| Title: | Identification and Prediction of Risk Factors for Immune-Mediated Inflammatory Diseases, with a Focus on Skin Disorders: An Integrated Epidemiological, Genetic, and Environmental Approach |
| Lead Institution: | The First Affiliated Hospital of Chongqing Medical University |
| Principal investigator: |
Application 747364
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About
Immune-mediated inflammatory diseases (IMIDs) affect approximately 3-5% of the population worldwide with an increasing incidence. As a part of IMIDs, immune-mediated inflammatory skin diseases (IMSDs) can occur at all ages, affecting 30-70% of individuals. Although their clinical manifestations are different from each other, they share a similar chronic inflammatory background of the skin and adversely affect patients' physical conditions and quality of life. Current risk prediction models for IMIDs and IMSDs are limited, often focusing on single factor, without integrating multi-dimensional data. Moreover, IMIDs often experience the occurrence of some comorbidities. Therefore, prevention of IMSDs is very important, and timely management is necessary to prevent the occurrence of disease complications. This project aims to uncover why some people develop chronic inflammatory conditions, particularly those affecting the skin. By studying how genes, lifestyle habits, and environmental factors work together, we hope to identify what increases the risk of patients and improve early detection. This helps us solve problems such as: 1. Which genetic traits or environmental exposures most strongly link to these conditions? 2. Can combining genetic and environmental data create better tools to predict who might get sick? 3. How can we use this knowledge to prevent diseases before they start? Over three years, we will: 1. Identify risk factors: Analyze genetic data, health records, and environmental exposures (e.g., air quality, UV exposure) to pinpoint what raises the risk of these diseases. 2. Test cause-and-effect relationships: Use advanced statistical methods to confirm whether factors like vitamin D levels or smoking directly cause disease or are just linked to it. 3. Build prediction tools: Develop computer models that combine genetic risks, environmental triggers, and clinical data to estimate a person's likelihood of developing these conditions.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17182 | Association between triglyceride-glucose index combined with a body shape index and atherosclerotic cardiovascular disease risk varies by glycemic status: insights from the UK Biobank | Xinyi Shao (+7) | 2025 | Cardiovascular Diabetology |
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