Assessing the Causal Role of Sleep Traits on Glycated Hemoglobin: A Mendelian Randomization Study

Objective: To examine the effects of sleep traits on glycated haemoglobin (HbA1c). Design: Observational multivariable regression (MVR), one-sample Mendelian randomization (1SMR), and two-sample summary data Mendelian randomization (2SMR). Setting: UK Biobank (UKB) prospective cohort study and genome-wide association studies from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). Participants: In MVR and 1SMR, participants were adults (mean (SD) age 57 (8) years; 54% female) from the UKB (n=336,999); in 2SMR, participants were adults (53 (11) years; 52% female) from MAGIC (n=46,368). All participants were adults of European ancestry. Exposures: Self-reported insomnia frequency (usually vs sometimes or rarely/never); sleep duration: 24-hour sleep duration (hours/day); short sleep ([≤]6 hours vs 7-8 hours) and long sleep ([≥]9 hours vs 7-8 hours); daytime sleepiness and daytime napping (each consisting of 3 categories: never/rarely, sometimes, usually); chronotype (5 categories from definite morning to definite evening preference). Main outcome measure: HbA1c in standard deviation (SD) units. Results: Across MV, 1SMR, 2SMR, and their sensitivity analyses we found a higher frequency of insomnia (usually vs sometimes or rarely/never) was associated with higher HbA1c (MVR: 0.053 SD units, 95% confidence interval (0.046 to 0.061), 1SMR: 0.52, (0.42 to 0.63), 2SMR: 0.22, (0.10 to 0.35)). Results remained significant but point estimates were somewhat attenuated after excluding people with diagnosed diabetes. For other sleep traits, there was less consistency with significant associations when using some, but not all methods. Conclusions: This study suggests that insomnia increases HbA1c levels. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycaemia and prevent diabetes.

[1]  M. Borges,et al.  Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization , 2019, European Journal of Epidemiology.

[2]  F. Dudbridge,et al.  Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data , 2020, medRxiv.

[3]  C. Pieh,et al.  Metabolomics in Sleep, Insomnia and Sleep Apnea , 2020, International journal of molecular sciences.

[4]  S. Larsson,et al.  An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study , 2020, Diabetologia.

[5]  Samuel E. Jones,et al.  Genetic determinants of daytime napping and effects on cardiometabolic health , 2020, Nature Communications.

[6]  Aqeel M. Alenazi,et al.  The effects of cognitive behavioral therapy for insomnia in people with type 2 diabetes mellitus, pilot RCT part II: diabetes health outcomes , 2020, BMC Endocrine Disorders.

[7]  X. Zhuang,et al.  Exploring the causal pathway from body mass index to coronary heart disease: a network Mendelian randomization study , 2020, Therapeutic advances in chronic disease.

[8]  Max A. Little,et al.  Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes , 2019, Nature Communications.

[9]  S. Neuhausen,et al.  Evaluating causal associations between chronotype and fatty acids and between fatty acids and type 2 diabetes: A Mendelian randomization study. , 2019, Nutrition, metabolism, and cardiovascular diseases : NMCD.

[10]  R. Noordam,et al.  The Association between Habitual Sleep Duration and Sleep Quality with Glycemic Traits: Assessment by Cross-Sectional and Mendelian Randomization Analyses , 2019, Journal of clinical medicine.

[11]  Max A. Little,et al.  Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates , 2019, Nature Communications.

[12]  L. Hui,et al.  Sleep duration and risk of diabetes: Observational and Mendelian randomization studies. , 2019, Preventive medicine.

[13]  Samuel E. Jones,et al.  Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms , 2019, Nature Communications.

[14]  G. Hemani,et al.  UK Biobank Genetic Data: MRC-IEU Quality Control, version 2 , 2019 .

[15]  A. Auton,et al.  Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways , 2019, Nature Genetics.

[16]  S. Kyle,et al.  Effect of Digital Cognitive Behavioral Therapy for Insomnia on Health, Psychological Well-being, and Sleep-Related Quality of Life: A Randomized Clinical Trial , 2019, JAMA psychiatry.

[17]  C. Lindgren,et al.  GWAS identifies 14 loci for device-measured physical activity and sleep duration , 2018, Nature Communications.

[18]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[19]  F. Windmeijer,et al.  An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings , 2018, bioRxiv.

[20]  S. Burgess,et al.  Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates , 2018, European Journal of Epidemiology.

[21]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.

[22]  Max A. Little,et al.  Biological and clinical insights from genetics of insomnia symptoms , 2019, Nature Genetics.

[23]  X. Lao,et al.  Short Sleep Duration Increases Metabolic Impact in Healthy Adults: A Population-Based Cohort Study , 2017, Sleep.

