December 2025 to March 2026
Author: Claudia Iannone
Prevalence, distribution and associations of the scleroderma capillaroscopic patterns: new insights from the Italian SPRING-SIR registry
De Angeli et al. conducted a cross-sectional analysis of 1,689 SSc patients with standardised nailfold videocapillaroscopy (NVC) from the Italian SPRING-SIR registry. NVC pattern prevalence was: early 21.6%, active 47.4%, late 25.7%, and normal/non-specific 5.3%. Crucially, all three scleroderma patterns were present across all disease durations, challenging the assumption of strict temporal progression and suggesting that microvascular damage in SSc follows a heterogeneous rather than a uniformly time-dependent course.
Clinical and molecular data to predict flares in DMARD optimization in rheumatoid arthritis: a randomized, controlled, open-label, non-inferiority trial
This randomized phase IV trial (OPTIBIO) evaluated whether tapering bDMARDs in rheumatoid arthritis patients in remission is non-inferior to standard dosing. Among 195 patients, flare rates were slightly higher in the optimization group but not statistically significant. Predictive models incorporating clinical and molecular biomarkers improved identification of patients at risk of flare or sustained remission. Although dose reduction was not non-inferior, safety was comparable. The study highlights the importance of personalized approaches using combined clinical and molecular predictors.
Lipid-rich pericoronary adipose tissue in systemic lupus erythematosus
Azoulay et al. investigated in this case-control study pericoronary adipose tissue (PCAT) density in systemic lupus erythematosus (SLE) patients compared to matched controls. Using CT coronary angiography, SLE patients showed significantly lower PCAT density, suggesting altered pericoronary inflammation linked to coronary artery disease risk. No strong correlation was found with disease activity. The findings support PCAT density as a potential imaging biomarker for cardiovascular risk stratification in SLE, addressing a key unmet clinical need in assessing atherosclerosis risk.
Novel deep-learning analysis for connective tissue disease–related interstitial lung disease extent assessment on CT: a preliminary cross-sectional study
This study conducte by Ito et al. assessed a deep-learning software (QZIP-ILD®) for quantifying interstitial lung disease (ILD) extent in connective tissue diseases. In 80 patients, automated measurements correlated well with visual assessment but showed superior performance in identifying severe functional impairment (FVC <70%). The method improved objectivity and reproducibility compared to conventional visual scoring. These findings suggest deep-learning tools can enhance ILD evaluation and better stratify disease severity, supporting their integration into clinical assessment of CTD-related ILD.
Claudia Iannone
Claudia Iannone is a Rheumatologist at IRCCS Ospedale San Raffaele and Gaetano Pini in Milan, Italy. Her research focuses on systemic sclerosis, interstitial lung disease in RMD, and vasculitis.
She is a member of the Italian Society of Rheumatology and the EULAR Lung Study Group and serves as Clinical Group Coordinator of the EUSTAR Young Investigators Group (YIG).
Dr. Iannone is a member of the EMEUNET Social Media Subcommittee.