Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring

Novel susceptibility genes for sleep apnea revealed by a cross-tissue transcriptome-wide association study

Sleep apnea (SA) is a sleep disorder characterized by frequent interruptions in breathing during sleep and is widely recognized as a significant global public health concern. Although genome-wide association studies (GWAS) have identified several loci associated with SA susceptibility, the underlying genes and biological mechanisms remain largely unknown. A cross-tissue transcriptome-wide association study (TWAS) was performed to integrate SA GWAS summary statistics from 410,385 individuals (43,901 cases and 366,484 controls) and gene expression data from 49 distinct tissues and obtained from 838 post-mortem donors. Functional Summary-based Imputation was employed to validate these findings in whole blood tissue. Additionally, candidate susceptibility genes were further verified using Gene Analysis combined with Multi-marker Analysis of Genomic Annotation. Subsequent Mendelian randomization and colocalization analyses were conducted. In the cross-tissue TWAS analysis, 60 susceptibility genes were identified. Two novel susceptibility genes, GPD2 and L3MBTL2, were validated through both single tissue TWAS and MAGMA analysis. Mitochondrial glycerophosphate dehydrogenase (GPD2) may reduce the SA risk by regulating energy metabolism, while Lethal (3) malignant brain tumor-like protein 2 (L3MBTL2) may increase the risk of SA by disturbing DNA damage repair pathway and by regulating the process of the cell cycle. In summary, two novel biological macromolecules were identified in our study whose expression was predicted to be associated with SA risk, providing new insight into the genetic basis of this condition.

Bidirectional causal associations between frailty measures and sleep disturbances: a two-sample Mendelian randomization study

Background

Observational studies have demonstrated a strong association between sleep disturbances and frailty. However, the causality remains inconclusive. We aimed to investigate the bidirectional causal relationships between frailty measures and sleep disturbances employing a two-sample Mendelian randomization (MR) analysis.

Methods

Two-sample MR analyses were performed based on large-scale genome-wide association studies (GWAS) of the European population for frailty index (FI) (N = 175,226), Fried Frailty Score (FFS) (N = 386,565), insomnia (N = 283,595), sleep duration (N = 445,966) and sleep apnea (N = 523,366). We conducted the causal estimates using the inverse variance-weighted method (IVW), with sensitivity analyses using MR-Egger, weighted median, weighted mode, and MR pleiotropy residual sum and outlier (MR-PRESSO) analysis. Cochran's Q test was performed to assess heterogeneity.

Results

We found that genetically predicted FI was associated with shorter sleep duration and sleep apnea. The genetically predicted FFS was associated with insomnia, shorter sleep duration, and sleep apnea. In the reverse direction analysis, genetic liability to insomnia, short sleep duration, and long sleep duration were associated with an increase in FI. Genetic liability to short sleep duration and long sleep duration were associated with an increase in FFS.

Conclusion

Our study provided genetic evidence supporting the bidirectional causality between frailty measures and sleep disturbances. The findings contribute to the prevention and management of frailty and sleep disturbances.

Comorbidity of obstructive sleep apnea and narcolepsy: A challenging diagnosis and complex management

Introduction

Narcolepsy and obstructive sleep apnea syndrome (OSA) are relevant causes of excessive daytime sleepiness (EDS); although different for etiopathogenesis and symptoms, differential diagnosis is sometimes difficult, and guidelines are lacking concerning their management when coexisting in a same patient.

Methods

A narrative review of the literature was realized including PubMed, Scopus and Embase, aimed to regroup studies and case reports evaluating epidemiology, clinical and instrumental features and treatment of patients presenting comorbid NT1 and OSA. Moreover, a snowball search on the pathophysiology underpinnings of the association of the two disorder was realized.

Results

For adults, the prevalence of OSA in NT1 ranged from 24.8 % to 51.4 %. No studies were found concerning the treatment of EDS in double-diagnosis patients, but only case reports; these latter and the experience on patients with either NT or OSA suggest that modafinil, methylphenidate, pitolisant and solriamfetol are effective.

