Association between fibromyalgia symptoms and Fourier-transform infrared (ATR-FTIR) spectroscopy analysis of blood combined with chemometrics

Association between fibromyalgia symptoms and Fourier-transform infrared (ATR-FTIR) spectroscopy analysis of blood combined with chemometrics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between fibromyalgia symptoms and Fourier-transform infrared (ATR-FTIR) spectroscopy analysis of blood combined with chemometrics João Octávio Sales Passos, Marcelo Victor dos Santos Alves, Antônio Felipe Cavalcante, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4165415/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fibromyalgia typically involves pain, fatigue, and mood disruptions, often necessitating over two years and around four medical consultations for diagnosis. The combination of spectroscopy and chemometric techniques holds promise as a cost-effective and accurate strategy for screening fibromyalgia according to the association between the symptoms and spectral data. The study aimed to explore the association between spectrochemical analysis coupled to chemometric techniques with fibromyalgia symptoms. A total of 126 controls and 126 patients with fibromyalgia participated in the study. Blood plasma was analyzed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques for posterior association between pain, kinesiophobia, pain catastrophizing, impact of fibromyalgia, quality of life and anxiety. The datasets underwent multivariate classification using supervised models. Different chemometric algorithms were tested to classify the spectral data and the association between symptoms. A clear accuracy discrimination was observed to moderate and severe pain (82.1%; 100%); kinesiophobia (84.6%; 80.8%), catastrophizing (87.5%; 81.8%), impact of fibromyalgia (74.8%; 77.8%), anxiety (100%; 76.9%) and mild and regular quality of life (93.2%; 81.4%). The obtained favorable classification results validate the effectiveness of this technique as an analytical tool for fibromyalgia detection. Health sciences/Rheumatology/Musculoskeletal system Physical sciences/Chemistry/Analytical chemistry/Infrared spectroscopy Infrared spectroscopy chemometrics algorithm chronic pain diagnosis Figures Figure 1 INTRODUCTION Fibromyalgia syndrome is an intriguing rheumatological disorder with no consensus about the etiology and physiological mechanism of symptoms ( 1 , 2 ). Fibromyalgia is the second most common rheumatic disorder with 2%-5% of the adult population in America and a mean estimated global prevalence of 2.7% ( 3 , 4 ). This is a chronic debilitating disorder with devastating effects in functional, emotional, and social aspects ( 5 ). Patients commonly relate persistent complex polysymptomatology that include widespread pain (with allodynia and hyperalgesia), chronic fatigue, stiffness, sleep disturbances, depression, anxiety, elevated negative affectivity and in some cases, cognitive dysfunction (fibro-fog) ( 3 ). This clinical condition reduces the quality of life, physical function, and work productivity ( 3 , 6 ). This complex and heterogeneous condition makes the diagnostic a challenger for healthcare professionals and could be considered underdiagnosed ( 7 ). According to a patient survey, the diagnosis of fibromyalgia is a journey passing through primary care physicians (rheumatologists, neurologists, psychiatrists, and pain specialists) to highly specialized support care including hospitalization and image exams ( 8 ). It is necessary an average of 2.3 years and presenting to 3.7 different physicians before receiving a diagnosis of fibromyalgia ( 8 ). In addition, several biochemical and imaging exams are routinely requested to exclude other possible neurological, orthopaedic, and rheumatologic diseases ( 8 ). The American College of Rheumatology (ACR) presented the first approved classification criterion for diagnostic of fibromyalgia in 1990 that was modified in 2010/2011 by Wolf and colleagues ( 9 , 10 ). Reanalyzes of the 2010/2011 criteria showed mean and median unweighted sensitivities of 84% and 84% and specificities of 83% and 87% ( 11 ). Alternative diagnostic criteria of fibromyalgia was proposed with a modified 2010/2011 criteria ( 12 ). These alternative criteria had a diagnostic sensitivity of 81%, a specificity of 80%, and a correct classification of 80% ( 12 ). Even after thirty-three years of the first guideline, fibromyalgia remains misdiagnosed, and some investigators call the criteria into question ( 13 ). Primarily because the cause of FM is unknown and others pain or musculoskeletal syndromes could mask the diagnoses ( 13 ). Furthermore, the diversity of symptoms is commonly found throughout several rheumatic and nonrheumatic diseases ( 13 ). Underdiagnosis or misdiagnosis of fibromyalgia still occurs for any reasons including the variability of symptoms severity ( 14 ). New strategies are needed to improve diagnosis of fibromyalgia in a fast, low-cost, accurate and less invasive manner. Establishing new protocols for diagnosis of fibromyalgia could provide substantial relief for patients and additional security for clinicians ( 7 ). The diagnosis of fibromyalgia is therefore completely clinical, due to non-specific imaging exam, electrophysiological or biomarkers findings ( 15 ). Recently, studies focused on finding a biomarker for fibromyalgia using metabolomics, spectroscopy, neuroretinal evaluation and artificial intelligence ( 2 , 16 – 18 ). Some biomarkers could therefore contribute to the disease phenotype and could be involved in the etiology or physiological mechanism of fibromyalgia ( 18 ). For a relevant finding, it is necessary that a certain biomarker has a direct association with the clinical symptom’s severity in fibromyalgia ( 18 ). Studies suggest using mid-infrared microspectroscopy (IRMS) and metabolomics to differentiate patients with fibromyalgia from those with osteoarthritis (OA) and rheumatoid arthritis (RA) ( 19 ). It was possible to find differences in metabolomic analysis in fibromyalgia and a relationship between infrared spectral patterns and disease activity assessed using pain score and self-reported symptom ( 19 ). It is suggested an association or possible prediction of fibromyalgia according to IRMS and metabolomics approaches also ( 19 ). Spectroscopy has been increasingly recognized as a powerful technology in biomedical and microbiology ( 20 ). Created by physicists and chemists, it has traditionally been used to identify molecular entities present in unknown substances ( 21 ). Currently, it is used to determine the relative amounts of biochemical components present in cells and tissues, leading to biospectroscopy ( 22 ). The biospectroscopy provides information related to the molecular property of the analyzed material with a relatively fast and easy to use ( 20 ). This method is a non-destructive technique that requires a small number of samples (nano-microgram) as blood plasma ( 22 ). Results of previous studies suggest that biospectroscopy could be used as a minimally invasive method to distinguish patients with Alzheimer’s disease ( 6 ), dengue, Zika, chikungunya ( 20 ) and some types of cancer ( 21 ). Recently, Passos et al. used Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques and showed 84.2% accuracy, 89.5% sensitivity and 79.0% specificity to detect fibromyalgia ( 22 ). When compared with previous ACR classification criteria, these metrics demonstrate a satisfactory rate for distinguishing fibromyalgia and health controls. Therefore, a secondary analysis is now proposed aiming to explore the possible association between spectrochemical analysis coupled to chemometric techniques with fibromyalgia symptoms of pain, disease severity, functional ability, kinesiophobia, catastrophizing, anxiety, and quality of life. Presenting the relationships between biospectroscopy and the clinical findings of fibromyalgia enhances this technique as a valid tool to detect the disease. RESULTS A total of 246 participants completed all study procedures. The datasets were submitted to multivariate analysis to distinguish between the control group and fibromyalgia group. The classifications were performed with Principal Component Analysis (PCA), the Successive Projections Algorithm (SPA), and the Genetic Algorithm (GA), associated with supervised Linear Discriminant Analysis (LDA), thus three combinations of algorithms: PCA-LDA, SPA-LDA, GA-LDA seeking to identify the combination with the best performance for each dataset. The samples for each symptom were allocated across different levels that aligned with the stratification of the clinical variable ( 24 , 25 , 27 , 30 – 32 ). This allocation was based on the responses provided by patients on scales and questionnaires administered at the time of blood collection. Before model construction, 70% of samples were assigned to the training set, 15% to the validation set, and 15% to the lest set using the Kennard-Stone uniform sampling algorithm ( 33 ). The training set was used for model construction, the validation set for internal model optimization, and the test set for final model evaluation, where figures of merit (accuracy, sensitivity and specificity) reflecting