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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Clin Hypertens (Greenwich). Author manuscript; available in PMC 2013 August 1.
Published in final edited form as:
PMCID: PMC3698958
NIHMSID: NIHMS366571

Are Sleep Symptoms Predictors of Resistant Hypertension in a Population Based Sample: National Health and Nutritional Examination Survey (NHANES)?

Harneet Walia, M.D.,1 Kingman Strohl, M.D., F.A.A.S.M,1,5 Brian Koo, M.D.,4,5 Andreea Seicean, MPH,2 and Sinziana Seicean, M.D., PhD, MPH.1,3

Abstract

Study Objectives

To test the association of self-reported sleep symptoms to those identified with severe hypertension in a nationally representative sample of adults

Setting

Self-reported and study measured health and sleep characteristics collected by National Health and Nutrition Examination Survey (NHANES) from 2005–2008.

Methods

From 10,526 individuals with completed sleep surveys participating in the study, we identified 379 subjects with severe hypertension defined as those treated with three or more anti-hypertensive medications including a diuretic, 110 of these had resistant hypertension (RHTN) despite therapy, while 269 were controlled for severe hypertension (CSHTN).

Results

Subjects with RHTN were more likely to be married, less educated, smoke and self-report unsatisfactory health and diabetes when compared to CSHTN. Multivariate analyses showed that poorly controlled diabetes (Hga1c > 7%) was the strongest predictor of RHTN [OR 3.0 95%CI (1.2–7.9)]. Unsatisfactory health [OR 1.7 95%CI (1.7–2.7)] was also associated with RHTN.

Conclusions

Poorly controlled diabetes and self-reported unsatisfactory heath showed significant association with RHTN. Contrary to expectations, there was no significant association between self-reported snoring/snorting and RHTN, when other factors were examined. The association between poorly controlled diabetes and RHTN warrants further emphasis on strict control of diabetes in these individuals.

Keywords: Resistant Hypertension, Obstructive sleep apnea, snoring, snorting

Introduction

Hypertension (HTN) is a highly prevalent chronic condition (29–31%) in the United States, and is reported as insufficiently controlled in up to two-thirds of individuals. 12 Resistant Hypertension (RHTN) is defined as blood pressure values in excess of 140/90 mmHg or 130/80 mm Hg in the presence of diabetes or chronic kidney disease despite treatment with three or more antihypertensive medications including a diuretic.3 The prevalence of RHTN in the United States adult hypertensive clinic population is estimated to be as high as 15–20%.4 Known risk factors associated with RHTN include low socioeconomic status and behavioral factors including a potentially complex medical regimen.5 Obesity, a high salt diet, physical inactivity, and heavy alcohol intake may alone or in combination contribute to poorly controlled, high blood pressures. In one study switching from a high to low salt diet was associated with an average reduction in office blood pressure of 23/9 mmHg in those with resistant hypertension.6 Hence, identifying and addressing not only non-physiologic as well as clinically modifiable factors may be important.

The role of sleep symptoms including snoring, snorting and poor or insufficient sleep in the etiology of RHTN is of interest for a number of reasons. First, obstructive sleep apnea (OSA) or insufficient sleep, both commonly present in the population and can be a predictor of future HTN in normotensive individuals. 79 Second, both OSA and its clinical marker of chronic, frequent snoring have shown to be associated with RHTN in hospital based case-series; both these entities are readily treatable. 1011, 26

The National Health and Nutritional Examination Survey (NHANES) was designed as a public health survey tool to detect and monitor over time disease and illness. Questions have been recently added to assess the potential relationships among sleep disorders, sleep duration, sleep quality and prevalent conditions such as HTN and diabetes. Prior overview surveys found increased risk in hypertensive vs, non-hypertensive adults of having concurrent sleep disorders with poor or short sleep.12 The current study looks more closely at this database in regard to hypertension control with the aim to assess if these same self-reported sleep symptoms predict RHTN in those identified with severe hypertension.

METHODS

Data Source

Data was obtained from the 2005–2008 National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics, Center for Disease Control. NHANES is a cross-sectional stratified multistage probability sample of the civilian non-institutionalized US adults 18 years of age and older. Complete details on recruitment, design, and surveys used are described on the NHANES website http://www.cdc.gov/nchs/nhanes.htm. 13

Sleep variables were obtained by merging the sleep-related data collected by questionnaire with files containing information concerning demographics, health insurance, health status, medical history, depression screening, body measurements, physical activity, alcohol use, food frequency and drug use questionnaire, cholesterol screening, and current medications by respondent sequence number. The NHANES study was approved by a CDC human subjects committee. Since all data in the NHANES database is de-identified, the study obtained exemption by University Hospitals Case Medical Center Institutional Review Board.

