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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Circ Heart Fail. Author manuscript; available in PMC 2014 January 12.
Published in final edited form as:
PMCID: PMC3888807

Insulin Resistance and Risk of Incident Heart Failure: Cardiovascular Health Study



Patients with heart failure (HF) have higher fasting insulin levels and a higher prevalence of insulin resistance (IR) as compared with matched controls. IR leads to structural abnormalities in the heart, such as increased left atrial (LA) size, left ventricular (LV) mass, and alterations in transmitral velocity that can precede the diagnosis of HF. It is not known whether IR precedes the development of HF or whether the relationship between IR and HF is present among adults with HF due to non-ischemic heart disease.

Methods and Results

We examined 4425 participants (60% female) from the Cardiovascular Health Study after excluding those with HF, myocardial infarction, or treated diabetes at baseline. We used Cox proportional hazards models to estimate the relative risk of incident HF associated with fasting insulin measured at study entry.

There were 1216 cases of incident HF (1103 without antecedent MI) during a median follow-up of 12 years (maximum, 19 years). Fasting insulin levels were positively associated with the risk of incident HF (HR = 1.10, 95% CI 1.05, 1.15, per SD change) when adjusted for age, gender, race, field center, physical activity, smoking, alcohol intake, HDL cholesterol, total cholesterol, and systolic blood pressure, and waist circumference. The association between fasting insulin levels and incident HF was similar for HF without antecedent MI (HR= 1.10, 95% CI 1.05, 1.15). Measures of LA size, LV mass, and peak A velocity at baseline were associated both with fasting insulin levels and with heart failure ; however, additional statistical adjustment for these parameters did not completely attenuate the insulin-HF estimate (HR= 1.08, 95% CI 1.03, 1.14 per1-SD increase in fasting insulin).


Fasting insulin was positively associated with adverse echocardiographic features and risk of subsequent HF in CHS participants, including those without an antecedent MI.

Keywords: heart failure, insulin, epidemiology


Adults with cardiomyopathy exhibit a higher prevalence of abnormal glucose tolerance and insulin resistance (IR) as compared with matched controls or patients with valvular heart disease.1, 2 Although the direction of the association between insulin resistance and cardiomyopathy is unknown, some authors have proposed that insulin resistance predates cardiomyopathy.35 Patients with microvascular angina also exhibit insulin resistance, suggesting that insulin resistance is involved in the pathogenesis of subsequent cardiomyopathy.6

The seminal study investigating the association between insulin resistance and incident congestive heart failure (CHF) by Ingelsson7 followed elderly Scandinavian men for a median follow-up of 9 years after obtaining baseline risk factors for heart failure, including insulin resistance. They found that insulin resistance by euglycemic clamp (the gold standard for measuring insulin sensitivity), 2-hour oral glucose tolerance test (OGTT) values and fasting serum proinsulin levels were each associated with CHF incidence independent of established risk factors. However, their analysis did not distinguish between types of heart failure, nor did they examine the role of echocardiographic parameters, BNP levels, or subclinical atherosclerosis on the association. An analysis of a subset of Cardiovascular Health Study (CHS) participants by Kaplan et al8 did not observe an association with incident congestive heart failure, but was focused upon insulin-like growth factors and did not include all CHS participants.

In this investigation, we sought to determine the relationship between insulin resistance and incident heart failure in a prospective cohort of older adults who were free of diabetes. Secondary questions included determining whether associations were consistent among participants without an antecedent MI, persisted following adjustment for confounders and mediators, and varied across different measures of insulin resistance.


