Evidence map›Paper›PMID 30500833›Full record

ArticlePloS one2018

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females.

Zimin Li, Yuxi Zhang, Ting Hu, Sergei Likhodii, Guang Sun, Guangju Zhai, Zhaozhi Fan, Chunji Xuan, Weidong Zhang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Sex differences in the human metabolome.Biology of sex differences · 2022
    Review
  6. Article
  7. Endogenous cortisol excess confers a unique lipid signature and metabolic network.Journal of molecular medicine (Berlin, Germany) · 2021
    Article
  8. Network Analysis in Systems Epidemiology.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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

9 authors at 4 institutions in 2 countries.

Zimin LiSchool of Pharmaceutical Sciences, Jilin University, Changchun, China.
Yuxi ZhangSchool of Pharmaceutical Sciences, Jilin University, Changchun, China.
Ting HuDepartment of Computer Science, Memorial University, St John's, NL, Canada.
Sergei LikhodiiProvincial Toxicology Centre, Provincial Health Services Authority, Vancouver, British Columbia, Canada.
Guang SunDiscipline of Medicine, Faculty of Medicine, Memorial University, St. John's, NL, Canada.
Guangju ZhaiDiscipline of Genetics, Faculty of Medicine, Memorial University, St. John's, NL, Canada.
Zhaozhi FanDepartment of Mathematics and Statistics, Memorial University, St. John's, NL, Canada.
Chunji XuanNortheast Asian Studies College, Jilin University, Changchun, China.
Weidong ZhangSchool of Pharmaceutical Sciences, Jilin University, Changchun, China.ORCID 0000-0002-0843-5688
Memorial University of Newfoundland · CAJilin University · CNHarbin Medical University · CNProvincial Health Services Authority · CA

Funding

Canadian Institutes of Health Research MOP192552Canadian Institutes of Health Research RNL-132178
6 · The paper itself

Abstract

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.

Indexed as

Metabolic Networks and PathwaysMetabolomicsSex CharacteristicsFemaleHumansMaleMiddle Aged

Identifiers

PMID30500833
PMCPMC6267973
OpenAlexW2902758369

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.