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Epigenetic Aging and Immune Senescence in Women With Insomnia Symptoms: Findings From the Women’s Health Initiative Study

      Abstract

      Background

      Insomnia symptoms are associated with vulnerability to age-related morbidity and mortality. Cross-sectional data suggest that accelerated biological aging may be a mechanism through which sleep influences risk. A novel method for determining age acceleration using epigenetic methylation to DNA has demonstrated predictive utility as an epigenetic clock and prognostic of age-related morbidity and mortality.

      Methods

      We examined the association of epigenetic age and immune cell aging with sleep in the Women’s Health Initiative study (N = 2078; mean 64.5 ± 7.1 years of age) with assessment of insomnia symptoms (restlessness, difficulty falling asleep, waking at night, trouble getting back to sleep, and early awakenings), sleep duration (short sleep 5 hours or less; long sleep greater than 8 hours), epigenetic age, naive T cell (CD8+CD45RA+CCR7+), and late differentiated T cells (CD8+CD28–CD45RA–).

      Results

      Insomnia symptoms were related to advanced epigenetic age (β ± SE = 1.02 ± 0.37, p = .005) after adjustments for covariates. Insomnia symptoms were also associated with more late differentiated T cells (β ± SE = 0.59 ± 0.21, p = .006), but not with naive T cells. Self-reported short and long sleep duration were unrelated to epigenetic age. Short sleep, but not long sleep, was associated with fewer naive T cells (p < .005) and neither was related to late differentiated T cells.

      Conclusions

      Symptoms of insomnia were associated with increased epigenetic age of blood tissue and were associated with higher counts of late differentiated CD8+ T cells. Short sleep was unrelated to epigenetic age and late differentiated cell counts, but was related to a decline in naive T cells. In this large population-based study of women in the United States, insomnia symptoms are implicated in accelerated aging.

      Keywords

      Insomnia symptoms are associated with increased vulnerability to physical and mental declines, increased frailty in the elderly, elevated inflammation, and age-related morbidity and mortality (
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      ).
      An alternative biomarker of aging has recently been developed and is based on DNA methylation (DNAm), referred to as the epigenetic clock (
      • Horvath S.
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      ). This epigenetic clock method for estimating age is highly correlated with chronological age across cell types and complex tissues (
      • Horvath S.
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      Obesity accelerates epigenetic aging of human liver.
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      The cerebellum ages slowly according to the epigenetic clock.
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      DNA methylation age of blood predicts all-cause mortality in later life.
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      DNA methylation age is associated with mortality in a longitudinal Danish twin study.
      ), younger epigenetic age relates to cognitive and physical fitness in the elderly (
      • Marioni R.E.
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      • Muniz-Terrera G.
      • Harris S.E.
      • et al.
      The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936.
      ), and epigenetic age is younger in the offspring of Italian semisupercentenarians (i.e., subjects 105 years of age or older) compared with age-matched controls (
      • Horvath S.
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      • Di Blasio A.M.
      • Delledonne M.
      • et al.
      Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring.
      ). The epigenetic clock method has been used in applications surrounding obesity (
      • Horvath S.
      • Erhart W.
      • Brosch M.
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      • von Schönfels W.
      • Ahrens M.
      • et al.
      Obesity accelerates epigenetic aging of human liver.
      ), Down syndrome (
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      Accelerated epigenetic aging in Down syndrome.
      ), human immunodeficiency virus infection (
      • Horvath S.
      • Levine A.J.
      HIV-1 infection accelerates age according to the epigenetic clock.
      ), Parkinson׳s disease (
      • Horvath S.
      • Ritz B.R.
      Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients.
      ), Alzheimer׳s disease–related neuropathologies (
      • Levine M.E.
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      Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning.
      ), and lung cancer (
      • Levine M.E.
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      • Absher D.
      • Assimes T.
      • Horvath S.
      DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative.
      ). Estimates are that 40% of epigenetic age acceleration is inheritable (
      • Horvath S.
      DNA methylation age of human tissues and cell types.
      ,
      • Marioni R.E.
      • Shah S.
      • McRae A.F.
      • Ritchie S.J.
      • Muniz-Terrera G.
      • Harris S.E.
      • et al.
      The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936.
      ), with the remaining 60% thought to be accounted for by unidentified environmental and behavioral contributions. Along this line, initial work has begun to examine the role of environmental factors that may contribute to accelerated aging, with evidence that accelerated epigenetic aging is associate with lifetime stress (
      • Zannas A.S.
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      Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling.
      ), low socioeconomic status, and psychological trauma (
      • Miller G.E.
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      • Brody G.H.
      Self-control forecasts better psychosocial outcomes but faster epigenetic aging in low-SES youth.
      ,
      • Boks M.P.
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      • et al.
      Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder.
      ). However, it is not yet known whether epigenetic age acceleration, using the DNAm-based biomarker of aging, relates to measures of sleep disturbances or sleep duration.
      We hypothesized that insomnia symptoms and sleep duration would be associated with epigenetic age acceleration among women from the Women’s Health Initiative (WHI) study. Consistent with findings that symptoms of insomnia increase inflammation (
      • Irwin M.R.
      • Olmstead R.
      • Carroll J.E.
      Sleep disturbance, sleep duration, and inflammation: A systematic review and meta-analysis of cohort studies and experimental sleep deprivation.
      ) and morbidity and mortality risk (
      • Li Y.
      • Zhang X.
      • Winkelman J.W.
      • Redline S.
      • Hu F.B.
      • Stampfer M.
      • et al.
      Association between insomnia symptoms and mortality: A prospective study of U.S. men.
      ,
      • Vgontzas A.N.
      • Liao D.
      • Pejovic S.
      • Calhoun S.
      • Karataraki M.
      • Basta M.
      • et al.
      Insomnia with short sleep duration and mortality: The Penn State cohort.
      ,
      • Althuis M.D.
      • Fredman L.
      • Langenberg P.W.
      • Magaziner J.
      The relationship between insomnia and mortality among community-dwelling older women.
      ,
      • Carroll J.E.
      • Irwin M.R.
      • Merkin S.S.
      • Seeman T.E.
      Sleep and multisystem biological risk: A population-based study.
      ,
      • Carroll J.E.
      • Seeman T.E.
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      • Melendez G.
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      • et al.
      Improved sleep quality in older adults with insomnia reduces biomarkers of disease risk: Pilot results from a randomized controlled comparative efficacy trial.
      ), we predicted that greater insomnia symptoms would be associated with an older epigenetic age. Similar to findings of sleep duration with mortality (
      • Cappuccio F.P.
      • D’Elia L.
      • Strazzullo P.
      • Miller M.A.
      Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies.
      ), we also predicted that both short and long sleep duration would be associated with greater epigenetic aging.

