Interplay of Social Determinants of Health and Traditional Risk Factors in Predicting Cardiac Aging.

Abstract

To create a model to predict cardiac aging and mortality along with examining the interconnection between psychosocial and conventional determinants. This is a cross-sectional study of adult patients who sought care at Mayo Clinic between 2018 and 2023. Social determinants of health status was assessed using a questionnaire. The artificial intelligence-enhanced electrocardiogram was used to estimate cardiac age. Structural equation modeling was used to map the connection between SDoH, conventional risk factors, and the cardiac age gap, and Cox proportional hazard models were applied to conduct survival analysis. A total of 280,323 patients were included (50.8% were female, the average chronological age was 59.8±16.4 years). Among SDoH, financial strain (β= -1.02; 95% CI, -1.03 to -1.01; P<.001) and food insecurity (β= -0.74; 95% CI, -0.74 to -0.733; P<.001) were the strongest determinants. Compared with comorbidities and demographic factors, SDoH had the highest contribution to predict age gap in the total population (β= -0.197; 95% CI, -0.2 to -0.18; P<.001) and sex-stratified analysis. Cox proportional hazard analysis showed a strong association between SDoH domains, particularly financial resource strain (HR, 1.6; 95% CI, 1.5 to 1.72; P<.001) adjusted to conventional risk factors. The study highlights the contribution of SDoH with interaction to comorbidities and demographic risk factors in cardiac aging and mortality. Identifying the most important determinants enables targeted preventive intervention in the community and enables physicians to engage in patient-centered care.