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

NCT07248371 | Recruiting

• 1 other identifier: 202400297A3
• Study Type: observational
• Target: 6,266
• Locations: 1 country
• Sites: 1

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.

Conditions

Metabolic Syndrome (MetS); Obesity & Overweight; Cardiovascular Diseases (CVD); Chronic Kidney Disease; Nonalcoholic Fatty Liver Disease; Healthy

Keywords

Metabolic Syndrome; Multi-Omics; Precision Medicine; Metabolomics; Genomics; Transcriptomics; Microbiome; Machine Learning

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Longitudinal changes in metabolomic and microbiome profiles

  Annually for 5 years

• Association between omics-derived biomarkers and clinical outcomes

  Up to 5 years

• Development of an integrative risk prediction model

  5 years

Study Arms (1)

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

Interventions

No intervention OTHER

no intervention

whole cohort

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Individuals (male or female) aged 20 years or older
• Willing and able to provide written informed consent to participate in the study

You may not qualify if:

• 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

Sponsors & Collaborators

• Chang Gung Memorial Hospital: lead

Study Sites (1)

Chang Gung Memorial Hospitals, Linkou

Taoyuan District, Taiwan

RECRUITING

Related Publications (1)

• 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.

  PMID: 40830908 RESULT

Related Links

• Related Info

Biospecimen

Retention: SAMPLES WITH DNA

genomics (DNA sequencing), transcriptomics (RNA sequencing), metabolomics (serum and urine metabolite profiling), and microbiomics (stool microbiota analysis).

MeSH Terms

Conditions

Metabolic Syndrome; Obesity; Overweight; Cardiovascular Diseases; Renal Insufficiency, Chronic; Non-alcoholic Fatty Liver Disease

Condition Hierarchy (Ancestors)

Insulin Resistance; Hyperinsulinism; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Overnutrition; Nutrition Disorders; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Fatty Liver; Liver Diseases; Digestive System Diseases

Central Study Contacts

Chi-Hsiao Yeh, MD PhD

CONTACT

+886-3-3281200; yehccl@cgmh.org.tw

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 18, 2025
• First Posted: November 25, 2025
• Study Start: June 9, 2025
• Primary Completion (Estimated): September 30, 2028
• Study Completion (Estimated): September 30, 2035
• Last Updated: November 25, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)