Prediction of 5-year risk of diabetes mellitus in relatively low risk middle-aged and elderly adults

  • Hua Hu
  • Jing Wang
  • Xu Han
  • Yaru Li
  • Xiaoping Miao
  • Jing Yuan
  • Handong Yang
  • Meian HeEmail author
Original Article



To determine the potential risk factors and construct the predictive model of diabetic risk among a relatively low risk middle-aged and elderly Chinese population.


Information of participants was collected in the Dongfeng-Tongji cohort study, a perspective cohort study of Chinese occupational population. The main outcome was incident type 2 diabetes (T2DM). Based on the conventional risk factors of diabetes, we defined low risk participants without underlying diseases such as coronary heart disease, stroke, cancer, dyslipidemia, hypertension, metabolic syndrome, obesity and family history of diabetes. Totally, 4833 participants from the Dongfeng-Tongji cohort study were enrolled, and of them, 171 had an incident diagnosis of T2DM during 4.6 years of follow-up period. A Cox proportional hazards model was used to estimate effects of risk factors. The restricted cubic spline regression and the Youden index were used to explore the optimal cutoffs of risk factors, and the C index was used to assess the discrimination power of prediction models.


There were significant linear relationships between BMI/TG level/fasting glucose level and incident diabetic risk among low risk participants. In the restricted cubic spline regression, when fasting glucose level was above 5.4 mmol/L, TG above 1.06 mmol/L and BMI above 22 kg/m2, the HRs (95% CIs) of diabetes were above 1.0. The detailed HRs (95% CI) were 1.29 (1.01, 1.64), 2.57 (1.00, 6.58), and 1.49 (1.00, 2.22), respectively. The optimal cutoff determined by the Yonden index was 1.1 mmol/L for TG, 24 kg/m2 for BMI and 5.89 mmol/L for fasting plasma glucose, respectively. The C index was 0.75 (95% CI: 0.7–0.81) when age, sex, smoke status, physical activity, BMI (< 24 kg/m2 and ≥ 24 kg/m2), TG (< 1.1 mmol/L and ≥ 1.1 mmol/L), and FPG (< 5.89 mmol/L and ≥ 5.89 mmol/L) were introduced into the diabetes predictive model.


Fasting plasma glucose level, BMI, and triglyceride level were still dominated factors to predict 5-year diabetic risk among the relatively low risk participants. The cutoff values for fasting plasma glucose, TG, and BMI set as 5.89 mmol/L, 1.1 mmol/L, and 24 kg/m2, respectively, had the best predictive discrimination of diabetes.


Prediction model Perspective cohort study Type 2 diabetes 


Author’s contribution

Hua Hu, and Meian He conceived and designed the study. All authors acquired, analyzed, or interpreted data and critically revised the manuscript for important intellectual content. Jing Yuan, Xiaoping Miao checked the data extraction. Hua Hu did the statistical analysis and drafted the manuscript. Meian He obtained funding and supervised the study. Meian He had full access to all of the data and took responsibility for the integrity of the data and the accuracy of the data analysis.


This work was supported by the Grants from the National Natural Science Foundation (Grants NSFC-81473051 and 81522040) and the Program for HUST Academic Frontier Youth Team.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

The study protocol was approved by the Ethics and Human Subject Committee of School of Public Health, Tongji Medical College, Huazhong University of Science and Technology and Dongfeng General Hospital, DMC.

Informed consent

All participants in this study gave written informed consent.

