Evidence map›Paper›PMID 32587867›Full record

ArticleJournal of diabetes research2020

IgG Glycosylation Profile and the Glycan Score Are Associated with Type 2 Diabetes in Independent Chinese Populations: A Case-Control Study.

Zhiyuan Wu, Haibin Li, Di Liu, Lixin Tao, Jie Zhang, Baolu Liang, Xiangtong Liu, Xiaonan Wang, Xia Li, Youxin Wang and 2 more

Abstract read
In one paragraph

Article in Journal of diabetes research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. High-Throughput Glycomic Methods.Chemical reviews · 2022
    Review
  10. Article
  11. Molecules (Basel, Switzerland) · 2021
    Article
  12. Review
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.

Zhiyuan WuBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-5694-2441
Haibin LiBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-6932-982X
Di LiuBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Lixin TaoBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Jie ZhangBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-0721-0321
Baolu LiangBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-2540-8411
Xiangtong LiuBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-8219-5064
Xiaonan WangBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Xia LiDepartment of Mathematics and Statistics, La Trobe University, Australia.ORCID https://orcid.org/0000-0003-1890-5548
Youxin WangBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-6574-6706
Wei WangBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-1430-1360
Xiuhua GuoBeijing Municipal Key Laboratory of Clinical Epidemiology, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0001-6657-6940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe relationship between the IgG glycan panel and type 2 diabetes remains unclear in Chinese population. We aimed to investigate the association of the IgG glycan profile and glycan score with type 2 diabetes.

methodsIn the discovery population, 162 individuals diagnosed with type 2 diabetes and 162 matched controls from Beijing health management cohort were included. We analyzed the IgG glycan profile and composed a glycan score for type 2 diabetes. Findings were validated in the replication population from Beijing Xuanwu community cohort (280 cases and 508 controls). Area under curve (AUC) using 10-fold and bootstrap validation, net reclassification index (NRI), and integrated discrimination index (IDI) were calculated for the glycan score.

resultsIn the discovery population, 5 initial IgG glycans and 7 derived traits were significantly associated with type 2 diabetes after Bonferroni correction and Lasso selection, which were validated in the replication population subsequently. The glycan score composed of these IgG glycans and traits showed a strong association with type 2 diabetes (combined odds ratio (OR): 3.78) and its risk factors. In the replication population, AUC of the model involving clinical traits improved from 0.74 to above 0.90, and the values of NRI and IDI were 0.35 and 0.42, respectively, with the glycan score added.

conclusionsIgG glycosylation profiles were associated with type 2 diabetes and the glycan score may be a novel indicator for diabetes which reflected a proinflammatory status.

Indexed as

AdultAgedBlood GlucoseCase-Control StudiesChinaDiabetes Mellitus, Type 2Down-RegulationFemaleGlycoproteinsHumansImmunoglobulin GMaleMiddle AgedPolysaccharidesReproducibility of ResultsUp-RegulationBlood GlucoseGlycoproteinsglycosylated IgGImmunoglobulin GPolysaccharides

Identifiers

PMID32587867
PMCPMC7301241

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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.