Prediction equations and point system derived from large-scale health check-up data for estimating diabetic risk in the Chinese population of Taiwan
Section snippets
Background
Prevalence and incidence rates of diabetes and diabetic complications are rapidly increasing in the world. In Taiwan, diabetes and associated cardiovascular consequences such as heart disease, stroke and end-stage renal disease are among the leading causes of death. Although diabetes has become more prevalent in recent years, studies [1], [2] have shown that diabetes can be prevented and its progression halted by appropriate lifestyle interventions. In order to stop disease progression early in
Study population
The MJ Health Screening Center is a chained private membership cooperation with four health check-up centers located in northern (Taipei), north western (Taoyuan), central (Taichung), and southern (Kaohsiung) Taiwan. These clinics provide routine health examination to members. A total of 24,899 non-diabetic subjects aged more than 35 year-old participated the exam during the period of 1994–1996 and had at least one or more health examinations during the period of 1997–2006. Use was made of the
Results
There was no significant difference between the training set and the testing set in baseline characteristics such as age, gender, BMI, and WC (Table 1). There was also no difference in cumulative diabetes incidence between the two sets (Table 1). However, diabetic patients had significantly higher levels of baseline age, body mass index, waist circumference, triglycerides, systolic blood pressure, diastolic blood pressure, and fasting glucose (Table 2).
The average and the standard deviation of
Main study findings
Six risk prediction equations for diabetes, varying in class and number of variables included in the model, were constructed in the current study. The performance of these equations differed in AUCs which ranged from 0.713 to 0.835. The simplest prediction model (model 1), including only sociodemographic and anthropometric variables, performed reasonably well. The second model (model 2), adding BP status information to model 1, performed better. The third model (model 3), including all the
Conflict of interest
There are no conflicts of interest.
Acknowledgements
This study was supported by a grant from the National Science Council, Taiwan (NSC-95-2314-001-012-MY3). We thanked MJ Health Life Company to provide their assistance in data acquirement.
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