The interval within 1 year of FMP is a critical period for women when vascular functional alterations occur. These findings underscore the importance of more intensive lifestyle modifications in women transitioning through menopause.

We evaluated 339 participants from the SWAN (Study of Women’s Health Across the Nation) Heart Ancillary study (Study of Women’s Health Across the Nation). Women had ≤2 carotid-femoral pulse-wave velocity (cfPWV) exams over a mean±SD of 2.3±0.5 years of follow-up. Annual percentage changes in cfPWV were estimated in 3 time segments relative to FMP and compared using piecewise linear mixed-effects models. At baseline, women were 51.1±2.8 years of age and 36% black. Annual percentage change (95% CI) in cfPWV varied by time segments: 0.9% (−0.6% to 2.3%) for >1 year before FMP, 7.5% (4.1% to 11.1%) within 1 year of FMP, and −1.0% (−2.8% to 0.8%) for >1 year after FMP. Annual percentage change in cfPWV within 1 year of FMP was significantly greater than the other 2 time segments; P <0.05 for both comparisons. Adjusting for concurrent cardiovascular disease risk factors explained part of the change estimates but did not eliminate the difference. Black women had greater increase in cfPWV compared with white women in the first segment; P for interaction, 0.04.

Menopause may augment age-dependent increases in arterial stiffness, with black women having greater progression in midlife compared with white women. We sought to determine whether and when women experience changes in arterial stiffness relative to the final menstrual period (FMP) and whether these changes differ between black and white midlife women.

Highlights The menopause transition can augment age-dependent increase in arterial stiffness in midlife women.

Modifying cardiovascular disease risk factors including blood pressure, central adiposity, and lipid profile may mitigate arterial stiffness progression in women transitioning through menopause.

Our findings underscore the importance of frequent and timely monitoring of cardiovascular disease risk factors and stressing more intensive lifestyle modifications in women transitioning through menopause.

The risk of cardiovascular disease (CVD) increases with age in both sexes, with noticed risk acceleration in postmenopausal years in women.1 This observation suggests that the menopause transition might augment the age-dependent increase in CVD risk. In fact, women are subjected to increases in carotid intima-media thickness and adventitial diameter starting from late perimenopause2,3—a stage that typically starts 1 to 3 years before the final menstrual period (FMP) and extends to 12 months after FMP.4 These structural vascular changes in the late peri- and post-menopause are accompanied by declining endothelial function as measured by brachial artery flow-mediated dilation.5 SWAN (Study of Women’s Health Across the Nation) recently showed that women who transitioned through menopause had a significantly greater increase in central arterial stiffness compared with women who remained premenopausal or were postmenopausal at baseline.6 These findings suggest that women undergo structural and functional vascular changes as they traverse menopause, which may impact their risk of CVD later in life.

See accompanying article on page 850

The menopause transition, specifically, the few years before and after FMP constitute a critical period in women’s lives when dynamic metabolic and physiological changes begin to accumulate.7 This period is characterized by adverse structural changes in the carotids2,3 and increases in lipids,8 waist circumference,9 and metabolic syndrome.10 Additionally, dynamic hormonal changes in estradiol and FSH (follicle-stimulating hormone) occur within 1 year of FMP.11 Whether concurrent changes in central arterial stiffness—a measure of vascular aging and an independent predictor of CVD risk in the general population12—occur in this period remains to be determined.

Racial differences have been reported in arterial stiffness progression in women at midlife, with black women having greater progression compared with white women.13 SWAN is a multiethnic longitudinal cohort study that concurrently measured central arterial stiffness, CVD risk factors, and endogenous sex hormones as women traversed menopause. This study provides a unique opportunity to characterize vascular functional changes in the years around the FMP while adjusting for other risk factors that are modified during the menopause transition. Therefore, the aims of this study were to determine whether women experience changes in central arterial stiffness, as measured by carotid-femoral pulse-wave velocity (cfPWV), relative to the FMP and whether the magnitude of these changes differ between black and white women.

Materials and Methods

The authors declare that all supporting data are available within the article and in the Data Supplement.

