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Development and validation of a nomogram to estimate future risk of type 2 diabetes mellitus in adults with metabolic syndrome: prospective cohort study

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Abstract

Objectives

To develop and validate the 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome.

Design

Retrospective cohort study of a large multicenter cohort with broad validation.

Settings

The derivation cohort was from 32 sites in China and the geographic validation cohort was from Henan population-based cohort study.

Results

568 (17.63) and 53 (18.67%) participants diagnosed diabetes during 4-year follow-up in the develo** and validation cohort, separately. Age, gender, body mass index, diastolic blood pressure, fasting plasma glucose and alanine aminotransferase were included in the final model. The area under curve for the training and external validation cohort was 0.824 (95% CI, 0.759–0.889) and 0.732 (95% CI, 0.594–0.871), respectively. Both the internal and external validation have good calibration plot. A nomogram was constructed to predict the probability of diabetes during 4-year follow-up, and on online calculator is also available for a more convenient usage (https://lucky0708.shinyapps.io/dynnomapp/).

Conclusion

We developed a simple diagnostic model to predict 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome, which is also available as web-based tools (https://lucky0708.shinyapps.io/dynnomapp/).

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Data availability

The datasets generated and/or analyzed during the current study are available in the DRYAD database repository, for the derivation cohort can be downloaded from https://doi.org/10.5061/dryad.ft8750v, and for the validation part can be downloaded from https://datadryad.org/stash/share/HNXtM6qk-4uSRGSP90XkBKzIi818a4R40i5EWblEog0.

References

  1. Mortality, G.B.D. and C. Causes of Death, Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385(9963), 117–171 (2015)

    Article  Google Scholar 

  2. Y. Zheng, S.H. Ley, F.B. Hu, Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14(2), 88–98 (2018)

    Article  PubMed  Google Scholar 

  3. American Diabetes Association, 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care 43(Suppl 1), S14–S31 (2020)

    Article  Google Scholar 

  4. L. Wang et al. Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013. JAMA 317(24), 2515–2523 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  5. S.H. Yang, K.F. Dou, W.J. Song, Prevalence of diabetes among men and women in China. N. Engl. J. Med. 362(25), 2425–2426 (2010).

    Article  PubMed  Google Scholar 

  6. Diabetes Prevention Program Research Group, Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study. Lancet Diabetes Endocrinol. 3(11), 866–875 (2015)

    Article  PubMed Central  Google Scholar 

  7. J. Lindstrom et al. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet 368(9548), 1673–1679 (2006)

    Article  PubMed  Google Scholar 

  8. Q. Gong et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol. 7(6), 452–461 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  9. W.H. Herman et al. Impact of lifestyle and metformin interventions on the risk of progression to diabetes and regression to normal glucose regulation in overweight or obese people with impaired glucose regulation. Diabetes Care 40(12), 1668–1677 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  10. M.G. Saklayen, The global epidemic of the metabolic syndrome. Curr. Hypertens. Rep. 20(2), 12 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  11. P. Ranasinghe et al. Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: a systematic review. BMC Public Health 17(1), 101 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Z. Gu et al. Obesity and lipid-related parameters for predicting metabolic syndrome in Chinese elderly population. Lipids Health Dis. 17(1), 289 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. E. Muzurovic, D.P. Mikhailidis, C. Mantzoros, Non-alcoholic fatty liver disease, insulin resistance, metabolic syndrome and their association with vascular risk. Metabolism 119, 154770 (2021)

    Article  CAS  PubMed  Google Scholar 

  14. N.C. do Vale Moreira et al. Prevalence of Metabolic Syndrome by different definitions, and its association with type 2 diabetes, pre-diabetes, and cardiovascular disease risk in Brazil. Diabetes Metab. Syndr. 14(5), 1217–1224 (2020)

    Article  PubMed  Google Scholar 

  15. Y. Chen et al. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open 8(9), e021768 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  16. J. Lu et al. Predictive value of fasting glucose, postload glucose, and hemoglobin A1c on risk of diabetes and complications in Chinese adults. Diabetes Care 42(8), 1539–1548 (2019)

