CORRELATION BETWEEN BIOSPECTROSCOPIC DATA AND CLINICAL VARIABLES IN INDIVIDUALS WITH FIBROMYALGIA
Near-infrared spectroscopy, algorithm, chronic pain, biomarkers, fibromyalgia.
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 cat
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 costeffective and accurate strategy for screening fibromyalgia according to the correlation between the symptoms and spectral data. The study aimed to explore the correlation between spectroscopic analysis coupled with 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 correlation 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 correlation between symptoms. A clear accuracy discrimination for the fibromyalgia group was observed for moderate pain (82.1%) and severe pain (100%), moderate (84.6%) and severe (80.8%) kinesiophobia, moderate (87.5%) and severe (81.8%) catastrophizing, moderate (74.8%) and severe (77.8%) impact of fibromyalgia, and moderate (100%) and severe (76.9%) anxiety. Additionally, accuracy was 93.2% for the mild and 81.4% for the regular quality of life. ATR-FTIR spectroscopy integrated with chemometric algorithms suggested favorable classification performance for fibromyalgia and showed significant correspondence with core clinical characteristics. Future work should include external validation in independent cohorts, calibration analyses, and longitudinal designs to test responsiveness to therapeutic change and to establish robust operating thresholds for use in clinical and rehabilitation settings.
astrophizing, 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.