Machine Learning for Reclassification of Obesity

Study Overview
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Brief Summary
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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.

Official Title
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Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning

Conditions
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Obesity

Intervention / Treatment
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* Diagnostic Test: AI classification of patients with obesity


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

Ages Eligible for Study
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Minimum Age: 10 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| NW<br>normal weight control | |
| MHO<br>metabolic healthy obesity | Diagnostic Test: AI classification of patients with obesity<br>* 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.<br>|
| LMO<br>hypometabolic obesity | Diagnostic Test: AI classification of patients with obesity<br>* 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.<br>|
| HMO-U<br>hypermetabolic obesity with hyperuricemia | Diagnostic Test: AI classification of patients with obesity<br>* 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.<br>|
| HMO-I<br>hypermetabolic obesity with hyperinsulinemia | Diagnostic Test: AI classification of patients with obesity<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Metabolic classification of patients with obesity using machine learning |  | baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Metabolic features in patients of different subgroups |  | baseline | 
| Risks for noncommunicable disease in patients of different subgroups |  | baseline | 
| Effect of bariatric surgery in patients of different subgroups |  | 1 year after bariatric surgery | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Obesity, Machine learning, Metabolic classification, Clustering, Bariatric surgery