Racial Differences in Heart Rate Variability During Sleep in Women: The Study of Women Across the Nation Sleep Study
Hall, Martica H. PhD; Middleton, Kellie MPH; Thayer, Julian F. PhD; Lewis, Tené T. PhD; Kline, Christopher E. PhD; Matthews, Karen A. PhD; Kravitz, Howard M. DO, MPH; Krafty, Robert T. PhD; Buysse, Daniel J. MD
From the Department of Psychiatry (M.H.H., C.E.K., K.A.M., D.J.B.) and Department of Surgery (K.M.), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychology (J.F.T.), The Ohio State University, Columbus, Ohio; Department of Epidemiology (T.T.L.), Rollins School of Public Health, Emory University, Atlanta, Georgia; Departments of Psychiatry and Preventive Medicine (H.M.K.), Rush University Medical Center, Chicago, Illinois; and Department of Statistics (R.T.K.), Temple University, Philadelphia, Pennsylvania.
Address correspondence and reprint requests to Martica H. Hall, PhD, Department of Psychiatry, University of Pittsburgh School of Medicine, Suite E-1101, 3811 O’Hara Street, Pittsburgh, PA 15213. E-mail: firstname.lastname@example.org
Received for publication November 7, 2012; revision received May 6, 2013.
Background: Heart rate variability (HRV) differs markedly by race, yet few studies have evaluated these relationships in women, and none have done so during sleep (sHRV).
Methods: We addressed these gaps by examining sHRV in women of African American, Chinese American, or European American origin or descent (mean [standard deviation] age = 51.2 [2.2] years).
Results: HRV during Stage 2 non–rapid eye movement (NREM) and rapid eye movement (REM) sleep differed significantly by race after adjusting for possible confounders. Normalized high-frequency HRV was significantly lower in European American compared with African American and Chinese American participants (European American NREM = 0.35 [0.01], REM = 0.23 [0.01]; African American NREM = 0.43 [0.02], REM = 0.29 [0.02]; Chinese American NREM = 0.47 [0.03], REM = 0.33 [0.02]; p values <.001). European Americans also exhibited higher low-to-high-frequency HRV ratios during sleep compared with African American and Chinese American women (European American NREM = 2.42 [1.07], REM = 5.05 [1.07]; African American NREM = 1.69 [1.09], REM = 3.51 [1.09]; Chinese American NREM = 1.35 [1.07], REM = 2.88 [1.13]; p values <.001).
Conclusions: Race was robustly related to sHRV. Compared with women of African American or Chinese American origin or descent, European American women exhibited decreased vagally mediated control of the heart during sleep. Prospective data are needed to evaluate whether sHRV, including race differences, predicts cardiovasular disease.
Mounting evidence suggests that heart rate variability (HRV) is a robust predictor of cardiovascular morbidity and mortality (1). Identification of key correlates of HRV is critical to primary prevention efforts in vulnerable populations, as well as to our understanding of the mechanisms through which HRV influences cardiovascular health. Race is one of the most consistent correlates of HRV. Paradoxically, many studies have reported higher indices of cardiac vagal control in African Americans compared with European Americans (2–8), although others have found contradictory results (9,10). The overall direction of these effects is somewhat surprising in light of marked disparities in cardiovascular morbidity and mortality among African Americans relative to European Americans (11). Significantly, most studies that have examined racial/ethnic differences in HRV have been conducted in men; few have included appreciable numbers of females. This is an important oversight, given that cardiovascular disease (CVD) is the leading cause of death for women in the United States (12).
A second oversight in the extant literature is a near-universal focus on HRV assessed during wakefulness, primarily based on brief electrocardiogram (ECG) recordings. Without exception, studies examining racial differences in HRV have used data gathered during short, controlled laboratory protocols or during 24-hour Holter monitoring. None of these studies, including the Holter monitoring studies, specifically examined HRV during sleep (sHRV), despite mounting evidence that sleep and sleep disruptions play a key role in cardiovascular health (13). Among patients with sleep apnea, for example, disruptions in nocturnal physiology are associated with cardiovascular morbidity, including incident hypertension and stroke (14). Although it has been long recognized that daytime blood pressure is a strong predictor of future mortality and cardiovascular events, a recent meta-analysis found that nighttime blood pressure was a stronger predictor of mortality in both individuals with hypertension and the population at large (15).
