Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

Abdul-Hamid Emwas, Raja Roy, Ryan T McKay, Danielle Ryan, Lorraine Brennan, Leonardo Tenori, Claudio Luchinat, Xin Gao, Ana Carolina Zeri, G A Nagana Gowda, Daniel Raftery, Christoph Steinbeck, Reza M Salek, David S Wishart, Abdul-Hamid Emwas, Raja Roy, Ryan T McKay, Danielle Ryan, Lorraine Brennan, Leonardo Tenori, Claudio Luchinat, Xin Gao, Ana Carolina Zeri, G A Nagana Gowda, Daniel Raftery, Christoph Steinbeck, Reza M Salek, David S Wishart

Abstract

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Keywords: NMR; disease; metabolites; quantification; quantitative analysis; recommendations; standardization; urine.

Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Demonstration of the biomarker prediction test with two Gaussian curves indicating the distributions of measured values, with positive cases on the right side and negative cases on the left. The dashed lines indicate the cutoff threshold of hypothetical biomarker concentration that can be used to separate positive from negative tests. The overlap between the biomarker concentrations of the two populations represents the misclassification ratio between the left-hand side of the positive cases and the right-hand side of the negative cases. TP, the number of true positives; TN, the number of true negatives; FP, the number of false positives; FN, the number of false negatives, respectively.

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