AB0812 BIOMARKERS FOR CONNECTIVE TISSUE DISEASE-ASSOCIATED INTERSTITIAL LUNG DISEASE: A PILOT STUDY
Authors: Y. H. Chiu, M. Voortman, E. M. Delemarre, S. Nierkens, P. De Jong, F. Mohamed Hoesein, J. M. Van Laar, J. Spierings
Journal: Annals of the Rheumatic Diseases (2021)

Background: Connective tissue disease (CTD)-associated interstitial lung disease (ILD) has high morbidity and increased mortality risk. Biomarkers that can predict prognosis and response to therapy have been studied in recent years, including cytokines, chemokines, adhesion molecules, growth factors, extracellular remodelling molecules and pneumocyte associated proteins. However, there is still a knowledge gap for interpreting biomarkers in the context of imaging and response. Objectives: The aim of our study was to identify biomarkers that are associated with inflammatory and fibrotic changes over time on high-resolution computed tomography (HRCT) and response to treatment in CTD-ILD. Methods: An exploratory set of 38 biomarkers were measured in serum of patients with CTD-ILD using a multiplex immunoassay, chemiluminescence enzyme immunoassay and enzyme-linked immunosorbent assay. Samples were taken at baseline and one year of follow-up. Clinical data was extracted from electronic medical patients records. Two experienced chest radiologists independently and blindly reviewed the HRCTs. A third expert reviewed the scan in case of discrepancies. They classified ILD patterns according to the classification for idiopathic interstitial pneumonia and categorised into fibrotic or inflammatory. Association between biomarkers at baseline and pulmonary function test (PFT) and HRCT changes in response to treatment were analysed by logistic regression. Responsiveness of biomarkers to treatment was determined using the paired Wilcoxon sign rank test. Correlations between the variation of biomarkers and PFT after one year of treatment were examined using Spearman’s rank correlation coefficient. Results: Sixteen patients were included (12 females (75.0%), median age 51 years (IQR 45–62). Underlying CTDs were systemic sclerosis (n=5), Sjogren’s syndrome (n=1), inflammatory myopathies (n=6), rheumatoid arthritis (n=1), systemic lupus erythematosus (n=1), mixed connective tissue disease (n=1), and undifferentiated connective tissue disease (n=1). There were three patients with a fibrotic HRCT pattern and 13 patients with an inflammatory HRCT pattern. Biomarkers attenuation in CXCL11, CTGF, and KL-6 were associated with inflammatory HRCT patterns as well as less HRCT progression and fewer FVC decline. Furthermore, a decrease in CTGF was observed in patients treated with MMF and corticoids, while a reduction in CXCL11 and SPD were seen in patients treated with hydroxychloroquine. (Table 1) Additionally, an increase in levels of galectin-3 at one-year follow-up was associated with improved predicted FVC (Rho 0.5, p=0.048). (Figure 1) Table 1. Biomarker responses to clinical treatment Treatment Biomarkers Baseline, Median (IQR) Follow up, Median (IQR) Variation percentage p MMF CTGF (pg/ml) 110.69 (63.21–158.06) 4.73 (4.73–45.3) -95.73 0.036 (n=7) sVCAM (pg/ml) 2061300 (1939200–3042250) 1767600 (1707100–1986350) -14.25 0.031 HCQ CXCL11 (pg/ml) 699.30 (392.50–764.50) 333.39 (223.1–487.92) -52.33 0.031 (n=6) SPD (ng/ml) 18.70 (13.40–28.73) 16.80 (11.70–22.80) -10.16 0.031 Steroid CTGF (pg/ml) 81.93 (20.80–158.06) 12.76 (4.73–56.74) -84.43 0.044 (n=11) MMP-7 (pg/ml) 3146.3 (1779.7–3997.0) 4563.3 (2951.9–7625.8) 45.04 0.005 IQR, interquartile range; MMF, mycophenolate mofetil; HCQ, hydroxychloroquine; CTGF, connective tissue growth factor; sVCAM, soluble vascular cell adhesion molecule-1; SPD, surfactant protein D; MMP-7, matrix metalloproteinase 7. Conclusion: In this pilot study, CXCL11, CTGF, and KL-6 reduction were associated with inflammatory HRCT patterns and better pulmonary outcome. In contrast to previous research in severe ILD, there was a positive correlation between changes of Galectin-3 and FVC in our study. Further research in a larger group and focusing on combining biomarkers to predict outcome and prognosis are needed. Figure 1. Acknowledgements: We want to thank the Utrecht Immunity and Infection cohort for providing the serum samples, Marieke Vianen for the support in data management and Annemieke Sloeserwij for her help in collecting the samples. We want to thank Henk Ruven from St. Antonius Hospital Utrecht for measuring Krebs von den Lungen 6, and surfactant protein D. We acknowledge Boehringer Ingelheim B.V. the Netherlands for providing financial support for this study. Disclosure of Interests: None declared