the model performance towards external sample were calculated. Concerning the efficacy results in group classification, sensitivity and specificity values were employed for both the control and fibromyalgia groups within each dataset. Table 1 presents the optimal outcomes achieved for each symptom level or cumulative level. According to multivariate analyzes it was possible to identify the huge association between fibromyalgia symptoms with spectral data. The severe level had indices of 100% accuracy classification in the control group and fibromyalgia of the clinical variable VAS (PCA/SPA/GA-LDA pp), 100% in the control group for TSK (GA-LDA), 100% in the control group and fibromyalgia for PCS, 73,4 and 77.8% (GA-LDA pp) of accuracy in the control group and fibromyalgia respectively. Severe symptoms of anxiety (HAS) showed an accuracy of 55.1 and 76.9 (GA-LDA) in the control group and fibromyalgia respectively. The moderate level of the kinesiophobia symptom also showed good distinction between the groups, with an accuracy of 84.6% in the fibromyalgia (PCA-LDA). For anxiety, moderate levels showed 100% accuracy classification in the fibromyalgia group (PCA-LDA pp). The mild level was present in the VAS TSK, PCS, SF-36 and HAS. Best results could be noted with 100% (SPA-LDA) of accuracy for TSK, 83.3% (PCA-LDA pp) for PCS, 93.2 (GA-LDA) for SF-36, all for the fibromyalgia group. Models with the best performance in each data set, followed by the respective sensitivity (Sens.), specificity (Spec.) and accuracy values are available in Table 2 . Table 1 Socio-demographic and clinical characteristics. Outcomes Fibromyalgia Control p value (mean ± SD) (mean ± SD) Age (years) 48.02 ± 10.03 49.84 ± 11.42 0.18 VAS 5.74 ± 2.41 1.77 ± 2.25 0.0001 TSK 49.12 ± 7.9 38.85 ± 10.91 0.0001 PCS 36.46 ± 11.38 17.76 ± 13.51 0.0001 FIQ 75.03 ± 13.97 27.2 ± 21.35 0.0001 SF-36 53.39 ± 20.51 113.6 ± 44.58 0.0001 Anxiety (HAS) 38.05 ± 9.26 18.18 ± 11.93 0.001 Income* (%) 0.0003 1 Minimum Wage 6.7 29.4 2 to 3 Minimum Wage 53.3 41.2 4 Minimum Wage or more 33.3 11.8 Unreported 6.7 17.6 Marital status (%) 0.03 Married 60 41.2 Never Married 26.7 41.2 Widowed 6.7 5.9 Divorced 6.7 11.8 Not respond Education (%) 0.802 Elementary (incomplete) 0 5.9 Elementary 26.7 23.5 Secondary 26.7 41.2 University 46.7 29.4 SD = Standard Deviation; VAS = Visual Analogue Scale; TSK = Tampa Scale for Kinesiophobia; PCS = Pain Catastrophizing Scale; FIQ = Fibromyalgia Impact Questionnaire; SF-36 = Short Form 36 Health Survey; HAS = Hamilton Anxiety Scale. Numeric data were calculated using unpaired t test. Categorical data were calculated using the Chi-Square test. *Brazilian National Minimum Wage, US $ 252.14 per month. Table 2 Sensitivity (Sens.), specificity (Spec.) and accuracy values of best performance model in each data set for control group (G1) and fibromyalgia (G2). Clinical variable Symptom level Best model Sens. (%) Spec. (%) Accuracy (%) G1 G2 G1 G2 G1 G2 VAS No pain SPA-LDA pp 80 100 80 100 80 100 Mild GA-LDA pp 83.3 50.1 82.5 56.7 82.9 53.4 Moderate SPA-LDA pp 60 82.1 60 82.1 60 82.1 Severe PCA/SPA/GA-LDA pp 100 100 100 100 100 100 TSK Mild SPA-LDA pp 91.7 100 91.7 100 91.7 100 Moderate PCA-LDA 38.2 84.6 38.2 84.6 38.2 84.6 Severe GA-LDA 100 76.4 72.2 85.2 86.1 80.8 PCS Mild PCA-LDA pp 36.7 83.3 36.7 83.3 36.7 83.3 Moderate PCA-LDA pp 62.5 87.5 62.5 87.5 62.5 87.5 Severe PCA-LDA 100 81.8 100 81.8 100 81.8 FIQ Moderate GA-LDA pp 87.4 68.7 75.3 80.9 81.4 74.8 Severe GA-LDA pp 88.9 77.7 57.9 77.8 73.4 77.8 SF-36 Mild GA-LDA 100 91.9 91.7 94.5 95.8 93.2 Regular GA-LDA pp 51.7 81.4 51.3 81.3 51.5 81.4 HAS Mild GA-LDA 100 55.6 94.9 66.7 97.5 61.2 Moderate PCA-LDA pp 41.7 100 41.7 100 41.7 100 Severe GA-LDA 54.6 55.6 74 79.8 55.1 76.9 G1 = Control group; G2: Fibromyalgia group; VAS = Visual Analogue Scale; TSK = Tampa Scale for Kinesiophobia; PCS = Pain Catastrophizing Scale; FIQ = Fibromyalgia Impact Questionnaire; SF-36 = Short Form 36 Health Survey; HAS = Hamilton Anxiety Scale. DISCUSSION Fibromyalgia diagnosis continues to be a difficult clinical decision for physicians and new technologies could help to enhance both the accuracy and efficiency of diagnosis ( 2 ). This study is characterized by case-control classifications that use ATR-FTIR spectroscopy with chemometric techniques, revealing significant correlations with clinical characteristics. Samples were organized based on the characteristic symptoms of pain, kinesiophobia, catastrophizing, disease impact, quality of life and anxiety. Each symptom was assessed by distributing the samples across levels corresponding to the stratification of the clinical variable, determined by the responses provided. PCA-LDA was the best model to associate with clinical outcomes. All the outcomes examined constitute classical findings in patients with fibromyalgia ( 3 ). Pain aspects were assessed by level of pain (VAS), kinesiophobia and catastrophizing and have shown huge association with the chemometric classification. Important to note that FIQ is a specific questionnaire for fibromyalgia and usually used to quantify the impact of the disease in daily life ( 28 , 29 ). However, in this study VAS, TSK, and PCS showed better association with the chemometric classification. This finding may be attributed to pain being the primary symptom associated with fibromyalgia, with elevated levels potentially indicating increased accuracy in diagnoses ( 11 , 12 ). Despite a significant difference in the mean FIQ scores between groups (75.03 ± 13.97 for fibromyalgia and 27.2 ± 21.35 for control), this outcome demonstrated a moderate association with the best chemometric classification model (GA-LDA pp). FIQ indirectly measure functional capacity and includes items about pain, physical impairment, ability to work, restfulness, and mood ( 28 ). This is largely used in fibromyalgia studies with prior analyses indicating that a 14% alteration in the FIQ total score holds clinical significance ( 30 ). Bennett et al. proposed a severity categorization based on the endpoint for FIQ, which served as the basis for the analysis in this study. Our finds suggest that pain aspects could be more suitable for associations with the models of chemometric classification. In this way, a classic clinical symptom of fibromyalgia, widely used for diagnosis, exhibited a stronger association than the disease-specific questionnaire with the spectrum of problems related to fibromyalgia. Quality of life (SF-36) showed a huge association with chemometric classification for fibromyalgia group, but not for controls. It is expected that control group exhibit good levels of quality of life when compared with a clinical population. Thus, the chemometric model seems to distinguish and shows better association for high levels of each outcome assessed. HAS showed mixed results with huge association for mild anxiety for control group and for moderate anxiety for fibromyalgia group. For the analysis of the chemometric models, it was possible to find important associations between clinical outcomes and spectroscopy analyses. This is an interesting finding suggesting that it is possible to use ATR-FTIR spectroscopy to detect fibromyalgia and associate it with the symptoms. Several other outcomes that include depression, affectivity, cognitive problems, fatigue, sleep disturbance and gastrointestinal symptoms could be included for future analyzes to obtain more spectroscopy analysis associations. Authors previously published an article regarding the use of ATR-FTIR spectroscopy in conjunction with chemometric techniques to detect fibromyalgia with an 84.2% accuracy ( 22 ). Now it is possible to establish an association between clinical parameters and ATR-FTIR findings. These associations enhance the efficacy of this technique and imply a robust correlation with clinical implications. By utilizing this clinical aspect, the model behaves as an interesting tool for fibromyalgia screening using ATR-FTIR combined with chemometrics ( 22 ). Blood biomarkers indicative of disease constitute a developing field with great promise for musculoskeletal disorders, neurology, and oncology ( 20 , 22 , 37 ). A significant number of new molecular tests for blood, saliva, and urine have emerged, demonstrating satisfactory results with low cost, simple collection procedures, and non-destructive material ( 20 , 22 , 37 ). Identifying distinct biological markers for each clinical condition is feasible, and establishing their association with symptoms is essential for enhancing screening methodologies ( 39 ). Nuguri et al. used an OPLS-DA algorithm to differentiate fibromyalgia and others rheumatic condition and found an 84% of accuracy ( 39 ). Very nearly to 84,2% showed by Passos et al. ( 22 ). Nuguri et al. not only presented the accuracy, but also distinguished it from other rheumatic diseases that includes rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain ( 38 – 40 ). Near-infrared spectroscopy was also used to detect Alzheimer's disease (AD) with 92.8% accuracy ( 6 ), dengue and chikungunya with 100% accuracy and 90% accuracy for Zika ( 20 ). The same group showed an 89% accuracy to distinguish the samples from patients with osteosarcopenia ( 40 ). Chemometric algorithms