Skip to main content

Advertisement

SpringerLink
Log in

Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint

  • Research
  • Published:
EPMA Journal Aims and scope Submit manuscript

Abstract

Objectives

Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM.

Methods

Case–control and nested case–control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument.

Results

After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case–control setting, baseline SHS, and baseline optimal health participants from the nested case–control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case–control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case–control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone.

Conclusions

This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The software or software package used for the data analysis is indicated in the text, and the code used for the specific analysis can be obtained from the corresponding author.

Abbreviations

ADCC :

Antibody-dependent cell-mediated cytotoxicity

ALT :

Alanine aminotransferase

AP :

Attributable proportion due to interaction

AST :

Aspartate aminotransferase

AUC :

Area under the ROC curve

BH :

Benjamini–Hochberg

BMI :

Body mass index

CCA :

Canonical correlation analysis

CNY :

Chinese Yuan

Cr :

Creatinine

CVD :

Cardiovascular disease

DBP :

Diastolic blood pressures

DG :

Derived glycan

EUR :

Euro

FcγRs :

Fragment crystallizable γ receptors

FDR :

False discovery rate

FPG :

Fasting plasma glucose

GlcNAc :

N-acetylglucosamine

GP :

Glycan peak

HbA1c :

Glycosylated hemoglobin A1c

HDL :

High-density lipoprotein cholesterol

HILIC :

Hydrophilic interaction liquid chromatography

IDF :

International Diabetes Federation

IgG :

Immunoglobulin G

LASSO :

Least absolute shrinkage and selection operator

LC–MS :

Liquid chromatography–mass spectrometry

LDL :

Low-density lipoprotein cholesterol

NCDs :

Non-communicable diseases

OGTT :

Oral glucose tolerance tests

PBG :

Postprandial blood glucose

RERI :

Relative excess risk due to interaction

ROC :

Receiver operating characteristic

SBP :

Systolic blood pressures

SD :

Standard deviation

SHS :

Suboptimal health status

SHSQ-25 :

SHS questionnaire 25 items

S index :

Synergy index

TC :

Total cholesterol

TGs :

Total triglycerides

T2DM :

Type 2 diabetes mellitus

UA :

Uric acid

UPLC :

Ultra-performance liquid chromatography

USD :

United States dollar

WHO :

World Health Organization

WHR :

Waist-to-hip ratio

2-AB :

2-Aminobenzamide

3PM/PPPM :

Predictive, preventive, and personalized medicine

References

  1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119.

    Article  PubMed  Google Scholar 

  2. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. https://doi.org/10.1038/nrendo.2017.151.

    Article  PubMed  Google Scholar 

  3. Heald AH, Stedman M, Davies M, Livingston M, Alshames R, Lunt M, et al. Estimating life years lost to diabetes: outcomes from analysis of National Diabetes Audit and Office of National Statistics data. Cardiovasc Endocrinol Metab. 2020;9(4):183–5. https://doi.org/10.1097/xce.0000000000000210.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10(1):14790. https://doi.org/10.1038/s41598-020-71908-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. American Diabetes Association. Economic costs of diabetes in the US in 2017. Diabetes Care. 2018;41(5):917–28.

    Article  PubMed Central  Google Scholar 

  6. Pradeepa R, Mohan V. Prevalence of type 2 diabetes and its complications in India and economic costs to the nation. Eur J Clin Nutr. 2017;71(7):816–24. https://doi.org/10.1038/ejcn.2017.40.

    Article  CAS  PubMed  Google Scholar 

  7. Shen C, Ge J. Epidemic of cardiovascular disease in China: current perspective and prospects for the future. Circulation. 2018;138(4):342–4. https://doi.org/10.1161/circulationaha.118.033484.

    Article  PubMed  Google Scholar 

  8. Ogurtsova K, Guariguata L, Barengo NC, Ruiz PL, Sacre JW, Karuranga S, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract. 2022;183:109118. https://doi.org/10.1016/j.diabres.2021.109118.

    Article  PubMed  Google Scholar 

  9. American Diabetes Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S17-s38. https://doi.org/10.2337/dc22-S002.

  10. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53. https://doi.org/10.1002/(sici)1096-9136(199807)15:7%3c539::Aid-dia668%3e3.0.Co;2-s.

