Evidence map›Paper›PMID 27510537›Full record

ArticleThe American journal of clinical nutrition2016

Comparing metabolite profiles of habitual diet in serum and urine.

Mary C Playdon, Joshua N Sampson, Amanda J Cross, Rashmi Sinha, Kristin A Guertin, Kristin A Moy, Nathaniel Rothman, Melinda L Irwin, Susan T Mayne, Rachael Stolzenberg-Solomon and 1 more

Open access · bronzeAbstract readComparative StudyValidation Study
In one paragraph

Article in The American journal of clinical nutrition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 1 pooled it
8.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

114 citing papers in PubMed, 1 synthesis or guideline pooled it, 173 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Article
  15. Metabolomics Based Biomarkers of Green Beans Intake.Molecular nutrition & food research · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

54 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Mary C PlaydonYale School of Public Health, Yale University, New Haven, CT; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD; mary.playdon@nih.gov.
Joshua N SampsonDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Amanda J CrossFaculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom;
Rashmi SinhaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Kristin A GuertinDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Kristin A MoyDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Nathaniel RothmanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Melinda L IrwinYale School of Public Health, Yale University, New Haven, CT; Yale Cancer Center, New Haven, CT; and.
Susan T MayneYale School of Public Health, Yale University, New Haven, CT; Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, MD.
Rachael Stolzenberg-SolomonDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
Steven C MooreDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD;
National Cancer Institute · USYale Cancer Center · USCenter for Food Safety and Applied Nutrition · USImperial College London · GB

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Diabetes Research CenterP30DK045735 · NIDDK · YALE UNIVERSITY · PI Richard A Flavell · 1993 to 2026
$44.0M
NCATS NIH HHS UL1 TR001863NIDDK NIH HHS P30 DK045735
6 · The paper itself

Abstract

backgroundDiet 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.

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

designWe 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.

resultsWe 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.

conclusionsCandidate 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.

Indexed as

Diet, HealthyFeeding BehaviorModels, BiologicalNutrition AssessmentPatient ComplianceAdenomaAgedBiomarkersCase-Control StudiesColonic NeoplasmsDietary SupplementsEarly Detection of CancerFemaleHospitals, MilitaryHumansMachine LearningBiomarkersbiomarkerdietfoodmetabolitemetabolomicsnutrition assessmentserumurine

Identifiers

PMID27510537
PMCPMC4997302
OpenAlexW2476039382

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.