Evidence map›Paper›PMID 36810000›Full record

ArticleClinical proteomics2023

Changes of serum IgG glycosylation patterns in rheumatoid arthritis.

Xiaoyue Deng, Xiaomin Liu, Yan Zhang, Dan Ke, Rui Yan, Qian Wang, Xinping Tian, Mengtao Li, Xiaofeng Zeng, Chaojun Hu

Abstract read
In one paragraph

Article in Clinical proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Quantitative Modeling of IgG N-Glycosylation Profiles from Population Data.International journal of molecular sciences · 2025
    Article
  2. Article
  3. Article
  4. Glycosylation Regulation by TMEM230 in Aging and Autoimmunity.International journal of molecular sciences · 2025
    Article
  5. Review
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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

10 authors.

Xiaoyue Deng *Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Xiaomin Liu *Department of Rheumatology, Shunyi District Hospital, Beijing, 101300, China.
Yan ZhangDepartment of Rheumatology, Shunyi District Hospital, Beijing, 101300, China.
Dan KeDepartment of Rheumatology, Shunyi District Hospital, Beijing, 101300, China.
Rui YanDepartment of Rheumatology, Shunyi District Hospital, Beijing, 101300, China.
Qian WangDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Xinping TianDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Mengtao LiDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
Xiaofeng ZengDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China. zengxfpumc@163.com.
Chaojun HuDepartment of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China. huchaojun818@qq.com.

Funding

Chinese National Key Technology R&D Program, Ministry of Science and Technology 2022YFC2504600, 2019YFC0840603National High Level Hospital Clinical Research Funding 2022-PUMCHA-039National Natural Science Foundation of China 81771780
6 · The paper itself

Abstract

backgroundRA is a common chronic and systemic autoimmune disease, and the diagnosis is based significantly on autoantibody detection. This study aims to investigate the glycosylation profile of serum IgG in RA patients using high-throughput lectin microarray technology.

methodLectin microarray containing 56 lectins was applied to detect and analyze the expression profile of serum IgG glycosylation in 214 RA patients, 150 disease controls (DC), and 100 healthy controls (HC). Significant differential glycan profiles between the groups of RA and DC/HC as well as RA subgroups were explored and verified by lectin blot technique. The prediction models were created to evaluate the feasibility of those candidate biomarkers.

resultsAs a comprehensive analysis of lectin microarray and lectin blot, results showed that compare with HC or DC groups, serum IgG from RA patients had a higher affinity to the SBA lectin (recognizing glycan GalNAc). For RA subgroups, RA-seropositive group had higher affinities to the lectins of MNA-M (recognizing glycan mannose) and AAL (recognizing glycan fucose), and RA-ILD group had higher affinities to the lectins of ConA (recognizing glycan mannose) and MNA-M while a lower affinity to the PHA-E (recognizing glycan Galβ4GlcNAc) lectin. The predicted models indicated corresponding feasibility of those biomarkers.

conclusionLectin microarray is an effective and reliable technique for analyzing multiple lectin-glycan interactions. RA, RA-seropositive, and RA-ILD patients exhibit distinct glycan profiles, respectively. Altered levels of glycosylation may be related to the pathogenesis of the disease, which could provide a direction for new biomarkers identification.

Indexed as

GlycosylationILDImmunoglobulin GLectin microarrayRA

Identifiers

PMID36810000
PMCPMC9942403

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