Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.

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.

Validation of non-invasive indicators in the screening of metabolic dysfunction-associated fatty liver disease: a cross-sectional study among Uighurs in rural Xinjiang.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease in China. Our study aimed to evaluate the screening value of the fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Zhejiang University index (ZJU), as well as other single indicators for MAFLD. We aimed to find the optimal screening tool and its appropriate cut-off values for rural Uyghur adults. METHODS: We completed a survey of 14,321 Uyghur adults in 51 groups in Kashgar, Xinjiang, in 2016 using a typical sampling method, with 12,794 patients ultimately included in statistical analyses. Fatty liver was diagnosed using ultrasonography. RESULTS: The prevalence of fatty liver disease (FLD) and MAFLD was 16.73% and 16.55%, respectively, and the FLI, HSI, LAP, VAI, and ZJU were all independently associated with an increased risk of MAFLD. The areas under the receiver operating characteristic curves (AUCs) of the FLI for diagnosing MAFLD in men and women were 0.853 and 0.847, respectively. The AUCs of the body mass index (BMI) for diagnosing MAFLD in men and women were 0.850 and 0.852, respectively. Compared with other metabolic-related markers, FLI had the largest AUC. In men, the optimal cut-off values of FLI and BMI for diagnosing MAFLD were 45 (sensitivity 84.83%, specificity 69.57%) and 27.4 (sensitivity 78.47%, specificity 76.30%), respectively. In women, the optimal cut-off values of FLI and BMI for diagnosing MAFLD were 45 (sensitivity 80.11%, specificity 74.23%) and 28.0 (sensitivity 79.56%, specificity 75.41%), respectively. In men and women, an FLI score of < 30 ruled out MAFLD, while a score of ≥ 50 was a basis for diagnosis. CONCLUSION: FLI and BMI had good screening ability for MAFLD and were superior to HSI, LAP, VAI, and ZJU in both sexes.

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.

PURPOSE: With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis. METHODS: Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD. RESULTS: After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang. CONCLUSION: The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.

Gallbladder disease is associated with the risk of cardiovascular disease among Uyghurs in Xinjiang: a prospective cohort study.

BACKGROUND: Gallbladder disease (GBD) can increase the risk of cardiovascular disease (CVD). However, GBD has rarely been reported in the less developed, rural areas of Xinjiang. This study aimed to determine the prevalence of GBD and incidence of CVD in a prospective cohort study in rural Xinjiang. Moreover, the study aimed to explore the association between GBD and CVD within this cohort. METHODS: The study cohort included 11,444 Uyghur adults in Xinjiang, 3rd division, from the 51st Mission. Study groups were classified according to whether GBD was present or absent at baseline. The occurrence of CVD was the end event. Demographic, anthropometric, and biochemical data were recorded, and the incidence of CVD in the GBD and non-GBD groups analysed. Cox proportional hazards regression models were used to assess the association between GBD and CVD and factors associated with their incidence. Several subgroup analyses were performed to assess CVD incidence in different subgroups. The interaction between GBD and cardiometabolic risk factors, and subsequent risk of developing CVD, was evaluated. RESULTS: Prevalence of GBD in the study cohort was 10.29%. After a median follow-up of 4.92 years, the cumulative incidence of CVD in the study cohort was 10.49%, 8.43% in males and 12.65% in females. CVD incidence was higher in the GBD group (34.04% vs. 7.78%, HR = 4.96, 95% CI: 4.40-5.59). After multivariate adjustment, the risk of CVD remained higher in the GBD group (HR = 2.89, 95% CI: 2.54-3.29). Subgroup analyses showed male sex, smoking, alcohol consumption, lack of exercise, and abnormal renal function were all associated with increased risk of CVD. Moreover, the risk of CVD was markedly higher in GBD combined with cardiometabolic risk factors (hypertension, T2DM, dyslipidaemia, overweight, and abdominal obesity), than in cardiometabolic risk factors alone and this was higher in the GBD group than in the non-GBD group regardless of whether cardiometabolic risk factors were combined. CONCLUSION: GBD is an important independent risk factor for CVD development. Awareness of these associations will raise concerns among clinicians about the risk of cardiovascular disease in patients with GBD.

