Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics

Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30–40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-tre...

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Bibliographic Details
Published in:Scientific reports 2021-03, Vol.11 (1), p.7266-7266, Article 7266
Main Authors: Maciejewski, Mateusz, Sands, Caroline, Nair, Nisha, Ling, Stephanie, Verstappen, Suzanne, Hyrich, Kimme, Barton, Anne, Ziemek, Daniel, Lewis, Matthew R., Plant, Darren
Format: Article
Language:English
Subjects:
631/114
631/114/1305
631/1647/320
631/1647/48
631/45/608
692/4023/1670/498
692/53/2423
Humanities and Social Sciences
Learning algorithms
Lipids
Liquid chromatography
Mass spectrometry
Mass spectroscopy
Mathematical models
Methotrexate
multidisciplinary
Patients
Rheumatoid arthritis
Science
Science (multidisciplinary)
Serum lipids
Statistical analysis
Statistical models
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Summary:Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30–40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra‐performance liquid chromatography–mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-86729-7