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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Psoriasis Glucose Metabolism Disorders Hyperinsulinism Insulin Resistance Metabolic Diseases Nutritional and Metabolic Diseases Skin Diseases Skin Diseases, Papulosquamous Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Chinese PLA General Hosptial

Beijing, None Selected, 100853, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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)

Sponsors and collaborators

Lead sponsor

Chinese PLA General Hospital

Other

Registry information

Official study title

Identification of Risk Factors and Development of an Interpretable Machine Learning Model for Predicting Insulin Resistance in Patients With Psoriasis

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jan 7, 2026
Registry last updated: May 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed