Aim: Current clinical classification paradigms for obesity are unrefined and demand more precise classification.
Methods: Unsupervised ML is used to cluster patients with obesity on 2 independent cohorts at 1 institution: an outpatient (n=507) and an inpatient (n=229) cohort. The clustering is performed separately on the 2 cohorts, based on 4 physician expert-selected clinical variables (age, AUC of insulin, FSH, urine acid). Statistics of a lean cohort (n=702) are measured as control.
Results: The classification reveals 4 metabolic different obese clusters on each cohort (Fig 1). Jaccard similarity is 0.865 between the 2 cohorts’ clusters. MHO shows a moderate basal metabolic rate (BMR) with relative healthy hormone levels and glucometabolism. LMO shows the lowest BMR and hormone, oldest age, and most severe glucometabolism. Both HMO-T1 and HMO-T2 show the highest BMR and hormone, lowest glucose, highest incidents of hyperuricemia and female hyperandrogenemia. Moreover, higher urine acid and testosterone in female are observed in HMO-T1, while extremely high insulin secretion and low incidents of diabetes are seen in HMO-T2.
Conclusion: Clinical characteristics-separable subgroups of obesity can be autonomously identified by ML, where the sub-grouping is generalizable across 2 independent cohorts. This may provide interpretation of pathogenesis and enable more precise therapy decision-making for patients with obesity.
Z. Lin: None. Y. Hui: None. S. Wu: Other Relationship; Spouse/Partner; LifeVantage. S. Qu: None.
National Key R&D Program of China (2018YFC1314100); National Natural Science Foundation of China for Youth (81500687); Fundamental Research Funds for the Central Universities of Tongji University (22120190210)
