Variability in sleep duration and cardiovascular health have been infrequently investigated, particularly among reproductive-age women. We examined these associations across the menstrual cycle among a cohort of 250 healthy premenopausal women, aged 18-44y. The BioCycle study had collected cardiovascular biomarkers (serum high- and low-density lipoprotein (HDL,LDL), total cholesterol, triglycerides, and C-reactive protein(CRP)) at key time points along the menstrual cycle (follicular, ovulatory, luteal phases). Women also recorded sleep duration in daily diaries. From these data, we computed L-moments, robust versions of location, dispersion, skewness, and kurtosis. We fitted linear mixed models with random intercepts and inverse-probability-weighting to estimate associations between sleep variability and cardiovascular biomarkers, accounting for demographic, lifestyle, health and reproductive factors. Sleep dispersion (any deviation from mean duration) was associated with lower mean LDL for non-shift workers (64%) and non-White women (44%). Skewed sleep duration was associated with higher mean CRP and lower mean total cholesterol. Sleep durations with extreme short and long bouts (kurtosis) were associated with a lower mean HDL, but not mean CRP, LDL or triglycerides. Sleep duration modified associations between sleep dispersion and LDL, HDL, and total cholesterol. Even in young and healthy women, sleep duration variability could influence cardiovascular health.
Introduction Irregular sleep duration may disrupt circadian rhythms necessary for optimal cardiovascular function. Yet, few studies have examined irregular sleep duration in relation to cardiovascular health, particularly among diverse cohorts of reproductive-age women. This study examined associations between sleep duration irregularities across the menstrual cycle and cardiovascular disease biomarkers in a cohort of healthy, premenopausal women. Methods We utilized the BioCycle micro-longitudinal cohort study of 259 regularly menstruating women aged 18–44 years. This measurement-intense study collected cardiovascular disease biomarkers at key reproductive time-points along the menstrual cycle (approximately days 2,7,12,13,14,18,22,27 of a 28-day cycle) across two cycles. Specifically, we assessed serum high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, and C-reactive protein (CRP). Women recorded sleep duration in daily diaries concurrently. We computed a system of four mathematical measures, the L-moments, robust versions of location, dispersion, skewness, and kurtosis for series of recorded sleep durations. Using linear mixed models with random intercepts and inverse probability weighting we estimated associations between irregular sleep duration and cardiovascular disease biomarkers in all women and within a subset of non-white women. Adjusted analysis accounted for baseline characteristics and time-varying hormonal changes across the menstrual cycle. Results Woman-specific mean sleep duration ranged from 4.4 to 10.6 hours. A one-hour increase in dispersion of sleep duration was associated with a lower mean LDL and higher mean HDL for non-white women (-19.4%, 95%CI -30.9,-6.0% and 24.7%, 95%CI 8.2,43.0, respectively). Unbalanced (skewed) sleep duration, frequent short or long hours, was associated with higher mean CRP for all women and non-white women (99.3%, 95%CI 17.2,238.9 and 126.7%, 95%CI 3.1,398.2, respectively), but lower total cholesterol (-10.9%, 95%CI -19.9,-1.0). Finally, irregular sleep durations, extreme short and long sleep bouts (kurtosis), were associated with reduced mean HDL for all women, and non-white women (-17.1%, 95%CI -31.1,-0.2 and -25.4%, 95%CI -39.5,-8.0, respectively). Conclusion This micro-longitudinal study of premenopausal women found associations between irregularities in sleep duration and differences in CRP, LDL, HDL and total cholesterol, but not with triglycerides. These data suggest that even in young and healthy women, irregularities in sleep duration could have a potential impact on cardiometabolic health. Support (If Any) Dr. Dunietz was supported by a Mentored Research Scientist Development Award from the National Heart, Lung, and Blood Institute (K01 HL144914). This work was supported in part by the Intramural Research Program of the NIH, Eunice Kennedy Shriver National Institute of Child Health and Human Development (authors KAM, JRF, EFS, SLM; The BioCycle Study was funded under the following intramural contracts: HHSN275200403394C HHSN275201100002I, and Task 1 HHSN27500001).
Introduction Poor sleep quality has been reported in the unemployed compared with employed. How sleep varies by employment status has been rarely examined at a population level. Therefore, we investigated sleep-wake patterns among employed, unemployed but actively seeking a job, and not-in-the-labor-force participants by gender and race/ethnicity. Methods Methods We used data from the American Time Use Survey (ATUS), a nationally representative sample of US residents aged ≥15years, which records weekday/weekend activities in a 24-hour period (4:00am-4:00am). This sample was restricted to participants aged 25–60 years (n=130,062). This analysis utilized functional nonparametric regression based on dimension reduction and neighborhood matching. We modeled the relationship between participant-specific sleep-wake trajectories, coded by minute, and employment status. Implementing the counterfactual approach, we estimated the effects of each employment scenario on participant-level expected sleep trajectory. This approach allowed the examination of hypothetical sleep-wake trajectories for each participant if their employment status differed from the observed. We then marginalized these findings to gender and race/ethnic subpopulations, controlling for confounders and secular trends. Results Mean age was 42□0.01 years, nearly half (51%) of participants were women and 68% were Whites. The proportions of employed, unemployed, and not-in-the-labor-force were 79%, 16.5% and 4.5%, respectively. On average, unemployed and not-in-the-labor-force participants had a later bedtime and wake-time compared with employed. With the exception of Whites, each individual race/ethnicity group showed pronounced differences in sleep-wake patterns by employment status. Of note, the likelihood of still being asleep up to 9:00am was greater when unemployed in comparison to had they been employed. Compared with employed, differences in sleep-wake patterns were pronounced among Blacks and Hispanics had they been unemployed, but attenuated if they were out-of-the-labor-force. Gender alone was not a strong moderator of the relationship between sleep-wake patterns and employment status. Unemployed participants had bedtime after 11pm, regardless of gender or race/ethnicity. Conclusion Using the counterfactual approach, we predicted sleep-wake patterns among individuals had they been employed, unemployed, or out-of-the-labor-force by gender and race/ethnicity. Though cross-sectional, our data suggest that the sleep schedules of racial/ethnic minorities in comparison to Whites may be more affected by employment status. Support (if any):
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