Objective:To investigate the effects of rosuvastatin and lfuvastatin on patients with acute coronary syndrome (ACS) combing impaired glucose tolerance (IGT).
Methods: A total of 215 consecutive ACS patients combing IGT treated in our hospital from 2009-05 to 2011-05 were studied. The patients were randomly divided into 2 groups, Rosuvastatin group, the patients received rosuvastatin10mg/day, n=108 and Fluvastatin group, the patients received fluvastatin 40mg/day, n=107. The total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) levels before and at 6, 12, 24 months after medication, fasting blood glucose (FBG), 2-hour postprandial glucose (2hPBG) and the number of new-onset of diabetes patients were compared between 2 groups.
Results: After treatment, the TC, LDL-C levels were decreased (6, 12, 24 months) and the HDL-C level (12, 24 months), 2hPBG (24 months) were increased in both groups. Compared with Fluvastatin group, Rosuvastatin group had decreased TC and LDL-C (6, 12, 24 months), and increased LDL-C (24 months). With 6, 12, 24 months treatment, the blood lipids reached the standard were more in Rosuvastatin group than those in Fluvastatin group as 35.3%vs 26.1%, 36.4% vs 22.0%, 43.1% vs 31.8% respectively, all P<0.05. With 12 and 24 months treatment, the new-onset diabetes patients in Rosuvastatin group were 11 and 18, in Fluvastatin group were 12 and 17. With 12, 24 months treatment, FBG, 2hPBG levels and the number of new-onset diabetes patients were similar between 2 groups, P>0.05.
Conclusion: Compared with lfuvastatin, the conventional dose of rosuvastatin could better reduce the blood lipids level in ACS patients combing IGT, the effects for preventing ACS patients from IGT to diabetes were similar for both drugs.

Objective: To compare metabolic syndrome(MS)with Framingham risk score as predictors of cardiovascular disease(CVD)among Kazakhs population. Methods: The participants were the residents who had been followed up for more than 5 years in representative areas of Kazakhs in Xinjiang. We assigned MS a continuous risk score for predicting the development of CVD based on the weights of MS components. MS and Framingham risk score were compared in terms of their ability in predicting years in representative areas of Kazakhs in Xinjiang. We assigned MS a continuous risk score for predicting the development of CVD based on the weights of MS components. MS and Framingham risk score were compared in terms of their ability in predicting development of CVD using Cox regression and receiver operating characteristic curve. Results: The incidence of CVD was 13.87%. The incidence of CVD was higher in the MS group than it in the non-MS group(21.59% vs 11.10%, P<0.001). The area under the receiver operating characteristic(ROC)curve of MS risk score was significantly larger than that of MS classification(0.727 vs 0.585, P<0.001); the area under the curve of MS risk score was close to that of Framingham risk score(0.732 vs 0.727, P=0.673). The association between CVD and each quintile of MS risk score was more significant than that between Framingham risk score and CVD under the same exposed condition(4.61, 9.33, 14.15, 22.29 vs 3.69, 6.36, 8.47, 16.99). Conclusion MS risk score that included age may be a better predictor of CVD among Kazakhs population.

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.