• Dinesh Khanna, Donald P Tashkin, Christopher P Denton, Elisabetta A Renzoni, Sujal R Desai, and John Varga.
• Division of Rheumatology, Department of Internal Medicine, University of Michigan Scleroderma Program, Ann Arbor, Michigan.
• Am. J. Respir. Crit. Care Med. 2020 Mar 15; 201 (6): 650-660.

AbstractSystemic sclerosis (SSc) is a complex, multiorgan, autoimmune disease. Lung fibrosis occurs in ∼80% of patients with SSc; 25% to 30% develop progressive interstitial lung disease (ILD). The pathogenesis of fibrosis in SSc-associated ILD (SSc-ILD) involves cellular injury, activation/differentiation of mesenchymal cells, and morphological/biological changes in epithelial/endothelial cells. Risk factors for progressive SSc-ILD include older age, male sex, degree of lung involvement on baseline high-resolution computed tomography imaging, reduced DlCO, and reduced FVC. SSc-ILD does not share the genetic risk architecture observed in idiopathic pulmonary fibrosis (IPF), with key risk factors yet to be identified. Presence of anti-Scl-70 antibodies and absence of anti-centromere antibodies indicate increased likelihood of progressive ILD. Elevated levels of serum Krebs von den Lungen-6 and C-reactive protein are both associated with SSc-ILD severity and predict SSc-ILD progression. A promising prognostic indicator is serum chemokine (C-C motif) ligand 18. SSc-ILD shares similarities with IPF, although clear differences exist. Histologically, a nonspecific interstitial pneumonia pattern is commonly observed in SSc-ILD, whereas IPF is defined by usual interstitial pneumonia. The course of SSc-ILD is variable, ranging from minor, stable disease to a progressive course, whereas all patients with IPF experience progression of disease. Although appropriately treated patients with SSc-ILD have better chances of stabilization and survival, a relentlessly progressive course, akin to IPF, is seen in a minority. Better understanding of cellular and molecular pathogenesis, genetic risk, and distinctive features of SSc-ILD and identification of robust prognostic biomarkers are needed for optimal disease management.

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