Loneliness-the subjective experience of social disconnection-is now widely regarded as a health risk factor. However, whether the associations between loneliness and multiple diseases are consistent with causal effects remains largely unexplored. Here we combined behavioural, genetic and hospitalization data from the UK Biobank to examine the associations of loneliness with a wide range of non-overlapping diseases. During a median 12.2-year follow-up, loneliness was associated with greater risks in 13 of 14 disease categories and 30 of 56 individual diseases considered. Of the 30 diseases significantly associated with loneliness, 26 had genetic data available for Mendelian randomization (MR) analyses. After Benjamini‒Hochberg correction and multiple sensitivity analyses within the MR framework, non-causal associations were identified between genetic liability to loneliness and 20 out of the 26 specific diseases, including cardiovascular diseases, type 2 diabetes mellitus, obesity, chronic liver diseases, chronic kidney disease, most neurological diseases and the other common diseases. Genetic liability to loneliness was only potentially causally associated with the remaining six diseases. Socioeconomic factors, health behaviours, baseline depressive symptoms and comorbidities largely explained the associations between loneliness and diseases. Overall, our study revealed a dissociation between observational and genetic evidence regarding the associations of loneliness with multiple diseases. These findings suggest that loneliness may serve as a potential surrogate marker rather than a causal risk factor for most diseases tested here.

The associations between sleep duration and cardiovascular diseases (CVDs) have been explored in many observational studies. However, the causality of sleep duration and many CVDs, such as coronary artery disease (CAD), heart failure (HF), and stroke, remains unclear. In this study, we conducted a systematic meta-review and meta-analysis of the results of observational and Mendelian randomization (MR) studies to examine how sleep duration impacts the risk of CVDs. We searched articles published in English and before 10 September 2021 in PubMed, Web of Science, and Embase. The articles were screened independently by two reviewers to minimize potential bias. We combined the meta-analyses of observational studies and 11 MR studies and summarized evidence of the effect of sleep duration on the risk of CAD, HF, stroke, and cardiovascular and all-cause mortality. Results showed that (a) evidence is accumulating that short sleep duration is a causal risk factor for CAD and HF; (b) abundant evidence from observational studies supports that long sleep duration is associated with the risk of CAD, stroke, and mortality, and long sleep duration has no causal associations with stroke and CAD in the MR studies; the causation of long sleep duration and other CVDs should be further studied; and (c) emerging evidence indicates that an increase in hours of sleep is associated with a decreased risk of CAD. Finally, we discussed the underlying pathophysiological mechanisms underlying short sleep duration and CVDs and suggested that increasing sleep duration benefits cardiovascular health.

Background: Observational studies have found that both short and long sleep duration are associated with increased risk of metabolic syndrome (MetS). This study aimed to examine the associations of genetically determined sleep durations with MetS and its five components (i.e., central obesity, high blood pressure, dyslipidemia, hypertriglyceridemia, and hyperglycemia) among a group of elderly population. Methods: In 335,727 participants of White British from the UK Biobank, linear Mendelian randomization (MR) methods were first employed to examine the causal association of genetically predicted continuous sleep duration with MetS and its each component. Nonlinear MR analyses were performed to determine the nonlinearity of these associations. The causal associations of short and long sleep duration with MetS and its components were further assessed by using genetic variants that associated with short (≤ 6 h) and long sleep (≥ 9 h) durations. Results: Linear MR analyses demonstrated that genetically predicted 1-h longer sleep duration was associated with a 13% lower risk of MetS, a 30% lower risk of central obesity, and a 26% lower risk of hyperglycemia. Non-linear MR analyses provided evidence for non-linear associations of genetically predicted sleep duration with MetS and its five components (all P values < 0.008). Genetically predicted short sleep duration was moderately associated with MetS and its four components, including central obesity, dyslipidemia, hypertriglyceridemia, and hyperglycemia (all P values < 0.002), whereas genetically long sleep duration was not associated with MetS and any of its components. Conclusions: Genetically predicted short sleep duration, but not genetically predicted long sleep duration, is a potentially causal risk factor for MetS. Graphical

