Skip to main content

Advertisement

Log in

Knowledge mapping of biological disease-modifying anti-rheumatic drugs for axial spondyloarthritis: a bibliometric study

  • REVIEW ARTICLE
  • Published:
Clinical Rheumatology Aims and scope Submit manuscript

Abstract

Various biological disease-modifying anti-rheumatic drugs (bDMARDs) have been applied for treating axial spondyloarthritis (axSpA). However, there is a glaring absence of a bibliometric analysis on bDMARDs against axSpA. Articles related to use of bDMARDs in treating axSpA published from 2004 to 2022 were searched from the Web of Science Core Collection. VOS viewer 1.6.18 and CiteSpace 6.1.R2 were used to analyze and visualize the quantity and citations of publications, as well as to identify “research hotspots” and trends in this field. BibExcel version 1.0.0 and gCLUTO version 1.0 were used to build matrices for bi-clustering analysis. A total of 2546 articles referring to bDMARDs for treatment of axSpA were included in this bibliometric analysis. Overall, the number of publications has been increasing steadily annually. The USA (23.21%, 591 publications) ranked first with the largest output of papers, followed by Germany, and the Netherlands. Rheumazentrum Ruhrgebiet ranked first as the most frequent publisher (119 articles). Annals of the Rheumatic Diseases published the most documents (6.76%, 172 publications) in this field. The predominant hotspots have been “tuberculosis,” “IL-17,” and “quality of life” in the field until 2020. Since 2015, “biosimilar pharmaceuticals” has retained the popularity. Current research hotspots are “spinal radiographic progression,” Janus kinase (JAK) inhibitors, and adverse events (AEs). Machine learning has become popular gradually. Globally, there has been a steady increase in the number of studies on bDMARDs use against axSpA. JAK inhibitors, spinal radiographic progression, biosimilar pharmaceuticals, and AEs are current research hotspots. Machine learning is emerging research hotspots and trends in this field.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files (Appendix 1 and 2).

References

  1. Sieper J, Rudwaleit M, Baraliakos X, Brandt J, Braun J, Burgos-Vargas R, Dougados M, Hermann KG, Landewé R, Maksymowych W, & van der Heijde D (2009) The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis. Annals of the rheumatic diseases 68: Suppl 2, ii1–ii44. https://doi.org/10.1136/ard.2008.104018

  2. Klavdianou K, Tsiami S, & Baraliakos X (2021) New developments in ankylosing spondylitis-status in 2021. Rheumatology (Oxford, England) 60: Suppl 6, vi29–vi37. https://doi.org/10.1093/rheumatology/keab523

  3. Rudwaleit M, van der Heijde D, Landewé R, Listing J et al (2009) The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis 68(6):777–783. https://doi.org/10.1136/ard.2009.108233

    Article  CAS  PubMed  Google Scholar 

  4. Benavent D, Navarro-Compán V (2021) Understanding the paradigm of non-radiographic axial spondyloarthritis. Clin Rheumatol 40(2):501–512. https://doi.org/10.1007/s10067-020-05423-7

    Article  PubMed  Google Scholar 

  5. Taurog JD, Chhabra A, Colbert RA (2016) Ankylosing spondylitis and axial spondyloarthritis. N Engl J Med 374(26):2563–2574. https://doi.org/10.1056/NEJMra1406182

    Article  PubMed  Google Scholar 

  6. Dean LE, Jones GT, MacDonald AG, Downham C, Sturrock RD, Macfarlane GJ (2014) Global prevalence of ankylosing spondylitis. Rheumatology (Oxford) 53(4):650–657. https://doi.org/10.1093/rheumatology/ket387

    Article  PubMed  Google Scholar 

  7. Navarro-Compan V, Sepriano A, El-Zorkany B, van der Heijde D (2021) Axial spondyloarthritis. Ann Rheum Dis 80(12):1511–1521. https://doi.org/10.1136/annrheumdis-2021-221035

    Article  CAS  PubMed  Google Scholar 

  8. Ritchlin C, & Adamopoulos IE (2021) Axial spondyloarthritis: new advances in diagnosis and management. BMJ (Clinical research ed.) 372: m4447. https://doi.org/10.1136/bmj.m4447

