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Serum TGF-β1 and CD14 Predicts Response to Anti-TNF-α Therapy in IBD
S. Coufal, M. Kverka, J. Kreisinger, T. Thon, F. Rob, M. Kolar, Z. Reiss, D. Schierova, K. Kostovcikova, R. Roubalova, L. Bajer, Z. Jackova, M. Mihula, P. Drastich, J. Tresnak Hercogova, M. Novakova, M. Vasatko, M. Lukas, H. Tlaskalova-Hogenova,...
Jazyk angličtina Země Egypt
Typ dokumentu časopisecké články
NLK
Directory of Open Access Journals
od 1990
Free Medical Journals
od 2014
Hindawi Publishing Open Access
od 1990-01-01
PubMed Central
od 2014
Europe PubMed Central
od 2014
ProQuest Central
od 2008-01-01
Open Access Digital Library
od 1990-01-01
Open Access Digital Library
od 2014-01-08
Open Access Digital Library
od 2014-01-01
Medline Complete (EBSCOhost)
od 2014-01-01
Health & Medicine (ProQuest)
od 2008-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2014
PubMed
37383609
DOI
10.1155/2023/1535484
Knihovny.cz E-zdroje
- MeSH
- inhibitory TNF MeSH
- insulinu podobný růstový faktor II * MeSH
- lidé MeSH
- matrixová metaloproteinasa 9 MeSH
- transformující růstový faktor beta1 * MeSH
- vaskulární endoteliální růstový faktor A MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
Dermatology Prof Hercogova Center for Biological Therapy Prague Czech Republic
ISCARE a s IBD Clinical and Research Centre Prague Czech Republic
Citace poskytuje Crossref.org
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- $a 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.
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