This study aims to validate integrative multi-omics approaches for understanding complications related to metabolic syndrome. By combining genetic, transcriptomic, metabolomic, and microbiome data from participants with and without metabolic syndrome, the research seeks to determine which biological factors predict disease progression and how these insights can inform precision prevention and treatment strategies for metabolic disorders.
Individuals (male or female) aged 20 years or older
Willing and able to provide written informed consent to participate in the study
Pregnant or breastfeeding women
Patients with end-stage renal disease receiving hemodialysis or peritoneal dialysis
Individuals currently undergoing active cancer treatment
Recipients of any organ transplantation
Participants will be recruited from outpatient clinics at Chang Gung Memorial Hospitals in Taiwan. Recruitment will occur through the Departments of Endocrinology, Cardiology, Cardiac Surgery, Nephrology, and Gastroenterology. The study population will include adults receiving routine clinical care at these sites, representing both individuals with metabolic syndrome and those without the condition who serve as healthy controls. This hospital-based community cohort reflects a diverse urban and suburban Taiwanese population, enabling comprehensive multi-omics analysis of metabolic syndrome-related diseases within a real-world clinical setting.
Cohort
Prospective
No intervention
Other interventionno intervention
Identification and validation of multi-omics biomarkers associated with metabolic syndrome and its complications
Comprehensive integration of genomic, transcriptomic, metabolomic, and microbiome datasets to identify molecular signatures predictive of metabolic syndrome progression and related complications (e.g., cardiovascular disease, chronic kidney disease, fatty liver).
Longitudinal changes in metabolomic and microbiome profiles
Evaluation of yearly changes in serum metabolite and gut microbiota composition and their correlation with metabolic parameters such as fasting glucose, triglycerides, HDL-C, and blood pressure.
Association between omics-derived biomarkers and clinical outcomes
Analysis of associations between identified omics signatures and incident cardiometabolic events (e.g., myocardial infarction, heart failure, renal impairment, fatty liver progression).
Development of an integrative risk prediction model
Construction and internal validation of a machine-learning-based model incorporating multi-omics and clinical data to predict metabolic syndrome-related complications.
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