Assessment of sHRV offers several advantages over brief waking or 24-hour Holter recordings. First, sHRV can be measured continuously and noninvasively throughout the night in relation to specific stages of sleep (16,17). Second, assessment of sHRV limits the amount of random error attributable to differences in physical activity and postural changes that occur during 24-hour Holter recordings. This is particularly important in a multiracial/ethnic context, given known racial differences in work and social environments (18). A third advantage of sHRV is that these data may be especially important to understanding circadian variation in the timing of CVD events, which are more likely to occur during the early morning hours (19). Finally, assessment of sHRV provides a quantitative measure of autonomic tone during a consolidated period of physiological restoration critical to the maintenance of cardiovascular health (13,20,21).
The present study examined racial/ethnic differences in HRV throughout sleep in a community sample of women of African American, Chinese American, or European American origin or descent from the Study of Women Across the Nation (SWAN) ancillary Sleep Study. Critically, these data address the “biological mechanism” evidence gap cited in the 10Q Report on CAD in women by seeking to evaluate interrelationships among race and HRV in midlife women (22).
The SWAN Sleep Study is an observational study of sleep in a multiracial/ethnic sample of midlife women conducted over a 5-year period starting in the fall of 2002. A total of 368 European American, African American, and Chinese American participants were enrolled from four study sites: Chicago, IL; Detroit area, MI; Oakland, CA; and Pittsburgh, PA. Race was assessed by self-identification of racial/ethnic origin or descent. Exclusions for the Sleep Study were as follows: current menopausal hormone replacement therapy or oral corticosteroid use, current chemotherapy or radiation treatment, self-reported diagnosis of sleep apnea, regular nocturnal shiftwork, regular consumption of more than 4 alcoholic drinks per day, and noncompliance with Core SWAN procedures. Informed consent was obtained in accordance with approved protocols and guidelines of the institutional review board at each participating institution. Participants were paid for their participation.
Participants underwent three consecutive nights of in-home polysomnography (PSG), as previously described (23). Self-report data including daily sleep diaries were collected concurrently with sleep studies. For the present report, analyses were restricted to SWAN Sleep Study participants in whom HRV was available and of sufficient quality for analysis (n = 332), as described below. SWAN Sleep Study participants excluded from the present analyses had a higher body mass index (BMI), on average, compared with women included in the analyses. Average BMI for the present sample was 29.70 (7.45) kg/m2 compared with 32.36 (8.88) kg/m2 for participants in whom sufficient HRV data were not available. Other sample characteristics including race did not differ as a function of availability of HRV data.
Vitaport-3 (TEMEC VP3) ambulatory monitors were used to collect sleep, breathing, and leg movement signals throughout the sleep period, as previously described (23). Quality assurance assessments, scoring, and processing of all sleep study records were performed by trained PSG technologists with established reliability at the University of Pittsburgh Neuroscience–Clinical and Translational Research Center. Visual sleep stage scoring was based on the Rechtschaffen and Kales (24) criteria and scored in 20-second epochs, as data were collected before the updated American Academy of Sleep Medicine manual. Sleep staging was used to characterize the sample and to identify specific HRV epochs for analysis, as described below.
A modified two-lead electrode placement was used to collect the ECG signal continuously throughout sleep at a sampling rate of 1024 Hz. Commercially available software was used to identify successive R waves and suspected artifacts (Mindware Heart Rate Variability Scoring Module; Mindware Technologies Ltd, Gahanna, OH). Artifact editing was accomplished using an automated artifact detection algorithm and visual inspection, and interbeat intervals were then calculated for each successive pair of R waves. The time series of successive R-R intervals was used to quantify the variability between R waves, or HRV. Specifically, the Fast Fourier Transform was used to derive HRV power spectral estimates for each 2-minute epoch during sleep.
