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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
  • 59 Downloads

Abstract

Aims

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Prediction model Perspective cohort study Type 2 diabetes 

Notes

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.

Funding

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)

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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

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