Working…
COVID-19 is an emerging, rapidly evolving situation.
Get the latest public health information from CDC: https://www.coronavirus.gov.

Get the latest research information from NIH: https://www.nih.gov/coronavirus.
ClinicalTrials.gov
ClinicalTrials.gov Menu

Machine Learning for Reclassification of Obesity

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04282837
Recruitment Status : Completed
First Posted : February 25, 2020
Last Update Posted : June 25, 2020
Sponsor:
Collaborators:
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
The Third People's Hospital of Chengdu
Shanghai East Hospital
University of Pittsburgh
Information provided by (Responsible Party):
Shen Qu, Shanghai 10th People's Hospital

Brief Summary:
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.

Condition or disease Intervention/treatment
Obesity Diagnostic Test: AI classification of patients with obesity

Layout table for study information
Study Type : Observational
Actual Enrollment : 2495 participants
Observational Model: Case-Control
Time Perspective: Retrospective
Official Title: Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning
Actual Study Start Date : March 1, 2020
Actual Primary Completion Date : April 30, 2020
Actual Study Completion Date : June 20, 2020

Group/Cohort Intervention/treatment
NW
normal weight control
MHO
metabolic healthy obesity
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.

LMO
hypometabolic obesity
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.

HMO-U
hypermetabolic obesity with hyperuricemia
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.

HMO-I
hypermetabolic obesity with hyperinsulinemia
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.




Primary Outcome Measures :
  1. Metabolic classification of patients with obesity using machine learning [ Time Frame: baseline ]

Secondary Outcome Measures :
  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 ]


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   10 Years to 70 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
Patients with overweight/obesity.
Criteria

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04282837


Locations
Layout table for location information
China, Shanghai
Shanghai Tenth People's Hospital
Shanghai, Shanghai, China, 200072
Sponsors and Collaborators
Shanghai 10th People's Hospital
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
The Third People's Hospital of Chengdu
Shanghai East Hospital
University of Pittsburgh
Layout table for additonal information
Responsible Party: Shen Qu, Clinical Professor and Principal Investigator, Shanghai 10th People's Hospital
ClinicalTrials.gov Identifier: NCT04282837    
Other Study ID Numbers: Obesity Reclassification
First Posted: February 25, 2020    Key Record Dates
Last Update Posted: June 25, 2020
Last Verified: June 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Shen Qu, Shanghai 10th People's Hospital:
Obesity
Machine learning
Metabolic classification
Clustering
Bariatric surgery
Additional relevant MeSH terms:
Layout table for MeSH terms
Obesity
Overnutrition
Nutrition Disorders
Overweight
Body Weight
Signs and Symptoms