The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable, person-specific RAR measures that can be estimated from actigraphy. Actigraphs are wearable devices that dynamically record acceleration and provide indirect measures of physical activity over time. One class of useful RAR measures are those that quantify variability around a mean circadian pattern. However, current parametric and nonparametric RAR measures used by applied researchers can only quantify variability from a limited or undefined number of rhythmic sources. The primary goal of this article is to consider a new measure of RAR variability: the log-power spectrum of stochastic error around a circadian mean. This functional measure quantifies the relative contributions of variability about a circadian mean from all possibly frequencies, including weekly, daily, and high-frequency sources of variation. It can be estimated through a two-stage procedure that smooths the log-periodogram of residuals after estimating a circadian mean. The development of this measure was motivated by a study of depression in older adults and revealed that slow, rhythmic variations in activity from a circadian pattern are correlated with depression symptoms.

Background The science of stress exposure and health in humans has been hampered by differences in operational definitions of exposures and approaches to defining timing, leading to results that lack consistency and specificity. In the present study we aim to empirically derive variability in type, timing and chronicity of stress exposure for Black and White females using prospectively collected data in the Pittsburgh Girls Study (PGS). Methods The PGS is an ongoing 20-year longitudinal, community-based study. In this paper we focused on annual caregiver reports of three domains of stress: subsistence (e.g., resource strain, overcrowding); safety (e.g., community violence, inter-adult aggression), and caregiving (e.g., separation, maternal depression) from early childhood through adolescence. Z-scores were used to conduct a finite mixture model-based latent class trajectory analysis. Model fit was compared using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). We examined differences in timing and chronicity of stress exposure between Black and White girls. Results Distinct trajectory groups characterized by differential timing and chronicity of stress exposure were observed across all stress domains. Six trajectories characterized subsistence and safety stress, and five characterized caregiving stress. Variability in initial level, chronicity, and magnitude and timing of change was observed within and across domains of stressors. Race differences also varied across the domains: race differences in timing and chronicity were most pronounced for the subsistence and safety domains, whereas Black and White girls had similar levels of exposure to caregiving stress. Conclusions Substantial variability in timing and chronicity was observed within and across stress domains. Modeling specific domains and dimensions of stress exposure is likely important in testing associations between exposure and health; such specificity may lead to more effective deployment of preventive interventions based on stress exposure.

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The negative effects of prenatal stress on offspring health are well established, but there remains little understanding of the influence of stress prior to conception despite known effects on biological systems that are important for a healthy pregnancy. Furthermore, operational definitions of stress vary considerably, and exposure is often characterized via summed, ordinal scales of events. We hypothesized that type, severity, and consistency of preconception stress would be associated with birthweight and gestational age (GA) at birth. Data were drawn from a subsample of participants in the 21-year longitudinal Pittsburgh Girls Study (PGS, N = 2,450) that has followed women annually since childhood. Prior work in the PGS derived three domains of stress exposure between ages 7-17 years related to subsistence (e.g., resource strain, overcrowding), safety (e.g., community violence, inter-adult aggression), and caregiving (e.g., separation, maternal depression). We tested the effects of dimensions of preconception stress on birthweight and GA among offspring of 490 PGS participants who delivered at age 18 or older (n = 490; 76% Black, 20% White, 4% Multiracial). Our hypotheses were partially supported with results varying by stress type and severity and by infant sex. Severity of preconception exposure to subsistence stress was prospectively associated with lower offspring birthweight (B = −146.94, SE = 69.07, 95% CI = −282.66, −11.22). The association between severity of caregiving stress in childhood and adolescence and GA at birth was moderated by infant sex (B = 0.85, SE = .41, 95% CI = 0.04, 1.66), suggesting greater vulnerability to this type of stress for male compared to female infants. Exposure to safety stressors did not predict birth outcomes. Infants of Black compared with White mothers had lower birthweight in all models regardless of preconception stress type, severity or consistency. However, we observed no moderating effects of race on preconception stress-birth outcome associations. Demonstrating specificity of associations between preconception stress exposure and prenatal health has the potential to inform preventive interventions targeting profiles of exposure to optimize birth outcomes.

Background While preeclampsia (PE) is a leading cause of pregnancy-related morbidity/mortality, its underlying mechanisms are not fully understood. DNA methylation (DNAm) is a dynamic regulator of gene expression that may offer insight into PE pathophysiology and/or serve as a biomarker (e.g., risk, subtype, a therapeutic response). This study’s purpose was to evaluate for differences in blood-based DNAm across all trimesters between individuals eventually diagnosed with PE (cases) and individuals who remained normotensive throughout pregnancy, did not develop proteinuria, and birthed a normally grown infant (controls). Results In the discovery phase, longitudinal, genome-wide DNAm data were generated across three trimesters of pregnancy in 56 participants (n=28 cases, n=28 controls) individually matched on self-identified race, pre-pregnancy body mass index, smoking, and gestational age at sample collection. An epigenome-wide association study (EWAS) was conducted, using surrogate variable analysis to account for unwanted sources of variation. No CpGs met the genome-wide significance p value threshold of 9×10-8, but 16 CpGs (trimester 1: 5; trimester 2: 1; trimester 3: 10) met the suggestive significance threshold of 1×10-5. DNAm data were also evaluated for differentially methylated regions (DMRs) by PE status. Three DMRs in each trimester were significant after Bonferonni-adjustment. Since only third-trimester samples were available from an independent replication sample (n=64 cases, n=50 controls), the top suggestive hits from trimester 3 (cg16155413 and cg21882990 associated with TRAF3IP2-AS1/TRAF3IP2 genes, which also made up the top DMR) were carried forward for replication. During replication, DNAm data were also generated for validation purposes from discovery phase third trimester samples. While significant associations between DNAm and PE status were observed at both sites in the validation sample, no associations between DNAm and PE status were observed in the independent replication sample. Conclusions The discovery phase findings for cg16155413/cg21882990 (TRAF3IP2-AS1/TRAF3IP2) were validated with a new platform but were not replicated in an independent sample. Given the differences in participant characteristics between the discovery and replication samples, we cannot rule out important signals for these CpGs. Additional research is warranted for cg16155413/cg21882990, as well as top hits in trimesters 1–2 and significant DMRs that were not examined in the replication phase.