Background: Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease characterized by persistent synovial inflammation and progressive joint destruction. Despite the implementation of the treat-to-target (T2T) strategy and the introduction of several classes of biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs), a considerable proportion of patients continues to exhibit active, refractory disease. In 2021, the European Alliance of Associations for Rheumatology (EULAR) defined this condition as Difficult-to-Treat Rheumatoid Arthritis (D2T-RA). This study aimed to identify clinical, laboratory, and therapeutic factors associated with D2T-RA. Methods: A total of 344 patients with established RA were retrospectively evaluated. Among them, 164 fulfilled the 2021 EULAR criteria for D2T-RA (D2T group), while 180 did not (NO-D2T group). Clinical (age, sex, disease duration, BMI, smoking, comorbidities), laboratory (RF, ACPA, ESR, CRP), clinimetric (DAS28, CDAI, PhGA, PGA, HAQ), and therapeutic data (glucocorticoid use, methotrexate treatment and dose, monotherapy, advanced therapy exposure, number of failed advanced therapies, current DMARD regimen) were analyzed. Results: Factors significantly associated with D2T-RA included female sex, longer disease duration, higher RF and ACPA titers, elevated ESR levels, glucocorticoid therapy, and a greater number of failed advanced therapies. Although both groups achieved low disease activity or remission by DAS28 and CDAI, JAK inhibitors—particularly Filgotinib and Upadacitinib—were significantly more common in the D2T cohort and appeared associated with clinical stabilization. Conclusions: This study strengthens the understanding of the predictive profile of D2T-RA, confirming the role of disease chronicity and persistent inflammation in the development of treatment resistance. Importantly, the observed trend toward clinical stabilization achieved under JAK inhibitor therapy reinforces their potential to address unmet therapeutic needs in D2T-RA, providing a mechanistically grounded strategy for patients refractory to conventional and biologic DMARDs.

Background: Polymyalgia rheumatica (PMR) is a clinically heterogeneous inflammatory disorder in which conventional acute-phase reactants may inadequately reflect the complexity and biological variability of disease activity. Precision medicine approaches integrating multidimensional clinical and laboratory data may offer a more comprehensive characterization of inflammatory phenotypes. This study investigated whether the combined assessment of cytokine profiles, routine laboratory biomarkers, ultrasound findings, and clinical variables could improve disease activity stratification in PMR. Methods: A total of 103 consecutive patients with PMR were retrospectively analyzed. Disease activity was assessed using the PMR Activity Score (PMR-AS) and, for exploratory purposes, dichotomized according to the cohort median (≥11 vs. <11). Group differences were evaluated using non-parametric statistical methods. The multidimensional structure of the dataset was explored through Spearman correlation analysis, principal component analysis (PCA), and unsupervised clustering. Predictive models, including logistic regression, random forest, and gradient boosting, were developed to evaluate the potential contribution of integrated analytical approaches to patient stratification. Results: The study cohort included 103 patients (63.1% female), with a median age of 76 years (IQR 71–81); 57 patients (55.3%) presented PMR-AS ≥11. Patients with higher disease activity exhibited significantly increased levels of CRP, fibrinogen, platelet count, IL-6, serum amyloid A (SAA), and myeloid-related protein (MRP), together with a higher prevalence of joint effusion and Power Doppler positivity. Among the evaluated biomarkers, SAA demonstrated the strongest correlation with disease activity (Spearman ρ = 0.878; p < 0.001). Multivariate predictive modeling showed high discriminative performance, with random forest achieving the highest cross-validated AUC (0.934), whereas gradient boosting demonstrated the best overall accuracy (0.883). Unsupervised clustering analysis identified a subgroup characterized by a more pronounced inflammatory signature associated with higher PMR-AS values. Conclusions: These findings support the concept that disease activity in PMR may be more effectively represented through an integrated multidimensional inflammatory profile rather than isolated biomarkers. The combined evaluation of routine laboratory parameters, cytokines, and imaging features may contribute to more refined patient stratification within a precision medicine framework. Although exploratory, these results highlight the potential value of advanced data integration strategies for supporting biologically informed disease characterization in PMR, while underscoring the need for external validation before translation into clinical practice.