Power was integrated in the low-frequency (LF-HRV; 0.04–0.15 Hz) and the high-frequency (HF-HRV; 0.15–4.0 Hz) bands over a total power spectrum of 0.04 to 4.0 Hz. Outcomes in the present analyses included three indices of sHRV: LF-HRV, HF-HRV, and the ratio of LF/HF, which provide a comprehensive assessment of the race-sHRV relationship and for comparability with the published literature on race and HRV during wakefulness. Analyses were conducted using both absolute and normalized LF-HRV (nLF-HRV) and normalized HF-HRV (nHF-HRV), as total sHRV power during non–rapid eye movement (NREM) sleep differed significantly by race (F(2,329) = 4.83, p < .009). Normalized variables were identified as nLF-HRV and nHF-HRV.
Time-series data files were used to align sleep and HRV data, with a total of six 20-second PSG epochs for each 2-minute HRV epoch. HRV epochs that contained either Stage 2 NREM sleep or rapid eye movement (REM) sleep for the entire 120 seconds were extracted for analysis and averaged across the night. Stage 2 NREM and REM sleep were selected for analysis because they comprise most of the sleep period in midlife women.
Only participants with at least fifteen 2-minute epochs of Stage 2 NREM (n = 332) or REM sleep (n = 321) were included in the analyses, to ensure that sufficient data were available to render reliable estimates. There were too few participants with fifteen 2-minute epochs of Stage 3 + 4 NREM sleep (n = 11) to evaluate race differences in HRV during slow-wave sleep, which is known to differ as a function of race (23). Epochs of Stage 1 NREM sleep were not included because it is a transitional sleep stage with electroencephalographic characteristics similar to wakefulness. Heterogeneous HRV epochs, which contained a mixture of sleep stages, were not used in analyses because of recognized differences in HRV across sleep stages (25,26). For each participant, HRV data were generated for either Night 2 or 3 of sleep, given the high short-term stability of sHRV (27). Thus, 10 variables were created for each participant based on all-night averages of homogeneous HRV epochs: LF-HRV, nLF-HRV, HF-HRV, nLF-HRV, and the LF/HF ratio during both Stage 2 NREM sleep and REM sleep. Respiration rate was also collected for each 2-minute epoch of sleep based on the respiratory effort signal.
Potential covariates included sociodemographic, psychological, physiological, and behavioral factors previously linked to racial differences in HRV and/or cardiovascular risk (28). Age was established by self-report. Marital status at the time of sleep studies was coded as “married/living as married” or “unmarried”; this latter category included participants who were single, separated, divorced, or widowed. Educational attainment was used as an indicator of socioeconomic status and was dichotomized as a comparison of those participants with a college or advanced degree versus those women without a college degree.
Daily sleep diaries used throughout the study were used to assess vasomotor symptoms and smoking status. Each morning upon awakening, participants were asked to report the number of hot flashes and the number of night sweats they experienced on the previous night of sleep. Total number of reported hot flashes and night sweats was calculated, and vasomotor symptoms were dichotomized as none reported on HRV nights versus at least one reported on HRV nights. Each evening before going to bed, participants recorded the number of cigarettes smoked during the previous waking day. Participants who reported any cigarette use on most days were coded as current smokers. BMI was calculated as weight in kilograms/height in meters squared. Daily medication use was coded according to the World Health Organization Anatomical Therapeutic Chemical Classification System classification (http://www.whocc.no/atcddd). The use of medications that affect sleep, including opioids, antiepileptics, anxiolytics, sedatives and hypnotics, antidepressants, and antihistamines; use was dichotomized as “present” or “absent.” β-Blocker use concurrent with sleep studies was also dichotomized as “present” or “absent.” National Cholesterol Education Program Adult Treatment Panel III guidelines for women were used to identify hypertension and diabetes as follows: hypertension was defined as blood pressure of at least 130 mm Hg systolic, at least 85 mm Hg diastolic, or use of antihypertensive medication, and diabetes was defined as fasting serum glucose of at least 100 mg/dl, use of diabetic medication, or having ever been classified as diabetic (29). Fasting blood draws were collected during the morning hours, and blood pressure was recorded using standard mercury sphygmomanometers after a 5-minute rest in the seated position. Two sequential readings were taken on the right arm, with a 2-minute intervening rest, and values were averaged for each participant. These measures were collected in conjunction with Core SWAN annual visits immediately preceding the Sleep Study.
