Integrating genetic and imaging information to enhance cardiovascular risk stratification: rationale and design of
Emanuele Di Angelantonio1,2,3,4,5,6, Massimo Piepoli7, Serenella Castelvecchio8
1Health Data Science Research Centre, Human Technopole, Viale Rita Levi- Montalcini, 1 Area MIND, 20157 Milan, Italy.
Insights
The CVRISK-IT trial investigates if imaging and genetic data can improve cardiovascular disease (CVD) risk prediction. This study aims to enhance CVD prevention strategies for better public health outcomes.
Area of Science:
- Cardiology
- Genetics
- Medical Imaging
Background:
- Cardiovascular disease (CVD) is a leading global cause of death.
- Current CVD risk prediction models have limited precision.
- Integrating imaging and genetic data may improve risk stratification.
Purpose of the Study:
- To evaluate the impact of imaging and polygenic risk scores (PRS) on cardiovascular disease (CVD) risk assessment.
- To compare precision-based CVD prevention strategies against standard care.
- To inform public health policy and routine screening practices.
Main Methods:
- The CVRISK-IT trial is a multicentre, randomized controlled study of 30,000 adults aged 40-80.
- 12,000 participants are randomized into four arms: standard care, imaging, PRS, or combined imaging and PRS.
- Co-primary endpoints include 10-year CVD risk at 12 months and CVD event incidence at 5 years.
Main Results:
- Results are pending as the study is ongoing.
- The trial will provide data on the effectiveness of novel risk stratification methods.
- Analysis will focus on changes in CVD risk, event rates, and adherence to preventive measures.
Conclusions:
- CV RISK-IT will assess the feasibility and impact of integrating genetic and imaging data for CVD risk stratification.
- Findings will guide the implementation of advanced risk assessment in clinical practice.
- The study aims to enhance cardiovascular disease prevention through personalized strategies.
Aims:
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Current guidelines recommend algorithms to estimate 10-year CVD risk in apparently healthy individuals, but their precision remains limited. Whether the integration of imaging and genetic information can improve individual risk stratification remains uncertain.
Methods And Results:
The CVRISK-IT trial is a multicentre, open-label, randomized controlled study designed to enhance CVD prevention through precision-based risk assessment and targeted communication strategies. A total of 30 000 adults aged 40-80 years without previous CVD or diabetes are recruited across Italy. In Phase I, participants undergo a baseline assessment including clinical examination, blood sampling, and calculation of CVD risk. Approximately 12 000 eligible enter Phase II and are randomized equally into four intervention arms: (i) standard care (control), (ii) imaging-based risk refinement (coronary artery calcium score or carotid ultrasound), (iii) polygenic risk score (PRS), or (iv) combination of imaging and PRS. The co-primary endpoints are differences in estimated 10-year CVD risk at 12 months and the incidence of major CVD events, CVD mortality, and all-cause mortality at 5 years. Secondary endpoints include differences in CVD risk factors, prescription and adherence to preventive drug adherence, and behavioural or psychological measures.
Conclusion:
By prospectively evaluating the feasibility and impact of incorporating genetic and imaging information in CVD risk stratification, CVRISK-IT will generate robust evidence to inform its integration into routine screening practices and public health policy.
Trial Registration:
ClinicalTrials.gov Identifier NCT06832644.
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