Evidence map›Paper›PMID 28951559›Full record

ArticleScientific reports2017

Subclass-specific IgG glycosylation is associated with markers of inflammation and metabolic health.

Rosina Plomp, L Renee Ruhaak, Hae-Won Uh, Karli R Reiding, Maurice Selman, Jeanine J Houwing-Duistermaat, P Eline Slagboom, Marian Beekman, Manfred Wuhrer

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed
10.7field-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

101 citing papers in PubMed, 166 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. Article
  7. Article
  8. Immunoglobulin G in aging and cancer.Frontiers in immunology · 2026
    Review
  9. Review
  10. Article
  11. Not all antibodies are created equal: total IgG glycosylation and severity of antibody-mediated rejection in kidney transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2026
    Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

41 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

9 authors at 1 institution in 1 country.

Rosina PlompCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
L Renee RuhaakCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Hae-Won UhDepartment of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Karli R ReidingCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Maurice SelmanCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
Jeanine J Houwing-DuistermaatDepartment of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
P Eline SlagboomDepartment of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Marian BeekmanDepartment of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.ORCID 0000-0003-0585-6206
Manfred WuhrerCenter for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands. m.wuhrer@lumc.nl.
Leiden University Medical Center · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study indicates that glycosylation of immunoglobulin G, the most abundant antibody in human blood, may convey useful information with regard to inflammation and metabolic health. IgG occurs in the form of different subclasses, of which the effector functions show significant variation. Our method provides subclass-specific IgG glycosylation profiling, while previous large-scale studies neglected to measure IgG2-specific glycosylation. We analysed the plasma Fc glycosylation profiles of IgG1, IgG2 and IgG4 in a cohort of 1826 individuals by liquid chromatography-mass spectrometry. For all subclasses, a low level of galactosylation and sialylation and a high degree of core fucosylation associated with poor metabolic health, i.e. increased inflammation as assessed by C-reactive protein, low serum high-density lipoprotein cholesterol and high triglycerides, which are all known to indicate increased risk of cardiovascular disease. IgG2 consistently showed weaker associations of its galactosylation and sialylation with the metabolic markers, compared to IgG1 and IgG4, while the direction of the associations were overall similar for the different IgG subclasses. These findings demonstrate the potential of IgG glycosylation as a biomarker for inflammation and metabolic health, and further research is required to determine the additive value of IgG glycosylation on top of biomarkers which are currently used.

Indexed as

Health StatusAdultAgedBiomarkersCohort StudiesFemaleGlycoproteinsGlycosylationHumansImmunoglobulin Fc FragmentsImmunoglobulin GInflammationMaleMetabolomicsMiddle AgedBiomarkersGlycoproteinsglycosylated IgGImmunoglobulin Fc FragmentsImmunoglobulin G

Identifiers

PMID28951559
PMCPMC5615071
OpenAlexW2754240404

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.