Urine metabolomics based prediction model approach for radiation exposure.
Animals
Biomarkers
/ urine
Gamma Rays
Logistic Models
Magnetic Resonance Spectroscopy
Male
Metabolic Networks and Pathways
Metabolome
Metabolomics
/ methods
Mice
Mice, Inbred C57BL
Models, Animal
Multivariate Analysis
Proof of Concept Study
ROC Curve
Radiation Exposure
/ analysis
Reproducibility of Results
Whole-Body Irradiation
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 09 2020
30 09 2020
Historique:
received:
06
01
2020
accepted:
13
08
2020
entrez:
1
10
2020
pubmed:
2
10
2020
medline:
5
1
2021
Statut:
epublish
Résumé
The radiological incidents and terrorism have demanded the need for the development of rapid, precise, and non-invasive technique for detection and quantification of exposed dose of radiation. Though radiation induced metabolic markers have been thoroughly investigated, but reproducibility still needs to be elucidated. The present study aims at assessing the reliability and reproducibility of markers using nuclear magnetic resonance (NMR) spectroscopy and further deriving a logistic regression model based on these markers. C57BL/6 male mice (8-10 weeks) whole body γ-irradiated and sham irradiated controls were used. Urine samples collected at 24 h post dose were investigated using high resolution NMR spectroscopy and the datasets were analyzed using multivariate analysis. Fifteen distinguishable metabolites and 3 metabolic pathways (TCA cycle, taurine and hypotaurine metabolism, primary bile acid biosynthesis) were found to be amended. ROC curve and logistic regression was used to establish a diagnostic model as Logit (p) = log (p/1 - p) = -0.498 + 13.771 (tau) - 3.412 (citrate) - 34.461 (α-KG) + 515.183 (fumarate) with a sensitivity and specificity of 1.00 and 0.964 respectively. The findings demonstrate the proof of concept and the potential of NMR based metabolomics to establish a prediction model that can be implemented as a promising mass screening tool during triage.
Identifiants
pubmed: 32999294
doi: 10.1038/s41598-020-72426-4
pii: 10.1038/s41598-020-72426-4
pmc: PMC7527994
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
16063Références
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