Decoding the genetic crosstalk network for cardiometabolic diseases and metabolic traits

Metabolic malfunctions are commonly observed in cardiometabolic diseases (CMDs), yet their genetic connections are not fully explored. We leverage multi-omics to decode the genetic crosstalk between 17 cardiometabolic diseases and 16 metabolic traits. Through genomic structural equation modeling, we identify 7 disease clusters anchored on 7 distinct metabolic axes, revealing subtype-specific variants, candidate genes, pathways, tissues and cell types. Among these are four metabolically distinct subtypes of arterial disorders, two adiposity subtypes with opposing associations with CMDs, and a heart-brain-kidney subtype characterized by blood pressure and BMI with dysregulated muscle-vessel coupling. Additionally, we uncover the genetic links between cardiometabolic processes and female health diagnoses. Finally, we leverage the shared risk genes to discover candidate drugs for CMDs, offering potential to improve comorbidity treatments. Overall, our study reveals the genetic basis of the metabolic networks underlying CMDs and extends their relevance into the female health. These results provide a foundation for future mechanistic studies and precise stratification of complex human diseases.