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Objective@#To construct and confirm a predictive model for the risks of cardiovascular diseases (CVD) with metabolic syndrome (MS) and its factors in Xinjiang Kazakh population.@*Methods@#A total of 2 286 Kazakh individuals were followed for 5 years from 2010 to 2012 as baseline survey. They were recruited in Xinyuan county, Yili city, Xinjiang. CVD cases were identified via medical records of the local hospitals in 2013, 2016 and 2017, respectively. Factor analysis was performed on 706 MS patients at baseline, and main factors, age, and sex were extracted from 18 medical examination indexs to construct a predictive model of CVD risk. After excluding the subjects with CVD at baseline and incomplete data, 2007 were used as internal validation, and 219 Kazakhs in Halabra Township were used as external validation. Logistic regression discriminations were used for internal validation and external validation, as well as to calculate the probability of CVD for each participant and receiver operating characteristic curves.@*Results@#The prevalence of MS in Kazakh was 30.88%. Seven main factors were extracted from the Kazakh MS population, namely obesity factor, blood lipid and blood glucose factor, liver function factor, blood lipid factor, renal metabolic factor, blood pressure factor, and liver enzyme factor. The area under the curve (AUC) for predicting CVD in the internal validation was 0.773 (95%CI 0.754-0.792). In the external validation, the AUC for predicting CVD was 0.858 (95%CI 0.805-0.901).@*Conclusions@#The CVD risk prediction model constructed by 7 main factors extracted from Kazakh MS patients has high validation efficiency and can be used for risk assessment of CVD in Xinjiang Kazakh population.
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Objective:To construct and confirm a predictive model for the risks of cardiovascular diseases (CVD) with metabolic syndrome (MS) and its factors in Xinjiang Kazakh population.Methods:A total of 2 286 Kazakh individuals were followed for 5 years from 2010 to 2012 as baseline survey. They were recruited in Xinyuan county, Yili city, Xinjiang. CVD cases were identified via medical records of the local hospitals in 2013, 2016 and 2017, respectively. Factor analysis was performed on 706 MS patients at baseline, and main factors, age, and sex were extracted from 18 medical examination indexs to construct a predictive model of CVD risk. After excluding the subjects with CVD at baseline and incomplete data, 2007 were used as internal validation, and 219 Kazakhs in Halabra Township were used as external validation. Logistic regression discriminations were used for internal validation and external validation, as well as to calculate the probability of CVD for each participant and receiver operating characteristic curves.Results:The prevalence of MS in Kazakh was 30.88%. Seven main factors were extracted from the Kazakh MS population, namely obesity factor, blood lipid and blood glucose factor, liver function factor, blood lipid factor, renal metabolic factor, blood pressure factor, and liver enzyme factor. The area under the curve (AUC) for predicting CVD in the internal validation was 0.773 (95% CI 0.754-0.792). In the external validation, the AUC for predicting CVD was 0.858 (95% CI 0.805-0.901). Conclusions:The CVD risk prediction model constructed by 7 main factors extracted from Kazakh MS patients has high validation efficiency and can be used for risk assessment of CVD in Xinjiang Kazakh population.
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Objective@#To study the epidemiological characteristics and related factors of dyslipidemia among adult residents in Xinjiang Uygur Autonomous Region (Xinjiang) in 2013-2014.@*Methods@#A total of 4 120 adult residents consisting of Han and Uygur group aged over 18 years old were selected by using a stratified cluster random sampling method in 8 counties of Xinjiang from 2013 to 2014. The related factors of dyslipidemia were collected by questionnaire and physical measurement. The total cholesterol, triglyceride, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol were detected by enzyme method. Factors associated with dyslipidemia were analyzed by chi-squared test and a multivariate unconditioned logistic regression model adjusted for gender, urban or rural area, age-group, body mass index (BMI), central obesity, smoking, drinking, education attainment, diabetes mellitus and hypertension.@*Results@#The prevalence of dyslipidemia was 45.00% (1 854 cases). The prevalence of dyslipidemia was higher in Uygur group (47.80% (977/2 044)) than that in Han group (42.24% (877/2 076)) (χ2=12.84, P<0.001). The analysis showed that dyslipidemia was related with gender (OR=0.41, 95%CI: 0.33-0.51), urban area (OR=0.54, 95%CI: 0.39-0.76), BMI (overweight group (OR=1.52, 95%CI: 1.18-1.96); obesity group (OR=2.20, 95%CI: 1.64-2.96)), central obesity (OR=1.66, 95%CI: 1.29-2.14) and diabetes mellitus (OR=1.49, 95%CI: 1.06-2.11) in Uygur group. The analysis also showed that dyslipidemia was related with BMI (overweight group (OR=1.72, 95%CI: 1.32-2.25), obesity group (OR=2.60, 95%CI: 1.85-3.64)), central obesity (OR=1.45, 95%CI: 1.13-1.87), smoking (OR=1.46, 95%CI: 1.09-1.95), diabetes mellitus (OR=1.77, 95%CI: 1.38-2.25) and hypertension (OR=1.62, 95%CI: 1.31-2.00) in Han group.@*Conclusions@#The prevalence of dyslipidemia in Xinjiang was higher than the national average prevalence. The prevalence of dyslipidemia in Uygur group was significantly higher than that in Han group. The gender, living area, BMI, central obesity and diabetes mellitus were risk factors of dyslipidemia in Uygur group, and BMI, central obesity, smoking, diabetes mellitus and hypertension were risk factors of dyslipidemia in Han group in Xinjiang.