      Methods and Materials

      Participants

      Participants included women in the WHI study, with detailed methods previously published (
      The Women׳s Health Initiative Study Group
      Design of the Women’s Health Initiative clinical trial and observational study.
      ,
      • Hays J.
      • Hunt J.R.
      • Hubbell F.A.
      • Anderson G.L.
      • Limacher M.
      • Allen C.
      • Rossouw J.E.
      The Women’s Health Initiative recruitment methods and results.
      ,
      • Langer R.D.
      • White E.
      • Lewis C.E.
      • Kotchen J.M.
      • Hendrix S.L.
      • Trevisan M.
      The Women’s Health Initiative Observational Study: Baseline characteristics of participants and reliability of baseline measures.
      ). Exclusion criterion for the observational study was minimal to ensure generalizability. Women were eligible to participate if they were 50–79 years of age, were postmenopausal, were willing to provide written informed consent, and resided in a nearby area within proximity of 40 WHI clinical centers across the United States for 3+ years after enrollment. Recruitment for the baseline assessment occurred from 1993 to 1999. The current analyses include a subset of 2078 participants who were selected for an integrative genomics study with the aim to identify genomic determinants of coronary heart disease, as reported previously (
      • Levine M.E.
      • Hosgood H.D.
      • Chen B.
      • Absher D.
      • Assimes T.
      • Horvath S.
      DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative.
      ). Included in the current study are individuals with both epigenetic and sleep data available at baseline. Demographic characteristics of this sample are reported in Table 1.

      DNAm Profiling

      Methylation analyses were performed at HudsonAlpha Institute of Biotechnology (Huntsville, AL) using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA), which includes 485,577 different CpG sites, and was described previously (
      • Levine M.E.
      • Hosgood H.D.
      • Chen B.
      • Absher D.
      • Assimes T.
      • Horvath S.
      DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative.
      ).

      Estimating Blood Cell Counts

      We estimated blood cell proportions using the advanced analysis option of the epigenetic clock software (
      • Horvath S.
      DNA methylation age of human tissues and cell types.
      ) available online (https://labs.genetics.ucla.edu/horvath/dnamage/), which estimates the percentage of late differentiated CD8+ T cells (CD8+CD28–CD45RA–) and the number (count) of naive CD8+ T cells (CD8+CD45RA+CCR7+) (
      • Horvath S.
      • Levine A.J.
      HIV-1 infection accelerates age according to the epigenetic clock.
      ). Final counts were statistically adjusted for chronological age. Additional cell subsets are reported in the Supplement.

      DNAm Age and the Epigenetic Clock

      We estimated the epigenetic age (also known as DNAm age) of each blood sample using two well-defined methods. First, we measured extrinsic epigenetic age acceleration (EEAA), which is highly correlated with immune senescence. EEAA is based on the DNAm age measure proposed in Hannum et al. (
      • Hannum G.
      • Guinney J.
      • Zhao L.
      • Zhang L.
      • Hughes G.
      • Sadda S.
      • et al.
      Genome-wide methylation profiles reveal quantitative views of human aging rates.
      ) that relies on 71 CpGs and is enhanced by forming a weighted average of this with the estimated blood cell counts from three blood cell types that are known to change with age—naive (CD45RA+CCR7+) cytotoxic T cells, late differentiated (CD28–CD45RA–) cytotoxic T cells, and plasma B cells—using the approach of Klemera and Doubal (
      • Klemera P.
      • Doubal S.
      A new approach to the concept and computation of biological age.
      ). The (static) weights that are used in the weighted average are determined by the correlation between the respective variables and chronological age in the WHI data (
      • Klemera P.
      • Doubal S.
      A new approach to the concept and computation of biological age.
      ). By definition, EEAA is positively correlated with the estimated abundance of exhausted CD8+ T cells and plasma B cells, and is negatively correlated with naive CD8+ T cells. Therefore, the measures of EEAA track both age-related changes in blood cell composition and intrinsic epigenetic changes.
      The second approach uses the Horvath (
      • Horvath S.
      DNA methylation age of human tissues and cell types.
      ) method, using 353 CpGs and coefficient values, to define DNAm age. This measure, intrinsic epigenetic age acceleration (IEAA), rather than using blood cell types to form a weighted average (as EEAA does), adjusts for imputed measures of blood cell counts: naive cytotoxic T cells and late differentiated cytotoxic T cells and plasma B cells. Further detail of these methods and comparisons between EEAA and IEAA can be found in the Supplement. Both the EEAA and IEAA measure are expressed as the deviation between DNAm age and chronological age and are used to define measures of epigenetic age acceleration, which is computed from the residual when regressing DNAm age on chronological age. A positive value indicates that epigenetic age is higher than chronological age.