Self-reported symptoms of depression were measured concurrently with sleep studies using the 16-item Inventory of Depressive Symptomatology (30). The Inventory of Depressive Symptomatology, minus sleep items, was calculated as a continuous variable. Symptoms of anxiety and perceived stress were assessed concurrently with sleep studies using the 20-item Spielberger State Anxiety Inventory and the 4-item Perceived Stress Scale, respectively (31,32). Finally, respiration rate, sleep-disordered breathing (apnea-hypopnea index; AHI) and leg movements (leg movement arousal index), assessed by PSG, were included as covariates because of their known association with HRV (33,34).
Descriptive statistics were used to characterize the study sample. The following sHRV variables were transformed by natural logarithm to reduce skewness: absolute LF-HRV, absolute HF-HRV, and the LF/HF ratio; nLF-HRV and nHF-HRV were not transformed for analyses. Differences in background characteristics between the races were assessed using analysis of variance for continuous variables and χ2 tests for categorical variables. Analysis of covariance was used to evaluate race differences in HRV during Stage 2 NREM and REM sleep, with significance set at p < .01. Post hoc tests (Tukey honestly significant difference) were used to evaluate significant race differences. Covariates were age, education, marital status, vasomotor symptoms, BMI, hypertension, smoking, use of medications that affect sleep and/or β-blockers, symptoms of depression, respiration rate, apnea-hypopnea index, and leg movement arousal index. Diabetes, symptoms of anxiety, and perceived stress were not included in the final models because they were unrelated to race and/or sHRV in our sample. Sensitivity analyses were conducted in participants with AHI values less than 15 (n = 259) to confirm that sleep-disordered breathing did not confound race differences in sHRV. Race-by-AHI interactions were also used to evaluate possible confounding by sleep-disordered breathing. Interaction terms were calculated using the median split for AHI (AHI = 5.05).
On average, participants were 51 years of age (range, 46–57 years), married or living with a partner, and overweight (Table 1). As previously reported in this cohort (23), race was a significant correlate of sociodemographic, mental and physical health, and health behavior characteristics. Compared with their European American and Chinese American counterparts, African American participants were more likely to be unmarried, to be less educated, to be smokers, to have a higher BMI, to meet the criteria for hypertension, and to report more depressive and nighttime vasomotor symptoms. These sample characteristics were among the covariates included in statistical models of the race-sHRV relationship. As shown in Table 2, participants spent an average of 64% (standard deviation = 8%) of the night in Stage 2 NREM sleep and 25% (standard deviation = 5%) of the night in REM sleep. Significant race effects were observed during NREM and REM sleep. Percent Stage 1 NREM sleep was lower in European Americans compared with African Americans (p = .014), and percent slow-wave sleep was greater in European Americans compared both with African American (p = .001) and Chinese American participants (p = .017). Finally, percent REM sleep was lower in African American compared with Chinese American participants (p = .039). Other sleep parameters did not differ as a function of race.
Figure 1 illustrates the contribution of LF- and HF-HRV to total power during NREM and REM sleep by race. As shown in Table 3, significant race effects were observed for LF-HRV (absolute and normalized), nHF-HRV, and the LF/HF ratio during both Stage 2 NREM and REM sleep. Absolute LF-HRV was significantly higher in European American compared with Chinese American participants during Stage 2 NREM (p = .001) and REM sleep (p = .004). European Americans also exhibited higher absolute LF-HRV during Stage 2 NREM sleep compared with African American participants (p = .001). nLF-HRV was significantly higher in European American compared with African American and Chinese American participants during both Stage 2 NREM (p = .001) and REM sleep (p = .001). Although absolute HF-sHRV did not differ as a function of race, significant effects were observed for normalized HF power during Stage 2 NREM sleep (p < .001) and REM sleep (p < .001). Normalized HF-HRV was lower in European American compared with African American and Chinese American participants during Stage 2 NREM and REM sleep (p values <.001). The LF/HF ratio during both NREM and REM sleep was significantly lower in African American and Chinese American, compared with European American participants (p values <.010). Chinese American and African American participants did not differ on LF/HF ratio values during NREM or REM sleep. These results were observed after adjusting for all covariates.
