Hua Hu1, Jing Wang1, Xu Han1, Yaru Li1, Xiaoping Miao2, Jing Yuan1, Handong Yang3, Meian He4. 1. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, Hubei, China. 2. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College Huazhong University of Science and Technology, Wuhan, Hubei, China. 3. Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China. 4. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, 430030, Hubei, China. hemeian@hotmail.com.
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.
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.
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Keywords:
Perspective cohort study; Prediction model; Type 2 diabetes