Phenotyping obesity through a two-dimensional tree structure reveals cardiometabolic heterogeneity.

Abstract

Obesity, a major public health challenge, is characterized by substantial phenotypic heterogeneity. Here, we employ the discriminative dimensionality reduction tree (DDRTree) method to routine clinical data from 18,733 Chinese individuals with obesity enrolled in the nationwide China Cardiometabolic Disease and Cancer Cohort (4C) study. We identify five distinct metabolic phenotypes, among which the phenotype characterized by hyperglycemia and insulin resistance exhibits a higher risk of glycemic deterioration, while the phenotype characterized by hypertension and dyslipidemia demonstrates an elevated risk of microvascular and macrovascular diseases. These findings are validated in an independent prospective cohort. Additionally, we reveal distinctive metabolomic features that contribute to the heterogeneity of obesity in the 4C study. To translate our findings into practice, we develop a user-friendly online tool to assess event risks in the obese population. Overall, our analysis illustrates the underlying phenotypic variations influencing subsequent obesity-related outcomes, emphasizing the importance of precision medicine in obesity management.