Multi-omics search for clotting and bleeding markers in coronary heart disease

Multi-Omics-Based Development of Novel Thrombosis and Bleeding Markers and Construction of a Risk Prediction Model in Coronary Heart Disease

This project will test whether combining proteins, metabolites, genes, sugar tags, and heart images can better predict clotting and major bleeding in people with coronary heart disease on dual antiplatelet therapy.

Quick facts

• Study type: Observational
• Enrollment: 12154 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: Chinese Academy of Medical Sciences, Fuwai Hospital (Academic / other)
• Locations: 3 sites (Zhengzhou, Henan and 2 other locations)
• Trial ID: NCT07523880 on ClinicalTrials.gov

What this trial studies

Researchers will use a large, long-term Chinese cohort of patients with coronary heart disease, including those with acute coronary syndrome and patients undergoing PCI, to discover new biomarkers linked to thrombosis and major bleeding. They will analyze multiple molecular layers (proteomics, metabolomics, glycomics, genomics) plus medical imaging and clinical data. Machine learning methods will identify the most informative markers and the team will build new risk models that add these markers to existing clinical scores like GRACE, PARIS, and Precise-DAPT. The new models will be tested within the cohort to see if they predict clotting and bleeding events better than current tools.

Who should consider this trial

Good fit: Adults (age ≥18) with coronary heart disease who are hospitalized and planned for long-term antithrombotic therapy—including ACS patients or stable CAD patients undergoing PCI—with available blood samples and follow-up data are ideal candidates.

Not a fit: Patients without available blood specimens or complete follow-up data, or those not on long-term antiplatelet therapy, are unlikely to benefit from the study's findings.

Why it matters

Potential benefit: If successful, the models could help doctors predict who is at high risk of clotting or bleeding and personalize antiplatelet therapy to reduce those risks.

How similar studies have performed: Prior work has shown some single-biomarker and clinical-score approaches can predict events, but combining multi-omics with imaging and machine learning for DAPT-related clotting and bleeding risk is relatively novel and not yet widely validated.

Eligibility criteria

Inclusion Criteria:

1. Age ≥ 18 years.
2. Hospitalized due to symptoms or objective evidence of coronary heart disease and planned for long-term antithrombotic therapy.
3. Diagnosis of acute coronary syndrome or stable coronary heart disease undergoing percutaneous coronary intervention (PCI), with clinical stability meeting discharge criteria after treatment.
4. For patients from the existing cohort: minimum 2 years of follow-up with complete clinical data and blood specimens available; approval for use of these data and specimens has been obtained, with a waiver of re-consent.

For newly enrolled patients: voluntary written informed consent provided by the patient or legal representative, agreement to provide blood samples, and acceptance of follow-up procedures.

Exclusion Criteria:

For patients from the existing cohort:

1. Severe missing or erroneous baseline or clinical data that cannot be corrected by source verification.
2. No available blood specimen, or specimen that does not meet testing requirements.

For newly enrolled patients:

1. Presence of serious comorbid conditions with life expectancy ≤ 6 months.
2. Conditions that significantly affect study compliance or the ability to complete follow-up.
3. Contraindications to blood sampling.

Where this trial is running

Zhengzhou, Henan and 2 other locations

• Fuwai Central China Cardiovascular Hospital — Zhengzhou, Henan, China (Recruiting)
• General Hospital of Northern Theater Command of Chinese People's Liberation Army — Shenyang, Liaoning, China (Recruiting)
• Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College — Beijing, China (Recruiting)

Study contacts

• Study coordinator: Xueyan Zhao, PhD

Conditions Thrombosis; Bleeding; Coronary Artery Disease; Coronary Heart Disease; Dual Antiplatelet Therapy; Multi-Omics; Biomarkers; Risk Prediction Model