Impact of the baseline insulin resistance surrogates and their longitudinal trajectories on cardiovascular disease (coronary heart disease and stroke): a prospective cohort study in rural China.

Background: This study aimed to assess the association of baseline insulin resistance (IR) surrogates and their longitudinal trajectories with cardiovascular diseases (CVD) to provide a useful reference for preventing CVD. Methods: This study was a prospective cohort study conducted in the 51st Regiment of the Third Division of Xinjiang Corps. A total of 6362 participants were recruited in 2016 to conduct the baseline survey, and the follow-up surveys in 2019, 2020, 2021, and 2022. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD according to the baseline IR surrogates of metabolic insulin resistance score (METS-IR) and triglyceride-glucose (TyG) index. Cox regression models were used to assess the association between the baseline IR surrogates and CVD. The impact of the longitudinal trajectories of the IR surrogates on CVD was analyzed after excluding those with IR surrogate data measured ≤2 times. Based on the group-based trajectory model (GBTM), the trajectory patterns of IR surrogates were determined. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD in each trajectory group of METS-IR and TyG index. Cox regression models were used to analyze the association between different trajectory groups of each index and CVD. In addition, the Framingham model was utilized to evaluate whether the addition of the baseline IR surrogates increased the predictive potential of the model. Results: Baseline data analysis included 4712 participants. During a median follow-up of 5.66 years, 572 CVD events were recorded (mean age, 39.42 ± 13.67 years; males, 42.9%). The cumulative CVD incidence increased with the ascending baseline METS-IR and TyG index quartiles (Q1-Q4). The hazard ratio and 95% confidence interval for CVD risk in Q4 of the METS-IR and TyG index were 1.79 (1.25, 2.58) and 1.66 (1.28, 2.17), respectively, when compared with Q1. 4343 participants were included in the trajectory analysis, based on the longitudinal change patterns of the METS-IR and TyG index, the following three trajectory groups were identified: low-increasing, moderate-stable, and elevated-increasing groups. Multivariate Cox regression revealed that the hazard ratio (95% confidence interval) for CVD risk in the elevated-increasing trajectory group of the METS-IR and TyG index was 2.13 (1.48, 3.06) and 2.63 (1.68, 4.13), respectively, when compared with the low-rising group. The C-index, integrated discrimination improvement value, and net reclassification improvement value were enhanced after adding the baseline METS-IR and TyG index values to the Framingham model (P<0.05). Conclusions: Elevated baseline IR surrogates and their higher long-term trajectories were strongly associated with a high risk of CVD incidence in Xinjiang's rural areas. Regular METS-IR and TyG index monitoring can aid in the early detection of CVD-risk groups.

Association of three insulin resistance indices with hypertension and body weight among Uyghur adults in rural areas of Xinjiang, China.

Obesity and insulin resistance are significant contributors to hypertension. There is a high prevalence of obesity among Uyghurs in the rural areas of Xinjiang, China. Therefore, this study aimed to explore the association between insulin resistance indices and hypertension according to different body weights in rural Uyghur residents of Xinjiang, China. A total of 12 813 local Uyghur residents were recruited for the study. Excluding those with incomplete data and those using antihypertensive, lipid-lowering, or glycemic drugs, 9577 permanent residents were eligible for the study. Three insulin resistance indicators were calculated: triglyceride to high-density lipoprotein cholesterol ratio, product of fasting triglyceride and glucose (TYG), and metabolic score for insulin resistance. Multivariate logistic regression analysis was performed to estimate the association between the three non-insulin-based insulin resistance indices and the risk of hypertension for different body weights. TYG was significantly associated with hypertension in the normal-weight group, particularly in women. In the obese group that was obese, all three indicators were associated with hypertension. Since TYG was associated with hypertension in the groups with normal weight and obesity, it may be useful as a reference indicator for insulin resistance. This indicator may provide a basis for the identification and management of hypertension risk among adults in the Uyghur population.

