Comparing metabolite profiles of habitual diet in serum and urine123

• Published in: The American journal of clinical nutrition Vol. 104; no. 3; pp. 776 - 789
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2016; American Society for Nutrition
• Subjects: biomarker; diet; food; metabolite; metabolomics; nutrition assessment; Nutritional Epidemiology and Public Health; serum; urine; LOD; TMAO; CEHC; nutrition assessment; urine; biomarker; food; FFQ; metabolite; α-CEHC; metabolomics; FDR; LASSO; diet; serum; CMPF
• ISSN: 0002-9165; 1938-3207
• DOI: 10.3945/ajcn.116.135301

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.