      Measurement of Sleep

      Sleep Duration

      Subjective reports of sleep duration were obtained at the baseline visit, in which subjects reported how many hours of sleep they got on a typical night during the past 4 weeks. Response options included the following: 5 hours or less, 6 hours, 7 hours, 8 hours, 9 hours, or 10 or more hours. Consistent with research linking sleep duration with mortality risk (
      • Cappuccio F.P.
      • D’Elia L.
      • Strazzullo P.
      • Miller M.A.
      Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies.
      ), and consistent with the newly released joint consensus statement of the American Academy of Sleep Medicine and the Sleep Research Society on the recommended amount of sleep for healthy adults (
      • Watson N.F.
      • Badr M.S.
      • Belenky G.
      • Bliwise D.L.
      • Buxton O.M.
      • Buysse D.
      • et al.
      Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for a Healthy Adult: Methodology and Discussion.
      ), we created dummy variables to categorize short sleepers as 5 hours or less (sensitivity analyses categorized as 6 hours or less, given disagreement as to whether the risk for morbidity or mortality is also elevated in this group), normal sleepers as 7–8 hours (the recommended amount of optimal sleep), and long sleepers as greater than 8 hours (identified in the optimal dose of sleep model as elevated risk for disease).

      Global Sleep Disturbances

      Subjective reports of sleep disturbances were derived from the WHI Insomnia Rating Scale (WHIIRS) (
      • Levine D.W.
      • Kripke D.F.
      • Kaplan R.M.
      • Lewis M.A.
      • Naughton M.J.
      • Bowen D.J.
      • Shumaker S.A.
      Reliability and validity of the Women’s Health Initiative Insomnia Rating Scale.
      ). Five items (0–4) from the scale are summed to produce an overall global sleep disturbances score ranging from 0 to 20, with higher scores reflecting greater sleep disturbance and predictive of cardiovascular disease (
      • Sands-Lincoln M.
      • Loucks E.B.
      • Lu B.
      • Carskadon M.A.
      • Sharkey K.
      • Stefanick M.L.
      • et al.
      Sleep duration, insomnia, and coronary heart disease among postmenopausal women in the Women’s Health Initiative.
      ). Previous literature has used an elevated WHIIRS scores of greater than 10 to indicate a significant sleep disturbance (
      • Chen X.
      • Wang R.
      • Zee P.
      • Lutsey P.L.
      • Javaheri S.
      • Alcántara C.
      • et al.
      Racial/ethnic differences in sleep disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA).
      ), which has reasonable specificity for identifying insomnia cases (
      • Levine D.W.
      • Kripke D.F.
      • Kaplan R.M.
      • Lewis M.A.
      • Naughton M.J.
      • Bowen D.J.
      • Shumaker S.A.
      Reliability and validity of the Women’s Health Initiative Insomnia Rating Scale.
      ,
      • Levine D.W.
      • Kaplan R.M.
      • Kripke D.F.
      • Bowen D.J.
      • Naughton M.J.
      • Shumaker S.A.
      Factor structure and measurement invariance of the Women’s Health Initiative Insomnia Rating Scale.
      ).

      Insomnia Symptoms

      Whereas the WHIIRS reflects a global sleep disturbances assessment, the global score does not provide specificity and sensitivity for detecting discrete insomnia symptoms. Thus, we examined the five individual insomnia symptoms reported in the WHIIRS to create an insomnia symptoms specific measure, allowing us a more sensitive assessment of the relationship between insomnia symptoms and epigenetic age. Individuals were classified as having insomnia symptoms if they reported any one of the following occurring 1–2 or more times per week (and in sensitivity analyses occurring three or more times a week): restlessness, difficulty falling asleep, difficulty waking during the night, early awakenings, and inability to fall back to sleep. We computed a sum score reflecting the number of insomnia symptoms reported (0–5) to estimate severity, and a yes or no score if reporting any insomnia symptom.

      Snoring

      Self-reported snoring was also included as a covariate in models to adjust for possible sleep apnea, given the predictive utility of this measure in WHI for cardiovascular disease (
      • Sands M.
      • Loucks E.B.
      • Lu B.
      • Carskadon M.A.
      • Sharkey K.
      • Stefanick M.
      • et al.
      Self-reported snoring and risk of cardiovascular disease among postmenopausal women (from the Women’s Health Initiative).
      ).

      Depressive Symptoms

      The Center for Epidemiological Studies Depression Scale (
      • Radloff L.S.
      The CES-D scale: A self-report depression scale for research in the general population.
      ) 6-item version was administered to participants, as was reported previously (
      The Women׳s Health Initiative Study Group
      Design of the Women’s Health Initiative clinical trial and observational study.
      ,
      • Wassertheil-Smoller S.
      • Shumaker S.
      • Ockene J.
      • Talavera G.A.
      • Greenland P.
      • Cochrane B.
      • et al.
      Depression and cardiovascular sequelae in postmenopausal women. The Women’s Health Initiative (WHI).
      ), and is used to control for depressive symptoms.

      Statistical Analysis

      The measures of epigenetic age and cell counts include adjustment for chronological age (i.e., an estimate of accelerated aging); therefore, further adjustment for chronological age in the models was not necessary. Cross-sectional associations of sleep measures with epigenetic age acceleration, naive CD8+ cell counts, and late differentiated CD8+ cell counts were performed using biweight midcorrelation analyses. To test for differences in epigenetic age acceleration among sleep duration categories, sleep disturbances categories, and insomnia symptoms, we used a linear multivariate models. Model 1 adjusts for race or ethnicity (black or non-black, Hispanic or non-Hispanic), education (less than high school, high school diploma, some college, bachelor’s degree, graduate school), and body mass index (BMI) categories (less than 18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, 40+) and tested differences in EEAA and IEAA among 1) any WHIIRS insomnia symptoms (yes or no); 2) sleep disturbances groups with normal quality sleepers (WHIIRS 10 or less) as the reference; and 3) short-duration sleepers, long-duration sleepers, and normal duration sleepers (reference). In addition to adjusting for model 1 covariates, model 2 adjusts for comorbid chronic conditions including diabetes, hypertension, and cardiovascular disease. Secondary analyses employed pairwise comparisons using the least significant difference method to test differences in EEAA between number of WHIIRS insomnia symptoms categories (0, 1–2, 3–4, and 5).

