Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients.

Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions.

Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort.

Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.

[Cost analysis in a cohort of rheumatoid arthritis patients managed in rheumatology units in Spain]

OBJECTIVE

To assess the annual costs of rheumatoid arthritis (RA) patients attended at rheumatology units in Spanish public hospitals.

METHODS

A longitudinal, prospective, multicenter, observational, 1-year study was performed in the rheumatology units of randomly selected Spanish public hospitals. Randomly selected RA patients were included. The patients made four visits (at baseline and every 4 months). Resource use and costs were collected from patient diaries and structured questionnaires.

RESULTS

A total of 301 patients were included and 190 (83% women) completed the study. The mean (± SD) age was 59±13 years and the mean disease duration was 10±10 years. The median annual cost per patient was 3,845 euros (318-36,783). The estimated total annual cost of the Spanish RA population managed in the rheumatology units of public hospitals was 590,110,000 euros. Of total costs, 74% were direct costs and 26% were indirect costs. Medical costs represented 81% of direct costs. The main components of medical costs were drugs (56%), medical visits (21%), complementary tests (12%), and hospitalizations (11%). Permanent work disability represented 66% of indirect costs.

CONCLUSIONS

Direct costs were substantially higher than indirect costs. The main components of medical costs were drugs. There was high variability in resource use with a wide range of annual costs per patient.