Evidence map›Paper›PMID 27118561›Full record

ReviewInternational journal of epidemiology2016

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks.

Liam G Fearnley, Michael Inouye

Abstract readReview
In one paragraph

Review in International journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Article
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  4. Review
  5. Article
  6. Article
  7. Article
  8. Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021
    Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Review of recent developments in GC-MS approaches to metabolomics-based research.Metabolomics : Official journal of the Metabolomic Society · 2018
    Review
  14. Article
  15. Review
  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

2 authors.

Liam G FearnleyCentre for Systems Genomics.
Michael InouyeCentre for Systems Genomics minouye@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology.

Indexed as

BiomarkersMetabolomicsSystems BiologyBiomedical ResearchEpidemiologic StudiesHumansBiomarkersepidemiologygenomicsmetabolomicssystems biologytranscriptomics

Identifiers

PMID27118561
PMCPMC5100607

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.