      Results

      Descriptive statistics on demographic data and distribution of sleep measures can be seen in Table 1. Among the sample of 2078 women, mean age was 64.5 ± 7.1 years, with variations in epigenetic age relative to chronological age (–27.6 to +27.9 years of age for EEAA; –21.5 to +42.7 years of age for IEAA). The WHI sample is racially and ethnically diverse with approximately half non-Hispanic white, a third black, and the remainder of Hispanic ethnicity.
      Table 1Demographic Characteristics of the WHI Participants
      VariableStatistic (%)
      Race/Ethnicity
       White989 (47.6)
       Black668 (32.1)
       Hispanic421 (20.3)
      Chronological Age at Draw
       50–59 years of age526 (25.3)
       60–69 years of age972 (46.9)
       70–80 years of age580 (27.9)
      Educational Attainment
       Less than high school264 (12.7)
       High school diploma697 (33.5)
       Some college523 (25.2)
       Bachelor’s degree342 (16.5)
       Graduate school252 (12.1)
      Health Conditions
       Cardiovascular disease293 (14.1)
       Diabetes271 (13)
       Hypertension907 (43.6)
      BMI Category
       <18.513 (0.6)
       18.5–24.9443 (21.3)
       25–29.9716 (34.5)
       30–34.9530 (25.5)
       35–39.9246 (11.8)
       40+130 (6.3)
      Sleep Duration
       <6 hours271 (13.1)
       6 hours626 (30.1)
       7–8 hours1096 (52.7)
       >8 hours85 (4.1)
      WHI Insomnia Rating Scale
       No sleep disturbances (0–10)1667 (80.2)
       Sleep disturbance (>10)411 (19.8)
      Wake Up Several Times at Night
       No, not in past 4 weeks430 (20.7)
       Yes, less than once a week354 (17.1)
       Yes, 1 or 2 times a week481 (23.1)
       Yes, 3 or 4 times a week345 (16.6)
       Yes, 5 or more times a week468 (22.5)
      Wake Earlier Than Planned
       No, not in past 4 weeks904 (43.5)
       Yes, less than once a week452 (21.7)
       Yes, 1 or 2 times a week364 (17.5)
       Yes, 3 or 4 times a week211 (10.2)
       Yes, 5 or more times a week147 (7.1)
      Trouble Getting Back to Sleep
       No, not in past 4 weeks1069 (51.4)
       Yes, less than once a week390 (18.8)
       Yes, 1 or 2 times a week299 (14.4)
       Yes, 3 or 4 times a week174 (8.4)
       Yes, 5 or more times a week146 (7)
      Snore
       No, not in past 4 weeks315 (15.1)
       Yes, less than once a week97 (4.7)
       Yes, 1 or 2 times a week102 (4.9)
       Yes, 3 or 4 times a week111 (5.3)
       Yes, 5 or more times a week313 (15.1)
       Don’t know1140 (54.8)
      Trouble Falling Asleep
       No, not in past 4 weeks1176 (56.6)
       Yes, less than once a week378 (18.2)
       Yes, 1 or 2 times a week286 (13.8)
       Yes, 3 or 4 times a week144 (6.9)
       Yes, 5 or more times a week94 (4.5)
      Restless Sleep
       Very restless52 (2.5)
       Restless286 (13.8)
       Average quality890 (42.8)
       Sound or restful590 (28.4)
       Very sound or restful260 (12.5)
      Any Insomnia Symptom
       Yes1706 (82.1)
       No372 (17.9)
      Number of Insomnia Symptoms
       0372 (17.9)
       1–21091 (52.5)
       3–4467 (22.5)
       5148 (7.1)
      Epigenetic Age
       EEAA
      Range, 27.6 to 27.9 years.
      0.00 ± 6.4
       IEAA
      Range, 24.5 to 42.7 years.
      0.00 ± 5.0
      Range, 24.5 to 42.7 years.
      Values are n (%) or mean ± SD.
      BMI, body mass index; EEAA, extrinsic epigenetic age acceleration; IEAA, intrinsic epigenetic age acceleration; WHI, Women’s Health Initiative.
      a Range, 27.6 to 27.9 years.
      b Range, 24.5 to 42.7 years.

