Background: Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. Methods: The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). Results: The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p<0.05. Conclusion: KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks. Methods: The final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of included variables. Twenty-two variables, including sociodemographic characteristics, medical history, cytokines, and synthetic indices, were used for model development. Results: Among 1508 subjects, 203 were diagnosed with CVD over a median follow-up of 5.17 years. All 7 models had moderate to excellent discrimination (AUC ranged from 0.770 to 0.872) and were well calibrated. LR and SVM performed identically with an AUC of 0.872 (95% CI: 0.829– 0.907) and 0.868 (95% CI: 0.825– 0.904), respectively. LR had the lowest Brier score (0.078) and the highest sensitivity (97.1%). Decision curve analysis indicated that SVM was slightly better than LR. The inflammatory cytokines, such as hs-CRP and IL-6, were identified as strong predictors of CVD. Conclusion: SVM and LR can be applied to guide clinical decision-making in the Kazakh Chinese population, and further study is required to ensure their accuracies.

Objective: This study aimed to estimate the prevalence of diabetes mellitus (DM) and impaired fasting glucose (IFG) in a Kazakh population aged ≥18 years living in the YiLi District of Xinjiang, China and to evaluate the associated risk factors of diabetes. Methods: Randomly selected adults, living for at least 6 months in the YiLi District in Xinjiang had their clinical characteristics and standard blood chemistries measured. DM and IFG were defined according to WHO 1999 criteria. The adjusted odds ratio (ORs) and 95% confidence intervals were calculated for the association of diabetes risk factors in multivariate logistic regression models. Results: A total of 3919 subjects were randomly selected. The age-and gender-standardized prevalence of DM and IFG were 5.9% and 10.0%, respectively. The prevalence of DM and IFG increased with age and BMI. Prevalence of 7.4%, 12.2% in males and 4.9%, 8.6% in females for DM and IFG. Compared by sex, prevalence of DM and IFG was higher in males. Prevalence of 3.4%, 8.1% in normal, 6.7%, 11.9% in overweight and 12.0%, 13.0% in obesity for diabetes and IFG. In the multivariable logistic models, male sex, older age, unmarried, overweight, obesity, hypertension, triglycerides and smoking were all significantly associated with an increased risk of diabetes. Conclusions: The prevalence of DM and IFG among minorities was lower than the overall national level both in men and women (9.7% in total, 10.6% in males, 8.8% in females), and also lower than among the Han ethnicity (9.26%) which predominates in China today.

Objective: To compare the screening ability of various anthropometric and atherogenic indices for Metabolic syndrome (MetS) using three common criteria and to evaluate the validity of suitable parameters in combination for the screening of MetS among a Kazakh population in Xinjiang. Methods: A total of 3752 individuals were selected using the stratified cluster random sampling method from nomadic Kazakhs (≥18 years old) in Xinyuan county, Xinjiang, China, which is approximately 4407 km away from the capital Beijing. MetS was defined by the International Diabetes Federation (IDF), National Cholesterol Education Program Adult Treatment Panel III (ATP III) and Joint Interim Statement (JIS) criteria. The receiver operating characteristic curve (ROC) was used to compare the area under the ROC curve (AUC) of each index. The sensitivity, specificity, Youden’s index and cut-offs of each index for the screening of MetS were calculated. Results: According to the IDF, ATP III and JIS criteria, 18.61%, 10.51%, and 24.83% of males and 23.25%, 14.88%, and 25.33% of females had MetS. According to the IDF criteria, the waist-to-height ratio (WHtR) was the index that most accurately identified individuals with and without MetS both in males (AUC = 0.872) and females (AUC = 0.804), with the optimal cut-offs of 0.53 and 0.52, respectively. According to both the ATP III and JIS criteria, the lipid accumulation product (LAP) was the best index to discriminate between individuals with and without MetS in males (AUC = 0.856 and 0.816, respectively) and females (AUC = 0.832 and 0.788, respectively), with optimal cut-offs of 41.21 and 34.76 in males and 28.16 and 26.49 in females, respectively. On the basis of the IDF standard, Youden’s indices of WHtR and LAP serial tests for the screening of MetS were 0.590 and 0.455 in males and females, respectively, and those of WHtR and LAP parallel tests were 0.608 and 0.479, accordingly. Conclusion: According to the IDF, ATP III and JIS criteria, both the WHtR and LAP were better indices for the screening of MetS. The WHtR and LAP parallel test was the most accurate.

Background: Previous studies have evaluated the associations between the cholesteryl ester transfer protein (CETP) TaqIB polymorphism (rs708272), the risk of developing composite ischemic cardiovascular disease (CVD) and the concentration of high-density lipoprotein cholesterol (HDL-C), but results remain controversial. The objective of this study was to investigate whether a relationship exists between these factors. Methods: We conducted a meta-analysis of available studies to clarify the associations of the CETP TaqIB polymorphism with HDL-C concentration and the composite ischemic CVD risk in both Asians and Caucasians. All statistical analyses were done with Stata 12.0. Results: Through utilization of the Cochrane Library, Embase, PubMed, Web of Science, Springer, China Science and Technology Journal Database, China National Knowledge Infrastructure, Google Scholar, and Baidu Library, a total of 45 studies from 44 papers with 20,866 cases and 21,298 controls were combined showing a significant association between the CETP TaqIB variant and composite ischemic CVD risk. Carriers of allele TaqIB-B1 were found to have a higher risk of composite ischemic CVD than non-carriers: OR = 1.15, 95% CI = 1.09–1.21, p < 0.001. Meanwhile, 28 studies with 23,959 subjects were included in the association between the CETP TaqIB polymorphism and the concentration of HDL-C. Results suggested that carriers of the B1B1 genotype had lower concentrations of HDL-C than those of the B2B2 genotype: SMD = 0.50, 95% CI = 0.36–0.65, p < 0.001. Conclusions: The synthesis of available evidence demonstrates that the CETP TaqIB polymorphism protects against composite ischemic CVD risk and is associated with a higher HDL-C concentration in both Asians and Caucasians.

In 2020, a group of international experts proposed a new term ‘metabolic dysfunction-associated fatty liver disease’ (MAFLD) to replace ‘non-alcoholic fatty liver disease’. This study aimed to describe the epidemic characteristics of MAFLD, incidence of cardiovascular disease (CVD), and relationship between MAFLD and incident CVD. In 2016, 12,794 Uyghur adults from Kashgar, Xinjiang, were grouped according to the presence or absence of MAFLD. The primary outcome was the occurrence of CVD events. Fatty liver was diagnosed using ultrasound. The prevalence of MAFLD was 16.55%. After excluding patients with previous CVD, 11,444 participants were followed up for a median period of 4.7 years. During the follow-up period, the overall CVD incidence was 10.40% (1190/11,444). The incidence of CVD in the patients with MAFLD was significantly higher than that in the non-MAFLD patients (18.38% vs. 9.02%, p < 0.001; multivariable-adjusted hazard ratio = 1.37, 95% CI = 1.20–1.56). The prevalence of MAFLD was relatively low, whereas the incidence of CVD was relatively high among the Uyghur adults in rural Xinjiang. Individuals with MAFLD have a higher risk of developing CVD independent of traditional cardiovascular risk factors, obesity, type 2 diabetes mellitus (T2DM), and dyslipidaemia.