CD206 and dust particles are prognostic biomarkers of progressive fibrosing interstitial lung disease associated with air pollutant exposure

Abstract

Current management strategies for progressive fibrosing interstitial lung disease (PF-ILD) and non-PF- ILD differ significantly, underscoring the need for early identification of PF-ILD patients. We analyzed the expression of macrophage markers and the number of dust particles (DP) in lung tissue, as well as complete blood count and blood chemistry tests to identify biomarkers of PF-ILD, and examined the effect of certain pollutants on these biomarkers. Lung biopsies were collected from 73 non-PF-ILD patients and 36 PF-ILD patients. DP were quantified in alveolar wall cells (DP-aw) and desquamated epithelial cells (DP-desq) using polarizing light microscopy. Expression of CD206, transforming growth factor β1 (TGF-β1), connective tissue growth factor (CTGF), C-X-C motif ligand 13 (CXCL13), fibroblast growth factor 2 (FGF-2), tumor necrosis factor α (TNFα), and interleukin 1β (IL-1β) was assessed in lung tissue by immunohistochemistry. The numbers of DP-desq, pulmonary expression of CXCL13, IL-1β and CD206 were higher in ILD patients resided for ≥ 15 days per year in places with 24-hour ambient PM 10 level of ≥ 50 µg/m 3 compared with ILD patients exposed for < 15 days per year to the similar PM 10 concentration. Additionally, CXCL13 expression in lung tissue was higher in smoking ILD patients than in non-smoking ILD patients. Compared with non-PF-ILD patients, PF-ILD patients exhibited higher numbers of DP-aw and DP-desq, as well as increased expression of CD206, CXCL13, IL-1β, TGF-β1, and CTGF in lung tissue. Elevated blood neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios were also observed in PF-ILD patients. These biomarkers were found to be independent predictors of PF-ILD. A regression logistic model incorporating NLR, CD206, and DP-desq predicted PF-ILD with an AUC of 0.847, sensitivity of 84.6%, and specificity of 83.3%. Our findings may be useful in predicting PF-ILD and highlight the need for reducing pollutant emission.