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Objectives Autoantibody responses increase years before the onset of inflammatory arthritis (IA) and are stable during transitioning from clinically suspect arthralgia (CSA) to IA. Cytokine and chemokine levels also increase years before IA onset. However, the course in the at-risk stage of CSA during progression to disease or non-progression is unknown. To increase the understanding of processes mediating disease development, we studied the course of cytokine, chemokine and related receptors gene expression in CSA patients during progression to IA and in CSA patients who ultimately did not develop IA. Methods Whole-blood RNA expression of 37 inflammatory cytokines, chemokines and related receptors was determined by dual-colour reverse transcription multiplex ligation-dependent probe amplification in paired samples of CSA patients at CSA onset and either at IA development or after 24 months without IA development. ACPA-positive and ACPA-negative CSA patients developing IA were compared at CSA onset and during progression to IA. Generalised estimating equations tested changes over time. A false discovery rate approach was applied. Results None of the cytokine/chemokine genes significantly changed in expression between CSA onset and IA development. In CSA patients without IA development, G-CSF expression decreased (P  = 0.001), whereas CCR6 and TNIP1 expression increased (P  < 0.001 and P  = 0.002, respectively) over a 2 year period. Expression levels in ACPA-positive and ACPA-negative CSA patients who developed IA were similar. Conclusion Whole-blood gene expression of assessed cytokines, chemokines and related receptors did not change significantly from CSA to IA development. This suggests that changes in expression of these molecules may not be related to the final process of developing chronicity and may have occurred preceding CSA onset. Changes in gene expression in CSA patients without IA development may provide clues for processes related to resolution. [ABSTRACT FROM AUTHOR]

Objectives: Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers., Methods: Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response., Results: We included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 [95% CI (0.63, 0.80)] on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% [95% CI (20%, 58%)] chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% [95% CI: (17%, 29%)]. A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond., Conclusion: Our study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)