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ABSTRACT: Background
There is a strong association between vascular risk factors, particularly in midlife, and stroke risk; therefore, the co-occurrence of multiple risk factors may be especially informative. This study used a machine-learning-based cluster analysis to group individuals into clusters based on similar clinical profiles in midlife and assessed the clusters' associations with stroke risk and severity.Methods
Participants (N=15 404) without prevalent stroke from the ARIC study (Atherosclerosis Risk in Communities) were included. An unsupervised agglomerative hierarchical clustering approach was used to allocate participants into clusters based on the presence of clinical risk factors in midlife: hypertension, diabetes, coronary heart disease, heart failure, atrial fibril
SUBMITTER: Egle M
PROVIDER: S-EPMC12447826 | biostudies-literature | 2025 Oct
REPOSITORIES: biostudies-literature