Study Participants

SWAN is an ongoing multiethnic, multisite longitudinal study of the physical, biological, and psychosocial changes of the menopause transition. Detailed methods are presented elsewhere.14 In brief, 3302 women were recruited between 1996 and 1997 from 7 research sites within the United States (Detroit, MI; Boston, MA; Chicago, IL; Oakland, CA; Los Angeles, CA; Newark, NJ; and Pittsburgh, PA). To be eligible for SWAN, women had to be 42 to 52 years of age at enrollment, have an intact uterus and at least one ovary with menstrual bleeding within the past 3 months, not be pregnant or breastfeeding, and not have used hormone therapy within the past 3 months. Women self-identified as a member of 1 of 5 ethnic groups depending on the research site: white (all sites), black (Boston, Chicago, Detroit, and Pittsburgh), Chinese/Chinese American (Oakland), Hispanic (Newark), or Japanese/Japanese American (Los Angeles). The institutional review board at each participating site approved the study protocol, and all participants signed informed consent before participation.

Central arterial stiffness was measured as part of the SWAN Heart study, an ancillary study to SWAN designed to evaluate subclinical atherosclerosis measures in women at midlife. Between 2001 and 2003, women from the SWAN Pittsburgh and Chicago sites were recruited to SWAN Heart (n=608) if they had no self-reported clinical CVD. By design, Pittsburgh and Chicago sites recruited only white and black women. After a mean±SD of 2.3±0.5 years of SWAN Heart baseline visit, women were invited for a follow-up visit.

For the current analysis, we excluded women who reported CVD during the SWAN Heart follow-up period (n=11) or did not have arterial stiffness measurements (n=56). We additionally excluded 202 women for whom FMP date was not observed (see below). The final analytic sample included 339 women who contributed 537 observations with each woman having 1 or 2 measurement time points. Compared with the excluded participants, women in analytic sample were less likely to be hormone users (3% versus 24%) at baseline; otherwise, they shared similar baseline clinical characteristics.

Study Outcome: Central Arterial Stiffness

Central arterial stiffness was assessed by cfPWV at both SWAN Heart baseline and follow-up visits using a protocol described previously.15 In brief, arterial flow waves were simultaneously and noninvasively recorded at the right carotid and femoral arteries of supine participants, using unidirectional transcutaneous Doppler flow probes (model 810-a, 10 MHz; Parks Medical Electronics, Aloha, OR). Data were scored using software developed by the Laboratory of Cardiovascular Science, Gerontology Research Center, National Institute on Aging, which averages 20 seconds worth of waveforms and determines time from the R wave on the ECG to the foot of the averaged waveforms. Transit time of the pulse wave was calculated as the delay between the averaged carotid and femoral waveforms. Three runs were performed for each participant, and the average was used in the analysis. Distance traveled by the pulse waveform was estimated by measurement over the participant’s torso. Distance from the carotid to aortic site (at the suprasternal notch) was subtracted from the sum of the aortic to umbilicus and umbilicus to femoral sites to adjust for opposite direction of blood flow in that arterial branch. cfPWV was calculated as distance/transit time (cm/s) with higher cfPWV indicating a stiffer artery. Reproducibility study of cfPWV measurement was done in the same laboratory that conducted cfPWV measurements for SWAN participants, with an overall laboratory intraclass correlation of 0.77.15

Main Independent Variable: Time Since FMP

As part of each SWAN annual visit, date of the most recent menstrual period was determined, and FMP was assigned retrospectively as the date of the participant’s last menstrual period before 12 consecutive months of amenorrhea. As a sensitivity analysis, we added 128 women to our analytic sample for whom the FMP date could not be observed due to hormone therapy, hysterectomy, or bilateral oophorectomy and, therefore, were multiply imputed as described in the Data Supplement. For observations in our analytic sample, time in years since FMP for cfPWV exam was calculated as the date on which cfPWV exam was performed minus the FMP date.

Blood Assays

A fasting blood sample was obtained and analyzed with standardized protocols at both the baseline and follow-up visits of the SWAN Heart ancillary study. Lipids, glucose, and insulin were measured at the Medical Research Laboratories (Lexington, KY). homeostasis model assessment—an index of insulin resistance derived from glucose and insulin measures—was calculated as (fasting insulin [mU/L]×fasting glucose [mmol/L])/22.5.16 Endogenous sex hormones were measured at the University of Michigan Endocrine Laboratory. Since estradiol and FSH show dynamic changes throughout the menstrual cycle, cycle day of blood draw (within 2–5 days of the menstrual cycle or not) was considered when adjusting for these hormones. A detailed description of blood assay measurement is presented in the Data Supplement.17

