Urinary Metabonome Differentiates Athletes and Labor Workers

doi:10.11938/cjmr20160304

Abstract

Abstract:

Under the concept of personal-based health care, different health management strategies are needed for different populations. To achieve this goal, the first step is to characterize the health-related differences among different populations. To this end, we recruited a total of 31 athletes and 42 labor workers to exam population-level differences in their urinary metabonome. First morning urine was collected and stored at -80℃ until use. ¹H NMR spectra of the urine samples were collected on a 600 MHz spectrometer. The data collected were then used to build supervised and unsupervised pattern recognition models (PCA model and OPLS-DA model) to differentiate the two populations. Metabolites contributing significantly to the population difference in urinary metabonome were identified by VIP plot, among which false positives were discovered by receiver operating characteristic curve (ROC) and t-test. Predictive PLS-DA model was built, and validated by internal cross-validation, permutation tests and external prediction. The results showed that a PLS-DA model built upon 20 discriminating metabolites had the best predictive accuracy (AUC = 0.998), and the most significant level (p = 3.34×10^-5). In addition, all samples from the external prediction set were classified correctly, suggesting that the PLS-DA model built upon 20 discriminating metabolites had high sensitivity and specificity.

Key words: nuclear magnetic resonance (NMR), metabonomics, pattern recognition, model verification

CLC Number:

CHEN Pu, YU Yan-bo, HUANG Jian-ying, LI Hong-yi, DONG Hai-sheng, CHEN Bin. Urinary Metabonome Differentiates Athletes and Labor Workers[J]. Chinese Journal of Magnetic Resonance, 2016, 33(3): 395-405.

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