Machine Learning-Based Prediction of Insulin Resistance in Psoriasis Patients Emphasizing Interpretability

NCT ID: NCT07321288

Last Updated: 2026-01-07

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 1265 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-09-01
• Study Completion Date: 2026-09-01

Brief 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. 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.

Conditions

Psoriasis; Insulin Resistance

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Chinese PLA General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Chongli Yu

PhD Candidate

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Chinese PLA General Hosptial

Beijing, None Selected, China

Countries

China

Other Identifiers

S2025-72-01

Identifier Type: -

Identifier Source: org_study_id