Association of H-Type Hypertension with miR-21, miR-29, and miR-199 in Kazahks of Xinjiang, China.

Objective: This study aims to analyze the expressions of miR-21, miR-29, and miR-199 in the serum of the patients with H-type hypertension among Kazakhs. Then, we analyzed the effect of MTHFR 677C > T polymorphism on the association between the above miRNA and H-type hypertension. Method: In this study, the expression of miR-21, miR-29, and miR-199 was quantitatively measured in 120 serum samples and then stratified according to the C677T polymorphism to analyze the relationship between target miRNAs and HHcy. Results: The expression of miR-21/-29 in the hypertension group was higher than the normal group (P < 0.001). And the expression of miR-199 was higher in the hcy group than in the normal group (P < 0.001). In the CC and CT genotypes of MTHFR 677C > T, the expression of miR-21 was lower in the HHcy patients than in the normal individuals (P = 0.005 and P = 0.001) and miR-199 was significantly higher in the HHcy patients than in the normal ones (P = 0.002 and P = 0.048). No such difference was found in the TT genotype. Logistic regression analysis showed that after adjusting for sex, age, BMI, systolic blood pressure, diastolic blood pressure, and MTHFRC677 T gene polymorphism, miR-21 was negatively correlated with hcy (OR = 0.222, 95% CI (0.101-0.485), P < 0.001) and miR-199 was positively correlated with hcy (OR = 1.823,95%CI (1.272∼2.614), P = 0.001). Conclusion: miR-21, miR-29, and miR-199 are associated with H-type hypertension in the Kazakhs, especially hyperhomocysteinemia. And these three miRNAs may serve as biomarkers to provide clues to the potential pathogenesis of H-type hypertension.

Updating Framingham CVD risk score using waist circumference and estimated cardiopulmonary function: a cohort study based on a southern Xinjiang population.

PURPOSE: To explore the association between waist circumference (WC), estimated cardiopulmonary function (eCRF), and cardiovascular disease (CVD) risk in southern Xinjiang. Update the Framingham model to make it more suitable for the southern Xinjiang population. METHODS: Data were collected from 7705 subjects aged 30-74 years old in Tumushuke City, the 51st Regiment of Xinjiang Production and Construction Corps. CVD was defined as an individual's first diagnosis of non-fatal acute myocardial infarction, death from coronary heart disease, and fatal or non-fatal stroke. The Cox proportional hazards regression analysis was used to analyze the association between WC, eCRF and CVD risk. Restricted cubic spline plots were drawn to describe the association of the two indicators with CVD risk. We update the model by incorporating the new variables into the Framingham model and re-estimating the coefficients. The discrimination of the model is evaluated using AUC, NRI, and IDI metrics. Model calibration is evaluated using pseudo R2 values. RESULTS: WC was an independent risk factor for CVD (multivariate HR: 1.603 (1.323, 1.942)), eCRF was an independent protective factor for CVD (multivariate HR: 0.499 (0.369, 0.674)). There was a nonlinear relationship between WC and CVD risk (nonlinear χ2 = 12.43, P = 0.002). There was a linear association between eCRF and CVD risk (non-linear χ2 = 0.27, P = 0.6027). In the male, the best risk prediction effect was obtained when WC and eCRF were added to the model (AUC = 0.763((0.734,0.792)); pseudo R2 = 0.069). In the female, the best risk prediction effect was obtained by adding eCRF to the model (AUC = 0.757 (0.734,0.779); pseudo R2 = 0.107). CONCLUSION: In southern Xinjiang, WC is an independent risk factor for CVD. eCRF is an independent protective factor for CVD. We recommended adding WC and eCRF in the male model and only eCRF in the female model for better risk prediction.

Comparison of obesity-related indicators for identifying metabolic syndrome among normal-weight adults in rural Xinjiang, China.