      Insomnia Symptoms, Sleep Duration, and Epigenetic Age Acceleration

      Initial unadjusted analyses of differences in EEAA between women with and without WHIIRS insomnia symptoms revealed a significant effect (see Table 2). Further adjustment in model 1 for race or ethnicity, BMI, educational attainment, and snoring found that the linear coefficient for insomnia symptoms was significant (p = .005; Table 3). Further adjustment for health conditions in model 2 did not modify this effect (p = .02). A closer examination of whether the number of insomnia symptoms was related to EEAA suggested that greater difference in epigenetic age correlated with increasing number of WHIIRS insomnia symptoms (p = .007; see Figure 1) such that the epigenetic age (EEAA) of women with no insomnia symptoms was younger than those with 1–2 symptoms (mean difference = 1.17, p = .002), 3–4 symptoms (mean difference = 1.10, p = .01), and five symptoms (mean difference = 1.77, p = .005). Further analyses adjusting for depressive symptoms did not modify these results. In addition, we performed sensitivity analyses to examine whether the effect was predominantly from those reporting insomnia symptoms three or more times per week, as opposed to one or two times per week. We found that individuals reporting any insomnia symptoms, selected if occurring either three or more times per week or one or two or more times per week, exhibited significantly older epigenetic age when compared with those with no insomnia symptoms (all p < .05).
      Table 2Point-Biserial Correlations With EEAA and IEAA
      EEAAIEAA
      rp Valuerp Value
      Short Sleep Sleep Duration (<6 hours)–.01.73–.06.06
      Long Sleep Duration (9–10 hours)–.02.46–.02.50
      WHIIRS Sleep Disturbance (>10).05
      p < .05.
      .02.01.53
      Trouble Falling Asleep.04.08–.01.83
      Waking at Night.07
      p < .05.
      .001.04.09
      Waking Too Early.03.23.01.64
      Trouble Going Back to Sleep.04.10–.01.69
      Snore.03.13–.001.97
      Restless Sleep.02.28.02.50
      Any Insomnia Symptom.08
      p < .05.
      .001.03.20
      EEAA, extrinsic epigenetic age acceleration; IEAA, intrinsic epigenetic age acceleration; WHIIRS, Women’s Health Initiative Insomnia Rating Scale.
      a p < .05.
      Table 3Linear Effect Model Coefficient (β) and SE of Each Sleep Characteristic Predicting EEAA
      Model 1Model 2
      Independent Predictorβ ± SEp Valueβ ± SEp Value
      Sleep Duration
       Short sleep (<6 hours)–0.005 ± 0.44.99–0.08 ± 0.44.86
       Normal sleep (7–8 hours; reference)REFREFREFREF
       Long sleep (> 8 hours)–0.75 ± 0.70.29–0.73 ± 0.71.31
      Sleep Disturbance (WHIIRS >10 vs. 10 or less)0.61 ± 0.35.090.64 ± 0.36.08
      Wake at Night1.00 ± 0.35.0040.92 ± 0.35.008
      Restless0.12 ± 0.38.740.11 ± 0.38.78
      Trouble Falling Asleep0.11 ± 0.32.740.07 ± 0.32.82
      Waking Too Early0.20 ± 0.29.510.19 ± 0.29.52
      Trouble Going Back to Sleep0.20 ± 0.31.500.18 ± 0.31.57
      Any Insomnia Symptom (yes vs. no)1.02 ± 0.37.0050.85 ± 0.36.02
      WHIIRS, Women’s Health Initiative Insomnia Rating Scale.
      aEach independent predictor is entered in a separate model with extrinsic epigenetic age acceleration (EEAA) as the dependent variable. Model 1 adjusts for race (black vs. nonblack Hispanic vs. non-Hispanic), education (category), body mass index (category), and snore (yes = 1). Model 2 adjusts for comorbid chronic conditions: diabetes, hypertension, and cardiovascular disease.
      Figure 1
      Figure 1Mean difference in extrinsic epigenetic age acceleration by number of insomnia symptoms.
      To characterize whether unique dimensions of insomnia were more strongly related to epigenetic age, we further analyzed the individual insomnia symptoms: trouble going to sleep, frequent waking at night, trouble getting back to sleep, waking earlier than planned, and restless sleep. Older epigenetic age (higher EEAA) was present in women who reported waking in the night compared with women with no nighttime awakenings (p = .004). Model 2 adjustments did not alter this effect (p = .008). Women reporting waking at night were on average one year older epigenetically than those without nighttime awakenings. The other insomnia symptoms alone did not predict epigenetic age acceleration (see Table 3).
      Examination of EEAA with the global WHIIRS sleep disturbance scores, cutoff >10, revealed differences in women with elevated WHIIRS scores (mean = 0.573, n = 399) compared with women with scores 10 or less (mean = –0.183, n = 1615; p < .05). Linear effect model 1 (see Table 3) adjusted for race or ethnicity, BMI, educational attainment, and snoring, and model 2 further adjusted for health conditions, with subsequent reduction in the strength of the effect of sleep disturbances (p = .09 and p = .08, respectively). Additional adjustment for depressive symptoms did not modify these results (p = .10).
      Analyses examining differences in EEAA by short sleep and long sleep compared to normal sleep duration found no significant effects (all p > .10; see Table 2, Table 3). Further sensitivity analyses tested whether categorization of short sleep as 6 hours per night or less would generate different findings. Sleeping 6 hours or less compared with 7–8 hours was unrelated to EEAA, p = .95. Given findings by Vgontzas et al. (
      • Vgontzas A.N.
      • Liao D.
      • Pejovic S.
      • Calhoun S.
      • Karataraki M.
      • Basta M.
      • et al.
      Insomnia with short sleep duration and mortality: The Penn State cohort.
      ) that demonstrate a significant increased risk for mortality among short sleepers with insomnia symptoms, further analyses examined the interaction of short sleep (6 hours or less; 5 hours or less) with insomnia symptoms. There were no significant interaction effects (all p > .4). Analyses examining associations of sleep duration and disturbances with IEAA found significant differences in relation to neither insomnia symptoms nor extremes of sleep duration. See Supplemental Table S1 for model coefficients and p values.

      Insomnia Symptoms, Sleep Duration, and Cell Subtypes: Naive and Late Differentiated T Cells