    Article  CAS  PubMed  Google Scholar 

  17. T. Wang et al. Ideal cardiovascular health metrics and major cardiovascular events in patients with prediabetes and diabetes. JAMA Cardiol. 4(9), 874–883 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  18. C. Chen, F.C. Lu; P.R.C. Department of Disease Control Ministry of Health, The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed. Environ. Sci. 17(Suppl), 1–36 (2004)

    PubMed  Google Scholar 

  19. Chinese Preventive Medicine, Association et al., [Chinese guideline on healthy lifestyle to prevent cardiometabolic diseases]. Zhonghua Yu Fang Yi Xue Za Zhi 54(3), 256–277 (2020)

  20. American Diabetes, A., Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1), S62–9 (2010)

    Article  Google Scholar 

  21. G.S. Collins et al. Transparent reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br. J. Surg. 102(3), 148–158 (2015)

    Article  CAS  PubMed  Google Scholar 

  22. P. Lunde et al. The effectiveness of smartphone apps for lifestyle improvement in noncommunicable diseases: systematic review and meta-analyses. J. Med. Internet Res. 20(5), e162 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  23. S. Chatterjee, K. Khunti, M.J. Davies, Type 2 diabetes. Lancet 389(10085), 2239–2251 (2017)

    Article  CAS  PubMed  Google Scholar 

  24. R.A. DeFronzo et al. Type 2 diabetes mellitus. Nat. Rev. Dis. Prim. 1, 15019 (2015)

    Article  PubMed  Google Scholar 

  25. U. Galicia-Garcia et al. Pathophysiology of type 2 diabetes mellitus. Int J. Mol. Sci. 21, 17 (2020)

    Article  Google Scholar 

  26. Z.T. Bloomgarden, Dyslipidemia and the metabolic syndrome. Diabetes Care 27(12), 3009–3016 (2004)

    Article  PubMed  Google Scholar 

  27. J. Beulens et al. Risk and management of pre-diabetes. Eur. J. Prev. Cardiol. 26(2_suppl), 47–54 (2019)

    Article  PubMed  Google Scholar 

  28. R. Williams et al. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin. Pract. 162, 108072 (2020)

    Article  PubMed  Google Scholar 

  29. T. Xu et al. A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants. EPMA J. 13(3), 397–405 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  30. K. Wang et al. Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents. EPMA J. 10(3), 227–237 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  31. K. Liang et al. Nomogram predicting the risk of progression from prediabetes to diabetes after a 3-year follow-up in chinese adults. Diabetes Metab. Syndr. Obes. 14, 2641–2649 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  32. Y. Han et al. Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: development and validation of a novel model. Diabetes Obes. Metab. 25(3), 675–687 (2023)

    Article  CAS  PubMed  Google Scholar 

  33. Q. Liu et al. Development and validation of a nomogram to predict type 2 diabetes mellitus in overweight and obese adults: a prospective cohort study from 82938 adults in China. Int J. Endocrinol. 2020, 8899556 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  34. R.H. Eckel et al. The metabolic syndrome. Lancet 375(9710), 181–183 (2010)

    Article  PubMed  Google Scholar 

  35. B.M. Cheung et al. Development of diabetes in Chinese with the metabolic syndrome: a 6-year prospective study. Diabetes Care 30(6), 1430–1436 (2007)

    Article  PubMed  Google Scholar 

  36. C. Ding et al. The associations of metabolic syndrome with incident hypertension, type 2 diabetes mellitus and chronic kidney disease: a cohort study. Endocrine 60(2), 282–291 (2018)

    Article  CAS  PubMed  Google Scholar 

  37. C.M. Ferrara, A.P. Goldberg, Limited value of the homeostasis model assessment to predict insulin resistance in older men with impaired glucose tolerance. Diabetes Care 24(2), 245–249 (2001)

    Article  CAS  PubMed  Google Scholar 

  38. M. Ng et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384(9945), 766–781 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  39. M.L. Power, J. Schulkin, Sex differences in fat storage, fat metabolism, and the health risks from obesity: possible evolutionary origins. Br. J. Nutr. 99(5), 931–940 (2008)

