Investigating Causal Relations Between Sleep-Related Traits and Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study

U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study

Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.

Long-term trajectories of sleep duration are associated with incident diabetes in middle-to-older-aged Black and White Americans

AIMS/HYPOTHESIS

Both short and long sleep durations have been linked to higher diabetes risk. However, sleep duration may vary over time, and there has been limited research focusing on individual sleep trajectories and diabetes risk. There are substantial racial disparities in both sleep health and diabetes risk in the USA. Thus, it is important to understand the role of suboptimal sleep patterns in diabetes risk in different racial groups.

METHODS

We assessed long-term trajectories of sleep duration and incident diabetes in 22,285 Black adults (mean age ± SD, 51.1 ± 8.2 years; 64.8% women) and 13,737 White adults (mean age ± SD, 54.4 ± 9.0 years; 63.8% women) enrolled in the Southern Community Cohort Study. Nine sleep trajectories were derived based on self-reported sleep duration at baseline and after a mean of 5 years of follow-up: normal-normal (reference), short-normal, normal-short, short-short, long-normal, normal-long, long-long, long-short and short-long. Diabetes was reported using a validated questionnaire. Multivariable-adjusted logistic regression was used to determine relationships between sleep trajectories and incident diabetes.

RESULTS

When compared with the normal-normal trajectory, suboptimal sleep trajectories were associated with higher likelihoods of developing diabetes (OR; 95% CI: short-normal 1.19; 1.09, 1.31; normal-short 1.14; 1.02, 1.27; short-short 1.17; 1.07, 1.28; long-normal 1.13; 0.98, 1.30; normal-long 1.16; 1.00, 1.34; long-long 1.23; 1.02, 1.48; long-short 1.45; 1.19, 1.77; short-long 1.51; 1.28, 1.77). Stratified analyses by race and socioeconomic status (i.e. education and household income) showed that most suboptimal sleep trajectories were consistently associated with incident diabetes in all sociodemographic subgroups. We also noted potential interaction with race and education for several sleep trajectories (i.e. short-long and normal-short with race; long-long and short-short with education).

CONCLUSIONS/INTERPRETATION

Adults with suboptimal sleep duration trajectories are more likely to develop incident diabetes. Future research is needed to study how sociodemographic factors modulate this relationship.

Sleep patterns and risks of incident cardiovascular disease and mortality among people with type 2 diabetes: a prospective study of the UK Biobank

BACKGROUND

To explore the relationship between sleep patterns and cardiovascular disease (CVD) incidence and mortality risk in a population with type 2 diabetes through a UK Biobank sample.

METHODS

A total of 6860 patients with type 2 diabetes were included in this study. Five sleep factors (including Chronotype, sleep duration, insomnia, daytime sleepiness, and snoring) were collected as a questionnaire. The calculation generates a sleep score of 0-5, and then three sleep patterns were defined based on the sleep scores: poor sleep pattern (0-2), Intermediate sleep pattern (3-4), and healthy sleep pattern (5). HRs and 95% confidence intervals were calculated by multivariate COX proportional risk model adjustment. Restricted cubic splines were used to validate linear associations between sleep scores CVD events.

RESULTS

Our results found a reduced risk of CVD events in individuals with healthy sleep patterns compared to participants with poor sleep patterns. CVD Mortality (HR, 0.690; 95% CI 0.519-0.916), ASCVD (Atherosclerosis CVD) (HR, 0.784; 95% CI 0.671-0.915), CAD (Coronary Artery Disease) (HR, 0.737; 95% CI 0.618-0.879), PAD (Peripheral Arterial Disease) (HR, 0.612; 95% CI 0.418-0.896), Heart Failure (HR, 0.653; 95% CI 0.488-0.875). Restricted cubic spline responded to a negative linear correlation between sleep scores and CVD Mortality, ASCVD, CAD, PAD, and Heart Failure.

CONCLUSIONS

Healthy sleep patterns are significantly associated with a reduced risk of CVD Mortality, ASCVD, CAD, PAD, and Heart Failure in the diabetes population.