  9. Menegatti S, Bianchi E, Rogge L (2019) Anti-TNF therapy in spondyloarthritis and related diseases, impact on the immune system and prediction of treatment responses. Front Immunol 10:382. https://doi.org/10.3389/fimmu.2019.00382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ward MM, Deodhar A, Gensler LS, Dubreuil M et al (2019) 2019 Update of the American College of Rheumatology/Spondylitis Association of America/Spondyloarthritis Research and Treatment Network recommendations for the treatment of ankylosing spondylitis and nonradiographic axial spondyloarthritis. Arthritis Rheumatol 71(10):1599–1613. https://doi.org/10.1002/art.41042

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ortolan A, Webers C, Sepriano A, Falzon L, Baraliakos X, Landewé RB, Ramiro S, van der Heijde D, & Nikiphorou E (2022) Efficacy and safety of non-pharmacological and non-biological interventions: a systematic literature review informing the 2022 update of the ASAS/EULAR recommendations for the management of axial spondyloarthritis. Annals of the rheumatic diseases, ard-2022–223297. Advance online publication. https://doi.org/10.1136/ard-2022-223297

  12. Cherqaoui B, Araujo LM, Glatigny S, Breban M (2021) Axial spondyloarthritis: emerging drug targets. Expert Opin Ther Targets 25(8):633–644. https://doi.org/10.1080/14728222.2021.1973429

    Article  CAS  PubMed  Google Scholar 

  13. Sfikakis PP, Bournia VK, Sidiropoulos P, Boumpas DT, Drosos AA, Kitas GD, Konstantonis G, Liossis SN, Manoussakis MN, Sakkas L, Tektonidou M, Tzioufas AG, Vlachoyiannopoulos PG, Kani C, Paterakis P, Litsa P, Vassilopoulos D (2017) Biologic treatment for rheumatic disease: real-world big data analysis from the Greek country-wide prescription database. Clin Exp Rheumatol 35(4):579–585

    PubMed  Google Scholar 

  14. Prieto-Peña D, Dasgupta B (2021) Biologic agents and small-molecule inhibitors in systemic autoimmune conditions: an update. Pol Arch Intern Med 131(2):171–181. https://doi.org/10.20452/pamw.15438

  15. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

    Article  Google Scholar 

  16. Liu X, Zhao S, Tan L, Tan Y, Wang Y, Ye Z, Hou C, Xu Y, Liu S, Wang G (2022) Frontier and hot topics in electrochemiluminescence sensing technology based on CiteSpace bibliometric analysis. Biosens Bioelectron 201:113932. https://doi.org/10.1016/j.bios.2021.113932

  17. Webers C, Ortolan A, Sepriano A, Falzon L, Baraliakos X, Landewé RBM, Ramiro S, van der Heijde D, Nikiphorou E (2023) Efficacy and safety of biological DMARDs: a systematic literature review informing the 2022 update of the ASAS-EULAR recommendations for the management of axial spondyloarthritis. Ann Rheum Dis 82(1):130–141. https://doi.org/10.1136/ard-2022-223298

    Article  CAS  PubMed  Google Scholar 

  18. Miao L, Zhang J, Zhang Z, Wang S, Tang F, Teng M, Li Y (2022) A bibliometric and knowledge-map analysis of CAR-T cells from 2009 to 2021. Front Immunol 13:840956. https://doi.org/10.3389/fimmu.2022.840956

  19. Chen J, He Q, Jiang B, Song W, Wu Z, Yang J, Huang Z, Yu X, Luo J, Tao Q (2022) Research on primary Sjögren’s syndrome in 2004–2021: a Web of Science-based cross-sectional bibliometric analysis. Rheumatol Int 42(12):2221–2229. https://doi.org/10.1007/s00296-022-05138-9

    Article  PubMed  Google Scholar 

  20. Zhang J, Song L, Jia J, Tian W, Lai R, Zhang Z, Li J, Ju J, & Xu H (2022) Knowledge mapping of necroptosis from 2012 to 2021: a bibliometric analysis. Frontiers in immunology 13: 917155. https://doi.org/10.3389/fimmu.2022.917155

  21. Van der Heijde D, Ramiro S, Landewe R, Baraliakos X et al (2017) 2016 update of the ASAS-EULAR management recommendations for axial spondyloarthritis. Ann Rheum Dis 76(6):978–991. https://doi.org/10.1136/annrheumdis-2016-210770

    Article  PubMed  Google Scholar 

  22. Braun J, Brandt J, Listing J, Zink A, Alten R, Golder W, Gromnica-Ihle E, Kellner H, Krause A, Schneider M, Sörensen H, Zeidler H, Thriene W, Sieper J (2002) Treatment of active ankylosing spondylitis with infliximab: a randomised controlled multicentre trial. Lancet (London, England) 359(9313):1187–1193. https://doi.org/10.1016/s0140-6736(02)08215-6

