Skip to main content

Advertisement

SpringerLink
Log in

Predictors of response to TNF-α antagonist therapy in Chinese rheumatoid arthritis

  • Original Article
  • Published:
Clinical Rheumatology Aims and scope Submit manuscript

Abstract

This study aimed to investigate the clinical, immunological, and radiologic predictors of response to tumor necrosis factor (TNF)-α antagonist therapy in Chinese rheumatoid arthritis (RA). Ninety RA patients were divided into two groups according to their responsiveness to TNF-α antagonist therapy at 1 month: group A (responders) and group B (non-responders). After 3 months of therapy, all the 90 patients were re-assessed and re-divided into another two groups: group C (responders) and group D (non-responders). Serum samples and clinical characteristics as well as radiographic features were collected at baseline, first month, and third month post-initial administration of TNF-α antagonist. Serum TNF-α, interleukin (IL)-6, IL-8, IL-34, and matrix metalloproteinase (MMP)-3 were measured by enzyme-linked immunosorbent assay (ELISA). Disease activity and Sharp score were evaluated. (1) Comparisons between groups A and B: subjects in group A showed a lower level of erythrocyte sedimentation rate (ESR) and a higher level of albumin (ALB) at baseline than that of group B (p < 0.05). The cutoff value of ALB for prediction was ≥34.9 g/l and that of ESR was ≤55.5 mm/h. (2) Comparisons between groups C and D: group C showed lower levels of ESR, health assessment questionnaire (HAQ), and IL-34 at baseline (p < 0.05). The threshold for prediction were as follows: ESR ≤60 mm/h, HAQ ≤1.3125, and IL-34 ≤194.12 pg/ml. (3) The serum cytokines were positively correlated with C-reactive protein (CRP) and disease activity index, while ALB was negatively correlated with CRP and disease activity. Baseline ALB ≥34.9 g/l or ESR ≤55.5 mm/h might predict a good response at 1-month treatment of TNF-α antagonist, while baseline ESR ≤60 mm/h, HAQ ≤1.3125, and IL-34 ≤194.12 pg/ml might predict a good response at 3-month treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bathon JM, Martin RW, Fleischmann RM et al (2000) A comparison of etanercept and methotrexate in patients with early rheumatoid arthritis. N Engl J Med 343:1586–1593

    Article  CAS  PubMed  Google Scholar 

  2. Maini RN, Breedveld FC, Kalden JR et al (2004) Sustained improvement over two years in physical function, structural damages, and signs and symptoms among patients with rheumatoid arthritis treated with infliximab and methotrexate. Arthritis Rheum 50:1051–1065

    Article  CAS  PubMed  Google Scholar 

  3. Simsek I (2012) Predictors of Response to TNF inhibitors in rheumatoid arthritis—do we have new tools for personalized medicine? Bull NYU Hospital Joint Dis 70:187–190

    Google Scholar 

  4. Smolen JS, Landewé R, Breedveld FC (2014) EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2013 update. Ann Rheum Dis 73:492–509

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. der Broeder A, van de Putte L, Rau R et al (2002) A single dose, placebo controlled study of the fully human anti-tumor necrosis factor-alpha antibody adalimumab (D2E7) in patients with rheumatoid arthritis. J Rheumatol 29:2288–2298

    Google Scholar 

  6. Salgado E, Maneiro JR, Gomez-Reino JJ (2014) Predictors of response to TNF antagonists. Curr Pharm Des 21:221–232

    Article  Google Scholar 

  7. Cuchacovich M, Bueno D, Carvajal R, Bravo N, Aguillón JC, Catalán D, Soto L (2014) Clinical parameters and biomarkers for anti-TNF treatment prognosis in rheumatoid arthritis patients. Clin Rheumatol 33:1707–1714

    Article  PubMed  Google Scholar 

  8. Wright HL, Bucknall RC, Moots RJ et al (2012) Analysis of SF and plasma cytokines provides insights into the mechanisms of inflammatory arthritis and may predict response to therapy. Rheumatology (Oxford) 51:451–459

    Article  CAS  Google Scholar 

  9. Fabre S, Dupuy AM, Dossat N et al (2008) Protein biochip array technology for cytokine profiling predicts etanercept responsiveness in rheumatoid arthritis. Clin Exp Immunol 153:188–195

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Yuasa S, Yamaguchi H, Nakanishi Y et al (2013) Treatment responses and their predictors in patients with rheumatoid arthritis treated with biological agents. J Med Invest 60:77–90

    Article  PubMed  Google Scholar 

  11. Pomirleanu C, Ancuta C, Miu S et al (2013) A predictive model for remission and low disease activity in patients with established rheumatoid arthritis receiving TNF blockers. Clin Rheumatol 32:665–670

    Article  PubMed  Google Scholar 

  12. Gonzalez-Alvaro I, Ortiz AM, Tomero EG et al (2007) Baseline serum RANKL levels may serve to predict remission in rheumatoid arthritis patients treated with TNF antagonists. Ann Rheum Dis 66:1675–1678

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Takahashi R, SalkikoIsojima MU et al (2014) Serum anticyclic citrullinated protein antibody titers are correlated with the response to biological agents in patients with rheumatoid arthritis. Open Access Rheumatol: Res Rev 6:57–64