are evidenced as a powerful technique for cancer diagnosis and classification ( 41 ), but not previously study have shown the association between spectrochemical analysis combined with chemometric techniques and clinical outcomes. The potential applications of this technology in health science will help with screening, diagnosis, and surveillance through classification for different clinical conditions. In this study, we first suggested an association between spectrochemical analysis and clinical outcomes. Future studies and secondary analyses regarding other populations and clinical conditions could reveal these associations, predicting respondents to treatments and disease progression. New robust studies are necessary to improve the accuracy of algorithm and the associations of clinical findings in fibromyalgia. CONCLUSION This study evaluated the association of ATR-FTIR spectroscopy analysis of blood combined with chemometrics with clinical symptoms of fibromyalgia. Pain aspects, as assessed by VAS, kinesiophobia, and catastrophizing, emerged as the most significant clinical outcomes linked to the model. All clinical variables assessed showed moderate to strong association with algorithms models. METHODS Study design and participants This case-control study recruited a total 126 control subjects (G1) and 126 patients (G2) meeting the American College of Rheumatology criteria for diagnosis of fibromyalgia ( 9 ). Based on a fibromyalgia (FM) incidence rate of 6.6% in the Brazilian population ( 23 ), a significance level of 0.5, and a study power of 0.8, a sample size of 252 patients was calculated. Participants were recruited from social media and at the medical clinic of the Onofre Lopes University Hospital (HUOL) from July 2018 to March 2019. The following inclusion criteria were adopted: (a) medical diagnosis of fibromyalgia according to the ACR/2010; (b) ability to answer questionnaire and understand this study aim; (c) patients not undergoing physical therapy or rehabilitation programs during the three previous months; and (d) age ranging from 18 to 80 years old. The exclusion criteria were: (a) physical and/or organic problems, when these compromised questionnaire applications; and (b) rheumatic and/or autoimmune diseases including chronic fatigue syndrome, rheumatoid arthritis, gout, and lupus. The study was performed following the ethics standards of the Declaration of Helsinki. The protocol and consent form were approved by the Human Research Committee of the HUOL (Federal University of Rio Grande do Norte, Natal, Brazil) under registration number 2.631.168. All participants provided written informed consent prior to the beginning of the study. The study was conducted at the Clinical and Epidemiological Laboratory at the HUOL and at the Chemistry Institute of University of Rio Grande do Norte. Clinical variables All study participants underwent a clinical assessment, and on the same day, 10 mL of blood was collected from each participant along with the completion of questionnaires (Fig. 1 ). The sociodemographic variables of sex (female or male), age (years), weight (kilograms), height (meters), educational level (illiterate, primary school, secondary school, tertiary education), profession, marital status (single, married, divorced, widowed, or preferred not to respond), and race/ethnicity (white, yellow, black, mixed race, indigenous, unknown, preferred not to respond) were analyzed using a multiple-choice questionnaire developed by the research team. Clinical outcomes were assessed in terms of pain, pain catastrophizing, kinesiophobia, impact of fibromyalgia, quality of life and anxiety. Pain was assessed using a Visual Analog Scale of pain (VAS) ( 24 ). VAS was widely used to evaluate rheumatic diseases and chronic pain syndromes in clinical studies ( 24 ). This is a self-completion test in which the participant is asked to place a line perpendicular to the VAS line that represents the level of pain. The method involves measuring the distance (mm) on a 10-cm line between the "no pain" to “worst pain imaginable ( 24 ). Cut points were described as no pain (0–4 mm), mild pain (5–44 mm), moderate pain (45–74 mm), and severe pain (75– 100 mm). Catastrophizing was evaluated using the Pain Catastrophizing Scale (PCS) that consists of 13 items divided into three domains: helplessness, magnification, and rumination ( 25 ). The total score on the assessment ranges from 0 to 52 points, with higher scores indicating a greater tendency towards catastrophic thinking ( 25 ). The Brazilian-Portuguese version of the Tampa Scale for Kinesiophobia (TSK) was used ( 26 ). This is a questionnaire with 17 questions that evaluate the fear of movement and score for each item ranges from 1 to 4 (where: strongly disagree = 1; somewhat disagree = 2; somewhat agree = 3; and strongly agree = 4). The total score of TSK ranges from 17 to 68 points, with a higher score indicating a higher level of kinesiophobia ( 26 ). Kinesiophobia was classified as mild (17 to 34), moderate (35 to 50) and severe (51 to 68) ( 27 ). Impact of fibromyalgia was evaluated using a Fibromyalgia Impact Questionnaire (FIQ) ( 28 ). FIQ investigates the functional limitations of daily activities according to physical impairment, feeling good, work missed, doing work, pain, fatigue, rest, stiffness, anxiety, and depression ( 29 ). The score of FIQ ranges from 0 to 100 and higher values indicate low physical function and great impact of the syndrome. FIQ could be classified as mild (0 to < 39), moderate (39 to < 59) and severe (59 to 100) ( 30 ). The overall health status was assessed using the Brazilian version of the Short Form 36 Health Survey (SF-36), which consists of 36 items divided into eight domains: functional capacity (10 items), physical aspects (4 items), pain (2 items), general health perception (5 items), vitality (4 items), social functioning (2 items), emotional aspects (3 items), and mental health (5 items), along with one additional item that evaluates the comparison between current health conditions and those of one year ago ( 31 ). The results range from 0 to 100, with 0 indicating the poorest overall health status and 100 representing the best ( 31 ). Anxiety was assessed using Hamilton Anxiety Rating Scale (HAM-A) scores. HAM-A is a 14-item questionnaire where each item is scored on a scale of 0 (not present) to 4 (severe). Higher scores indicating greater anxiety symptom severity, where mild anxiety = 8–14; moderate = 15–23; and severe ≥ 24 (scores ≤ 7 were considered to represent no/minimal anxiety) ( 32 ). Plasma samples A total of 10 mL of peripheral blood was collected from the non-dominant upper limb using a Vacutainer™ BD tube with EDTA. The blood was then processed and frozen at -15°C for subsequent analysis using an infrared spectroscopic instrument. The collection and storage of the samples until their analysis were performed at the Clinical and Epidemiological Research Laboratory, located at university hospital. The researchers involved in the study are committed to the proper storage, handling, and disposal of the samples at the end of the study, ensuring that the samples will not be used for any purpose other than that explicitly stated in the informed consent form and the project methodology. These actions are in accordance with the definitions established in Resolution CNS No. 441/2011. During the blood collection procedure, the participants were instructed to have a free diet, a restful night of sleep, and to arrive at the collection site at the scheduled time. Spectrochemical analysis The samples were stored at − 15°C before spectrochemical analysis. Measurements were performed at the Institute of Chemistry of the Federal University of Rio Grande do Norte, Natal, Brazil. A Bruker Vertex 70 FTIR spectrometer (Bruker, Coventry, UK) coupled to an ATR Helios attachment was used for spectral acquisition. Spectra were acquired with 32 scans (4cm − 1 resolution) and in triplicate for each sample. Before every new sample the ATR crystal was cleaned, and a new background was set in order to account for ambient variability. The blood plasma samples were measured in the liquid state. Data analysis The spectral data were processed using the MATLAB R2014b software (MathWorks Inc., Natick, USA) with the PLS Toolbox version 7.8 (Eigenvector Research Inc., Wenatchee, USA) and lab-made routines. The spectral data were initially cropped to the bio-fingerprint region (900–1,800 cm − 1 ) and pre-processed by automatic weighted least squares baseline correction and vector normalization. The spectral samples are divided into training (70%), validation (15%) and test (15%) sets using the Kennard-Stone uniform sample selection algorithm ( 33 ). The training set is used for model construction, the validation set for model internal validation and optimization, and the test set for evaluating the model predictive performance towards external samples through the calculation of figures of merit (accuracy, sensitivity and specificity). Several algorithms of feature extraction and selection coupled to discriminant analysis techniques were tested on the spectral data; these were: principal component analysis linear discriminant analysis (PCA-LDA), principal