Supplementary material

592_2019_1375_MOESM1_ESM.docx (161 kb)
Supplementary material 1 (DOCX 161 kb)


  1. 1.
    International Diabetes Federation (2017) IDF diabetes atlas, 8th edn. International Diabetes Federation, Brussel, BelgiumGoogle Scholar
  2. 2.
    Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P (2009) Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 338:b880CrossRefGoogle Scholar
  3. 3.
    Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes. Diabetes Care 24(5):1047–1053CrossRefGoogle Scholar
  4. 4.
    Griffin SJ, Little PS, Hales CN, Kinmonth AL, Wareham NJ (2000) Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes/Metab Res Rev 16(3):164–171CrossRefGoogle Scholar
  5. 5.
    Lindström J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26(3):725–731CrossRefGoogle Scholar
  6. 6.
    Gray LJ, Taub NA, Khunti K et al (2010) The leicester risk assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabet Med 27(8):887–895CrossRefGoogle Scholar
  7. 7.
    Wilson PWF (2007) Prediction of incident diabetes mellitus in middle-aged adults. Arch Intern Med 167(10):1068CrossRefGoogle Scholar
  8. 8.
    Beshara A, Cohen E, Goldberg E, Lilos P, Garty M, Krause I (2016) Triglyceride levels and risk of type 2 diabetes mellitus: a longitudinal large study. J Investig Med 64(2):383–387CrossRefGoogle Scholar
  9. 9.
    Tirosh A, Shai I, Tekes-Manova D et al (2005) Normal fasting plasma glucose levels and type 2 diabetes in young men. New England J Med 353(14):1454–1462CrossRefGoogle Scholar
  10. 10.
    Odegaard AO, Koh WP, Vazquez G et al (2009) BMI and diabetes risk in Singaporean Chinese. Diabetes Care 32(6):1104–1106CrossRefGoogle Scholar
  11. 11.
    Wang F, Zhu J, Yao P et al (2013) Cohort profile: the Dongfeng-Tongji cohort study of retired workers. Int J Epidemiol 42(3):731–740CrossRefGoogle Scholar
  12. 12.
    Zhou X, Qiao Q, Ji L et al (2013) Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nation-wide diabetes survey. Diabetes Care 36:3944–3952CrossRefGoogle Scholar
  13. 13.
    Xu Y, Wang L, He J et al (2013) Prevalence and control of diabetes in Chinese adults. JAMA 310(9):948–959CrossRefGoogle Scholar
  14. 14.
    Daviglus ML, Pirzada A, Durazo-Arvizu R et al (2016) Prevalence of low cardiovascular risk profile among diverse hispanic/latino adults in the united states by age, sex, and level of acculturation: the hispanic community health study/study of latinos. J Am Heart Assoc 5(8):e003929CrossRefGoogle Scholar
  15. 15.
    Sun D, Li F, Zhang Y, Xu X (2014) Associations of the pre-pregnancy BMI and gestational BMI gain with pregnancy outcomes in Chinese women with gestational diabetes mellitus. Int J Clin Exp Med 7(12):5784–5789Google Scholar
  16. 16.
    Alberti KG, Eckel RH, Grundy SM et al (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120(16):1640–1645CrossRefGoogle Scholar
  17. 17.
    Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statist Med 23:2109–2123CrossRefGoogle Scholar
  18. 18.
    Qiu G, Zheng Y, Wang H et al (2016) Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults. Int J Epidemiol 45(5):1507–1516CrossRefGoogle Scholar
  19. 19.
    Hara K, Fujita H, Johnson TA et al (2014) Genome-wide association study identifies three novel loci for type 2 diabetes. Hum Mol Genet 23(1):239–246CrossRefGoogle Scholar
  20. 20.
    Gray BJ, Bracken RM, Turner D et al (2015) Different type 2 diabetes risk assessments predict dissimilar numbers at ‘high risk’: a retrospective analysis of diabetes risk-assessment tools. Br J Gen Pract 65(641):e852–860CrossRefGoogle Scholar
  21. 21.
    Besseling J, Kastelein JJ, Defesche JC, Hutten BA, Hovingh GK (2015) Association between familial hypercholesterolemia and prevalence of type 2 diabetes mellitus. JAMA 313(10):1029–1036CrossRefGoogle Scholar
