UNASSIGNED: This Mendelian Randomization (MR) study aims to explore the potential causal relationships between four sleep traits and pain in 10 different body sites.
UNASSIGNED: The study utilizes exposure and outcome data from the GWAS database, employing the Inverse Variance Weighting Method (IVW) for primary causal estimates. Cochran Q and Rücker Q heterogeneity tests are conducted using IVW and MR-Egger methods, with the Egger-intercept method for pleiotropy testing, leave-one-out sensitivity analysis, and calculation of F-statistics to assess the presence of weak instrument bias.
UNASSIGNED: The study reveals that genetically predicted insomnia significantly increases the risk of unspecified pain, chest pain, gum pain, upper abdominal pain, and lower abdominal pain occurrence. Daytime napping is associated with a moderate reduction in the likelihood of joint pain but may concomitantly elevate the risk of chest pain, upper abdominal pain, and generalized abdominal pain. Neither sleep chronotype nor sleep duration demonstrated a definitive causal relationship with pain perception.
UNASSIGNED: This study elucidates the causal relationships between four sleep characteristics and pain across 10 different body regions. Overall, the contribution of insomnia and sleep deficiency to pain in multiple body regions is more pronounced. Conversely, the association between adequate sleep and the likelihood of somatic pain is relatively lower and less significant.

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Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

BACKGROUND: Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses.
METHODS: In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations.
RESULTS: There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations.
CONCLUSIONS: The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant biliary tract cancer with poor prognosis. Previous studies have implicated the gut microbiota in CCA, but evidence for causal mechanisms is lacking.
OBJECTIVE: To investigate the causal relationship between gut microbiota and CCA risk.
METHODS: We performed a two-sample mendelian randomization study to evaluate potential causal associations between gut microbiota and CCA risk using genome-wide association study summary statistics for 196 gut microbial taxa and CCA. Genetic variants were used as instrumental variables. Multiple sensitivity analyses assessed result robustness.
RESULTS: Fifteen gut microbial taxa showed significant causal associations with CCA risk. Higher genetically predicted abundance of genus Eubacteriumnodatum group, genus Ruminococcustorques group, genus Coprococcus, genus Dorea, and phylum Actinobacteria were associated with reduced risk of gallbladder cancer and extrahepatic CCA. Increased intrahepatic CCA risk was associated with higher abundance of family Veillonellaceae, genus Alistipes, order Enterobacteriales, and phylum Firmicutes. Protective effects against CCA were suggested for genus Collinsella, genus Eisenbergiella, genus Anaerostipes, genus Paraprevotella, genus Parasutterella, and phylum Verrucomicrobia. Sensitivity analyses indicated these findings were reliable without pleiotropy.
CONCLUSIONS: This pioneering study provides novel evidence that specific gut microbiota may play causal roles in CCA risk. Further experimental validation of these candidate microbes is warranted to consolidate causality and mechanisms.

Aim: This simulation study is to assess the utility of physician\'s prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.

UNASSIGNED: Many studies have shown that omega-3 fatty acids may play critical roles in cardiovascular diseases. Myocardial infarction (MI) typically results from a thrombotic occlusion of a coronary artery leading to myocardial ischemia. Thus, this study aims to examine the association between omega-3 fatty acids and MI.
UNASSIGNED: A two-sample Mendelian randomization study was used to explore the causal relationship between circulating omega-3 fatty acids and the risk of MI performed by MR-Egger regression, inverse-variance weighted (IVW), weighted median, and weighted mode.
UNASSIGNED: Five single-nucleotide polymorphisms strongly related to circulating omega-3 fatty acids were selected as instrumental variables from a published genome-wide association study (GWAS) meta-analysis including 13,544 subjects. We extracted summary data for the risk of MI from another GWAS meta-analysis including 171,875 individuals (43,676 cases and 128,199 controls). The genetically predicted lower circulating omega-3 increased the risk of myocardial infarction showed by the results of IVW [odds ratio (OR) = 1.224, 95% CI = 1.045-1.433, P = 0.012], weighted median method (OR = 1.171, 95% CI = 1.042-1.315, P = 0.008), and weighted mode (OR = 1.149, 95% CI = 1.002-1.317, P = 0.117), although the result of MR-Egger was not significant (OR = 0.950, 95% CI = 0.513-1.760, P = 0.880) with a wider confidence interval.
UNASSIGNED: The findings from our Mendelian randomization analysis suggest that the association between omega-3 fatty acid levels and MI is likely causal.

