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| Name | Class |
|---|---|
| The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School | OTHER |
| The Third People's Hospital of Chengdu | OTHER |
| Shanghai East Hospital | OTHER |
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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| NW | normal weight control | ||
| MHO | metabolic healthy obesity |
| |
| LMO | hypometabolic obesity |
| |
| HMO-U | hypermetabolic obesity with hyperuricemia |
| |
| HMO-I | hypermetabolic obesity with hyperinsulinemia |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Metabolic classification of patients with obesity using machine learning | baseline |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with overweight/obesity.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shanghai Tenth People's Hospital | Shanghai | Shanghai Municipality | 200072 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34335479 | Derived | 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. |
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| ID | Term |
|---|---|
| D009765 | Obesity |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D050154 | Adiposity |
| ID | Term |
|---|---|
| D050218 | Body Fat Distribution |
| D001837 | Body Weights and Measures |
| D001824 | Body Constitution |
| D010808 | Physical Examination |
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| University of Pittsburgh |
| OTHER |
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|
| 1 year after bariatric surgery |
| D001835 |
| Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D019937 |
| Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D001823 | Body Composition |
| D001669 | Biochemical Phenomena |
| D055598 | Chemical Phenomena |
| D008660 | Metabolism |
| D010829 | Physiological Phenomena |