Exploratory analyses showed that the race-sHRV relationship did not differ as a function of sleep-disordered breathing. Sensitivity analyses revealed that results were unchanged by excluding participants with moderate to severe sleep apnea as defined by an AHI of at least 15 for all outcomes, except for absolute LF-HRV during NREM sleep, which no longer differed by race (p = .064; data not shown). Nor were there significant race-by-AHI interactions for any of the sHRV outcomes.
Emerging evidence suggests that HRV is a critical biological mechanism affecting cardiovascular risk and that race and/or ethnicity plays a significant role in its expression (1,9,35). However, few studies of race and HRV have been conducted in women, and none have evaluated sHRV, despite its importance to cardiovascular health (13). We report here the first data regarding racial/ethnic differences in sHRV. Our major finding is that African American and Chinese American women exhibited increased cardiac parasympathetic control (nHF-HRV) during NREM and REM sleep in comparison with European American women. Conversely, LF-HRV and the LF/HF ratio were elevated during sleep in European American compared with African American and Chinese American participants. Race effects on sHRV were unrelated to sleep-disordered breathing.
We found robust race differences in nHF-HRV during Stage 2 NREM and REM sleep, after adjusting for other sociodemographic, psychological, behavioral, and medical factors known to influence HRV. Our results for sHRV are consistent with most studies that have evaluated race effects on HF-HRV during wakefulness (2–8). For instance, Earnest and colleagues (36) reported that cardiac vagal activity during wakefulness was higher in postmenopausal African American compared with European American participants. We know of only one study that has compared HF-HRV in European and South Asian adults, all of whom were men (37). In that study, HF-HRV during wakefulness did not vary as a function of race/ethnicity. In addition to studies that assessed HRV during wakefulness, some (38) but not all (10) studies based on 24-hour Holter monitoring have found HF-HRV differences between African Americans and European Americans. However, because of small sample sizes, not all effects were statistically reliable. Guzzetti and colleagues (38) reported an effect size of d = 0.47 but had only 26 participants in each ethnic group, whereas Lampert et al. (10) reported higher HF-HRV in European Americans but had only 41 African American participants. Although it has been noted that ambulatory studies introduce confounds associated with waking behaviors that may differ as a function of race, race differences in sleep, too, may confound 24-hour monitoring (23). For this reason, we restricted the current comparisons of HRV and race to Stage 2 NREM and REM sleep. Different outcomes for absolute and normalized HF-HRV suggest that although European American women exhibit higher total power during sleep compared with African American and Chinese American women, relatively less power can be attributed to vagal modulation of the heart. It is important to note that although the greater HF-HRV in the Chinese American women is consistent with their generally better cardiovascular risk profile, the greater HF-HRV in the African Americans is not consistent with their generally poorer cardiovascular risk profiles. More research is to further explicate this HRV race paradox.
We observed significant race effects for LF-HRV and the LF/HF ratio during Stage 2 NREM and REM sleep. Several previous studies have similarly reported increased LF-HRV during wakefulness and during 24-hour Holter recordings in European American compared with African American participants (3,9,10,38). In contrast, results for race and the LF/HF ratio have not been consistent across studies. Choi and colleagues (9) reported no significant race differences in their sample of younger (23–54 years) African American and European American men and women, whereas the LF/HF ratio was significantly lower in European American compared with African American participants in the Atherosclerosis Risk in Communities cohort, irrespective of age and sex (3). In contrast, LF/HF ratio values were higher in European American compared with African American participants both in our study of sHRV and in a 24-hour Holter monitoring study of 52 patients with unmedicated essential hypertension. Similar to results for HF-HRV, European and South Asian men did not differ in terms of LF-HRV or the LF/HF ratio (37). Too few studies have evaluated Chinese or other Asian populations in comparison with African American and European American participants to warrant firm conclusions about HRV in these racial/ethnic groups.