    Article  CAS  PubMed  Google Scholar 

  40. M.F. Yao et al. Gender differences in risks of coronary heart disease and stroke in patients with type 2 diabetes mellitus and their association with metabolic syndrome in China. Int J. Endocrinol. 2016, 8483405 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  41. A. Vaiserman, O. Lushchak, Developmental origins of type 2 diabetes: focus on epigenetics. Ageing Res Rev. 55, 100957 (2019)

    Article  CAS  PubMed  Google Scholar 

  42. C. Aguayo-Mazzucato et al. Acceleration of beta cell aging determines diabetes and senolysis improves disease outcomes. Cell Metab. 30(1), 129–142.e4 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. P. Muntner et al. Comparison of the Framingham Heart Study hypertension model with blood pressure alone in the prediction of risk of hypertension: the Multi-Ethnic Study of Atherosclerosis. Hypertension 55(6), 1339–1345 (2010)

    Article  CAS  PubMed  Google Scholar 

  44. S.S. Mahmood et al. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383(9921), 999–1008 (2014)

    Article  PubMed  Google Scholar 

  45. C.E. Ruhl, J.E. Everhart, Determinants of the association of overweight with elevated serum alanine aminotransferase activity in the United States. Gastroenterology 124(1), 71–79 (2003)

    Article  CAS  PubMed  Google Scholar 

  46. R. Wejstal et al. Persistent alanine aminotransferase elevation in healthy Swedish blood donors–mainly caused by obesity. Vox Sang. 55(3), 152–156 (1988)

    Article  CAS  PubMed  Google Scholar 

  47. L.S. Friedman et al. Evaluation of blood donors with elevated serum alanine aminotransferase levels. Ann. Intern. Med. 107(2), 137–144 (1987)

    Article  CAS  PubMed  Google Scholar 

  48. R.K. Schindhelm et al. Alanine aminotransferase as a marker of non-alcoholic fatty liver disease in relation to type 2 diabetes mellitus and cardiovascular disease. Diabetes Metab. Res Rev. 22(6), 437–443 (2006)

    Article  CAS  PubMed  Google Scholar 

  49. J. Westerbacka et al. Women and men have similar amounts of liver and intra-abdominal fat, despite more subcutaneous fat in women: implications for sex differences in markers of cardiovascular risk. Diabetologia 47(8), 1360–1369 (2004)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Jiayi Yi, National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Bei**g, China, and Dr. Zhongheng Zhang, Department of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China, for their advice on the study design.

Author contributions

Design: T.Y.; J.W.; L.W.; Gu.Q.; Y.Z.; Data collection: T.Y.; N.J.; M.P.; Analysis: T.Y.; F.G.; F.H.; Y.Song; Y.Z.; Writing manuscript: T.Y.; Y.Z.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Natural Science Foundation of China (U2004116, 81800734), the National Key Research and Development Program of China (2017YFC1309800) and the Key Project of Medical Science and Technology of Henan Province (SB201901046).

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Authors and Affiliations

  1. Division of Endocrinology, Department of Internal Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China

    Tongyue Yang, Jiao Wang, Lina Wu, Feng Guo, Fengjuan Huang, Yi Song, Na **g, Mengxing Pan, **aoxu Ding, Zhe Cao, Shiyu Liu, Guijun Qin & Yanyan Zhao

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  1. Tongyue Yang
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Corresponding author

Correspondence to Yanyan Zhao.

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Conflict of interest

The authors declare no competing interests.

Ethics approval and consent to participate

The Rich Healthcare Group Review Board examined and granted approval for the investigations using human subjects in the derivation cohort. In compliance with national law and institutional standards, written informed permission was not needed for participation in this study. Additionally, the Ethical Review Committee of Rui** Hospital reviewed and authorized the human participants in the broad validation cohort (RUIJIN-2011-14). To take part in this study, the patients/participants gave their written informed consent.

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Yang, T., Wang, J., Wu, L. et al. Development and validation of a nomogram to estimate future risk of type 2 diabetes mellitus in adults with metabolic syndrome: prospective cohort study. Endocrine 80, 336–345 (2023). https://doi.org/10.1007/s12020-023-03329-3

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