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      Genetically Predicted Insomnia in Relation to 14 Cardiovascular Conditions and 17 Cardiometabolic Risk Factors: A Mendelian Randomization Study

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          Abstract

          Background

          This Mendelian randomization study aims to investigate causal associations between genetically predicted insomnia and 14 cardiovascular diseases (CVDs) as well as the potential mediator role of 17 cardiometabolic risk factors.

          Methods and Results

          Using genetic association estimates from large genome‐wide association studies and UK Biobank, we performed a 2‐sample Mendelian randomization analysis to estimate the associations of insomnia with 14 CVD conditions in the primary analysis. Then mediation analysis was conducted to explore the potential mediator role of 17 cardiometabolic risk factors using a network Mendelian randomization design. After correcting for multiple testing, genetically predicted insomnia was consistent significantly positively associated with 9 of 14 CVDs, those odds ratios ranged from 1.13 (95% CI, 1.08–1.18) for atrial fibrillation to 1.24 (95% CI, 1.16–1.32) for heart failure. Moreover, genetically predicted insomnia was consistently associated with higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol, each of which may act as a mediator in the causal pathway from insomnia to several CVD outcomes. Additionally, we found very little evidence to support a causal link between insomnia with abdominal aortic aneurysm, thoracic aortic aneurysm, total cholesterol, low‐density lipoprotein cholesterol, glycemic traits, renal function, and heart rate increase during exercise. Finally, we found no evidence of causal associations of genetically predicted body mass index, high‐density lipoprotein cholesterol, or triglycerides on insomnia.

          Conclusions

          This study provides evidence that insomnia is associated with 9 of 14 CVD outcomes, some of which may be partially mediated by 1 or more of higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol.

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          Most cited references95

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          Measuring inconsistency in meta-analyses.

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            Quantifying heterogeneity in a meta-analysis.

            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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              UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

              Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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                Author and article information

                Contributors
                lihongkaiyouxiang@163.com
                xuefzh@sdu.edu.cn
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                28 July 2021
                03 August 2021
                : 10
                : 15 ( doiID: 10.1002/jah3.v10.15 )
                : e020187
                Affiliations
                [ 1 ] Department of Biostatistics School of Public Health Cheeloo College of Medicine Shandong University Jinan Shandong China
                [ 2 ] Institute for Medical Dataology Cheeloo College of Medicine Shandong University Jinan Shandong China
                [ 3 ] Department of Emergency and Chest Pain Center Qilu Hospital Cheeloo College of Medicine Shandong University Jinan Shandong China
                [ 4 ] Center for Big Data Research in Health and Medicine Shandong Qianfoshan Hospital Cheeloo College of Medicine Shandong University Jinan Shandong China
                Author notes
                [*] [* ] Correspondence to: Fuzhong Xue, PhD, Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, PO Box 100, 44 Wenhuaxi Road, Jinan, Shandong, 250012, China. E‐mail: xuefzh@ 123456sdu.edu.cn and Hongkai Li, PhD, Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, PO Box 100, 44 Wenhuaxi Road, Jinan, Shandong, 250012, China. E‐mail: lihongkaiyouxiang@ 123456163.com

                [*]

                X. Liu and C. Li contributed equally.

                Author information
                https://orcid.org/0000-0002-7894-1172
                https://orcid.org/0000-0001-6304-0817
                https://orcid.org/0000-0001-9858-7089
                https://orcid.org/0000-0002-5636-4028
                https://orcid.org/0000-0003-0446-2088
                https://orcid.org/0000-0003-1848-937X
                https://orcid.org/0000-0003-0378-7956
                Article
                JAH36430
                10.1161/JAHA.120.020187
                8475657
                34315237
                6d7cb8c8-aba6-460d-8a49-07f4ae1764fe
                © 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 15 November 2020
                : 13 May 2021
                Page count
                Figures: 3, Tables: 1, Pages: 88, Words: 35929
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81773547
                Award ID: 82003557
                Funded by: National Key Research and Development Program of China
                Award ID: 2020YFC2003500
                Funded by: the Shandong Provincial Natural Science Foundation of China
                Award ID: ZR2019ZD02
                Funded by: Shandong Provincial Key Research and Development project
                Award ID: 2018CXGC1210
                Categories
                Original Research
                Original Research
                Epidemiology
                Custom metadata
                2.0
                August 3, 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.4 mode:remove_FC converted:03.08.2021

                Cardiovascular Medicine
                cardiometabolic risk factors,cardiovascular disease,insomnia,mediator,mendelian randomization,epidemiology,mental health

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