Subjects

3306 adults in the sample were deemed as hypertensive from a cohort of 10,526 subjects with complete sleep symptoms data available. HTN was defined based of either self report (HTN previously diagnosed by a physician) or if the NHANES measured blood pressure readings were above 140/90 mm Hg, or 130/80 mm Hg if patients were diabetic or with chronic kidney disease. Diabetes and chronic kidney disease was defined by self report. Blood pressure measurements were collected at the time of the interview using a mobile examination center where two physicians obtained three consecutive readings of blood pressure for each participant, with 5 minutes of rest in a sitting position between each reading. 379 subjects were identified as severe hypertensive based on treatment with minimum of three anti-hypertensive medications including a diuretic at the time of the data collection. Initially self-reported, this information was validated by direct medication check-up by an NHANES personnel at the time of data collection.

Outcome Variable: RHTN

The sample population was dichotomized into controlled severe hypertension (CSHTN) and RHTN according to collected blood pressure measurements in the NHANES database. CSHTN was defined as having a BP ≤ 140/90 and ≤ 130/80 in diabetes and chronic kidney disease on treatment with three or more antihypertensive medications including a diuretic. Subjects were considered to have RHTN if one or more of the three collected blood pressures were ≥ 140/90 mm Hg or ≥ 130/80 mm Hg in patients with diabetes or chronic kidney disease despite therapy with three or more blood pressure medications of which one was a diuretic.

Predictor Variables: Sleep Characteristics

Symptoms of OSA included 1) self-report of having physician diagnosed OSA, 2) self-report of symptoms of snoring ≥3 times per week in the past 12 months, 3) self-report of symptoms of snorting ≥3 times per week in the past 12 months, 4) combined symptoms of sleep apnea that is snoring or snorting ≥3 times/week in the past 12 months, 5) severe sleep apnea based on symptoms was defined as symptoms of snoring or snorting ≥5 times/week in the past 12 months and 6) severe combined sleep apnea was defined as symptoms of snoring and snorting ≥5 times/week in the past 12 months. Sleep duration was collected as continuous variable (in hours) based on response to the question: “How much sleep do you usually get at night on weekdays or workdays?” Two different cut points were used for defining short habitual sleep time (dichotomized yes/no) : < 7hrs/weeknight and < 6 hrs/weeknight. 14

Insomnia was defined as one self-reported sleep complaint plus one or more self-reported daytime functional impairment due to lack of sleep.15 The following sleep complaints were inquired about: “trouble falling asleep”, “waking up during the night and had trouble getting back to sleep”, “waking up too early in the morning and unable to get back to sleep”, and “feeling un rested during the day, no matter how many hours of sleep had.” Daytime functional impairments related to sleepiness measures included difficulties carrying out specific regular daily activities in the last month in the following nine areas: “concentrating on the things”, “remembering things”, “getting things done because too sleepy or tired to drive or take public transportation?”, “performing employed or volunteer work or attending school”, “working on a hobby, for example, sewing, collecting, gardening”, and “taking care of financial affairs and doing paperwork (for example, paying bills or keeping financial records)”. Subjects who reported ≥1 daytime functional impairment and any one sleep complaint occurring 5–15 times per month were defined as having “mild/moderate insomnia”. Those with ≥1 daytime functional impairment and any sleep complaint occurring >15 times/month were considered to have “severe insomnia”. Insomnia with short sleep duration was defined as having any one sleep complaint occurring ≥5 times per month and any one daytime functional impairment and, and sleep duration <7 hrs/weeknight.

Covariates

Covariates included self-reported demographics, health related variables, and substance use (use of alcohol and illegal drug usage). Age was reported in years at the time of screening. Race was dichotomized as Caucasian vs. other. Financial strain was measured as a continuous variable by poverty income ratio (PIR), a variable obtained by dividing the family income by the poverty threshold. Education was dichotomized with a cutoff set at high school graduation vs. no high school graduate. Insurance status was dichotomized as covered by any type of health insurance vs. not insured. Marital status was defined based on living with partner/married or other. Study collected serum nicotine was used to determine current smoking status with ≤10 ng/mL considered nonsmokers16 Self-reported use of any alcohol, available for participants’ ≥20 years of age was defined as having at ≥1 drink per month. Illegal narcotics or stimulant drug use was coded positive subjects self-reporting current or past use of marijuana, hashish, cocaine, heroin or methamphetamine.

