Integrative Omics Defines Metabolic Biomarkers and Genetic Regulatory Mechanisms of Mortality Risk

The genetic and metabolic architecture of mortality risk represents a fundamental, yet poorly understood, challenge in human medicine and livestock breeding. Here serum metabolomics and multi-omics data is integrated in a designed 3-generation chicken model (n = 1,277) with divergent mortality. The analysis reveals a trade-off between heightened inflammatory responses and impaired growth in susceptible animals. To uncover the genetic underpinnings, 45,585 metabolite quantitative trait loci are identified, which are predominantly enriched among liver-specific regulatory variants. Using a machine learning approach, a robust 16-metabolite signature is established, including hexyl glucoside and pyrraline, that accurately predicts mortality risk. Importantly, these metabolites and their genetic loci offer practical targets for genomic selection in chicken breeding, providing a direct approach to enhance disease resistance and survival. Cross-species comparison with human data revealed conserved metabolic dysregulation pathways, while also highlighting species-specific immuno-metabolic pathophysiology. Furthermore, the findings pinpoint butyrate-mediated microbiota-host interactions and the dual antioxidant functions of L-cysteine as critical regulatory mechanisms. Together, these results delineate an evolutionarily conserved immuno-metabolic framework for mortality risk, offering novel biomarkers for selective breeding and potential therapeutic targets for human metabolic diseases.