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

Abstract 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...

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Autores principales:	Mateusz Maciejewski, Caroline Sands, Nisha Nair, Stephanie Ling, Suzanne Verstappen, Kimme Hyrich, Anne Barton, Daniel Ziemek, Matthew R. Lewis, Darren Plant
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/15aaa01033fa4c62b85b8be3ce377903
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spelling	oai:doaj.org-article:15aaa01033fa4c62b85b8be3ce3779032021-12-02T13:26:58ZPrediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics10.1038/s41598-021-86729-72045-2322https://doaj.org/article/15aaa01033fa4c62b85b8be3ce3779032021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86729-7https://doaj.org/toc/2045-2322Abstract 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.Mateusz MaciejewskiCaroline SandsNisha NairStephanie LingSuzanne VerstappenKimme HyrichAnne BartonDaniel ZiemekMatthew R. LewisDarren PlantNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mateusz Maciejewski Caroline Sands Nisha Nair Stephanie Ling Suzanne Verstappen Kimme Hyrich Anne Barton Daniel Ziemek Matthew R. Lewis Darren Plant Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
description	Abstract 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.
format	article
author	Mateusz Maciejewski Caroline Sands Nisha Nair Stephanie Ling Suzanne Verstappen Kimme Hyrich Anne Barton Daniel Ziemek Matthew R. Lewis Darren Plant
author_facet	Mateusz Maciejewski Caroline Sands Nisha Nair Stephanie Ling Suzanne Verstappen Kimme Hyrich Anne Barton Daniel Ziemek Matthew R. Lewis Darren Plant
author_sort	Mateusz Maciejewski
title	Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
title_short	Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
title_full	Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
title_fullStr	Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
title_full_unstemmed	Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
title_sort	prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/15aaa01033fa4c62b85b8be3ce377903
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Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics

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