Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning

Author:

Koussiouris John,Looby Nikita,Kotlyar Max,Kulasingam Vathany,Jurisica Igor,Chandran Vinod

Abstract

Abstract Introduction Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult. There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management. Objectives To identify metabolites capable of classifying patients with PsA according to their disease activity. Methods An in-house solid-phase microextraction (SPME)—liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance. Results The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids. Conclusion Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin. Quantitative MS assays based on selected reaction monitoring, are required to quantify the candidate biomarkers identified.

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Canada Foundation for Innovation

Ontario Research Fund

IBM and Ian Lawson van Toch Fund

Canadian Institutes of Health Research

Pfizer Chair Research Award

Krembil Foundation

Publisher

Springer Science and Business Media LLC

Reference40 articles.

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