Serum TGF-β1 and CD14 Predicts Response to Anti-TNF-α Therapy in IBD - Coufal, Stepan; Kverka, Miloslav; Kreisinger, Jakub; Thon, Tomas; Rob, Filip; Kolar, Martin; Reiss, Zuzana; Schierova, Dagmar; Kostovcikova, Klara; Roubalova, Radka; Bajer, Lukas; Jackova, Zuzana; et al. (Journal of immunology research, 2023)
BACKGROUND: Tumor necrosis factor-alpha (TNF-α) agonists revolutionized therapeutic algorithms in inflammatory bowel disease (IBD) management. However, approximately every third IBD patient does not respond to this therapy in the long term, which delays efficient control of the intestinal inflammation. METHODS: We analyzed the power of serum biomarkers to predict the failure of anti-TNF-α. We collected serum of 38 IBD patients at therapy prescription and 38 weeks later and analyzed them with relation to therapy response (no-, partial-, and full response). We used enzyme-linked immunosorbent assay to quantify 16 biomarkers related to gut barrier (intestinal fatty acid-binding protein, liver fatty acid-binding protein, trefoil factor 3, and interleukin (IL)-33), microbial translocation, immune system regulation (TNF-α, CD14, lipopolysaccharide-binding protein, mannan-binding lectin, IL-18, transforming growth factor-β1 (TGF-β1), osteoprotegerin (OPG), insulin-like growth factor 2 (IGF-2), endocrine-gland-derived vascular endothelial growth factor), and matrix metalloproteinase system (MMP-9, MMP-14, and tissue inhibitors of metalloproteinase-1). RESULTS: We found that future full-responders have different biomarker profiles than non-responders, while partial-responders cannot be distinguished from either group. When future non-responders were compared to responders, their baseline contained significantly more TGF-β1, less CD14, and increased level of MMP-9, and concentration of these factors could predict non-responders with high accuracy (AUC = 0.938). Interestingly, during the 38 weeks, levels of MMP-9 decreased in all patients, irrespective of the outcome, while OPG, IGF-2, and TGF-β1 were higher in non-responders compared to full-responders both at the beginning and the end of the treatment. CONCLUSIONS: The TGF-β1 and CD14 can distinguish non-responders from responders. The changes in biomarker dynamics during the therapy suggest that growth factors (such as OPG, IGF-2, and TGF-β) are not markedly influenced by the treatment and that anti-TNF-α therapy decreases MMP-9 without influencing the treatment outcome.
Keywords: vaskulární endoteliální růstový faktor A; inhibitory TNF; insulinu podobný růstový faktor II; lidé; matrixová metaloproteinasa 9; Transforming Growth Factor beta1; Vascular Endothelial Growth Factor A; Insulin-Like Growth Factor II; Matrix Metalloproteinase 9; transformující růstový faktor beta1; Tumor Necrosis Factor Inhibitors; Humans