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Influence of acute phytochemical intake on human urinary metabolomic profiles^1,2,3

¹ From the Centre for Food and Health (MCW and MJG), the School of Biomolecular and Biomedical Sciences, Conway Institute (LB), and the School of Medicine and Medical Science, Conway Institute (AF and DGH), University College Dublin, Ireland, and the Unite de Nutrition Humaine, Institute National de la Recherche Agronomique, St-Genès-Champanelle, France (EPG, JLS, and AS)

Background: Diversity in dietary intake contributes to variation in human metabolomic profiles and artifacts from acute dietary intake can affect metabolomics data.

Objective: We investigated the role of dietary phytochemicals on shaping human urinary metabolomic profiles.

Design: First void urine samples were collected from 21 healthy volunteers (12 women, 9 men) following their normal diet (ND), a 2-d low-phytochemical diet (LPD), or a 2-d standard phytochemical diet (SPD). Nutrient intake was assessed during the study. Urine samples were analyzed by using ¹H nuclear magnetic resonance spectroscopy (¹H NMR) and mass spectrometry (MS), which was followed by multivariate data analysis.

Results: Macronutrient intake did not change throughout the study. Partial least-squares-discriminant analysis indicated a clear distinction between the LPD samples and the ND and SPD samples, relating to creatinine and methylhistidine excretion after the LPD and hippurate excretion after the ND and SPD. The predictive power of the LPD versus the ND model was 74 ± 3% and 82 ± 6% with the ¹H NMR and MS data sets, respectively. The predictive power of the LPD versus the SPD model was 83 ± 8% and 69 ± 4% for the ¹H NMR and MS data sets respectively. A cross platform comparison of both data sets by co-inertia analysis showed a similar distinction between the LPD and SPD.

Conclusions: Acute changes in urinary metabolomic profiles occur after the consumption of dietary phytochemicals. Dietary restrictions in the 24 h before sample collection may reduce diversity in phytochemical intakes and therefore reduce variation and improve data interpretation in metabolomics studies using urine.

Key Words: Nutritional metabolomics • metabonomics • metabolic profiling • nutrigenomics

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