component analysis quadratic discriminant analysis (PCA-QDA), principal component analysis support vector machines (PCA-SVM), successive projections algorithm linear discriminant analysis (SPA-LDA), successive projections algorithm quadratic discriminant analysis (SPA-QDA), successive projections algorithm support vector machines (SPA-SVM), genetic algorithm linear discriminant analysis (GA-LDA), genetic algorithm quadratic discriminant analysis (GA-QDA), and genetic algorithm support vector machines (GA-SVM). PCA decomposes the pre-processed spectral data into a small number of principal components (PCs) that are orthogonal to each other and explain most of the original data variance. Each PC is composed of scores, representing the variance on sample direction, hence, being used to assess similarities/dissimilarities between the samples; and loadings, representing the variance on wavenumber direction, thus being used to assess variable importance. Therefore, PCA can be used for data reduction, feature extraction, pattern recognition, sample selection, exploratory analysis, among others ( 34 ). Successive projections algorithm (SPA) and genetic algorithm (GA) are forward feature selection algorithms that select sets of wavenumbers responsible for maximizing class differences. SPA is a forward feature selection method that works by minimizing the data multicollinearity through a series of projections of the original wavenumbers in an iterative way ( 35 ). GA is another iterative method that works based on the principle of natural evolution where a set of wavenumbers (chromosomes) undergo an evolution-like model of combinations, crossovers and mutations until the best set of wavenumbers achieve the best fitness according to a predetermined cost-function that maximizes class differences ( 36 ). The outputs from PCA (scores), SPA and GA can be used as input variables for discriminant analysis. LDA and QDA are discriminant analysis techniques based on a Mahalanobis distance calculation between the samples, where the LDA ( \({L}_{ik}\) ) and QDA ( \({Q}_{ik}\) ) classification scores are calculated as follows ( 37 ): $${L}_{ik}=({x}_{i}- \underset{\_}{{x}_{k}}{)}^{T} {C}_{pooled}^{-1}\left({x}_{i}- \underset{\_}{{x}_{k}}\right)-2lo{g}_{e}{\pi }_{k}$$ $${Q}_{ik}=({x}_{i}- \underset{\_}{{x}_{k}}{)}^{T} {C}_{k}^{-1}\left({x}_{i}- \underset{\_}{{x}_{k}}\right)+lo{g}_{e}\left|{C}_{k}\right|-2lo{g}_{e}{\pi }_{k}$$ where \({x}_{i}\) is vector containing the input variables for sample i ; \({x}_{k}\) is the mean vector of class k ; Cpooled is the pooled covariance matrix; Ck is pooled variance–covariance matrix of class k ; and \({\pi }_{k}\) is the prior probability of class k . The SVM classification takes the form ( 37 ): $$f\left(x\right)= sign \left({\sum }_{i=1}^{{N}_{SV}}{\alpha }_{í}{y}_{i}K\left({x}_{i}, {z}_{j}\right)+b\right)$$ K ( \({x}_{i}, {z}_{j}\) ) is the kernel function for \({x}_{i},\) and \({z}_{j}\) which are input variables for different classes; \({\alpha }_{í}\) is the Lagrange multiplier; \({y}_{i}\) is the training class membership; and b is the bias parameter. Declarations Author contributions J.O.S.P., AFC and SM collected clinical data and wrote the manuscript. M.V.S.A. performed the chemometric analyses, conceptualization and wrote the manuscript. K.M.G.L. conceptualization, planning and wrote of the manuscript. R.P. conceptualization, planning and wrote the manuscript. Data availability The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Additional information Correspondenceand requests for materials should be addressed to R.P. Competing interests There is no competing interest regarding this study. Funding The funding was provided by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (305562/2020-7). Competing interests The authors declare no competing interests. References Giorgi V, Sirotti S, Romano ME, Marotto D, Ablin JN, Salaffi F, et al. Fibromyalgia: one year in review 2022. Clin Exp Rheumatol. 40(6):1065–1072 (2022). Boquete, L. et al. Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence. Int J Clin Health Psychol. 22(2):100294 (2022). Sarzi-Puttini, P., Giorgi, V., Marotto, D., Atzeni, F. Fibromyalgia: an update on clinical characteristics, aetiopathogenesis and treatment. Nat Rev Rheumatol. 16(11):645–60 (2020). Clauw, D.J., Arnold, L.M., McCarberg, B.H. The Science of Fibromyalgia. Mayo Clin Proc . 86(9):907–11 (2011). Häuser, W., Sarzi-Puttini, P., Fitzcharles, M.A. Fibromyalgia syndrome: under-, over- and misdiagnosis. 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PLoS ONE. 7(5):3–8 (2012). Malatji, B.G. et al. A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls. BMC Neurology. 17(1):1–15 (2017). Hackshaw, K. V., Rodriguez-Saona, L., Plans, M., Bell, L.N., Buffington, C.A.T. A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders. Analyst. 138(16):4453–62 (2013). Santos, M.C.D., et al. ATR-FTIR spectroscopy with chemometric algorithms of multivariate classification in the discrimination between healthy: Vs. dengue vs. chikungunya vs. zika clinical samples. Anal Methods. 10(10):1280–5 (2018). Siqueira, L.F.S., Lima, K.M.G. MIR-biospectroscopy coupled with chemometrics in cancer studies. Analyst. 141(16):4833–47 (2016). Passos, J.O.S. et al. Spectrochemical analysis in blood plasma combined with subsequent chemometrics for fibromyalgia detection. Sci Rep. 10(1):1–8. (2020). Marques, A. P., Santo, A. S. D. E., Berssaneti, A. A., Matsutani, L. A., & Yuan, S. L. K. Prevalence of fibromyalgia: literature review update. Rev Bras Reumatol. 57 (4), 356–363 (2017). Hawker, G.A., Mian, S., Kendzerska, T., French, M. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res. 63 Suppl 11:S240-S252 (2011). Sehn. F., et al. Cross-Cultural Adaptation and Validation of the Brazilian Portuguese Version of the Pain Catastrophizing Scale. Pain Med. 13(11):1425–35 (2012). Siqueira, F.B., Teixeira-Salmela, L.F., Magalhães, L. de C. Análise das propriedades psicométricas da versão brasileira da escala tampa de cinesiofobia. Acta Ortop Bras. 15(1):19–24 (2007). Trocoli, T.O., Botelho, R. V. Prevalência de ansiedade, depressão e cinesiofobia em pacientes com lombalgia e sua associação com os sintomas da lombalgia. Rev Bras Reumatol. 56(4):330–6 (2016). Paiva, E.S. et al. A Brazilian Portuguese version of the Revised Fibromyalgia Impact Questionnaire (FIQR): A validation study. Clin Rheumatol. 32(8):1199–206 (2013). Schaefer, C. et al. The comparative burden of mild, moderate and severe Fibromyalgia: Results from a cross-sectional survey in the United States. Health Qual Life Outcomes. 9(1):71 (2011). Bennett, R.M., Bushmakin, A.G., Cappelleri, J.C., Zlateva, G., Sadosky, A.B. Minimal clinically important difference in the fibromyalgia impact questionnaire. J Rheumatol. 36(6):1304–11 (2009). Ciconelli, R.M., Ferraz, M.B., Santos, W., Meinao, I., Quaresma, M.R. Brazilian-Portuguese version of the SF-36. A reliable and valid quality of life outcome measure. Rev Bras Reumatol. 39(3):143–50 (1999). Matza, L.S., Morlock, R., Sexton, C., Malley, K., Feltner, D. Identifying HAM-A cutoffs for mild, moderate, and severe generalized anxiety disorder. Int J Methods Psychiatr Res.19(4):223–32 (2010). Kennard, R. W. & Stone, L. A. Computer aided design of experiments. Technometrics.11, 137–148 (1969). Bro, R. & Smilde, A. K. Principal component analysis. Anal. Methods. 6,2812–2831. (2014). Soares, S. F. C., Gomes, A. A., Araujo, M. C. U., Galvão Filho, A. R. & Galvão, R. K. H. The successive projections algorithm. Trends Anal. Chem. 42, 84–98 (2013). McCall, J. Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math.184, 205–222 (2005). Morais, C.L.M., Costa, F.S.L., Lima, K.M.G. Variable selection with a support vector machine for discriminating: Cryptococcus fungal species based on ATR-FTIR spectroscopy. Analytical Methods . 28;9(20):2964–70 (2017). Morais, C.L.M. & Lima, K.M.G. Principal component analysis with linear and quadratic discriminant analysis for identification of cancer samples based on mass spectrometry. J Braz Chem Soc. 29(3):472–81 (2018). Nuguri, S.M. el al. Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling. Molecules . 29(2):413 (2024). da Silva, T.G. et al. Spectrochemical analysis of blood combined with chemometric techniques for detecting osteosarcopenia. Sci Rep. 13(1):9686 (2023). Siqueira, L. F. S. & Lima, K. M. G. MIR-biospectroscopy coupled with chemometrics in cancer studies. Analyst. 141, 4833–4847 (2016). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Fibromyalgia is the second most common rheumatic disorder with 2%-5% of the adult population in America and a mean estimated global prevalence of 2.7% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This is a chronic debilitating disorder with devastating effects in functional, emotional, and social aspects (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Patients commonly relate persistent complex polysymptomatology that include widespread pain (with allodynia and hyperalgesia), chronic fatigue, stiffness, sleep disturbances, depression, anxiety, elevated negative affectivity and in some cases, cognitive dysfunction (fibro-fog) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This clinical condition reduces the quality of life, physical function, and work productivity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis complex and heterogeneous condition makes the diagnostic a challenger for healthcare professionals and could be considered underdiagnosed (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). According to a patient survey, the diagnosis of fibromyalgia is a journey passing through primary care physicians (rheumatologists, neurologists, psychiatrists, and pain specialists) to highly specialized support care including hospitalization and image exams (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It is necessary an average of 2.3 years and presenting to 3.7 different physicians before receiving a diagnosis of fibromyalgia (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, several biochemical and imaging exams are routinely requested to exclude other possible neurological, orthopaedic, and rheumatologic diseases (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe American College of Rheumatology (ACR) presented the first approved classification criterion for diagnostic of fibromyalgia in 1990 that was modified in 2010/2011 by Wolf and colleagues (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Reanalyzes of the 2010/2011 criteria showed mean and median unweighted sensitivities of 84% and 84% and specificities of 83% and 87% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Alternative diagnostic criteria of fibromyalgia was proposed with a modified 2010/2011 criteria (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These alternative criteria had a diagnostic sensitivity of 81%, a specificity of 80%, and a correct classification of 80% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Even after thirty-three years of the first guideline, fibromyalgia remains misdiagnosed, and some investigators call the criteria into question (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Primarily because the cause of FM is unknown and others pain or musculoskeletal syndromes could mask the diagnoses (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Furthermore, the diversity of symptoms is commonly found throughout several rheumatic and nonrheumatic diseases (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Underdiagnosis or misdiagnosis of fibromyalgia still occurs for any reasons including the variability of symptoms severity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). New strategies are needed to improve diagnosis of fibromyalgia in a fast, low-cost, accurate and less invasive manner. Establishing new protocols for diagnosis of fibromyalgia could provide substantial relief for patients and additional security for clinicians (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diagnosis of fibromyalgia is therefore completely clinical, due to non-specific imaging exam, electrophysiological or biomarkers findings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Recently, studies focused on finding a biomarker for fibromyalgia using metabolomics, spectroscopy, neuroretinal evaluation and artificial intelligence (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Some biomarkers could therefore contribute to the disease phenotype and could be involved in the etiology or physiological mechanism of fibromyalgia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). For a relevant finding, it is necessary that a certain biomarker has a direct association with the clinical symptom\u0026rsquo;s severity in fibromyalgia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Studies suggest using mid-infrared microspectroscopy (IRMS) and metabolomics to differentiate patients with fibromyalgia from those with osteoarthritis (OA) and rheumatoid arthritis (RA) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It was possible to find differences in metabolomic analysis in fibromyalgia and a relationship between infrared spectral patterns and disease activity assessed using pain score and self-reported symptom (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It is suggested an association or possible prediction of fibromyalgia according to IRMS and metabolomics approaches also (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpectroscopy has been increasingly recognized as a powerful technology in biomedical and microbiology (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Created by physicists and chemists, it has traditionally been used to identify molecular entities present in unknown substances (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Currently, it is used to determine the relative amounts of biochemical components present in cells and tissues, leading to biospectroscopy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The biospectroscopy provides information related to the molecular property of the analyzed material with a relatively fast and easy to use (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This method is a non-destructive technique that requires a small number of samples (nano-microgram) as blood plasma (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Results of previous studies suggest that biospectroscopy could be used as a minimally invasive method to distinguish patients with Alzheimer\u0026rsquo;s disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), dengue, Zika, chikungunya (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and some types of cancer (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, Passos et al. used Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques and showed 84.2% accuracy, 89.5% sensitivity and 79.0% specificity to detect fibromyalgia (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). When compared with previous ACR classification criteria, these metrics demonstrate a satisfactory rate for distinguishing fibromyalgia and health controls. Therefore, a secondary analysis is now proposed aiming to explore the possible association between spectrochemical analysis coupled to chemometric techniques with fibromyalgia symptoms of pain, disease severity, functional ability, kinesiophobia, catastrophizing, anxiety, and quality of life. Presenting the relationships between biospectroscopy and the clinical findings of fibromyalgia enhances this technique as a valid tool to detect the disease.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 246 participants completed all study procedures. The datasets were submitted to multivariate analysis to distinguish between the control group and fibromyalgia group. The classifications were performed with Principal Component Analysis (PCA), the Successive Projections Algorithm (SPA), and the Genetic Algorithm (GA), associated with supervised Linear Discriminant Analysis (LDA), thus three combinations of algorithms: PCA-LDA, SPA-LDA, GA-LDA seeking to identify the combination with the best performance for each dataset. The samples for each symptom were allocated across different levels that aligned with the stratification of the clinical variable (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This allocation was based on the responses provided by patients on scales and questionnaires administered at the time of blood collection.\u003c/p\u003e \u003cp\u003eBefore model construction, 70% of samples were assigned to the training set, 15% to the validation set, and 15% to the lest set using the Kennard-Stone uniform sampling algorithm (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The training set was used for model construction, the validation set for internal model optimization, and the test set for final model evaluation, where figures of merit (accuracy, sensitivity and specificity) reflecting the model performance towards external sample were calculated.\u003c/p\u003e \u003cp\u003eConcerning the efficacy results in group classification, sensitivity and specificity values were employed for both the control and fibromyalgia groups within each dataset. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the optimal outcomes achieved for each symptom level or cumulative level. According to multivariate analyzes it was possible to identify the huge association between fibromyalgia symptoms with spectral data.\u003c/p\u003e \u003cp\u003eThe severe level had indices of 100% accuracy classification in the control group and fibromyalgia of the clinical variable VAS (PCA/SPA/GA-LDA pp), 100% in the control group for TSK (GA-LDA), 100% in the control group and fibromyalgia for PCS, 73,4 and 77.8% (GA-LDA pp) of accuracy in the control group and fibromyalgia respectively. Severe symptoms of anxiety (HAS) showed an accuracy of 55.1 and 76.9 (GA-LDA) in the control group and fibromyalgia respectively. The moderate level of the kinesiophobia symptom also showed good distinction between the groups, with an accuracy of 84.6% in the fibromyalgia (PCA-LDA). For anxiety, moderate levels showed 100% accuracy classification in the fibromyalgia group (PCA-LDA pp). The mild level was present in the VAS TSK, PCS, SF-36 and HAS. Best results could be noted with 100% (SPA-LDA) of accuracy for TSK, 83.3% (PCA-LDA pp) for PCS, 93.2 (GA-LDA) for SF-36, all for the fibromyalgia group. Models with the best performance in each data set, followed by the respective sensitivity (Sens.), specificity (Spec.) and accuracy values are available in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and clinical characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibromyalgia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.12\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.03\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.39\u0026thinsp;\u0026plusmn;\u0026thinsp;20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.6\u0026thinsp;\u0026plusmn;\u0026thinsp;44.