Weight, height, and waist circumference at the time of the interview were collected by trained health technicians. Body mass index (BMI) 17 was computed as the ratio of weight in kg to height in cm squared and overweight/obesity was defined as BMI ≥25kg/m2. Waist circumference was used to define central obesity as >88 cm in women or >102 cm in men. Direct high density lipoprotein (HDL) levels were assessed from blood drawn at the time of data collection in mg/dL. Participants were considered to have diabetes based on self-reported physician diagnosed diabetes. Subjects with hemoglobin A1C ≥7% at the time of NHANES data collection were considered to have poorly controlled diabetes.18 Self-reported “overall general health” was also dichotomized by collapsing “fair” and “poor health” groups into the newly created variable “unsatisfactory health” vs. “satisfactory health”. Depression was assessed by using the nine-item Patient Health Questionnaire (PHQ), and a PHQ score >10 was considered as an indication of major depression.19

Statistical Analyses

General and sleep characteristics of the analytic sample were analyzed according to RHTN status, by using sample weights analyses in SAS 9.2 (Proc Survey, SAS Institute, Inc., Cary, NC) and the Taylor Series Linearization approach (Rust 1985). Univariate and multivariate nested hierarchical logistical regression modeling for this sample was obtained after the weights were normalized (standardized) to the size of the subsamples (Delgado 1990). Models were adjusted for age, gender, race, family income, education, health insurance, marital status, smoking, alcohol use, illegal drug use, sedentary leisure time, unsatisfactory health status, overweight/obesity, direct high density lipoprotein (HDL), poorly controlled diabetes, and depression. The final model was additionally adjusted for physician diagnosed sleep apnea, insomnia, and short habitual sleep time (SHST) <6 hours/weeknight. The consistency of the presented weighted results was tested in unweighted analyses (Patel, 2004). Two-tailed p-values of < 0.05 were considered significant.

Results

Of 3306 subjects with HTN, there were 379 with severe HTN; 269 with CSHTN and 110 with RHTN. Amongst RHTN subjects 94 had diabetes or CKD. The descriptive characteristics for the final cohorts are described in Table 1. Age, gender and ethnic distribution, PIR, sedentary leisure time, illicit drug usage, obesity, and alcohol use and depression scores did not differ significantly between the two groups. Subjects with RHTN were more likely to be married, have a lower education level, be current smokers, have unsatisfactory health and have poorly controlled diabetes as compared to control subjects. In addition, they were more likely to have diabetes and poorly controlled diabetes.

Table 1
General Characteristics of Severe Hypertension Patients According to Hypertension Control Status (N = 379)

In relation to sleep characteristics (table 2); there was no difference in any of the surrogate symptom measures of sleep apnea, sleep duration or insomnia between RHTN and CSHTN groups. History of physician diagnosed OSA was marginally greater in the RHTN group as compared to CSHTN.

Table 2
Sleep Characteristics of Severe Hypertension Patients According to Hypertension Control Status (N = 379) §

Univariate analysis (table 3 and and4)4) for the RHTN group revealed that people not completing high school had about 1.7 higher odds (95 % CI, 1.1–2.7) of having RHTN. Also diabetes was associated with 12.8 higher odds (95 % CI, 7.4–21.9) and poorly controlled diabetes was associated with 3.1 higher odds (95% CI, 1.3–7.2) of having RHTN compared to CSHTN. The subjects who reported poor health had 1.8 times higher odds (95% CI, 1.1–2.8) of having RHTN. The subjects who reported snoring over 3 times per week in the past 12 months trended towards a reduced odds of RHTN (that was marginally significant, OR=0.6, 95% CI, 0.3–1.0). No other sleep symptoms were found to be significantly associated with RHTN in this cohort.

Table 3
Univariate Odds Ratios (OR) for Resistant Hypertension (RHTN) in Patients with Severe Hypertension
Table 4
Univariate Odds Ratios (OR) of Sleep Symptoms for Resistant Hypertension (RHTN) in Patients with Severe Hypertension

Multivariate analyses showed consistent association between poorly controlled diabetes and RHTN (OR= 3.0, 95% CI, 1.2–7.9). Self reporting fair or poor health was also a significant predictor of RHTN (OR= 1.7, 95% CI, 1.1–2.7). Snoring at least 3 times per week trended towards a reduced risk of RHTN (OR=0.5, 95% CI, 0.3–1.1).

