Objective:

To summarize methodological innovations in cardiovascular disease (CVD) epidemiology, interrogate their impact on people with disabilities (PwD), propose a framework for inclusive research, and outline a research agenda for the next five years.

Approach:

• Methodological Innovations: Summarizes the integration of multi-omic data, machine learning applications, and population-scale linked-data infrastructures in CVD epidemiology.
• Impact on PwD: Examines why innovations have largely bypassed PwD, highlighting their disproportionate CVD burden and under-representation in clinical datasets.
• Proposed Framework: Presents a layered architecture for disability-inclusive big-data and AI-enabled cardiovascular research.
• Research Agenda: Articulates a tractable research agenda for the next five years focused on improving CVD outcomes for PwD.

Key Findings:

• CVD is the leading cause of premature mortality and a major contributor to disability-adjusted life years lost.
• PwD experience a higher prevalence of heart disease and stroke, with mortality ratios significantly elevated compared to non-disabled individuals.
• Disability-related exclusions in clinical trials are common and often lack justification, impacting the generalizability of findings.

Interpretation:

Disability inclusion is essential for the validity, generalizability, and ethical legitimacy of precision cardiovascular research.

Limitations:

• The article is a narrative review and does not follow a systematic synthesis protocol.
• It does not apply formal risk-of-bias appraisal or quantitative pooling.

Conclusion:

The article emphasizes the need for disability-inclusive approaches in CVD research to address significant health disparities faced by PwD.

Sources:

• World Health Organization
• National Institutes of Health
• National Institute on Minority Health and Health Disparities