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OpenTrials
Completed

NCT Number: NCT04282837

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.

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Key information

Conditions

Obesity Body Weight Nutrition Disorders Nutritional and Metabolic Diseases Overnutrition Overweight Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

10 year–70 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Shanghai Tenth People's Hospital

Shanghai, Shanghai Municipality, 200072, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

AI classification of patients with obesity

Diagnostic Test

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.

Primary outcomes

  1. Metabolic classification of patients with obesity using machine learning

    Time frame: baseline

Secondary outcomes

  1. Metabolic features in patients of different subgroups

    Time frame: baseline

  2. Risks for noncommunicable disease in patients of different subgroups

    Time frame: baseline

  3. Effect of bariatric surgery in patients of different subgroups

    Time frame: 1 year after bariatric surgery

Sponsors and collaborators

Lead sponsor

Shanghai 10th People's Hospital

Other

Collaborators

  • Shanghai East Hospital
  • The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
  • The Third People's Hospital of Chengdu
  • University of Pittsburgh

Registry information

Official study title

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

Important dates

Study start
2020
Primary completion
2020
Study completion
2020
First posted
Feb 25, 2020
Registry last updated
Jun 25, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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