      Analyses of CD8+ naive cell with sleep dimensions were performed. Although WHIIRS insomnia symptoms were not associated with naive CD8+ cells, short sleep and trouble falling asleep were significantly associated with fewer naive CD8+ cells (all p < .05; see Table 4).
      Table 4Point-Biserial Correlations With Age-Adjusted Naive and Late Differentiated Subsets
      Naive (CD8+CD45RA+CCR7+)Late Differentiated (CD8+CD28–CD45RA–)
      rp Valuerp Value
      Short Sleep Duration (<6 hours)–.07.008.00.29
      Long Sleep Duration (9–10 hours).03.39.04.22
      WHIIRS Sleep Disturbance (>10)–.03.14.06.005
      Trouble Falling Asleep–.05.03.07.003
      Waking at Night–.01.58.05.02
      Waking Too Early–.03.25.03.17
      Trouble Going Back to Sleep–.03.12.05.03
      Snore–.001.97.02.41
      Restless Sleep–.03.19.01.65
      Any Insomnia Symptom–.03.18.08<.001
      WHIIRS, Women’s Health Initiative Insomnia Rating Scale.
      CD8+CD28–CD45– late differentiated T cells were significantly positively correlated with WHIIRS global sleep disturbance scores greater than 10, trouble falling asleep, waking at night, trouble going back to sleep, and having any insomnia symptom (all p < .05; see Table 4). The findings were such that having a high global sleep disturbance score, discrete insomnia symptoms, or any insomnia symptom was related to a greater amount of these late differentiated, exhausted T cells in circulating blood. Sleep duration was unrelated to the late differentiated T cell subtype (see Table 4).
      In subsequent adjusted models, reporting any insomnia symptoms continued to be significantly associated with having more CD8+CD28–CD45RA– T cells, after adjustments for race or ethnicity, BMI, educational attainment, snoring, and health conditions (β ± SE = 0.59 ± 0.21; p = .006). Further adjustment for depressive symptoms had no effect on these results (p = .008). Examination of each insomnia symptom, adjusting for model 1 and 2 covariates, revealed modest effects for trouble falling asleep (β ± SE = 0.32 ± 0.19; p = .09), waking at night (β ± SE = 0.40 ± 0.20; p = .05), and going back to sleep (β ± SE = 0.34 ± 0.18; p = .06). Linear effect models, adjusting for race or ethnicity, BMI, educational attainment, snoring, and health conditions, demonstrated the significant associations of the WHIIRS measure of global sleep disturbances with CD8+CD28–CD45RA– was retained (β ± SE = 0.41 ± 0.21; p = .05).

