Evidence map›Paper›PMID 34209622›Full record

ArticleJournal of personalized medicine2021

Glycosylation of IgG Associates with Hypertension and Type 2 Diabetes Mellitus Comorbidity in the Chinese Muslim Ethnic Minorities and the Han Chinese.

Xiaoni Meng, Manshu Song, Marija Vilaj, Jerko Štambuk, Mamatyusupu Dolikun, Jie Zhang, Di Liu, Hao Wang, Xiaoyu Zhang, Jinxia Zhang and 10 more

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. High-Throughput Glycomic Methods.Chemical reviews · 2022
    Review
  9. Personalized Medicine in Epidemics.Journal of personalized medicine · 2022
    Article
  10. 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

20 authors.

Xiaoni MengBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.ORCID 0000-0002-6544-5225
Manshu SongBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.ORCID 0000-0003-1433-7192
Marija VilajGenos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.
Jerko ŠtambukGenos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.
Mamatyusupu DolikunCollege of the Life Sciences and Technology, Xinjiang University, Urumqi 830046, China.
Jie ZhangBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Di LiuBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Hao WangDepartment of Clinical Epidemiology and Evidence-Based Medicine, National Clinical Research Center for Digestive Disease, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.ORCID 0000-0002-9131-0199
Xiaoyu ZhangBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Jinxia ZhangBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Weijie CaoBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Ana MomčilovićGenos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.
Irena Trbojević-AkmačićGenos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.ORCID 0000-0003-0106-0155
Xingang LiSchool of Medical and Health Sciences, Edith Cowan University, Perth, WA 6027, Australia.ORCID 0000-0003-0252-154X
Deqiang ZhengBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Lijuan WuBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Xiuhua GuoBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.
Youxin WangBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.ORCID 0000-0002-6574-6706
Gordan LaucGenos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.ORCID 0000-0003-1840-9560
Wei WangBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China.ORCID 0000-0002-1430-1360

Funding

China-Australian Collaborative Grant NSFC 81561128020-NHMRC APP1112767National Natural Science Foundation of China 81573215, 31460285, 81673247
6 · The paper itself

Abstract

objectivesHypertension and type 2 diabetes mellitus comorbidity (HDC) is common, which confers a higher risk of cardiovascular disease than the presence of either condition alone. Describing the underlying glycomic changes of immunoglobulin G (IgG) that predispose individuals to HDC may help develop novel protective immune-targeted and anti-inflammatory therapies. Therefore, we investigated glycosylation changes of IgG associated with HDC.

methodsThe IgG N-glycan profiles of 883 plasma samples from the three northwestern Chinese Muslim ethnic minorities and the Han Chinese were analyzed by ultra-performance liquid chromatography instrument.

resultsWe found that 12 and six IgG N-glycan traits showed significant associations with HDC in the Chinese Muslim ethnic minorities and the Han Chinese, respectively, after adjustment for potential confounders and false discovery rate. Adding the IgG N-glycan traits to the baseline models, the area under the receiver operating characteristic curves (AUCs) of the combined models differentiating HDC from hypertension (HTN), type 2 diabetes mellitus (T2DM), and healthy individuals were 0.717, 0.747, and 0.786 in the pooled samples of Chinese Muslim ethnic minorities, and 0.828, 0.689, and 0.901 in the Han Chinese, respectively, showing improved discriminating performance than both the baseline models and the glycan-based models.

conclusionAltered IgG N-glycan profiles were shown to associate with HDC, suggesting the involvement of inflammatory processes of IgG glycosylation. The alterations of IgG N-glycome, illustrated here for the first time in HDC, demonstrate a biomarker potential, which may shed light on future studies investigating their potential for monitoring or preventing the progression from HTN or T2DM towards HDC.

Indexed as

biomarkershypertension and type 2 diabetes mellitus comorbidityIgGN-glycosylation

Identifiers

PMID34209622
PMCPMC8307283

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Registered trials

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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.