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Blending Samples to Increase Accuracy and Precision of ¹H NMR Urine Metabolomics.

ABSTRACT: Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

SUBMITTER: Bay Nord A

PROVIDER: S-EPMC11325295 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Blending Samples to Increase Accuracy and Precision of <sup>1</sup>H NMR Urine Metabolomics.

Bay Nord Anders A Lindqvist Helen H Rådjursöga Millie M Winkvist Anna A Karlsson B Göran BG Malmodin Daniel D

Analytical chemistry 20240731 32

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use ...[more]

PMID: 39084612

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Project description:Objective: Millions of sport-related concussions (SRC) occur annually in North America, and current diagnosis of concussion is based largely on clinical evaluations. The objective of this study was to determine whether urinary metabolites are significantly altered post-SRC compared to pre-injury. Setting: Outpatient sports medicine clinic. Participants: Twenty-six male youth sport participants. Methods: Urine was analyzed pre-injury and after SRC by 1H NMR spectroscopy. Data were analyzed using multivariate statistics, pairwise t-test, and metabolic pathway analysis. Variable importance analysis based on random variable combination (VIAVC) was applied to the entire data set and resulted in a panel of 18 features. Partial least square discriminant analysis was performed exploring the separation between pre-injury and post-SRC groups. Pathway topography analysis was completed to identify biological pathway involvement. Spearman correlations provide support for the relationships between symptom burden and length of return to play and quantifiable metabolic changes in the human urinary metabolome. Results: Phenylalanine and 3-indoxysulfate were upregulated, while citrate, propylene glycol, 1-methylhistidine, 3-methylhistidine, anserine, and carnosine were downregulated following SRC. A receiver operator curve (ROC) tool constructed using the 18-feature classifier had an area under the curve (AUC) of 0.887. A pairwise t-test found an additional 19 altered features, 7 of which overlapped with the VIAVC analysis. Pathway topology analysis indicated that aminoacyl-tRNA biosynthesis and beta-alanine metabolism were the two pathways most significantly changed. There was a significant positive correlation between post-SRC 2-hydroxybutyrate and the length of return to play (ρ = 0.482, p = 0.02) as well as the number of symptoms and post-SRC lactose (ρ = 0.422, p = 0.036). Conclusion: We found that 1H NMR metabolomic urinary analysis can identify a set of metabolites that can correctly classify SRC with an accuracy of 81.6%, suggesting potential for a more objective method of characterizing SRC. Correlations to both the number of symptoms and length of return to play indicated that 2-hydroxybutyrate and lactose may have potential applications as biomarkers for sport-related concussion.

Dataset Information

Blending Samples to Increase Accuracy and Precision of ¹H NMR Urine Metabolomics.

Publications

Blending Samples to Increase Accuracy and Precision of <sup>1</sup>H NMR Urine Metabolomics.

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Dataset Information

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Publications

Blending Samples to Increase Accuracy and Precision of <sup>1</sup>H NMR Urine Metabolomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Blending Samples to Increase Accuracy and Precision of ¹H NMR Urine Metabolomics.