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

Area of Science:

• Metabolic Phenotyping
• Precision Medicine
• Public Health

Background:

• Obesity presents significant phenotypic diversity, impacting public health.
• Understanding this heterogeneity is crucial for effective management.

Purpose of the Study:

• To identify distinct metabolic phenotypes within an obese Chinese population.
• To link these phenotypes to specific health risks and underlying metabolomic features.
• To develop tools for personalized risk assessment in obesity.

Main Methods:

• Utilized discriminative dimensionality reduction tree (DDRTree) on clinical data from 18,733 individuals in the China Cardiometabolic Disease and Cancer Cohort (4C) study.
• Validated findings in an independent prospective cohort.
• Analyzed metabolomic data to identify contributing features.
• Developed an online risk assessment tool.

Main Results:

• Identified five distinct metabolic phenotypes of obesity.
• Phenotype with hyperglycemia and insulin resistance showed higher glycemic deterioration risk.
• Phenotype with hypertension and dyslipidemia showed increased microvascular and macrovascular disease risk.
• Distinct metabolomic signatures were associated with obesity phenotypes.

Conclusions:

• Obesity heterogeneity significantly influences disease outcomes.
• Precision medicine approaches are essential for tailored obesity management.
• Identified phenotypes and risk associations can guide clinical practice and preventative strategies.