Sepsis and subsequent multi-organ failure represent life-threatening conditions with high mortality rates, characterized by dysregulated host responses to infection leading to systemic inflammation and organ damage. The pathophysiology involves complex interactions between genetic susceptibility, metabolic reprogramming, inflammatory protein cascades, and structural organ alterations. However, current understanding of the molecular drivers and progression pathways remains incomplete, limiting early detection and targeted interventions. This study will leverage multi-omics data to address key research questions: How do genetic variants influence susceptibility to sepsis and organ dysfunction? What metabolomic and proteomic signatures distinguish patients at risk of progressive multi-organ failure? How do imaging biomarkers reflect underlying molecular mechanisms? We will integrate genomic data (GWAS, exome sequencing), metabolomic profiles (Nightingale platform), proteomic data (Olink panels), and clinical imaging (chest/abdominal CT, echocardiography) from UK Biobank participants with sepsis and organ dysfunction. Analytical approaches will include machine learning for risk stratification, pathway analysis to elucidate inflammatory and metabolic mechanisms, and Mendelian randomization to infer causality. The research aims to develop predictive models for early identification of high-risk patients, uncover novel therapeutic targets, and ultimately improve clinical outcomes through personalized management strategies.
