Issue 18, 2023

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

Abstract

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Graphical abstract: Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

Associated articles

Supplementary files

Article information

Article type
Paper
Submitted
26 Հնս 2023
Accepted
26 Հլս 2023
First published
27 Հլս 2023

Analyst, 2023,148, 4318-4330

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

Q. Du, X. Wang, J. Chen, Y. Wang, W. Liu, L. Wang, H. Liu, L. Jiang and Z. Nie, Analyst, 2023, 148, 4318 DOI: 10.1039/D3AN01051A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements