in the earlier time period to 8.7% in the later time period (attributable risk ratio, 1.62; P=0.04). Adjustment for CVD risk factors (age, sex, hypertension, current smoking, high cholesterol, and obesity) weakened this attributable risk ratio to 1.5 (P=0.12).40
    • Other data from FHS show that over a 30-year period, CVD among women with DM was 54.8% among normal-weight women but 78.8% among obese women. Among normal-weight men with DM, the lifetime risk of CVD was 78.6%, whereas it was 86.9% among obese men.41
  • Other studies show that the increased prevalence of DM is being followed by an increasing prevalence of CVD morbidity and mortality. New York City death certificate data for 1989 to 1991 and 1999 to 2001 and hospital discharge data for 1988 to 2002 show increases in all-cause and cause-specific mortality between 1990 and 2000, as well as in annual hospitalization rates for DM and its complications among patients hospitalized with AMI and/or DM. During this decade, all-cause and cause-specific mortality rates declined, although not for patients with DM; rates increased 61% and 52% for diabetic men and women, respectively, as did hospitalization rates for DM and its complications. The percentage of all AMIs occurring in patients with DM increased from 21% to 36%, and the absolute number more than doubled, from 2951 to 6048. Although hospital days for AMI fell overall, for those with DM, they increased 51% (from 34 188 to 51 566). These data suggest that increases in DM rates threaten the long-established nationwide trend toward reduced coronary artery events.42
  • Data from the ARIC study of the NHLBI found that the magnitude of incremental CHD risk associated with DM was smaller in blacks than in whites.43
  • A subgroup analysis was conducted of patients with DM enrolled in randomized clinical trials that evaluated ACS therapies. The data included 62 036 patients from Thrombolysis in Myocardial Infarction studies (46 577 with STEMI and 15 459 with UA/NSTEMI). Of these, 17.1% had DM. Modeling showed that mortality at 30 days was significantly higher among patients with DM than among those without DM who presented with UA/NSTEMI (2.1% versus 1.1%; P≤0.001) and STEMI (8.5% versus 5.4%; P=0.001), with adjusted risks for 30-day mortality in DM versus no DM of 1.78 for UA/NSTEMI (95% CI, 1.24–2.56) and 1.40 (95% CI, 1.24–1.57) for STEMI. DM was also associated with significantly higher mortality 1 year after UA/NSTEMI or STEMI. By 1 year after ACS, patients with DM who presented with UA/NSTEMI had a risk of death that approached that of patients without DM who presented with STEMI (7.2% versus 8.1%).44
  • In analyses from the National Registry of Myocardial Infarction comprising data registered on 1 734 431 patients admitted with AMI to 1964 participating US hospitals, the incremental adjusted OR for hospital mortality associated with DM declined from 1.24 (95% CI, 1.16–1.32) in 1994 to 1.08 (95% CI, 0.99–1.19) in 2006, which demonstrates a closing of the acute hospital mortality gap associated with DM.45
  • In an analysis of provincial health claims data for adults living in Ontario, Canada, between 1992 and 2000, the rate of patients admitted for AMI and stroke decreased to a greater extent in the diabetic than the nondiabetic population (AMI, −15.1% versus −9.1%, P=0.0001; stroke, −24.2% versus −19.4%, P=0.0001). Patients with DM experienced reductions in case fatality rates related to AMI and stroke similar to those without DM (−44.1% versus −33.2%, P=0.1, and −17.1% versus −16.6%, P=0.9, respectively) and similarly comparable decreases in all-cause mortality. Over the same period, the number of DM cases increased by 165%, which translates to a marked increase in the proportion of CVD events occurring among patients with DM: AMI, 44.6%; stroke, 26.1%; AMI deaths, 17.2%; and stroke deaths, 13.2%.46
  • In the same data set, the transition to a high-risk category (an event rate equivalent to a 10-year risk of 20% or an event rate equivalent to that associated with previous MI) occurred at a younger age for men and women with DM than for those without DM (mean difference, 14.6 years). For the outcome of AMI, stroke, or death resulting from any cause, men and women with DM entered the high-risk category at 47.9 and 54.3 years of age, respectively. The data suggest that DM confers a risk equivalent to aging 15 years. In North America, diverse data show lower rates of CVD among people with DM, but as the prevalence of DM has increased, so has the absolute burden of CVD, especially among middle-aged and older individuals.47
  • DM increases the risk of HF and adversely affects outcomes among patients with HF.
    • DM alone qualifies for the most recent ACC Foundation/AHA diagnostic criteria for stages A and B HF, a classification of patients without HF but at notably high risk for its development.48
    • In MESA, DM was associated with a 2-fold increased adjusted risk of incident HF among 6814 individuals free of CVD at baseline over a mean follow-up of 4 years (HR, 1.99; 95% CI, 1.08–3.68).49
    • Post hoc analysis of data from the EVEREST randomized trial of patients hospitalized with decompensated systolic HF stratified by DM status, which evaluated cardiovascular outcomes over a follow-up period of 9.9 months, demonstrated an increased adjusted HR for the composite of cardiovascular mortality and HF rehospitalization associated with DM (HR, 1.17; 95% CI, 1.04–1.31).50
  • DM increases the risk of AF. On the basis of meta-analysis of published observational data comprising 11 studies and >1.6 million participants, DM was crudely associated with a 40% increased risk for AF (RR, 1.39; 95% CI, 1.10–1.75) with the association remaining significant after multivariable adjustment (adjusted RR, 1.24; 95% CI, 1.06–1.44), yielding an estimate of the population attributable fraction of AF attributable to DM of 2.5%.51
  • DM increases the risk of stroke, with the RR ranging from 1.8- to 6-fold increased risk.37,52
    • DM is associated with increased ischemic stroke incidence at all ages, with the incremental risk associated with DM being most prominent before 55 years of age in blacks and before 65 years of age in whites.52
    • Ischemic stroke patients with DM are younger, more likely to be black, and more likely to have hypertension, prior MI, and high cholesterol than nondiabetic patients.52
  • DM accounted for 44% of the new cases of ESRD in 2007.53
  • In 2011, the incidence rate of ESRD attributed to DM in adults ≥20 years increased with age from 5.02 per 100 000 in those aged 20 to 29 years to 109.81 per 100 000 in those ≥70 years, compared with rates of 2.41 and 83.19, respectively, in those without DM.54
  • According to NHANES data, the prevalence of diabetic kidney disease has increased from 2.2% in NHANES III to 3.3% in NHANES 2005 to 2008. These increases were observed in direct proportion to increases in DM.55
  • HbA1c levels ≥6.5% can be used to diagnose DM.55a In the population-based ARIC study, over a 14-year follow-up period that preceded the endorsement of HbA1c as a diagnostic criterion, HbA1c levels ≥6.5% at study entry were associated with a multivariable-adjusted HR of 16.5 (95% CI, 14.2–19.1) for diagnosed DM based on contemporaneous diagnostic criteria and 1.95 (95% CI, 1.53–2.48) for CHD relative to those with HbA1c <5.0%.56
  • According to data from the ARIC study and NHANES III, the sensitivity and specificity for diagnosing DM with HbA1c criteria (compared with a single fasting glucose measurement of ≥126 mg/dL) were 47% and 98%, respectively.

Risk Factors

  • DM, especially type 2 DM, is associated with clustered risk factors for CHD, with a prevalence of 75% to 85% for hypertension among adults with DM, 70% to 80% for elevated LDL, and 60% to 70% for obesity.57
  • Aggressive treatment of hypertension is recommended for adults with DM to prevent cardiovascular complications. Between NHANES III (1984–1992) and NHANES 1999 to 2004, the proportion of patients with DM whose BP was treated increased from 76.5% to 87.8%, and the proportion whose BP was controlled nearly doubled (from 15.9% to 29.6%).58
  • Aggressive treatment of hypercholesterolemia is recommended for adults with DM, with the cornerstone of treatment being statin therapy, which is recommended for all patients with DM >40 years of age independent of baseline cholesterol, with targeted LDL cholesterol <100 mg/dL and optimally <70 mg/dL.59
  • CHD risk factors among patients with DM remain suboptimally treated, although improvements have been observed over the past decade. Between 1999 and 2008, in up to 2623 adult participants with DM, data from NHANES showed that improvements were observed for the achieved targets for control of HbA1c (from 37.0% to 55.2%), BP (from 35.2% to 51.0%), and LDL cholesterol (from 32.5% to 52.9%).60
  • Data from the 2012 National Healthcare Disparities Report (AHRQ, US Department of Health and Human Services) found that only about 23% of adults over age 40 years with DM received all 4 interventions to reduce risk factors recommended for comprehensive DM care in 2009. The proportion receiving all 4 interventions was lower among blacks and Hispanics than whites.61
    • In multivariable models, among those aged 40 to 64 years, only about 65% had their blood pressure <140/80 mm Hg, with blacks less likely than whites to achieve this blood pressure level.61
  • In 1 large academic medical center, outpatients with type 2 DM were observed during an 18-month period for proportions of patients who had HbA1c levels, BP, or total cholesterol levels measured; who had been prescribed any drug therapy if HbA1c levels, SBP, or LDL cholesterol levels exceeded recommended treatment goals; and who had been prescribed greater-than-starting-dose therapy if these values were above treatment goals. Patients were less likely to have cholesterol levels measured (76%) than HbA1c levels (92%) or BP (99%; P<0.0001 for either comparison). The proportion of patients who received any drug therapy was greater for above-goal HbA1c (92%) than for above-goal SBP (78%) or LDL cholesterol (38%; P<0.0001 for each comparison). Similarly, patients whose HbA1c levels were above the treatment goal (80%) were more likely to receive greater-than-starting-dose therapy than were those who had above-goal SBP (62%) and LDL cholesterol levels (13%; P<0.0001).62
    • Data from the same academic medical center also showed that CVD risk factors among women with DM were managed less aggressively than among men with DM. Women were less likely than men to have HbA1c <7% (without CHD: adjusted OR for women versus men 0.84, P=0.005; with CHD: 0.63, P<0.0001). Women without CHD were less likely than men to be treated with lipid-lowering medication (0.82; P=0.01) or, when treated, to have LDL cholesterol levels <100 mg/dL (0.75; P=0.004) and were less likely than men to be prescribed aspirin (0.63; P<0.0001). Women with DM and CHD were less likely than men to be prescribed aspirin (0.70, P<0.0001) and, when treated for hypertension or hyperlipidemia, were less likely to have BP levels <130/80 mm Hg (0.75; P<0.0001) or LDL cholesterol levels <100 mg/dL (0.80; P=0.006).63
  • Analysis of data from the CHS of the NHLBI found that lifestyle risk factors, including PA level, dietary habits, smoking habits, alcohol use, and adiposity measures, assessed late in life, were each independently associated with risk of new-onset DM. Participants whose PA level and dietary, smoking, and alcohol habits were all in the low-risk group had an 82% lower incidence of DM than all other participants. When absence of adiposity was added to the other 4 low-risk lifestyle factors, incidence of DM was 89% lower.64
  • According to 2007 data from the BRFSS, only 25% of adults with DM achieved recommended levels of total PA based on the 2007 American Diabetes Association guidelines.65

Hospitalizations

(See Table 10-1.)

Youths

  • Nationwide Inpatient Sample data from 1993 to 2004 were analyzed for individuals 0 to 29 years of age with a diagnosis of DM. Rates of hospitalizations increased by 38%. Hospitalization rates were higher for females (42%) than for males (29%). Inflation-adjusted total charges for DM hospitalizations increased 130%, from $1.05 billion in 1993 to $2.42 billion in 2004.66

Adults

  • According to NHDS data reported by the CDC in an analysis of data from 2010, DM was a listed diagnosis in 16% of US adult hospital discharges. Of the 5.1 million discharges with DM listed, circulatory diseases was the most common first-listed diagnosis (24.1%; 1.3 million discharges) and DM the second most common (11.5%; 610 000 discharges).67

Hypoglycemia

  • Hypoglycemia is a common side effect of DM treatment, typically defined as a blood glucose level <50 mg/dL; severe hypoglycemia is additionally defined as patients needing assistance to treat themselves.
  • In the ADVANCE trial, 2.1% of patients had an episode of severe hypoglycemia.
  • Severe hypoglycemia was associated with an increased risk of major macrovascular events (HR, 2.88; 95% CI, 2.01–4.12), cardiovascular death (HR, 2.68; 95% CI, 1.72–4.19), and all-cause death (HR, 2.69; 95% CI, 1.97–3.67), including nonvascular outcomes. The lack of specificity of hypoglycemia with vascular outcomes suggests that it might be a marker for susceptibility. Risk factors for hypoglycemia included older age, DM duration, worse renal function, lower BMI, lower cognitive function, use of multiple glucose-lowering medications, and randomization to the intensive glucose control arm.68
  • According to data from the 2004 to 2008 MarketScan database of type 2 DM, which consisted of 536 581 individuals, the incidence rate of hypoglycemia was 153.8 per 10 000 person-years and was highest in adults aged 18 to 34 years (218.8 per 10 000 person-years).69
  • According to data from 2956 adults >55 years of age from the ACCORD trial, poor cognitive function, defined as a 5-point poorer baseline score on the Digit Symbol Substitution Test, was associated with a 13% increased risk of severe hypoglycemia that required medical assistance.70
  • In a sample of 813 adults with type 2 DM enrolled in commercial health plans, 71% reported experiencing symptoms of hypoglycemia.71

Cost

(See Table10-1.)

  • In 2012, the cost of DM was estimated at $245 billion, up from $174 billion in 2007, accounting for 1 in 5 healthcare dollars. Of these costs, $176 billion were direct medical costs and $69 billion resulted from reduced productivity. Inpatient care accounted for 43% of these costs, 18% were attributable to prescription costs to treat DM complications, and 12% were related to antidiabetes agents and supplies.72
  • After adjustment for age and sex, medical costs for patients with DM were 2.3 times higher than for people without DM.5
  • According to the insurance claims and MarketScan data from 7556 youths <19 years of age with insulin-treated DM, costs for youths with hypoglycemia were $12 850 compared with $8970 for youths without hypoglycemia. For diabetic ketoacidosis, costs were $14 236 for youths with versus $8398 for youths without diabetic ketoacidosis.73
  • The cost of hypoglycemia, according to data from 536 581 individuals with type 2 DM from the 2004 to 2008 MarketScan database, was $52 223 675, which accounted for 1.0% of inpatient costs, 2.7% of ED costs, and 0.3% of out-patient costs. This resulted in a mean cost of $17 564 for an inpatient admission, $1387 for an ED visit, and $394 for an outpatient visit.69

