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ObjectiveSelf-reported data can be a valuable resource for understanding health outcomes, behaviors, disease prevalence, and risk factors, yet underutilized in epidemiological research. While observational studies have linked sleep traits with diabetes, evidence using self-reported diabetes data for causal connection is lacking.MethodsWe performed a two-sample Mendelian randomization (MR) analysis using Inverse Variance Weighting (IVW), IVW with multiplicative random effects (IVW-MRE), Maximum Likelihood (ML), MR-Egger regression, and Weighted Median models, with genetic variants linked to five sleep traits (sleep duration, insomnia, snoring, daytime dozing, and chronotype) and self-reported diabetes from the UK Biobank dataset. The study utilized MR-Egger and MR-PRESSO regression to evaluate pleiotropy and outliers, IVW Q statistics to detect heterogeneity, the MR-Steiger test to assess directionality, and leave-one-out sensitivity analysis to ensure the reliability.ResultsML provided positive causal associations between genetically predicted insomnia (p = 0.002, OR = 1.021, 95% CI: 1.008–1.035) and daytime dozing (p = 0.014, OR = 1.029, 95% CI: 1.006–1.052) with diabetes, while IVW and IVW-MRE analysis showed a trend towards significance. Snoring showed mixed evidence, while genetically predicted sleep duration was marginally associated with diabetes (p = 0.053, OR = 0.992, 95% CI: 0.984–1.000) with the weighted median method, indicating a potential small protective effect. No causal association was found between chronotype and diabetes.ConclusionThis exploratory MR study provides evidence for the effect of insomnia, daytime dozing, sleep duration and snoring on diabetes risk. These findings underscore the importance of considering self-reported health outcomes in epidemiological research.Article HighlightsWhy did we undertake this study?We undertook this study to explore the causal relationships between sleep-related traits and diabetes using self-reported data, as previous prospective, retrospective or other observational studies have shown associations but lacked causal evidence using self-reported data.What is the specific question(s) we wanted to answer?We aimed to answer whether sleep-related traits, such as sleep duration, insomnia, snoring, daytime dozing, and chronotype, have a causal impact on diabetes which is self-reported.What did we find?Our two-sample Mendelian Randomization (MR) analysis found that genetically predicted insomnia and daytime dozing have a positive causal association and sleep duration was marginally associated with diabetes.What are the implications of our findings?These findings suggest that certain sleep traits may contribute to diabetes risk, highlighting the importance of considering sleep in diabetes prevention and treatment strategies. The results also emphasize the value of using self-reported health outcomes in epidemiological research and clinical interventions.
ObjectiveSelf-reported data can be a valuable resource for understanding health outcomes, behaviors, disease prevalence, and risk factors, yet underutilized in epidemiological research. While observational studies have linked sleep traits with diabetes, evidence using self-reported diabetes data for causal connection is lacking.MethodsWe performed a two-sample Mendelian randomization (MR) analysis using Inverse Variance Weighting (IVW), IVW with multiplicative random effects (IVW-MRE), Maximum Likelihood (ML), MR-Egger regression, and Weighted Median models, with genetic variants linked to five sleep traits (sleep duration, insomnia, snoring, daytime dozing, and chronotype) and self-reported diabetes from the UK Biobank dataset. The study utilized MR-Egger and MR-PRESSO regression to evaluate pleiotropy and outliers, IVW Q statistics to detect heterogeneity, the MR-Steiger test to assess directionality, and leave-one-out sensitivity analysis to ensure the reliability.ResultsML provided positive causal associations between genetically predicted insomnia (p = 0.002, OR = 1.021, 95% CI: 1.008–1.035) and daytime dozing (p = 0.014, OR = 1.029, 95% CI: 1.006–1.052) with diabetes, while IVW and IVW-MRE analysis showed a trend towards significance. Snoring showed mixed evidence, while genetically predicted sleep duration was marginally associated with diabetes (p = 0.053, OR = 0.992, 95% CI: 0.984–1.000) with the weighted median method, indicating a potential small protective effect. No causal association was found between chronotype and diabetes.ConclusionThis exploratory MR study provides evidence for the effect of insomnia, daytime dozing, sleep duration and snoring on diabetes risk. These findings underscore the importance of considering self-reported health outcomes in epidemiological research.Article HighlightsWhy did we undertake this study?We undertook this study to explore the causal relationships between sleep-related traits and diabetes using self-reported data, as previous prospective, retrospective or other observational studies have shown associations but lacked causal evidence using self-reported data.What is the specific question(s) we wanted to answer?We aimed to answer whether sleep-related traits, such as sleep duration, insomnia, snoring, daytime dozing, and chronotype, have a causal impact on diabetes which is self-reported.What did we find?Our two-sample Mendelian Randomization (MR) analysis found that genetically predicted insomnia and daytime dozing have a positive causal association and sleep duration was marginally associated with diabetes.What are the implications of our findings?These findings suggest that certain sleep traits may contribute to diabetes risk, highlighting the importance of considering sleep in diabetes prevention and treatment strategies. The results also emphasize the value of using self-reported health outcomes in epidemiological research and clinical interventions.
Background Most adult patients with depression complain about sleep symptoms, including insufficient and excessive sleep. However, previous studies investigating the impact of sleep duration on depression have yielded conflicting results. Therefore, this study aimed to analyse the link between depression and sleep duration, daytime napping, and snoring among rural Chinese adults. Methods A cross-sectional study was conducted with 9104 individuals. Interviews were conducted with the participants regarding their sleep patterns and their daytime napping routines. The individuals were then assessed for depression using the Patient Health Questionnaire-9. The risk of depression was assessed using a multifactor binary logistic regression analysis. A generalized additive model was used to evaluate the nonlinear relationship between depression and sleep duration/nap time. Additionally, subgroup analysis was conducted to investigate the correlation between sleep duration, daytime napping, snoring, and depression. Results Less than 6 h or more than 8 h of nighttime sleep, daytime napping for more than 1 h, and snoring were all significantly associated with an increased risk of depression. A U-shaped relationship was found between the duration of nighttime sleep and depression. In addition, we found that the nighttime duration of sleep, daytime naps, and snoring had a significant combined effect on the risk of depression. The subgroup analysis further revealed that lack of sleep at night significantly increased the risk of depression in all subgroups. However, snoring and excessive nighttime sleep and napping were only associated with the risk of depression in some subgroups. Conclusions Lack of nighttime sleep (short sleep duration), excessive sleep, and napping for more than one hour during the day were associated with a high risk of depression and had a combined effect with snoring.
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