Recent observational studies revealed an association between gut microbiota and aging, but whether gut microbiota are causally associated with the aging process remains unknown. We used a two-sample Mendelian randomization approach to investigate the causal association between gut microbiota and biological age acceleration using the largest available gut microbiota GWAS summary data from the MiBioGen consortium and GWAS data on biological age acceleration. We further conducted sensitivity analysis using MR-PRESSO, MR-Egger regression, Cochran Q test, and reverse MR analysis. Streptococcus (IVW, β = 0.16, p = 0.0001) was causally associated with Bioage acceleration. Eubacterium (rectale group) (IVW, β = 0.20, p = 0.0190), Sellimonas (IVW, β = 0.06, p = 0.019), and Lachnospira (IVW, β = -0.18, p = 0.01) were suggestive of causal associations with Bioage acceleration, with the latter being protective. Actinomyces (IVW, β = 0.26, p = 0.0083), Butyricimonas (IVW, β = 0.21, p = 0.0184), and Lachnospiraceae (FCS020 group) (IVW, β = 0.24, p = 0.0194) were suggestive of causal associations with Phenoage acceleration. This Mendelian randomization study found that Streptococcus was causally associated with Bioage acceleration. Further randomized controlled trials are needed to investigate its role in the aging process.

We systematically reviewed how investigators argued for and justified the validity of their instrumental variables (IV) in clinical studies of dementia and neurodegenerative disease.
We included studies using IV analysis with observational data to investigate causal effects in clinical research studies of dementia and neurodegenerative disease. We reported the subject-matter argumentation, falsification test, and study design strategies used to satisfy the three assumptions of a valid IV: relevance, exclusion restriction, and exchangeability.
Justification for the relevance assumption was performed in all 12 included studies, exclusion restriction in seven studies, and exchangeability in nine studies. Two subject-matter argumentation strategies emerged from seven studies on the relevance of their IV. All studies except one provided quantitative evidence for the strength of the association between the IV and exposure variable. Four argumentation strategies emerged for exclusion restriction from six studies. Four falsification tests were performed across three studies. Three argumentation strategies emerged for exchangeability across four studies. Nine falsification tests were performed across nine studies. Two notable study design strategies were reported.
Our results reinforce IV analysis as a feasible option for clinical researchers in dementia and neurodegenerative disease by clarifying known strategies used to validate an IV.

OBJECTIVE: Previous observational studies have suggested that gastroesophageal reflux disease (GERD) increases the risk of stroke, but the specific underlying mechanisms are unclear. We investigated the causal associations of GERD with stroke and its subtypes using Mendelian randomization (MR), and evaluated the potential mediating effects of modifiable stroke risk factors in the causal pathway.
METHODS: Genetic instrumental variables for GERD were extracted from the latest genome-wide association study (GWAS) summary level data. We initially performed two-sample MR to examine the association of GERD with stroke and its subtypes, including ischemic stroke, intracranial hemorrhage, and the major subtypes of ischemic stroke. Two-step MR was further employed to investigate the mediating effect of 15 risk factors in the causal pathway.
RESULTS: We found significant causal associations of genetically predicted GERD with increased risk of stroke (OR: 1.22 95% CI: 1.126-1.322), ischemic stroke (OR: 1.19 95% CI: 1.098-1.299), and large-artery stroke (OR: 1.49 95% CI: 1.214-1.836). Replication and sensitivity analyses yielded consistent effect directions and similar estimates. Further mediation analyses indicated that hypertension (HTN), systolic blood pressure (SBP), and type 2 diabetes (T2D) mediated 36.0%, 9.0%, and 15.8% of the effect of GERD on stroke; 42.9%, 10.8%, and 21.4% for ischemic stroke, and 23.3%; 7.9%, and 18.7% for large-artery stroke, respectively.
CONCLUSIONS: This study supports that GERD increases susceptibility to stroke, ischemic stroke, and large-artery stroke, and is partially mediated by HTN, SBP, and T2D.