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Am J Clin Nutr. 2016 Sep;104(3):776-89. doi: 10.3945/ajcn.116.135301. Epub 2016 Aug 10.

Comparing metabolite profiles of habitual diet in serum and urine.

Author information

1
Yale School of Public Health, Yale University, New Haven, CT; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD; mary.playdon@nih.gov.
2
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
3
Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom;
4
Yale School of Public Health, Yale University, New Haven, CT; Yale Cancer Center, New Haven, CT; and.
5
Yale School of Public Health, Yale University, New Haven, CT; Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, MD.

Abstract

BACKGROUND:

Diet plays an important role in chronic disease etiology, but some diet-disease associations remain inconclusive because of methodologic limitations in dietary assessment. Metabolomics is a novel method for identifying objective dietary biomarkers, although it is unclear what dietary information is captured from metabolites found in serum compared with urine.

OBJECTIVE:

We compared metabolite profiles of habitual diet measured from serum with those measured from urine.

DESIGN:

We first estimated correlations between consumption of 56 foods, beverages, and supplements assessed by a food-frequency questionnaire, with 676 serum and 848 urine metabolites identified by untargeted liquid chromatography mass spectrometry, ultra-high performance liquid chromatography tandem mass spectrometry, and gas chromatography mass spectrometry in a colon adenoma case-control study (n = 125 cases and 128 controls) while adjusting for age, sex, smoking, fasting, case-control status, body mass index, physical activity, education, and caloric intake. We controlled for multiple comparisons with the use of a false discovery rate of <0.1. Next, we created serum and urine multiple-metabolite models to predict food intake with the use of 10-fold crossvalidation least absolute shrinkage and selection operator regression for 80% of the data; predicted values were created in the remaining 20%. Finally, we compared predicted values with estimates obtained from self-reported intake for metabolites measured in serum and urine.

RESULTS:

We identified metabolites associated with 46 of 56 dietary items; 417 urine and 105 serum metabolites were correlated with ≥1 food, beverage, or supplement. More metabolites in urine (n = 154) than in serum (n = 39) were associated uniquely with one food. We found previously unreported metabolite associations with leafy green vegetables, sugar-sweetened beverages, citrus, added sugar, red meat, shellfish, desserts, and wine. Prediction of dietary intake from multiple-metabolite profiles was similar between biofluids.

CONCLUSIONS:

Candidate metabolite biomarkers of habitual diet are identifiable in both serum and urine. Urine samples offer a valid alternative or complement to serum for metabolite biomarkers of diet in large-scale clinical or epidemiologic studies.

KEYWORDS:

biomarker; diet; food; metabolite; metabolomics; nutrition assessment; serum; urine

PMID:
27510537
PMCID:
PMC4997302
DOI:
10.3945/ajcn.116.135301
[Indexed for MEDLINE]
Free PMC Article
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