Background and Aims:The risk factors of cholelithiasis have not been clearly identified, especially for total cholesterol. Here, we try to identify these causal risk factors. Approach and Results:We obtained genetic variants associated with the exposures at the genome-wide significance (p < 5 × 10 −8 ) level from corresponding genome-wide association studies. Summary-level statistical data for cholelithiasis were obtained from FinnGen and UK Biobank (UKB) consortia. Both univariable and multivariable Mendelian randomization (MR) analyses were conducted to identify causal risk factors of cholelithiasis. Results from FinnGen and UKB were combined using the fixed-effect model. In FinnGen, the odds of cholelithiasis increased per 1-SD increase of body mass index (BMI) (OR = 1.631, p = 2.16 × 10 −7 ), together with body fat percentage (OR = 2.108, p = 4.56 × 10 −3 ) and fasting insulin (OR = 2.340, p = 9.09 × 10 −3 ). The odds of cholelithiasis would also increase with lowering of total cholesterol (OR = 0.789, p = 8.34 × 10 −5 ) and low-density lipoproteincholesterol (LDL-C) (OR = 0.792, p = 2.45 × 10 −4 ). However, LDL-C was not significant in multivariable MR. In UKB, the results of BMI, body fat percentage, total cholesterol, and LDL-C were replicated. In meta-analysis, the liability to type 2 diabetes mellitus and smoking could also increase the risk of cholelithiasis. Moreover, there were no associations with other predominant risk factors.Conclusions: Our MR study corroborated the risk factors of cholelithiasis from previous MR studies. Furthermore, lower total cholesterol level could be an independent risk factor.

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Background The relationship between serum lipids and cholecystitis is still under investigation. To examine the causal effect of serum lipids on cholecystitis using the Mendelian randomization method. Methods We conducted univariable Mendelian randomization (MR) analyses using summary statistics from two independent genome-wide association studies (GWAS) on serum lipids (n = 132,908) and cholecystitis (n = 361,194). Mainly, the inverse-variance weighted (IVW) method was utilized to combine each SNP’s causal estimation, and the MR-Egger was adopted as a complementary method, together with the weighted median. Cochrane’s Q value was employed to appraise heterogeneity. The MR-Egger intercept and MR-PRESSO were used to detect the horizontal pleiotropy. Results Our univariable results displayed a minor protective effect of serum low-density lipoprotein (LDL) cholesterol (OR [95% CI] = 0.9984483 [0.9984499, 0.9984468]; p = 0.008) on cholecystitis. No significant causal effect of total cholesterol (TC) (OR [95% CI] = 0.9994228 [0.9994222, 0.9994233]; p = 0.296), triglycerides (OR [95% CI] = 0.9990893 [0.9990882, 0.9990903]; p = 0.238) and high-density lipoprotein (HDL) cholesterol (OR [95% CI] = 0.9997020 [0.9997017, 0.9997023]; p = 0.565) was found on cholecystitis. Conclusion These findings suggest that LDL cholesterolhas a slight protective effect on cholecystitis, which can be easily affected by confounding factors. TC, triglycerides and HDL cholesterol don’t have causal effect on cholecystitis. The protective effect of serum lipids on cholecystitis, though possible, remain less certain.

Context Although several risk proteins for hypothyroidism have been reported in recent years, many more plasma proteins have not been tested. Objective To determine potential mechanisms and novel causal plasma proteins for hypothyroidism using Mendelian randomization (MR). Methods A large-scale plasma proteome MR analysis was conducted using protein quantitative trait loci (pQTLs) for 2297 plasma proteins. We classified pQTLs into four different groups. MR analyses were conducted within the four groups simultaneously. Significant proteins were discovered and validated in two different cohorts. Colocalization analysis and enrichment analysis were conducted using proteins found with MR. Results Thirty-one proteins were identified in the discovery cohort. Among them, 13 proteins were validated in the validation cohort. Nine of the 13 proteins are risk factors (ISG15, FcRL2, LIGHT, Rab-2A, FcRL3, thrombomodulin, IFN-lambda-1, GPIbA, IL-7RA) for hypothyroidism, while others are protective proteins (POGLUT1, RANKL, HIBYL-CoA-H, TfR1). Among the significant proteins, POGLUT1 strongly colocalized with eQTLs from whole blood (posterior probability of colocalization (PP4) = 0.978) and the thyroid (PP4 = 0.978). Two different trans-pQTLs (rs2111485 PP4 = 0.998; rs35103715 PP4 = 0.998) for IFN-lambda-1 strongly colocalized with hypothyroidism in different chromosomes. Conclusions Thirteen various proteins were identified and validated to be associated with hypothyroidism using univariable MR. We reinforced and expanded the effect of IFN on hypothyroidism. Several proteins identified in this study could explain part of the association between the coagulation system and hypothyroidism. Our study broadens the causal proteins for hypothyroidism and provides the relationships between plasma proteins and hypothyroidism. The proteins identified in this study can be used as early screening biomarkers for hypothyroidism.