DataSheet_1_Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.pdf (2.01 MB)
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DataSheet_1_Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.pdf

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posted on 14.07.2021, 05:20 authored by Ziwei Lin, Wenhuan Feng, Yanjun Liu, Chiye Ma, Dooman Arefan, Donglei Zhou, Xiaoyun Cheng, Jiahui Yu, Long Gao, Lei Du, Hui You, Jiangfan Zhu, Dalong Zhu, Shandong Wu, Shen Qu
Background and objective

Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.

Subjects and Methods

In a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People’s hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-weight cohort (n=1057) were measured as controls.

Results

Machine learning revealed four stable metabolic different obese clusters on each cohort. Metabolic healthy obesity (MHO, 44% patients) was characterized by a relatively healthy-metabolic status with lowest incidents of comorbidities. Hypermetabolic obesity-hyperuricemia (HMO-U, 33% patients) was characterized by extremely high uric acid and a large increased incidence of hyperuricemia (adjusted odds ratio [AOR] 73.67 to MHO, 95%CI 35.46-153.06). Hypermetabolic obesity-hyperinsulinemia (HMO-I, 8% patients) was distinguished by overcompensated insulin secretion and a large increased incidence of polycystic ovary syndrome (AOR 14.44 to MHO, 95%CI 1.75-118.99). Hypometabolic obesity (LMO, 15% patients) was characterized by extremely high glucose, decompensated insulin secretion, and the worst glucolipid metabolism (diabetes: AOR 105.85 to MHO, 95%CI 42.00-266.74; metabolic syndrome: AOR 13.50 to MHO, 95%CI 7.34-24.83). The assignment of patients in the verification cohorts to the main model showed a mean accuracy of 0.941 in all clusters.

Conclusion

Machine learning automatically identified four subtypes of obesity in terms of clinical characteristics on four independent patient cohorts. This proof-of-concept study provided evidence that precise diagnosis of obesity is feasible to potentially guide therapeutic planning and decisions for different subtypes of obesity.

Clinical Trial Registration

www.ClinicalTrials.gov, NCT04282837.

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