Machine Learning for Reclassification of Obesity
Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
-
-
Shanghai
-
Shanghai, Shanghai, China, 200072
- Shanghai Tenth People's Hospital
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
NW
normal weight control
|
|
|
MHO
metabolic healthy 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
|
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
|
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
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Metabolic classification of patients with obesity using machine learning
Time Frame: baseline
|
baseline
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Metabolic features in patients of different subgroups
Time Frame: baseline
|
baseline
|
|
Risks for noncommunicable disease in patients of different subgroups
Time Frame: baseline
|
baseline
|
|
Effect of bariatric surgery in patients of different subgroups
Time Frame: 1 year after bariatric surgery
|
1 year after bariatric surgery
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- Obesity Reclassification
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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