Discussion

To the best of our knowledge this is the first study assessing the role of self-reported sleep complaints in severe hypertension as it exists in the community and distinguishing RHTN from medically matched patients with CSHTN. From this analysis, sleep symptoms did not predict RHTN, but other common health problems did. This analysis has the advantage of sampling from a non-clinic sample in a nationally representative US population cohort.

Poorly controlled diabetes and self-reported fair or poor health were significant, and probably more important, predictors for RHTN. The general association of diabetes and HTN is well known. For instance, in the ALLHAT study, diabetes predicted lack of blood pressure control during the course of the study.20 Conversely, clinical trials have indicated that in order to achieve the lower blood pressure goal recommended for patients with diabetes, an average of 2.8 to 4.2 antihypertensive medications are required.21 The degree to which insulin resistance directly contributes to the development of HTN versus simply being associated with HTN because of common underlying causes (i.e. obesity) remains to be determined. Pathophysiologic effects of insulin resistance per se may also contribute to HTN; these include increased sympathetic nervous activity, vascular smooth muscle cell proliferation, and increased sodium retention. Therefore, diabetic control could be proposed as an important health priority to help control RHTN. There has been a thought that patient’s overall level of well-being and perception of functional capacity may be more sensitive to the pharmacotherapy of antihypertensive drugs. Also compliance, frequently related to a patient’s sense of deterioration in quality of life secondary to medical treatment, may well be the determinate of success with any antihypertensive regimen.22 One possibility is that RHTN cohort with greater unsatisfactory health may not be as compliant to the pharmacological therapy. While all of these factors can be indirectly attributed to poor sleep, chronic snoring, or untreated sleep apnea, self-reports of such conditions appear to take a backseat when RHTN is detected in the community.

It was contrary to our expectations that self-reported sleep symptoms did not relate to RHTN. In fact, there was a trend toward a protective effect of frequent snoring in terms of RHTN. Past studies including small hospital case series have shown that among cohorts with RHTN, OSA was highly prevalent.9, 26 Our findings of a lack of association among RHTN, sleep symptoms and sleep apnea could have resulted from a relative insensitivity or nonspecificity of these symptoms to detect OSA whereas past studies used the “gold standard” polysomnography to diagnose OSA using values of apnea-hypopnea index (AHI). The use of polysomnography is cost prohibitive and thus was not done in the NHANES study. Further mechanistic and interventional studies are needed to address the role of sleep apnea in RHTN, to understand what features of clinical studies have led to the belief that OSA is a risk factor of RHTN. 3, 23, 24

While many studies have shown a significantly higher prevalence of HTN in habitual snorers than among non snorers2526, other studies have reported no association after the adjustment of confounding factors such as smoking, alcohol usage, age and obesity. 2728 These observations are based on clinic cohorts where attributes of the presenting population in both medical and social domains could differ and offer different associations. The cause of marginally significant protective role of snoring is unknown but could result from artifact secondary to screening and treatment decisions of OSA in RHTN vs. CSHTN.

The strengths of the study include the national community- based sample of hypertensive subjects. Limitations of the NHANES data include a cross sectional design, self-reported data on sleep, and data paucity in respect to OSA treatment for patients diagnosed already with OSA in the cohort. The prevalence of severe HTN was found to be lower compared to previous published clinical studies. Since NHANES does not include recently hospitalized or institutionalized adults, some of the members of the population might be excluded from NHANES.

In conclusion, this study shows significant association between self reported poor health and poor controlled diabetes and RHTN in a representative sample of US population with severe hypertension, independently of confounders including sleep symptoms. Longitudinal and interventional community based studies are warranted to assess the predictive value of the diabetic control and assess the role of OSA symptoms in screening, diagnosis and treatment in severe HTN.

Table 5
Multivariate Odds Ratios (OR) for Resistant Hypertension (RHTN) in Patients with Severe Hypertension§

Acknowledgments

Supported by National Institute of Health grants HL007913 and HS00059-14

Footnotes

The other authors have no conflict of interests and no financial disclosures to make.

Dr. Kingman Strohl serves in the Medical Advisory Board: SleepMed, Novasom And have Grants from the VA Research Foundation and from the NIH NHLBI

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