Potential genetic and epigenetic mechanisms in insomnia: A systematic review

Insomnia is a stress-related sleep disorder conceptualised within a diathesis-stress framework, which it is thought to result from predisposing factors interacting with precipitating stressful events that trigger the development of insomnia. Among predisposing factors genetics and epigenetics may play a role. A systematic review of the current evidence for the genetic and epigenetic basis of insomnia was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) system. A total of 24 studies were collected for twins and family heritability, 55 for genome-wide association studies, 26 about candidate genes for insomnia, and eight for epigenetics. Data showed that insomnia is a complex polygenic stress-related disorder, and it is likely to be caused by a synergy of genetic and environmental factors, with stress-related sleep reactivity being the important trait. Even if few studies have been conducted to date on insomnia, epigenetics may be the framework to understand long-lasting consequences of the interaction between genetic and environmental factors and effects of stress on the brain in insomnia. Interestingly, polygenic risk for insomnia has been causally linked to different mental and medical disorders. Probably, by treating insomnia it would be possible to intervene on the effect of stress on the brain and prevent some medical and mental conditions.

Unraveling the associations and causalities between glucose metabolism and multiple sleep traits

Purpose

The aim of our study is to estimate the associations and causalities of glucose metabolism traits of fasting blood glucose (FBG), fasting insulin (FINS), glycosylated hemoglobin (HbA1c), and 2-h glucose post-challenge (2hGlu) with sleep traits consisting of excessive daytime sleepiness (EDS), insomnia, and sleep duration.

Methods

We employed standard quantitative analysis procedures to assess the associations between sleep traits and glucose metabolism. Moreover, we acquired published genome-wide association studies (GWAS) summary statistics for these traits and conducted Mendelian randomization (MR) analyses to estimate their causal directions and effects. Inverse variance weighting (IVW) was employed as the primary approach, followed by sensitivity analyses.

Results

A total of 116 studies with over 840,000 participants were included in the quantitative analysis. Our results revealed that participants with abnormal glucose metabolism had higher risks for EDS (OR [95% CI] = 1.37 [1.10,1.69]), insomnia (OR [95% CI] = 1.65 [1.24,2.20]), and both short and long sleep duration (OR [95% CI] = 1.35 [1.12,1.63]; OR [95% CI] = 1.38 [1.13,1.67] respectively). In addition, individuals with these sleep traits exhibited alterations in several glycemic traits compared with non-affected controls. In MR analysis, the primary analysis demonstrated causal effects of 2hGlu on risks of EDS (OR [95% CI] = 1.022 [1.002,1.042]) and insomnia (OR [95% CI] = 1.020[1.001,1.039]). Furthermore, FINS was associated with short sleep duration (OR [95% CI] = 1.043 [1.018,1.068]), which reversely presented a causal influence on HbA1c (β [95% CI] = 0.131 [0.022,0.239]). These results were confirmed by sensitivity analysis.

Conclusion

Our results suggested mutual risk and causal associations between the sleep traits and glycemic traits, shedding new light on clinical strategies for preventing sleep disorders and regulating glucose metabolism. Future studies targeting these associations may hold a promising prospect for public health.

The causal associations of altered inflammatory proteins with sleep duration, insomnia and daytime sleepiness

STUDY OBJECTIVES

Growing evidence linked inflammation with sleep. This study aimed to evaluate the associations and causal effects of sleep traits including insomnia, excessive daytime sleepiness (EDS), and sleep duration (short: <7 h; normal: 7-9 h; long: ≥9 h), with levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and interleukins.

METHODS

Standard procedures of quantitative analysis were applied to estimate the expression differences for each protein in compared groups. Then, a two-sample Mendelian randomization (MR) analysis was performed to explore their causal relationships with published genome-wide association study summary statistics. The inverse-variance weighted was used as the primary method, followed by several complementary approaches as sensitivity analyses.

RESULTS

A total of 44 publications with 51 879 participants were included in the quantitative analysis. Our results showed that the levels of CRP, interleukin-1β (IL-1β), IL-6, and TNF-α were higher from 0.36 to 0.58 (after standardization) in insomnia compared with controls, while there was no significant difference between participants with EDS and controls. Besides, there was a U/J-shaped expression of CRP and IL-6 with sleep durations. In MR analysis, the primary results demonstrated the causal effects of CRP on sleep duration (estimate: 0.017; 95% confidence intervals [CI], [0.003, 0.031]) and short sleep duration (estimate: -0.006; 95% CI, [-0.011, -0.001]). Also, IL-6 was found to be associated with long sleep duration (estimate: 0.006; 95% CI, [0.000, 0.013]). These results were consistent in sensitivity analyses.