Type 1 DM

  • Type 1 DM constitutes 5% to 10% of DM in the United States.74
  • The Colorado IDDM Study Registry and SEARCH for Diabetes in Youth registry demonstrated an increasing incidence of type 1 DM among Colorado youths ≤17 years of age, with an increase in the incidence of 2.3% (95% CI, 1.6%–3.1%) per year over the past 26 years.75
  • Between 1996 and 2010, the number of youths with type 1 DM increased by 5.7% per year.76
  • Among youths with type 1 DM, the prevalence of overweight is 22.1% and the prevalence of obesity is 12.6%.8
  • A long-term study of patients with type 1 DM that began in 1966 showed that over 30 years of follow-up, overall risk of mortality associated with type 1 DM was 7 times greater than that of the general population. Females had a 13.2-fold incremental mortality risk compared with a 5.0-fold increased risk in males. During the course of study, the incremental mortality risk associated with type 1 DM declined from 9.3 to 5.6 times that of nondiabetic control subjects.77
  • According to 30-year mortality data from Allegheny County, PA, those with type 1 DM have a mortality rate 5.6 times higher than the general population.78
  • The leading cause of death among patients with type 1 DM is CVD, which accounted for 22% of deaths among those in the Allegheny County, PA, type 1 DM registry, followed by renal (20%) and infectious (18%) causes.79
  • Long-term follow-up data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group showed that intensive versus conventional treatment in the Diabetes Control and Complications Trial was associated with a 42% reduced risk of CVD (P=0.02) and a 57% reduced risk of the composite end point (P=0.02; included nonfatal MI, stroke, and CVD death).80
  • Observational data from the Swedish National Diabetes Register showed that most CVD risk factors were more adverse among patients with HbA1c between 8.0% and 11.9% than among those with HbA1c between 5.0% and 7.9%. Per 1% unit increase in HbA1c, the HR of fatal and nonfatal CHD was 1.30 in multivariable-adjusted models and 1.27 for fatal and nonfatal CVD. Among patients with HbA1c 8.0% to 11.9% compared with those with HbA1c 5.0% to 7.9%, the HR of fatal/nonfatal CHD was 1.71 and the risk of fatal/nonfatal CVD was 1.59.81
  • Among 2787 patients from the EURODIAB Prospective Complications Study, age, waist-hip ratio, pulse pressure, non-HDL cholesterol, microalbuminuria, and peripheral and autonomic neuropathy were risk factors for all-cause, CVD, and non-CVD mortality.81a
  • Among 3610 older patients (>60 years of age) with type 1 DM, the risk of severe hypoglycemia was twice as high as for those <60 years of age (40.1 versus 24.3 per 100 patient-years).82
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33. Carnethon MR, Biggs ML, Barzilay J, Kuller LH, Mozaffarian D, Mukamal K, Smith NL, Siscovick D. Diabetes and coronary heart disease as risk factors for mortality in older adults. Am J Med. 2010;123:556.e1–556.e9. [PMC free article] [PubMed]
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36. Cowie CC, Rust KF, Byrd-Holt DD, Eberhardt MS, Flegal KM, Engelgau MM, Saydah SH, Williams DE, Geiss LS, Gregg EW. Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population: National Health And Nutrition Examination Survey 1999–2002. Diabetes Care. 2006;29:1263–1268. [PubMed]
37. Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, Creager MA, Culebras A, Eckel RH, Hart RG, Hinchey JA, Howard VJ, Jauch EC, Levine SR, Meschia JF, Moore WS, Nixon JV, Pearson TA. on behalf of the American Heart Association Stroke Council; Council on Cardiovascular Nursing; Council on Epidemiology and Prevention; Council for High Blood Pressure Research, Council on Peripheral Vascular Disease, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published correction appears in Stroke. 2011;42:e26] Stroke. 2011;42:517–584. [PubMed]
38. Centers for Disease Control and Prevention (CDC) Prevalence of self-reported cardiovascular disease among persons aged > or =35 years with diabetes: United States, 1997–2005. MMWR Morb Mortal Wkly Rep. 2007;56:1129–1132. [PubMed]
39. Fox CS, Coady S, Sorlie PD, Levy D, Meigs JB, D’Agostino RB, Sr, Wilson PW, Savage PJ. Trends in cardiovascular complications of diabetes. JAMA. 2004;292:2495–2499. [PubMed]
40. Fox CS, Coady S, Sorlie PD, D’Agostino RB, Sr, Pencina MJ, Vasan RS, Meigs JB, Levy D, Savage PJ. Increasing cardiovascular disease burden due to diabetes mellitus: the Framingham Heart Study. Circulation. 2007;115:1544–1550. [PubMed]
41. Fox CS, Pencina MJ, Wilson PW, Paynter NP, Vasan RS, D’Agostino RB., Sr Lifetime risk of cardiovascular disease among individuals with and without diabetes stratified by obesity status in the Framingham heart study. Diabetes Care. 2008;31:1582–1584. [PMC free article] [PubMed]
42. Fang J, Alderman MH. Impact of the increasing burden of diabetes on acute myocardial infarction in New York City: 1990–2000. Diabetes. 2006;55:768–773. [PubMed]
43. Jones DW, Chambless LE, Folsom AR, Heiss G, Hutchinson RG, Sharrett AR, Szklo M, Taylor HA., Jr Risk factors for coronary heart disease in African Americans: the Atherosclerosis Risk in Communities study, 1987–1997. Arch Intern Med. 2002;162:2565–2571. [PubMed]
44. Donahoe SM, Stewart GC, McCabe CH, Mohanavelu S, Murphy SA, Cannon CP, Antman EM. Diabetes and mortality following acute coronary syndromes. JAMA. 2007;298:765–775. [PubMed]
45. Gore MO, Patel MJ, Kosiborod M, Parsons LS, Khera A, de Lemos JA, Rogers WJ, Peterson ED, Canto JC, McGuire DK. National Registry of Myocardial Infarction Investigators. Diabetes mellitus and trends in hospital survival after myocardial infarction, 1994 to 2006: data from the National Registry of Myocardial Infarction. Circ Cardiovasc Qual Outcomes. 2012;5:791–797. [PubMed]
46. Booth GL, Kapral MK, Fung K, Tu JV. Recent trends in cardiovascular complications among men and women with and without diabetes. Diabetes Care. 2006;29:32–37. [PubMed]
47. Booth GL, Kapral MK, Fung K, Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet. 2006;368:29–36. [PubMed]
48. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, Jessup M, Konstam MA, Mancini DM, Michl K, Oates JA, Rahko PS, Silver MA, Stevenson LW, Yancy CW. 2009 Focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation [published correction appears in Circulation. 2010;121:e258] Circulation. 2009;119:e391–e479. [PubMed]
49. Bahrami H, Bluemke DA, Kronmal R, Bertoni AG, Lloyd-Jones DM, Shahar E, Szklo M, Lima JA. Novel metabolic risk factors for incident heart failure and their relationship with obesity: the MESA (Multi-Ethnic Study of Atherosclerosis) study. J Am Coll Cardiol. 2008;51:1775–1783. [PubMed]
50. Sarma S, Mentz RJ, Kwasny MJ, Fought AJ, Huffman M, Subacius H, Nodari S, Konstam M, Swedberg K, Maggioni AP, Zannad F, Bonow RO, Gheorghiade M. EVEREST Investigators. Association between diabetes mellitus and post-discharge outcomes in patients hospitalized with heart failure: findings from the EVEREST trial. Eur J Heart Fail. 2013;15:194–202. [PMC free article] [PubMed]
51. Huxley RR, Filion KB, Konety S, Alonso A. Meta-analysis of cohort and case-control studies of type 2 diabetes mellitus and risk of atrial fibrillation. Am J Cardiol. 2011;108:56–62. [PMC free article] [PubMed]
52. Kissela BM, Khoury J, Kleindorfer D, Woo D, Schneider A, Alwell K, Miller R, Ewing I, Moomaw CJ, Szaflarski JP, Gebel J, Shukla R, Broderick JP. Epidemiology of ischemic stroke in patients with diabetes: the Greater Cincinnati/Northern Kentucky Stroke Study. Diabetes Care. 2005;28:355–359. [PubMed]
53. Centers for Disease Control and Prevention (CDC) Incidence of end-stage renal disease attributed to diabetes among persons with diagnosed diabetes: United States and Puerto Rico, 1996–2007. MMWR Morb Mortal Wkly Rep. 2010;59:1361–1366. [PubMed]
54. Centers for Disease Control and Prevention. US Chronic Kidney Disease Surveillance System. Atlanta, GA: US Department of Health and Human Services; 2011. [Accessed September 20, 2013]. http://www.cdc.gov/ckd.
55. de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA. 2011;305:2532–2539. [PMC free article] [PubMed]
55a. American Diabetes Association. Diagnosis and classification of diabetes mellitus [published correction appears in Diabetes Care. 2010;33:e57] Diabetes Care. 2010;33(suppl 1):S62–S69. [PMC free article] [PubMed]
56. Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, Coresh J, Brancati FL. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med. 2010;362:800–811. [PMC free article] [PubMed]
57. Preis SR, Pencina MJ, Hwang SJ, D’Agostino RB, Sr, Savage PJ, Levy D, Fox CS. Trends in cardiovascular disease risk factors in individuals with and without diabetes mellitus in the Framingham Heart Study. Circulation. 2009;120:212–220. [PMC free article] [PubMed]
58. Suh DC, Kim CM, Choi IS, Plauschinat CA, Barone JA. Trends in blood pressure control and treatment among type 2 diabetes with comorbid hypertension in the United States: 1988–2004. J Hypertens. 2009;27:1908–1916. [PubMed]
59. Grundy SM, Cleeman JI, Merz CN, Brewer HB, Jr, Clark LT, Hunninghake DB, Pasternak RC, Smith SC, Jr, Stone NJ. National Heart, Lung, and Blood Institute; American College of Cardiology Foundation; American Heart Association. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines [published correction appears in Circulation. 2004;110:763] Circulation. 2004;110:227–239. [PubMed]
60. Ford ES. Trends in the control of risk factors for cardiovascular disease among adults with diagnosed diabetes: findings from the National Health and Nutrition Examination Survey 1999–2008. J Diabetes. 2011;3:337–347. [PubMed]
61. US Department of Health and Human Services, Agency for Healthcare Research and Quality. National Healthcare Disparities Report, 2012. Rockville, MD: Agency for Healthcare Research and Quality; 2012. [Accessed October 30, 2013]. http://www.ahrq.gov/research/findings/nhqrdr/nhdr12/2012nhdr.pdf.
62. Grant RW, Cagliero E, Murphy-Sheehy P, Singer DE, Nathan DM, Meigs JB. Comparison of hyperglycemia, hypertension, and hypercholesterolemia management in patients with type 2 diabetes. Am J Med. 2002;112:603–609. [PubMed]
63. Wexler DJ, Grant RW, Meigs JB, Nathan DM, Cagliero E. Sex disparities in treatment of cardiac risk factors in patients with type 2 diabetes. Diabetes Care. 2005;28:514–520. [PubMed]
64. Mozaffarian D, Kamineni A, Carnethon M, Djoussé L, Mukamal KJ, Siscovick D. Lifestyle risk factors and new-onset diabetes mellitus in older adults: the cardiovascular health study. Arch Intern Med. 2009;169:798–807. [PMC free article] [PubMed]
65. Zhao G, Ford ES, Li C, Balluz LS. Physical activity in U.S. older adults with diabetes mellitus: prevalence and correlates of meeting physical activity recommendations. J Am Geriatr Soc. 2011;59:132–137. [PubMed]
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67. Centers for Disease Control and Prevention (CDC), National Center for Health Statistics, Division of Health Care Statistics. Distribution of first-listed diagnoses among hospital discharges with diabetes as any listed diagnosis, adults aged 18 years and older, United States, 2010. [Accessed July 22, 2013];Centers for Disease Control and Prevention Web site. http://www.cdc.gov/diabetes/statistics/hosp/adulttable1.htm.
68. Zoungas S, Patel A, Chalmers J, de Galan BE, Li Q, Billot L, Woodward M, Ninomiya T, Neal B, MacMahon S, Grobbee DE, Kengne AP, Marre M, Heller S. ADVANCE Collaborative Group. Severe hypoglycemia and risks of vascular events and death. N Engl J Med. 2010;363:1410–1418. [PubMed]
69. Quilliam BJ, Simeone JC, Ozbay AB, Kogut SJ. The incidence and costs of hypoglycemia in type 2 diabetes. Am J Manag Care. 2011;17:673–680. [PubMed]
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78. Secrest AM, Becker DJ, Kelsey SF, LaPorte RE, Orchard TJ. All-cause mortality trends in a large population-based cohort with long-standing childhood-onset type 1 diabetes: the Allegheny County Type 1 Diabetes Registry. Diabetes Care. 2010;33:2573–2579. [PMC free article] [PubMed]
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11. Metabolic Syndrome

