Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI‑MS) and multivariate classifcation

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dc.creatorAlves, Marcelo Victor dos Santos
dc.creatorMaciel, Lanaia Ítala Louzeiro
dc.creatorPassos, João Octávio Sales
dc.creatorMorais, Camilo de Lelis Medeiros de
dc.creatorSantos, Marfran Claudino Domingos dos
dc.creatorLima, Leomir Aires Silva de
dc.creatorVaz, Boniek Gontijo
dc.creatorFreitas, Rodrigo Pegado de Abreu
dc.creatorLima, Kassio Michell Gomes de
dc.date.accessioned2023-06-27T13:09:22Z
dc.date.available2023-06-27T13:09:22Z
dc.date.issued2023
dc.description.abstractThis study performs a chemical investigation of blood plasma samples from patients with and without fbromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fbromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire—physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classifcation using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/ GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fbromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classifcation sensitivity, and associated with the VAS symptom achieved 100% classifcation specifcity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially signifcant for classifcation according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fbromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classifcation ability of diferent datasets. The good classifcation results obtained confrm this technique is as a good analytical tool for the detection of fbromyalgia, and provides theoretical support for other studies about fbromyalgia diagnosis.pt_BR
dc.identifier.citationALVES, Marcelo V. S. et al. Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI‑MS) and multivariate classifcation. Scientific Reports, London, v. 13, e4658, 2023. DOI: 10.1038/s41598-023-31565-0. Disponível em: https://www.nature.com/articles/s41598-023-31565-0. Acesso em: 21 jun. 2023.pt_BR
dc.identifier.doihttps://doi.org/10.1038/s41598-023-31565-0
dc.identifier.issne- 2045-2322
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/22765
dc.language.isoengpt_BR
dc.publisher.countryGra-bretanhapt_BR
dc.publisher.departmentInstituto de Química - IQ (RMG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleSpectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI‑MS) and multivariate classifcationpt_BR
dc.typeArtigopt_BR

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