Study Covariates

At visits that coincide with the cfPWV exam dates, participants completed self- and interviewer-administered questionnaires that included assessment of sociodemographic and lifestyle factors and medical history. Age, race, whether woman had financial strain, current smoking status, and ever used hormone therapy were self-reported. Physical activity was assessed via Modified Baecke scores of habitual physical activity, with higher scores indicating more physical activity.18 Menopause status was determined based on reported frequency and regularity of menstrual bleeding as follows: (1) premenopause: monthly bleeding with no perceived change in cycle interval; (2) early perimenopause: monthly bleeding with a perceived change in cycle interval but at least one menstrual period within the past 3 months; (3) late perimenopause: 3 consecutive months of amenorrhea; and (4) natural menopause: 12 consecutive months of amenorrhea ([a] early natural menopause: <2 years since became menopause; [b] late natural menopause: ≥2 years past menopause). Medications including cholesterol, blood pressure, and diabetes mellitus medications were self-reported and interviewer-verified from medication container label. If a woman reported taking any of these medications, she was labeled as current medication user. Participants had their physical measurements obtained at each study visit using standardized protocols.

Age at FMP, race, and study site were time-independent variables. For financial strain, we used values that coincided with the first cfPWV scan date. Time-varying physical activity scores that coincided or were the closest in time with each participant’s cfPWV scan date were used. For all other covariates, we used time-varying values that coincided in time with each participant’s cfPWV scan date.

Statistical Analysis

Clinical characteristics at baseline were compared between women with observed and imputed FMP. cfPWV was log-transformed due to skewed distribution. A nonparametric locally weighted scatter-plot smoothing curve was used to fit cfPWV as a function of time since FMP.19 From this curve, inflection points associated with increase/decrease in cfPWV were determined.20 Since the observed inflection points were 1 year before and 1 year after the FMP, the cfPWV trajectory was divided into 3 time segments: segment 1: >1 year before FMP; segment 2: within 1 year before and after FMP; and segment 3: >1 year after FMP. Piecewise linear mixed-effects models were used to estimate and compare annual changes of cfPWV among time segments. Annual percentage change of cfPWV in each time segment was calculated as follows: annual percentage change of cfPWV=(eestimated annual change in log-cfPWV−1)×100.21 A random intercept for each woman was included to account for baseline heterogeneity. To visualize the cfPWV trajectory, we calculated annual means of cfPWV over time since FMP, as well as cfPWV estimates from the piecewise linear model. This figure was truncated at 5 years before and after FMP because of small sample size.

Study covariates were chosen based on a priori knowledge of risk factors of arterial stiffness and by univariate analysis. The first model adjusted for age at FMP and sociodemographic factors including race, study site, and financial strain. The second model further adjusted for hormone therapy and lifestyle factors including current smoking and physical activity. The third model additionally adjusted for CVD risk factors including systolic blood pressure, waist circumference, LDL (low-density lipoprotein) cholesterol, and homeostasis model assessment to determine whether they explain any observed changes in cfPWV around FMP. Finally, we built a parsimonious model (final model) by removing variables from the third model using backward stepwise selection methods with a P threshold of >0.2; financial strain, physical activity, and homeostasis model assessment were removed, and their removal did not change the effect estimates (β) in time segments by >10%.

To test whether annual changes in arterial stiffness vary between black and white women, we included an interaction term of race with each of the 3 time segments separately to avoid collinearity. Race-specific annual percentage changes in cfPWV were calculated separately for each time segment using the corresponding final model.

In subsequent analyses, we tested separately whether estradiol or FSH can attenuate changes in arterial stiffness relative to time since FMP in the final model controlling for cycle day of blood draw. Additionally, we reran the final model adjusting separately for C-reactive protein (CRP), heart rate, and medications to test whether they can explain changes in arterial stiffness relative to time since FMP. As sensitivity analyses, we reran the final model combining women with imputed and observed FMP and on women for whom cfPWV was measured at both SWAN Heart visits.

Results

Study Population

On average, the baseline cfPWV was measured (mean±SD) 1.0±3.2 years before the FMP, and the follow-up cfPWV was 1.1±3.1 years after the FMP. Of the 339 women in our study, 198 (58%) had cfPWV measured at baseline and follow-up visits, 115 (34%) at baseline visit only, and 26 (8%) at follow-up visit only. The number of women/observations within time segments 1, 2, and 3 were 149 (44%)/189 (35%), 86 (25%)/144 (27%), and 104 (31%)/204 (38%), respectively. Characteristics of all women at baseline are shown in Table 1. The largest difference between women with observed and imputed FMP was ever use of hormone therapy (Table I in the Data Supplement). The unadjusted association between each study covariate and cfPWV is presented in Table II in the Data Supplement.

