Background
Carpal tunnel syndrome (CTS) is a prevalent peripheral neuropathy with unclear pathogenesis. This study investigated the causal relationships between metabolic diseases and CTS using univariable and multivariable Mendelian randomization (MR) analyses.
Methods
Single nucleotide polymorphisms from large genetic databases served as instrumental variables. Genome-wide data on type 2 diabetes (T2D), obesity, gout, hypothyroidism, and CTS were obtained from the IEU OpenGWAS Project (Integrative Epidemiology Unit Open Genome-Wide Association Studies Project). Univariable MR analysis assessed individual causal effects, and multivariable MR evaluated combined effects. Sensitivity analyses examined heterogeneity and pleiotropy.
Results
The univariable MR analysis identified significant associations of CTS with obesity (odds ratio [OR], 1.0135; 95% confidence interval [CI], 1.0118–1.0151; P<0.001), T2D (OR, 1.0028; 95% CI, 1.0011–1.0045; P=0.0011), gout (OR, 1.0559; 95% CI, 1.0152–1.0982; P=0.0067), and hypothyroidism (OR, 1.0517; 95% CI, 1.013–1.0919; P=0.0084). The multivariable MR analysis confirmed obesity (OR, 1.0129; 95% CI, 1.0088–1.0171; P<0.001) and T2D (OR, 1.002; 95% CI, 1.0006–1.0033; P=0.0042) as significant independent risk factors. Gout and hypothyroidism lost significance after adjustment. No notable pleiotropy was observed.
Conclusion
Obesity and T2D independently increase CTS risk, whereas the univariable associations for gout and hypothyroidism were attenuated after adjustment for other metabolic conditions, suggesting mediation by or confounding with obesity and glucose metabolism in this dataset.

Background
This study aimed to examine the relationship between genetically predicted metabolite levels and gastric cancer (GC) risk using Mendelian randomization (MR), and to identify the metabolic pathways potentially involved.
Methods
We selected genetic instruments for metabolites from 64 genome-wide association studies covering 362,750 participants. A two-sample MR design was applied to evaluate the associations with GC using summary-level data from a combined analysis of the UK Biobank and FinnGen. The primary analysis relied on the inverse-variance weighted method, while the median-weighted and MR-Egger methods were used to account for potential violations of instrumental variable assumptions and provide the estimate even when a subset of instruments was invalid. The MR-Egger intercept test was performed to detect directional pleiotropy. Metabolites showing significant associations with GC were further examined using pathway enrichment analysis to identify relevant metabolic and lipid processes.
Results
MR analyses identified 25 and 17 metabolites that were positively and inversely associated with GC risk, respectively. Notably, hexanoylcarnitine and cis-4-decenoylcarnitine were strongly associated with increased risk, whereas pregnanediol disulfate, acetylcarnitine, prolyl-hydroxyproline, and X-18914 were associated with reduced risk, with no evidence of heterogeneity or directional pleiotropy. Enrichment analyses highlighted key metabolic pathways, including cysteine and methionine catabolism, beta-oxidation of pristanoyl-CoA (coenzyme A), oxidation of branched-chain fatty acids, and peroxisomal lipid metabolism.
Conclusion
This study identified a set of genetically predicted metabolites associated with GC risk, highlighting the potential utility of metabolite panels and lipid-based biomarkers for risk stratification and early detection. However, further standardization and extensive validation are necessary prior to clinical application.