The goal of this study is to employ or develop computational modeling techniques for the precise reclassification of obesity into subgroups. Clinical features, risks of noncommunicable diseases, as well as weight loss effects of bariatric surgery will also be studied and compared within the subgroups.
Study Type
OBSERVATIONAL
Enrollment
2,495
Computational modeling techniques will be used for the precise reclassification of obesity into four subgroups, several variables according to the clinical experience and the modeling results will be selected for the cluster analysis.
Shanghai Tenth People's Hospital
Shanghai, Shanghai Municipality, China
Metabolic classification of patients with obesity using machine learning
Time frame: baseline
Metabolic features in patients of different subgroups
Time frame: baseline
Risks for noncommunicable disease in patients of different subgroups
Time frame: baseline
Effect of bariatric surgery in patients of different subgroups
Time frame: 1 year after bariatric surgery
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