Diversity and Representation in Cardiovascular Research: Evidence Gaps, Emerging Models, and Policy Implications

Area of Science:

• Cardiovascular research
• Health equity
• Genomic data science

Background:

• Cardiovascular disease (CVD) is a leading global cause of death.
• Current CVD research relies on homogenous datasets, primarily from White, male participants in high-income countries.
• This lack of diversity results in biased evidence, reduced diagnostic accuracy, and limited generalizability of findings.

Purpose of the Study:

• To highlight the scientific necessity of diversity in cardiovascular data for valid, globally applicable knowledge.
• To propose a global roadmap for reshaping cardiovascular research methodologies.
• To advocate for inclusive practices in CVD research and guideline development.

Main Methods:

• Discussion of existing scientific literature and emerging initiatives.
• Analysis of the impact of data homogeneity on CVD research outcomes.
• Proposal of strategies including data donation, inclusive biobanking, equitable AI development, and international policy change.

Main Results:

• Homogenous datasets in CVD research lead to biased evidence and inequitable health outcomes.
• Integrating diverse data is essential for accurate risk prediction, diagnostics, and treatment.
• Current research practices limit the effectiveness and generalizability of CVD guidelines worldwide.

Conclusions:

• Diversity in cardiovascular data is a scientific imperative, not merely an ethical consideration.
• A global roadmap integrating genomics, digital health, and participatory research is needed.
• Implementing inclusive methodologies will ensure cardiovascular guidelines are effective, equitable, and just for all populations.