BACKGROUND: This study aimed to compare the ability of certain obesity-related indicators to identify metabolic syndrome (MetS) among normal-weight adults in rural Xinjiang. METHODS: A total of 4315 subjects were recruited in rural Xinjiang. The questionnaire, biochemical and anthropometric data were collected from them. Binary logistic regression was used to analyze the association between the z-score of each index and MetS. The area under the receiver-operating characteristic (ROC) curves were used to compare the diagnostic ability of each index. According to the cut-off value of each index, nomogram models were established and their diagnostic ability were evaluated. RESULTS: After adjusting for confounding factors, each indicator in different genders was correlated with MetS. Triglyceride-glucose index (TyG index) showed the strongest association with MetS in both males (OR = 3.749, 95%CI: 3.173-4.429) and females (OR = 3.521,95%CI: 2.990-4.148). Lipid accumulation product (LAP) showed the strongest diagnostic ability in both males (AUC = 0.831, 95%CI: 0.806-0.856) and females (AUC = 0.842, 95%CI: 0.820-0.864), and its optimal cut-off values were 39.700 and 35.065, respectively. The identification ability of the TyG index in different genders (males AUC: 0.817, females AUC: 0.817) was slightly weaker than LAP. Waist-to-height ratio (WHtR) had the similar AUC (males: 0.717, females: 0.747) to conicity index (CI) (males: 0.734, females: 0.749), whereas the identification ability of a body shape index (ABSI) (males AUC: 0.700, females AUC: 0.717) was relatively weak. Compared with the diagnostic ability of a single indicator, the AUC of the male nomogram model was 0.876 (95%CI: 0.856-0.895) and the AUC of the female model was 0.877 (95%CI: 0.856-0.896). The identification ability had been significantly improved. CONCLUSION: LAP and TyG index are effective indicators for identifying MetS among normal-weight adults in rural Xinjiang. Nomogram models including age, CI, LAP, and TyG index can significantly improve diagnostic ability.

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.

Background: Cardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learning (ML) algorithms to establish a CVD prediction model based on routine physical examination indicators suitable for the Xinjiang rural population. Method: The research cohort data collection was divided into two stages. The first stage involved a baseline survey from 2010 to 2012, with follow-up ending in December 2017. The second-phase baseline survey was conducted from September to December 2016, and follow-up ended in August 2021. A total of 12,692 participants (10,407 Uyghur and 2,285 Kazak) were included in the study. Screening predictors and establishing variable subsets were based on least absolute shrinkage and selection operator (Lasso) regression, logistic regression forward partial likelihood estimation (FLR), random forest (RF) feature importance, and RF variable importance. The selected subset of variables was compared with L1 regularized logistic regression (L1-LR), RF, support vector machine (SVM), and AdaBoost algorithm to establish a CVD prediction model suitable for this population. The incidence of CVD in this population was then analyzed. Result: After 4.94 years of follow-up, a total of 1,176 people were diagnosed with CVD (cumulative incidence: 9.27%). In the comparison of discrimination and calibration, the prediction performance of the subset of variables selected based on FLR was better than that of other models. Combining the results of discrimination, calibration, and clinical validity, the prediction model based on L1-LR had the best prediction performance. Age, systolic blood pressure, low-density lipoprotein-L/high-density lipoproteins-C, triglyceride blood glucose index, body mass index, and body adiposity index were all important predictors of the onset of CVD in the Xinjiang rural population. Conclusion: In the Xinjiang rural population, the prediction model based on L1-LR had the best prediction performance.

Metabolic Dysfunction-Associated Fatty Liver Disease Is Associated with the Risk of Incident Cardiovascular Disease: A Prospective Cohort Study in Xinjiang.

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.

TFEB ameliorates autophagy flux disturbance induced by PBDE-47 via up-regulating autophagy-lysosome fusion.

