ArticleDiabetes, obesity & metabolism2025
Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes.
Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Sex-specific associations of social determinants of health and genetic risk factors with atherosclerotic cardiovascular diseases incidence in the general population.American journal of preventive cardiology · 2026Article
- Quantifying the Predictive Power of Social Determinants of Health in Cardiovascular Disease and Type 2 Diabetes Progression Using XGBoost: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Risk for Diabetes From Long Working Hours and Night Work in the United States: Prospective Associations and Machine Learning Techniques.Safety and health at work · 2025Article
- Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes.Diabetes, obesity & metabolism · 2025Article
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Authors and funding
8 authors.
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Abstract
aimsIdentifying individuals at the highest risk of progression to type 2 diabetes (T2D) using clinical and social determinants of health (SDoH) measures will help prioritize prevention efforts. We aimed to investigate model performance after adding SDoH to a previously validated cardiometabolic disease staging diabetes risk prediction model. MATERIALS AND
methodsWe developed a Bayesian predictive model using data [clinical factors: fasting glucose, blood pressure, body mass index, high-density lipoprotein cholesterol, triglycerides; individual SDoH: income, education, health insurance status, relationship status, self-reported stress and neighbourhood SDoH: census-tract level social vulnerability index] from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study to predict T2D with external validation using the Coronary Artery Risk Development in Young Adults (CARDIA) study.
resultsThe analysis included 9907 REGARDS participants without T2D at baseline [mean age 63 years (SD 8.5), 54% female, 33% non-Hispanic Black] who completed a follow-up visit 10 years later. N = 1268 (12.8%) developed T2D. Adding SDoH to the clinical model modestly improved performance [Area Under the Curve: 0.802 vs. 0.804, p = 0.01]. Calibration plots indicated that the clinical model underpredicted risk in disadvantaged SDoH subgroups, whereas the clinical plus SDoH model improved prediction accuracy in subgroups. Classification tables revealed that the clinical plus SDoH model accurately reclassified individuals categorized as borderline risk in a clinical-only model.
conclusionIncluding SDoH in T2D risk prediction and stratification at the population level may aid in better classifying T2D risk among vulnerable populations, which has important implications for screening strategies.
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