      Discussion

      Overall, we find modest correlations between self-reported insomnia symptoms and measures of epigenetic age acceleration in peripheral blood cells. Insomnia symptoms were significantly associated with the extrinsic measure of age acceleration, which measures the age of the immune system. Moreover, and consistent with the findings with extrinsic age acceleration, we report that insomnia symptoms were significantly associated with a greater proportion of late differentiated cytotoxic T cells (CD8+CD28–CD45RA–), indicative of an aged immune system (
      • Arosa F.A.
      CD8+CD28- T cells: certainties and uncertainties of a prevalent human T-cell subset.
      ). Insomnia symptoms did not seem to relate to intrinsic age acceleration, which is independent of age-related changes in blood cell counts. More careful examination of insomnia symptoms revealed that the association with the extrinsic measure of epigenetic age was graded; increasing number of insomnia symptoms was associated with an older epigenetic age, in which those reporting five symptoms had the oldest epigenetic age compared with women with no insomnia symptoms (see Figure 1). This effect was present after adjustment for potential confounds including race or ethnicity, BMI, educational attainment, snoring, depressive symptoms, and health conditions. When we analyzed whether specific dimensions of insomnia symptoms uniquely related to epigenetic age acceleration, we found that self-reported waking at night was significantly related to an older epigenetic age, more so than other insomnia symptoms reported. In parallel with the findings with the number of insomnia symptoms, the global measure of sleep disturbances showed a more modest relationship with epigenetic age, suggesting that use of the WHIIRS cutoff greater than 10, which has high specificity for identifying insomnia cases, is a less sensitive measure than examination of the cumulative effect of individual insomnia symptoms in the context of age acceleration.
      In addition, we found that late differentiated T cells, which are in a senescent or near-senescent state, were higher in those reporting insomnia symptoms. This finding suggests that poor sleep may accelerate CD8+ cell replication and progression to cellular senescence. This is in line with findings of Prather et al. (
      • Prather A.A.
      • Gurfein B.
      • Moran P.
      • Daubenmier J.
      • Acree M.
      • Bacchetti P.
      • et al.
      Tired telomeres: Poor global sleep quality, perceived stress, and telomere length in immune cell subsets in obese men and women.
      ), reporting marked telomere shortening in CD8+ T cells with sleep disturbances. Importantly, CD8+CD28– T cells have noticeably shorter telomere length and reduced telomerase activity, have significantly impaired replicative capacity, and are at an increased proportion in circulation among those with increasing chronological age, individuals who are human immunodeficiency virus positive (an accelerated aging disease) (
      • Horvath S.
      • Levine A.J.
      HIV-1 infection accelerates age according to the epigenetic clock.
      ), and those with diseases and conditions of later life (
      • Arosa F.A.
      CD8+CD28- T cells: certainties and uncertainties of a prevalent human T-cell subset.
      ,
      • Lin J.
      • Epel E.
      • Cheon J.
      • Kroenke C.
      • Sinclair E.
      • Bigos M.
      • et al.
      Analyses and comparisons of telomerase activity and telomere length in human T and B cells: Insights for epidemiology of telomere maintenance.
      ,
      • Effros R.B.
      The role of CD8 T cell replicative senescence in human aging.
      ). Likewise, telomeric shortening within CD8+CD28– cells was predictive of susceptibility to a novel virus, further implicating this cell subset in cellular aging and immune compromise (
      • Cohen S.
      • Janicki-Deverts D.
      • Turner R.B.
      • Casselbrant M.L.
      • Li-Korotky H.S.
      • Epel E.S.
      • Doyle W.J.
      Association between telomere length and experimentally induced upper respiratory viral infection in healthy adults.
      ). Taken together, our results advocate that experiencing sleep disturbances is associated with accelerated aging, particularly within the immune system.
      In contrast, our hypothesis that extremes of sleep duration would be related to an accelerated epigenetic age was not supported by the data. We found no significant effects of short or long sleep on extrinsic or intrinsic epigenetic age. There was a trend for a difference in intrinsic epigenetic age to be associated with short as compared with normal sleep duration, although this did not reach statistical significance (Table 2). We did see a significant inverse association of naive T cell counts with short sleep duration (Table 4), indicating a reduction in the number of naive T cells in the T cell compartment among those with short sleep. These findings have implications for early immune responses to novel antigens, such as susceptibility to a new cold or flu virus or in response to vaccinations (
      • Prather A.A.
      • Hall M.
      • Fury J.M.
      • Ross D.C.
      • Muldoon M.F.
      • Cohen S.
      • Marsland A.L.
      Sleep and antibody response to hepatitis B vaccination.
      ), which in part contributes to reduced immune competence seen in late life. Indeed, recent evidence has linked short sleep with an increased risk for developing the common cold after exposure (
      • Prather A.A.
      • Janicki-Deverts D.
      • Hall M.H.
      • Cohen S.
      Behaviorally assessed sleep and susceptibility to the common cold.
      ).
      A lack of association of epigenetic age acceleration with extremes of sleep duration is contrasted with the considerable epidemiological data linking short and long sleep with morbidity and mortality outcomes (
      • Carroll J.E.
      • Irwin M.R.
      • Merkin S.S.
      • Seeman T.E.
      Sleep and multisystem biological risk: A population-based study.
      ,
      • Cappuccio F.P.
      • Cooper D.
      • D’Elia L.
      • Strazzullo P.
      • Miller M.A.
      Sleep duration predicts cardiovascular outcomes: A systematic review and meta-analysis of prospective studies.
      ). Our null results may be due to measurement error of sleep duration. Considerable differences exist between sleep duration obtained when assessed by objective measures such as actigraphy as compared with self-reports of sleep duration, as used in the current report. Indeed, the correlation (r) between objective and subjective measures is .45, with average self-report being 0.8 hours more than objectively recorded sleep duration (
      • Lauderdale D.S.
      • Knutson K.L.
      • Yan L.L.
      • Liu K.
      • Rathouz P.J.
      Self-reported and measured sleep duration: How similar are they?.
      ). Future research should address this question using an objective assessment of sleep duration over repeated samplings and not rely solely on retrospective self-report of sleep duration.
      In contrast with self-reported sleep duration, self-reported insomnia symptoms using the WHIIRS has been demonstrated to have test–retest reliability and construct validity compared with objective indicators of sleep (
      • Levine D.W.
      • Kaplan R.M.
      • Kripke D.F.
      • Bowen D.J.
      • Naughton M.J.
      • Shumaker S.A.
      Factor structure and measurement invariance of the Women’s Health Initiative Insomnia Rating Scale.
      ). Although objectively derived assessments of waking after sleep onset are likely to capture more nuanced information about frequency and length of waking, self-reports of waking during the night, waking early, or difficulty falling asleep are all subjective self-reports of insomnia symptoms and are considered valid methods in epidemiological studies.
      These cross-sectional results need to be replicated in a prospective design to determine causation, but they suggest that sleep disturbances may increase risk for morbidity and mortality by accelerating the rate at which the immune, and possibly other bodily systems, age. Should replication and prospective designs support these initial findings, this raises the possibility that maintenance of sleep health (i.e., absence of sleep disturbance) slows the aging process and is vital for the prevention of early onset of disease and premature death (
      • Buysse D.J.
      Sleep health: Can we define it? Does it matter?.
      ). Alternatively, persistent insomnia symptoms may accelerate aging (
      • Parthasarathy S.
      • Vasquez M.M.
      • Halonen M.
      • Bootzin R.
      • Quan S.F.
      • Martinez F.D.
      • Guerra S.
      Persistent insomnia is associated with mortality risk.
      ) by disrupting healthy cellular function; increasing energy demands; and promoting DNA damage, inflammation, and cellular senescence (
      • Carroll J.E.
      • Cole S.W.
      • Seeman T.E.
      • Breen E.C.
      • Witarama T.
      • Arevalo J.M.G.
      • et al.
      Partial sleep deprivation activates the DNA damage response (DDR) and the senescence-associated secretory phenotype (SASP) in aged adult humans.
      ,
      • Irwin M.R.
      • Wang M.
      • Campomayor C.O.
      • Collado-Hidalgo A.
      • Cole S.
      Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation.
      ,
      • Everson C.A.
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      • Szabo A.
      • Hogg N.
      Cell injury and repair resulting from sleep loss and sleep recovery in laboratory rats.
      ,
      • Naidoo N.
      Cellular stress/the unfolded protein response: Relevance to sleep and sleep disorders.
      ,
      • Naidoo N.
      Roles of endoplasmic reticulum and energetic stress in disturbed sleep.
      ,
      • Naidoo N.
      • Ferber M.
      • Master M.
      • Zhu Y.
      • Pack A.I.
      Aging impairs the unfolded protein response to sleep deprivation and leads to proapoptotic signaling.
      ,
      • Brown M.K.
      • Naidoo N.
      The UPR and the anti-oxidant response: Relevance to sleep and sleep loss.
      ), which may lead to early mortality (
      • Parthasarathy S.
      • Vasquez M.M.
      • Halonen M.
      • Bootzin R.
      • Quan S.F.
      • Martinez F.D.
      • Guerra S.
      Persistent insomnia is associated with mortality risk.
      ). Together these biological alterations result in epigenetic modifications that appear with more advanced cellular age, which supports the hypothesis that DNAm age captures the cumulative work of the epigenomic maintenance system (
      • Horvath S.
      • Erhart W.
      • Brosch M.
      • Ammerpohl O.
      • von Schönfels W.
      • Ahrens M.
      • et al.
      Obesity accelerates epigenetic aging of human liver.
      ).
      There are several limitations to the current findings. First, the results are limited by the cross-sectional design, and an observation of associations between variables does not provide evidence of causality. Future studies should explore the alternative hypothesis that older epigenetic age is a driver of disruptions in sleep. Disruptions in circadian patterns with age may be the consequence of biological aging of the suprachiasmatic nucleus, the circadian clock of the body (
      • Hofman M.A.
      • Swaab D.F.
      Living by the clock: The circadian pacemaker in older people.
      ). Along this line, aging is associated with alterations in many aspects of the suprachiasmatic nucleus that influence sleep timing, duration, rhythm, depth, and quality. Therefore the direction of this association cannot be determined in the present results. However, we did not find associations of sleep disturbances with IEAA (an indicator of more global aging), but rather primarily with EEAA, an indicator of immune system aging. Likewise, there are several potential unidentified confounds that might influence the data, including unmeasured subclinical disease. As mentioned previously, the current results are limited by self-report measures of sleep taken from one sampling time point and may not be representative of long-term sleep patterns, adding error to the measure. Future research should establish repeated assessments of objectively defined sleep characteristics as they relate to changes in biological aging over time. Likewise, although our findings point to a link between insomnia symptoms and epigenetic age, the effect sizes are small; further, caution should be taken when interpreting these cross-sectional results where directionality and causation is not tested. The current sample comprised women only and is not representative of men in the population. Finally, we focused on blood tissue. Here we did see associations of insomnia symptoms with alterations in cell subsets representative of aging immunity including reductions in naive cytotoxic T cells and increases in late differentiated or exhausted T cells. Future studies should explore whether sleep is associated with the epigenetic ages of other tissues outside of the immune system. Physiological factors appear to have tissue-specific aging effects (e.g., obesity is strongly correlated with epigenetic age acceleration of liver tissue but much less so in blood) (
      • Horvath S.
      • Erhart W.
      • Brosch M.
      • Ammerpohl O.
      • von Schönfels W.
      • Ahrens M.
      • et al.
      Obesity accelerates epigenetic aging of human liver.
      ).
      Several strengths of the study design should also be noted, particularly the high quality of the WHI study design, measurements, and sample collection, and the diverse sampling of women across various strata of the United States. The study is well powered to test the hypotheses. The study also included a validated measure of sleep, the WHIIRS. Another notable strength is the highly accurate biomarkers of epigenetic age as mentioned in the introduction.
      In summary, insomnia symptoms were significantly associated with a marker of epigenetic age acceleration in a sample of more than 2000 women from the WHI study, representing the first findings to date to document insomnia symptoms to be associated with the epigenetic clock. Given that sleep disturbances are also associated with increase vulnerability to physical and mental declines, and age-related morbidity and mortality risk (
      • Ohayon M.M.
      Epidemiology of insomnia: What we know and what we still need to learn.
      ,

