PsoriasisNCT07321288
Machine Learning-Based Prediction of Insulin Resistance in Psoriasis Patients Emphasizing Interpretability
An observational study of Psoriasis and Insulin Resistance, run by Chinese PLA General Hospital at 1 site in China.
Trial summary
Psoriasis is a long-term inflammatory skin disease that can affect overall health. People with psoriasis have a higher risk of developing insulin resistance, a condition in which the body does not respond properly to insulin. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Psoriasis, Insulin Resistance
- How many people
- 1,265 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2025to 1 Sep 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population includes adult patients with psoriasis who received routine clinical care at Chinese PLA General Hospital (First Medical Center) and a collaborating center (Fourth Medical Center) in China. Participants are identified from hospital-based clinical databases and electronic medical records collected during routine dermatology practice between January 2015 and June 2025. The population represents a real-world cohort of patients with psoriasis receiving standard medical care in tertiary hospitals. Data used in this observational study are collected retrospectively, and all patient information is anonymized prior to analysis. No interventions or additional procedures are performed as part of the study.
Who may be able to join
- Adults aged 18 years or older
- Diagnosed with psoriasis (including plaque psoriasis, pustular psoriasis, erythrodermic psoriasis, or other clinically recognized subtypes), according to established clinical diagnostic guidelines
- Received medical care at Chinese PLA General Hospital (First Medical Center) or the collaborating center (Fourth Medical Center) during the study period
- Availability of complete medical records, including demographic information, relevant clinical characteristics, and laboratory data required to assess insulin resistance
Who cannot join
- Previous or current diagnosis of diabetes mellitus
- Presence of severe systemic diseases that may significantly affect glucose metabolism (such as malignant tumors, hyperthyroidism, or other serious endocrine disorders)
- Current or recent use of systemic medications known to affect insulin sensitivity, including:Systemic corticosteroids,Glucose-lowering medications, or Other medications with known significant effects on insulin resistance
- Pregnant or breastfeeding women
- Medical records with missing key variables required for the assessment of insulin resistance or model development
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Identification of Risk Factors and Development of an Interpretable Machine Learning Model for Predicting Insulin Resistance in Patients With Psoriasis
Psoriasis is a long-term inflammatory skin disease that can affect overall health. People with psoriasis have a higher risk of developing insulin resistance, a condition in which the body does not respond properly to insulin. Insulin resistance can increase the risk of diabetes, heart disease, and other serious health problems. Because insulin resistance often develops without clear symptoms, many patients are not diagnosed early.
The purpose of this study is to identify which patients with psoriasis are more likely to develop insulin resistance and to create a tool that can help doctors estimate this risk for individual patients. The study will use existing medical records from two medical centers. Researchers will analyze information such as age, body weight, psoriasis severity, blood test results, other medical conditions, and medication history.
Read more
Machine learning methods will be used to analyze these data and build a prediction model. The model will be designed to be easy to understand, so doctors can see which factors contribute most to insulin resistance risk.
This study does not involve any new treatments or procedures. All patient information will be anonymized to protect privacy. The results may help doctors identify high-risk patients earlier and support timely monitoring and preventive care.
- Conditions
- Psoriasis, Insulin Resistance
- Medical subject headings
- Psoriasis, Insulin Resistance
What is being measured
The primary outcome is the main question the study is designed to answer.
Insulin Resistance Status Assessed by the TyG Index
Insulin resistance will be evaluated using the triglyceride-glucose (TyG) index, calculated from fasting triglyceride and fasting plasma glucose levels obtained from medical records. Participants will be classified as having insulin resistance or not based on a predefined TyG index cutoff value (TyG ≥ 8.5)
Measured: At baseline (using existing medical record data collected between January 2015 and June 2025)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2026
- Study start1 Sep 2025
- Primary completion1 Sep 2026 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PsoriasisStudies of the same condition, open and under way.
