BACKGROUNDS & AIMS: There is no accurate and reliable circulating biomarker to diagnose Crohn's disease (CD). Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD. METHODS: We analyzed by Raman spectroscopy dry plasma samples obtained from 77 CD patients (CD) and 45 healthy controls (HC). In the dataset obtained, we analyzed spectra differences between CD and HC, as well as among CD patients with different disease behavior. We also developed a method, based on Principal Component Analysis followed by a Linear Discrimination Analysis (PCA-LDA), for the automatic classification of individuals based on plasma spectra analysis. RESULTS: Compared to HC, the CD spectra were characterized by less intense peaks corresponding to carotenoids (p<10-4) and by more intense peaks corresponding to proteins with β-sheet secondary structure (p<10-4). Differences were also found on Raman peaks relative to lipids (p=0.0007) and aromatic amino acids (p<10-4). The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy (sensitivity 80.0% and specificity 85.7%) and F1-score of 86.8%. CONCLUSION: Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use prior to further specific evaluation.
BACKGROUNDS & AIMS: There is no accurate and reliable circulating biomarker to diagnose Crohn's disease (CD). Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD. METHODS: We analyzed by Raman spectroscopy dry plasma samples obtained from 77 CD patients (CD) and 45 healthy controls (HC). In the dataset obtained, we analyzed spectra differences between CD and HC, as well as among CD patients with different disease behavior. We also developed a method, based on Principal Component Analysis followed by a Linear Discrimination Analysis (PCA-LDA), for the automatic classification of individuals based on plasma spectra analysis. RESULTS: Compared to HC, the CD spectra were characterized by less intense peaks corresponding to carotenoids (p<10-4) and by more intense peaks corresponding to proteins with β-sheet secondary structure (p<10-4). Differences were also found on Raman peaks relative to lipids (p=0.0007) and aromatic amino acids (p<10-4). The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy (sensitivity 80.0% and specificity 85.7%) and F1-score of 86.8%. CONCLUSION: Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use prior to further specific evaluation.