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Fingerprinting Acute Digestive Diseases by Untargeted NMR Based Metabolomics.

ABSTRACT: Precision medicine may significantly contribute to rapid disease diagnosis and targeted therapy, but relies on the availability of detailed, subject specific, clinical information. Proton nuclear magnetic resonance (¹H?NMR) spectroscopy of body fluids can extract individual metabolic fingerprints. Herein, we studied 64 patients admitted to the Florence main hospital emergency room with severe abdominal pain. A blood sample was drawn from each patient at admission, and the corresponding sera underwent ¹H?NMR metabolomics fingerprinting. Unsupervised Principal Component Analysis (PCA) analysis showed a significant discrimination between a group of patients with symptoms of upper abdominal pain and a second group consisting of patients with diffuse abdominal/intestinal pain. Prompted by this observation, supervised statistical analysis (Orthogonal Partial Least Squares?Discriminant Analysis (OPLS-DA)) showed a very good discrimination (>90%) between the two groups of symptoms. This is a surprising finding, given that neither of the two symptoms points directly to a specific disease among those studied here. Actually herein, upper abdominal pain may result from either symptomatic gallstones, cholecystitis, or pancreatitis, while diffuse abdominal/intestinal pain may result from either intestinal ischemia, strangulated obstruction, or mechanical obstruction. Although limited by the small number of samples from each of these six conditions, discrimination of these diseases was attempted. In the first symptom group, >70% discrimination accuracy was obtained among symptomatic gallstones, pancreatitis, and cholecystitis, while for the second symptom group >85% classification accuracy was obtained for intestinal ischemia, strangulated obstruction, and mechanical obstruction. No single metabolite stands up as a possible biomarker for any of these diseases, while the contribution of the whole ¹H?NMR serum fingerprint seems to be a promising candidate, to be confirmed on larger cohorts, as a first-line discriminator for these diseases.

SUBMITTER: Takis PG

PROVIDER: S-EPMC6274841 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Fingerprinting Acute Digestive Diseases by Untargeted NMR Based Metabolomics.

Takis Panteleimon G PG Taddei Antonio A Pini Riccardo R Grifoni Stefano S Tarantini Francesca F Bechi Paolo P Luchinat Claudio C

International journal of molecular sciences 20181023 11

Precision medicine may significantly contribute to rapid disease diagnosis and targeted therapy, but relies on the availability of detailed, subject specific, clinical information. Proton nuclear magnetic resonance (¹H⁻NMR) spectroscopy of body fluids can extract individual metabolic fingerprints. Herein, we studied 64 patients admitted to the Florence main hospital emergency room with severe abdominal pain. A blood sample was drawn from each patient at admission, and the corresponding sera unde ...[more]

PMID: 30360494

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Project description:INTRODUCTION:When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES:Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS:We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS:We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability,?~?85% of all peaks had an adequately low coefficient of variation (<?30%) in replicate samples. CONCLUSION:Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.

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Fingerprinting Acute Digestive Diseases by Untargeted NMR Based Metabolomics.

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Fingerprinting Acute Digestive Diseases by Untargeted NMR Based Metabolomics.

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