Study participants

The CHS is a prospective population-based cohort study of 5,888 Medicare-eligible adults 65 years and older in 4 US communities. Two cohorts were recruited. In the original cohort, 5,201 eligible men and women were enrolled during 1989–1990. In the second recruitment during 1992–93, designed to increase the number of black participants, 687 predominantly black men and women were enrolled. Clinic examinations were performed at study baseline and at annual visits through 1998–1999, and again in 2005–2006. Participants were contacted by telephone annually between exams, and twice per year during 2000–2004 and 2007-present when no clinic examinations occurred. Data collection included fasting blood specimens, resting electrocardiograms (ECGs), physical examinations (including height, weight, and blood pressure (BP)), and questionnaires comprising medical history, use of prescription medications, and health-related behaviors. Each center's institutional review board approved the study and all participants gave informed written consent. A description of the design, sampling, and recruitment in the CHS has been published previously.9

After excluding participants with prevalent heart failure (n=275), prior myocardial infarction (451), treated diabetes at baseline (n=375), missing fasting glucose (n=259), and missing insulin measures (n=103), 4425 participants were available for analysis.

Ascertainment of CHF and Myocardial Infarction

All incident CHF events, myocardial infarctions, as well as other vascular events, hospitalizations, and deaths, were identified through semiannual participant contacts, notification by participants and proxies, and review of Medicare hospitalization records. Potential incident events were investigated by review of medical records and final classification was assigned by the CHS Events Subcommittee using standardized criteria. Details of the adjudication processes have been published previously.10

Confirmation of CHF required, in addition to a physician diagnosis, supporting evidence consisting of at least one of the following: 1) documentation of symptoms including shortness of breath, fatigue, orthopnea and paroxysmal nocturnal dyspnea, plus physical signs (edema, rales, tachycardia, a gallop, or a displaced left ventricular (LV) apical impulse); or 2) supportive clinical findings such as cardiomegaly and pulmonary edema on chest x-ray, evidence of a dilated left ventricle, and global or segmental wall-motion abnormalities with decreased LV systolic function either by echocardiography or by contrast ventriculography; or 3) medical therapy for CHF, defined as diuretics plus either digitalis or a vasodilator (angiotensin-converting enzyme inhibitors, hydralazine, or long-acting nitrates).

At baseline, self-report of a history of physician-diagnosed myocardial infarction was confirmed by ECG evidence of a previous MI defined as the presence of major Q waves or the combination of minor q waves and ST-T wave changes.11 When ECG results did not match the participant's report of past history of MI, medical records and physician questionnaires were reviewed to validate the reported history. Incident myocardial infarction was defined as electrocardiographic evidence of MI (Q waves on EKG), elevated serum markers of myocardial damage, and segmental wall motion abnormalities on echocardiography or by contrast ventriculography.10

Laboratory methods

Fasting blood specimens from each visit were stored at a central laboratory (CHS Central Laboratory, University of Vermont, Burlington, VT). Fasting insulin, glucose, lipids, and other laboratory parameters were measured at the central laboratory as previously described.10 Oral glucose tolerance testing (OGTT) performed in the original cohort at baseline entailed ingestion of 75 grams of glucose after fasting, with glucose and insulin levels measured at baseline and 2 hours after ingestion.


Diabetes was defined as use of anti-hyperglycemic medication or a fasting glucose level above 126 mg/dL. Post-load glucose was not used to define diabetes because it was not available at the baseline examination for the supplemental cohort of black participants. Measures of insulin resistance apart from fasting insulin levels included HOMA-IR, calculated as fasting plasma insulin (µIU/mL) × fasting plasma glucose (mmol/L) 22.5), the Gutt index (75,000 _ +_(fasting glucose −_ 2-h glucose)_ ×_ 0.19 _× body weight/120 ×_ log ([fasting insulin + 2-h insulin]/2) _ ×_(fasting glucose +_ 2-h glucose]/2,12 and triglyceride/HDL ratio. Carotid–intima thickness was determined by carotid ultrasonography.13 Echocardiographic examinations were performed at baseline in the original CHS cohort, recorded on super-VHS tape using a standard protocol, and all studies were sent to a reading center.14

