NCT04282837

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
2,495

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Mar 2020

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

February 21, 2020

Completed
4 days until next milestone

First Posted

Study publicly available on registry

February 25, 2020

Completed
5 days until next milestone

Study Start

First participant enrolled

March 1, 2020

Completed
2 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 30, 2020

Completed
2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

June 20, 2020

Completed
Last Updated

June 25, 2020

Status Verified

June 1, 2020

Enrollment Period

2 months

First QC Date

February 21, 2020

Last Update Submit

June 23, 2020

Conditions

Obesity

Keywords

ObesityMachine learningMetabolic classificationClusteringBariatric surgery

Outcome Measures

Primary Outcomes (1)

  • Metabolic classification of patients with obesity using machine learning

    baseline

Secondary Outcomes (3)

  • 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

Study Arms (5)

NW

normal weight control

MHO

metabolic healthy obesity

Diagnostic Test: AI classification of patients with obesity

LMO

hypometabolic obesity

Diagnostic Test: AI classification of patients with obesity

HMO-U

hypermetabolic obesity with hyperuricemia

Diagnostic Test: AI classification of patients with obesity

HMO-I

hypermetabolic obesity with hyperinsulinemia

Diagnostic Test: AI classification of patients with obesity

Interventions

AI classification of patients with obesityDIAGNOSTIC_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.

HMO-IHMO-ULMOMHO

Eligibility Criteria

Age10 Years - 70 Years
Sexall
Healthy VolunteersYes
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Patients with overweight/obesity.

You may qualify if:

  • Patients with overweight/obesity
  • Patients with normal weight as controls

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Shanghai Tenth People's Hospital

Shanghai, Shanghai Municipality, 200072, China

Location

Related Publications (1)

  • Lin Z, Feng W, Liu Y, Ma C, Arefan D, Zhou D, Cheng X, Yu J, Gao L, Du L, You H, Zhu J, Zhu D, Wu S, Qu S. Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study. Front Endocrinol (Lausanne). 2021 Jul 14;12:713592. doi: 10.3389/fendo.2021.713592. eCollection 2021.

    PMID: 34335479DERIVED

MeSH Terms

Conditions

Obesity

Interventions

Adiposity

Condition Hierarchy (Ancestors)

OverweightOvernutritionNutrition DisordersNutritional and Metabolic DiseasesBody WeightSigns and SymptomsPathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Body Fat DistributionBody Weights and MeasuresBody ConstitutionPhysical ExaminationDiagnostic Techniques and ProceduresDiagnosisBody CompositionBiochemical PhenomenaChemical PhenomenaMetabolismPhysiological Phenomena

Study Design

Study Type
observational
Observational Model
CASE CONTROL
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Clinical Professor and Principal Investigator

Study Record Dates

First Submitted

February 21, 2020

First Posted

February 25, 2020

Study Start

March 1, 2020

Primary Completion

April 30, 2020

Study Completion

June 20, 2020

Last Updated

June 25, 2020

Record last verified: 2020-06

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)