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety (HAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.05\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome* (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 Minimum Wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 3 Minimum Wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 Minimum Wage or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnreported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary (incomplete)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSD\u0026thinsp;=\u0026thinsp;Standard Deviation; VAS\u0026thinsp;=\u0026thinsp;Visual Analogue Scale; TSK\u0026thinsp;=\u0026thinsp;Tampa Scale for Kinesiophobia; PCS\u0026thinsp;=\u0026thinsp;Pain Catastrophizing Scale; FIQ\u0026thinsp;=\u0026thinsp;Fibromyalgia Impact Questionnaire; SF-36\u0026thinsp;=\u0026thinsp;Short Form 36 Health Survey; HAS\u0026thinsp;=\u0026thinsp;Hamilton Anxiety Scale. Numeric data were calculated using unpaired t test. Categorical data were calculated using the Chi-Square test. *Brazilian National Minimum Wage, US\u003cspan\u003e$\u003c/span\u003e 252.14 per month.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity (Sens.), specificity (Spec.) and accuracy values of best performance model in each data set for control group (G1) and fibromyalgia (G2).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSymptom level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBest model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSens. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSpec. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA/SPA/GA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSF-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA-LDA pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGA-LDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eG1\u0026thinsp;=\u0026thinsp;Control group; G2: Fibromyalgia group; VAS\u0026thinsp;=\u0026thinsp;Visual Analogue Scale; TSK\u0026thinsp;=\u0026thinsp;Tampa Scale for Kinesiophobia; PCS\u0026thinsp;=\u0026thinsp;Pain Catastrophizing Scale; FIQ\u0026thinsp;=\u0026thinsp;Fibromyalgia Impact Questionnaire; SF-36\u0026thinsp;=\u0026thinsp;Short Form 36 Health Survey; HAS\u0026thinsp;=\u0026thinsp;Hamilton Anxiety Scale.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFibromyalgia diagnosis continues to be a difficult clinical decision for physicians and new technologies could help to enhance both the accuracy and efficiency of diagnosis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This study is characterized by case-control classifications that use ATR-FTIR spectroscopy with chemometric techniques, revealing significant correlations with clinical characteristics. Samples were organized based on the characteristic symptoms of pain, kinesiophobia, catastrophizing, disease impact, quality of life and anxiety. Each symptom was assessed by distributing the samples across levels corresponding to the stratification of the clinical variable, determined by the responses provided. PCA-LDA was the best model to associate with clinical outcomes.\u003c/p\u003e \u003cp\u003eAll the outcomes examined constitute classical findings in patients with fibromyalgia (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Pain aspects were assessed by level of pain (VAS), kinesiophobia and catastrophizing and have shown huge association with the chemometric classification. Important to note that FIQ is a specific questionnaire for fibromyalgia and usually used to quantify the impact of the disease in daily life (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, in this study VAS, TSK, and PCS showed better association with the chemometric classification. This finding may be attributed to pain being the primary symptom associated with fibromyalgia, with elevated levels potentially indicating increased accuracy in diagnoses (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Despite a significant difference in the mean FIQ scores between groups (75.03\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97 for fibromyalgia and 27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.35 for control), this outcome demonstrated a moderate association with the best chemometric classification model (GA-LDA pp). FIQ indirectly measure functional capacity and includes items about pain, physical impairment, ability to work, restfulness, and mood (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This is largely used in fibromyalgia studies with prior analyses indicating that a 14% alteration in the FIQ total score holds clinical significance (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Bennett et al. proposed a severity categorization based on the endpoint for FIQ, which served as the basis for the analysis in this study. Our finds suggest that pain aspects could be more suitable for associations with the models of chemometric classification. In this way, a classic clinical symptom of fibromyalgia, widely used for diagnosis, exhibited a stronger association than the disease-specific questionnaire with the spectrum of problems related to fibromyalgia.\u003c/p\u003e \u003cp\u003eQuality of life (SF-36) showed a huge association with chemometric classification for fibromyalgia group, but not for controls. It is expected that control group exhibit good levels of quality of life when compared with a clinical population. Thus, the chemometric model seems to distinguish and shows better association for high levels of each outcome assessed. HAS showed mixed results with huge association for mild anxiety for control group and for moderate anxiety for fibromyalgia group.\u003c/p\u003e \u003cp\u003eFor the analysis of the chemometric models, it was possible to find important associations between clinical outcomes and spectroscopy analyses. This is an interesting finding suggesting that it is possible to use ATR-FTIR spectroscopy to detect fibromyalgia and associate it with the symptoms. Several other outcomes that include depression, affectivity, cognitive problems, fatigue, sleep disturbance and gastrointestinal symptoms could be included for future analyzes to obtain more spectroscopy analysis associations.\u003c/p\u003e \u003cp\u003eAuthors previously published an article regarding the use of ATR-FTIR spectroscopy in conjunction with chemometric techniques to detect fibromyalgia with an 84.2% accuracy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Now it is possible to establish an association between clinical parameters and ATR-FTIR findings. These associations enhance the efficacy of this technique and imply a robust correlation with clinical implications. By utilizing this clinical aspect, the model behaves as an interesting tool for fibromyalgia screening using ATR-FTIR combined with chemometrics (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Blood biomarkers indicative of disease constitute a developing field with great promise for musculoskeletal disorders, neurology, and oncology (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). A significant number of new molecular tests for blood, saliva, and urine have emerged, demonstrating satisfactory results with low cost, simple collection procedures, and non-destructive material (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Identifying distinct biological markers for each clinical condition is feasible, and establishing their association with symptoms is essential for enhancing screening methodologies (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNuguri et al. used an OPLS-DA algorithm to differentiate fibromyalgia and others rheumatic condition and found an 84% of accuracy (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Very nearly to 84,2% showed by Passos et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Nuguri et al. not only presented the accuracy, but also distinguished it from other rheumatic diseases that includes rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain (\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Near-infrared spectroscopy was also used to detect Alzheimer's disease (AD) with 92.8% accuracy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), dengue and chikungunya with 100% accuracy and 90% accuracy for Zika (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The same group showed an 89% accuracy to distinguish the samples from patients with osteosarcopenia (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Chemometric algorithms are evidenced as a powerful technique for cancer diagnosis and classification (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), but not previously study have shown the association between spectrochemical analysis combined with chemometric techniques and clinical outcomes.