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OP0250 Can rankl serum levels predict future progression to rheumatoid arthritis in acpa negative patients?
  1. A Burska1,
  2. J El-Jawhari1,
  3. AL Tan1,
  4. R Wakefield1,
  5. H Marzo-Ortega2,
  6. P Conaghan1,
  7. P Emery1,
  8. F Ponchel1,
  9. J Freeston1,2,3
  1. 1LIRMM, University of Leeds
  2. 2NIHR Leeds Musculoskeletal Biomedical Research Centre
  3. 3Rheumatology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom


Background Making the earliest diagnosis of rheumatoid arthritis (RA) is crucial to initiate treatment and prevent further disease progression and joint damage. Despite recent advances with the discovery and integration of anti–cyclic citrullinated protein antibody (ACPA) in classification criteria, there is still an unmet need for new diagnostic biomarkers, notably for ACPA-negative disease. Power Doppler ultrasound has been shown to identify poor prognosis disease in ACPA negative patients.

Objectives The receptor-activator-nuclear-factor-κB axis (RANK/RANKL) is known to regulate bone homeostasis. The aim of this pilot study is to establish whether serum RANKL levels in people with early inflammatory arthritis are associated with RA diagnosis at follow-up and to evaluate the added value of RANKL with ultrasound for early RA diagnosis.

Methods Serum from 298 subjects (95/204 Male/Female) was collected at the baseline participant visit to the Leeds Early Arthritis clinic. Demographic (age, gender symptom duration) and clinical data (swollen and tender joint counts (SJC, TJC), CRP, DAS28, rheumatoid factor (RF) and ACPA, shared epitope (SE)) were collected.

A commercial ELISA (BioVENDOR) was used to measure RANKL. Ultrasound of 26 joints (bilateral elbows, wrists, MCP 2–3, PIP 2–3, knees, ankles and MTP 1–5) was performed at baseline recording summative scores for power Doppler (PD), grey scale hypertrophy (GS) and erosions (ERO).

Results At 1 year follow-up, 151 patients had a confirmed diagnosis of RA (EULAR 2010 criteria) and 147 were classified as non-RA (undifferentiated arthritis, other inflammatory diagnoses or non-persistent inflammation). All routinely used biomarkers were associated with RA diagnosis (ACPA, RF, SE, TJC, SJC, CRP, DAS28, p<0.0001), as were imaging biomarkers (PD, GS, ERO, p<0.001). RANKL levels were significantly higher in RA (RA 1002.4±1053.2pmol/L, non-RA 339.2±451.5pmol/L, p<0.0001). A regression analysis suggested that four parameters were sufficient to account for all associations with RA: RANKL, age, SJC, and PD with 75.3% accurate prediction. An AUROC analysis suggested a cut-off for each parameter and a score was calculated, adding 1 point for each of the factors (RANKL>700, age>62, TPD>3, SJC>4). This score predicted RA with an AUROC of 0.782 ((0.23–0.840), p<0.0001). The same analysis repeated for ACPA negative patients only (n=193) showed similar results, providing accurate diagnosis of RA (77.6% correct by regression) and with an AUROC of 0.774 ((0.690–0.858), p<0.0001).

Conclusions A score incorporating RANKL, age, SJC and PD showed good predictive value for non-RA when low and for RA when high. Furthermore, the analysis redone in ACPA-negative patients performed particularly well for predicting RA with a good AUROC value.

Disclosure of Interest None declared

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