Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity

BACKGROUND: Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having cardio-metabolic co-morbidities. This study is aimed to examine the cardio-metabolic characteristics and comorbidity profile of the middle-aged and elderly Chinese with general and central obesity by clustering them into different subgroups, which would lead to a deepened understanding of their distinct medical needs. METHODS: Adopting an unsupervised machine learning approach, we conducted a clustering analysis of the adiposity and cardio-metabolic profiles of the middle-aged and elderly Chinese with general obesity and central obesity. The data was obtained from the China Health and Retirement Longitudinal Study (CHARLS). The subgroup features were examined. The risks of having obesity-related co-morbidities (i.e. hypertension, dyslipidemia, diabetes, heart problem, stroke) in each cluster were then compared. RESULTS: Among the 7,970 subjects selected from the baseline cohort, 41.88% (n = 3,338) had general obesity, while 71.29% (n = 5,682) had central obesity. These individuals with either general obesity or central obesity were clustered into four groups, respectively: (1) obesity with relatively healthier metabolites; (2) hyperuricemia subtype; (3) hyperglycemia-insulin resistance subtype; and (4) the average subtype. The results indicated among people with either general obesity or central obesity, those with high levels in HbA1c level and TyG index concurrently demonstrated more severe adiposity issues and unhealthier cardio-metabolic profile. CONCLUSIONS: This data-driven study identified a novel classification strategy to identify subtypes of the middle-aged and elderly Chinese with general obesity and central obesity and classify their adiposity and cardio-metabolic profiles. With clinically accessible metrics, this approach could inform precise risk stratification by revealing subtype-specific heterogeneity during initial assessments.