Large-scale plasma proteomics identifies proteins and pathways associated with incident interstitial lung disease
6 affiliations
- 1Department of Rheumatology and Immunology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
- 2Senior Department of Respiratory and Critical Care Medicine, The Eighth Medical Center of Chinese PLA General Hospital
- 3Senior Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital
- 4College of Economics and Management, China Agricultural University
- 5College of Biological Science and Medical Engineering, Donghua University
- 6Department of Rheumatology and Immunology, Beijing Jishuitan Hospital, Capital Medical University
Abstract
Objective: Reliable biomarkers for early interstitial lung disease (ILD) risk stratification are limited; therefore, we aimed to identify prediagnostic proteins associated with incident ILD and develop a long-term prediction model.
Methods: We analyzed 52,743 UK Biobank participants without baseline ILD and measured 2,923 plasma proteins. Cox models were used to identify proteins associated with incident ILD. Key predictors were selected using LASSO and sequential forward selection within a LightGBM framework. Model performance was assessed using the area under the curve (AUC) and SHAP. Prediagnostic protein trajectories were evaluated using LOESS and clustering. Single-cell transcriptomic data from human idiopathic pulmonary fibrosis lungs were used to explore cellular sources.
Results: During a mean follow-up period of 14.4 years, 814 ILD cases were identified in the cohort. A total of 277 proteins were significantly associated with incident ILD and enriched in immune activation and cytokine signaling pathways. Five proteins (GDF15, LAMP3, SFTPD, EDA2R, and WFDC2) were identified as dominant predictors. Trajectory analysis showed that several ILD-associated proteins deviated from control levels years before diagnosis and could be grouped into four distinct temporal clusters. GDF15, LAMP3, SFTPD, WFDC2, and EGFR showed detectable prediagnostic alterations more than a decade before ILD onset. Furthermore, connective tissue disease (CTD)-ILD displayed a distinct inflammatory proteomic profile, including CXCL13, CCL7, PDCD1, and TNFRSF4. When the five protein biomarkers were combined with clinical variables, the integrated model achieved relatively good predictive accuracy for ILD, with AUCs of 0.938 and 0.908 for 5-year and 10-year ILD risks, respectively. Single-cell analysis indicated that these proteins may predominantly originate from dysregulated alveolar and airway epithelial cells.
Conclusion: Plasma proteomics identified prediagnostic protein signatures associated with future ILD risk and improved long-term prediction when combined with clinical variables.
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BibTeX
@article{kang2026largescale,
title = {{Large-scale plasma proteomics identifies proteins and pathways associated with incident interstitial lung disease}},
author = {Zijian Kang and Ying Zhu and Jianzheng Zhang and Chen Zhu and Ping Li and Kai Li and Qiang Tong and Sheng-Ming Dai},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1962219},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1962219/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Large-scale plasma proteomics identifies proteins and pathways associated with incident interstitial lung disease
AU - Zijian Kang
AU - Ying Zhu
AU - Jianzheng Zhang
AU - Chen Zhu
AU - Ping Li
AU - Kai Li
AU - Qiang Tong
AU - Sheng-Ming Dai
PY - 2026
DA - 2026/10/21
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1962219
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1962219/full
AB - Objective: Reliable biomarkers for early interstitial lung disease (ILD) risk stratification are limited; therefore, we aimed to identify prediagnostic proteins associated with incident ILD and develop a long-term prediction model. Methods: We analyzed 52,743 UK Biobank participants without baseline ILD and measured 2,923 plasma proteins. Cox models were used to identify proteins associated with incident ILD. Key predictors were selected using LASSO and sequential forward selection within a LightGBM framework. Model performance was assessed using the area under the curve (AUC) and SHAP. Prediagnostic protein trajectories were evaluated using LOESS and clustering. Single-cell transcriptomic data from human idiopathic pulmonary fibrosis lungs were used to explore cellular sources. Results: During a mean follow-up period of 14.4 years, 814 ILD cases were identified in the cohort. A total of 277 proteins were significantly associated with incident ILD and enriched in immune activation and cytokine signaling pathways. Five proteins (GDF15, LAMP3, SFTPD, EDA2R, and WFDC2) were identified as dominant predictors. Trajectory analysis showed that several ILD-associated proteins deviated from control levels years before diagnosis and could be grouped into four distinct temporal clusters. GDF15, LAMP3, SFTPD, WFDC2, and EGFR showed detectable prediagnostic alterations more than a decade before ILD onset. Furthermore, connective tissue disease (CTD)-ILD displayed a distinct inflammatory proteomic profile, including CXCL13, CCL7, PDCD1, and TNFRSF4. When the five protein biomarkers were combined with clinical variables, the integrated model achieved relatively good predictive accuracy for ILD, with AUCs of 0.938 and 0.908 for 5-year and 10-year ILD risks, respectively. Single-cell analysis indicated that these proteins may predominantly originate from dysregulated alveolar and airway epithelial cells. Conclusion: Plasma proteomics identified prediagnostic protein signatures associated with future ILD risk and improved long-term prediction when combined with clinical variables.
SN - 1664-3224
ER -