CONCLUSIONS

There are high inflammatory profiles in insomnia and extremes of sleep duration. Meanwhile, elevated CRP and IL-6 have causal effects on longer sleep duration. Further studies can focus on related upstream and downstream mechanisms.

Sleep Patterns, Plasma Metabolome, and Risk of Incident Type 2 Diabetes Mellitus

CONTEXT

A healthy sleep pattern has been related to a lower risk of type 2 diabetes mellitus (T2DM).

OBJECTIVE

We aimed to identify the metabolomic signature for the healthy sleep pattern and assess its potential causality with T2DM.

METHODS

This study included 78 659 participants with complete phenotypic data (sleep information and metabolomic measurements) from the UK Biobank study. Elastic net regularized regression was applied to calculate a metabolomic signature reflecting overall sleep patterns. We also performed genome-wide association analysis of the metabolomic signature and one-sample mendelian randomization (MR) with T2DM risk.

RESULTS

During a median of 8.8 years of follow-up, we documented 1489 incident T2DM cases. Compared with individuals who had an unhealthy sleep pattern, those with a healthy sleep pattern had a 49% lower risk of T2DM (multivariable-adjusted hazard ratio [HR], 0.51; 95% CI, 0.40-0.63). We further constructed a metabolomic signature using elastic net regularized regressions that comprised 153 metabolites, and robustly correlated with sleep pattern (r = 0.19; P = 3×10-325). In multivariable Cox regressions, the metabolomic signature showed a statistically significant inverse association with T2DM risk (HR per SD increment in the signature, 0.56; 95% CI, 0.52-0.60). Additionally, MR analyses indicated a significant causal relation between the genetically predicted metabolomic signature and incident T2DM (P for trend < .001).

CONCLUSION

In this large prospective study, we identified a metabolomic signature for the healthy sleep pattern, and such a signature showed a potential causality with T2DM risk independent of traditional risk factors.

Interplay between chronic widespread pain and lifestyle factors on the risk of type 2 diabetes: longitudinal data from the Norwegian HUNT Study

INTRODUCTION

Chronic widespread pain (CWP) and diabetes commonly co-occur; however, it is unclear whether CWP infers an additional risk for diabetes among those with known risk factors for type 2 diabetes. We aimed to examine if CWP magnifies the effect of adverse lifestyle factors on the risk of diabetes.

RESEARCH DESIGN AND METHODS

The study comprised data on 25 528 adults in the Norwegian HUNT Study without diabetes at baseline (2006-2008). We calculated adjusted risk ratios (RRs) with 95% CIs for diabetes at follow-up (2017-2019), associated with CWP and body mass index (BMI), physical activity, and insomnia symptoms. The relative excess risk due to interaction (RERI) was calculated to investigate the synergistic effect between CWP and adverse lifestyle factors.

RESULTS

Compared with the reference group without chronic pain and no adverse lifestyle factors, those with BMI ≥30 kg/m2 with and without CWP had RRs for diabetes of 10.85 (95% CI 7.83 to 15.05) and 8.87 (95% CI 6.49 to 12.12), respectively; those with physical activity <2 hours/week with and without CWP had RRs for diabetes of 2.26 (95% CI 1.78 to 2.88) and 1.54 (95% CI 1.24 to 1.93), respectively; and those with insomnia symptoms with and without CWP had RRs for diabetes of 1.31 (95% CI 1.07 to 1.60) and 1.27 (95% CI 1.04 to 1.56), respectively. There was little evidence of synergistic effect between CWP and BMI ≥30 kg/m2 (RERI=1.66, 95% CI -0.44 to 3.76), low physical activity (RERI=0.37, 95% CI -0.29 to 1.03) or insomnia symptoms (RERI=-0.09, 95% CI -0.51 to 0.34) on the risk of diabetes.

CONCLUSIONS

These findings show no clear interaction between CWP and adverse lifestyle factors on the risk of diabetes.