  • Metabolic syndrome is a multicomponent risk factor for CVD and type 2 DM that reflects the clustering of individual cardiometabolic risk factors related to abdominal obesity and insulin resistance. Although several different clinical definitions for metabolic syndrome have been proposed, the International Diabetes Federation, NHLBI, AHA, and others recently proposed a harmonized definition for metabolic syndrome.1 By this definition, metabolic syndrome is diagnosed when any 3 of the following 5 risk factors are present (most but not all people with DM will be classified as having metabolic syndrome by this definition because they will have ≥2 other factors besides the glucose criterion; many will prefer to separate those with DM into a separate group for risk stratification or treatment purposes):
    • Fasting plasma glucose ≥100 mg/dL or undergoing drug treatment for elevated glucose
    • HDL cholesterol <40 mg/dL in men or <50 mg/dL in women or undergoing drug treatment for reduced HDL cholesterol
    • Triglycerides ≥150 mg/dL or undergoing drug treatment for elevated triglycerides
    • Waist circumference >102 cm in men or >88 cm in women for people of most ancestries living in the United States. Ethnicity and country-specific thresholds can be used for diagnosis in other groups, particularly Asians and individuals of non-European ancestry who have predominantly resided outside the United States.
    • BP ≥130 mm Hg systolic or ≥85 mm Hg diastolic or undergoing drug treatment for hypertension or antihypertensive drug treatment in a patient with a history of hypertension.
  • Those with a fasting glucose level ≥126 mg/dL or a casual glucose value ≥200 mg/dL or taking hypoglycemic medication can normally be classified separately as having DM; many of these people will also have metabolic syndrome from the presence of additional risk factors noted above.
  • The new harmonized metabolic syndrome definition identifies a similar risk group and predicts CVD risk similarly to the prior metabolic syndrome definitions.2
  • There are many adverse health conditions that are related to metabolic syndrome but are not part of its clinical definition. These include nonalcoholic fatty liver disease, sexual dysfunction (erectile dysfunction in men and polycystic ovarian syndrome in women), and obstructive sleep apnea, as well as a general proinflammatory and pro-thrombotic state.3
  • Identification and treatment of metabolic syndrome fits closely with the current AHA 2020 Impact Goals, including emphasis on PA, healthy diet, and healthy weight for attainment of ideal BP, serum cholesterol, and fasting blood glucose. Metabolic syndrome should be considered largely a disease of unhealthy lifestyle. Prevalence of metabolic syndrome is a secondary metric in the 2020 Impact Goals. Identification of metabolic syndrome represents a call to action for the healthcare provider and patient to address the underlying lifestyle-related risk factors. A multidisciplinary team of healthcare professionals is desirable to adequately address these multiple issues in patients with metabolic syndrome.4

Abbreviations Used in Chapter 11

AFatrial fibrillation
AHAAmerican Heart Association
ARICAtherosclerosis Risk in Communities
BMIbody mass index
BPblood pressure
CACcoronary artery calcification
CADcoronary artery disease
CHDcoronary heart disease
CIconfidence interval
COURAGEClinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation
CRPC-reactive protein
CTcomputed tomography
CVDcardiovascular disease
DMdiabetes mellitus
ECGelectrocardiogram
FRSFramingham Risk Score
HDLhigh-density lipoprotein
HFheart failure
HIVhuman immunodeficiency virus
HRhazard ratio
IMTintima-media thickness
LDLlow-density lipoprotein
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
NCHSNational Center for Health Statistics
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
PAphysical activity
PARpopulation attributable risk
PCIpercutaneous coronary intervention
RRrelative risk
WHOWorld Health Organization

Prevalence

Adults

The following estimates include many of those who have DM, in addition to those with metabolic syndrome without DM:

  • Prevalence of metabolic syndrome varies by the definition used, with definitions such as that from the International Diabetes Federation and the harmonized definition suggesting lower thresholds for defining central obesity in European whites, Asians (in particular, South Asians), Middle Easterners, Sub-Saharan Africans, and Hispanics, which results in higher prevalence estimates.5
  • On the basis of NHANES 2003 to 2006 data and National Cholesterol Education Program/Adult Treatment Panel III guidelines, ≈34% of adults ≥20 years of age met the criteria for metabolic syndrome.6
  • Also based on NHANES 2003 to 2006 data6
    • The age-adjusted prevalence was 35.1% for men and 32.6% for women.
    • Among men, the age-specific prevalence ranged from 20.3% among people 20 to 39 years of age to 40.8% for people 40 to 59 years of age and 51.5% for people ≥60 years of age. Among women, the age-specific prevalence ranged from 15.6% among people 20 to 39 years of age to 37.2% for people 40 to 59 years of age and 54.4% for those ≥60 years of age.
    • The age-adjusted prevalences of people with metabolic syndrome were 37.2%, 25.3%, and 33.2% for non-Hispanic white, non-Hispanic black, and Mexican American men, respectively. Among women, the percentages were 31.5%, 38.8%, and 40.6%, respectively.
    • The age-adjusted prevalence was ≈53% higher among non-Hispanic black women than among non-Hispanic black men and ≈22% higher among Mexican American women than among Mexican American men.
  • The prevalence of metabolic syndrome is also high among immigrant Asian Indians, ranging between 26.8% and 38.2% depending on the definition used.7
  • Among American Indian and Alaska Native people living in the southwestern United States, the prevalence of metabolic syndrome was reported to be 43.2% in men and 47.3% in women; among Alaska Native people, prevalences were 26.5% and 31.2%, respectively.8
  • The prevalence of metabolic syndrome among pregnant women increased to 26.5% during 1999 to 2004 from 17.8% during 1988 to 1994.9
  • The prevalence of metabolic syndrome has been noted to be high among select special populations, including those taking atypical antipsychotic drugs,10 those receiving prior organ transplants,11 HIV-infected individuals,12 and individuals in select professions, including law enforcement13 and firefighters.14
  • There is a bidirectional relationship between metabolic syndrome and depression. In prospective studies, the presence of depression increases the risk of metabolic syndrome (OR, 1.49; 95% CI, 1.19–1.87), whereas metabolic syndrome increases the risk of depression (OR, 1.52; 95% CI, 1.20–1.91).15
  • Metabolic syndrome is becoming hyperendemic around the world. Recent evidence has described the prevalence of metabolic syndrome in Canada,16 Latin America,17 India,18 and China,19 as well as many other countries.
  • In the INTER-HEART case-control study of MI in 26 903 subjects from 52 countries, metabolic syndrome was present in 29.1% of case subjects and just 16.8% of control subjects. The age- and obesity-adjusted prevalence of metabolic syndrome was highest in cases among women (32.1%), South Asians (29.8%), and other Asians (28.7%).20
  • Despite its prevalence, the public’s recognition of metabolic syndrome is limited.21 A diagnosis of metabolic syndrome may increase risk perception and motivation toward a healthier behavior.22