  22. 22.
    Wang B, Zhang M, Liu Y et al (2018) Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. J Diabetes 10(8):641–652CrossRefGoogle Scholar
  23. 23.
    Capurso C, Capurso A (2012) From excess adiposity to insulin resistance: the role of free fatty acids. Vascul Pharmacol 57(2–4):91–97CrossRefGoogle Scholar
  24. 24.
    Boden G (1997) Role of fatty acids in the pathogenesis of insulin resistance and NIDDM. Diabetes 46(1):3–10CrossRefGoogle Scholar
  25. 25.
    Trauner M, Arrese M, Wagner M (2010) Fatty liver and lipotoxicity. Biochim Biophys Acta 1801(3):299–310CrossRefGoogle Scholar
  26. 26.
    Dotevall A, Johansson S, Wilhelmsen L, Rosengren A (2004) Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women: a prospective 18-year follow-up of the BEDA study. Diabetic Med 21(6):615–622CrossRefGoogle Scholar
  27. 27.
    Russo GT, De Cosmo S, Viazzi F, Pacilli A, Ceriello A, Genovese S (2016) Plasma triglycerides and HDL-C levels predict the development of diabetic kidney disease in subjects with type 2 diabetes: the AMD annals initiative. Diabetes Care 39(12):2278–2287CrossRefGoogle Scholar
  28. 28.
    Federation ID (2008) Efficacy of cholesterol-lowering therapy in 18 686 people with diabetes in 14 randomised trials of statins: a meta-analysis. The Lancet 371(9607):117–125CrossRefGoogle Scholar
  29. 29.
    Drew BG, Duffy SJ, Formosa MF et al (2009) High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus. Circulation 119(15):2103–2111CrossRefGoogle Scholar
  30. 30.
    Sone H, Tanaka S, Tanaka S et al (2011) Serum level of triglycerides is a potent risk factor comparable to LDL cholesterol for coronary heart disease in Japanese patients with type 2 diabetes: subanalysis of the Japan Diabetes Complications Study (JDCS). J Clin Endocrinol Metab 96(11):3448–3456CrossRefGoogle Scholar
  31. 31.
    Bragg F, Tang K, Guo Y et al (2018) Associations of general and central adiposity with incident diabetes in chinese men and women. Diabetes Care 41(3):494–502CrossRefGoogle Scholar
  32. 32.
    Dale CE, Fatemifar G, Palmer TM et al (2017) Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a mendelian randomization analysis. Circulation 135(24):2373–2388CrossRefGoogle Scholar
  33. 33.
    Meigs JB, Shrader P, Sullivan LM et al (2008) Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 359(21):2208–2219CrossRefGoogle Scholar
  34. 34.
    Lam TK, Pocai A, Gutierrez-Juarez R et al (2005) Hypothalamic sensing of circulating fatty acids is required for glucose homeostasis. Nat Med 11(3):320–327CrossRefGoogle Scholar
  35. 35.
    Razak F, Anand SS, Shannon H et al (2007) Defining obesity cut points in a multiethnic population. Circulation 115(16):2111–2118CrossRefGoogle Scholar
  36. 36.
    Association AD (2017) Classification and diagnosis of diabetes. Diabetes Care 40(Supplement 1):S11–S24CrossRefGoogle Scholar
  37. 37.
    Pich´e M (2004) What is a normal glucose value? Diabetes Care 27(10):2470–2477CrossRefGoogle Scholar
  38. 38.
    Pratley RE, Weyer C (2001) The role of impaired early insulin secretion in the pathogenesis of type II diabetes mellitus. Diabetologia 44(8):929–945CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2019

Authors and Affiliations

  • Hua Hu
    • 1
  • Jing Wang
    • 1
  • Xu Han
    • 1
  • Yaru Li
    • 1
  • Xiaoping Miao
    • 2
  • Jing Yuan
    • 1
  • Handong Yang
    • 3
  • Meian He
    • 1
    Email author
  1. 1.Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Epidemiology and Biostatistics, School of Public HealthTongji Medical College Huazhong University of Science and TechnologyWuhanChina
  3. 3.Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of MedicineShiyanChina

Personalised recommendations