Table 1. Characteristics of Study Participants at Baseline (n=339) Variables* Values Age, y 51.10±2.80 Race, n (%) White 216 (64) Black 123 (36) Financial strain, n (%) Hard to pay for basics 107 (34) No financial strain 222 (66) Current smoker, n (%) 64 (19) Physical activity score† 7.94±1.78 BMI, kg/m2 29.40±6.59 Waist circumference, cm 89.55±14.85 Systolic blood pressure, mm Hg 119.94±16.63 Diastolic blood pressure, mm Hg 76.01±9.58 Heart rate, bpm 71.40±8.93 Total cholesterol, mg/dL 201.75±38.75 High-density lipoprotein cholesterol, mg/dL 57.40±14.22 Low-density lipoprotein cholesterol, mg/dL 121.62±34.04 Triglycerides, mg/dL; median (Q1–Q3) 97.0 (75.0–135.0) Glucose, mg/dL; median (Q1–Q3) 88.0 (83.0–96.0) Insulin, µIU/mL; median (Q1–Q3) 9.1 (6.9–13.5) HOMA, median (Q1–Q3) 2.0 (1.4–3.1) Current medication user,‡ n (%) 66 (20) Menopausal status, n (%) Premenopause 28 (9) Early perimenopause 161 (52) Late perimenopause 36 (11) Early natural menopause 25 (8) Late natural menopause 53 (17) Age at FMP, y 52.02±2.84 Estradiol, pg/mL; median (Q1–Q3) 28.4 (15.5–83.6) FSH, mIU/mL; median (Q1–Q3) 36.8 (16.6–83.9) Ever used hormone therapy, n (%) 48 (14) cfPWV, cm/s; median (Q1–Q3) 774.7 (677.8–905.2)

Changes in cfPWV Around Menopause

In segment 2, cfPWV increased 6.5% per year and that estimated increase was significantly different from zero percent (ie, no change) adjusting for study covariates (Table 2; Figure 1). There was no significant change in cfPWV in segments 1 and 3. The increase in cfPWV in segment 2 was significantly greater than the changes in segments 1 and 3. However, adjusting for midlife CVD risk factors including systolic blood pressure, waist circumference, and LDL cholesterol explained part of the estimated annual change in segment 2 (from 7.5% in model 2 to 5.9% in model 3). Based on model fit statistics, systolic blood pressure and waist circumference, but not LDL cholesterol, explained most of the variability. The estimated annual changes in cfPWV in segments 1 and 3 were not statistically different from one another.

Table 2. Annual Percentage Change in cfPWV in Time Segments Relative to FMP Model* Annual Percentage Change† in cfPWV (95% CI) P Value for Pairwise Difference in Annual Change Segment 1 Segment 2 Segment 3 Segment 1 vs 2 Segment 1 vs 3 Segment 2 vs 3 >1 y Before FMP Within 1 y of FMP >1 y After FMP Unadjusted −0.20 (−1.54 to 1.16) 6.51 (3.10 to 10.04)‡ −1.31 (−3.11 to 0.52) 0.002 0.313 0.001 Model 1 0.95 (−0.47 to 2.39) 7.72 (4.30 to 11.24)‡ −0.64 (−2.43 to 1.18) 0.002 0.146 0.001 Model 2 0.87 (−0.55 to 2.30) 7.54 (4.13 to 11.07)‡ −1.03 (−2.81 to 0.79) 0.002 0.082 0.0003 Model 3 0.90 (−0.53 to 2.34) 5.94 (2.38 to 9.62)‡ −1.01 (−2.98 to 1.00) 0.024 0.102 0.006 Final model 0.88 (−0.50 to 2.29) 5.74 (2.37 to 9.21)‡ −0.81 (−2.66 to 1.08) 0.023 0.127 0.006

Figure 1. Means of carotid-femoral pulse-wave velocity (cfPWV) in years around the final menstrual period (FMP). Figure showing annual mean values compared with estimated values from piecewise linear model of cfPWV over time since FMP for women from the SWAN (Study of Women’s Health Across the Nation) Heart Ancillary study. Model adjusted for age at FMP, race, study site, hormone therapy, smoking status, systolic blood pressure, waist circumference, and low-density lipoprotein cholesterol (final model). Error bars represent 95% CI.

