Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders.

ObjectiveThe gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.ApproachWe used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.ResultsThe authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.ConclusionsThe NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

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The aim of this study was to investigate the in vitro immunomodulatory effects of zoledronic acid (Zol) on peripheral blood Vc9/Vd2 (cd) T cells of normal donors and multiple myeloma (MM) patients. cd T cells were stimulated with Zol and low doses of interleukin-2 (IL-2), and then analyzed for proliferation, cytokine production, and generation of effector activity against myeloma cell lines and primary myeloma cells. Proliferation of cd T cells was observed in 100% of normal donors and 50% of MM patients. cd T cells produced IFN-c, surface mobilized the CD107a and CD107b antigens, and exerted direct cell-to-cell antimyeloma activity irrespective of the ability to proliferate to Zol and IL-2. The memory phenotype was predominant in the MM cd T cells that proliferated in response to Zol (responders), whereas effector cells were predominant in those that did not (nonresponders). Zol induced antimyeloma activity through the monocyte-dependent activation of cd T cells and by enhancing the immunosensitivity of myeloma cells to cd T cells. Mevastatin, a specific inhibitor of hydroxy-methylglutaryl-CoA reductase, completely abrogated this antimyeloma activity.

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This study evaluated the daily, temporal associations between sleep and daytime physical activity and sedentary behavior among adolescents from the Fragile Families & Child Wellbeing Study. A sub-sample of the cohort at age 15 (N = 417) wore actigraphy monitors for one week during the school year from which we derived daily minutes in sedentary and moderate-to-vigorous physical activity (MVPA) and nighttime sleep measures. Multilevel models tested temporal associations of nightly sleep onset, offset, duration, and sleep maintenance efficiency, with daily MVPA and sedentary behavior. More MVPA than an individual’s average was associated with earlier sleep onset (p < 0.0001), longer duration (p = 0.03), and higher sleep maintenance efficiency (p < 0.0001). On days with more sedentary behavior than an individual’s average, sleep onset and offset were delayed (p < 0.0001), duration was shorter (p < 0.0001), and sleep maintenance efficiency was higher (p = 0.0005). Conversely, nights with earlier sleep onset predicted more next-day sedentary behavior (p < 0.0001), and nights with later sleep offset and longer sleep duration were associated with less MVPA (p < 0.0001) and less sedentary time (p < 0.0001, p = 0.004) the next day. These bidirectional associations between sleep and physical activity suggest that promoting MVPA may help to elicit earlier bedtimes, lengthen sleep duration, and increase sleep efficiency, critical for healthy adolescent development.

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