Evidence map›Paper›PMID 21852955›Full record

ArticlePLoS genetics2011

Discovery of sexual dimorphisms in metabolic and genetic biomarkers.

Kirstin Mittelstrass, Janina S Ried, Zhonghao Yu, Jan Krumsiek, Christian Gieger, Cornelia Prehn, Werner Roemisch-Margl, Alexey Polonikov, Annette Peters, Fabian J Theis and 8 more

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 228 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
228citing papers in PubMed, 7 pooled it
11.4field-weighted citation impact, top 1% 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

228 citing papers in PubMed, 7 syntheses or guidelines pooled it, 400 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Trial
  9. Trial
  10. Trial
  11. Article
  12. Leisure-time physical activity and metabolomic trajectories over the transition from late midlife to old age.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  13. Article
  14. Article
  15. Metabolomics Data Analysis with TIGER.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 6 institutions in 4 countries.

Kirstin MittelstrassUnit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
Janina S Ried
Zhonghao Yu
Jan Krumsiek
Christian Gieger
Cornelia Prehn
Werner Roemisch-Margl
Alexey Polonikov
Annette Peters
Fabian J Theis
Thomas Meitinger
Florian Kronenberg
Stephan Weidinger
Heinz Erich Wichmann
Karsten Suhre
Rui Wang-Sattler
Jerzy Adamski
Thomas Illig
Helmholtz Zentrum München · DEInnsbruck Medical University · ATInstitute of Bioinformatics and Systems Biology · DEKursk State Medical University · RUUniversity Hospital Schleswig-Holstein · DEZimmer Biomet (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.

Indexed as

Polymorphism, Single NucleotideSex CharacteristicsAdultAgedAged, 80 and overAmino AcidsCarnitineFemaleGenetic MarkersGenome-Wide Association StudyGlycineHumansLipidsMaleMetabolomeMiddle AgedacylcarnitineAmino AcidsCarnitineGenetic MarkersGlycineLipids

Identifiers

PMID21852955
PMCPMC3154959
OpenAlexW2166354753

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.