Metabolic syndrome refers to a cluster of metabolic disorders that significantly increase the risk of developing cardio-cerebrovascular diseases, type 2 diabetes, and other health complications. While traditional factors such as age, ethnicity and lifestyle are established contributors to metabolic syndrome onset and progression , the roles of genetic susceptibility, multi-omics biomarkers, imaging-derived phenotypes, and environmental exposures in shaping long-term metabolic syndrome prognosis remain inadequately characterized.
To quantify how psychological, cognitive, genetic, metabolic, imaging and environmental factors shape long-term metabolic syndrome outcomes.
In this study, datasets from the UK Biobank were utilized, including baseline questionnaires (e.g., lifestyle, cognitive function, and environmental exposures), genotyping data, physical measurements, metabolic biomarkers, longitudinal health records, clinical outcomes (e.g., macrovascular and microvascular complications, cancer, mental disorders), and imaging data. Using statistical methods such as the Cox proportional hazards model, logistic regression model, mediation analysis, restricted cubic splines, we evaluated the independent effects, dose-response relationships, and interactions of relevant risk factors. Furthermore, by integrating data sources, a analysis was conducted to investigate the associations between risk factors (e.g., lipid profiles, dynamic changes in blood glucose, obesity indicators, genetic susceptibility, environmental exposure, and mental and cognitive characteristics) and the long-term prognosis of metabolic syndrome, including all-cause mortality, cardiovascular and cerebrovascular events, diabetic complications, and cancer incidence.
This study focuses on the interplay mechanisms of genetics, metabolism and environment, to provide a scientific basis for clinical decision-making and novel drug researches on metabolic syndrome.
