| The most updated research showed that prevalence of diabetes among Chinese adults is 9.7% hitting the top one across the world. The incidence of pre-diabetes is 15.5 per 100 person-years. It is estimated the number of people living with diabetes will keep increasing if there is no appropriate interventions. The epidemic will be more serious as diabetes result in complex complications, high morbidity and mortality rate, as well as heave disease burdens. Therefore, it is significant to identify high risk population more likely to develop diabetes and then to conduct life-style-related interventions at early stage. It is believed that incidence prediction model of diabetes individuals is effective to identify high risk population and then to propose appropriate interventive plans. The prediction model has been widely used to provide key information for disease prevention, policy making, and to evaluate the intervention outcomes in many countries. However, due to the lackage of effective population-based cohort study, China has not employ this model so far. Given the worsen epidemic of diabetes, it is of great importance to establish a prediction model which can fit into Chinese context at present.We systematically reviewed studies with unadjusted OR or RR values to address major high risk factors associated to diabetes and applied Meta analysis to measure the association. Synthesis analysis was applied to eliminate the collinearity of each risk factor and establish the individual level prediction model based on the data collected from National Nutrition and Health Status Survey conducted in 2002. To verify the prediction models, we used it to analyze a cohort study of a large enterprise in Beijing. We applied ROC approach of the interviewees to define the best cut point of the model with its sensitivity and specificity. We also applied the model on diabetes survey at Beijing and Zhejiang province.Through Meta analysis,10 risk factors, such as age, BMI, family history of diabetes, history of hypertension, TC, TG, LDL-C, hyperlipidemia, WC, and high concentration of fasting blood glucose and 2 protective factors, such as education level and concentration of serum HDL-C, were identified from 14 factors with OR values and 1 factor with RR value....We also found smoking, alcohol consumption and gender were weakly associated with diabetes.2 prediction models (Concise & Complex) were established based on the data of Nutrition and Health Status Survey (2002) to predict the incidence of diabetes in next 10 years among Chinese adults at the age of 20-70 years old. The concise model can be operated by the target population themselves without any professional assistant because only basic information and anthropometric data are needed in the model. Whereas, to apply the complex model, data of fasting blood glucose and lipid concentration are required. We verified the 2 models by applying them to analyzing data collected from a cohort study. It was showed the area under the curve of ROC was 0.68 with the sensitivity 57.9% and specificity 71.2% at the optimal cut-point 13.4% in the concise model. In the complex model, it was found the AUC was 0.81 and sensitivity and specificity was 74.7% and 80.0% respectively at the optimal cut-point. The prediction efficiency of complex model was similar to that of Taiwan's prediction model.If we used consice prediction model first and test fasting blood glucose and lipid of the individual whose diabetes risk in 10 years above 4%, than evaluated again with complex one, we will get a better ROC, which AUC would be 0.83 and the sensitivity and specificity was 80.0% and 77.3% respectively at the optimal cut-off (Youden index was 0.573). The prediction efficiency was better than that of the Taiwan's model (0.518) and also better than that of the combination of concise model and Taiwan's model (0.517).We calculated the chronic disease risk for each individual in Zhejiang and Beijing based on the software derived from the models. Considering many participants did not know they have diabetes before the appliaction of the model, we assessed the identification of the model to undiagnosed diabetes population and the result showed that the concise prediction model have the effect of prediction. The concise model could be applied based on individual's age, BMI, WC, family history of diabetes, and history of hypertension without biochemical testing. The area under the curve of ROC was 0.707. The sensitivity was 91.4% when the cutpoint was 4%. For population without diabetes, we could divide them into high risk and low risk group based on the individual risk of diabetes in 10 years and administer health management for those at high risk. Individual intervention measures can be made based on individual's own features and changed modifiable factors through the risk assessment, which was widely recognized and accepted by the assessment individuals and county level departments of disease control and prevention.A diabetes incidence prediction model for Chinese adults is established in this study. The model could be used to predict the risk of diabetes incidence in the next 10 years, to identify undiagnosed diabetes, to apply in diabetes health management, to develop individual intervention plans and to adjust health resources. It is the first model to predict the risk of diabetes of the next 10 years in China and it is the first time for the synthesis approach to be used in this field. The model is appropriate to be applied for health management and disease control in community hospitals and public health departments, for self health promotion, allocation of health resources, and for decision making on burden of disease related health policies.
|
PDF Full Text Request