2,2',4,4'-tetrabromodiphenyl ether (PBDE-47), the widely used brominated flame retardant, has remarkable neurotoxicity which is associated with autophagy disorder. However, the mechanism remains unclear. The results showed that PBDE-47 damaged lysosomal biogenesis and interfered with autophagy-lysosome fusion both in vivo and in vitro. Our investigation further demonstrated that PBDE-47 could downregulate TFEB expression and inhibit the nuclear translocation of TFEB. Knockdown of TFEB in PC12 cells increased the reduction of lysosomal-associated proteins and the expression of STX17-SNAP29-VAMP8 proteins involved in autophagy-lysosomal fusion. Conversely, Overexpression TFEB in vitro significantly improved lysosomal abundance and ameliorated the autophagosome-lysosome fusion inhibition, thus restoring autophagic flux and improving PC12 cells survival. In addition, TFEB biologically interacted with STX17 by not inducing or inducing TFEB overexpression. Collectively, our results indicate that the autophagy flux compromised by PBDE-47 is related to the defective fusion of autophagosome and lysosome. TFEB may serve as a promising molecular target for future study of PBDE-47 developmental neurotoxicity.

Association between methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and H-type hypertension: A systematic review and meta-analysis.

PURPOSE: The polymorphism of methylenetetrahydrofolate reductase (MTHFR) gene C677T has been linked to H-type hypertension. But the conclusion remained controversial. To elucidate this issue, we performed a comprehensive meta-analysis to analyze the MTHFR C677T polymorphism and H-type hypertension. MATERIALS AND METHODS: The English and Chinese databases were systematically searched to identify relevant studies until November 2020. RevMan 5.3 and Stata 12.0 software were used for meta-analysis. The odds ratio (ORs) and 95% confidence intervals (95% CIs) were used to assess the relationship between the MTHFR C677T polymorphism and H-type hypertension. RESULTS: A total of 14 studies involving 1769 cases and 1443 controls were included. The meta-analysis results showed the association between MTHFR C677T polymorphism and H-type hypertension with the homozygous codominant model (OR = 3.30, 95% CI = 1.94-5.60), heterozygous codominant model (OR = 2.34, 95% CI = 1.53-3.58), dominant model (OR = 1.79, 95% CI = 1.33-2.41), recessive model (OR = 2.70, 95% CI = 1.73-4.21),and the allelic model (OR = 1.82, 95% CI = 1.41-2.35). All p-values were less than 0.05. Therefore, MTHFR C677T polymorphism has a positive correlation with the risk of H-type hypertension. Among them, TT mutation has the greatest impact on the activity of this enzyme, which causes Hcy to rise and leads to H-type hypertension. CONCLUSION: In summary, our results provide sufficient data to support the hypothesis that the MTHFR C677T polymorphism is related to H-type hypertension susceptibility.

Construction of a Personalized Insulin Resistance Risk Assessment Tool in Xinjiang Kazakhs Based on Lipid- and Obesity-Related Indices.

Purpose: This study aimed to explore the relationship between obesity- and lipid-related indices and insulin resistance (IR) and construct a personalized IR risk model for Xinjiang Kazakhs based on representative indices. Methods: This cross-sectional study was performed from 2010 to 2012. A total of 2170 Kazakhs from Xinyuan County were selected as research subjects. IR was estimated using the homeostasis model assessment of insulin resistance. Multivariable logistic regression analysis, least absolute shrinkage and selection operator penalized regression analysis, and restricted cubic spline were applied to evaluate the association between lipid- and obesity-related indices and IR. The risk model was developed based on selected representative variables and presented using a nomogram. The model performance was assessed using the area under the ROC curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). Results: After screening out 25 of the variables, the final risk model included four independent risk factors: smoking, sex, triglyceride-glucose (TyG) index, and body mass index (BMI). A linear dose-response relationship was observed for the BMI and TyG indices against IR risk. The AUC of the risk model was 0.720 based on an independent test and 0.716 based on a 10-fold cross-validation. Calibration curves showed good consistency between actual and predicted IR risks. The DCA demonstrated that the risk model was clinically effective. Conclusion: The TyG index and BMI had the strongest association with IR among all obesity- and lipid-related indices, and the developed model was useful for predicting IR risk among Kazakh individuals.

Predictive power of a body shape index and traditional anthropometric indicators for cardiovascular disease: a cohort study in rural Xinjiang, China.