Method comparison and estimation of causal effects of insomnia on health outcomes in a survey sampled population

Applying causal inference methods, such as weighting and matching methods, to a survey sampled population requires properly incorporating the survey weights and design to obtain effect estimates that are representative of the target population and correct standard errors (SEs). With a simulation study, we compared various approaches for incorporating the survey weights and design into weighting and matching-based causal inference methods. When the models were correctly specified, most approaches performed well. However, when a variable was treated as an unmeasured confounder and the survey weights were constructed to depend on this variable, only the matching methods that used the survey weights in causal estimation and as a covariate in matching continued to perform well. If unmeasured confounders are potentially associated with the survey sample design, we recommend that investigators include the survey weights as a covariate in matching, in addition to incorporating them in causal effect estimation. Finally, we applied the various approaches to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and found that insomnia has a causal association with both mild cognitive impairment (MCI) and incident hypertension 6-7 years later in the US Hispanic/Latino population.

Diet, Lifestyle Behaviours and Other Risk Factors Associated With Type 2 Diabetes Beyond Body Mass Index: A Mendelian Randomization Study

OBJECTIVES

Our aim in this study was to identify promising targets for the prevention of type 2 diabetes in addition to weight loss. We conducted a Mendelian randomization (MR) study to investigate the body mass index (BMI)-independent associations of 16 risk factors, including diet, lifestyle behaviours and others, with type 2 diabetes.

METHODS

We selected genetic variants as instrumental variables for diet, sleep traits, smoking, physical activity, education and blood pressure (BP) from European-descent genome-wide association studies (GWASs). Summary statistics for type 2 diabetes were derived from a recent GWAS with 74,124 European cases and 824,006 European controls. The inverse-variance weighted MR method was used to assess the associations of the risk factors with type 2 diabetes, followed by validation of robustness using different MR methods in sensitivity analyses.

RESULTS

Genetically predicted insomnia (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.06 to 1.15), smoking initiation (OR, 1.14; 95% CI, 1.06 to 1.21), educational level (OR, 0.69; 95% CI, 0.65 to 0.74), hypertension (OR, 6.50; 95% CI, 3.13 to 13.50), systolic BP (OR, 1.02; 95% CI, 1.02 to 1.03) and diastolic BP (OR, 1.03; 95% CI, 1.02 to 1.03) had BMI-independent effects on type 2 diabetes risk. In addition, alcohol dependence (OR, 1.10; 95% CI, 1.05 to 1.16; BMI-adjusted OR, 1.04; 95% CI, 0.98 to 1.09) and vegetarian diet (OR, 0.50; 95% CI, 0.33 to 0.74; BMI-adjusted OR, 0.78; 95% CI, 0.57 to 1.06) appeared to be correlated with type 2 diabetes via a BMI-mediated pathway. Sensitivity analyses further confirmed the relationship between these factors and type 2 diabetes.

CONCLUSIONS

In this systematic MR study, insomnia, smoking, education and BP had BMI-independent causal effects on the risk of type 2 diabetes, whereas alcohol dependence and vegetarian diet were associated with type 2 diabetes through BMI.

Type 2 diabetes

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

Self-reported daytime napping, daytime sleepiness, and other sleep phenotypes in the development of cardiometabolic diseases: a Mendelian randomization study

AIMS

Sleep disorders are associated with an increased risk of cardiometabolic diseases in observational studies, but the causality remains unclear. In this study, we leveraged two-sample Mendelian randomization (MR) analyses to assess the causal associations of self-reported daytime napping, daytime sleepiness, and other sleep phenotypes with cardiometabolic diseases including ischaemic stroke (IS), coronary artery disease (CAD), heart failure (HF), and Type 2 diabetes mellitus (T2DM).

METHODS AND RESULTS

We selected genetic variants as instrumental variables for self-reported daytime napping, daytime sleepiness, morning person, insomnia, short sleep duration, and long sleep duration from European-descent genome-wide association studies (GWASs). Summary statistics for cardiometabolic diseases originated from four different GWASs with a total of 2 500 086 participants. We used the inverse-variance weighted method to explore the role of self-reported sleep phenotypes on the aetiology of cardiometabolic diseases in the main analyses, followed by several sensitivity analyses for robustness validation. Genetically predicted self-reported daytime napping [T2DM: OR, 1.56 (95% confidence interval, 1.21-2.02)], insomnia [IS: OR, 1.07 (1.04-1.11)]; CAD: OR, 1.13 (1.08-1.17); HF: OR, 1.10 (1.07-1.14); T2DM: OR, 1.16 (1.11-1.22); and short sleep duration [CAD: OR, 1.37 (1.21-1.55)] were causally associated with an elevated risk of cardiometabolic diseases. Moreover, genetically determined self-reported daytime sleepiness [CAD: OR, 2.05 (1.18-3.57); HF: OR, 1.82 (1.15-2.87)] and morning person [HF: 1.06 OR, (1.01-1.11)] had potential detrimental effect on cardiometabolic risks.

