Characterizing the genetic basis of Cardio-Renal-Metabolic multimorbidity using multivariate genomic modelling

Cardio-renal-metabolic multimorbidity (CRMM) denotes co-occurring cardiovascular, kidney, and metabolic diseases. The shared genetic architecture of CRMM across diverse ancestries remains incompletely understood. Using ancestry-specific GWAS-summary statistics from >3.5 million participants across seven biobanks, we applied genomic structural equation modelling to characterise shared genetic architecture of hypertension, heart failure, ischaemic heart disease, chronic kidney disease, and type 2 diabetes in European (EUR), East Asian (EAS), and African (AFR) populations. Conditional analysis of the hierarchical latent-factor model identified 148, 16, and 138 independent CRMM-associated loci in EUR, AFR, and EAS populations, respectively. Integrative analyses implicated neurological pathways and brain-tissue enrichment, nominating ATXN2L as a putative functional gene. Drug repurposing analyses highlighted existing diabetes and hypertension therapies as potential therapeutic candidates, while Mendelian randomisation identified adiposity, cholesterol, and other traits as candidate causal risk factors for CRMM. These findings delineate cross-ancestry and ancestry-specific CRMM genetics, informing therapeutic targets and precision medicine.