Evidence map›Paper›PMID 34015815›Full record

ReviewAdvances in nutrition (Bethesda, Md.)2021

Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review.

Talha Rafiq, Sandi M Azab, Koon K Teo, Lehana Thabane, Sonia S Anand, Katherine M Morrison, Russell J de Souza, Philip Britz-McKibbin

Abstract readReview
In one paragraph

Review in Advances in nutrition (Bethesda, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
–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

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Expanding the pig metabolomics QTL catalog.Genetics, selection, evolution : GSE · 2026
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

8 authors.

Talha RafiqMedical Sciences Graduate Program, Faculty of Health Sciences, McMaster University, Hamilton, Canada.
Sandi M AzabDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada.
Koon K TeoPopulation Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada.
Lehana ThabaneDepartment of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada.ORCID 0000-0003-0355-9734
Sonia S AnandPopulation Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada.ORCID 0000-0003-3692-7441
Katherine M MorrisonDepartment of Pediatrics, McMaster University, Hamilton, Canada.
Russell J de SouzaPopulation Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada.ORCID 0000-0001-8945-513X
Philip Britz-McKibbinDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.

Indexed as

DietMetabolomicsBiomarkersFruitHumansVegetablesBiomarkersdietary biomarkersfood exposuresmetabolomicsnutritionomics

Identifiers

PMID34015815
PMCPMC8634495

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.