    Article  CAS  PubMed  Google Scholar 

  23. Baeten D, Sieper J, Braun J, Baraliakos X, Dougados M, Emery P, Deodhar A, Porter B, Martin R, Andersson M, Mpofu S, Richards HB, MEASURE 1 Study Group, & MEASURE 2 Study Group (2015) Secukinumab, an interleukin-17A inhibitor, in ankylosing spondylitis. N Engl J Med 373(26):2534–2548. https://doi.org/10.1056/NEJMoa1505066

    Article  CAS  Google Scholar 

  24. Van der Heijde D, Dijkmans B, Geusens P, Sieper J, DeWoody K, Williamson P, Braun J, & Ankylosing Spondylitis Study for the Evaluation of Recombinant Infliximab Therapy Study Group (2005) Efficacy and safety of infliximab in patients with ankylosing spondylitis: results of a randomized, placebo-controlled trial (ASSERT). Arthritis Rheum 52(2):582–591. https://doi.org/10.1002/art.20852

    Article  CAS  Google Scholar 

  25. Braun J, van den Berg R, Baraliakos X, Boehm H et al (2011) 2010 update of the ASAS/EULAR recommendations for the management of ankylosing spondylitis. Ann Rheum Dis 70(6):896–904. https://doi.org/10.1136/ard.2011.151027

    Article  CAS  PubMed  Google Scholar 

  26. Davis JC, Van Der Heijde D, Braun J, Dougados M, Cush J, Clegg DO, Kivitz A, Fleischmann R, Inman R, Tsuji W, & Enbrel Ankylosing Spondylitis Study Group (2003) Recombinant human tumor necrosis factor receptor (etanercept) for treating ankylosing spondylitis: a randomized, controlled trial. Arthritis Rheum 48(11):3230–3236. https://doi.org/10.1002/art.11325

    Article  CAS  Google Scholar 

  27. Van der Heijde D, Kivitz A, Schiff MH, Sieper J, Dijkmans BA, Braun J, Dougados M, Reveille JD, Wong RL, Kupper H, Davis JC. Jr, & ATLAS Study Group (2006) Efficacy and safety of adalimumab in patients with ankylosing spondylitis: results of a multicenter, randomized, double-blind, placebo-controlled trial. Arthritis Rheum 54(7):2136–2146. https://doi.org/10.1002/art.21913

    Article  CAS  Google Scholar 

  28. Sieper J, van der Heijde D, Dougados M, Mease PJ, Maksymowych WP, Brown MA, Arora V, Pangan AL (2013) Efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis: results of a randomised placebo-controlled trial (ABILITY-1). Ann Rheum Dis 72(6):815–822. https://doi.org/10.1136/annrheumdis-2012-201766

    Article  CAS  PubMed  Google Scholar 

  29. Landewé R, Braun J, Deodhar A, Dougados M, Maksymowych WP, Mease PJ, Reveille JD, Rudwaleit M, van der Heijde D, Stach C, Hoepken B, Fichtner A, Coteur G, de Longueville M, Sieper J (2014) Efficacy of certolizumab pegol on signs and symptoms of axial spondyloarthritis including ankylosing spondylitis: 24-week results of a double-blind randomised placebo-controlled Phase 3 study. Ann Rheum Dis 73(1):39–47. https://doi.org/10.1136/annrheumdis-2013-204231

    Article  CAS  PubMed  Google Scholar 

  30. Sieper J, Poddubnyy D (2017) Axial spondyloarthritis. Lancet 390(10089):73–84. https://doi.org/10.1016/S0140-6736(16)31591-4

    Article  PubMed  Google Scholar 

  31. Kiltz U, van der Heijde D, Mielants H, Feldtkeller E, Braun J, group PEpi, (2009) ASAS/EULAR recommendations for the management of ankylosing spondylitis: the patient version. Ann Rheum Dis 68(9):1381–1386. https://doi.org/10.1136/ard.2008.096073

    Article  CAS  PubMed  Google Scholar 

  32. Poddubnyy D, Hermann KG, Callhoff J, Listing J, Sieper J (2014) Ustekinumab for the treatment of patients with active ankylosing spondylitis: results of a 28-week, prospective, open-label, proof-of-concept study (TOPAS). Ann Rheum Dis 73(5):817–823. https://doi.org/10.1136/annrheumdis-2013-204248

