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THU0145 Novel Peptide Biomarkers Detected by Mass Spectrometry Predict Arthritis in Seropositive Arthralgia Patients
  1. T. V. Pham1,
  2. L. A. van de Stadt2,
  3. J. C. Knol1,
  4. P. M. van de Ven3,
  5. M. Boers3,4,
  6. C. R. Jimenez1,
  7. D. van Schaardenburg2
  1. 1OncoProteomics Laboratory, VU University Medical Center
  2. 2Jan van Breemen Research Institute | Reade
  3. 3Department of Epidemiology and Biostatistics
  4. 4Department of Rheumatology, VU University Medical Center, Amsterdam, Netherlands


Background Persons with IgM-rheumatoid factor and/or anti-CCP in their blood (seropositive) are at increased risk for developing rheumatoid arthritis (RA). In our ongoing seropositive arthralgia cohort, this risk is 40% after 2 years if there is a high level of anti-CCP or anti-CCP and RF are both positive [Bos ARD 2010]. Other biomarkers may further improve the predictive value of these tests for developing RA. Serum mass spectrometry-based peptide profiles of different patient groups can be compared and correlated with clinical data to assist in diagnosis, monitoring and/or prediction.

Objectives To identify peptides in patient plasma associated with risk for RA.

Methods Out of 370 seropositive arthralgia patients (arthritis conversion rate of 35% after two years), we selected 40 patients who had developed arthritis during follow up (after a mean of 1 year, 90% RA according to the 2010 criteria) and 40 who had not developed arthritis. Their plasma had been stored at -20 degrees. Serum peptide profiling was carried out using automated magnetic C18 bead-assisted serum peptide capture coupled to matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS). The raw mass spectra were pre-processed with in-house bioinformatics software tools [Voortman, Proteome Science 2009]. Statistical analysis was conducted by using the Mann–Whitney U test with adjustment for multiple testing by the Benjamini-Hochberg method. Significance was defined as an adjusted p-value below 0.05. Logistic regression with forward selection was used to select from the peptides found in univariate analyses those that improved prediction of group membership over RF and anti-CCP.

Results Five peptides were significantly elevated in patients who developed arthritis compared to those who did not (Figure 1). In four out of five peptides this association was independent of RF, anti-CCP or double positivity. One peptide was associated with anti-CCP status. In logistic regression two peptides were identified that, when added to RF and anti-CCP, significantly improved the predictive accuracy for RA (the Nagelkerke R2 increased from 30% to 60%, p-value for likelihood ratio test for model comparison < 0.001).

Conclusions We found five peptides strongly related to arthritis development. This result was independent of the presence of (the combination of) RF and anti-CCP. A panel of two peptides, and RF and anti-CCP were found to be highly predictive of arthritis development. Validation in an independent cohort is necessary to verify the added value of these peptides in the prediction of RA.

Disclosure of Interest None Declared

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