- More trials run by Chinese PLA General HospitalThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,265 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese PLA General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chongli Yu, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07321288
- Sponsor's protocol ID
- S2025-72-01
- Status last verified
- Sep 2025
- First submitted
- 22 Dec 2025
- Last updated
- 8 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Identification of Risk Factors and Development of an Interpretable Machine Learning Model for Predicting Insulin Resistance in Patients With Psoriasis
- ClinicalTrials.gov ID
- NCT07321288
- Acronym / sponsor ID
- S2025-72-01
- Conditions
- Psoriasis, Insulin Resistance
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,265 participants (estimated)
- Lead sponsor
- Chinese PLA General HospitalUniversity, hospital or other
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 7 Jan 2026 (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.
Psoriasis is a long-term inflammatory skin disease that can affect overall health. People with psoriasis have a higher risk of developing insulin resistance, a condition in which the body does not respond properly to insulin. Insulin resistance can increase the risk of diabetes, heart disease, and other serious health problems. Because insulin resistance often develops without clear symptoms, many patients are not diagnosed early.
The purpose of this study is to identify which patients with psoriasis are more likely to develop insulin resistance and to create a tool that can help doctors estimate this risk for individual patients. The study will use existing medical records from two medical centers. Researchers will analyze information such as age, body weight, psoriasis severity, blood test results, other medical conditions, and medication history.
Machine learning methods will be used to analyze these data and build a prediction model. The model will be designed to be easy to understand, so doctors can see which factors contribute most to insulin resistance risk.
This study does not involve any new treatments or procedures. All patient information will be anonymized to protect privacy. The results may help doctors identify high-risk patients earlier and support timely monitoring and preventive care.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population includes adult patients with psoriasis who received routine clinical care at Chinese PLA General Hospital (First Medical Center) and a collaborating center (Fourth Medical Center) in China. Participants are identified from hospital-based clinical databases and electronic medical records collected during routine dermatology practice between January 2015 and June 2025. The population represents a real-world cohort of patients with psoriasis receiving standard medical care in tertiary hospitals. Data used in this observational study are collected retrospectively, and all patient information is anonymized prior to analysis. No interventions or additional procedures are performed as part of the study.
Key inclusion criteria (4 listed)
- Adults aged 18 years or older
- Diagnosed with psoriasis (including plaque psoriasis, pustular psoriasis, erythrodermic psoriasis, or other clinically recognized subtypes), according to established clinical diagnostic guidelines
- Received medical care at Chinese PLA General Hospital (First Medical Center) or the collaborating center (Fourth Medical Center) during the study period
- Availability of complete medical records, including demographic information, relevant clinical characteristics, and laboratory data required to assess insulin resistance
Key exclusion criteria (5 listed)
- Previous or current diagnosis of diabetes mellitus
- Presence of severe systemic diseases that may significantly affect glucose metabolism (such as malignant tumors, hyperthyroidism, or other serious endocrine disorders)
- Current or recent use of systemic medications known to affect insulin sensitivity, including:Systemic corticosteroids,Glucose-lowering medications, or Other medications with known significant effects on insulin resistance
- Pregnant or breastfeeding women
- Medical records with missing key variables required for the assessment of insulin resistance or model development
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Insulin Resistance Status Assessed by the TyG Index
Time frame: At baseline (using existing medical record data collected between January 2015 and June 2025)
Insulin resistance will be evaluated using the triglyceride-glucose (TyG) index, calculated from fasting triglyceride and fasting plasma glucose levels obtained from medical records. Participants will be classified as having insulin resistance or not based on a predefined TyG index cutoff value (TyG ≥ 8.5)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 May 2026.
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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 Psoriasis and Insulin Resistance, run by Chinese PLA General 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 4 inclusion and 5 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: under way, not looking for participants, as of the sponsor's last update on 8 May 2026. 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?
This study is still under way. Results are not available in the source record yet.
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?
Insulin Resistance Status Assessed by the TyG Index (time frame: At baseline (using existing medical record data collected between January 2015 and June 2025)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 8 May 2026.