Studying Multi-omics to Understand and Predict Metabolic Syndrome and Related Diseases for Better Personalized Care

What this Trial Is About

Researchers are evaluating integrative multi-omics approaches to better understand complications related to metabolic syndrome. The study aims to identify biological factors that predict disease progression by combining genetic, transcriptomic, metabolomic, and microbiome data from participants with and without metabolic syndrome. This research seeks to support precision prevention and treatment strategies for metabolic disorders. This longitudinal, multi-center observational study will recruit individuals diagnosed with metabolic syndrome as well as age- and sex-matched controls without the condition. Participants will provide annual blood, urine, and stool samples for comprehensive profiling including DNA sequencing, RNA sequencing, metabolite analysis, and stool microbiota assessment. These biological samples will be linked with clinical data from a research database to create a unified framework for integrative analysis. Participants will be followed for up to five years, during which researchers will collect biological samples and clinical information annually. The study will use advanced bioinformatics and machine learning tools to identify molecular networks and biomarkers associated with metabolic complications such as cardiovascular disease, chronic kidney disease, and fatty liver disease. Data will be securely stored with participant confidentiality maintained. The main outcome is the identification and validation of multi-omics biomarkers related to metabolic syndrome and its complications over a five-year period.

Validating Integrative Multi-omics Approaches in Metabolic Syndrome-related Diseases