Inflammatory markers, lifestyle, and health status explained mortality risk associated with short sleep, while the mortality risk associated with long sleep was explained predominantly by lifestyle and health status.

Childhood trauma may affect sleep health in adulthood. These findings align with the growing body of evidence linking childhood trauma to adverse health outcomes later in life.

Opportunities for restorative sleep and optimal sleep-wake schedules are becoming luxuries in industrialized cultures, yet accumulating research has revealed multiple adverse health effects of disruptions in sleep and circadian rhythms, including increased risk of breast cancer. The literature on breast cancer risk has focused largely on adverse effects of night shift work and exposure to light at night (LAN), without considering potential effects of associated sleep disruptions. As it stands, studies on breast cancer risk have not considered the impact of both sleep and circadian disruption, and the possible interaction of the two through bidirectional pathways, on breast cancer risk in the population at large. We review and synthesize this literature, including: 1) studies of circadian disruption and incident breast cancer; 2) evidence for bidirectional interactions between sleep and circadian systems; 3) studies of sleep and incident breast cancer; and 4) potential mechanistic pathways by which interrelated sleep and circadian disruption may contribute to the etiology of breast cancer.

Study Objectives Polysomnography (PSG) is considered the “gold standard” for assessing sleep, but cost and burden limit its use. Although wrist actigraphy and self-report diaries are feasible alternatives to PSG, few studies have compared all three modalities concurrently across multiple nights in the home to assess their relative validity across multiple sleep outcomes. This study compared sleep duration and continuity measured by PSG, actigraphy, and sleep diaries and examined moderation by race/ethnicity. Methods Participants from the Study of Women’s Health Across the Nation Sleep Study included 323 White (n = 147), African American (n = 120), and Chinese (n = 56) middle-aged community-dwelling women (mean age: 51 years, range: 48–57). PSG, wrist actigraphy (AW-64; Philips Respironics, McMurray, USA), and sleep diaries were collected concurrently in participants’ homes over three consecutive nights. Multivariable repeated-measures linear models compared time in bed (TIB), total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO) across modalities. Results Actigraphy and PSG produced similar estimates of sleep duration and efficiency. Diaries yielded higher estimates of TIB, TST, and SE vs. PSG and actigraphy, and lower estimates of SL and WASO vs. PSG. Diary SL was shorter than PSG SL only among White women, and diary WASO was lower than PSG and actigraphy WASO among African American vs. White women. Conclusions Given concordance with PSG, Actigraphy may be preferred as an alternative to PSG for measuring sleep in the home. Future research should consider racial/ethnic differences in diary-reported sleep continuity.

Objective Dried blood spot (DBS) methodology offers significant advantages over venipuncture in vulnerable populations or large-scale studies, including reduced participant burden and higher response rates. Uncertainty about validity of cardiovascular risk biomarkers remains a barrier to wide-scale use. We determined the validity of DBS-derived biomarkers of CVD risk versus gold-standard assessments, and study-specific, serum-equivalency values for clinical relevance of DBS-derived values. Methods Concurrent venipuncture serum and DBS samples (n=150 adults) were assayed in CLIA-certified and DBS laboratories, respectively. Time controls of DBS standard samples were assayed single-blind along with test samples. Linear regression analyses evaluated DBS-to-serum equivalency values; agreement and bias were assessed via Bland-Altman plots. Results Linear regressions of venipuncture values on DBS-to-serum equivalencies provided R2 values for TC, HDL-C, CRP of 0.484, 0.118, 0.666, respectively. Bland-Altman plots revealed minimal systematic bias between DBS-to-serum and venipuncture values; precision worsened at higher mean values of CRP. Time controls reveal little degradation or change in analyte values for HDL-C and CRP over 30 weeks. Conclusions DBS-assessed biomarkers represent a valid alternative to venipuncture assessments. Large studies using DBS should include study-specific serum-equivalency determinations to optimize individual-level sensitivity, viability of detecting intervention effects, and generalizability in community-level, primary prevention interventions.

Objectively assessed short sleep duration was prospectively associated with increased BAD over a 12- to 30-year period.

Background Snoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objective measures of snoring. One reason for this methodological limitation is the difficulty of quantifying snoring. Conventional methods may rely on manual scoring of snore events by trained human scorers, but this process is both time- and labor-intensive, making the measurement of objective snoring impractical for large or multi-night studies. Methods The current study is a proof-of-concept to validate the use of support vector machines (SVM), a form of machine learning, for the automated scoring of an objective snoring signal. An SVM algorithm was trained and tested on a set of approximately 150,000 snoring and non-snoring data segments, and F-scores for SVM performance compared to visual scoring performance were calculated using the Wilcoxon signed rank test for paired data. Results The ability of the SVM algorithm to discriminate snore from non-snore segments of data did not differ statistically from visual scorer performance (SVM F-score=82.46 ± 7.93 versus average visual F-score=88.35 ± 4.61, p=0.2786), supporting SVM snore classification ability comparable to visual scorers. Conclusion In this proof-of-concept, we established that the SVM algorithm performs comparably to trained visual scorers, supporting the use of SVM for automated snoring detection in future studies.

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