Cardiovascular Risk Assessment: A Review of Current Models and Emerging Determinants, including Biomarkers, Genetics,...

Area of Science:

• Cardiology
• Preventive Medicine
• Genetics

Background:

• Cardiovascular disease (CVD) is a leading global cause of mortality.
• Primary prevention strategies are crucial for public health gains in CVD.
• Traditional risk stratification tools like PCEs and SCORE2 have limitations in capturing individual variability and non-traditional risk factors.

Purpose of the Study:

• To critically discuss emerging risk determinants for primary CVD prevention.
• To evaluate the utility and limitations of novel risk stratification tools.
• To provide guidance on integrating new data into clinical decision-making for CVD prevention.

Main Methods:

• Literature review and critical discussion of emerging risk factors and stratification tools.
• Analysis of limitations and potential clinical applications of novel biomarkers, genetic scores, and imaging techniques.
• Evaluation of evidence for inflammation markers and multiomics approaches in CVD risk assessment.

Main Results:

• Coronary artery calcium scoring, apolipoprotein B, lipoprotein(a), polygenic risk scores, and multiomics offer potential for risk reclassification but face challenges in validation, cost, access, and clinical utility.
• Inflammation targeting (e.g., colchicine) and clonal hematopoiesis of indeterminate potential have unclear roles in primary CVD prevention.
• Artificial intelligence shows promise for prediction but requires broader validation.

Conclusions:

• New risk stratification tools show promise but require robust, outcome-based evidence before widespread clinical adoption.
• Risk enhancers should guide, not dictate, therapy in selected patients, pending further evidence.
• The proliferation of tools necessitates careful consideration of when additional data meaningfully alter clinical decisions.