Serum TGF-β1 and CD14 Predicts Response to Anti-TNF-α Therapy in IBD

Author:

Coufal Stepan1ORCID,Kverka Miloslav1,Kreisinger Jakub2,Thon Tomas1,Rob Filip3,Kolar Martin4,Reiss Zuzana1,Schierova Dagmar1,Kostovcikova Klara1,Roubalova Radka1,Bajer Lukas15,Jackova Zuzana1,Mihula Martin1,Drastich Pavel5,Tresnak Hercogova Jana36,Novakova Michaela3,Vasatko Martin4,Lukas Milan47,Tlaskalova-Hogenova Helena1,Jiraskova Zakostelska Zuzana1ORCID

Affiliation:

1. Laboratory of Cellular and Molecular Immunology, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic

2. Laboratory of Animal Evolutionary Biology, Faculty of Science, Department of Zoology, Charles University, Prague, Czech Republic

3. Second Faculty of Medicine, University Hospital Bulovka, Dermatovenerology Department, Charles University, Prague, Czech Republic

4. ISCARE a.s., IBD Clinical and Research Centre, Prague, Czech Republic

5. Department of Gastroenterology and Hepatology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic

6. Dermatology Prof. Hercogova, Center for Biological Therapy, Prague, Czech Republic

7. Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and First Faculty of Medicine, Charles University, Prague, Czech Republic

Abstract

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.

Funder

Ministerstvo Zdravotnictví Ceské Republiky

Publisher

Hindawi Limited

Subject

Immunology,General Medicine,Immunology and Allergy

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