Investigating the common genetic basis between inflammatory bowel disease and metabolic syndrome through genomic structural equation modeling

Abstract

Background Inflammatory bowel disease (IBD) and metabolic syndrome (MetS) exhibit a complex interplay, with clinical evidence indicating an increasing incidence of their co-occurrence. However, current research lacks a systematic framework to model the pleiotropic genetic architecture linking gastrointestinal and liver-metabolic phenotypes, thereby hindering a comprehensive understanding of how multiple genetic risk factors converge to drive IBD–MetS comorbidity. Methods This study employed genomic structural equation modeling (SEM) to integrate genome-wide association study (GWAS) summary datasets for IBD and MetS-related traits (body mass index, triglycerides, non-alcoholic fatty liver disease, hypertension, and type 2 diabetes), creating the multivariate GWAS summary datasets. Post-GWAS analytical approaches were subsequently utilized to assess risky loci, gene functionality, and tissue-specific regulatory networks, aiming to elucidate the pathological connections between chronic low-grade inflammation and the gut-liver-metabolic axis. Results Genomic SEM identified a shared latent genetic factor between IBD and MetS (Comparative Fit Index = 0.9864, Standardized Root Mean Square Residual = 0.0602). A total of 522 lead single nucleotide polymorphism (SNP) loci were identified, including 21 novel SNPs specific to the multivariate model that were not detected in univariate GWAS. Fine-mapping with SuSiE and FINEMAP identified 29 high-confidence causal SNPs. Integrating SNP fine-mapping with MAGMA, FUSION, and FOCUS analyses confirmed seven core genes. Conclusion To the best of our knowledge, this study provides the first comprehensive characterization of the shared genetic architecture of IBD and MetS through a multivariate genetic model. The results deepen the understanding of the genetic mechanisms underlying IBD and MetS and offer potential therapeutic targets and a conceptual framework for developing interventions for cross-system diseases.