Race Interaction

Race modified the rate of change in cfPWV only in segment 1 (Figure 2); black and white women showed 2.0% and 0.6% annual increase in cfPWV (P for racial difference is 0.04), respectively. Interestingly, only black women had a statistically significant increase in cfPWV within segment 1.

Figure 2. Effect modification of race on annual percentage change in carotid-femoral pulse-wave velocity (cfPWV) by time segments relative to the final menstrual period (FMP). Interaction between race with each time segment was tested separately, and annual percentage change for each segment was estimated from the corresponding model. Models adjusted for the other 2 time segments, age at FMP, study site, hormone therapy, smoking status, systolic blood pressure, waist circumference, and low-density lipoprotein cholesterol (final model). Error bars represent 95% CI.

Additional Analyses (Data Not Shown)

Adjusting for estradiol, FSH, medications, or heart rate did not attenuate changes in cfPWV relative to the FMP. Adjusting for CRP among women with available CRP measurement (n=259) did not influence the overall conclusions.

Sensitivity Analyses

Rerunning the final model to women with imputed and observed FMP and to women who had 2 cfPWV measurements resulted in similar findings (Table III in the Data Supplement).

Discussion

The present study characterized functional vascular changes, specifically, central arterial stiffness, in relation to FMP and showed racial differences using a well-characterized cohort of women while adjusting for midlife covariates. We demonstrated significant increases in central arterial stiffness in the 1-year period surrounding the FMP in women transitioning through menopause. This increase was independent of aging, midlife CVD risk factors, estradiol, and FSH. Additionally, we showed that black women may experience greater adverse changes in arterial stiffness starting early in the transition than white women.

Previous cross-sectional analyses suggest that postmenopausal women have faster PWV measures compared with premenopausal women.22 However, controlling for age in such analyses is critical as aging may greatly confound these associations.23 To disentangle the relative contributions of chronologic and reproductive aging to vascular stiffness, longitudinal repeated-measures studies that follow a cohort of women through the menopause transition are required.24 Using such design, previous results from SWAN have shown that women who transitioned through menopause during the study follow-up period had greater increase in central arterial stiffness compared with women who remained premenopausal or were postmenopausal at baseline.6 In the current analysis, we used time-oriented analysis to describe when critical vascular changes occur in relation to the FMP.

A similar, and slightly smaller, estimate of change in central arterial stiffness within 1 year of the FMP was observed after adding women with imputed FMP to women with observed FMP. However, women with imputed FMP were healthier as they were less likely to smoke and had lower blood pressure compared with women with observed FMP, suggesting that analyses including women with imputed FMP may better reflect the population represented by SWAN Heart.

The aorta regulates the pulsatile blood flow from the heart into steady flow of blood supply to peripheral organs.25 Aging and other CVD risk factors including hypertension, hyperlipidemia, and central adiposity can increase arterial stiffness through loss of vascular elasticity and increased collagen deposition.26,27 Consequently, arterial stiffness can result in left ventricular dysfunction,28 low coronary perfusion,29 or end-organ damage.30,31 Interestingly, the increase in arterial stiffness within 1 year of FMP remained significantly greater than the increase before 1 year before FMP; however, this difference was attenuated after adjusting for CVD risk factors. Although the annual percentage increase in cfPWV within 1 year of FMP itself remained significantly different from zero percent after adjusting for midlife CVD risk factors, our results suggest that midlife CVD risk factors may explain part of the difference in arterial stiffness progression across the menopause transition.

Estradiol and FSH dynamically change during the menopause transition.11 Furthermore, estrogen receptors α and β are detected on vessel walls in human vasculature, and estradiol is thought to preserve arterial stiffness through vasodilation and vascular matrix formation.32 Therefore, one would expect that estradiol or FSH may explain functional changes in central arteries observed during the menopause transition. In the current study, these hormones did not explain changes or differences in arterial stiffness as women transitioned through menopause. Apart from traditional CVD risk factors, it seems that other mechanisms including inflammation and adipocytokine secretion may contribute to the changes in central arteries during the menopause transition. Several studies suggest that systemic inflammation is involved in the pathogenesis of arterial stiffness33 and that the menopause transition is associated with a rise in chronic low-grade inflammation,34 which may result in accelerating arterial stiffness.35 However, adjustment for CRP among women with available CRP measurement did not explain our findings. Additionally, women transitioning through menopause experience increased waist circumference and visceral adiposity.9,36 These ectopic fat depots are thought to alter serum levels of adipocytokines with increase in leptin and decrease in adiponectin, which may have deleterious health effects on midlife women.37 Consequently, greater levels of leptin and lower levels of adiponectin in women transitioning through menopause may contribute to worsening arterial stiffness.38 However, adipocytokine data were not available for most SWAN Heart participants.