Discussion

Adults with NT1 showed a higher prevalence of OSA compared to the general population, but the reach of the results reviewed here is limited by the retrospective design of most of the studies and by the inhomogeneous utilization of diagnostic criteria. The association with OSA is likely to be explained by the involvement of orexin in hypercapnic-hypoxic responses: a deficit of orexin may promote obstructive events during sleep. Open questions warrant further investigation, especially orexin's involvement in other sleep disorders associated with EDS, and the more appropriate treatment for the OSA-narcolepsy comorbidity.

Oxytocin Receptor Polymorphism Is Associated With Sleep Apnea Symptoms

Context

Oxytocin supplementation improves obstructive sleep apnea (OSA), and animal studies suggest involvement of oxytocin in respiratory control. However, the relationship between endogenous oxytocin signaling and human sleep status remains undetermined.

Objective

In this study, we approached the contribution of the intrinsic oxytocin-oxytocin receptor (OXTR) system to OSA by genetic association analysis.

Methods

We analyzed the relationship between OXTR gene polymorphisms and sleep parameters using questionnaire data and sleep measurements in 305 Japanese participants. OSA symptoms were assessed in 225 of these individuals.

Results

The OXTR rs2254298 A allele was more frequent in those with OSA symptoms than in those without (P = .0087). Although total scores on the Pittsburgh Sleep Quality Index questionnaire did not differ between the genotypes, breathlessness and snoring symptoms associated with OSA were significantly more frequent in individuals with rs2254298 A genotype (P = .00045 and P = .0089 for recessive models, respectively) than the G genotype. A multivariable analysis confirmed these genotype-phenotype associations even after adjusting for age, sex, and body mass index in a sensitivity analysis. Furthermore, objective sleep efficiency measured by actigraph was not significantly different between genotypes; however, subjective sleep efficiency was significantly lower in the rs2254298 A genotype (P = .013) compared with the G genotype. The frequency of the A allele is higher in East Asians, which may contribute to their lean OSA phenotype.

Conclusion

The OXTR gene may contribute to OSA symptoms via the respiratory control system, although it could be in linkage disequilibrium with a true causal gene.

The Importance of Genetic Background and Neurotransmission in the Pathogenesis of the Co-Occurrence of Sleep Bruxism and Sleep-Disordered Breathing-Review of a New Perspective

Sleep bruxism (SB) and sleep-disordered breathing (SDB) are two prevalent conditions that significantly impact overall health. Studies suggest that up to 49.7% of individuals with SDB also exhibit symptoms of SB. This review aims to provide a comprehensive analysis of the role of genetic background and neurotransmission in the pathogenesis of the co-occurrence of SB and SDB. It seeks to synthesize current knowledge, highlight gaps in the existing literature, and propose a new perspective that integrates genetic and neurobiological factors. This review shows that both SB and SDB may be influenced by a combination of genetic, neurochemical, and environmental factors that contribute to their shared pathophysiology. The key neurotransmitters-dopamine, serotonin, and GABA-may play a significant role in their co-occurrence by regulating motor activity, sleep architecture, and respiratory control. Understanding genetic and neurochemical mechanisms may allow for more precise diagnostic tools and more personalized treatment approaches regarding SB and SDB. Clinically, there is a need for interdisciplinary collaboration between sleep specialists, dentists, neurologists, and geneticists. There is also a need to conduct large-scale genetic studies, coupled with neuroimaging and neurophysiological research, uncovering additional insights into the shared mechanisms of SB and SDB.

Impact of sleep problems on the cardiometabolic risks: an integrated epidemiological and metabolomics study

BACKGROUND

We investigated the association between sleep problems and cardiometabolic risks and the potential linking effect of metabolites and metabolic pathways based on multi-layered research, including observational, mendelian randomization (MR), and metabolomics analysis.

METHODS

A cross-sectional analysis of the 2015-2018 National Health and Nutrition Examination Survey (NHANES) dataset was conducted to identify the association between sleep problems and cardiometabolic risks. A subsequent MR study based on genetic data was performed to explore the causal correlation of significant associations in the NHANES study. The underlying alteration of metabolism was explored by constructing zebrafish models and wide-targeted metabolomics analysis.

