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Updated: Jun 1, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Novel HLA class I and II insights into the pathogenesis of systemic sclerosis-associated interstitial lung disease
Carlos Rosa-Baez1, Carlos Rangel-Peláez1, Inmaculada Rodriguez-Martin1
1Department of Cell Biology and Immunology, Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas, Granada, Spain.
Objectives:
Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of mortality in systemic sclerosis (SSc), yet its genetic architecture remains incompletely understood. Therefore, given the key role of the major histocompatibility complex (MHC) in SSc, we aimed to perform a comprehensive MHC-wide association study in the largest SSc-ILD cohort to date.
Methods:
We analysed 2412 patients with SSc-ILD⁺, 3550 patients with SSc-ILD⁻, and 15,076 controls of European ancestry from 10 international cohorts. After quality control, the MHC region was imputed, and inverse variance weighted meta-analysis was performed. Subsequently, conditional stepwise analyses, adjustment for antitopoisomerase autoantibody (ATA) status, and functional annotation of significant single-nucleotide polymorphisms were performed. Finally, we constructed a composite score combining genetic, clinical, and demographic variables to predict SSc-ILD.
Results:
After conditional analysis, we detected 12 significant associations within class I and class II human leukocyte antigen (HLA) genes. ATA adjustment reduced the significance of class II HLA variants, whereas class I HLA variants remained unaffected. Finally, the built composite score had an area under the curve of 0.754, significantly outperforming the models including any of the variables alone.
Conclusions:
In this study, we identify genetic mechanisms underlying SSc-ILD that support the potential implication of CD8+ T cells and ATAs in its pathogenesis. Moreover, we also demonstrate the enhanced efficacy of integrating genetic information into predictive models to detect patients at high risk of SSc-ILD. These findings provide new insights into disease pathogenesis and suggest potential biomarkers and therapeutic targets for improved patient management.
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