Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learn
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.
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Metadaten
Author:Jörn Lötsch, Susanne Schiffmann, Katja Schmitz, Robert Brunkhorst, Florian Lerch, Nerea Ferreiros, Sabine Wicker, Irmgard Tegeder, Gerd Geisslinger, Alfred Ultsch
URN:urn:nbn:de:hebis:30:3-476733
DOI:http://dx.doi.org/10.1038/s41598-018-33077-8
ISSN:2045-2322
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=30291263
Parent Title (English):Scientific reports
Publisher:Macmillan Publishers Limited, part of Springer Nature
Place of publication:[London]
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/10/05
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/10/09
Tag:Diagnostic markers; Multiple sclerosis
Volume:8
Issue:1, Art. 14884
Pagenumber:16
First Page:1
Last Page:16
Note:
Rights and permissions: Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
HeBIS PPN:438464346
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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