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AB0192 Serum measures of type i collagen degradation are surrogate markers of joint destruction and progression; first steps towards a prognostic score
  1. AC Bay-Jensen1,
  2. C Bager2,
  3. AS Siebuhr1,
  4. H Bay Nielsen2,
  5. MA Karsdal1
  1. 1Rheumatology, Nordic Bioscience Biomarkers and Research
  2. 2Proscion, Herlev, Denmark

Abstract

Background Monitoring of patients with rheumatoid arthritis (RA) requires assessment of biomarkers reflecting disease activity and its progression. There is a need for non-invasive markers for frequent monitoring of disease severity and progression as well as response to therapy.

Objectives Serological markers together with clinical parameters was tested in a multi-marker model to assess its ability to objectively predict progression of RA.

Methods Current post-hoc analysis included RA patients from the biomarker substudy of the phase III clinical study LITHE investigating the safety and efficacy of tocilizumab1–4. Patients had moderate/severe, active RA. In addition, only patients of the placebo arm and with total sharp score (SHP) recorded at baseline (BL), week 24 (W24) and W52 were included. Progressors were defined as the delta from BL to W24 and W24 to W52. Biochemical markers reflecting tissue turnover (table) were assessed at BL and W16. Associations with structural progression (deltaSHP) were investigated by spearman's r, least squared multivariate and logistic regression. Covariates were CRP, sex, BMI, age, disease duration, DAS-ESR, no. prior DMARDs/aTNF use and SHP/BL. The data were divided into a training and confirmation set; 1) association between markers/W16 and deltaSHP/W52 (n=31 prog./42 non-prog.), 2) association between markers/BL and deltaSHP/W24 (n=33/48).

Results The training set. Eight markers were correlated (R>0.2) with deltaSHP/W52. Of these C1M, PINP, ICTP and MMP3 were predictive for of progression (deltaSHP/W52>0) with ORs of 3.2 [1.3–8.0], 4.0 [1.4–12], 8.5 [2.4–31], and 2.5 [1.3–5.1]; all p<0.01, respectively. A logistic model for prediction of disease progression incorporating C1M, ICTP, disease duration and BMI demonstrated an AUC of 0.77 [0.66–0.86], p<0.01. The model correctly identified 72% of the progressors. The confirmation set: The results were confirmed in the second dataset with an AUC of 0.75 [0.64–0.81], p<0.01. The model correctly identified 65% of the progressors.

Conclusions We demonstrated that a multi-marker model was able to pint-point, which patients were more likely to be structural progressors. Such first steps to build a progression model, rather than a score reflecting disease activity only, may enrich clinical studies with structurally active diseaese. Importantly, the markers with strongest influence were those associated with MMP-driven bone (ICTP) and connective tissue (C1M) remodelling.

References

  1. Smolen JS.Tocilizumab inhibits progression of joint damage in rheumatoid arthritis irrespective of its anti-inflammatory effects: disassociation of the link between inflammation and destruction. Ann Rheum Dis 2012.

  2. Kremer JM.Tocilizumab inhibits structural joint damage in rheumatoid arthritis patients with inadequate responses to methotrexate: results from the double-blind treatment phase of a randomized placebo-controlled trial of tocilizumab safety and prevention of structu. Arthritis Rheum 2011.

  3. Bay-Jensen AC. Effect of tocilizumab combined with methotrexate on circulating biomarkers of synovium, cartilage, and bone in the LITHE study. Semin Arthritis Rheum 2014.

  4. Bay-Jensen AC. Early changes in blood-based joint tissue destruction biomarkers are predictive of response to tocilizumab in the LITHE study.Arthritis Res Ther 2016.

References

Disclosure of Interest A. Bay-Jensen Shareholder of: Nordic Bioscience, Employee of: Nordic Bioscience, C. Bager Employee of: Proscion, A. S. Siebuhr Employee of: Nordic Bioscience, H. Bay Nielsen Employee of: Proscion, M. Karsdal Shareholder of: Nordic Bioscience, Employee of: Nordic Bioscience

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