Nailfold capillary scleroderma pattern may be associated with disease damage in childhood-onset systemic lupus erythematosus: Important lessons from longitudinal follow-up

Objectives To observe if capillary patterns in childhood-onset SLE (cSLE) change over time and find associations between a capillary scleroderma pattern with disease activity, damage or scleroderma-like features. Methods Clinical and (yearly) capillaroscopy data from a longitudinal cohort of patients with cSLE (minimum of four Systemic Lupus International Collaborating Clinics (SLICC) criteria, onset <18 years) were analysed. Disease activity was measured by Systemic Lupus Erythematosus Activity Index (SLEDAI) and disease damage by SLICC Damage Index. A scleroderma pattern was defined according to the fast track algorithm' from the European League Against Rheumatism Study Group on Microcirculation in Rheumatic Diseases. An abnormal capillary pattern, not matching a scleroderma pattern, was defined as microangiopathy'. Results Our cohort consisted of 53 patients with cSLE with a median disease onset of 14 years (IQR 12.5-15.5 years), median SLEDAI score at diagnosis was 11 (IQR 8-16), median SLEDAI at follow-up was 2 (IQR 1-6). A scleroderma pattern (ever) was seen in 18.9%, while only 13.2% of patients had a normal capillary pattern. Thirty-three patients had follow-up capillaroscopy of which 21.2% showed changes in type of capillary pattern over time. Type of capillary pattern was not associated with disease activity. Raynaud's phenomenon (ever) was equally distributed among patients with different capillaroscopy patterns (p=0.26). Anti-ribonucleoprotein antibodies (ever) were significantly more detected (Χ 2, p=0.016) in the scleroderma pattern subgroup (n=7 of 10, 70%). Already 5 years after disease onset, more than 50% of patients with a scleroderma pattern had SLE-related disease damage (HR 4.5, 95% CI 1.1 to 18.8, p=0.034), but they did not develop clinical features of systemic sclerosis at follow-up. Number of detected fingers with a scleroderma pattern was similar between cSLE, juvenile systemic sclerosis and juvenile undifferentiated connective tissue disease. Conclusion This longitudinal study shows that the majority of capillary patterns in cSLE are abnormal and they can change over time. Irrespective of disease activity, a capillary scleroderma pattern in cSLE may be associated with higher risk of SLE-related disease damage

Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps

International audienceIt is well known that the probability distribution function (PDF) of galaxy density contrast is approximately lognormal; whether the PDF of mass fluctuations derived from weak lensing convergence (κ_WL) is lognormal is less well established. We derive PDFs of the galaxy and projected matter density distributions via the counts-in-cells (CiC) method. We use maps of galaxies and weak lensing convergence produced from the Dark Energy Survey Science Verification data over 139 deg^2. We test whether the underlying density contrast is well described by a lognormal distribution for the galaxies, the convergence and their joint PDF. We confirm that the galaxy density contrast distribution is well modelled by a lognormal PDF convolved with Poisson noise at angular scales from 10 to 40 arcmin (corresponding to physical scales of 3–10 Mpc). We note that as κ_WL is a weighted sum of the mass fluctuations along the line of sight, its PDF is expected to be only approximately lognormal. We find that the κ_WL distribution is well modelled by a lognormal PDF convolved with Gaussian shape noise at scales between 10 and 20 arcmin, with a best-fitting χ^2/dof of 1.11 compared to 1.84 for a Gaussian model, corresponding to p-values 0.35 and 0.07, respectively, at a scale of 10 arcmin. Above 20 arcmin a simple Gaussian model is sufficient. The joint PDF is also reasonably fitted by a bivariate lognormal. As a consistency check, we compare the variances derived from the lognormal modelling with those directly measured via CiC. Our methods are validated against maps from the MICE Grand Challenge N-body simulation