Evidence map›Paper›PMID 36174116›Full record

ArticleDiabetes care2022

Immunoglobulin G N-Glycosylation Signatures in Incident Type 2 Diabetes and Cardiovascular Disease.

Anna Birukov, Branimir Plavša, Fabian Eichelmann, Olga Kuxhaus, Rosangela Akemi Hoshi, Najda Rudman, Tamara Štambuk, Irena Trbojević-Akmačić, Catarina Schiborn, Jakub Morze and 9 more

Abstract read
In one paragraph

Article in Diabetes care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Immunoglobulin GFrontiers in immunology · 2026
    Article
  7. Article
  8. Article
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  10. Article
  11. Article
  12. Review
  13. Article
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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

19 authors.

Anna BirukovDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Branimir PlavšaUniversity of Zagreb Faculty of Pharmacy and Biochemistry, Zagreb, Croatia.
Fabian EichelmannDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Olga KuxhausDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Rosangela Akemi HoshiCenter for Lipid Metabolomics, Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Najda RudmanUniversity of Zagreb Faculty of Pharmacy and Biochemistry, Zagreb, Croatia.
Tamara ŠtambukGenos Glycoscience Research Laboratory, Zagreb, Croatia.
Irena Trbojević-AkmačićGenos Glycoscience Research Laboratory, Zagreb, Croatia.
Catarina SchibornDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Jakub MorzeDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Matea MihelčićGenos Glycoscience Research Laboratory, Zagreb, Croatia.
Ana CindrićGenos Glycoscience Research Laboratory, Zagreb, Croatia.
Yanyan LiuCenter for Lipid Metabolomics, Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Olga DemlerCenter for Lipid Metabolomics, Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Markus PerolaFinnish Institute for Health and Welfare (THL), Helsinki, Finland.
Samia MoraCenter for Lipid Metabolomics, Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Matthias B SchulzeDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.ORCID 0000-0002-0830-5277
Gordan LaucUniversity of Zagreb Faculty of Pharmacy and Biochemistry, Zagreb, Croatia.
Clemens WittenbecherDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.ORCID 0000-0001-7792-877X

Funding

Total plasma and IgG glycomes, statin therapy and ASCVD eventsR01HL117861 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MORA, SAMIA · 2013 to 2022
$3.8M
Reducing Inflammation for the Prevention of CVD: A Focus on Eicosanoid PathwaysR01HL134811 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MORA, SAMIA · 2017 to 2020
$3.0M
Circulating plasma metabolites, diet, and risk of type 2 diabetesR01DK112940 · NIDDK · BRIGHAM AND WOMEN'S HOSPITAL · PI HU, FRANK B, MORA, SAMIA · 2018 to 2021
$2.9M
Patient Centered Approaches to Preventing ASCVD EventsK24HL136852 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SAMIA MORA · 2018 to 2026
$1.1M
CHD Risk and Metabolomic Profiles of Discordant LipidsK01HL135342 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMLER, OLGA · 2017 to 2021
$857k
Consensus Framework for Cardiovascular Risk Prediction in a Clinical SettingR21HL167173 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMLER, OLGA · 2023 to 2024
$269k
NHLBI NIH HHS K01 HL135342NHLBI NIH HHS K24 HL136852NHLBI NIH HHS R01 HL117861NHLBI NIH HHS R01 HL134811NHLBI NIH HHS R21 HL167173NIDDK NIH HHS R01 DK112940
6 · The paper itself

Abstract

objectiveN-glycosylation is a functional posttranslational modification of immunoglobulins (Igs). We hypothesized that specific IgG N-glycans are associated with incident type 2 diabetes and cardiovascular disease (CVD). RESEARCH DESIGN AND

methodsWe performed case-cohort studies within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (2,127 in the type 2 diabetes subcohort [741 incident cases]; 2,175 in the CVD subcohort [417 myocardial infarction and stroke cases]). Relative abundances of 24 IgG N-glycan peaks (IgG-GPs) were measured by ultraperformance liquid chromatography, and eight glycosylation traits were derived based on structural similarity. End point-associated IgG-GPs were preselected with fractional polynomials, and prospective associations were estimated in confounder-adjusted Cox models. Diabetes risk associations were validated in three independent studies.

resultsAfter adjustment for confounders and multiple testing correction, IgG-GP7, IgG-GP8, IgG-GP9, IgG-GP11, and IgG-GP19 were associated with type 2 diabetes risk. A score based on these IgG-GPs was associated with a higher diabetes risk in EPIC-Potsdam and independent validation studies (843 total cases, 3,149 total non-cases, pooled estimate per SD increase 1.50 [95% CI 1.37-1.64]). Associations of IgG-GPs with CVD risk differed between men and women. In women, IgG-GP9 was inversely associated with CVD risk (hazard ratio [HR] per SD 0.80 [95% CI 0.65-0.98]). In men, a weighted score based on IgG-GP19 and IgG-GP23 was associated with higher CVD risk (HR per SD 1.47 [95% CI 1.20-1.80]). In addition, several derived traits were associated with cardiometabolic disease incidence.

conclusionsSelected IgG N-glycans are associated with cardiometabolic risk beyond classic risk factors, including clinical biomarkers.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2FemaleGlycosylationHumansImmunoglobulin GIncidenceMalePolysaccharidesRisk FactorsImmunoglobulin GPolysaccharides

Identifiers

PMID36174116
PMCPMC9679264

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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.