ObesityNCT04282837
Machine Learning for Reclassification of Obesity
An observational study of Obesity, run by Shanghai 10th People's Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 10 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Obesity
- What is being tested?
- AI classification of patients with obesity
- How many people
- 2,495 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2020to 20 Jun 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 10 to 70 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients with overweight/obesity.
Who may be able to join
- Patients with overweight/obesity
- Patients with normal weight as controls
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai Tenth People's Hospital
Shanghai, Shanghai Municipality, 200072
About this study
Official title: Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning
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.
- Conditions
- Obesity
- Being tested
- AI classification of patients with obesity
- Keywords
- Obesity, Machine learning, Metabolic classification, Clustering, Bariatric surgery
- Medical subject headings
- Obesity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| NW | normal weight control | |
| MHO | metabolic healthy obesity Receives: Diagnostic Test: AI classification of patients with obesity | |
| LMO | hypometabolic obesity Receives: Diagnostic Test: AI classification of patients with obesity | |
| HMO-U | hypermetabolic obesity with hyperuricemia Receives: Diagnostic Test: AI classification of patients with obesity | |
| HMO-I | hypermetabolic obesity with hyperinsulinemia Receives: Diagnostic Test: AI classification of patients with obesity |
What is being measured
The primary outcome is the main question the study is designed to answer.
Metabolic classification of patients with obesity using machine learning
Measured: baseline
Secondary outcomes (3)
Metabolic features in patients of different subgroups
Measured: baseline
Risks for noncommunicable disease in patients of different subgroups
Measured: baseline
Effect of bariatric surgery in patients of different subgroups
Measured: 1 year after bariatric surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2020
- Study start1 Mar 2020
- Primary completion30 Apr 2020
- Study completion20 Jun 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for ObesityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 2,495 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai 10th People's Hospital
- Sponsor type
- University, hospital or other
- 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
- Responsible party
- Principal investigator, Shen Qu, Shanghai 10th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04282837
- Sponsor's protocol ID
- Obesity Reclassification
- Status last verified
- Jun 2020
- First submitted
- 21 Feb 2020
- Last updated
- 25 Jun 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 34335479
Study at a glance
From the registry record, last updated by the sponsor on 25 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Data-driven Clustering for Metabolic Classification of Obesity Using Machine Learning
- ClinicalTrials.gov ID
- NCT04282837
- Acronym / sponsor ID
- Obesity Reclassification
- Conditions
- Obesity
- Keywords
- Obesity, Machine learning, Metabolic classification, Clustering, Bariatric surgery
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,495 participants (actual)
- Lead sponsor
- Shanghai 10th People's HospitalUniversity, hospital or other
- 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
- Study start
- 1 Mar 2020 (actual)
- Primary completion
- 30 Apr 2020 (actual)
- Study completion
- 20 Jun 2020 (actual)
- First posted
- 25 Feb 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| NW | – | –normal weight control |
| MHO | – | Diagnostic Test: AI classification of patients with obesitymetabolic healthy obesity |
| LMO | – | Diagnostic Test: AI classification of patients with obesityhypometabolic obesity |
| HMO-U | – | Diagnostic Test: AI classification of patients with obesityhypermetabolic obesity with hyperuricemia |
| HMO-I | – | Diagnostic Test: AI classification of patients with obesityhypermetabolic obesity with hyperinsulinemia |
Who may qualify
- Ages
- 10 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with overweight/obesity.
Key inclusion criteria (2 listed)
- Patients with overweight/obesity
- Patients with normal weight as controls
Key exclusion criteria (6 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI classification of patients with obesityComputational 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. | Diagnostic test | HMO-I, HMO-U, LMO, MHO |
Primary outcome
Metabolic classification of patients with obesity using machine learning
Time frame: baseline
3 secondary outcomes
Metabolic features in patients of different subgroups
Time frame: baseline
Risks for noncommunicable disease in patients of different subgroups
Time frame: baseline
Effect of bariatric surgery in patients of different subgroups
Time frame: 1 year after bariatric surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jun 2020.
- Derived from this study: PubMed 34335479Lin 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.
- Individual participant dataSharing planned: No
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Obesity
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obesity, run by Shanghai 10th People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Metabolic classification of patients with obesity using machine learning (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04282837, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Jun 2020.