    Article  CAS  PubMed  Google Scholar 

  11. Wang W, Russell A, Yan Y. Traditional Chinese medicine and new concepts of predictive, preventive and personalized medicine in diagnosis and treatment of suboptimal health. EPMA J. 2014;5(1):4. https://doi.org/10.1186/1878-5085-5-4.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28. https://doi.org/10.1186/2001-1326-1-28.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang W, Yan Y, Guo Z, Hou H, Garcia M, Tan X, et al All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine 2021 EPMA J. 1–31. https://doi.org/10.1007/s13167-021-00253-2.

  14. Ge S, Xu X, Zhang J, Hou H, Wang H, Liu D, et al. Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study. EPMA J. 2019;10(1):65–72. https://doi.org/10.1007/s13167-019-0159-9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang Y, Liu X, Qiu J, Wang H, Liu D, Zhao Z, et al. Association between Ideal Cardiovascular Health Metrics and Suboptimal Health Status in Chinese Population. Sci Rep. 2017;7(1):14975. https://doi.org/10.1038/s41598-017-15101-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yan YX, Dong J, Liu YQ, Yang XH, Li M, Shia G, et al. Association of suboptimal health status and cardiovascular risk factors in urban Chinese workers. J Urban Health. 2012;89(2):329–38. https://doi.org/10.1007/s11524-011-9636-8.

    Article  PubMed  Google Scholar 

  17. Adua E, Roberts P, Wang W. Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population. EPMA J. 2017;8(4):345–55. https://doi.org/10.1007/s13167-017-0119-1.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279–90. https://doi.org/10.1016/s0140-6736(12)60283-9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7(1):23. https://doi.org/10.1186/s13167-016-0072-4.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Golubnitschaja O, Kinkorova J, Costigliola V. Predictive, preventive and personalised medicine as the hardcore of “Horizon 2020”: EPMA position paper. EPMA J. 2014;5(1):6. https://doi.org/10.1186/1878-5085-5-6.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hirata T, Kizuka Y. N-Glycosylation. Adv Exp Med Biol. 2021;1325:3–24. https://doi.org/10.1007/978-3-030-70115-4_1.

    Article  CAS  PubMed  Google Scholar 

  22. Ohtsubo K, Marth JD. Glycosylation in cellular mechanisms of health and disease. Cell. 2006;126(5):855–67. https://doi.org/10.1016/j.cell.2006.08.019.

    Article  CAS  PubMed  Google Scholar 

  23. Moremen KW, Tiemeyer M, Nairn AV. Vertebrate protein glycosylation: diversity, synthesis and function. Nat Rev Mol Cell Biol. 2012;13(7):448–62. https://doi.org/10.1038/nrm3383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yu X, Wang Y, Kristic J, Dong J, Chu X, Ge S, et al. Profiling IgG N-glycans as potential biomarker of chronological and biological ages: a community-based study in a Han Chinese population. Medicine (Baltimore). 2016;95(28):e4112. https://doi.org/10.1097/md.0000000000004112.

    Article  CAS  PubMed  Google Scholar 

  25. Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health and disease. Nat Rev Nephrol. 2019;15(6):346–66. https://doi.org/10.1038/s41581-019-0129-4.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Cobb BA. The history of IgG glycosylation and where we are now. Glycobiology. 2020;30(4):202–13. https://doi.org/10.1093/glycob/cwz065.

    Article  CAS  PubMed  Google Scholar 

  27. Gudelj I, Lauc G, Pezer M. Immunoglobulin G glycosylation in aging and diseases. Cell Immunol. 2018;333:65–79. https://doi.org/10.1016/j.cellimm.2018.07.009.

    Article  CAS  PubMed  Google Scholar 

  28. Frkatovic A, Zaytseva OO, Klaric L. Genetic Regulation of Immunoglobulin G Glycosylation. Exp Suppl. 2021;112:259–87. https://doi.org/10.1007/978-3-030-76912-3_8.

