rss
Ann Rheum Dis 62:427-430 doi:10.1136/ard.62.5.427
  • Extended report

Autoantibodies can be prognostic markers of an erosive disease in early rheumatoid arthritis

  1. J Vencovský,
  2. S Macháček,
  3. L Šedová,
  4. J Kafková,
  5. J Gatterová,
  6. V Pešáková,
  7. Š Růžičková
  1. Institute of Rheumatology, Prague, Czech Republic
  1. Correspondence to:
    Dr J Vencovský, Institute of Rheumatology, Na Slupi 4, 12850 Praha 2, Czech Republic;
    venc{at}revma.cz
  • Accepted 25 September 2002

Abstract

Objective: To evaluate a contribution of selected laboratory parameters for a prediction of progressive and erosive development in patients with early rheumatoid arthritis (RA).

Methods: In a prospective study baseline levels of antibodies to cyclic citrullinated peptide (anti-CCP), IgM, IgA, and IgG rheumatoid factors (RFs) were measured by enzyme linked immunosorbent assay (ELISA) in 104 patients with RA with disease duration <2 years. Antikeratin antibodies (AKA) and antiperinuclear factor (APF) were detected by indirect immunofluorescence. Patients were divided into two groups based either on the presence or absence of erosions or according to progression of Larsen score at the end of the 24 months’ follow up.

Results: Sixty seven (64%) patients developed radiographic erosions, 49 (47%) had progression in Larsen score, and 36 (35%) progressed by more than 10 Larsen units. Significant differences in erosions and progression between the two groups were detected for anti-CCP, AKA, APF, IgM RF, IgA RF, and IgG RF. Baseline Larsen score correlated significantly with anti-CCP, IgM RF, and IgA RF levels, and all measured antibodies correlated with the progression >10 units. The combination of anti-CCP and IgM RF increased the ability to predict erosive and progressive disease.

Conclusion: The data confirmed that measurement of anti-CCP, AKA, APF, and individual isotypes of RFs was useful for prediction of structural damage early in the disease course. Combined analysis of anti-CCP and IgM RF provides the most accurate prediction.

Footnotes