An accurate risk score for estimation 5-year risk of type 2 diabetes based on a health screening population in Taiwan
Introduction
Along with India, China is one of the countries with the largest number of people suffering from diabetes mellitus [1]. With the increasing popularity of fast foods, fried foods, instant noodles and sweets, Taiwanese are now facing an enormous threat. It is estimated that near a million people in Taiwan in 2005 (190 million worldwide) have Type 2 Diabetes Mellitus, and around 350,000 people do not even know they have it. On average, diabetes kills around 30 people a day in Taiwan alone, indicating that diabetes mellitus has become one of the major public health problems in Taiwan [2], [3]. Hence an accurate screening tool to identify those at high risk of developing diabetes will be of great value, it will be beneficial for early recognition of and intervention for type 2 diabetes, particularly as cardiovascular complications set in early after the onset of diabetes. Algorithms published [4], [5], [6], [7] previously had suggested the concept of integrating the risk of type 2 diabetes into a single model and then using the resulting model in clinical practice is particularly valuable in countries where type 2 diabetes risk is increasing rapidly, as it is in China.
Some longitudinal studies have contributed to the identification of risk factors for type 2 diabetes and have developed multivariate functions to predict absolute type 2 diabetes risk [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. However, there are limitations in applications of the risk functions. Most of them have been developed among western population [4], [8], [9], [10], [11], [12], [13], [14], [15], other ethnic populations may differ from Caucasians as Western in terms of diet, life style, social environment, or genetic predisposition [17]. Only four diabetes risk scores has been involved in Asian population [5], [6], [7], [16], and one [16] was a prevalence study carried out in a part of Asia very different from Chinese, the other two [5], [6] from Thai and part of South Asian people with low the areas under the ROC curve. The latest published model developed from Taiwan population [7] was lack of inclusion of important risk factors (e.g., history of type 2 diabetes, blood pressure variables, smoking and alcohol consumption), which may result in its low validity.
Therefore, we constructed a risk prediction model of type 2 diabetes among Taiwanese enrolled in MJ Health Check-up Corporation to evaluate the onset risk of diabetes mellitus in Taiwanese periodic check-up people. The development of this prediction model was based on a cohort with a follow up of 10 years between 1997 and 2006, using clinical information, alone or combined with simple laboratory measures.
Section snippets
Study subjects and measurements
Taiwan MJ Longitudinal health-check-up-based Population Database (MJLPD) has been described in detail elsewhere previously [18], [19], [20], [21], [22]. MJ Health Screening Center is a private membership chain clinic with 4 health screening centers (Taipei, Taoyuan, Taichung, and Kaohsiung, respectively) around the Taiwan Island, which provides periodic health examination to its members, whose laboratory data have been recorded electrically since 1994. Records from the MJLPD database can be
Baseline characteristics
Table 1 compares the baseline clinical and laboratory characteristics of patients with and without incident type 2 diabetes in the exploratory subsample and the validation subsample, and shows that relationships of variables with incident diabetes in the validation cohort were broadly similar to those in the exploratory cohort.
Construction of Cox model
We produced four risk functions (see Table 2) for detecting incident diabetes on the MJLPD study cohort using Cox models with stepwise selection in training sample
Discussion
Estimation of absolute risk required for prevention or treatment of type 2 diabetes commonly relies on prediction models developed from the experience of prospective cohort studies. Although other risk prediction algorithms have been developed elsewhere, but few models were developed using Cox proportional hazards model based on follow-up study except two. One was Germany study [4], the performance of which, however, we could not validate because of lack of quantitative dietary factors in our
Conflict of interest
The authors state that they have no conflict of interest.
Acknowledgment
This study data was supplied by the MJ Health Screening Center.
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