Objective:To identify circulating proteins associated with cardiovascular, renal, and cardiorenal comorbidity events in individuals with metabolic syndrome, to construct a predictive model incorporating these proteins to improve prediction accuracy and to investigate their mediating effects on the interplay between cardiovascular and renal diseases.Methods:Data from the UK Biobank cohort were utilized. Cox proportional hazards models were applied to identify circulating proteins associated with various outcomes, followed by time-truncated sensitivity analyses. A predictive model incorporating protein scores was then developed using the LightGBM algorithm and compared with other models. Gene Ontology(GO) functional enrichment analysis was performed to explore the biological pathways of the identified proteins. Finally, mediation effect analysis was conducted to assess the role of circulating proteins in cardiorenal interactions. Results:The Cox analysis identified 180, 275, and 322 circulating proteins associated with cardiovascular events, renal events, and cardiorenal comorbidity events, respectively. Incorporating protein scores significantly improved model performance; the areas under the curve(AUC) for cardiovascular, renal, and cardiorenal events were 0.833, 0.907, and 0.890, respectively. GO functional enrichment analysis demonstrated significant enrichment in pathways such as cytokine activity(GO: 0005125), glycosaminoglycan binding(GO: 0005539), and humoral immune response(GO: 0006959) among all outcome-related proteins. Notably, EDA2R, GDF15, and WFDC2 exhibited significant mediating effects, each with mediation proportions exceeding 10%. Conclusions:A predictive model incorporating circulating protein scores can substantially improve prediction accuracy for cardiovascular and renal outcomes in individuls with metabolic syndrome.

Objective:To identify circulating proteins associated with cardiovascular, renal, and cardiorenal comorbidity events in individuals with metabolic syndrome, to construct a predictive model incorporating these proteins to improve prediction accuracy and to investigate their mediating effects on the interplay between cardiovascular and renal diseases.Methods:Data from the UK Biobank cohort were utilized. Cox proportional hazards models were applied to identify circulating proteins associated with various outcomes, followed by time-truncated sensitivity analyses. A predictive model incorporating protein scores was then developed using the LightGBM algorithm and compared with other models. Gene Ontology(GO) functional enrichment analysis was performed to explore the biological pathways of the identified proteins. Finally, mediation effect analysis was conducted to assess the role of circulating proteins in cardiorenal interactions. Results:The Cox analysis identified 180, 275, and 322 circulating proteins associated with cardiovascular events, renal events, and cardiorenal comorbidity events, respectively. Incorporating protein scores significantly improved model performance; the areas under the curve(AUC) for cardiovascular, renal, and cardiorenal events were 0.833, 0.907, and 0.890, respectively. GO functional enrichment analysis demonstrated significant enrichment in pathways such as cytokine activity(GO: 0005125), glycosaminoglycan binding(GO: 0005539), and humoral immune response(GO: 0006959) among all outcome-related proteins. Notably, EDA2R, GDF15, and WFDC2 exhibited significant mediating effects, each with mediation proportions exceeding 10%. Conclusions:A predictive model incorporating circulating protein scores can substantially improve prediction accuracy for cardiovascular and renal outcomes in individuls with metabolic syndrome.

Objective:To investigate the causal association between testosterone and nonalcoholic fatty liver disease(NAFLD) in men and women using a two-sample Mendelian randomization(MR) approach.Methods:Genetic variation in testosterone(total testosterone, bioavailable testosterone) and sex hormone-binding globulin(SHBG) in females and males was used as an instrumental variable using the genome-wide association study(GWAS) pooled data, and the inverse variance weighting method was applied. Inverse variance weighted(IVW) was used as the main analytical method, along with six univariate MR methods based on other modeling assumptions to assess the causal relationship between testosterone(total testosterone, bioavailable testosterone) as well as SHBG and NAFLD in women and men. In addition, NAFLD data from Finnish Biobank(FinnGen) were applied to validate the results of the exploratory analysis. Further, sensitivity analyses were performed to assess the level of heterogeneity, genetic pleiotropy, and stability of the instrumental variables using Cochran′ s Q test, MR-Egger regression, and leave-one-out methods. Results:The results of exploratory analysis of IVW model showed that bioavailable testosterone and SHBG were causally associated with NAFLD in women, for each unit increase in bioavailable testosterone levels, the risk of developing non-alcoholic fatty liver disease(NAFLD) rose by 24%( OR=1.24, 95% CI 1.07-1.43, P=0.004); and with each unit decrease in women′s SHBG, the NAFLD risk increased by 31%( OR=0.69, 95% CI 0.57-0.83, P<0.001). However, testosterone(total testosterone, bioavailable testosterone) as well as SHBG in men and female total testosterone did not show a causal relationship with NAFLD. The results of the other six MR methods were generally consistent with the IVW method. The results of the external validation data provided further evidence of a causal relationship between female bioavailable testosterone and SHBG and NAFLD. Conclusion:Elevated levels of bioavailable testosterone along lower levels of SHBG may increase the risk of developing NAFLD in women.