Results from the current analysis are in line with previous studies, highlighting the time around menopause as a period when critical changes in the vasculature and CVD risk factors accumulate.2,3,8–10,36 These changes may increase the likelihood of CVD later in life.39 Our findings emphasize that women should be made aware that their cardiovascular health is expected to be adversely affected during the menopause transition. Therefore, frequent monitoring of CVD risk factors early in the menopause transition may be prudent as women can be counseled to stress lifestyle changes.40 Interestingly, healthy lifestyle during the midlife is prospectively associated with less carotid atherosclerosis.41 Future research should examine whether modifying arterial stiffness in women transitioning through menopause is associated with favorable cardiovascular outcomes later in life.

Racial Differences in Arterial Stiffness

Arterial stiffness is generally greater in black than in white women.42 This racial difference is also evident in midlife women, with black women showing greater progression of arterial stiffness compared with their white counterparts.13 In the current study, black and white women did not have uniform trajectories of arterial stiffness around the FMP, such that arterial stiffness increased in black women starting in the years preceding the 1 year before the FMP, while a similar increase earlier in the transition was not evident in white women. The reported effect modification of race is supported by previous studies showing racial differences among midlife women in subclinical vascular measures.13,43,44 Given that blacks have greater prevalence and earlier onset of CVD risk factors and higher CVD mortality compared with whites, these results underscore the importance of aggressive and frequent monitoring of risk factors early in the menopause transition among black women.45,46

Study Limitations

About 42% of women in this study had a single measurement of arterial stiffness. However, the statistical analysis method we used is appropriate for handling unbalanced data with 1 to 2 time points of data per subject and yielding unbiased estimates of change.47 To further support this approach, similar estimates of change and overall conclusions were found from analysis of women with 2 time points of data. Another limitation is that the follow-up was relatively short (mean of 2.3 years). Nevertheless, about 40% of study participants had traversed menopause between the baseline and follow-up visits.

Conclusions

The 1 year before and after FMP is a critical period in women’s lives when vascular functional alterations occur. In our study, arterial stiffness significantly increased within 1 year of the FMP independent of aging, CVD risk factors, estradiol, and FSH. Our results are consistent with previous findings showing significant vascular structural changes and CVD risk factors worsening around the time of FMP. Collectively, our findings underscore the importance of frequent and timely monitoring of CVD risk factors and stressing more intensive lifestyle modifications in women transitioning through menopause.

Nonstandard Abbreviations and Acronyms cfPWV carotid-femoral pulse-wave velocity CVD cardiovascular disease FMP final menstrual period FSH follicle-stimulating hormone LDL low-density lipoprotein SWAN Study of Women’s Health Across the Nation

Acknowledgments

Clinical centers: University of Michigan, Ann Arbor, MI—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, CA—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); and the 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, MI—Daniel McConnell (Central Ligand Assay Satellite Services). Coordinating Center: University of Pittsburgh, Pittsburgh, PA—Maria Mori Brooks (PI: 2012–present), Kim Sutton-Tyrrell (PI: 2001–2012); New England Research Institutes, Watertown, MA—Sonja McKinlay (PI: 1995–2001). Steering Committee: Susan Johnson (current chair); Chris Gallagher (former chair). We thank the study staff at each site and all the women who participated in SWAN.

Sources of Funding The SWAN (Study of Women’s Health Across the Nation) has grant support from the National Institutes of Health (NIH), Department of Health and Human Services, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), and the NIH Office of Research on Women’s Health (ORWH; grants U01NR004061, U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, and U01AG012495). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH. SWAN Heart was supported by grants from the National Heart, Lung, and Blood Institute (HL065581 and HL065591).

Disclosures M.M. Brooks serves as a Data and Safety Monitoring Board member for Cerus Corporation. The other authors report no conflicts.

Footnotes