Abstract

Objectives: To determine the prevalence of anti-Ku antibodies in 625 patients with systemic sclerosis (SSc) from six European rheumatological centres and to evaluate their clinical and serological characteristics.

Methods: Sera of 625 consecutive patients with either limited cutaneous or diffuse cutaneous SSc were tested for antibodies to Ku antigen together with other extractable nuclear antigens by counterimmunoelectrophoresis. A case–control design with calculation of bootstrap 95% confidence intervals derived from anti-Ku negative control patients was used to evaluate clinical associations of anti-Ku antibodies. Sera from anti-Ku positive patients with SSc and a control group were additionally tested by immunofluorescence on Hep-2 cell substrates and line immunoassay.

Results: Anti-Ku antibodies were found in the sera of 14/625 (2.2%) patients with SSc. Of 14 anti-Ku positive patients with SSc, 10 had no other anti-extractable nuclear antigen (ENA) antibodies detected by counterimmunoelectrophoresis. Using a case–control study design, anti-Ku antibodies were significantly associated with musculoskeletal manifestations such as clinical markers of myositis, arthritis and joint contractures. In addition, a significant negative correlation of anti-Ku antibodies was found with vascular manifestation such as fingertip ulcers and teleangiectasias. There was a striking absence of anti-centromere antibodies as well as anti- polymyositis (PM)/scleroderma (Scl) antibodies in patients that were anti-Ku positive. As expected, anti-Scl70 and punctate nucleolar immunofluorescence patterns were present only in single cases.

Conclusion: This is the largest cohort to date focusing on the prevalence of anti-Ku antibodies in patients with SSc. The case–control approach was able to demonstrate a clinically distinct subset of anti-Ku positive patients with SSc with only relative clinical differences in skeletal features. However, the notable exceptions were signs of myositis. This shows the importance of anti-Ku antibody detection for the prediction of this specific clinical subset.