Context Insomnia is associated with insulin resistance (IR) in observational studies, however, whether insomnia is causally associated with IR remains unestablished. Objective This study aims to estimate the causal associations of insomnia with IR and its related traits. Methods In primary analyses, multivariable regression (MVR) and one-sample Mendelian randomization (1SMR) analyses were performed to estimate the associations of insomnia with IR (triglyceride-glucose [TyG] index and triglyceride to high-density lipoprotein cholesterol [TG/HDL-C] ratio) and its related traits (glucose level, TG, and HDL-C) in UK Biobank. Thereafter, two-sample MR (2SMR) analyses were used to validate the findings from primary analyses. Finally, the potential mediating effects of IR on the pathway of insomnia giving rise to T2D were examined using a two-step MR design. Results Across the MVR, 1SMR, and their sensitivity analyses, we found consistent evidence suggesting that more frequent insomnia symptoms were significantly associated with higher values of TyG index (MVR: β = 0.024, P < 2.00E-16; 1SMR: β = 0.343, P < 2.00E-16), TG/HDL-C ratio (MVR: β = 0.016, P = 1.75E-13; 1SMR: β = 0.445, P < 2.00E-16), and TG level (MVR: β = 0.019 log mg/dl, P < 2.00E-16; 1SMR: β = 0.289 log mg/dL, P < 2.00E-16) after Bonferroni adjustment. Similar evidence was obtained by using 2SMR, and mediation analysis suggested that about a quarter (25.21%) of the association between insomnia symptoms and T2D was mediated by IR. Conclusions This study provides robust evidence supporting that more frequent insomnia symptoms are associated with IR and its related traits across different angles. These findings indicate that insomnia symptoms can be served as a promising target to improve IR and prevent subsequent T2D.

A growing body of evidence supports the comorbidity between depression (DEP) and obesity, yet the genetic mechanisms underlying this association remain unclear. Our study explored the shared genetic architecture and causal associations of DEP with BMI. We investigated the multigene overlap and genetic correlation between DEP (N > 1.3 million) and BMI (N = 806,834) based on genome-wide association studies (GWAS) and using the bivariate causal mixture model and linkage disequilibrium score regression (LDSC). The causal association was explored by bi-directional Mendelian randomization (MR). Common risk loci were identified through cross-trait meta-analyses. Stratified LDSC and multi-marker gene annotation analyses were applied to investigate single-nucleotide polymorphisms enrichment across tissue types, cell types, and functional categories. Finally, we explored shared functional genes by Summary Data-Based Mendelian Randomization (SMR) and further detected differential expression genes (DEG) in brain tissues of individuals with depression and obesity. We found a positive genetic correlation between DEP and BMI (rg = 0.19, P = 4.07 × 10−26), which was more evident in local genomic regions. Cross-trait meta-analyses identified 16 shared genetic loci, 5 of which were newly identified, and they had influence on both diseases in the same direction. MR analysis showed a bidirectional causal association between DEP and BMI, with comparable effect sizes estimated in both directions. Combined with gene expression information, we found that genetic correlations between DEP and BMI were enriched in 6 brain regions, predominantly in the nucleus accumbens and anterior cingulate cortex. Moreover, 6 specific cell types and 23 functional genes were found to have an impact on both DEP and BMI across the brain regions. Of which, NEGR1 was identified as the most significant functional gene and associated with DEP and BMI at the genome-wide significance level (P < 5 × 10−8). Compared with healthy controls, the expression levels of NEGR1 gene were significant lower in brain tissues of individuals with depression and obesity. Our study reveals shared genetic basis underpinnings between DEP and BMI, including genetic correlations and common genes. These insights offer novel opportunities and avenues for future research into their comorbidities.