Statistical methods

We categorized participants by quartiles of fasting insulin and generated Kaplan-Meier curves to describe the association between insulin concentrations and time to incident CHF. Time at risk was calculated as the interval in days from the date of the baseline visit to the earliest of: date of incident CHF, death, or end of event follow-up (June, 2008). We used Cox proportional hazards models to estimate the relative risk (RR) of incident CHF associated with fasting insulin categories, using the lowest quartile as the reference group. We also calculated RR estimates for a 1-standard deviation change in fasting insulin. To evaluate the association between fasting insulin and incident CHF without an intervening myocardial infarction, we refit Cox models, censoring participants at the time of myocardial infarction that occurred during follow-up.

The initial multivariable model included the following covariates considered to be potential confounders: age, gender, race (black vs non-black), field center, physical activity, smoking (never, former, current), alcohol intake (never, former, < 1 dk/wk, 1–<7 dk/wk, 7+ dk/wk), HDL cholesterol, total cholesterol, systolic blood pressure, and waist circumference. To assess the role of possible mediators of the relationship between fasting insulin and incident CHF, we fit additional sequential models containing variables added separately to the baseline model, including: carotid intima media thickness, presence/absence of a major ECG abnormality, and NT pro BNP.

Among participants enrolled in 1989–90 who had echocardiography measures available at baseline (n=3,952), we investigated cardiac parameters as potential intermediates by adding the following variables to the baseline model: left atrial size, peak E velocity (quadratic), peak A velocity (quadratic), and left ventricular mass. To determine the effect of diabetes upon the relationship between insulin resistance and heart failure, we repeated our analyses after adjusting for diabetes as a time varying covariate, and also after excluding individuals with a fasting glucose of 126 mg/dL at baseline.

Finally, to evaluate the association between other measures of insulin resistance and CHF, we repeated the main analysis using different measures of insulin resistance as the independent variable (Gutt index, HOMA-IR, Triglyceride/HDL ratio, and 2 hour glucose from oral glucose tolerance test).

The proportion of missing data was very low (<3% for any single variable) with the exceptions of the echocardiography measure of left ventricular mass and serum NT-proBNP levels, which were missing among 41% and 23% of participants, respectively. Missing covariate data, except for NT-proBNP, was imputed as described previously.15 We evaluated the validity of the proportional hazards assumption using Schoenfeld residuals and found no meaningful departures. Statistical significance was defined as P < .05. Analyses were conducted using STATA version 10 analysis software (College Station, TX).


The study sample comprised 4425 participants. The majority of participants (60%) were female and 14% were black. Higher levels of fasting insulin were associated with higher left atrial size, waist circumference, and left ventricular mass and with lower HDL cholesterol and NT-BNP levels. (Table 1)

Table 1
Baseline Characteristics1 by Serum Insulin Levels among Cardiovascular Health Study Participants.

In total, 1126 new cases of incident HF (1103 without antecedent MI) occurred over 52,690 person-years of follow-up. Participants with heart failure had higher SBP, NT-BNP levels, carotid intima media thickness, waist circumference, and left ventricular mass and lower HDL levels as compared with participants without heart failure. Participants whose heart failure was not preceded by MI exhibited lower left ventricular mass, carotid intima media thickness, and NT-BNP levels and higher alcohol use than those without antecedent, but were otherwise very similar. (Table 2) Similar results were noted when participants with prior MI were included in the baseline cohort.

Table 2
Baseline Characteristics1 by Presence or Absence of Heart Failure Among Cardiovascular Health Study Participants.

Figure 1 displays the unadjusted, positive relationship between quartile of fasting insulin and incident heart failure. When adjusted for baseline characteristics (age, gender, race, field center, physical activity, smoking, alcohol intake, HDL cholesterol, total cholesterol, systolic blood pressure, and waist circumference), there remained a significant relationship between fasting insulin levels and incident heart failure. (Table 3) No meaningful difference in the association between fasting insulin and CHF was noted for participants whose CHF was not preceded by MI.