RESULTS

The cross-sectional analysis of the NHANES database revealed a significant association of snoring with obesity [OR = 2.65, 95% confidence intervals (CI): 1.87, 3.74]; sleep apnea with hypertension (OR = 1.68, 95% CI: 1.14, 2.48) and obesity (OR = 1.44, 95% CI: 1.05, 1.96); trouble sleeping with hypertension (OR = 1.84, 95% CI: 1.18, 2.86), obesity (OR = 1.56, 95% CI: 1.07, 2.26), and type 2 diabetes (T2DM) (OR = 1.52, 95% CI: 1.02, 2.25). MR analysis verified the causal relationship between genetically proxied sleep apnea or snoring and obesity. The decreased activity levels and altered expression levels of six circadian genes (bmal1b, cry1aa, cry1ab, clock1a, per1b, per2) were identified in the zebrafish of the sleep disorder group. Multiple metabolites related to disturbed glucose metabolism (e.g., 20-HETE), lipid metabolism (e.g., inosine), and vascular-related metabolites (e.g., riboflavin) were finally identified, indicating the latent effect of metabolism.

CONCLUSIONS

This study identified the chain of sleep-circadian rhythm-metabolism-cardiometabolic risks. These findings can promote improved prevention implementation and therapeutic strategies.

The Association between Obstructive Sleep Apnea and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study

BACKGROUND

 Despite previous observational studies linking obstructive sleep apnea (OSA) to venous thromboembolism (VTE), these findings remain controversial. This study aimed to explore the association between OSA and VTE, including pulmonary embolism (PE) and deep vein thrombosis (DVT), at a genetic level using a bidirectional two-sample Mendelian randomization (MR) analysis.

METHODS

 Utilizing summary-level data from large-scale genome-wide association studies in European individuals, we designed a bidirectional two-sample MR analysis to comprehensively assess the genetic association between OSA and VTE. The inverse variance weighted was used as the primary method for MR analysis. In addition, MR-Egger, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO) were used for complementary analyses. Furthermore, a series of sensitivity analyses were performed to ensure the validity and robustness of the results.

RESULTS

 The initial and validation MR analyses indicated that genetically predicted OSA had no effects on the risk of VTE (including PE and DVT). Likewise, the reverse MR analysis did not find substantial support for a significant association between VTE (including PE and DVT) and OSA. Supplementary MR methods and sensitivity analyses provided additional confirmation of the reliability of the MR results.

CONCLUSION

 Our bidirectional two-sample MR analysis did not find genetic evidence supporting a significant association between OSA and VTE in either direction.

Causal Associations of Sleep Apnea With Alzheimer Disease and Cardiovascular Disease: A Bidirectional Mendelian Randomization Analysis

BACKGROUND

Sleep apnea (SA) has been linked to an increased risk of dementia in numerous observational studies; whether this is driven by neurodegenerative, vascular, or other mechanisms is not clear. We sought to examine the bidirectional causal relationships between SA, Alzheimer disease (AD), coronary artery disease (CAD), and ischemic stroke using Mendelian randomization.

METHODS AND RESULTS

Using summary statistics from 4 recent, large genome-wide association studies of SA (n=523 366), AD (n=94 437), CAD (n=1 165 690), and stroke (n=1 308 460), we conducted bidirectional 2-sample Mendelian randomization analyses. Our primary analytic method was fixed-effects inverse variance-weighted (IVW) Mendelian randomization; diagnostics tests and sensitivity analyses were conducted to verify the robustness of the results. We identified a significant causal effect of SA on the risk of CAD (odds ratio [ORIVW]=1.35 per log-odds increase in SA liability [95% CI=1.25-1.47]) and stroke (ORIVW=1.13 [95% CI=1.01-1.25]). These associations were somewhat attenuated after excluding single-nucleotide polymorphisms associated with body mass index (ORIVW=1.26 [95% CI=1.15-1.39] for CAD risk; ORIVW=1.08 [95% CI=0.96-1.22] for stroke risk). SA was not causally associated with a higher risk of AD (ORIVW=1.14 [95% CI=0.91-1.43]). We did not find causal effects of AD, CAD, or stroke on risk of SA.

