Donna D. Baird’s research while affiliated with National Institutes of Health and other places

Introduction Religious/spiritual beliefs and practices may positively or negatively influence sleep through myriad bidirectional pathways. For instance, religion could reduce psychosocial stress by providing social support or amplify it through religious conflict. Coincidently, Black/African American (AA) women are highly likely to attend religious services, experience substantial stress, and be burdened by poor sleep health. However, religion-stress-sleep relationships are understudied, especially among Black/AA women. Methods We investigated associations between religiosity, stress, and sleep using data collected during enrollment (2010-2012) and over three follow-ups (2014-2018) in the Study of Environment, Lifestyle, and Fibroids. At baseline, participants self-reported importance of faith (‘very-to-somewhat’ vs. ‘not at all’), religiosity/spirituality as a source of strength/comfort (‘very-to-somewhat’ vs. ‘not at all’), and prayer/meditation frequency (‘everyday’, ‘every week’, or ‘≥ once/month’ vs. ‘< once/month’). We defined self-reported sleep as: short sleep duration (SSD) < 7-hours, non-restorative sleep (NRS) as ‘yes’ vs. ‘no’ to waking up rested < 4 days/week, and insomnia symptoms (IS) as ‘yes’ vs. ‘no’ to difficulty falling or staying asleep. Day-to-day stress was dichotomized as ‘very high/moderate’ vs. ‘mild/not at all.’ Adjusting for sociodemographic and clinical characteristics, we estimated prevalence ratios (PRs) and 95% confidence intervals (CIs) using Poisson regression with robust variance and applied general estimating equations to estimate risk ratios. Interaction terms for religiosity/spirituality and stress were tested. Results Among 1,614 Black/AA women, mean age ± SD was 29.2 ± 3.4 years, 69.5% reported faith is of importance, 55.8% perceived religion/spirituality as a source of strength/comfort, 58.9% engaged in everyday prayer/meditation, and 43.4% reported very high or moderate day-to-day stress. Baseline prevalence of SSD was 54.6%, NRS 9.5%, and IS 17.9%. Compared to women who did not consider religion/spirituality a source of strength/comfort, women who did and reported very high/moderate day-to-day stress had higher prevalence of restorative sleep (PR=6.25 [95% CI:1.23-33.33]). Everyday prayer/meditation vs. < once per month/never was associated with higher prevalence of NRS (PR=3.11 [95% CI:1.15-8.41]). Religiosity/spirituality was not longitudinally associated with sleep. Conclusion Religion/spirituality as a source of strength was associated with restorative sleep among highly-stressed women while everyday prayer/meditation was associated with non-restorative sleep. Longitudinal studies are needed to address potential reverse causation. Support (if any)

Background: Uterine leiomyomata (UL), hormone-dependent neoplasms, are a major source of gynecologic morbidity. Metals are hypothesized to influence UL risk through endocrine disruption, and their effects may vary by vitamin D status. Objective: We estimated associations of a metal mixture with incident UL, overall and by vitamin D status. Methods: We analyzed data from the Study of Environment, Lifestyle and Fibroids, a Detroit-area prospective cohort study of 1,693 Black women aged 23-35 years. We measured concentrations of 17 metals/metalloids in whole blood and 25-hydroxyvitamin D (25[OH]D) in serum collected at baseline (2010-2012). Participants underwent ultrasonography at baseline and after 20 months to detect UL. We used Bayesian Kernel Machine Regression to estimate adjusted associations (β) of the metal mixture with probit of incident UL. We also ran Cox regression models with interaction terms to estimate incidence rate ratios (IRR) by vitamin D status. Results: Among 1,132 UL-free participants at baseline, 832 (73%) had vitamin D deficiency (25[OH]D<20 ng/mL) and 117 (10%) developed UL within 20 months. Increasing all metals from their 50th to 75th percentiles was weakly positively associated with UL overall (β=0.06, 95% credible interval [CrI]: -0.03, 0.16) and among vitamin D-deficient participants (β=0.13, 95% CrI: 0.01, 0.24), driven by cadmium (overall and vitamin D-deficient) and mercury (vitamin D-deficient only). Increasing cadmium from its 25th to 75th percentile was positively associated with UL overall (β=0.03, 95% CrI: -0.05, 0.11) and among vitamin D-deficient participants (β=0.13, 95% CrI: 0.02, 0.24). In Cox models, cadmium (IRR=1.55, 95% confidence interval [CI]: 1.07, 2.24, per 1-unit increase in standardized concentration) and mercury (IRR=1.38, 95% CI: 0.99, 1.92) were positively associated with UL among vitamin D-deficient participants. Discussion: The metal mixture was positively associated with incident UL, but the association was weak and imprecise. We observed a stronger association among vitamin D-deficient participants that was driven by cadmium and mercury. https://doi.org/10.1289/EHP15218.