\u003c/p\u003e \u003cp\u003eThe potential applications of this technology in health science will help with screening, diagnosis, and surveillance through classification for different clinical conditions. In this study, we first suggested an association between spectrochemical analysis and clinical outcomes. Future studies and secondary analyses regarding other populations and clinical conditions could reveal these associations, predicting respondents to treatments and disease progression. New robust studies are necessary to improve the accuracy of algorithm and the associations of clinical findings in fibromyalgia.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study evaluated the association of ATR-FTIR spectroscopy analysis of blood combined with chemometrics with clinical symptoms of fibromyalgia. Pain aspects, as assessed by VAS, kinesiophobia, and catastrophizing, emerged as the most significant clinical outcomes linked to the model. All clinical variables assessed showed moderate to strong association with algorithms models.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eThis case-control study recruited a total 126 control subjects (G1) and 126 patients (G2) meeting the American College of Rheumatology criteria for diagnosis of fibromyalgia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Based on a fibromyalgia (FM) incidence rate of 6.6% in the Brazilian population (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), a significance level of 0.5, and a study power of 0.8, a sample size of 252 patients was calculated. Participants were recruited from social media and at the medical clinic of the Onofre Lopes University Hospital (HUOL) from July 2018 to March 2019.\u003c/p\u003e \u003cp\u003eThe following inclusion criteria were adopted: (a) medical diagnosis of fibromyalgia according to the ACR/2010; (b) ability to answer questionnaire and understand this study aim; (c) patients not undergoing physical therapy or rehabilitation programs during the three previous months; and (d) age ranging from 18 to 80 years old. The exclusion criteria were: (a) physical and/or organic problems, when these compromised questionnaire applications; and (b) rheumatic and/or autoimmune diseases including chronic fatigue syndrome, rheumatoid arthritis, gout, and lupus.\u003c/p\u003e \u003cp\u003eThe study was performed following the ethics standards of the Declaration of Helsinki. The protocol and consent form were approved by the Human Research Committee of the HUOL (Federal University of Rio Grande do Norte, Natal, Brazil) under registration number 2.631.168. All participants provided written informed consent prior to the beginning of the study. The study was conducted at the Clinical and Epidemiological Laboratory at the HUOL and at the Chemistry Institute of University of Rio Grande do Norte.\u003c/p\u003e \u003cp\u003eClinical variables\u003c/p\u003e \u003cp\u003eAll study participants underwent a clinical assessment, and on the same day, 10 mL of blood was collected from each participant along with the completion of questionnaires (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sociodemographic variables of sex (female or male), age (years), weight (kilograms), height (meters), educational level (illiterate, primary school, secondary school, tertiary education), profession, marital status (single, married, divorced, widowed, or preferred not to respond), and race/ethnicity (white, yellow, black, mixed race, indigenous, unknown, preferred not to respond) were analyzed using a multiple-choice questionnaire developed by the research team. Clinical outcomes were assessed in terms of pain, pain catastrophizing, kinesiophobia, impact of fibromyalgia, quality of life and anxiety.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePain was assessed using a Visual Analog Scale of pain (VAS) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). VAS was widely used to evaluate rheumatic diseases and chronic pain syndromes in clinical studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This is a self-completion test in which the participant is asked to place a line perpendicular to the VAS line that represents the level of pain. The method involves measuring the distance (mm) on a 10-cm line between the \"no pain\" to \u0026ldquo;worst pain imaginable (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Cut points were described as no pain (0\u0026ndash;4 mm), mild pain (5\u0026ndash;44 mm), moderate pain (45\u0026ndash;74 mm), and severe pain (75\u0026ndash; 100 mm).\u003c/p\u003e \u003cp\u003eCatastrophizing was evaluated using the Pain Catastrophizing Scale (PCS) that consists of 13 items divided into three domains: helplessness, magnification, and rumination (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The total score on the assessment ranges from 0 to 52 points, with higher scores indicating a greater tendency towards catastrophic thinking (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Brazilian-Portuguese version of the Tampa Scale for Kinesiophobia (TSK) was used (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This is a questionnaire with 17 questions that evaluate the fear of movement and score for each item ranges from 1 to 4 (where: strongly disagree\u0026thinsp;=\u0026thinsp;1; somewhat disagree\u0026thinsp;=\u0026thinsp;2; somewhat agree\u0026thinsp;=\u0026thinsp;3; and strongly agree\u0026thinsp;=\u0026thinsp;4). The total score of TSK ranges from 17 to 68 points, with a higher score indicating a higher level of kinesiophobia (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Kinesiophobia was classified as mild (17 to 34), moderate (35 to 50) and severe (51 to 68) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImpact of fibromyalgia was evaluated using a Fibromyalgia Impact Questionnaire (FIQ) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). FIQ investigates the functional limitations of daily activities according to physical impairment, feeling good, work missed, doing work, pain, fatigue, rest, stiffness, anxiety, and depression (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The score of FIQ ranges from 0 to 100 and higher values indicate low physical function and great impact of the syndrome. FIQ could be classified as mild (0 to \u0026lt;\u0026thinsp;39), moderate (39 to \u0026lt;\u0026thinsp;59) and severe (59 to 100) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall health status was assessed using the Brazilian version of the Short Form 36 Health Survey (SF-36), which consists of 36 items divided into eight domains: functional capacity (10 items), physical aspects (4 items), pain (2 items), general health perception (5 items), vitality (4 items), social functioning (2 items), emotional aspects (3 items), and mental health (5 items), along with one additional item that evaluates the comparison between current health conditions and those of one year ago (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The results range from 0 to 100, with 0 indicating the poorest overall health status and 100 representing the best (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnxiety was assessed using Hamilton Anxiety Rating Scale (HAM-A) scores. HAM-A is a 14-item questionnaire where each item is scored on a scale of 0 (not present) to 4 (severe). Higher scores indicating greater anxiety symptom severity, where mild anxiety\u0026thinsp;=\u0026thinsp;8\u0026ndash;14; moderate\u0026thinsp;=\u0026thinsp;15\u0026ndash;23; and severe\u0026thinsp;\u0026ge;\u0026thinsp;24 (scores\u0026thinsp;\u0026le;\u0026thinsp;7 were considered to represent no/minimal anxiety) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlasma samples\u003c/p\u003e \u003cp\u003eA total of 10 mL of peripheral blood was collected from the non-dominant upper limb using a Vacutainer\u0026trade; BD tube with EDTA. The blood was then processed and frozen at -15\u0026deg;C for subsequent analysis using an infrared spectroscopic instrument. The collection and storage of the samples until their analysis were performed at the Clinical and Epidemiological Research Laboratory, located at university hospital. The researchers involved in the study are committed to the proper storage, handling, and disposal of the samples at the end of the study, ensuring that the samples will not be used for any purpose other than that explicitly stated in the informed consent form and the project methodology. These actions are in accordance with the definitions established in Resolution CNS No. 441/2011. During the blood collection procedure, the participants were instructed to have a free diet, a restful night of sleep, and to arrive at the collection site at the scheduled time.\u003c/p\u003e \u003cp\u003eSpectrochemical analysis\u003c/p\u003e \u003cp\u003eThe samples were stored at \u0026minus;\u0026thinsp;15\u0026deg;C before spectrochemical analysis. Measurements were performed at the Institute of Chemistry of the Federal University of Rio Grande do Norte, Natal, Brazil. A Bruker Vertex 70 FTIR spectrometer (Bruker, Coventry, UK) coupled to an ATR Helios attachment was used for spectral acquisition. Spectra were acquired with 32 scans (4cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e resolution) and in triplicate for each sample. Before every new sample the ATR crystal was cleaned, and a new background was set in order to account for ambient variability. The blood plasma samples were measured in the liquid state.