Children/Adolescents

  • According to the 2009 AHA scientific statement about metabolic syndrome in children and adolescents, metabolic syndrome should be diagnosed with caution in this age group because metabolic syndrome categorization in adolescents is not stable.23 Approximately half of the 1098 adolescent participants in the Princeton School District Study diagnosed with pediatric Adult Treatment Panel III metabolic syndrome lost the diagnosis over 3 years of follow-up.24
  • Additional evidence of the instability of the diagnosis of metabolic syndrome in children exists. In children 6 to 17 years of age participating in research studies in a single clinical research hospital, the diagnosis of metabolic syndrome was unstable in 46% of cases after a mean of 5.6 years of follow-up.25
  • On the basis of NHANES 1999 to 2002 data, the prevalence of metabolic syndrome in adolescents 12 to 19 years of age was 9.4%, which represents ≈2.9 million people. It was 13.2% in boys, 5.3% in girls, 10.7% in whites, 5.2% in blacks, and 11.1% in Mexican Americans.26
  • In 1999 to 2004, ≈4.5% of US adolescents 12 to 17 years of age had metabolic syndrome according to the definition developed by the International Diabetes Federation.27 In 2006, this prevalence would have represented ≈1.1 million adolescents 12 to 17 years of age with metabolic syndrome. It increased from 1.2% among those 12 to 13 years of age to 7.1% among those 14 to 15 years of age and was higher among boys (6.7%) than girls (2.1%). Furthermore, 4.5% of white adolescents, 3.0% of black adolescents, and 7.1% of Mexican American adolescents had metabolic syndrome. The prevalence of metabolic syndrome remained relatively stable during successive 2-year periods: 4.5% for 1999 to 2000, 4.4% to 4.5% for 2001 to 2002, and 3.7% to 3.9% for 2003 to 2004.
  • Recent NHANES data among those aged 10 to 18 years in 2007 to 2008 showed an overall prevalence of metabolic syndrome of 3.9% in boys and 3.6% in girls, with the highest prevalence among Mexican Americans (7.6%) compared with African-Americans (2.1%) and whites (3.1%).28
  • In 1999 to 2002, among overweight or obese adolescents, 44% had metabolic syndrome.26 In 1988 to 1994, two thirds of all adolescents had ≥1 metabolic abnormality.29
  • Of 31 participants in the NHLBI Lipid Research Clinics Princeton Prevalence Study and the Princeton Follow-up Study who had metabolic syndrome at baseline, 21 (68%) had metabolic syndrome 25 years later.30 After adjustment for age, sex, and race, the baseline status of metabolic syndrome was significantly associated with an increased risk of having metabolic syndrome during adulthood (OR, 6.2; 95% CI, 2.8–13.8).
  • In the Bogalusa Heart Study, 4 variables (BMI, homeostasis model assessment of insulin resistance, ratio of triglycerides to HDL cholesterol, and mean arterial pressure) considered to be part of metabolic syndrome clustered together in blacks and whites and in children and adults.31 The degree of clustering was stronger among adults than among children. The clustering of rates of change in the components of metabolic syndrome in blacks exceeded that in whites. Cardiovascular abnormalities are associated with metabolic syndrome in children and adolescents.32,33

Risk

Adults

  • Consistent with 2 earlier meta-analyses, a recent meta-analysis of prospective studies concluded that metabolic syndrome increased the risk of developing CVD (summary RR, 1.78; 95% CI, 1.58–2.00).34 The risk of CVD tended to be higher in women (summary RR, 2.63) than in men (summary RR, 1.98; P=0.09). On the basis of results from 3 studies, metabolic syndrome remained a predictor of cardiovascular events after adjustment for the individual components of the syndrome (summary RR, 1.54; 95% CI, 1.32–1.79). A more recent meta-analysis among 87 studies comprising 951 083 subjects showed an even higher risk of CVD associated with metabolic syndrome (summary RR, 2.35; 95% CI, 2.02–2.73), with significant increased risks (RRs ranging from 1.6 to 2.9) for all-cause mortality, CVD mortality, MI, and stroke, as well as for those with metabolic syndrome without DM.35
  • In one of the earlier studies among US adults, mortality follow-up of the second NHANES showed a stepwise increase in risk of CHD, CVD, and total mortality across the spectrum of no disease, metabolic syndrome (without DM), DM, prior CVD, and those with CVD and DM, with an HR for CHD mortality of 2.02 (95% CI, 1.42–2.89) associated with metabolic syndrome. Increased risk was seen with increased numbers of metabolic syndrome risk factors.36
  • Several studies suggest that the FRS is a better predictor of incident CVD than metabolic syndrome.3739 In the San Antonio Heart Study, the area under the receiver-operating characteristic curve was 0.816 for the FRS and 0.811 for the FRS plus metabolic syndrome.37 Furthermore, the sensitivity for CVD at a fixed specificity was significantly higher for the FRS than for metabolic syndrome. In ARIC, inclusion of metabolic syndrome did not improve the risk prediction achieved by the FRS.38 In the British Regional Heart Study, the area under the receiver-operating characteristic curve for the FRS was 0.73 for incident CHD during 10 years of follow-up, and the area under the receiver-operating characteristic curve for the number of metabolic syndrome components was 0.63.39 For CHD events during 20 years of follow-up, the areas under the receiver-operating characteristic curves were 0.68 for the FRS and 0.59 for the number of metabolic syndrome components.
  • Estimates of RR for CVD generally increase as the number of components of metabolic syndrome increases.39 Compared with men without an abnormal component in the Framingham Offspring Study, the HRs for CVD were 1.48 (95% CI, 0.69–3.16) for men with 1 or 2 components and 3.99 (95% CI, 1.89–8.41) for men with ≥3 components.40 Among women, the HRs were 3.39 (95% CI, 1.31–8.81) for 1 or 2 components and 5.95 (95% CI, 2.20–16.11) for ≥3 components. Compared with men without a metabolic abnormality in the British Regional Heart Study, the HRs were 1.74 (95% CI, 1.22–2.39) for 1 component, 2.34 (95% CI, 1.65–3.32) for 2 components, 2.88 (95% CI, 2.02–4.11) for 3 components, and 3.44 (95% CI, 2.35–5.03) for 4 or 5 components.39
  • The cardiovascular risk associated with metabolic syndrome varies on the basis of the combination of metabolic syndrome components present. Of all possible ways to have 3 metabolic syndrome components, the combination of central obesity, elevated BP, and hyperglycemia conferred the greatest risk for CVD (HR, 2.36; 95% CI, 1.54–3.61) and mortality (HR, 3.09; 95% CI, 1.93–4.94) in the Framingham Offspring Study.41
  • Data from the Aerobics Center Longitudinal Study indicate that risk for CVD mortality is increased in men without DM who have metabolic syndrome (HR, 1.8; 95% CI, 1.5–2.0); however, among those with metabolic syndrome, the presence of DM is associated with even greater risk for CVD mortality (HR, 2.1; 95% CI, 1.7–2.6).42 Analysis of data from NCHS was used to determine the number of disease-specific deaths attributable to all nonoptimal levels of each risk factor exposure by age and sex. The results of the analysis of dietary, lifestyle, and metabolic risk factors show that targeting a handful of risk factors has large potential to reduce mortality in the United States.43
  • Among stable CAD patients in the COURAGE trial, the presence of metabolic syndrome was associated with an increased risk of death or MI (unadjusted HR, 1.41; 95% CI, 1.15–1.73; P=0.001); however, after adjustment for its individual components, metabolic syndrome was no longer significantly associated with outcome (HR, 1.15; 95% CI, 0.79–1.68; P=0.46). Early PCI in addition to medical therapy did not significantly reduce the risk of death or MI regardless of metabolic syndrome or DM status.44
  • In the INTER-HEART case-control study of 26 903 subjects from 52 countries, metabolic syndrome was associated with an increased risk of MI, both according to the WHO (OR, 2.69; 95% CI, 2.45–2.95) and the International Diabetes Federation (OR, 2.20; 95% CI, 2.03–2.38) definitions, with a PAR of 14.5% (95% CI, 12.7%–16.3%) and 16.8% (95% CI, 14.8%–18.8%), respectively, and associations that were similar across all regions and ethnic groups. In addition, the presence of ≥3 risk factors with subthresh-old values was associated with increased risk of MI (OR, 1.50; 95% CI, 1.24–1.81) compared with having “normal” values. Similar results were observed when the International Diabetes Federation definition was used.20
  • In the Three-City Study, among 7612 participants aged ≥65 years who were followed up for 5.2 years, metabolic syndrome was associated with increased total CHD (HR, 1.78; 95% CI, 1.39–2.28) and fatal CHD (HR, 2.40; 95% CI, 1.41–4.09); however, metabolic syndrome was not associated with CHD beyond its individual risk components.45
  • In MESA, among 6603 people aged 45 to 84 years (1686 [25%] with metabolic syndrome without DM and 881 [13%] with DM), subclinical atherosclerosis assessed by CAC was more severe in people with metabolic syndrome and DM than in those without these conditions, and the extent of CAC was a strong predictor of CHD and CVD events in these groups.46 Furthermore, the progression of CAC was greater in people with metabolic syndrome and DM than in those without, and progression of CAC predicted future CVD event risk both in those with metabolic syndrome and in those with DM.47,48
  • In addition to CVD, metabolic syndrome has been associated with incident AF49 and HF.50
  • So-called metabolically benign obesity without metabolic syndrome is associated with similar all-cause mortality to lean individuals.51
  • Metabolic syndrome is associated with increased healthcare use and healthcare-related costs among individuals with and without DM. Overall, healthcare costs increase by ≈24% for each additional metabolic syndrome component present.52