Background: Psoriatic arthritis (PsA) is a chronic inflammatory disease frequently associated with obesity and metabolic comorbidities. Increasing evidence suggests that obesity is associated with systemic inflammation, insulin resistance, and adverse clinical outcomes; however, the immunometabolic profile associated with obesity in PsA has not been fully characterized. Objective: To investigate the relationship between obesity, inflammatory biomarkers, cytokine profiles, metabolic parameters, insulin resistance, and disease-related characteristics in patients with PsA. Methods: In this monocentric cross-sectional study, 224 consecutive patients fulfilling the Classification Criteria for Psoriatic Arthritis (CASPAR) for PsA were evaluated. Clinical, inflammatory, metabolic, and cytokine-related variables were analyzed within an integrated immunometabolic framework. Patients were stratified according to obesity status based on body mass index (BMI ≥ 30 kg/m²). Correlation analyses, multivariable regression analyses, and exploratory machine-learning approaches were applied to identify multidimensional patterns associated with obesity. Adjusted multivariable regression analyses were performed to account for available demographic, clinical, comorbidity-related, and therapeutic covariates. Results: Ninety-four patients (42.0%) were classified as obese and 130 (58.0%) as non-obese. Compared with non-obese patients, obese individuals exhibited significantly higher levels of C-reactive protein (CRP; 0.31 vs. 0.13 mg/dL, p < 0.001), erythrocyte sedimentation rate (ESR; 15.0 vs. 10.0 mm/h, p = 0.006), serum amyloid A (SAA; 7.7 vs. 6.4 mg/L, p = 0.008), insulin (8.0 vs. 6.1 μU/mL, p < 0.001), glycated hemoglobin (HbA1c; 38.0 vs. 37.0 mmol/mol, p = 0.003), Homeostasis Model Assessment of Insulin Resistance (HOMA-IR; 1.29 vs. 1.21, p = 0.007), triglycerides (106.0 vs. 85.0 mg/dL, p = 0.019), and Health Assessment Questionnaire (HAQ) scores (p < 0.001). Obese patients also showed a higher prevalence of hypertension (18.1% vs. 6.9%, p = 0.018). BMI was positively correlated with several inflammatory, metabolic, and disease-related variables, including CRP (ρ = 0.383), interleukin-6 (IL-6; ρ = 0.295), insulin (ρ = 0.289), ESR (ρ = 0.245), SAA (ρ = 0.228), HbA1c (ρ = 0.199), HAQ score (ρ = 0.351), and Disease Activity Index for Psoriatic Arthritis (DAPSA) score (ρ = 0.183), while showing an inverse correlation with high-density lipoprotein cholesterol (HDL-C) (ρ = −0.189). After adjustment for age, sex, disease duration, DAPSA, hypertension, type 2 diabetes mellitus, and current therapy class, obesity remained associated with higher CRP and fasting insulin levels and with greater odds of belonging to a higher HAQ category. Machine-learning analyses identified inflammatory biomarkers, insulin resistance indices, and lipid-related parameters as the most informative features associated with obesity-related phenotypes. Conclusions: Obesity in PsA was associated with a broader immunometabolic phenotype characterized by increased inflammatory burden, metabolic dysfunction, and worse functional outcomes. These findings support the integration of metabolic and cardiovascular assessment into routine rheumatology practice and highlight obesity as an important marker of increased immunometabolic and clinical burden in PsA. Although the cross-sectional design precludes causal inference, the observed associations may help identify patients requiring closer metabolic and cardiovascular assessment.

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most severe extra-articular manifestations of rheumatoid arthritis (RA), requiring reliable biomarkers for early detection. This scoping review synthesized current evidence regarding the diagnostic performance and clinical associations of anti-mutated citrullinated vimentin (anti-MCV) antibodies in patients with RA-ILD. A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library. Following systematic screening, two observational studies met the predefined inclusion criteria. Both included studies reported significantly higher anti-MCV positivity rates and/or serum levels in patients with RA-ILD compared with RA patients without pulmonary involvement. Specifically, one study identified an independent association between anti-MCV positivity and RA-ILD, while the other demonstrated significant correlations between anti-MCV titers and pulmonary function impairment, as well as disease activity markers. However, substantial heterogeneity was observed across the studies regarding assay platforms, positivity thresholds, and diagnostic cut-offs, which limits the direct comparability of results. While anti-MCV antibodies represent promising candidate biomarkers for RA-ILD, current evidence remains limited and is insufficient to establish definitive diagnostic, prognostic, or pathogenic significance. Consequently, larger, prospective, and multi-center studies utilizing standardized anti-MCV assay protocols are necessary to rigorously evaluate the clinical utility of these antibodies in the management of RA-ILD.