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      Genome-wide Analysis of Insomnia Disorder

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          Abstract

          Insomnia is a worldwide problem with substantial deleterious health effects. Twin studies have shown a heritable basis for various sleep-related traits, including insomnia, but robust genetic risk variants have just recently begun to be identified. We conducted genome-wide association studies (GWAS) of soldiers in the Army Study To Assess Risk and Resilience in Servicemembers (STARRS). GWAS were carried out separately for each ancestral group (EUR, AFR, LAT) using logistic regression for each of the STARRS component studies (including 3,237 cases and 14,414 controls), and then meta-analysis was conducted across studies and ancestral groups. Heritability (SNP-based) for lifetime insomnia disorder was significant (h 2 g=0.115, p=1.78×10 −4 in EUR). A meta-analysis including three ancestral groups and three study cohorts revealed a genome-wide significant locus on Chr 7 (q11.22) (top SNP rs186736700, OR = 0.607, p = 4.88×10 −9) and a genome-wide significant gene-based association (p = 7.61×10 −7) in EUR for RFX3 on Chr 9. Polygenic risk for sleeplessness/insomnia severity in UK Biobank was significantly positively associated with likelihood of insomnia disorder in STARRS. Genetic contributions to insomnia disorder in STARRS were significantly positively correlated with major depressive disorder (r g = 0.44, se = 0.22, p = 0.047) and type 2 diabetes (r g = 0.43, se = 0.20, p = 0.037), and negatively with morningness chronotype (r g = −0.34, se = 0.17, p = 0.039) and subjective well-being (r g = −0.59, se = 0.23, p = 0.009) in external datasets. Insomnia associated loci may contribute to the genetic risk underlying a range of health conditions including psychiatric disorders and metabolic disease.

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          Most cited references50

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          The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI)

          This paper presents an overview of the World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) and a discussion of the methodological research on which the development of the instrument was based. The WMH‐CIDI includes a screening module and 40 sections that focus on diagnoses (22 sections), functioning (four sections), treatment (two sections), risk factors (four sections), socio‐demographic correlates (seven sections), and methodological factors (two sections). Innovations compared to earlier versions of the CIDI include expansion of the diagnostic sections, a focus on 12‐month as well as lifetime disorders in the same interview, detailed assessment of clinical severity, and inclusion of information on treatment, risk factors, and consequences. A computer‐assisted version of the interview is available along with a direct data entry software system that can be used to keypunch responses to the paper‐and‐pencil version of the interview. Computer programs that generate diagnoses are also available based on both ICD‐10 and DSM‐IV criteria. Elaborate CD‐ROM‐based training materials are available to teach interviewers how to administer the interview as well as to teach supervisors how to monitor the quality of data collection. Copyright © 2004 Whurr Publishers Ltd.
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            The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI).

            This paper presents an overview of the World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) and a discussion of the methodological research on which the development of the instrument was based. The WMH-CIDI includes a screening module and 40 sections that focus on diagnoses (22 sections), functioning (four sections), treatment (two sections), risk factors (four sections), socio-demographic correlates (seven sections), and methodological factors (two sections). Innovations compared to earlier versions of the CIDI include expansion of the diagnostic sections, a focus on 12-month as well as lifetime disorders in the same interview, detailed assessment of clinical severity, and inclusion of information on treatment, risk factors, and consequences. A computer-assisted version of the interview is available along with a direct data entry software system that can be used to keypunch responses to the paper-and-pencil version of the interview. Computer programs that generate diagnoses are also available based on both ICD-10 and DSM-IV criteria. Elaborate CD-ROM-based training materials are available to teach interviewers how to administer the interview as well as to teach supervisors how to monitor the quality of data collection.
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              Sleep disturbances compared to traditional risk factors for diabetes development: Systematic review and meta-analysis.

              Sleep disturbances [short ( 8 h) sleeping time, insomnia (initiating or maintaining sleep), obstructive sleep apnea (OSA) and abnormal sleep timing] have been associated with increased diabetes risk but the effect size relative to that of traditional risk factors is unknown. We conducted a systematic review and meta-analysis to compare the risk associated with sleep disturbances to traditional risk factors. Studies were identified from Medline and Scopus. Cohort studies measuring the association between sleep disturbances and incident diabetes were eligible. For traditional risk factors (i.e., overweight, family history, and physical inactivity), systematic reviews with or without meta-analysis were included. Thirty-six studies (1,061,555 participants) were included. Pooled relative risks (RRs) of sleep variables were estimated using a random-effect model. Pooled RRs of sleeping ≤5 h, 6 h, and ≥9 h/d were respectively 1.48 (95%CI:1.25,1.76), 1.18 (1.10,1.26) and 1.36 (1.12,1.65). Poor sleep quality, OSA and shift work were associated with diabetes with a pooled RR of 1.40 (1.21,1.63), 2.02 (1.57, 2.61) and 1.40 (1.18,1.66), respectively. The pooled RRs of being overweight, having a family history of diabetes, and being physically inactive were 2.99 (2.42,3.72), 2.33 (1.79,2.79), and 1.20 (1.11,1.32), respectively. In conclusion, the risk of developing diabetes associated with sleep disturbances is comparable to that of traditional risk factors. Sleep disturbances should be considered in clinical guidelines for type 2 diabetes screening.
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                Author and article information

                Journal
                9607835
                20545
                Mol Psychiatry
                Mol. Psychiatry
                Molecular psychiatry
                1359-4184
                1476-5578
                16 January 2018
                08 March 2018
                09 September 2018
                : 10.1038/s41380-018-0033-5
                Affiliations
                [1 ]Department of Psychiatry, University of California San Diego, La Jolla, CA
                [2 ]Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA
                [3 ]VA San Diego Healthcare System, San Diego, CA
                [4 ]Department of Psychiatry, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
                [5 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
                [6 ]Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
                [7 ]Department of Psychiatry, Yale University, New Haven, CT
                [8 ]VA Connecticut Healthcare System, West Haven, CT
                [9 ]Departments of Genetics and Neurobiology, Yale University, New Haven, CT
                [10 ]Institute for Social Research, University of Michigan, Ann Arbor, MI
                [11 ]Department of Health Care Policy, Harvard Medical School, Boston, MA
                [12 ]Department of Psychology, Harvard University, Cambridge, MA
                [13 ]Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD
                Author notes
                Please address correspondence to: Murray B. Stein MD, MPH, Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego (Mailcode 0855), 9500 Gilman Drive, La Jolla, CA 92093-0855, Ph: 858-534-6400; Fax: 858-534-6460, mstein@ 123456ucsd.edu
                Article
                NIHMS935051
                10.1038/s41380-018-0033-5
                6129221
                29520036
                b250af6e-2563-4209-aadb-b09d49a49b19

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                Molecular medicine
                Molecular medicine

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