    Article  CAS  PubMed  Google Scholar 

  29. Liu D, Li Q, Dong J, Li D, Xu X, Xing W, et al. The association between normal BMI with central adiposity and proinflammatory potential immunoglobuliN G N-glycosylation. Diabetes Metab Syndr Obes. 2019;12:2373–85. https://doi.org/10.2147/dmso.S216318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang Y, Klarić L, Yu X, Thaqi K, Dong J, Novokmet M, et al. The association between glycosylation of immunoglobulin G and hypertension: a multiple ethnic cross-sectional study. Medicine (Baltimore). 2016;95(17):e3379. https://doi.org/10.1097/md.0000000000003379.

    Article  CAS  PubMed  Google Scholar 

  31. Liu D, Zhao Z, Wang A, Ge S, Wang H, Zhang X, et al. Ischemic stroke is associated with the pro-inflammatory potential of N-glycosylated immunoglobulin G. J Neuroinflammation. 2018;15(1):123. https://doi.org/10.1186/s12974-018-1161-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Benešová I, Paulin Urminský A, Halámková J, Hernychová L. Changes of serum protein N-glycosylation in cancer. Klin Onkol. 2022;35(3):174–80. https://doi.org/10.48095/ccko2022174.

    Article  PubMed  Google Scholar 

  33. Silsirivanit A. Glycosylation markers in cancer. Adv Clin Chem. 2019;89:189–213. https://doi.org/10.1016/bs.acc.2018.12.005.

    Article  CAS  PubMed  Google Scholar 

  34. Li X, Wang H, Russell A, Cao W, Wang X, Ge S, et al. Type 2 Diabetes Mellitus is Associated with the Immunoglobulin G N-Glycome through Putative Proinflammatory Mechanisms in an Australian Population. OMICS. 2019;23(12):631–9. https://doi.org/10.1089/omi.2019.0075.

    Article  CAS  PubMed  Google Scholar 

  35. Liu J, Dolikun M, Stambuk J, Trbojevic-Akmacic I, Zhang J, Zhang J, et al. Glycomics for Type 2 Diabetes Biomarker Discovery: Promise of Immunoglobulin G Subclass-Specific Fragment Crystallizable N-glycosylation in the Uyghur Population. OMICS. 2019. https://doi.org/10.1089/omi.2019.0052.

    Article  PubMed  Google Scholar 

  36. Lemmers RFH, Vilaj M, Urda D, Agakov F, Šimurina M, Klaric L, et al. IgG glycan patterns are associated with type 2 diabetes in independent European populations. Biochim Biophys Acta Gen Subj. 2017;1861(9):2240–9. https://doi.org/10.1016/j.bbagen.2017.06.020.

    Article  CAS  PubMed  Google Scholar 

  37. Wu Z, Li H, Liu D, Tao L, Zhang J, Liang B, et al. IgG glycosylation profile and the glycan score are associated with type 2 diabetes in independent chinese populations: a case-control study. J Diabetes Res. 2020;2020:5041346. https://doi.org/10.1155/2020/5041346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wang Y, Ge S, Yan Y, Wang A, Zhao Z, Yu X, et al. China suboptimal health cohort study: rationale, design and baseline characteristics. J Transl Med. 2016;14(1):291. https://doi.org/10.1186/s12967-016-1046-y.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yan YX, Liu YQ, Li M, Hu PF, Guo AM, Yang XH, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19(6):333–41. https://doi.org/10.2188/jea.je20080086.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Simunovic J, Vilaj M, Trbojevic-Akmacic I, Momcilovic A, Vuckovic F, Gudelj I, et al. Comprehensive N-glycosylation analysis of immunoglobulin G from dried blood spots. Glycobiology. 2019. https://doi.org/10.1093/glycob/cwz061.

    Article  PubMed  Google Scholar 

  41. Huffman JE, Pucic-Bakovic M, Klaric L, Hennig R, Selman MH, Vuckovic F, et al. Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research. Mol Cell Proteomics. 2014;13(6):1598–610. https://doi.org/10.1074/mcp.M113.037465.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Trbojević-Akmačić I, Ugrina I, Lauc G. Comparative analysis and validation of different steps in glycomics studies. Methods Enzymol. 2017;586:37–55. https://doi.org/10.1016/bs.mie.2016.09.027.

