Emery, Paul

Abstract Objectives To compare clinical characteristics, including the frequency of cutaneous, extramuscular manifestations and malignancy, between adults with anti-synthetase syndrome (ASyS) and DM. Methods Using data regarding adults from the MYONET registry, a cohort of DM patients with anti-Mi2/-TIF1γ/-NXP2/-SAE/-MDA5 autoantibodies, and a cohort of ASyS patients with anti-tRNA synthetase autoantibodies (anti-Jo1/-PL7/-PL12/-OJ/-EJ/-Zo/-KS) were identified. Patients with DM sine dermatitis or with discordant dual autoantibody specificities were excluded. Sub-cohorts of patients with ASyS with or without skin involvement were defined based on presence of DM-type rashes (heliotrope rash, Gottron’s papules/sign, violaceous rash, shawl sign, V-sign, erythroderma, and/or periorbital rash). Results In total 1054 patients were included (DM, n = 405; ASyS, n = 649). In the ASyS cohort, 31% (n = 203) had DM-type skin involvement (ASyS-DMskin). A higher frequency of extramuscular manifestations, including Mechanic’s hands, Raynaud’s phenomenon, arthritis, interstitial lung disease and cardiac involvement differentiated ASyS-DMskin from DM (all P < 0.001), whereas higher frequency of any of four DM-type rashes—heliotrope rash (n = 248, 61% vs n = 90, 44%), violaceous rash (n = 166, 41% vs n = 57, 9%), V-sign (n = 124, 31% vs n = 28, 4%), and shawl sign (n = 133, 33% vs n = 18, 3%)—differentiated DM from ASyS-DMskin (all P < 0.005). Cancer-associated myositis (CAM) was more frequent in DM (n = 67, 17%) compared with ASyS (n = 21, 3%) and ASyS-DMskin (n = 7, 3%) cohorts (both P < 0.001). Conclusion DM-type rashes are frequent in patients with ASyS; however, distinct clinical manifestations differentiate these patients from classical DM. Skin involvement in ASyS does not necessitate increased malignancy surveillance. These findings will inform future ASyS classification criteria and patient management.

Objective Interstitial lung disease (ILD) is an important cause of mortality in some patients with rheumatoid arthritis (RA). Patient-level factors may predict which patients with RA are at the highest risk of developing ILD and are therefore candidates for screening for this complication of the underlying disease. Methods A systematic literature review was performed using PubMed, Embase and Scopus over a 10-year period up to July 2021. Publications reporting patient-level factors in patients with RA with and without ILD that were assessed before development of ILD (or were unchanged over time and therefore could be extrapolated to before development of ILD) were retrieved for assessment of evidence. Genetic variation in MUC5B and treatment with methotrexate were not included in the assessment of evidence because these factors have already been widely investigated for association with ILD. Results We found consistent associations of age, sex, smoking status and autoantibodies with development of ILD. For biomarkers such as Krebs von den Lungen 6, which have been shown to be diagnostic for ILD, there were no publications meeting criteria for this study. Conclusions This analysis provides an initial step in the identification of patient-level factors for potential development of a risk algorithm to identify patients with RA who may be candidates for screening for ILD. The findings represent a useful basis for future research leading to an improved understanding of the disease course and improved care for patients with RA at risk of development and progression of ILD.

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single‐nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3‐month follow‐up. Supervised ML methods were trained with 5 repeats and 10‐fold cross‐validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 ( P = 0.05) in the training cohort and achieved a prediction accuracy of 76% ( P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.