People with darker-skin tone are more likely than their lighter-skin counterparts to have less access to health-promoting resources (e.g., economic security; quality housing; favorable neighborhood environment). These adverse conditions can affect sleep and result in poor health outcomes such as cardiovascular disease and depression. However, few studies have examined associations between skin tone and sleep disparities. To address this gap, we used cross-sectional (2010–2012) and longitudinal (until 2014–2018) data from the Study of Environment, Lifestyle and Fibroids cohort of 1674 Black women aged 23-35 years and residing in the Detroit, Michigan area. Skin tone was measured using a skin reflectance instrument and categorized as light [29.3–57.6] (25%), medium [57.7–72.4] (50%), and dark [72.5–106.1] (25%) brown. Self-reported sleep dimensions were dichotomized (yes vs. no): short sleep duration, non-restorative sleep, insomnia symptoms, and sleep apnea. Adjusting for age and educational attainment, we used Poisson regression with robust variance to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) and applied generalized estimating equations to log binomial models to determine risk ratios (RRs) and 95% CIs between skin tone and each sleep dimension, separately. Dark vs. light skin tone was associated with marginally higher prevalence and risk of short sleep (PR = 1.04 [95% CI:1.00–1.08]; RR = 1.07 [95% CI: 0.99–1.16]) and a lower prevalence of insomnia symptoms (PR = 0.95 [95% CI: 0.91–0.99]). Insomnia symptoms were more prevalent among women with light skin tone (21.1% vs. 17.7% [medium] and 15.6% [dark]). Our findings elucidate the critical importance and need to address the differential impact of historical ideologies, systems, policies, and practices on Black women, which can manifest health-damaging social phenomena like colorism.

STUDY QUESTION Can algorithms using wrist temperature, available on compatible models of iPhone and Apple Watch, retrospectively estimate the day of ovulation and predict the next menses start day? SUMMARY ANSWER Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths. WHAT IS KNOWN ALREADY Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events. STUDY DESIGN, SIZE, DURATION We conducted a prospective cohort study of 262 menstruating females (899 menstrual cycles) aged 14 and older who logged their menses, performed urine LH testing to define day of ovulation, recorded daily basal body temperature (BBT), and collected overnight wrist temperature. Participants contributed between 2 and 13 menstrual cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS Algorithm performance was evaluated for three algorithms: one for retrospective ovulation day estimate in ongoing cycles (Algorithm 1), one for retrospective ovulation day estimate in completed cycles (Algorithm 2), and one for prediction of next menses start day (Algorithm 3). Each algorithm’s performance was evaluated under multiple scenarios, including for participants with all typical cycle lengths (23–35 days) and those with some atypical cycle lengths (<23, >35 days), in cycles with the temperature change of ≥0.2°C typically associated with ovulation, and with any temperature change included. MAIN RESULTS AND ROLE OF CHANCE Two hundred and sixty participants provided 889 cycles. Algorithm 1 provided a retrospective ovulation day estimate in 80.5% of ongoing menstrual cycles of all cycle lengths with ≥0.2°C wrist temperature signal with a mean absolute error (MAE) of 1.59 days (95% CI 1.45, 1.74), with 80.0% of estimates being within ±2 days of ovulation. Retrospective ovulation day in an ongoing cycle (Algorithm 1) was estimated in 81.9% (MAE 1.53 days, 95% CI 1.35, 1.70) of cycles for participants with all typical cycle lengths and 77.7% (MAE 1.71 days, 95% CI 1.42, 2.01) of cycles for participants with atypical cycle lengths. Algorithm 2 provided a retrospective ovulation day estimate in 80.8% of completed menstrual cycles with ≥0.2°C wrist temperature signal with an MAE of 1.22 days (95% CI 1.11, 1.33), with 89.0% of estimates being within ±2 days of ovulation. Wrist temperature provided the next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within ±3 days of menses start) with an MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with ≥0.2°C wrist temperature signal. LIMITATIONS, REASONS FOR CAUTION There are several limitations, including reliance on LH testing to identify ovulation, which may mislabel some cycles. Additionally, the potential for false retrospective ovulation estimates when no ovulation occurred reinforces the idea that this estimate should not be used in isolation. WIDER IMPLICATIONS OF THE FINDINGS Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths. STUDY FUNDING/COMPETING INTEREST(S) Apple is the funding source for this manuscript. Y.W., C.Y.Z., J.P., S.Z., and C.L.C. own Apple stock and are employed by Apple. S.M. has research funding from Apple for a separate study, the Apple Women’s Health Study, including meeting and travel support to present research findings related to that separate study. A.M.Z.J., D.D.B., B.A.C., and J.P. had no conflicts of interest. TRIAL REGISTRATION NUMBER NCT05852951.