[24]  C. Sudlow,et al.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population , 2017, American journal of epidemiology.

[25]  David M. Evans,et al.  Collider scope: when selection bias can substantially influence observed associations , 2016, bioRxiv.

[26]  S. Redline,et al.  Association Between Sleep Timing, Obesity, Diabetes: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Cohort Study , 2017, Sleep.

[27]  N. Watanabe,et al.  Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. , 2017, Sleep medicine.

[28]  C. Espie,et al.  Insomnia symptoms as a cause of type 2 diabetes Incidence: a 20 year cohort study , 2017, BMC Psychiatry.

[29]  C. Wong,et al.  The association between daytime napping and risk of diabetes: a systematic review and meta-analysis of observational studies. , 2017, Sleep medicine.

[30]  K. Ng,et al.  The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: A systematic review and meta-analysis. , 2017, Sleep medicine reviews.

[31]  Xiaofeng Zhu,et al.  Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits , 2016, Nature Genetics.

[32]  D. Lawlor,et al.  Causal inference—so much more than statistics , 2016, International journal of epidemiology.

[33]  Debbie A Lawlor,et al.  Triangulation in aetiological epidemiology , 2016, International journal of epidemiology.

[34]  P. Gehrman,et al.  Genetic Pathways to Insomnia , 2016, Brain sciences.

[35]  Cathie Sudlow,et al.  Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank , 2016, PloS one.

[36]  Y. Bin Is Sleep Quality More Important Than Sleep Duration for Public Health? , 2016, Sleep.

[37]  G. Davey Smith,et al.  Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator , 2016, Genetic epidemiology.

[38]  Stephen Burgess,et al.  Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods , 2015, Statistics in medicine.

[39]  J. Kaprio,et al.  Napping and the risk of type 2 diabetes: a population-based prospective study. , 2016, Sleep medicine.

[40]  J. Bowden,et al.  Integrating summarized data from multiple genetic variants in Mendelian randomization: bias and coverage properties of inverse-variance weighted methods , 2015, 1512.04486.

[41]  W. Willett,et al.  Mismatch of Sleep and Work Timing and Risk of Type 2 Diabetes , 2015, Diabetes Care.

[42]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[43]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[44]  F. Hu,et al.  Sleep Duration and Risk of Type 2 Diabetes: A Meta-analysis of Prospective Studies , 2015, Diabetes Care.

[45]  M. Berk,et al.  Excessive daytime sleepiness and metabolic syndrome: a cross-sectional study. , 2015, Metabolism: clinical and experimental.

[46]  T. VanderWeele,et al.  Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates☆ , 2014, Economics and human biology.

[47]  Jen-Hao Chen,et al.  Sleep duration and all-cause mortality: a critical review of measurement and associations. , 2013, Annals of epidemiology.

[48]  Tyler J VanderWeele,et al.  The effect of non-differential measurement error on bias, precision and power in Mendelian randomization studies. , 2012, International journal of epidemiology.

[49]  J. Lindesay,et al.  Increased prevalence of insomnia and changes in hypnotics use in England over 15 years: analysis of the 1993, 2000, and 2007 National Psychiatric Morbidity Surveys. , 2012, Sleep.

[50]  S. Thompson,et al.  Avoiding bias from weak instruments in Mendelian randomization studies. , 2011, International journal of epidemiology.

[51]  Christian Gieger,et al.  Edinburgh Research Explorer Common variants at 10 genomic loci influence hemoglobin A(C) levels via glycemic and nonglycemic pathways , 2010 .

[52]  Christian Gieger,et al.  New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk , 2010, Nature Genetics.

[53]  F. Cappuccio,et al.  Quantity and Quality of Sleep and Incidence of Type 2 Diabetes A systematic review and meta-analysis , 2010 .

[54]  P. Penev,et al.  Exposure to recurrent sleep restriction in the setting of high caloric intake and physical inactivity results in increased insulin resistance and reduced glucose tolerance. , 2009, The Journal of clinical endocrinology and metabolism.

[55]  E. van Cauter,et al.  Metabolic consequences of sleep and sleep loss. , 2008, Sleep medicine.

[56]  George Davey Smith,et al.  Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology , 2008, Statistics in medicine.

[57]  Christopher F. Baum,et al.  Instrumental Variables and GMM: Estimation and Testing , 2003 .

[58]  K. Spiegel,et al.  Impact of sleep debt on metabolic and endocrine function , 1999, The Lancet.

[59]  D. Black HEALTH AND DEPRIVATION: Inequality and the north , 1988 .

[60]  P. Townsend,et al.  Health and Deprivation: Inequality and the North , 1987 .