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Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans^1,2,3

¹ From the Nutrition Unit, Division of Clinical and Molecular Medicine, Trinity College, Dublin, Ireland (MCW, HMR, and MJG), and the UCD School of Biomolecular and Biomedical Sciences, UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland (LB and JPGM)

Background: Metabolomics in human nutrition research is faced with the challenge that changes in metabolic profiles resulting from diet may be difficult to differentiate from normal physiologic variation.

Objective: We assessed the extent of intra- and interindividual variation in normal human metabolic profiles and investigated the effect of standardizing diet on reducing variation.

Design: Urine, plasma, and saliva were collected from 30 healthy volunteers (23 females, 7 males) on 4 separate mornings. For visits 1 and 2, free food choice was permitted on the day before biofluid collection. Food choice on the day before visit 3 was intended to mimic that for visit 2, and all foods were standardized on the day before visit 4. Samples were analyzed by using ¹H nuclear magnetic resonance spectroscopy followed by multivariate data analysis.

Results: Intra- and interindividual variations were considerable for each biofluid. Visual inspection of the principal components analysis scores plots indicated a reduction in interindividual variation in urine, but not in plasma or saliva, after the standard diet. Partial least-squares discriminant analysis indicated time-dependent changes in urinary and salivary samples, mainly resulting from creatinine in urine and acetate in saliva. The predictive power of each model to classify the samples as either night or morning was 85% for urine and 75% for saliva.

Conclusions: Urine represented a sensitive metabolic profile that reflected acute dietary intake, whereas plasma and saliva did not. Future metabolomics studies should consider recent dietary intake and time of sample collection as a means of reducing normal physiologic variation.

Key Words: Nutritional metabolomics • metabonomics • healthy human biofluids • metabolic profiles • multivariate data analysis

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