Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
2495 participants
OBSERVATIONAL
2020-03-01
2020-06-20
Brief Summary
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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NW
normal weight control
No interventions assigned to this group
MHO
metabolic healthy obesity
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
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
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
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
2. Patients with normal weight as controls
Exclusion Criteria
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.
10 Years
70 Years
ALL
Yes
Sponsors
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The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
OTHER
The Third People's Hospital of Chengdu
OTHER
Shanghai East Hospital
OTHER
University of Pittsburgh
OTHER
Shanghai 10th People's Hospital
OTHER
Responsible Party
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Shen Qu
Clinical Professor and Principal Investigator
Locations
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Shanghai Tenth People's Hospital
Shanghai, Shanghai Municipality, China
Countries
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References
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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.
Other Identifiers
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Obesity Reclassification
Identifier Type: -
Identifier Source: org_study_id
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