    CAS  Google Scholar 

  14. Braun-Moscovici Y, Markovits D, Zinder O et al (2006) Anti-cyclic citrullinated protein antibodies as a predictor of response to anti-tumor necrosis factor-alpha therapy in patients with rheumatoid arthritis. J Rheumatol 33:497–500

    CAS  PubMed  Google Scholar 

  15. Nishimoto T, Seta N, Anan R et al (2014) A single nucleotide polymorphism of TRAF1 predicts the clinical response to anti-TNF treatment in Japanese patients with rheumatoid arthritis. Clin Exp Rheumatol 32:211–217

    CAS  PubMed  Google Scholar 

  16. Dávila-Fajardo CL, Márquez A, Pascual-Salcedo D et al (2014) Confirmation of -174G/C interleukin-6 gene promoter polymorphism as a genetic marker predicting antitumor necrosis factor treatment outcome. Pharmacogenet Genomics 24:1–5

    Article  PubMed  Google Scholar 

  17. Kayakabe K, Kuroiwa T, Sakurai N et al (2012) Interleukin-1β measurement in stimulated whole blood cultures is useful to predict response to anti-TNF therapies in rheumatoid arthritis. Rheumatology (Oxford) 51:1639–1643

    Article  CAS  Google Scholar 

  18. Ribbens C, Andre B, Jaspar JM et al (2000) Matrix metalloproteinase-3 serum levels are correlated with disease activity and predict clinical response in rheumatoid arthritis. J Rheumatol 27:888–893

    CAS  PubMed  Google Scholar 

  19. Abhishek A, Butt S, Gadsby K et al (2010) Anti-TNF-alpha agents are less effective for the treatment of rheumatoid arthritis in current smokers. J Clin Rheumatol 16:15–18

    Article  CAS  PubMed  Google Scholar 

  20. Coulthard LR, Taylor JC, Eyre S et al (2011) Genetic variants within the MAP kinase signaling network and anti-TNF treatment response in rheumatoid arthritis patients. Ann Rheum Dis 70:98–103

    Article  PubMed  Google Scholar 

  21. Saevarsdottir S, Wedren S, Seddighzadeh M et al (2011) Patients with early rheumatoid arthritis who smoke are less likely to respond to treatment with methotrexate and tumor necrosis factor inhibitors: observations from the Epidemiological Investigation of Rheumatoid Arthritis and the Swedish Rheumatology Register cohorts. Arthritis Rheum 63:26–36

    Article  CAS  PubMed  Google Scholar 

  22. Soderlin MK, Petersson IF, Geborek P (2012) The effect of smoking on response and drug survival in rheumatoid arthritis patients treated with their first anti-TNF drug. Scand J Rheumatol 41:1–9

    Article  CAS  PubMed  Google Scholar 

  23. van der Heijde D (2000) How to read radiographs according to the Sharp/van der Heijde method. J Rheumatol 27:261–263

    PubMed  Google Scholar 

  24. Felson DT, Anderson JJ, Boers M et al (1995) American college of rheumatology. Preliminary definition of improvement in rheumatoid arthritis. Arthritis Rheum 38:727–735

    Article  CAS  PubMed  Google Scholar 

  25. Ward MM, Guthrie LC, Alba MI (2014) Brief report: rheumatoid arthritis response criteria and patient-reported improvement in arthritis activity: is an American College of Rheumatology twenty percent response meaningful to patients? Arthritis Rheumatol 66:2339–2343

    Article  PubMed  Google Scholar 

  26. Hyrich KL, Watson KD, Silman AJ et al (2006) Predictors of response to anti-TNF-alpha therapy among patients with rheumatoid arthritis: Results from the British Society for Rheumatology Biologics Register. Rheumatology 45:1558–1565

    Article  CAS  PubMed  Google Scholar 

  27. Hwang SJ, Choi B, Kang SS et al (2012) Interleukin-34 produced by human fibroblast-like synovial cells in rheumatoid arthritis supports osteoclastogenesis. Arthritis Res Ther 14:R14

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Wijbrandts CA, Dijkgraaf MGW, Kraan MC et al (2008) The clinical response to infliximab in rheumatoid arthritis is in part dependent on pretreatment tumour necrosis factor a expression in the synovium. Ann Rheum Dis 67:1139–1144

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This project was supported by the grants from the Science & Technology Ministry ‘11th and 12th Five-Year Plan’ to support science and technology of China (No.2008BAI59B01 and No.2014BAI07B00).

Disclosures

None.

Authors’ contributions

Liqi Bi and Rui Ding designed the study, analyzed the data, and wrote and revised the manuscript. Ping Li collected the clinical characteristics. Ding Song and others scored the van der Heijde-modified Sharp score. Rui Ding performed the experiments. Xin Zhang collected and stored the serum samples.

Author information

Authors and Affiliations

  1. Department of Rheumatology and Immunology, China-Japan Union Hospital of Jilin University, Changchun, 130033, China

    Rui Ding, Ping Li, Ding Song, Xin Zhang & Liqi Bi

Authors
  1. Rui Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ding Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liqi Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liqi Bi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, R., Li, P., Song, D. et al. Predictors of response to TNF-α antagonist therapy in Chinese rheumatoid arthritis. Clin Rheumatol 34, 1203–1210 (2015). https://doi.org/10.1007/s10067-015-2973-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10067-015-2973-3

Keywords

Search

Navigation