Cardiovascular Diseases (CVD), Chronic Kidney Disease, Healthy, Metabolic Syndrome (MetS), Nonalcoholic Fatty Liver Disease, Obesity & Overweight
Conditions
Keywords
Metabolic Syndrome, Multi-Omics, Precision Medicine, Metabolomics, Genomics, Transcriptomics, Microbiome, Machine Learning
Brief summary
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.
Detailed description
This longitudinal, multi-center study is designed to validate integrative multi-omics methodologies for predicting disease progression and complications in metabolic syndrome. Participants will be recruited from all branches of Chang Gung Memorial Hospitals. Individuals who meet the diagnostic criteria for metabolic syndrome will constitute the study group, while age- and sex-matched individuals without metabolic syndrome will serve as controls. The study will collect peripheral blood, urine, and stool samples for comprehensive multi-omics profiling, including genomics (DNA sequencing), transcriptomics (RNA sequencing), metabolomics (serum and urine metabolite profiling), and microbiomics (stool microbiota analysis). Blood samples (10 mL) will be obtained annually for genetic and metabolomic analyses, while urine (30 mL) and stool (1 mL) samples will be used to assess metabolite and microbial signatures. These biospecimens will be linked with participants' longitudinal clinical data and laboratory test results retrieved from the Chang Gung Research Database (CGRD), providing a unified framework for integrative analysis. Data integration will utilize advanced bioinformatics pipelines and systems biology tools to identify multi-layered molecular networks associated with disease onset and progression. Analytical methods include dimensionality reduction, clustering, and machine-learning-based feature selection to construct predictive models for metabolic complications such as cardiovascular disease, chronic kidney disease, and fatty liver disease. Identified biomarkers and pathways will be validated internally and cross-compared with pre-existing data from the Integrated Smart Healthcare Database for Obesity. All data will be de-identified and securely stored on institutional servers with restricted access. Each participant will be assigned a unique study code to ensure confidentiality. Data linkage between omics datasets and clinical outcomes will be performed through encrypted, privacy-preserving algorithms under the supervision of the institutional data governance committee. The study adheres to the ethical standards set by the Institutional Review Board, ensuring participant protection throughout data collection, analysis, and dissemination.
Interventions
OTHERNo intervention
no intervention
Sponsors
Chang Gung Memorial Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Individuals (male or female) aged 20 years or older * Willing and able to provide written informed consent to participate in the study
Exclusion criteria
* 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 * Patients diagnosed with dementia
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification and validation of multi-omics biomarkers associated with metabolic syndrome and its complications | 5 years | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Longitudinal changes in metabolomic and microbiome profiles | Annually for 5 years | 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 | Up to 5 years | 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 | 5 years | Construction and internal validation of a machine-learning-based model incorporating multi-omics and clinical data to predict metabolic syndrome-related complications. |
Countries
Taiwan
Contacts
Primary ContactChi-Hsiao Yeh, MD PhD
Outcome results
None listed