Evidence map›Paper›PMID 19043545›Full record

ArticlePLoS genetics2008

Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.

Christian Gieger, Ludwig Geistlinger, Elisabeth Altmaier, Martin Hrabé de Angelis, Florian Kronenberg, Thomas Meitinger, Hans-Werner Mewes, H-Erich Wichmann, Klaus M Weinberger, Jerzy Adamski and 2 more

Abstract read
In one paragraph

Article in PLoS genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 387 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
387citing papers in PubMed, 6 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

387 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Use of Mendelian Randomization to Unveil Metabolic Markers inJournal of Korean medical science · 2026
    Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Article
  18. Article
  19. Article
  20. Article

327 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

12 authors.

Christian GiegerInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Ludwig Geistlinger
Elisabeth Altmaier
Martin Hrabé de Angelis
Florian Kronenberg
Thomas Meitinger
Hans-Werner Mewes
H-Erich Wichmann
Klaus M Weinberger
Jerzy Adamski
Thomas Illig
Karsten Suhre

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

Indexed as

Blood ProteinsDelta-5 Fatty Acid DesaturaseFatty Acid DesaturasesGeneticsGenome, HumanGenome-Wide Association StudyHumansMaleMetabolomicsOrganic ChemicalsPhenotypePhosphoproteinsPolymorphism, Single NucleotideUbiquitin-Protein LigasesBlood ProteinsDelta-5 Fatty Acid DesaturaseFADS1 protein, humanFatty Acid DesaturasesOrganic Chemicalsparkin proteinPhosphoproteinsplatelet protein P47Ubiquitin-Protein Ligases

Identifiers

PMID19043545
PMCPMC2581785

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.