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AI / informaticsJournal of translational medicine · 2026

Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.

Yizhuo Tian, Jing Geng, Mingyao Wang … Huaping Dai

Allergy & ImmunologyAI / informatics

Machine learning model accurately distinguishes fibrotic hypersensitivity pneumonitis from idiopathic pulmonary fibrosis.

Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.

Yizhuo Tian et al. · Journal of translational medicine · 2026

Background

Hypersensitivity pneumonitis (HP) manifests as fibrotic (FHP) and non-fibrotic (NFHP) phenotypes.

Purpose

This investigation sought to develop a plasma proteomics-based framework for differential diagnosis between these entities.

Methods

A total of 119 subjects were enrolled from the Chinese Interstitial Lung Disease (ILD) National Cohort and the PORTRAY IPF Cohort between July 2018 and June 2022, comprising 32 healthy controls (HCs), 31 NFHPs, 28 FHPs, and 28 IPF patients.

n = 119 subjects

Results

the model correctly identified these two different types of lung scarring

n = 119 subjects

More results

WGCNA revealed significant enrichment of the glycolysis/gluconeogenesis and pyruvate metabolism pathways, implicating metabolic reprogramming in FHP pathogenesis.

More results

Differential analysis identified nine differentially expressed proteins, from which a six-protein signature (H2BC12, SHBG, APCS, PTPRG, IGHV1-58, and GAPDH) was derived through LASSO regression and recursive feature elimination.

This model represents a promising non-invasive molecular tool for the differential diagnosis of atypical interstitial lung diseases.

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Conclusion

This study established the plasma proteomic landscape of FHP, linking metabolic reprogramming to disease pathogenesis and providing a machine learning framework for biomarker-guided differential diagnosis.

Post

Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.

Machine learning model accurately distinguishes fibrotic hypersensitivity pneumonitis from idiopathic pulmonary fibrosis.

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