    Article  CAS  PubMed  Google Scholar 

  43. Assmann SF, Hosmer DW, Lemeshow S, Mundt KA. Confidence intervals for measures of interaction. Epidemiology. 1996;7(3):286–90. https://doi.org/10.1097/00001648-199605000-00012.

    Article  CAS  PubMed  Google Scholar 

  44. Antza C, Kostopoulos G, Mostafa S, Nirantharakumar K, Tahrani A. The links between sleep duration, obesity and type 2 diabetes mellitus. J Endocrinol. 2021;252(2):125–41. https://doi.org/10.1530/joe-21-0155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Li K, Feng T, Wang L, Chen Y, Zheng P, Pan P, et al. Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization. Mol Genet Genomics. 2021;296(3):605–13. https://doi.org/10.1007/s00438-020-01752-z.

    Article  CAS  PubMed  Google Scholar 

  46. Zhou H, Zhang C, Ni J, Han X. Prevalence of cardiovascular risk factors in non-menopausal and postmenopausal inpatients with type 2 diabetes mellitus in China. BMC Endocr Disord. 2019;19(1):98. https://doi.org/10.1186/s12902-019-0427-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289(1):76–9. https://doi.org/10.1001/jama.289.1.76.

    Article  PubMed  Google Scholar 

  48. Wang TT. IgG Fc Glycosylation in Human Immunity. Curr Top Microbiol Immunol. 2019;423:63–75. https://doi.org/10.1007/82_2019_152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Raju TS. Terminal sugars of Fc glycans influence antibody effector functions of IgGs. Curr Opin Immunol. 2008;20(4):471–8. https://doi.org/10.1016/j.coi.2008.06.007.

    Article  CAS  PubMed  Google Scholar 

  50. Liu D, Chu X, Wang H, Dong J, Ge SQ, Zhao ZY, et al. The changes of immunoglobulin G N-glycosylation in blood lipids and dyslipidaemia. J Transl Med. 2018;16(1):235. https://doi.org/10.1186/s12967-018-1616-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lu JP, Knezevic A, Wang YX, Rudan I, Campbell H, Zou ZK, et al. Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations. J Proteome Res. 2011;10(11):4959–69. https://doi.org/10.1021/pr2004067.

    Article  CAS  PubMed  Google Scholar 

  52. Indellicato R, Trinchera M. Epigenetic Regulation of Glycosylation in Cancer and Other Diseases. Int J Mol Sci. 2021;22(6). https://doi.org/10.3390/ijms22062980.

  53. Zoldoš V, Novokmet M, Bečeheli I, Lauc G. Genomics and epigenomics of the human glycome. Glycoconj J. 2013;30(1):41–50. https://doi.org/10.1007/s10719-012-9397-y.

    Article  CAS  PubMed  Google Scholar 

  54. Nikolac Perkovic M, Pucic Bakovic M, Kristic J, Novokmet M, Huffman JE, Vitart V, et al. The association between galactosylation of immunoglobulin G and body mass index. Prog Neuropsychopharmacol Biol Psychiatry. 2014;48:20–5. https://doi.org/10.1016/j.pnpbp.2013.08.014.

    Article  CAS  PubMed  Google Scholar 

  55. Seeling M, Brückner C, Nimmerjahn F. Differential antibody glycosylation in autoimmunity: sweet biomarker or modulator of disease activity? Nat Rev Rheumatol. 2017;13(10):621–30. https://doi.org/10.1038/nrrheum.2017.146.

    Article  CAS  PubMed  Google Scholar 

  56. Malhotra R, Wormald MR, Rudd PM, Fischer PB, Dwek RA, Sim RB. Glycosylation changes of IgG associated with rheumatoid arthritis can activate complement via the mannose-binding protein. Nat Med. 1995;1(3):237–43. https://doi.org/10.1038/nm0395-237.

    Article  CAS  PubMed  Google Scholar 

  57. Karsten CM, Pandey MK, Figge J, Kilchenstein R, Taylor PR, Rosas M, et al. Anti-inflammatory activity of IgG1 mediated by Fc galactosylation and association of FcγRIIB and dectin-1. Nat Med. 2012;18(9):1401–6. https://doi.org/10.1038/nm.2862.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Axford JS, Mackenzie L, Lydyard PM, Hay FC, Isenberg DA, Roitt IM. Reduced B-cell galactosyltransferase activity in rheumatoid arthritis. Lancet. 1987;2(8574):1486–8. https://doi.org/10.1016/s0140-6736(87)92621-3.