BACKGROUND: A body shape index (ABSI) has been proven to be related to a population's CVD incidence. However, the application of this indicator has produced different results. AIM: This study aimed to evaluate the applicability of the ABSI in predicting the incidence of CVD in rural Xinjiang, China, and compare it with waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and body mass index (BMI). SUBJECTS AND METHODS: 5375 people aged 18 years or older were included in the study. We used the Cox proportional hazard model to evaluate the relationship between WC, WHR, WHtR, BMI, and ABSI and the incidence of CVD, the area under the curve (AUC) to evaluate the predictive power of each anthropometric index for the incidence of CVD, and restricted cubic splines are used to analyse the trend relationship between anthropometric indicators and the incidence of CVD. RESULTS: After multivariate adjustment, standardised WC, WHR, WHtR, BMI, and ABSI all positively correlated with the incidence of CVD. WC had the highest HR (95% CI) value, 1.64 (1.51-1.78), and AUC (95% CI) value, 0.7743 (0.7537-0.7949). ABSI had the lowest HR (95% CI) value, 1.21(1.10-1.32), and AUC (95% CI) value, 0.7419 (0.7208-0.7630). In the sex-specific sensitivity analysis, the predictive ability of traditional anthropometric indicators for the incidence of CVD is higher than that of ABSI. CONCLUSIONS: In the rural areas of Xinjiang, the traditional anthropometric indicators of WC had better ability to predict the incidence of CVD than ABSI.

Comparison of the Incidence of Cardiovascular Diseases in Weight Groups with Healthy and Unhealthy Metabolism.

BACKGROUND: We aimed to identify the relationship between metabolically healthy obesity (MHO), a special subtype of obesity, and the incidence of cardiovascular disease (CVD) in rural Xinjiang. METHODS: Body mass index (BMI) and the Joint Interim Statement criteria were utilized to define obesity and metabolic status, respectively. A baseline survey was conducted between 2010 and 2012. The cohort was followed-up until 2017, including 5059 participants (2953 Uyghurs and 2106 Kazakhs) in the analysis. RESULTS: During 6.78 years of follow-up, 471 individuals developed CVD, 10.8% (n=545) of whom were obese, and the prevalence of MHO and MHNW was 5.2% and 54.5%, respectively. Compared with metabolically healthy normal weight subjects, the subjects with MHO had an increased risk of CVD (hazard ratio [HR]=1.76, 95% confidence interval [CI]: 1.23-2.51), while the metabolically unhealthy obesity (MUO) group had an even higher risk (HR=3.80, 95% CI: 2.87-5.03). Additionally, there were sex differences in the relationship between BMI-metabolic status and incident CVD (P interaction =0.027). Compared with the subjects with MHO, those with MUO had an increased risk of CVD (HR=1.84, 95% CI: 1.26-2.71). CONCLUSION: MHO was associated with a high risk of CVD among adults in rural Xinjiang. In each BMI category, metabolically unhealthy subjects had a higher risk of developing CVD than did metabolically healthy subjects.

Metabolically healthy obesity and unhealthy normal weight rural adults in Xinjiang: prevalence and the associated factors.

BACKGROUND: This study aimed to describe the prevalence of metabolically healthy obesity (MHO) and metabolically unhealthy normal weight (MUNW) rural adults in Xinjiang and to explore their influencing factors. METHODS: We selected 13,525 Uyghur, Kazakh and Han participants in Kashi, Yili and Shihezi areas in Xinjiang from 2009 to 2010. Weight status was classified according to body mass index. Metabolic phenotype was further defined based on the National Cholesterol Education Program Adult Treatment Panel III criteria. RESULTS: The prevalence of normal weight, overweight, and obesity were 51.6, 30.2, and 14.4%, respectively. The mean age of the population was 45.04 years. The prevalence of MHO was 5.5% overall and was 38.5% among obese participants. The prevalence of MUNW was 15.5% overall and was 30.1% among normal weight participants. A metabolically healthy phenotype among obese individuals was positively associated with females and vegetable consumption ≥4 plates per week. However, this was inversely associated with higher age, red meat consumption ≥2 kg per week, and larger waist circumference (WC). Conversely, a metabolically unhealthy phenotype among normal-weight individuals was positively associated with higher age, red meat consumption ≥2 kg per week, and larger WC; this was however inversely associated with vegetable consumption ≥4 plates per week. CONCLUSIONS: The prevalence of MHO among obese adults in Xinjiang is higher than that of Han adults, while the prevalence of MUNW among normal weight adults is lower than that among Han adults. In obese and normal weight participants, higher age, more red meat consumption, and larger WC increase the risk of metabolic abnormality, and more vegetable consumption reduces the risk of metabolic abnormality.