Background Associations between early‐life menstrual cycle characteristics (MCC) and gestational diabetes (GDM) remain unclear. Objectives To evaluate associations between early‐life MCCs and GDM in first pregnancy, across pregnancies and its recurrence. Methods This analysis included participants from a US‐based digital cohort enrolled between 11/2019 and 9/2023 who provided consent, completed relevant surveys, were without diabetes and aged ≥18 at first pregnancy ( n = 30,473). Age at menarche [<11 (early), 11–15 (referent), ≥16 (late) years] and time from menarche to cycle regularity [<1 (referent), 1–2, 3–4, ≥5 years, not yet regular, regular after hormones] were self‐recalled at enrolment. Additionally, the last three categories were considered prolonged time‐to‐regularity (PTTR). GDM history was recalled at enrolment for each pregnancy. We restricted to pregnancies of ≥24 weeks with a live birth. We evaluated associations of early‐life MCCs with GDM at first pregnancy using modified Poisson regression, across pregnancies using cluster‐weighted Poisson generalised estimating equation and GDM recurrence using multinomial logistic regression, adjusted for sociodemographic, early‐life factors and age at pregnancy. Missing variables were imputed with multiple imputation by chained equations. Results Among 30,473 participants, 20,591 had eligible first pregnancies, of which 5.9% reported GDM. In 17,512 participants with ≥2 pregnancies, 8.3% had GDM once and 3.7% had recurrent GDM. Early menarche (<11 years, vs. 11–15 years) was associated with GDM in first pregnancy (RR 1.34, 95% CI 1.15, 1.57), across pregnancies (RR 1.24, 95% CI 1.10, 1.39) and recurrence (OR 1.51, 95% CI 1.21, 1.89). PTTR was associated with GDM in the first pregnancy (RR 1.22, 95% CI 1.08, 1.38), across pregnancies (RR 1.16, 95% CI 1.05, 1.27) and recurrence (OR 1.19, 95% CI 0.99, 1.43). Conclusions Earlier menarche and prolonged time‐to‐regularity are associated with higher risk of GDM and recurrence, suggesting menstrual characteristics during childhood/adolescence as potential early‐life markers for GDM.

Context Androgen excess (AE)-related symptoms can vary widely and may appear across the life course. Objective We assessed the prevalence of signs of potential AE and heterogeneity by demographic/health characteristics. Methods We used data of 24 435 participants who consented and enrolled during November 2019 to December 2022 in a US digital cohort to evaluate the prevalence and heterogeneity of self-reported signs of potential AE: possible hirsutism (having thick coarse hair on ≥4 of 8 body locations), hair level on the chin, hair loss on top of the head, and moderate to severe acne. Results The prevalence of possible hirsutism, having several/a lot of hair on the chin, significantly reduced hair/visible scalp on top of the head, and moderate to severe acne were 6.9%, 12.6%, 1.7%, and 31.8%, respectively. While possible hirsutism and moderate to severe acne decreased with age (range: 18-86 years), hair on the chin and hair loss on the head increased with age. Participants who self-identified as Hispanic or South Asian reported a higher prevalence of possible hirsutism (11.2%, 16.9%, vs 6.3% among non-Hispanic White participants). Participants with higher body mass index had a higher prevalence of possible hirsutism. Moderate to severe acne was more common among those with polycystic ovary syndrome. Possible hirsutism and hair loss were less common among participants using hormones for contraception. Conclusion In this large cohort, signs of potential AE varied by demographic and health factors. These results could provide a new understanding of how potential AE may appear differently in diverse groups, informing future work to develop more inclusive evaluation at a population level.