Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

Abstract Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In thi...

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Autores principales:	Marcelo V. S. Alves, Lanaia I. L. Maciel, Ruver R. F. Ramalho, Leomir A. S. Lima, Boniek G. Vaz, Camilo L. M. Morais, João O. S. Passos, Rodrigo Pegado, Kássio M. G. Lima
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2bce6dbd4b643dba89e88019d0668ef
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spelling	oai:doaj.org-article:e2bce6dbd4b643dba89e88019d0668ef2021-11-21T12:24:43ZMultivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia10.1038/s41598-021-02141-12045-2322https://doaj.org/article/e2bce6dbd4b643dba89e88019d0668ef2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02141-1https://doaj.org/toc/2045-2322Abstract Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.Marcelo V. S. AlvesLanaia I. L. MacielRuver R. F. RamalhoLeomir A. S. LimaBoniek G. VazCamilo L. M. MoraisJoão O. S. PassosRodrigo PegadoKássio M. G. LimaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marcelo V. S. Alves Lanaia I. L. Maciel Ruver R. F. Ramalho Leomir A. S. Lima Boniek G. Vaz Camilo L. M. Morais João O. S. Passos Rodrigo Pegado Kássio M. G. Lima Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
description	Abstract Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.
format	article
author	Marcelo V. S. Alves Lanaia I. L. Maciel Ruver R. F. Ramalho Leomir A. S. Lima Boniek G. Vaz Camilo L. M. Morais João O. S. Passos Rodrigo Pegado Kássio M. G. Lima
author_facet	Marcelo V. S. Alves Lanaia I. L. Maciel Ruver R. F. Ramalho Leomir A. S. Lima Boniek G. Vaz Camilo L. M. Morais João O. S. Passos Rodrigo Pegado Kássio M. G. Lima
author_sort	Marcelo V. S. Alves
title	Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
title_short	Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
title_full	Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
title_fullStr	Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
title_full_unstemmed	Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
title_sort	multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e2bce6dbd4b643dba89e88019d0668ef
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Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

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