Sepsis impacts over 49 million people yearly, with 20-30% mortality. Sepsis-induced coagulopathy (SIC) affects 50-70% of cases, progressing to disseminated intravascular coagulation (DIC), organ failure, and higher death rates. Traditional diagnostics (e.g., SOFA score, thrombocytopenia) lack early sensitivity/specificity. Lifestyle factors (diet, activity, smoking, BMI) modulate immune/coagulation pathways, influencing sepsis risk/progression. High-throughput omics-genomics (GWAS identifying immune loci), proteomics (inflammatory/coagulation changes), metabolomics (metabolic disruptions like lipid peroxidation)-enable molecular insights. Integrating multi-omics with clinical phenotypes and lifestyle data, via resources like UK Biobank and SEPSIS-OMICS, can reveal biomarkers, mechanisms (e.g., glycocalyx degradation, inflammasome activation), and predictive models for early diagnosis and reduced mortality.
This study aims to integrate clinical indices, lifestyle characteristics, and multi-omics profiling to better understand the biological underpinnings of sepsis and SIC. We will collect patient data across clinical, environmental, and behavioral domains, along with biospecimens for genome/exome sequencing, targeted proteomics, and untargeted metabolomics. Analytical methods will include statistical modeling, machine learning (e.g., random forests), and network-based approaches to identify predictive biomarkers and molecular signatures associated with SIC development and poor outcomes.
Core questions include: How do omics-based alterations intersect with lifestyle and clinical factors to influence sepsis progression? Can integrative data models outperform single-domain predictors in forecasting SIC risk? The findings will inform mechanistic understanding, support early diagnosis, and contribute to precision medicine strategies for sepsis care. Results will be disseminated through academic publications and expert forums to promote clinical translation.