CONCLUSIONS

These results suggest that SA increased the risk of CAD, and the identified causal association with stroke risk may be confounded by body mass index. Moreover, no causal effect of SA on AD risk was found. Future studies are warranted to investigate cardiovascular pathways between sleep disorders, including SA, and dementia.

A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances

We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (P int <5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (P joint <5.0e-9 in combination with P int <6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.

Causal Links Between Systemic Disorders and Keratoconus in European Population

PURPOSE

To establish the presence of a causal linkage between prevalent systemic diseases and keratoconus (KC).

DESIGN

Mendelian randomization (MR) analysis.

METHODS

After an exhaustive screening process, genetic variants linked to various systemic diseases were identified as instrumental variables at the genome-wide significance level. Subsequently, MR analyses were conducted to elucidate their potential causal connection with KC (N = 26,742). The encompassed systemic ailments comprise diabetes, hay fever/allergic rhinitis/eczema, obstructive sleep apnea, thyroid dysfunction, aortic aneurysm, major depressive disorder, inflammatory bowel disease (including Crohn's disease and ulcerative colitis), and mitral valve prolapse. Our study adheres to the principles of Strengthening the Reporting of Observational Studies in Epidemiology Using MR guidelines.

RESULTS

Using inverse variance weighting as the primary MR analysis method, our findings revealed that hay fever/allergic rhinitis/eczema (odds ratio, 10.144; 95% CI, 2.441-42.149; P = .001) and ulcerative colitis (odds ratio, 1.147; 95% CI, 1.054-1.248; P = .002) were associated with an increased risk of KC within the largest population under scrutiny. Conversely, the prolonged hyperglycemic state did not exhibit a potentially protective effect in delaying the pathogenesis of KC, and no correlation was observed between the two (odds ratio, 0.320; 95% CI, 0.029-3.549; P = .353). Also, obstructive sleep apnea, thyroid function, aortic aneurysm, major depressive disorder, Crohn's disease, and mitral valve prolapse did not exhibit a causal association with KC (P > .05 for all comparisons).

CONCLUSIONS

This study indicates an increased risk of KC related to hay fever/allergic rhinitis/eczema and ulcerative colitis, with diabetes not providing a protective effect. These findings may potentially contribute some insights to inform clinical interventions.

The causal relationship between sleep and risk of psychiatric disorders: A two-sample mendelian randomization study

Introduction

Sleep is associated with psychiatric disorders. However, their causality remains unknown.

Methods

The study explored the causal relationship between seven sleep parameters (sleep duration, insomnia, sleep apnea, chronotype, daytime dozing, napping during the day, and snoring) and three psychiatric disorders including major depressive disorder (MDD), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS) summary data for sleep parameters were obtained from the United Kingdom biobank, FinnGen biobank, and EBI databases. MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW(fixed effects) were used to perform the MR analysis. The heterogeneity was detected by Cochran's Q statistic. The horizontal pleiotropy was detected by MR Egger. The sensitivity was investigated by the leave-one-out analysis.

Results

Insomnia (OR = 2.02, 95%CI = 1.34-3.03, p = 0.001, False-discovery rate (FDR) corrected p-value = 0.011) and napping during the day (OR = 1.81, 95%CI = 1.34-2.44, FDR corrected p-value<0.001) were associated with an increased risk of MDD. Longer sleep duration (OR = 2.20, 95%CI = 1.24-3.90, FDR corrected p-value = 0.049) had an association with the increased risk of schizophrenia, while daytime dozing (OR = 4.44, 95%CI = 1.20-16.41, corrected p-value = 0.088)and napping during the day (OR = 2.11, 95%CI = 1.11-4.02, FDR corrected p-value = 0.088) had a suggestive association with an increased risk of schizophrenia. Longer sleep duration had a suggestive association with a decreased risk of ADHD (OR = 0.66, 95%CI = 0.42-0.93, FDR corrected p-value = 0.088).

