Personalized cardiovascular risk assessment in Rheumatoid Arthritis patients using circulating molecular profiles and their modulation by TNFi, IL6Ri, and JAKinibs.

BACKGROUND & OBJECTIVES: This study aimed to: 1) analyze the inflammatory profile of Rheumatoid Arthritis (RA) patients, identifying clinical phenotypes associated with cardiovascular (CV) risk; 2) evaluate biologic and targeted-synthetic disease-modifying antirheumatic drugs (b-DMARDs and ts-DMARDs': TNFi, IL6Ri, JAKinibs) effects; and 3) characterize molecular mechanisms in immune-cell activation and endothelial dysfunction. PATIENTS & METHODS: A total of 387 RA patients and 45 healthy donors were recruited, forming three cohorts: i) 208 RA patients with established disease but without previous CV events; ii) RA-CVD: 96 RA patients with CV events, and iii) 83 RA patients treated with b-DMARDs/ts-DMARDs for 6 months. Serum inflammatory profiles (cytokines/chemokines/growth factors) and NETosis/oxidative stress-linked biomolecules were evaluated. Mechanistic in vitro studies were performed on monocytes, neutrophils and endothelial cells (EC). RESULTS: In the first RA-cohort, unsupervised clustering unveiled three distinct groups: cluster 3 (C3) displayed the highest inflammatory profile, significant CV-risk score, and greater atheroma plaques prevalence. In contrast, cluster 1 (C1) exhibited the lowest inflammatory profile and CV risk score, while cluster 2 (C2) displayed an intermediate phenotype. Notably, 2nd cohort RA-CVD patients mirrored C3's inflammation. Treatment with b-DMARDs or ts-DMARDs effectively reduced disease-activity scores (DAS28) and restored normal biomolecules levels, controlling CV risk. In vitro, serum from C3-RA or RA-CVD patients increased neutrophils activity and CV-related protein levels in cultured monocytes and EC, which were partially prevented by pre-incubation with TNFi, IL6Ri, and JAKinibs. CONCLUSIONS: Overall, analyzing circulating molecular profiles in RA patients holds potential for personalized clinical management, addressing CV risk and assisting healthcare professionals in tailoring treatment, ultimately improving outcomes.

A Monte Carlo-Based Bayesian Approach for Measuring Agreement in a Qualitative Scale.

Agreement analysis has been an active research area whose techniques have been widely applied in psychology and other fields. However, statistical agreement among raters has been mainly considered from a classical statistics point of view. Bayesian methodology is a viable alternative that allows the inclusion of subjective initial information coming from expert opinions, personal judgments, or historical data. A Bayesian approach is proposed by providing a unified Monte Carlo-based framework to estimate all types of measures of agreement in a qualitative scale of response. The approach is conceptually simple and it has a low computational cost. Both informative and non-informative scenarios are considered. In case no initial information is available, the results are in line with the classical methodology, but providing more information on the measures of agreement. For the informative case, some guidelines are presented to elicitate the prior distribution. The approach has been applied to two applications related to schizophrenia diagnosis and sensory analysis.