Children

  • Few prospective pediatric studies have examined the future risk for CVD or DM according to baseline metabolic syndrome status. Data from 771 participants 6 to 19 years of age from the NHLBI’s Lipid Research Clinics Princeton Prevalence Study and the Princeton Follow-up Study showed that the risk of developing CVD was substantially higher among those with metabolic syndrome than among those without this syndrome (OR, 14.6; 95% CI, 4.8–45.3) who were followed up for 25 years.30
  • Another analysis of 814 participants in this cohort showed that those 5 to 19 years of age who had metabolic syndrome at baseline had an increased risk of having DM 25 to 30 years later compared with those who did not have the syndrome at baseline (OR, 11.5; 95% CI, 2.1–63.7).53
  • Additional data from the Princeton Follow-up Study, the Fels Longitudinal Study, and the Muscatine Study suggest that the absence of components of metabolic syndrome in childhood has a high negative predictive value for the development of metabolic syndrome or DM in adulthood.54
  • In a study of 6328 subjects from 4 prospective studies, compared with people with normal BMI as children and as adults, those with consistently high adiposity from childhood to adulthood had an increased risk of the following metabolic syndrome components: hypertension (RR, 2.7; 95% CI, 2.2–3.3), low HDL (RR, 2.1; 95% CI, 1.8–2.5), elevated triglycerides (RR, 3.0; 95% CI, 2.4–3.8), type 2 DM (RR, 5.4; 95% CI, 3.4–8.5), and increased carotid IMT (RR, 1.7; 95% CI, 1.4–2.2). Those who were overweight or obese during childhood but were not obese as adults had no increased risk compared with those with consistently normal BMI.55
  • In 1757 youths from the Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study, those with metabolic syndrome in youth and adulthood were at 3.4 times increased risk of high carotid IMT and 12.2 times increased risk of type 2 DM in adulthood as those without metabolic syndrome at either time. Adults whose metabolic syndrome had resolved after their youth were at no increased risk of having high IMT or type 2 DM.56

Risk Factors

  • Risk of metabolic syndrome probably begins before birth. The Prediction of Metabolic Syndrome in Adolescence Study showed that the coexistence of low birth weight, small head circumference, and parental history of overweight or obesity places children at the highest risk for metabolic syndrome in adolescence. Other risk factors identified included parental history of DM, gestational hypertension in the mother, and lack of breastfeeding.57
  • In prospective or retrospective cohort studies, the following factors have been reported as being directly associated with incident metabolic syndrome, defined by one of the major definitions: age,35,3739 low educational attainment,58,59 low socioeconomic status,60 smoking,5962 parental smoking,63 low levels of PA,5962, 6466 low levels of physical fitness,64,6770 intake of soft drinks,71 intake of diet soda,72 magnesium intake,73 energy intake,66 carbohydrate intake,58,61,74 total fat intake,37,53 Western dietary pattern,72 meat intake,72 intake of fried foods,72 skipping breakfast,70 heavy alcohol consumption,75 abstention from alcohol use,58 parental history of DM,53 long-term stress at work,76 pediatric metabolic syndrome,53 obesity or BMI,37,38,42,46,56 childhood obesity,77 waist circumference,74,7882 intra-abdominal fat,83 gain in weight or BMI,37,63 change in weight or BMI,61,78,84 weight fluctuation,85 BP,74,78,81,86 heart rate,87 homeostasis model assessment,79,88 fasting insulin,79 2-hour insulin,79 proinsulin,79 fasting glucose or hyperglycemia,39,58,60 2-hour glucose,79 impaired glucose tolerance,79 triglycerides,74,7881,89 low HDL cholesterol,74,7779,81 oxidized LDL,88 uric acid,84,90 γ-glutamyltransferase,84,91,92 alanine transaminase,84,91,93,94 plasminogen activator inhibitor-1,95 aldosterone,95 leptin,96 CRP,97,98 adipocyte–fatty acid binding protein,99 free testosterone index,100 active periodontitis,101 and urinary bisphenol A levels.102
  • The following factors have been reported as being inversely associated with incident metabolic syndrome, defined by one of the major definitions, in prospective or retrospective cohort studies: muscular strength,103 change in PA or physical fitness,61,67 aerobic training,104 alcohol intake,40,46 Mediterranean diet,105 dairy consumption,72 vitamin D intake,106 intake of tree nuts,107 insulin sensitivity,79 ratio of aspartate aminotransferase to alanine transaminase,93 total testosterone,79,82,83 serum 25-hydroxyvitamin D,108 sex hormone–binding globulin,79,82,83 and Δ5-desaturase activity.109
  • In the Data From the Epidemiological Study on the Insulin Resistance Syndrome cohort, metabolic syndrome was associated with an unfavorable hemodynamic profile, including increased brachial central pulse pressure and increase pulse pressure amplification, compared with similar individuals with isolated hypertension but without metabolic syndrome.110 In MESA, metabolic syndrome was associated with major and minor ECG abnormalities, although this varied by sex.111
  • Individuals with metabolic syndrome have a higher degree of endothelial dysfunction than individuals with a similar burden of traditional cardiovascular risk factors.112 Metabolic syndrome is associated with increased thrombosis, including increased resistance to aspirin.113
  • In modern imaging studies using echocardiography, magnetic resonance imaging, cardiac CT, and positron emission tomography, metabolic syndrome has been shown to be closely related to increased epicardial adipose tissues,114 increased visceral fat in other locations,115 high-risk coronary plaque features including increased necrotic core,116 impaired coronary flow reserve,117 and left ventricular diastolic dysfunction.118
  • Men are more likely than women to develop metabolic syndrome,58,78 and blacks have been shown to be less likely to develop metabolic syndrome than whites.58
  • In >6 years of follow-up in the ARIC Study, 1970 individuals (25%) developed metabolic syndrome, and compared with the normal-weight group (BMI <25 kg/m2), the ORs of developing metabolic syndrome were 2.81 (95% CI, 2.50–3.17) and 5.24 (95% CI, 4.50–6.12) for the overweight (BMI 25–30 kg/m2) and obese (BMI ≥30 kg/m2) groups, respectively. Compared with the lowest quartile of leisure-time PA, the ORs of developing metabolic syndrome were 0.80 (95% CI, 0.71–0.91) and 0.92 (95% CI, 0.81–1.04) for people in the highest and middle quartiles, respectively.119
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60. Chichlowska KL, Rose KM, Diez-Roux AV, Golden SH, McNeill AM, Heiss G. Life course socioeconomic conditions and metabolic syndrome in adults: the Atherosclerosis Risk in Communities (ARIC) Study. Ann Epidemiol. 2009;19:875–883. [PMC free article] [PubMed]
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64. Laaksonen DE, Lakka HM, Salonen JT, Niskanen LK, Rauramaa R, Lakka TA. Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome. Diabetes Care. 2002;25:1612–1618. [PubMed]
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66. Ferreira I, Twisk JW, van Mechelen W, Kemper HC, Stehouwer CD. Development of fatness, fitness, and lifestyle from adolescence to the age of 36 years: determinants of the metabolic syndrome in young adults: the Amsterdam Growth and Health Longitudinal Study. Arch Intern Med. 2005;165:42–48. [PubMed]
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68. LaMonte MJ, Barlow CE, Jurca R, Kampert JB, Church TS, Blair SN. Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome: a prospective study of men and women. Circulation. 2005;112:505–512. [PubMed]
