Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI‑MS) and multivariate classifcation
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2023
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This 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.
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ALVES, 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.