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 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.

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

“…This m/z ratio value was indicated in the analysis of PC1 loadings, being present in most data sets. Another reason that highlights this finding is in a previous study 18 which also confirms the presence of compounds of the lysophosphatidylcholine class in the samples of the cases group, as well as in studies that present this class of metabolites as a possible biomarker or contributing factor to the fibromyalgia phenotype 31 , 32 .…”

“…Despite proposing some adjustments in the use of the criteria, the study concluded that the criteria had good sensitivity and specificity for all analyzed studies, with an average of 84% and 83%, respectively 8 . Recent studies have also demonstrated good sensitivity and specificity in classifying groups of patients with and without fibromyalgia, using blood plasma samples, among which: Passos et al 17 used ATR-FTIR spectroscopy, with results of 89.5% sensitivity and 79% specificity in the classification between controls and fibromyalgia patients using a GA-LDA model; while Alves et al 18 used PSI-MS mass spectrometry and reached values of 100% sensitivity and specificity using SPA-LDA and exploratory analyzes with PCA, with small groups of samples (10 controls and 10 fibromyalgia samples). The study presented herein obtained 100% sensitivity and 75% specificity (88% accuracy), with a total number of samples equal to 64 (27 controls and 37 fibromyalgia samples) for the classification performed with the PCA-LDA model in the set of data regarding the moderate CAT symptom.…”

“…Among these studies, it is possible to mention investigations of potential biomarkers for the disease with proteomic analysis using MALDI-TOF mass spectrometry in saliva sample 13 and metabolomics studies with the analysis of urine samples in Gas Chromatography Mass Spectrometry (GC–MS) 14 and blood plasma samples with Liquid Chromatography Mass Spectrometry (LC–MS) 15 . Classification studies are also mentioned, such as using Raman and Fourier-Transform Infrared (FTIR) spectroscopy in differentiating blood samples from patients with FM and other rheumatic diseases 16 ; and, discrimination between samples from patients with and without FM based on blood plasmas through Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy 17 and Paper Spray Ionization Mass Spectrometry (PSI-MS) 18 .…”

Fibromyalgia: one year in review 2022

“…Features (NFF) questionnaire may be a valuable primary screening tool (7). • Studies underlined high salivary cortisol levels, alterations in metabolites involved in free radical, lipid and amino acid metabolism and in blood cytokine profiles (13)(14)(15)(16)). • Neuro-inflammation has been highlighted by OCT and [11C]-(R)-PK11195 PET (18,19).…”

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

“…[ 65 ] reported that PCA does more feature classification, while LDA does more data separation which is in accordance with our results. Likewise, previously reported data by Alves et al [ 66 ] applied nine different algorithms to find the best identification tool for fibromyalgia. They inferred that SPA-LDA is a reliable tool in the clinical diagnosis of fibromyalgia.…”