Multi-omics Merge for Ensemble Subtyping for Atherosclerotic Cardiovascular Disease

Multi-omics Merge for Ensemble Subtyping for Atherosclerotic Cardiovascular Disease and Related Mechanism Study

The current biological issues driving the evolutionary progression of coronary artery disease are in focus: at this stage, the biological evidence for them is scarce and small in scale, with the exception of metabolomics and microbiomics. Issues such as histologic mapping of coronary atherosclerosis deterioration remain to be corroborated by more clinical and basic evidence! By analyzing the clinical data and multi-omics data of patients with coronary heart disease, investigators will explore the related risk factors and establish molecular subtypes and prognostic prediction models for individualized prediction of coronary heart disease risk, in order to guide the clinical screening of high-risk groups of coronary heart disease and formulate more targeted intervention countermeasures.

The biological mechanisms driving the progression of coronary artery disease (CAD) are complex and multifaceted. While there have been significant advances in understanding these mechanisms, much of the biological evidence remains limited and fragmented, especially beyond the realms of metabolomics and microbiomics. For instance, the detailed histologic mapping of the deterioration of coronary atherosclerosis still requires more extensive clinical and basic research to substantiate initial findings.

To address these gaps, researchers are turning to comprehensive analyses of clinical and multi-omics data from patients with coronary heart disease. This involves a deep dive into various data types, including genomics, proteomics, metabolomics, and microbiomics, to identify potential risk factors associated with CAD. By integrating these data, investigators aim to uncover molecular subtypes of the disease that can provide a more nuanced understanding of its progression.

Furthermore, the goal is to develop robust prognostic prediction models that can accurately forecast the risk of CAD in individual patients. These models will leverage the identified molecular subtypes and associated risk factors to offer personalized predictions, which are crucial for effective clinical decision-making. Through this individualized approach, it will be possible to enhance the screening processes for high-risk groups and design more precise and effective intervention strategies.

Ultimately, this research endeavors to bridge the gap between basic scientific discoveries and clinical applications, paving the way for tailored therapeutic interventions that can significantly improve patient outcomes in coronary artery disease.

Inclusion Criteria:

1. aged more than 18 years
2. meet the diagnostic criteria of coronary heart disease
3. undergo coronary angiography after admission and have at least 50% stenosis in at least one major coronary artery
4. able to sign the informed consent form

Exclusion Criteria:

1. severe valvular disease (defined as valvular disease stage C or D)
2. hypertrophic cardiomyopathy; pulmonary heart disease 2) gastrointestinal disease
3. hyperthyroidism, anemia, or any other high-intensity heart disease
4. malignant tumors
5. severe dysfunction of the liver (defined as alanine aminotransferase or total bilirubin greater than 3 times the upper limit of normal) or kidney (defined as eGFR) >20 mL/min/1.73m2 or requiring dialysis)
6. severe congenital heart disease
7. severe infectious or contagious disease
8. autoimmune disease
9. age <18 years
10. patients with incomplete clinical records