    Article  CAS  PubMed  Google Scholar 

  33. Van der Heijde D, Deodhar A, Wei JC, Drescher E, Fleishaker D, Hendrikx T, Li D, Menon S, Kanik KS (2017) Tofacitinib in patients with ankylosing spondylitis: a phase II, 16-week, randomised, placebo-controlled, dose-ranging study. Ann Rheum Dis 76(8):1340–1347. https://doi.org/10.1136/annrheumdis-2016-210322

    Article  CAS  PubMed  Google Scholar 

  34. Edwards CJ, Blanco FJ, Crowley J, Birbara CA, Jaworski J, Aelion J, Stevens RM, Vessey A, Zhan X, Bird P (2016) Apremilast, an oral phosphodiesterase 4 inhibitor, in patients with psoriatic arthritis and current skin involvement: a phase III, randomised, controlled trial (PALACE 3). Ann Rheum Dis 75(6):1065–1073. https://doi.org/10.1136/annrheumdis-2015-207963

    Article  CAS  PubMed  Google Scholar 

  35. Deodhar A, Sliwinska-Stanczyk P, Xu H, Baraliakos X, Gensler LS, Fleishaker D, Wu WL, J, Menon S, Wang C, Dina O, Fallon L, Kanik KS, & van der Heijde D, (2021) Tofacitinib for the treatment of ankylosing spondylitis: a phase III, randomised, double-blind, placebo-controlled study. Ann Rheum Dis 80(8):1004–1013. https://doi.org/10.1136/annrheumdis-2020-219601

    Article  CAS  PubMed  Google Scholar 

  36. Taylor PC, van der Heijde D, Landewé R, McCue S, Cheng S, Boonen A (2021) A phase III randomized study of apremilast, an oral phosphodiesterase 4 inhibitor, for active ankylosing spondylitis. J Rheumatol 48(8):1259–1267. https://doi.org/10.3899/jrheum.201088

    Article  CAS  PubMed  Google Scholar 

  37. Keeling S, Maksymowych WP (2021) JAK inhibitors, psoriatic arthritis, and axial spondyloarthritis: a critical review of clinical trials. Expert Rev Clin Immunol 17(7):701–715. https://doi.org/10.1080/1744666X.2021.1925541

    Article  CAS  PubMed  Google Scholar 

  38. Van der Linden S, Valkenburg HA, Cats A (1984) Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum 27(4):361–368. https://doi.org/10.1002/art.1780270401

  39. Diekhoff T, Lambert R, Hermann KG (2022) MRI in axial spondyloarthritis: understanding an ‘ASAS-positive MRI’ and the ASAS classification criteria. Skeletal Radiol 51(9):1721–1730. https://doi.org/10.1007/s00256-022-04018-4

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lambert RG, Bakker PA, van der Heijde D, Weber U et al (2016) Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group. Ann Rheum Dis 75(11):1958–1963. https://doi.org/10.1136/annrheumdis-2015-208642

    Article  PubMed  Google Scholar 

  41. Protopopov M, Proft F, Wichuk S, Machado PM, Lambert RG, Weber U, Juhl Pedersen S, Østergaard M, Sieper J, Rudwaleit M, Baraliakos X, Maksymowych WP, & Poddubnyy D (2022) Comparing MRI and conventional radiography for the detection of structural changes indicative of axial spondyloarthritis in the ASAS cohort. Rheumatology (Oxford, England) keac432. Advance online publication. https://doi.org/10.1093/rheumatology/keac432

  42. Sepriano A, Ramiro S, van der Heijde D, Dougados M, Claudepierre P, Feydy A, Reijnierse M, Loeuille D, Landewé R (2022) Imaging outcomes for axial spondyloarthritis and sensitivity to change: a five-year analysis of the DESIR cohort. Arthritis Care Res 74(2):251–258. https://doi.org/10.1002/acr.24459

    Article  Google Scholar 

  43. Liu D, Lin C, Liu B, Qi J, Wen H, Tu L, Wei Q, Kong Q, Xie Y, & Gu J (2022) Quantification of fat metaplasia in the sacroiliac joints of patients with axial spondyloarthritis by chemical shift-encoded MRI: a diagnostic trial. Frontiers in immunology 12: 811672. https://doi.org/10.3389/fimmu.2021.811672

  44. Wetterslev M, Georgiadis S, Sørensen IJ, Pedersen SJ et al (2022) Tapering of TNF inhibitors in axial spondyloarthritis in routine care - 2-year clinical and MRI outcomes and predictors of successful tapering. Rheumatology (Oxford) 61(6):2398–2412. https://doi.org/10.1093/rheumatology/keab755