CONCLUSION

Self-reported daytime napping, insomnia, and short sleep duration had causal roles in the development of cardiometabolic diseases, while self-reported daytime sleepiness and morning person was the potential risk factor for cardiometabolic diseases.

Variant-to-gene-mapping analyses reveal a role for pancreatic islet cells in conferring genetic susceptibility to sleep-related traits

We investigated the potential role of sleep-trait associated genetic loci in conferring a degree of their effect via pancreatic α- and β-cells, given that both sleep disturbances and metabolic disorders, including type 2 diabetes and obesity, involve polygenic contributions and complex interactions. We determined genetic commonalities between sleep and metabolic disorders, conducting linkage disequilibrium genetic correlation analyses with publicly available GWAS summary statistics. Then we investigated possible enrichment of sleep-trait associated SNPs in promoter-interacting open chromatin regions within α- and β-cells, intersecting public GWAS reports with our own ATAC-seq and high-resolution promoter-focused Capture C data generated from both sorted human α-cells and an established human beta-cell line (EndoC-βH1). Finally, we identified putative effector genes physically interacting with sleep-trait associated variants in α- and EndoC-βH1cells running variant-to-gene mapping and establish pathways in which these genes are significantly involved. We observed that insomnia, short and long sleep-but not morningness-were significantly correlated with type 2 diabetes, obesity and other metabolic traits. Both the EndoC-βH1 and α-cells were enriched for insomnia loci (p = .01; p = .0076), short sleep loci (p = .017; p = .022) and morningness loci (p = 2.2 × 10-7; p = .0016), while the α-cells were also enriched for long sleep loci (p = .034). Utilizing our promoter contact data, we identified 63 putative effector genes in EndoC-βH1 and 76 putative effector genes in α-cells, with these genes showing significant enrichment for organonitrogen and organophosphate biosynthesis, phosphatidylinositol and phosphorylation, intracellular transport and signaling, stress responses and cell differentiation. Our data suggest that a subset of sleep-related loci confer their effects via cells in pancreatic islets.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.

Ellagic acid regulates hyperglycemic state through modulation of pancreatic IL-6 and TNF- α immunoexpression

Background

Objective

Methods

Results

Conclusion

Integrative Identification of Genetic Loci Jointly Influencing Diabetes-Related Traits and Sleep Traits of Insomnia, Sleep Duration, and Chronotypes

Accumulating evidence suggests a relationship between type 2 diabetes mellitus and sleep problems. A comprehensive study is needed to decipher whether shared polygenic risk variants exist between diabetic traits and sleep traits. Methods: We integrated summary statistics from different genome-wide association studies and investigated overlap in single-nucleotide polymorphisms (SNPs) associated with diabetes-related traits (type 2 diabetes, fasting glucose, fasting insulin, and glycated hemoglobin) and sleep traits (insomnia symptoms, sleep duration, and chronotype) using a conditional/conjunctional false discovery rate approach. Pleiotropic genes were further evaluated for differential expression analysis, and we assessed their expression pattern effects on type 2 diabetes by Mendelian randomization (MR) analysis. Results: We observed extensive polygenic pleiotropy between diabetic traits and sleep traits. Fifty-eight independent genetic loci jointly influenced the risk of type 2 diabetes and the sleep traits of insomnia, sleep duration, and chronotype. The strongest shared locus between type 2 diabetes and sleep straits was FTO (lead SNP rs8047587). Type 2 diabetes (z score, 16.19; P = 6.29 × 10⁻⁵⁹) and two sleep traits, sleep duration (z score, −6.66; P = 2.66 × 10⁻¹¹) and chronotype (z score, 7.42; P = 1.19 × 10⁻¹³), were shared. Two of the pleiotropic genes, ENSA and PMPCA, were validated to be differentially expressed in type 2 diabetes, and PMPCA showed a slight protective effect on type 2 diabetes in MR analysis. Conclusions: Our study provided evidence for the polygenic overlap between diabetic traits and sleep traits, of which the expression of PMPCA may play a crucial role and provide support of the hazardous effect of being an “evening” person on diabetes risk.