Figure 1
Kaplan- Meier curves for incidence of heart failure stratified by quartile of fasting insulin
Table 3
Hazard Ratios and 95% Confidence Intervals of Incident Heart Failure by Quartile1 of Fasting Insulin (Quartile 1 = Referent Group) and Per Standard Deviation Change in Fasting Insulin.

Adjustment for possible mediators of the relationship between fasting insulin (major ECG abnormality and carotid intima media thickness) did not substantially alter the relationship between fasting insulin and incident heart failure. (Table 3). Fasting insulin was positively associated with LA size, LV mass, and peak A velocity at baseline ; however, additional statistical adjustment for these parameters among participants who had echocardiographic measures available modestly attenuated the insulin-HF estimate (Model 1 HR=1.10 95% CI:1.05, 1.15; Model 1 + LA size, LV mass, peak E velocity and peak A velocity HR= 1.08, 95% CI 1.03, 1.14 per1-SD increase in fasting insulin). Adjustment for NT-BNP levels in participants who had this measure available did not attenuate the insulin-HF estimates (Model 1 HR=1.09 95% CI:1.04, 1.15; Model 1 + NT-BNP levels HR= 1.09, 95% CI 1.04, 1.15 per1-SD increase in fasting insulin). Additional exclusion of individuals with a fasting glucose of 126 mg/dL or higher at baseline, or adjustment for prevalent and incident diabetes (defined as fasting glucose ≥ 126 mg/dL or use of diabetes medication) as a time-varying covariate, did not appreciably alter the results.

When analyzing alternate measures of insulin resistance in our analysis, the risk (per SD HR) for incident heart failure was strongest when using 2 hour glucose levels. The association was generally weaker for the triglyceride/HDL ratio, as compared to fasting insulin (Table 4).

Table 4
Hazard Ratios and 95% Confidence Intervals of Incident Heart Failure by Different Measures of Insulin Resistance among CHS Participants with All Measures (n=3,792).


In this analysis, fasting insulin levels were positively associated with the risk of incident HF, even after adjustment for a broad set of potential confounders. The insulin-HF association was present among participants without antecedent MI. Fasting insulin was also associated with abnormal echocardiographic parameters at baseline, but adjustment for these parameters only modestly attenuated the association between fasting insulin levels and incident HF. Adjustment for other possible mediators did not appreciably alter the relationship between fasting insulin and incident heart failure. Finally, the relationship between incident HF and alternative measures of insulin resistance was similar to the relationship between incident HF and fasting insulin levels, although 2-hour glucose levels were most strongly associated with incident heart failure.

Our findings corroborate previous investigations which established an association between insulin resistance/diabetes and incident heart failure.7, 8, 16, 17 We expand on these analyses by showing that the increased risk of incident heart failure conferred by higher fasting insulin levels was present in those without a history of antecedent MI. This finding runs counter to the theory that coronary artery disease is the primary mediator of the relationship between insulin resistance and heart failure,17, 18 but is consistent with a study finding a higher prevalence of insulin resistance in patients with nonischemic cardiomyopathy1 and others noting that the relationship between insulin resistance and HF is independent of coronary artery disease.19 The relationship between fasting insulin levels and echocardiographic abnormalities such as left ventricular mass and left atrial size, have been noted by other investigators,2022 though we were able to provide new information by showing that these abnormalities did not completely attenuate the relationship between fasting insulin and incident heart failure.