    Article  CAS  PubMed  Google Scholar 

  59. Wang TT, Ravetch JV. Functional diversification of IgGs through Fc glycosylation. J Clin Invest. 2019;129(9):3492–8. https://doi.org/10.1172/jci130029.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Hmiel LK, Brorson KA, Boyne MT 2nd. Post-translational structural modifications of immunoglobulin G and their effect on biological activity. Anal Bioanal Chem. 2015;407(1):79–94. https://doi.org/10.1007/s00216-014-8108-x.

    Article  CAS  PubMed  Google Scholar 

  61. Nakano M, Mishra SK, Tokoro Y, Sato K, Nakajima K, Yamaguchi Y, et al. Bisecting GlcNAc Is a General Suppressor of Terminal Modification of N-glycan. Mol Cell Proteomics. 2019;18(10):2044–57. https://doi.org/10.1074/mcp.RA119.001534.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Komaromy A, Reider B, Jarvas G, Guttman A. Glycoprotein biomarkers and analysis in chronic obstructive pulmonary disease and lung cancer with special focus on serum immunoglobulin G. Clin Chim Acta. 2020;506:204–13. https://doi.org/10.1016/j.cca.2020.03.041.

    Article  CAS  PubMed  Google Scholar 

  63. Kaneko Y, Nimmerjahn F, Ravetch JV. Anti-inflammatory activity of immunoglobulin G resulting from Fc sialylation. Science. 2006;313(5787):670–3. https://doi.org/10.1126/science.1129594.

    Article  CAS  PubMed  Google Scholar 

  64. Quast I, Keller CW, Maurer MA, Giddens JP, Tackenberg B, Wang LX, et al. Sialylation of IgG Fc domain impairs complement-dependent cytotoxicity. J Clin Invest. 2015;125(11):4160–70. https://doi.org/10.1172/jci82695.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Meng X, Wang B, Xu X, Song M, Hou H, Wang W, et al. Glycomic biomarkers are instrumental for suboptimal health status management in the context of predictive, preventive, and personalized medicine. EPMA J. 2022;13(2):195–207. https://doi.org/10.1007/s13167-022-00278-1.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the participants and their families who donated their time and effort in helping to make this study possible.

Funding

The study was supported by grants from the China-Australian Collaborative Grant (NSFC 81561128020-NHMRC APP1112767) and the National Natural Science Foundation of China (81872920). The funding organization had the role in the design and conduct of the study and the collection, management, analysis, and interpretation of the data.

Author information

Author notes

  1. Xiaoni Meng and Fei Wang have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Beijing, 100069, China

    Xiaoni Meng, Biyan Wang, Youxin Wang & Wei Wang

  2. Health Management Institute, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China

    Fei Wang, Xiangyang Gao & Qiang Zeng

  3. School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China

    Xizhu Xu & Wei Wang

  4. Centre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Perth, WA, 6027, Australia

    Wei Wang

Authors
  1. Xiaoni Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangyang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biyan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xizhu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Youxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qiang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QZ, WW, and YW contributed to the conception and design. XM, FW, XG, BW, and XX contributed to the acquisition and analysis of the data. XM and FW drafted the manuscript. All authors made important contributions to editing and critically revising the manuscript for important intellectual content. WW, QZ, and YW guarantee this work, have full access to all of the data, and take responsibility for the integrity of the data.

Corresponding authors

Correspondence to Youxin Wang, Wei Wang or Qiang Zeng.

Ethics declarations

Ethics approval

Ethical approval was granted by the Ethics Committee of the Chinese PLA General Hospital (no. S2016-0681-01), according to the Helsinki Declaration.

Consent to participate

All subjects agreed to participate in the study and signed written informed consent.

Consent for publication

All authors have approved the final version to be published.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 156 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, X., Wang, F., Gao, X. et al. Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint. EPMA Journal 14, 1–20 (2023). https://doi.org/10.1007/s13167-022-00311-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13167-022-00311-3

Keywords

Search

Navigation