Insomnia and risk of sepsis: A Mendelian randomization study
Thorkildsen, M. S.; Gustad, L. T.; Nilsen, T. I. L.; Damas, J. K.; Rogne, T.
Show abstract
ImportanceInsomnia has been associated with reduced immune function and increased risk of infections and sepsis in observational studies. These studies are prone to bias, such as residual confounding. To further understand the causal relation between insomnia and sepsis risk we used a two-sample Mendelian randomization (MR) approach. ObjectiveIs genetically predicted insomnia associated with risk of sepsis? DesignTwo-sample MR was performed to estimate the causal effect of genetically predicted insomnia on sepsis risk. Data was obtained from a genome-wide association study (GWAS) identifying 556 independent genetic variants (R2<0.01) strongly associated with insomnia (P < 5e-8). We conducted sensitivity analyses to address bias due to pleiotropy and sample overlap, along with mediation analyses. SettingObservational study using genetic variants as instrumental variables in large populations. ParticipantsFor insomnia, 2.4 million subjects of European ancestry from the UK Biobank and 23andMe. For sepsis, 462,918 subjects of European ancestry from the UK Biobank. ExposureGenetically predicted insomnia. Main Outcome and MeasureSepsis. ResultsA doubling in the population prevalence of genetically predicted insomnia was associated with an odds ratio of 1.42 (95% CI 1.23-1.63, P = 9.1e-7) for sepsis. Sensitivity analyses supported this observation. Three quarters of the effect was mediated through body mass index. Conclusions and RelevanceThe concordance between our findings and previous observational studies support of a causal role of genetically predicted insomnia in the risk of sepsis.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Poor sleep quality, insomnia, and short sleep duration before infection predict long-term symptoms after COVID-19 92%
- Immune-Neuroendocrine Patterning and Response to Stress. A latent profile analysis in the English Longitudinal Study of Ageing 91%
- Increased circulating IL-18 levels in severe mental disorders indicate systemic inflammasome activation 90%
Similar papers in this journal
- Cohort Profile: Post-hospitalisation COVID-19 study (PHOSP-COVID) 88%
- Association of pre-existing maternal cardiovascular diseases with neurodevelopmental disorders in offspring: a cohort study in Sweden and British Columbia, Canada 88%
- Adverse childhood experiences and lower urinary tract symptoms in adolescence: the mediating effect of inflammation 88%
Similar papers in this journal
- Refinement of post-COVID condition core symptoms, subtypes, determinants, and health impacts: A cohort study integrating real-world data and patient-reported outcomes 90%
- Older biological age is associated with adverse COVID-19 outcomes: A cohort study in UK Biobank 89%
- Transcriptional survey of peripheral blood links lower oxygen saturation during sleep with reduced expressions of CD1D and RAB20 that is reversed by CPAP therapy 88%
Similar papers in this journal
- Glycoprotein Acetyls and Depression: testing for directionality and potential causality using longitudinal data and Mendelian randomization analyses 91%
- Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression 89%
- Increased neurotoxicity due to activated immune-inflammatory and nitro-oxidative stress pathways in patients with suicide attempts: a systematic review and meta-analysis. 89%
Similar papers in this journal
- The Innate Immune Toll-Like Receptor-2 modulates the Depressogenic and Anorexiolytic Neuroinflammatory Response in Obstructive Sleep Apnoea 89%
- Sleep Regularity Index as a Novel Indicator of Sleep Disturbance in Stroke Survivors: A Secondary Data Analysis 89%
- Synergistic effects of cardiovascular health and social isolation on adverse pregnancy outcomes 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.