Network Mendelian randomization study: exploring the causal pathway from insomnia to type 2 diabetes

INTRODUCTION

Insomnia is a novel pathogen for type 2 diabetes mellitus (T2DM). However, mechanisms linking insomnia and T2DM are poorly understood. In this study, we apply a network Mendelian randomization (MR) framework to determine the causal association between insomnia and T2DM and identify the potential mediators, including overweight (body mass index (BMI), waist-to-hip ratio, and body fat percentage) and glycometabolism (HbA1c, fasting blood glucose, and fasting blood insulin).

RESEARCH DESIGN AND METHODS

We use the MR framework to detect effect estimates of the insomnia-T2DM, insomnia-mediator, and mediator-T2DM associations. A mediator between insomnia and T2DM is established if MR studies in all 3 steps prove causal associations.

RESULTS

In the Inverse variance weighted method, the results show that insomnia will increase the T2DM risk (OR 1.142; 95% CI 1.072 to 1.216; p=0.000), without heterogeneity nor horizontal pleiotropy, strongly suggesting that genetically predicted insomnia has a causal association with T2DM. Besides, our MR analysis provides strong evidence that insomnia is causally associated with BMI and body fat percentage. There is also suggestive evidence of an association between insomnia and the waist-to-hip ratio. At the same time, our results indicate that insomnia is not causally associated with glycometabolism. Higher BMI, waist-to-hip ratio, and body fat percentage levels are strongly associated with increased risk of T2DM.

CONCLUSIONS

Genetically predicted insomnia has a causal association with T2DM. Being overweight (especially BMI and body fat percentage) mediates the causal pathway from insomnia to T2DM.

Characterisation of insomnia as an environmental risk factor for asthma via Mendelian randomization and gene environment interaction

Asthma is a complex disease that is reportedly associated with insomnia. However, the causal directionality of this association is still unclear. We used asthma and insomnia-associated single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) summary statistics to test the causal directionality between insomnia and asthma via Mendelian randomization (MR) analysis. We also performed a cross-trait meta-analysis using UK Biobank GWAS summary statistics and a gene–environment interaction study using data from UK Biobank. The interaction of genetic risk score for asthma (GRS_asthma) with insomnia on asthma was tested by logistic regression. Insomnia was a risk factor for the incidence of asthma, as revealed by three different methods of MR analysis. However, asthma did not act as a risk factor for insomnia. The cross-trait meta-analysis identified 28 genetic loci shared between asthma and insomnia. In the gene–environment interaction study, GRS_asthma interacted with insomnia to significantly affect the risk of asthma. The results of this study highlight the importance of insomnia as a risk factor of asthma, and warrant further analysis of the mechanism through which insomnia affects the risk of asthma.

A Meta-Analysis of a Cohort Study on the Association between Sleep Duration and Type 2 Diabetes Mellitus

OBJECTIVE

To study the association between sleep duration and the incidence of type 2 diabetes mellitus (T2DM) and to provide a theoretical basis for the prevention of T2DM through a meta-analysis.

METHODS

PubMed, Web of Science, Scopus, Embase, Cochrane Library, ProQuest, CNKI, Wanfang, VIP, and SINOMED were searched from their inception until May 2020. All cohort studies on the relationship between sleep duration and T2DM in adults were included. According to the inclusion and exclusion criteria, two authors independently assessed the literature and extracted the data. Metaregression and publication bias were evaluated, and sensitivity and meta-analyses were conducted with RevMan 5.3.

RESULTS

A total of 17 studies were collected, involving 737002 adults. The incidence of T2DM was 4.73% in short sleep duration (SSD) (t ≤ 6 h), 4.39% in normal sleep duration (NSD) (6 h < t < 9 h), and 4.99% in long sleep duration (LSD) (t ≥ 9 h). The meta-analysis demonstrated that SSD increased the risk of T2DM compared with NSD (RR = 1.22, 95% CI: 1.15-1.29, P < 0.001), LSD increased the risk of T2DM compared with NSD (RR = 1.26, 95% CI: 1.15-1.39, P < 0.001), and the risk of T2DM has no significant difference between SSD and LSD (RR = 0.97, 95% CI: 0.89-1.05, P = 0.41). The sensitivity of each study was robust and the publication bias was weak.

CONCLUSION

SSD or LSD can increase the risk of T2DM.