Machine Learning for Reclassification of Obesity

Tongji University

Status: Completed

Conditions: Obesity

Treatments: Diagnostic Test: AI classification of patients with obesity

Study type: Observational

Funder types: Other

Identifiers

NCT04282837

Obesity Reclassification

Details and patient eligibility

About

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.

Enrollment: 2,495 patients

Sex: All

Ages: 10 to 70 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

2,495 participants in 5 patient groups

NW

Description:

normal weight control

MHO

Description:

metabolic healthy obesity

Treatment:

Diagnostic Test: AI classification of patients with obesity

LMO

Description:

hypometabolic obesity

Treatment:

Diagnostic Test: AI classification of patients with obesity

HMO-U

Description:

hypermetabolic obesity with hyperuricemia

Treatment:

Diagnostic Test: AI classification of patients with obesity

HMO-I

Description:

hypermetabolic obesity with hyperinsulinemia

Treatment:

Diagnostic Test: AI classification of patients with obesity