An unexpected observation was that 2 hour glucose levels were more strongly associated with incident heart failure than other measures of insulin resistance. Hyperglycemia, which develops when insulin levels are no longer sufficient to control serum glucose levels, may confer additional risk of heart failure beyond insulin resistance. Thus, hyperglycemic patients with insulin resistance may benefit the most from risk factor modification. By contrast, TG/HDL ratio was less strongly associated with incident heart failure, which is consistent with available data suggesting that this surrogate measure of insulin resistance is not as sensitive as fasting insulin levels in defining insulin resistance.23

There is strong evidence to suggest that the biological effects of higher fasting insulin levels/insulin resistance could lead to the development or worsening of heart failure. During periods of myocardial stress (ie in advanced stages of heart failure), the heart switches to a fetal gene program, leading to a relative increase in the utilization of glucose as a fuel rather than free fatty acids.24, 25 In the setting of insulin resistance, however, this switch is ineffective, since glucose utilization is impaired, and the heart enters an ‘energy starved state’; thus insulin resistance could initiate or exacerbate heart failure.2628 Insulin resistance may also contribute to the development of heart failure due to the direct effects of higher fasting insulin levels upon the myocardium and its vasculature, including chronic adrenergic stimulation, cellular apoptosis and endothelial dysfunction, as well as indirect effects, such as impairment of myocardial energy metabolism, hypertension and dyslipidemia.2932 Further evidence of the link between insulin resistance and heart failure lies in the association with insulin resistance and structural abnormalities that predispose to diastolic heart failure (increased left ventricular mass and left ventricular hypertrophy).20, 21

Another unexpected observation was that NT-BNP levels were inversely associated with fasting insulin levels. Typically, patients with heart failure exhibit higher levels of NT-BNP than those without heart failure, and we might have expected a positive association between fasting insulin and NT-BNP levels. However, NT-BNP levels are lower in obese individuals as compared to normal weight individuals,33 so this finding may simply reflect the fact that higher fasting insulin levels are found in more obese individuals.

Strengths of our investigation include the use of a large, well-established cohort study with extended follow-up, a significant number of events, standardized, protocol- driven adjudication of events and measured parameters (ie echocardiography), and the prospective study design. Limitations include an imperfect designation of ischemic vs. nonischemic heart failure, since the gold standard for defining ischemic cardiomyopathy requires the use coronary angiography, which was not part of the study data. Although we subdivided our cohort by the presence or absence of previous myocardial infarction, this may not be sufficient to differentiate patients with ischemic type heart failure and those with non-ischemic HF.

Insulin resistance, whether measured by fasting insulin levels or triglyceride/HDL ratio, can be detected prior to the development of clinical heart failure and structural abnormalities of the heart. Though there are no large-scale clinical trials of therapy targeting insulin resistance in individuals at risk for heart failure, there are multiple treatment modalities that can improve or delay the progression of insulin resistance, including weight loss through dietary modification and exercise, and insulin sensitization via medications. Heart failure is a prevalent condition and constitutes a large portion of health care expenditures. Targeted measures to treat insulin resistance in patients with other risk factors for heart failure could lead to the prevention of significant cardiovascular morbidity and a reduction in associated health care costs.


Study funding: Supported by contract numbers HHSN268201200036C, N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, and grant HL080295 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided through AG-023629, AG-15928, AG-20098, AG-027058 and grant 1R01AG031890 from the National Institute on Aging. A full list of principal CHS investigators and institutions can be found at


Disclosures: no conflicts to disclose

Contributor Information

Dipanjan Banerjee, Stanford University School of Medicine.

Mary L. Biggs, University of Washington.

Laina Mercer, University of Washington.

Kenneth Mukamal, Beth Israel Deaconess Medical Center.

Robert Kaplan, Albert Einstein College of Medicine.

Joshua Barzilay, Kaiser Permanente of Georgia.

Lewis Kuller, University of Pittsburgh.

Jorge R. Kizer, Weill Cornell Medical College.

Luc Djousse, Brigham and Women’s Hospital and Harvard Medical School.

Russell Tracy, University of Vermont.

Susan Zieman, National Institute on Aging.

Donald Lloyd-Jones, Northwestern University.

David Siscovick, University of Washington.

Mercedes Carnethon, Northwestern University.


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