69. Ferreira I, Henry RM, Twisk JW, van Mechelen W, Kemper HC, Stehouwer CD. Amsterdam Growth and Health Longitudinal Study. The metabolic syndrome, cardiopulmonary fitness, and subcutaneous trunk fat as independent determinants of arterial stiffness: the Amsterdam Growth and Health Longitudinal Study. Arch Intern Med. 2005;165:875–882. [PubMed]
70. Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, Wilmot EG, Yates T, Biddle SJ. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS One. 2012;7:e34916. [PMC free article] [PubMed]
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75. Baik I, Shin C. Prospective study of alcohol consumption and metabolic syndrome. Am J Clin Nutr. 2008;87:1455–1463. [PubMed]
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77. Sun SS, Liang R, Huang TT, Daniels SR, Arslanian S, Liu K, Grave GD, Siervogel RM. Childhood obesity predicts adult metabolic syndrome: the Fels Longitudinal Study. J Pediatr. 2008;152:191–200. [PMC free article] [PubMed]
78. Cheung BM, Wat NM, Tam S, Thomas GN, Leung GM, Cheng CH, Woo J, Janus ED, Lau CP, Lam TH, Lam KS. Components of the metabolic syndrome predictive of its development: a 6-year longitudinal study in Hong Kong Chinese. Clin Endocrinol (Oxf) 2008;68:730–737. [PubMed]
79. Palaniappan L, Carnethon MR, Wang Y, Hanley AJ, Fortmann SP, Haffner SM, Wagenknecht L. Insulin Resistance Atherosclerosis Study. Predictors of the incident metabolic syndrome in adults: the Insulin Resistance Atherosclerosis Study. Diabetes Care. 2004;27:788–793. [PubMed]
80. Morrison JA, Friedman LA, Harlan WR, Harlan LC, Barton BA, Schreiber GB, Klein DJ. Development of the metabolic syndrome in black and white adolescent girls: a longitudinal assessment. Pediatrics. 2005;116:1178–1182. [PubMed]
81. Sheu WH, Chuang SY, Lee WJ, Tsai ST, Chou P, Chen CH. Predictors of incident diabetes, metabolic syndrome in middle-aged adults: a 10-year follow-up study from Kinmen, Taiwan. Diabetes Res Clin Pract. 2006;74:162–168. [PubMed]
82. Onat A, Uyarel H, Hergenç G, Karabulut A, Albayrak S, Can G. Determinants and definition of abdominal obesity as related to risk of diabetes, metabolic syndrome and coronary disease in Turkish men: a prospective cohort study. Atherosclerosis. 2007;191:182–190. [PubMed]
83. Tong J, Boyko EJ, Utzschneider KM, McNeely MJ, Hayashi T, Carr DB, Wallace TM, Zraika S, Gerchman F, Leonetti DL, Fujimoto WY, Kahn SE. Intra-abdominal fat accumulation predicts the development of the metabolic syndrome in non-diabetic Japanese-Americans. Diabetologia. 2007;50:1156–1160. [PubMed]
84. Ryu S, Song J, Choi BY, Lee SJ, Kim WS, Chang Y, Kim DI, Suh BS, Sung KC. Incidence and risk factors for metabolic syndrome in Korean male workers, ages 30 to 39. Ann Epidemiol. 2007;17:245–252. [PubMed]
85. Vergnaud AC, Bertrais S, Oppert JM, Maillard-Teyssier L, Galan P, Hercberg S, Czernichow S. Weight fluctuations and risk for metabolic syndrome in an adult cohort. Int J Obes (Lond) 2008;32:315–321. [PubMed]
86. Sun SS, Grave GD, Siervogel RM, Pickoff AA, Arslanian SS, Daniels SR. Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life. Pediatrics. 2007;119:237–246. [PubMed]
87. Tomiyama H, Yamada J, Koji Y, Yambe M, Motobe K, Shiina K, Yamamoto Y, Yamashina A. Heart rate elevation precedes the development of metabolic syndrome in Japanese men: a prospective study. Hypertens Res. 2007;30:417–426. [PubMed]
88. Holvoet P, Lee DH, Steffes M, Gross M, Jacobs DR., Jr Association between circulating oxidized low-density lipoprotein and incidence of the metabolic syndrome. JAMA. 2008;299:2287–2293. [PMC free article] [PubMed]
89. Lim HS, Lip GY, Beevers DG, Blann AD. Factors predicting the development of metabolic syndrome and type II diabetes against a background of hypertension. Eur J Clin Invest. 2005;35:324–329. [PubMed]
90. Sui X, Church TS, Meriwether RA, Lobelo F, Blair SN. Uric acid and the development of metabolic syndrome in women and men. Metab Clin Exp. 2008;57:845–852. [PMC free article] [PubMed]
91. André P, Balkau B, Vol S, Charles MA, Eschwège E. DESIR Study Group. Gamma-glutamyltransferase activity and development of the metabolic syndrome (International Diabetes Federation definition) in middle-aged men and women: Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) cohort. Diabetes Care. 2007;30:2355–2361. [PubMed]
92. Lee DS, Evans JC, Robins SJ, Wilson PW, Albano I, Fox CS, Wang TJ, Benjamin EJ, D’Agostino RB, Vasan RS. Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham Heart Study. Arterioscler Thromb Vasc Biol. 2007;27:127–133. [PubMed]
93. Hanley AJ, Williams K, Festa A, Wagenknecht LE, D’Agostino RB, Jr, Haffner SM. Liver markers and development of the metabolic syndrome: the Insulin Resistance Atherosclerosis Study. Diabetes. 2005;54:3140–3147. [PubMed]
94. Schindhelm RK, Dekker JM, Nijpels G, Stehouwer CD, Bouter LM, Heine RJ, Diamant M. Alanine aminotransferase and the 6-year risk of the metabolic syndrome in Caucasian men and women: the Hoorn Study. Diabet Med. 2007;24:430–435. [PubMed]
95. Ingelsson E, Pencina MJ, Tofler GH, Benjamin EJ, Lanier KJ, Jacques PF, Fox CS, Meigs JB, Levy D, Larson MG, Selhub J, D’Agostino RB, Sr, Wang TJ, Vasan RS. Multimarker approach to evaluate the incidence of the metabolic syndrome and longitudinal changes in metabolic risk factors: the Framingham Offspring Study. Circulation. 2007;116:984–992. [PubMed]
96. Galletti F, Barbato A, Versiero M, Iacone R, Russo O, Barba G, Siani A, Cappuccio FP, Farinaro E, della Valle E, Strazzullo P. Circulating leptin levels predict the development of metabolic syndrome in middle-aged men: an 8-year follow-up study. J Hypertens. 2007;25:1671–1677. [PubMed]
97. Laaksonen DE, Niskanen L, Nyyssönen K, Punnonen K, Tuomainen TP, Valkonen VP, Salonen R, Salonen JT. C-reactive protein and the development of the metabolic syndrome and diabetes in middle-aged men. Diabetologia. 2004;47:1403–1410. [PubMed]
98. Hassinen M, Lakka TA, Komulainen P, Gylling H, Nissinen A, Rauramaa R. C-reactive protein and metabolic syndrome in elderly women: a 12-year follow-up study. Diabetes Care. 2006;29:931–932. [PubMed]
99. Xu A, Tso AW, Cheung BM, Wang Y, Wat NM, Fong CH, Yeung DC, Janus ED, Sham PC, Lam KS. Circulating adipocyte-fatty acid binding protein levels predict the development of the metabolic syndrome: a 5-year prospective study. Circulation. 2007;115:1537–1543. [PubMed]
100. Rodriguez A, Muller DC, Metter EJ, Maggio M, Harman SM, Blackman MR, Andres R. Aging, androgens, and the metabolic syndrome in a longitudinal study of aging. J Clin Endocrinol Metab. 2007;92:3568–3572. [PubMed]
101. Nibali L, Tatarakis N, Needleman I, Tu YK, D’Aiuto F, Rizzo M, Donos N. Clinical review: association between metabolic syndrome and periodontitis: a systematic review and meta-analysis. J Clin Endocrinol Metab. 2013;98:913–920. [PubMed]
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105. Tortosa A, Bes-Rastrollo M, Sanchez-Villegas A, Basterra-Gortari FJ, Nuñez-Cordoba JM, Martinez-Gonzalez MA. Mediterranean diet inversely associated with the incidence of metabolic syndrome: the SUN prospective cohort. Diabetes Care. 2007;30:2957–2959. [PubMed]
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107. O’Neil CE, Keast DR, Nicklas TA, Fulgoni VL., 3rd Nut consumption is associated with decreased health risk factors for cardiovascular disease and metabolic syndrome in U.S. adults: NHANES 1999–2004. J Am Coll Nutr. 2011;30:502–510. [PubMed]
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12. Chronic Kidney Disease