Conclusion

This study provides further evidence for a complex relationship between sleep and psychiatric disorders. Our findings highlight the potential benefits of addressing sleep problems in the prevention of psychiatric disorders.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Sleep and cardiovascular disease

This review centres around the recent evidence in examining the intersection of sleep and cardiovascular disease (CVD). Sleep in this review will be further subdivided to consider both sleep quantity and quality along and will also consider some of the more common sleep disorders, such as insomnia and obstructive sleep apnoea, in the context of CVD. Sleep disorders have been further explored in several specific populations which are both at risk of sleep disorders and CVD. Secondly, the review will present some of the risk factors for CVD that are affected by sleep and sleep disorders which include hypertension, diabetes, and obesity. It will also examine the potential underlying mechanisms including inflammation, appetite control, endocrine, and genetic processes that are affected by sleep and sleep disorders leading to increased risk of CVD development. In addition, we will consider the observed bi-directional relationships between sleep and cardiovascular risk factors. For example, obesity, a risk factor for CVD can be affected by sleep, but in turn can increase the risk of certain sleep disorder development which disrupts sleep, leading to further risk of obesity development and increased CVD risk. Finally, the review will explore emerging evidence around lifestyle interventions that have included a sleep component and how it impacts the management of CVD risk factor. The need for increased awareness of the health effects of poor sleep and sleep disorders will be discussed alongside the need for policy intervention to improve sleep to facilitate better health and well-being.

Snoring-related polygenic risk and its relationship with lifestyle factors in a Korean population: KoGES study

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76-2.23), followed by male sex (OR 1.54, 95% CI 1.28-1.86), older age (OR 1.23, 95% CI 1.03-1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08-1.29), drinking behavior (OR 1.18, 95% CI 1.03-1.35), late sleep mid-time (OR 1.17, 95% CI 1.02-1.33), smoking behavior (OR 0.99, 95% CI 0.82-1.19), and lower physical activity (OR 0.92, 95% CI 0.85-1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk.

Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex

BACKGROUND

Genome-wide association studies (GWAS) for obstructive sleep apnoea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence.

METHODS

We performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analysed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totalling 916,696 individuals.

FINDINGS

MVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value < 0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, previously unreported, genome-wide significant regions.

INTERPRETATION

Sex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared aetiology and causal pathways.

FUNDING

Described in acknowledgements.

Causal associations of sleep apnea and snoring with type 2 diabetes and glycemic traits and the role of BMI

OBJECTIVE

Sleep apnea and snoring have been associated with type 2 diabetes, with BMI playing a role in the pathway, but the directions of causality are unclear. This study examined the causal associations of sleep apnea and snoring with type 2 diabetes while assessing the role of BMI using multiple genetic methods.

METHODS

Five genetic methods were used: two-sample; bidirectional univariable Mendelian randomization (MR) inverse variance-weighted (MR-IVW); multivariable MR-IVW; network MR; and latent causal variable method.

RESULTS

Compared with univariable MR-IVW, the odds ratio (95% CI) of type 2 diabetes for genetically predicted sleep apnea and snoring using the largest genome-wide association study decreased dramatically, from 1.61 (95% CI: 1.16-2.23) to 1.08 (95% CI: 0.59-1.97) and from 1.98 (95% CI: 1.25-3.13) to 1.09 (95% CI: 0.64-1.86) after adjustment for BMI. Network MR showed that BMI accounts for 67% and 62% of the total effect of sleep apnea and snoring on type 2 diabetes, respectively. The latent causal variable suggested that sleep apnea and snoring have no direct causal effect on type 2 diabetes.

CONCLUSIONS

These results first suggest that the associations of sleep apnea and snoring with type 2 diabetes were mainly driven by BMI. The possible indirect effects of sleep apnea and snoring on type 2 diabetes through BMI cannot be ruled out.