Our understanding of the meaning of LF and the LF/HF ratio continues to evolve. There is a growing consensus that LF power (either absolute or normalized) may reflect modulation of cardiac autonomic outflow via the baroreflex (39) rather than sympathetic activity, as previously thought. Similarly, most criticisms of the LF/HF ratio highlight the lack of association of LF power with β-adrenergic activity (40). However, the baroreflex comprises at least three related feedback loops, only one of which has been explicitly linked to LF power (39,41). A major unexplored association is with the vascular limb of the baroreflex, which would be primarily α-adrenergic. Indeed, the best evidence linking LF and, thus, the LF/HF ratio to sympathetic activity comes from studies of orthostasis, where LF power has a graded relationship with the angle of the tilt (40); tilt induces α-adrenergic vasoconstriction consistent with the increased blood pressure needed to avoid syncope. Thus, broad statements that LF is not related to sympathetic activity are not consistent with the complex physiology of the sympathetic nervous system. Regardless of the precise autonomic origins of the LF/HF ratio, a recent study suggests important functional consequences of larger LF/HF ratios. In a large study of healthy middle-aged adults, it was found that a time domain measure of the LF/HF ratio was positively associated with elevated cholesterol levels after controlling for a large number of potential confounders including overnight urinary norepinephrine—a measure of β-adrenergic activity (42). Thus, elevated LF/HF values, regardless of explicit knowledge about their autonomic origins, seem to be deleterious and inversely related to healthy vagal modulation, as found in the present study.
Taken as a whole, our data suggest robust race differences in sHRV. The relevance of sHRV to cardiovascular health remains underexplored. A burgeoning literature continues to document the impact of sleep disturbances (e.g., short sleep duration and shift work) and disorders (e.g., sleep apnea and insomnia) on cardiovascular risk (13,43–45). Alterations in autonomic tone, in turn, have been documented in each of these sleep disturbances and disorders, suggesting one plausible mechanism through which sleep affects cardiovascular health (1,13). That many of these sleep disturbances and disorders are more prevalent in African Americans and, in some cases, other racial and ethnic minorities suggests that links between race, autonomic imbalance during sleep, and CAD are likely complex and multiply determined. For example, the increased prevalence of hypertension in racial and ethnic minorities has been hypothesized to be caused, at least in part, by greater sympathetic drive in at-risk minorities (46,47). Alterations in autonomic tone, as defined by increased LF-HRV and the LF/HF ratio during NREM sleep in European American compared with African Americans and Chinese American women in the current study, are at odds with this hypothesis. Clearly, if one is to understand these important health disparities, further research is needed to examine autonomic control in various racial and ethnic groups by including measures of α-adrenergic and β-adrenergic influences in addition to parasympathetic factors.
Several limitations to the present study should be considered. The sample did not include certain populations at increased risk for cardiac autonomic imbalance and CVD including men, other minority racial/ethnic groups (e.g., Hispanics), and shiftworkers. Nor can results be generalized to younger or elderly women, given age-related changes in sleep and HRV (28,48). As noted above, absolute LF power and the LF/HF ratio are multiply determined, complicating the interpretation of these differences and their implications for cardiovascular morbidity and mortality. However, evidence does exist that larger LF/HF ratios are associated with cardiovascular risk factors (42). Finally, we were not able to generate estimates of HRV during NREM Stage 3 + 4 sleep because of the limited number of consolidated 2-minute epochs of slow-wave sleep. In future studies, time-series modeling techniques may prove especially useful for characterizing the moment-to-moment dynamic interplay between HRV and sleep, including the extent to which these relationships differ by race and affect cardiovascular risk. It may also be useful to examine these relationships in younger individuals who have more slow-wave sleep than do midlife women.
Numerous strengths offset these limitations, including the large sample size, direct comparison of three racial groups, and statistical adjustment for multiple factors known to affect HRV. In addition, we collected ECG signals concurrently with PSG, which permitted accurate quantification of HRV in relation to specific stages of sleep. This level of precision limits the amount of random error attributable to environmental influences that may affect daytime laboratory or 24-hour ambulatory recordings. Other strengths of this study include the use of in-home PSG to enhance the ecological validity of the data. Power spectral analysis of HRV data was performed on Night 2 or 3 of sleep studies to reduce the possible influence of physiological habituation to PSG monitoring on cardiac autonomic tone during sleep. Our study also restricted analyses to equivalent sleep stages (Stage 2 NREM and REM), which is critical given marked racial differences in sleep duration, fragmentation, and depth (23).