ICD-10 N18.0. See Tables 12-1 through 12-3.

Table 12-1
BP and the Adjusted Risk of ESRD Among 316 675 Adults Without Evidence of Baseline Kidney Disease
Table 12-3
Adjusted HR for Death of Any Cause, Cardiovascular Events, and Hospitalization Among 1 120 295 Ambulatory Adults, According to eGFR*

End-stage Renal Disease

Prevalence, Incidence, and Risk

(See Tables 12-1 and 12-2.)

Table 12-2
Multivariable Association Between BMI and Risk of ESRD Among 320 252 Adults

ESRD is a condition that is most commonly associated with DM or HBP, occurs when the kidneys are functioning at a very low level, and is currently defined as the receipt of chronic renal replacement treatment such as hemodialysis, peritoneal dialysis, or kidney transplantation. The ESRD population is increasing in size and cost as those with CKD transition to ESRD and as a result of changing practice patterns in the United States.

  • Data from the 2010 annual report of the United States Renal Data System showed that in 2008, the prevalence of ESRD was 547 982, with 70% of these prevalent cases being treated with hemodialysis.1
  • In 2008, 112 476 new cases of ESRD were reported.1
  • In 2008, 17 413 kidney transplants were performed.1
  • Data from a large cohort of insured patients showed that in addition to established risk factors for ESRD, lower hemoglobin levels, higher serum uric acid levels, self-reported history of nocturia, and family history of kidney disease are independent risk factors for ESRD.2
  • Data from a large insured population revealed that among adults with a GFR >60 mL·min−1·1.73 m−2 and no evidence of proteinuria or hematuria at baseline, risks for ESRD increased dramatically with higher baseline BP level, and in this same patient population, BP-associated risks were greater in men than in women and in blacks than in whites.3
  • Compared with white patients with similar levels of kidney function, black patients are much more likely to progress to ESRD and are on average 10 years younger when they reach ESRD.4,5
  • Results from a large community-based population showed that higher BMI also independently increased the risk of ESRD. The higher risk of ESRD with overweight and obesity was consistent across age, sex, and race and in the presence or absence of DM, hypertension, or known baseline kidney disease.6

Abbreviations Used in Chapter 12

ACTIONAcute Coronary Treatment and Intervention Outcomes Network
AFatrial fibrillation
AMIacute myocardial infarction
BMIbody mass index
BPblood pressure
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CKDchronic kidney disease
CVDcardiovascular disease
DMdiabetes mellitus
eGFRestimated glomerular filtration rate
ESRDend-stage renal disease
GFRglomerular filtration rate
HBPhigh blood pressure
HFheart failure
HRhazard ratio
ICD-10International Classification of Diseases, 10th Revision
JNC Vfifth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure
MImyocardial infarction
NCHSNational Center for Health Statistics
NHANESNational Health and Nutrition Examination Survey
PADperipheral arterial disease
RRrelative risk

Age, Sex, Race, and Ethnicity

  • The median age of the population with ESRD in 2008 varied across different racial/ethnic groups: 57.4 years for blacks, 58.0 years for Native Americans, 59.3 years for Asians, and 60.6 years for whites.1
  • Treatment of ESRD is more common in men than in women.1
  • Blacks, Hispanics, Asian Americans, and Native Americans have significantly higher rates of ESRD than do whites/Europeans. Blacks represent nearly 32% of treated patients with ESRD.1

Chronic Kidney Disease

Prevalence

  • CKD, defined as reduced GFR, excess urinary protein excretion, or both, is a serious health condition and a worldwide public health problem. The incidence and prevalence of CKD are increasing in the United States and are associated with poor outcomes and a high cost to the US healthcare system. Controversy exists about whether CKD itself independently causes incident CVD, but it is clear that people with CKD, as well as those with ESRD, represent a population at very high risk for CVD events. In fact, individuals with CKD are more likely to die of CVD than to transition to ESRD. The United States Renal Data System estimates that by 2020, >700 000 Americans will have ESRD, with >500 000 requiring dialysis and >250 000 receiving a transplant.
  • The National Kidney Foundation Kidney Disease Outcome Quality Initiative developed guidelines in 2002 that provided a standardized definition for CKD. Prevalence estimates may differ depending on assumptions used in obtaining estimates, including which equation is used to estimate GFR and methods for measuring proteinuria.7
  • The most recent US prevalence estimates of CKD come from NHANES 1988 to 1994 and 1999 to 2004 (NCHS) in adults ≥20 years of age.8
    • The prevalence of CKD in 1999 to 2004 (stages 1 to 5)9 was 13.1%. This represents an increase from the 10.0% prevalence estimate from NHANES 1988 to 1994 (NCHS).
    • The prevalence of stage 1 CKD (eGFR ≥90 mL·min−1· 1.73 m−2 with kidney damage, ie, presence of albuminuria) is 1.8%.
    • The prevalence of stage 2 CKD (eGFR 60–89 mL·min−1·1.73 m−2 with kidney damage) is 3.2%.
    • The prevalence of stage 3 CKD (eGFR 30–59 mL·min−1·1.73 m−2) is 7.7%.
    • The prevalence of stages 4 and 5 CKD (eGFR <29 mL·min−1·1.73 m−2) is 0.4%.
  • More than 26 million people (13%) in the United States have CKD, and most are undiagnosed.8 Another 20 million are at increased risk for CKD.10

Demographics

  • According to current definitions, the prevalence of CKD was higher with older age,1 as follows:
    • 6.0% for those 20 to 39 years of age
    • 11.6% for those 40 to 59 years of age
    • 38.8% for those ≥60 years of age
  • CKD prevalence was greater among those with DM (43.8%) and hypertension (29.4%) than among those without these chronic conditions.1
  • The prevalence of CKD was slightly higher among Mexican Americans (18.7%) and non-Hispanic blacks (19.9%) than among non-Hispanic whites (16.1%). This disparity was most evident for those with stage 1 CKD; non-Hispanic whites had a CKD prevalence of 4.2% compared with prevalences among Mexican Americans and non-Hispanic blacks of 10.2% and 9.4%, respectively.11

Risk Factors

  • Many traditional CVD risk factors are also risk factors for CKD, including older age, male sex, hypertension, DM, smoking, and family history of CVD.
  • Recent evidence suggests that BMI is associated with worsening CKD.
    • In a cohort of 652 African American individuals with hypertensive nephrosclerosis, BMI was independently associated with urine total protein and albumin excretion.12
  • In addition, both the degree of CKD (ie, eGFR) and urine albumin are strongly associated with the progression from CKD to ESRD. Furthermore, urine albumin level is associated with progression to CKD across all levels of reduced eGFR.13
  • Other risk factors include systemic conditions such as auto-immune diseases, systemic infections, and drug exposure, as well as anatomically local conditions such as urinary tract infections, urinary stones, lower urinary tract obstruction, and neoplasia. Even after adjustment for these risk factors, excess CVD risk remains.14

ESRD/CKD and CVD

(See Table 12-3.)

  • CVD is the leading cause of death among those with ESRD, although the specific cardiovascular cause of death may be more likely to be arrhythmic than an AMI, end-stage HF, or stroke. CVD mortality is 5 to 30 times higher in dialysis patients than in subjects from the general population of the same age, sex, and race.15,16
    • Individuals with less severe forms of kidney disease are also at significantly increased CVD risk independent of typical CVD risk factors.17
    • CKD is a risk factor for recurrent CVD events.18
    • CKD is also a risk factor for AF.19
  • Studies from a broad range of cohorts demonstrate an association between reduced eGFR and elevated risk of CVD, CVD outcomes, and all-cause death17,2025 that appears to be largely independent of other known major CVD risk factors.
  • Although clinical practice guidelines recommend management of mineral and bone disorders secondary to CKD, a recent meta-analysis suggests that there is no consistent association between calcium and parathyroid hormone and the risk of death or cardiovascular events.26
  • Any degree of albuminuria, starting below the micro-albuminuria cut point, has been shown to be an independent risk factor for cardiovascular events, CHF hospitalization, PAD, and all-cause death in a wide variety of cohorts.2732
  • A recent meta-analysis of 21 published studies of albuminuria involving 105 872 participants (730 577 person-years) from 14 studies with urine albumin/creatinine ratio measurements and 1 128 310 participants (4 732 110 person-years) from 7 studies with urine dipstick measurements showed that excess albuminuria or proteinuria is independently associated with a higher risk of CVD and all-cause mortality.33
    • People with both albuminuria/proteinuria and reduced eGFR are at particularly high risk for CVD, CVD outcomes, and death.34
    • The exact reasons why CKD and ESRD increase the risk of CVD have not been completely delineated but are clearly multifactorial and likely involve pathological alterations in multiple organ systems and pathways.
  • One potential explanation for the higher CVD event rate in patients with CKD is the low uptake of standard therapies for patients presenting with MI. In a recent analysis from the ACTION registry, patients presenting with CKD had a substantially higher mortality rate. In addition, patients with CKD were less likely to receive standard therapies for the treatment of MI.35

Cost: ESRD

  • The total annual cost of treating ESRD in the United States was $26.8 billion in 2008, which represents nearly 6% of the total Medicare budget.1
  • The total annual cost associated with CKD has not been determined accurately to date.

Cystatin C: Kidney Function and CVD

Serum cystatin C, another marker of kidney function, has been proposed to be a more sensitive indicator of kidney function than serum creatinine and creatinine-based estimating formulas at higher levels of GFR. It is a low-molecular-weight protein produced at a constant rate by all nucleated cells and appears not to be affected significantly across age, sex, and levels of muscle mass. Cystatin C is excreted by the kidneys, filtered through the glomerulus, and nearly completely reabsorbed by proximal tubular cells.36 Several equations have been proposed using cystatin C alone and in combination with serum creatinine to estimate kidney function.37,38

All-Cause Mortality

Elevated levels of cystatin C have been shown to be associated with increased risk for all-cause mortality in studies from a broad range of cohorts.3941

  • In addition to GFR and urine albumin-to-creatinine ratio, cystatin C provides incremental information for the prediction of ESRD and mortality.
    • In a recent analysis of 26 643 US adults, the addition of cystatin C to the combination of creatinine and albumin-to-creatinine ratio resulted in a significant improvement in the prediction of both all-cause mortality and the development of ESRD.42

Cardiovascular Disease

  • Data from a large national cohort found higher values of cystatin C to be associated with prevalent stroke, angina, and MI,43 as well as higher BMI.44
  • Elevated cystatin C was an independent risk factor for HF,45,46 PAD events,47 clinical atherosclerosis, and subclinical measures of CVD in older adults,48 as well as for cardiovascular events among those with CHD.39,49
  • In several diverse cohorts, elevated cystatin C has been found to be associated with CVD-related mortality,41,50,51 including sudden cardiac death.52
  • In a recent clinical trial of 9270 patients with CKD, the effect of lipid-lowering therapy with simvastatin plus ezetimibe was associated with a lower risk for major atherosclerotic events compared with placebo.53
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