Factor Analysis of Metabolic Syndrome and Its Relationship with the Risk of Cardiovascular Disease in Ethnic Populations in Rural Xinjiang, China.

BACKGROUND: This cohort study created a risk equation of CVD for the Uyghur and Kazakh ethnic groups with metabolic syndrome (MetS) in Xinjiang and its associated factors, evaluated the model's feasibility, and provided theoretical support for the prevention and early diagnosis of CVD. METHODS: A total of 5655 participants from Xinyuan and Jiashi counties in Xinjiang from 2010 to 2012 were selected, including 3770 and 1885 training and validation samples, respectively. A factor analysis was performed on 975 patients with MetS in the training sample, whereas potential factors related to CVD were extracted from 21 MetS biomarkers. Cox regression was used to create and verify a CVD-risk prediction model based on training samples. The receiver operating characteristic curve was drawn to evaluate the model's prediction efficiency. RESULTS: The cumulative incidence of CVD was 9.20% (training sample, 9.12%; validation sample, 9.36%). Nine potential factors were extracted from the training sample population with MetS to predict the CVD risk: lipid (hazard ratio [HR], 1.205), obesity (HR, 1.047), liver function (HR, 1.042), myocardial enzyme (HR, 1.008), protein (HR, 1.024), blood pressure (HR, 1.027), liver enzyme (HR, 1.012), renal metabolic (HR, 1.015), and blood glucose (HR, 1.010). The area under the curve of the training and validation samples was 0.841 (95% confidence interval [CI], 0.821-0.861) and 0.889 (95% CI, 0.870-0.909), respectively. CONCLUSION: The CVD prediction model created with nine potential factors in patients with MetS in Kazakh and Uyghur has a good predictive power.

Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China.

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.

Visceral Obesity-Related Indices in the Identification of Individuals with Metabolic Syndrome Among Different Ethnicities in Xinjiang, China.

BACKGROUND: Few studies have focused on the predictive ability of visceral obesity-related indices for metabolic syndrome (MetS), especially in different ethnic groups. This study aimed to evaluate the applicability of visceral obesity-related indices for MetS screening among three major ethnic groups living in remote rural areas of Xinjiang. METHODS: Based on multistage stratified cluster random sampling method, 3,192 Uyghurs, 3,054 Kazakhs, and 3,658 Hans were recruited from Xinjiang, China. The Joint Interim Statement (JIS) criteria were used to define MetS. The receiver operating characteristic curve (ROC), area under the ROC curve (AUC), and predictive value of each visceral obesity-related index were used to evaluate the predictive ability of MetS. RESULTS: After adjusting for potential confounding factors, the lipid accumulation product (LAP), Chinese visceral adiposity index (CVAI), waist-to-height ratio (WHtR), and atherogenic index of plasma (AIP) were significantly correlated with MetS for each ethnic group, and the odds ratios (ORs) for MetS increased across quartiles. LAP was best able to identify MetS status in Kazakhs (AUC=0.853) and Uyghurs (AUC=0.851), with optimal cut-offs being 36.3 and 28.2, respectively. Both LAP (AUC=0.798) and CVAI (AUC=0.791) most accurately identified MetS status in Hans, with the optimal cut-offs being 27.3 and 85.0, respectively. Moreover, the AUC of the combination of these visceral obesity-related indices is higher for each ethnic group. However, compared with LAP, the improved value of combined screening was not significant. CONCLUSION: LAP had the best discriminative capability for the screening of MetS among Kazakhs, Uyghurs, and Hans. The screening ability of CVAI for MetS was similar to that of LAP in Hans. Thus, LAP may be a complementary indicator for assessing MetS in various ethnic groups.