Comparing metabolite profiles of habitual diet in serum and urine

卷:104

Playdon, Mary C. ; Sampson, Joshua N. ; Cross, Amanda J. ; Sinha, Rashmi ; Guertin, Kristin A. ; Moy, Kristin A. ; Rothman, Nathaniel ; Irwin, Melinda L. ; Mayne, Susan T. ; Stolzenberg-Solomon, Rachael ; Moore, Steven C.

Yale Univ

关键词: metabolomics;    metabolite;    serum;    urine;    diet;    food;    nutrition assessment;    biomarker;   

DOI  :  10.3945/ajcn.116.135301

学科分类：食品科学和技术

【 摘 要 】

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.