Evidence map›Paper›PMID 33212857›Full record

ArticleMetabolites2020

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake.

Julia Langenau, Kolade Oluwagbemigun, Christian Brachem, Wolfgang Lieb, Romina di Giuseppe, Anna Artati, Gabi Kastenmüller, Leonie Weinhold, Matthias Schmid, Ute Nöthlings

Abstract read
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Exploring Human Metabolome after Wine Intake-A Review.Molecules (Basel, Switzerland) · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. PPI-Induced Changes in Plasma Metabolite Levels Influence Total Hip Bone Mineral Density in a UK Cohort.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2023
    Article
  12. Article
  13. Article
  14. Article
  15. Article
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

10 authors.

Julia LangenauUnit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany.ORCID 0000-0002-3594-1121
Kolade OluwagbemigunUnit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany.ORCID 0000-0002-9454-5970
Christian BrachemUnit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany.ORCID 0000-0002-5892-0358
Wolfgang LiebInstitute of Epidemiology, Kiel University, 24105 Kiel, Germany.ORCID 0000-0003-2544-4460
Romina di GiuseppeInstitute of Epidemiology, Kiel University, 24105 Kiel, Germany.
Anna ArtatiResearch Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Munich, Germany.
Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764 Munich, Germany.
Leonie WeinholdInstitute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, 53127 Bonn, Germany.
Matthias SchmidInstitute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, 53127 Bonn, Germany.
Ute NöthlingsUnit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany.ORCID 0000-0002-5789-2252

Funding

Bundesministerium für Bildung und Forschung 01EA1809A
6 · The paper itself

Abstract

Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.

Indexed as

biomarkersdietary assessment methodsdietary intakemetabolitesuntargeted metabolomics

Identifiers

PMID33212857
PMCPMC7698441

What OpenQuestion holds

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Registered trials

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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.