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

Validating Integrative Multi-omics Approaches in Metabolic Syndrome-related Diseases: A Step Towards Precision Medicine

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.

Study Overview

• Status: Recruiting
• Conditions: Healthy; Chronic Kidney Disease; Nonalcoholic Fatty Liver Disease; Obesity & Overweight; Cardiovascular Diseases (CVD); Metabolic Syndrome (MetS)
• Intervention / Treatment: Other: No intervention

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.

• Study Type: Observational
• Enrollment (Estimated): 6266

Contacts and Locations

• Study Contact: Name: Chi-Hsiao Yeh, MD PhD; Phone Number: 2118 +886-3-3281200; Email: yehccl@cgmh.org.tw

Study Locations

• Taiwan
  • Taoyuan District, Taiwan
    • Recruiting
    • Chang Gung Memorial Hospitals, Linkou
    • Contact: Chi-Hsiao Yeh, MD PhD; Phone Number: +886-3-3281200; Email: yehccl@cgmh.org.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

whole cohort; Participants who meet the diagnostic criteria for metabolic syndrome, as defined by the International Diabetes Federation (IDF) and/or ATP III guidelines and those participants without metabolic syndrome who are matched to the study group by age and sex. These individuals will undergo annual biospecimen collection (blood, urine, and stool) and longitudinal clinical follow-up to identify molecular signatures associated with disease progression and metabolic complications.

Other: No intervention; no intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	5 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Annually for 5 years
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).	Up to 5 years
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.	5 years

Collaborators and Investigators

• Sponsor: Chang Gung Memorial Hospital

Publications and helpful links

General Publications

• Hsu PW, Yeh CH, Lo CJ, Tsai TH, Chan YH, Chou YJ, Yang NI, Cheng ML, Sheu WH, Lai CC, Sytwu HK, Tsai TF. Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease. Biomark Res. 2025 Aug 20;13(1):107. doi: 10.1186/s40364-025-00821-y.

Helpful Links

• Related Info

Study record dates

Study Major Dates

• Study Start (Actual): June 9, 2025
• Primary Completion (Estimated): September 30, 2028
• Study Completion (Estimated): September 30, 2035

Study Registration Dates

• First Submitted: November 18, 2025
• First Submitted That Met QC Criteria: November 18, 2025
• First Posted (Actual): November 25, 2025

Study Record Updates

• Last Update Posted (Actual): November 25, 2025
• Last Update Submitted That Met QC Criteria: November 18, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: Microbiome; Metabolomics; Metabolic Syndrome; Genomics; Precision Medicine; Machine Learning; Transcriptomics; Multi-Omics
• Additional Relevant MeSH Terms: Urogenital Diseases; Pathologic Processes; Nutrition Disorders; Male Urogenital Diseases; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Chronic Disease; Disease Attributes; Metabolic Diseases; Overnutrition; Body Weight; Digestive System Diseases; Glucose Metabolism Disorders; Liver Diseases; Renal Insufficiency; Insulin Resistance; Hyperinsulinism; Fatty Liver; Pathological Conditions, Signs and Symptoms; Nutritional and Metabolic Diseases; Signs and Symptoms; Overweight; Obesity; Cardiovascular Diseases; Metabolic Syndrome; Non-alcoholic Fatty Liver Disease; Renal Insufficiency, Chronic
• Other Study ID Numbers: 202400297A3

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No