Uncovering heterogeneous cardiometabolic risk profiles in US adults: the role of social and behavioral determinants of health

INTRODUCTION: Social and behavioral determinants of health (SBDH) have been linked to diabetes risk, but their role in explaining variations in cardiometabolic risk across race/ethnicity in US adults is unclear. This study aimed to classify adults into distinct cardiometabolic risk subgroups using SBDH and clinically measured metabolic risk factors, while comparing their associations with undiagnosed diabetes and pre-diabetes by race/ethnicity. RESEARCH DESIGN AND METHODS: We analyzed data from 38,476 US adults without prior diabetes diagnosis from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. The k-prototypes clustering algorithm was used to identify subgroups based on 16 SBDH and 13 metabolic risk factors. Each participant was classified as having no diabetes, pre-diabetes or undiagnosed diabetes using contemporaneous laboratory data. Logistic regression was used to assess associations between subgroups and diabetes status, focusing on differences by race/ethnicity. RESULTS: Three subgroups were identified: cluster 1, primarily middle-aged adults with high rates of smoking, alcohol use, short sleep duration, and low diet quality; cluster 2, mostly young non-white adults with low income, low health insurance coverage, and limited healthcare access; and cluster 3, mostly older males who were the least physically active, but with high insurance coverage and healthcare access. Compared with cluster 2, adjusted ORs (95% CI) for undiagnosed diabetes were 14.9 (10.9, 20.2) in cluster 3 and 3.7 (2.8, 4.8) in cluster 1. Clusters 1 and 3 (vs cluster 2) had high odds of pre-diabetes, with ORs of 1.8 (1.6, 1.9) and 2.1 (1.8, 2.4), respectively. Race/ethnicity was found to modify the relationship between identified subgroups and pre-diabetes risk. CONCLUSIONS: Self-reported SBDH combined with metabolic factors can be used to classify adults into subgroups with distinct cardiometabolic risk profiles. This approach may help identify individuals who would benefit from screening for diabetes and pre-diabetes and potentially suggest effective prevention strategies.