In conclusion, marked racial/ethnic differences are observed during Stage 2 NREM sleep and REM sleep in midlife women. These differences are observed among African American and Chinese American compared with European American women and after controlling for known cardiovascular risk factors such as smoking, BMI, hypertension, medication use, and sleep disordered breathing. It will be important for future studies to evaluate the extent to which racial and/or ethnic differences in sHRV contribute to cardiovascular morbidity and mortality, including mechanisms that underlie this relationship. Our data suggest that the pathways that link race, sHRV, and cardiovascular outcomes may differ from the pathways that contribute to health disparities in cardiovascular morbidity and mortality in African American and other ethnic minorities.
We thank the dedicated study staff at each site and all the women who participated in SWAN Sleep Ancillary Study.
Source of Funding and Conflicts of Interest: Funding for the SWAN Sleep Study is from the National Institute on Aging (Grants AG019360, AG019361, AG019362, AG019363). In addition, support for authors M. H. Hall, R. Krafty, and J. Thayer was provided by HL104607. The SWAN has grant support from the National Institutes of Health, Department of Health and Human Services, through the National Institute on Aging, the National Institute of Nursing Research, and the National Institutes of Health Office of Research on Women’s Health (Grants NR004061, AG012505, AG012535, AG012531, AG012539, AG012546, AG012553, AG012554, AG012495). Sleep data were processed with the support of RR024153 and UL1TR000005. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Aging, National Institute of Nursing Research, Office of Research on Women’s Health, or the National Institutes of Health. Dr. Buysse has served as a paid consultant on scientific advisory boards for the following companies: Eisai, GlaxoSmithKline, Merck, Philips Respironics, Purdue Pharma, sanofi-aventis, and Takeda. He has also spoken at single-sponsored educational meetings for sanofi-aventis and Servier. Total fees from each of these sources were less than $10,000 per year. No other authors report any conflicts.
Clinical Centers: University of Michigan, Ann Arbor: Siobán Harlow, principal investigator (PI) 2011-present; MaryFran Sowers, PI 1994–2011. Massachusetts General Hospital, Boston, MA: Joel Finkelstein, PI 1999-present; Robert Neer, PI 1994–1999. Rush University, Rush University Medical Center, Chicago, IL: Howard Kravitz, PI 2009-present; Lynda Powell, PI 1994–2009. University of California, Davis/Kaiser: Ellen Gold, PI. University of California, Los Angeles: Gail Greendale, PI. Albert Einstein College of Medicine, Bronx, NY: Carol Derby, PI 2011-present; Rachel Wildman, PI 2010–2011; Nanette Santoro, PI 2004–2010. University of Medicine and Dentistry–New Jersey Medical School, Newark: Gerson Weiss, PI 1994–2004. University of Pittsburgh, Pittsburgh, PA: Karen Matthews, PI.
National Institutes of Health Program Office: National Institute on Aging, Bethesda, MD: Winifred Rossi, 2012-present; Sherry Sherman, 1994–2012; Marcia Ory, 1994–2001. National Institute of Nursing Research, Bethesda, MD: program officers.
Central Laboratory: University of Michigan, Ann Arbor: Daniel McConnell (Central Ligand Assay Satellite Services).
Coordinating Center: University of Pittsburgh, Pittsburgh, PA: Kim Sutton-Tyrrell, co-PI 2001–2012; Maria Mori Brooks, co-PI 2012. New England Research Institutes, Watertown, MA: Sonja McKinlay, PI 1995–2001.
Steering Committee: Susan Johnson, Current Chair; Chris Gallagher, Former Chair.
BMI: body mass index
HRV: heart rate variability
sHRV: heart rate variability during sleep
LF: absolute low-frequency heart rate variability
nLF: normalized low-frequency heart rate variability
HF: high-frequency heart rate variability
nHF: normalized high-frequency heart rate variability
LF/HF ratio: ratio of low-to-high-frequency heart rate variability
REM: rapid eye movement
NREM: non–rapid eye movement
SWAN: Study of Women Across the Nation
AHI: apnea-hypopnea index
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heart rate variability; sleep; race; cardiovasular disease; autonomic tone; women
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