Clinical and experimental rheumatology最新文献

Objectives: This study aimed to analyse the relationship between distinct autoantibody combinations (immunological signatures) and systemic disease activity in patients with Sjögren's disease (SjD). The hypothesis was that specific multi-autoantibody signatures would be associated with higher systemic disease activity at diagnosis, serving as predictors of a more severe disease course.

Methods: A retrospective observational study was conducted using data from the Big Data Sjögren Project Consortium, an international multicentre registry. The serological status (positive/negative) at diagnosis for ANA, RF, anti-Ro, and anti-La was recorded for each patient. Systemic disease activity was assessed using the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) and a simplified Disease Activity Score (DAS) categorised as low, moderate, or high. Statistical analyses included pairwise comparisons, a sensitivity analysis grouping signatures by the number of positive antibodies, and demographic-adjusted ordinal models.

Results: Serum autoantibodies were highly prevalent, with over 94% of patients having at least one autoantibody. The mean ESSDAI values varied significantly across signatures. The fully seronegative group had the lowest mean ESSDAI at 3.61, while the fully seropositive group (ANA+/Ro+/La+/RF+) had the highest among common phenotypes, with a mean of 7.93. A strong dose-response relationship was observed, with each additional positive autoantibody associated with a 1.11-point mean increase in ESSDAI and a 35% increase in the odds of being in a higher DAS category. The rarest signatures, such as ANA-/Ro-/La+/RF+, exhibited the highest mean systemic activity (mean 13.20).

Conclusions: The number and combination of SjD-related autoantibodies at diagnosis are robustly associated with systemic disease activity. Multi-positive profiles, particularly those combining RF with anti-Ro, identify patients at higher risk of systemic activity. Interpreting combined serological patterns offers an immediate, low-cost method for patient stratification and can help guide clinical management.

Objectives: Ultrasound-guided core needle biopsy (CNB) of major salivary glands is a less invasive alternative to minor salivary gland biopsy for diagnosing Sjögren's disease (SjD), reducing risks of neurological deficits and pain. However, the optimal CNB specimen length for adequate glandular surface area remains uncertain. This study aimed to determine and validate the optimal CNB specimen length thresholds.

Methods: This retrospective, dual-phase study included 119 consecutive patients undergoing submandibular gland CNB with an 18-gauge needle for suspicious chronic inflammatory sialadenitis. Specimen length, total surface area, and glandular surface area were recorded. A validation cohort (n=37) was analysed separately. Statistical analyses included correlation, regression, and ROC curve analysis.

Results: Specimen length correlated with glandular surface area (ρ=0.69, p<0.001). Multivariable analysis confirmed specimen length as a positive predictor (p<0.001) and fatty infiltration as a negative predictor (p=0.003) of glandular surface area. ROC analysis identified 7.6 mm as optimal for glandular surface area ≥4 mm², and 10.5 mm for ≥8 mm². Clinically significant haematomas occurred in 1.7% of cases. In the validation cohort, using the 7.6 mm threshold, PPV was 86.2%, and NPV 87.5% for glandular surface area ≥4 mm². For the 10.5 mm threshold, PPV was 76%, and NPV 100% for ≥8 mm².

Conclusions: A CNB specimen length of ≥7.6 mm is sufficient for diagnosing SjD, while ≥10.5 mm may be required for clinical trials. These findings may support the integration of specimen length thresholds into CNB procedural guidelines.

Objectives: Primary Sjögren's disease (pSD) is a chronic autoimmune disease with significant heterogeneity. Pyroptosis, a highly inflammatory programmed cell death, plays a key role in autoimmune diseases, including pSD. However, the role and mechanisms of pyroptosis-related genes (PRGs) in pSD remain unclear.

Methods: This study integrated bioinformatics approaches to analyse gene expression datasets from Gene Expression Omnibus. Differentially expressed genes were identified, followed by weighted gene co-expression network analysis and functional enrichment analysis. Multi-model machine learning frameworks and SHapley Additive exPlanations were used to screen candidate genes. The CIBERSORT algorithm explored the correlation between hub pyroptosis-related and pSD-related genes (PSGs) and immune cell populations, validated by single-cell RNA sequencing data. Nomogram models were developed to assess pSD prevalence, and molecular docking studies predicted potential therapeutic agents targeting these genes.

Results: A total of 647 differentially expressed genes were identified between pSD patients and healthy controls. Through WGCNA and functional enrichment analysis, significant pathways related to oxidative phosphorylation, apoptosis, and cell adhesion were revealed. Machine learning models identified HADHA, JAK1, BRD4, ATG5, and NRAS as hub PSGs with high diagnostic potential. Nomogram models based on these genes showed promising diagnostic accuracy. Molecular docking results suggested that some compounds could modulate the activity of these key genes.

Conclusions: This study provides novel insights into the molecular mechanisms of pSD and highlights potential diagnostic biomarkers and therapeutic targets related to pyroptosis. The integration of bioinformatics and machine learning offers a robust framework for understanding the complex interplay between pyroptosis and pSD pathogenesis.

Objectives: The phenotype of Sjögren's disease (SjD) may be influenced by several variables. Among these, the role of patient geolocation has been poorly explored. The study compared epidemiologic, serologic, clinical features and comorbidities according to geographical origin in a large Italian multicentre SjD cohort.

Methods: This is a retrospective analysis of a multicentre SjD cohort (2016 ACR/EULAR criteria) consecutively included in the Italian SjD Study Group registry and grouped into three macrogeographic areas: North, Centre and South. Disease-specific epidemiologic, serologic, histologic and clinical variables were collected. Comorbidities, traditional cardiovascular (CV) risk factors and history of CV events were also recorded. All data were stratified by geographic area to assess regional differences.

Results: 1231 SjD patients, median 53 (42-63) years at diagnosis and 95% females, were included. No differences were observed in sex distribution or ethnicity among the three areas. Patients from the South had older age at diagnosis compared to the North (55 vs. 51 years, p=0.001) and Centre (55 vs. 51 years, p=0.002) and higher frequency of activity in the constitutional and articular but lower in biological domains (p<0.001 for all). Hypertension and hypercholesterolaemia were more prevalent in the Centre and obesity was more common in the South compared to the North (p<0.001). No significant differences were observed in other CV risk factors and CV events.

Conclusions: This study provides the first evidence of geo-epidemiological differences among Italian SjD patients, highlighting how geographic origin is associated with disease phenotype and comorbidities. These regional disparities likely reflect environmental, socio-cultural and healthcare system-related factors, underscoring the need for personalised disease management strategies.