      Centers for Disease Control and Prevention (2011): Insufficient sleep is a public health epidemic. Available at: http://www.cdc.gov/Features/dsSleep/. Accessed August 4, 2011.

      ,
      • Colten H.R.
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      • Motivala S.J.
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      ,
      • Vgontzas A.N.
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      • Calhoun S.
      • Karataraki M.
      • Basta M.
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      ,
      • Althuis M.D.
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      ,
      • Sands-Lincoln M.
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      ), these findings raise the possibility that accelerated epigenetic aging might serve as one of several mechanisms through which sleep disturbances influence risk for age-related disease and early mortality. These findings are in concordance with work examining sleep disturbances, including insomnia (
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      • Liang G.
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      ,
      • Prather A.A.
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      • Bacchetti P.
      • et al.
      Tired telomeres: Poor global sleep quality, perceived stress, and telomere length in immune cell subsets in obese men and women.
      ) and extend the findings to link insomnia symptoms with accelerated epigenetic aging and evidence of an aged immune system. Given the recognized importance of aging biology for many chronic diseases seen in later life (
      • Franceschi C.
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      • Kennedy B.K.
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      ) and the growing demand to address the needs of an aging population, this work provides initial evidence that addressing behavioral factors such as sleep disturbances, which are prevalent in older adults (
      • Ohayon M.M.
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      ), may promote an extension of healthspan, more years of healthy living without chronic disease in later life (
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      • Burch J.B.
      • Augustine A.D.
      • Frieden L.A.
      • Hadley E.
      • Howcroft T.K.
      • Johnson R.
      • et al.
      Advances in geroscience: Impact on healthspan and chronic disease.
      ).

      Acknowledgments and Disclosures

      This study was supported by the University of California, Los Angeles, Cousins Center for Psychoneuroimmunology; National Institutes of Health (NIH)/National Institute on Aging Grant Nos. K01 AG044462 (to JEC), 5R01AG042511–02 (to ML, SH), R01 AG034588, and R01 AG026364; NIH/National Cancer Institute Grant No. R01 CA160245; NIH/National Institute on Drug Abuse Grant No. R01 DA032922; and NIH/National Heart, Lung, and Blood Institute (NHLBI) Grant Nos. 60442456 BAA23 (DA, TA, SH), R01 HL095799-01 (MRI), P30 AG017265, and R24AG037898 (TES). The WHI program is funded by the NHLBI, NIH, and U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201600003C, HHSN268201600002C, HHSN268201600004C, HHSN268201600001C, and HHSN271201100004C.
      We wish to give recognition of the team of WHI Investigators, which included the following individuals: Program Office: Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller (National Heart, Lung, and Blood Institute, Bethesda, Maryland); Clinical Coordinating Center: Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg (Fred Hutchinson Cancer Research Center, Seattle, Washington); Investigators and Academic Centers: JoAnn E. Manson (Brigham and Women׳s Hospital, Harvard Medical School, Boston, Massachusetts), Barbara V. Howard (MedStar Health Research Institute/Howard University, Washington, DC), Marcia L. Stefanick (Stanford Prevention Research Center, Stanford, California), Rebecca Jackson (The Ohio State University, Columbus, Ohio), Cynthia A. Thomson (University of Arizona, Tucson/Phoenix, Arizona), Jean Wactawski-Wende (University at Buffalo, Buffalo, New York), Marian Limacher (University of Florida, Gainesville/Jacksonville, Florida), Robert Wallace (University of Iowa, Iowa City/Davenport, Iowa), Lewis Kuller (University of Pittsburgh, Pittsburgh, Pennsylvania), and Sally Shumaker (Wake Forest University School of Medicine, Winston-Salem, North Carolina); and the Women’s Health Initiative Memory Study: Sally Shumaker (Wake Forest University School of Medicine, Winston-Salem, North Carolina).
      All authors report no biomedical financial interests or potential conflicts of interest.

      Appendix A. Supplementary material

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