Raman Analysis Reveals Biochemical Differences in Plasma of Crohn's Disease Patients.


Journal

Journal of Crohn's & colitis
ISSN: 1876-4479
Titre abrégé: J Crohns Colitis
Pays: England
ID NLM: 101318676

Informations de publication

Date de publication:
07 Nov 2020
Historique:
pubmed: 29 4 2020
medline: 7 9 2021
entrez: 29 4 2020
Statut: ppublish

Résumé

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. We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. 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. Compared with HC, the CD spectra were characterised 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%. 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 before further specific evaluation.

Sections du résumé

BACKGROUNDS AND AIMS OBJECTIVE
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 METHODS
We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. 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 RESULTS
Compared with HC, the CD spectra were characterised 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%.
CONCLUSIONS CONCLUSIONS
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 before further specific evaluation.

Identifiants

pubmed: 32343792
pii: 5826348
doi: 10.1093/ecco-jcc/jjaa080
doi:

Substances chimiques

Amino Acids, Aromatic 0
Biomarkers 0
Lipids 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1572-1580

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Carlo Morasso (C)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.

Marta Truffi (M)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.

Renzo Vanna (R)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.

Sara Albasini (S)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.

Serena Mazzucchelli (S)

Department of Biomedical and Clinical Sciences 'Luigi Sacco', Università degli studi di Milano, Milano, Italy.

Francesco Colombo (F)

ASST Fatebenefratelli Sacco Ospedale 'Luigi Sacco', Polo Universitario, Milano, Italy.

Luca Sorrentino (L)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.

Gianluca Sampietro (G)

ASST Fatebenefratelli Sacco Ospedale 'Luigi Sacco', Polo Universitario, Milano, Italy.

Sandro Ardizzone (S)

ASST Fatebenefratelli Sacco Ospedale 'Luigi Sacco', Polo Universitario, Milano, Italy.

Fabio Corsi (F)

Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
Department of Biomedical and Clinical Sciences 'Luigi Sacco', Università degli studi di Milano, Milano, Italy.

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Classifications MeSH