Machine Learning for Reclassification of Obesity


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.

Full Title of Study: “Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Retrospective
  • Study Primary Completion Date: April 30, 2020


  • Diagnostic Test: AI classification of patients with obesity
    • 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.

Arms, Groups and Cohorts

  • NW
    • normal weight control
  • MHO
    • metabolic healthy obesity
  • LMO
    • hypometabolic obesity
  • HMO-U
    • hypermetabolic obesity with hyperuricemia
  • HMO-I
    • hypermetabolic obesity with hyperinsulinemia

Clinical Trial Outcome Measures

Primary Measures

  • Metabolic classification of patients with obesity using machine learning
    • Time Frame: baseline

Secondary Measures

  • 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

Participating in This Clinical Trial

Inclusion Criteria

1. Patients with overweight/obesity 2. Patients with normal weight as controls Exclusion Criteria:

1. had ever been performed with a bariatric surgery before the study's first visit is scheduled; 2. had taken exogenous insulin, medication that affects glucose metabolism, or uric acid drugs currently; 3. being diagnosed with type 1 diabetes, secondary diabetes, hereditary disease, or severe disease (e.g. malignant tumor, heart failure, liver failure, etc.); 4. in gestation of lactation; 5. did not have the complete data for model; 6. for normal-weight controls, patients with diabetes or hyperuricemia were excluded.

Gender Eligibility: All

Minimum Age: 10 Years

Maximum Age: 70 Years

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • Shanghai 10th People’s Hospital
  • Collaborator
    • The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
  • Provider of Information About this Clinical Study
    • Principal Investigator: Shen Qu, Clinical Professor and Principal Investigator – Shanghai 10th People’s Hospital

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