A new nucleosomic-based model to identify and diagnose SSc-ILD.

Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (SSc-ILD), driving its mortality. Specific biomarkers associated with the evolution of the lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. Nucleosomes are stable DNA/protein complexes that are shed into the blood stream making them ideal candidates for biomarkers.
Methods: We studied circulating cell-free nucleosomes (cf-nucleosomes) in SSc patients, 31 with ILD (SSc-ILD) and 67 without ILD. We analyzed plasma levels for cf-nucleosomes and investigated whether global circulating nucleosome levels in association with or without other biomarkers of interest for systemic sclerosis or lung fibrosis (e.g., serum growth factors: IGFBP-1 and the MMP enzyme: MMP-9), could be suitable potential biomarkers for the correct identification of SSc-ILD disease.
Results: We found that H3.1 nucleosome levels were significantly higher in patients with SSc-ILD compared SSc patients without ILD (p < 0.05) and levels of MMP-9 were significantly increased in patients with SSc-ILD compared to SSc patients without ILD (p < 0.05). Conversely, IGFBP-1 was significantly reduced in patients with SSc-ILD compared to SSc without ILD (p < 0.001). The combination of cf-nucleosomes H3.1 coupled to MMP-9 and IGFBP-1 increased the sensitivity for the differential detection of SSc-ILD. High levels of accuracy were reached with this combined model: its performances are strong with 68.4% of positive predictive value and 77.2% of negative predictive value for 90% of specificity. With our model, we identified a significant negative correlation with FVC % pred (r = -0.22) and TLC % pred (r = -0.31). The value of our model at T1 (baseline) has a predictive power over the Rodnan score at T2 (after 6-18 months), showed by a significant linear regression with R ² = 19% (p = 0.013). We identified in the sole group of SSc-ILD patients a significant linear regression with a R ² = 54.4% with the variation of DLCO between T1 and T2 (p < 0.05).
Conclusion: In our study, we identified a new blood-based model with nucleosomic biomarker in order to diagnose SSc-ILD in a SSc cohort. This model is correlated with TLC and FVC at baseline and predictive of the skin evolution and the DLCO. Further longitudinal exploration studies should be performed in order to evaluate the potential of such diagnostic and predictive model.