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      Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank

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          Abstract

          Sleep is a complex behavior regulated by genetic and environmental factors, and is known to influence health outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on diseases has yet to be fully elucidated. Using the Mass General Brigham Biobank, we aimed to examine the association of multidimensional sleep with health outcomes and investigate whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health outcomes. First, we generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms for sleep health and constructed a Sleep Lifestyle Index using data from self-reported sleep questions and electronic health records; second, we performed phenome-wide association analyses between these indexes and clinical phenotypes; and third, we analyzed the interaction between the indexes on prevalent mental health outcomes. Fifteen thousand eight hundred and eighty-four participants were included in the analysis (mean age 54.4; 58.6% female). The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (β=0.050, 95%CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10 −5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10 −5). No interactions were found between the indexes on prevalent mental health outcomes. These findings suggest that favorable sleep behaviors and genetic predisposition to healthy sleep may independently be protective of disease outcomes. This work provides novel insights into the role of multidimensional sleep on population health and highlights the need to develop prevention strategies focused on healthy sleep habits.

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          Most cited references66

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

            The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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              Sleep health: can we define it? Does it matter?

              Good sleep is essential to good health. Yet for most of its history, sleep medicine has focused on the definition, identification, and treatment of sleep problems. Sleep health is a term that is infrequently used and even less frequently defined. It is time for us to change this. Indeed, pressures in the research, clinical, and regulatory environments require that we do so. The health of populations is increasingly defined by positive attributes such as wellness, performance, and adaptation, and not merely by the absence of disease. Sleep health can be defined in such terms. Empirical data demonstrate several dimensions of sleep that are related to health outcomes, and that can be measured with self-report and objective methods. One suggested definition of sleep health and a description of self-report items for measuring it are provided as examples. The concept of sleep health synergizes with other health care agendas, such as empowering individuals and communities, improving population health, and reducing health care costs. Promoting sleep health also offers the field of sleep medicine new research and clinical opportunities. In this sense, defining sleep health is vital not only to the health of populations and individuals, but also to the health of sleep medicine itself.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                07 February 2024
                : 2024.02.06.24302416
                Affiliations
                [1. ] Instituto de Psicología Clínica, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay.
                [2. ] MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom.
                [3. ] Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
                [4. ] Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
                [5. ] Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
                [6. ] Broad Institute, Cambridge, Massachusetts, United States of America.
                [7. ] Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.
                [8. ] Division of Nutrition, Harvard Medical School, Boston, Massachusetts, United States of America.
                Author notes
                [* ] Corresponding authors: Valentina Paz. Tristán Narvaja 1674, Montevideo, 11200, Uruguay. Tel: +59824008555-300. vpaz@ 123456psico.edu.uy , Hassan S. Dashti. 55 Fruit Street, Edwards 4-410C, Boston, MA 02114, United States. Tel: +17167269132. hassan.dashti@ 123456mgh.harvard.edu
                Author information
                http://orcid.org/0000-0002-2401-5472
                Article
                10.1101/2024.02.06.24302416
                10871384
                38370718
                175fe8fd-19a2-4f6b-977a-3c759232b1fb

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                Funded by: National Institutes of Health
                Award ID: R00HL153795
                Categories
                Article

                multidimensional sleep,polygenic scores,lifestyle behaviors,phenome-wide association study,clinical disorders,mental health

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