    Article  PubMed  Google Scholar 

  45. Baraliakos X, Østergaard M, Gensler LS, Poddubnyy D, Lee EY, Kiltz U, Martin R, Sawata H, Readie A, Porter B, & SURPASS Study Group (2020) Comparison of the effects of secukinumab and adalimumab biosimilar on radiographic progression in patients with ankylosing spondylitis: design of a randomized, phase IIIb study (SURPASS). Clin Drug Investig 40(3):269–278. https://doi.org/10.1007/s40261-020-00886-7

    Article  CAS  Google Scholar 

  46. Scrivo R, Castellani C, Mancuso S, Sciarra G, Giardina F, Bevignani G, Ceccarelli F, Spinelli FR, Alessandri C, Di Franco M, Riccieri V, Priori R, & Conti F (2022) Effectiveness of non-medical switch from adalimumab bio-originator to SB5 biosimilar and from ABP501 adalimumab biosimilar to SB5 biosimilar in patients with chronic inflammatory arthropathies: a monocentric observational study. Clinical and experimental rheumatology. Advance online publication. https://doi.org/10.55563/clinexprheumatol/bf00j9

  47. Barberio B, Cingolani L, Canova C, Barbieri G, Sablich R, Urbano MT, Bertani L, Costa F, Bodini G, Demarzo MG, Ferronato A, Buda A, Melatti P, Massimi D, Savarino EV, Zingone F (2021) A propensity score-weighted comparison between adalimumab originator and its biosimilars, ABP501 and SB5, in inflammatory bowel disease: a multicenter Italian study. Ther Adv Gastroenterol 14:17562848211031420. https://doi.org/10.1177/17562848211031420

    Article  CAS  Google Scholar 

  48. Wang R, Dasgupta A, Ward MM (2022) Predicting probability of response to tumor necrosis factor inhibitors for individual patients with ankylosing spondylitis. JAMA Netw Open 5(3): e222312. https://doi.org/10.1001/jamanetworkopen.2022.2312

  49. Lee S, Eun Y, Kim H, Cha HS, Koh EM, Lee J (2020) Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis. Sci Rep 10(1):20299. https://doi.org/10.1038/s41598-020-75352-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee S, Kang S, Eun Y, Won HH, Kim H, Lee J, Koh EM, Cha HS (2021) Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Res Ther 23(1):254. https://doi.org/10.1186/s13075-021-02635-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work is supported by the National High Level Hospital Clinical Research Funding (2022-NHLHCRF-LX-02-0103), the Elite Medical Professionals project of China-Japan Friendship Hospital (ZRJY2021-QM14), the Capital’s Funds for Health Improvement and Research (2020–4-40610), the National Regional Traditional Chinese Medicine (Specialty) Treatment Center Project (2019-ZX-006), and the National Key Clinical Specialty Building Project (2011-ZDZK-001).

Author information

Authors and Affiliations

Authors

Contributions

Conception and design: Luo J, Tao QW. Analysis and interpretation of the data: all authors. Drafting of the article: He Q, Chen JQ. Critical revision of the article: all authors. Statistical analysis: He Q, Yu XB, Liao JH. Final approval of the article: all authors.

Corresponding authors

Correspondence to Jing Luo or Qing-wen Tao.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest. Figure 5B was created by Figdraw (www.figdraw.com), accessed on 23 December 2022.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Significance and innovations

We conducted the first bibliometric analysis and disclosed the topic trends and knowledge structure of bibliometric analysis concentrating on the knowledge mapping of biological disease-modifying anti-rheumatic drugs (bDMARDs) for treating axial spondyloarthritis (axSpA) over the past 18 years systematically. Clinical efficacy and safety of tumor necrosis factor inhibitor are included in mature and comprehensive studies, whereas spinal radiographic progression, Janus kinase inhibitors, biosimilar pharmaceuticals, adverse events of biologics, and machine learning are studies with high potential for advancement. The purpose of this study is to fill the information gap, outline the current state of research development, and forecast the research trend to future researchers on bDMARDs in treating axSpA.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 27 KB)

Supplementary file2 (DOCX 775 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, Q., Chen, Jq., Yu, Xb. et al. Knowledge mapping of biological disease-modifying anti-rheumatic drugs for axial spondyloarthritis: a bibliometric study. Clin Rheumatol 42, 1999–2011 (2023). https://doi.org/10.1007/s10067-023-06540-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10067-023-06540-9

Keywords

Navigation