title: Comparing metabolite profiles of habitual diet in serum and urine.
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.
  
  
  METHODS
  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.
authors: 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.
authors: Mary C
authors: MC
authors: Playdon
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Joshua N
authors: JN
authors: Sampson
authors: Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom;
authors: Amanda J
authors: AJ
authors: Cross
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Rashmi
authors: R
authors: Sinha
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Kristin A
authors: KA
authors: Guertin
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Kristin A
authors: KA
authors: Moy
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Nathaniel
authors: N
authors: Rothman
authors: Yale School of Public Health, Yale University, New Haven, CT; Yale Cancer Center, New Haven, CT; and.
authors: Melinda L
authors: ML
authors: Irwin
authors: Yale School of Public Health, Yale University, New Haven, CT; Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, MD.
authors: Susan T
authors: ST
authors: Mayne
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Rachael
authors: R
authors: Stolzenberg-Solomon
authors: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
authors: Steven C
authors: SC
authors: Moore
mesh_terms: D000236:Adenoma / Q000097:blood / Q000175:diagnosis / Q000378:metabolism / Q000652:urine; D000368:Aged; D015415:Biomarkers / Q000097:blood / Q000652:urine*; D016022:Case-Control Studies; D003110:Colonic Neoplasms / Q000097:blood / Q000175:diagnosis / Q000378:metabolism / Q000652:urine; D000072001:Diet, Healthy; D019587:Dietary Supplements; D055088:Early Detection of Cancer; D005247:Feeding Behavior; D005260:Female; D006772:Hospitals, Military; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008396:Maryland; D055432:Metabolomics / Q000379:methods; D008875:Middle Aged; D008954:Models, Biological; D015596:Nutrition Assessment; D010349:Patient Compliance; D012044:Regression Analysis; D057566:Self Report
chemical_list: D015415:Biomarkers
keywords: biomarker; diet; food; metabolite; metabolomics; nutrition assessment; serum; urine