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J Proteome Res. 2017 Apr 7;16(4):1483-1491. doi: 10.1021/acs.jproteome.6b00860. Epub 2017 Mar 16.

Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study.

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Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, XaRTA, INSA, Campus Torribera, Faculty of Pharmacy and Food Science, University of Barcelona , Barcelona 08028, Spain.
CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III , Barcelona 08028, Spain.
Department of Biological Sciences and Department of Computing Science, University of Alberta , Edmonton AB T6G 2E9, Canada.
Department of Internal Medicine, Hospital Clinic, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS) , Barcelona 08036, Spain.
CIBER OBN, The Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition, Instituto de Salud Carlos III , Madrid 28029, Spain.
Department of Preventive Medicine and Public Health, University of Valencia , Valencia 46010, Spain.


Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.


NMR; ROC curve; biomarkers; choline metabolism; energy; legumes; metabolomics; pulses

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