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe spectral data were processed using the MATLAB R2014b software (MathWorks Inc., Natick, USA) with the PLS Toolbox version 7.8 (Eigenvector Research Inc., Wenatchee, USA) and lab-made routines. The spectral data were initially cropped to the bio-fingerprint region (900\u0026ndash;1,800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and pre-processed by automatic weighted least squares baseline correction and vector normalization. The spectral samples are divided into training (70%), validation (15%) and test (15%) sets using the Kennard-Stone uniform sample selection algorithm (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The training set is used for model construction, the validation set for model internal validation and optimization, and the test set for evaluating the model predictive performance towards external samples through the calculation of figures of merit (accuracy, sensitivity and specificity). Several algorithms of feature extraction and selection coupled to discriminant analysis techniques were tested on the spectral data; these were: principal component analysis linear discriminant analysis (PCA-LDA), principal component analysis quadratic discriminant analysis (PCA-QDA), principal component analysis support vector machines (PCA-SVM), successive projections algorithm linear discriminant analysis (SPA-LDA), successive projections algorithm quadratic discriminant analysis (SPA-QDA), successive projections algorithm support vector machines (SPA-SVM), genetic algorithm linear discriminant analysis (GA-LDA), genetic algorithm quadratic discriminant analysis (GA-QDA), and genetic algorithm support vector machines (GA-SVM).\u003c/p\u003e \u003cp\u003ePCA decomposes the pre-processed spectral data into a small number of principal components (PCs) that are orthogonal to each other and explain most of the original data variance. Each PC is composed of scores, representing the variance on sample direction, hence, being used to assess similarities/dissimilarities between the samples; and loadings, representing the variance on wavenumber direction, thus being used to assess variable importance. Therefore, PCA can be used for data reduction, feature extraction, pattern recognition, sample selection, exploratory analysis, among others (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Successive projections algorithm (SPA) and genetic algorithm (GA) are forward feature selection algorithms that select sets of wavenumbers responsible for maximizing class differences. SPA is a forward feature selection method that works by minimizing the data multicollinearity through a series of projections of the original wavenumbers in an iterative way (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). GA is another iterative method that works based on the principle of natural evolution where a set of wavenumbers (chromosomes) undergo an evolution-like model of combinations, crossovers and mutations until the best set of wavenumbers achieve the best fitness according to a predetermined cost-function that maximizes class differences (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe outputs from PCA (scores), SPA and GA can be used as input variables for discriminant analysis. LDA and QDA are discriminant analysis techniques based on a Mahalanobis distance calculation between the samples, where the LDA (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{ik}\\)\u003c/span\u003e\u003c/span\u003e) and QDA (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}_{ik}\\)\u003c/span\u003e\u003c/span\u003e) classification scores are calculated as follows (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${L}_{ik}=({x}_{i}- \\underset{\\_}{{x}_{k}}{)}^{T} {C}_{pooled}^{-1}\\left({x}_{i}- \\underset{\\_}{{x}_{k}}\\right)-2lo{g}_{e}{\\pi }_{k}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${Q}_{ik}=({x}_{i}- \\underset{\\_}{{x}_{k}}{)}^{T} {C}_{k}^{-1}\\left({x}_{i}- \\underset{\\_}{{x}_{k}}\\right)+lo{g}_{e}\\left|{C}_{k}\\right|-2lo{g}_{e}{\\pi }_{k}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is vector containing the input variables for sample \u003cem\u003ei\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the mean vector of class \u003cem\u003ek\u003c/em\u003e; Cpooled is the pooled covariance matrix; Ck is pooled variance\u0026ndash;covariance matrix of class \u003cem\u003ek\u003c/em\u003e; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\pi }_{k}\\)\u003c/span\u003e\u003c/span\u003e is the prior probability of class \u003cem\u003ek\u003c/em\u003e. The SVM classification takes the form (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$f\\left(x\\right)= sign \\left({\\sum }_{i=1}^{{N}_{SV}}{\\alpha }_{\u0026iacute;}{y}_{i}K\\left({x}_{i}, {z}_{j}\\right)+b\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eK\u003c/em\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}, {z}_{j}\\)\u003c/span\u003e\u003c/span\u003e) is the kernel function for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i},\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({z}_{j}\\)\u003c/span\u003e\u003c/span\u003e which are input variables for different classes; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{\u0026iacute;}\\)\u003c/span\u003e\u003c/span\u003e is the Lagrange multiplier; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the training class membership; and \u003cem\u003eb\u003c/em\u003e is the bias parameter.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.O.S.P., AFC and SM collected clinical data and wrote the manuscript. M.V.S.A. performed the chemometric analyses, conceptualization and wrote the manuscript. K.M.G.L. conceptualization, planning and wrote of the manuscript. R.P. conceptualization, planning and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondenceand requests for materials should be addressed to R.P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interest regarding this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding was provided by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (305562/2020-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiorgi V, Sirotti S, Romano ME, Marotto D, Ablin JN, Salaffi F, et al. 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Analyst. 141, 4833\u0026ndash;4847 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Infrared spectroscopy, chemometrics, algorithm, chronic pain, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4165415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4165415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFibromyalgia typically involves pain, fatigue, and mood disruptions, often necessitating over two years and around four medical consultations for diagnosis. The combination of spectroscopy and chemometric techniques holds promise as a cost-effective and accurate strategy for screening fibromyalgia according to the association between the symptoms and spectral data. The study aimed to explore the association between spectrochemical analysis coupled to chemometric techniques with fibromyalgia symptoms. A total of 126 controls and 126 patients with fibromyalgia participated in the study. Blood plasma was analyzed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques for posterior association between pain, kinesiophobia, pain catastrophizing, impact of fibromyalgia, quality of life and anxiety. The datasets underwent multivariate classification using supervised models. Different chemometric algorithms were tested to classify the spectral data and the association between symptoms. A clear accuracy discrimination was observed to moderate and severe pain (82.1%; 100%); kinesiophobia (84.6%; 80.8%), catastrophizing (87.5%; 81.8%), impact of fibromyalgia (74.8%; 77.8%), anxiety (100%; 76.9%) and mild and regular quality of life (93.2%; 81.4%). The obtained favorable classification results validate the effectiveness of this technique as an analytical tool for fibromyalgia detection.\u003c/p\u003e","manuscriptTitle":"Association between fibromyalgia symptoms and Fourier-transform infrared (ATR-FTIR) spectroscopy analysis of blood combined with chemometrics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 17:20:51","doi":"10.21203/rs.3.rs-4165415/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81ac847d-4938-4919-85a4-dde2e9890504","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30330288,"name":"Health sciences/Rheumatology/Musculoskeletal system"},{"id":30330289,"name":"Physical sciences/Chemistry/Analytical chemistry/Infrared spectroscopy"}],"tags":[],"updatedAt":"2024-06-27T05:10:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 17:20:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4165415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4165415","identity":"rs-4165415","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}