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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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

Article  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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.

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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.

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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

Article  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

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  Google Scholar 

Raju TS. Termin