4.7 Article

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

Journal

SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41598-021-86729-7

Keywords

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Funding

  1. Medical Research Council (MRC)
  2. NIHR Manchester Biomedical Research Centre
  3. National Institute for Health Research [MC_PC_12025]
  4. Medical Research Council
  5. Versus Arthritis - MATURA [20385, 20380, MR/K015346/1, 20670]
  6. MRC [MR/K015346/1] Funding Source: UKRI

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Methotrexate is a common first-line treatment for rheumatoid arthritis, but it is ineffective for a significant portion of patients, with no reliable way to predict individual responses. Statistical models based on serum lipid levels were explored for predicting methotrexate response in patients, but clinical covariates alone outperformed lipid-based models in predicting response. Serum lipid profiles at pre-treatment or early-treatment time points are unlikely to be predictive of methotrexate response by 6 months in a way that would be useful for clinical management of rheumatoid arthritis.
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.

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