Fluoride Induces Neurocytotoxicity by Disrupting Lysosomal Iron Metabolism and Membrane Permeability.

The detrimental effects of fluoride on neurotoxicity have been widely recorded, yet the detailed mechanisms underlying these effects remain unclear. This study explores lysosomal iron metabolism in fluoride-related neurotoxicity, with a focus on the Steap3/TRPML1 axis. Utilizing sodium fluoride (NaF)-treated human neuroblastoma (SH-SY5Y) and mouse hippocampal neuron (HT22) cell lines, our research demonstrates that NaF enhances the accumulation of ferrous ions (Fe2+) in these cells, disrupting lysosomal iron metabolism through the Steap3/TRPML1 axis. Notably, NaF exposure upregulated ACSL4 and downregulated GPX4, accompanied by reduced glutathione (GSH) levels and superoxide dismutase (SOD) activity and increased malondialdehyde (MDA) levels. These changes indicate increased vulnerability to ferroptosis within neuronal cells. The iron chelator deferoxamine (DFO) mitigates this disruption. DFO binds to lysosomal Fe2+ and inhibits the Steap3/TRPML1 axis, restoring normal lysosomal iron metabolism, preventing lysosomal membrane permeabilization (LMP), and reducing neuronal cell ferroptosis. Our findings suggest that interference in lysosomal iron metabolism may mitigate fluoride-induced neurotoxicity, underscoring the critical role of the Steap3/TRPML1 axis in this pathological process.

Global burden of maternal disorders attributable to malnutrition from 1990 to 2019 and predictions to 2035: worsening or improving?

Background and aims: Maternal malnutrition is a major global public health problem that can lead to serious maternal diseases. This study aimed to analyze and predict the spatio-temporal trends in the burden of maternal disorders attributable to malnutrition, and to provide a basis for scientific improvement of maternal malnutrition and targeted prevention of maternal disorders. Methods: Data on maternal disorders attributable to malnutrition, including number of deaths, disability-adjusted life years (DALYs), population attributable fractions (PAFs), age-standardized mortality rates (ASMRs), and age-standardized DALY rates (ASDRs) were obtained from the Global Burden of Disease Study 2019 to describe their epidemiological characteristics by age, region, year, and type of disease. A log-linear regression model was used to calculate the annual percentage change (AAPC) of ASMR or ASDR to reflect their temporal trends. Bayesian age-period-cohort model was used to predict the number of deaths and mortality rates to 2035. Results: Global number of deaths and DALYs for maternal disorders attributable to malnutrition declined by 42.35 and 41.61% from 1990 to 2019, with an AAPC of -3.09 (95% CI: -3.31, -2.88) and -2.98 (95% CI: -3.20, -2.77) for ASMR and ASDR, respectively. The burden was higher among younger pregnant women (20-29 years) in low and low-middle socio-demographic index (SDI) regions, whereas it was higher among older pregnant women (30-39 years) in high SDI region. Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. Maternal hemorrhage had the highest burden of all diseases. Global deaths are predicted to decline from 42,350 in 2019 to 38,461 in 2035, with the ASMR declining from 1.08 (95% UI: 0.38, 1.79) to 0.89 (95% UI: 0.47, 1.31). Conclusion: Maternal malnutrition is improving globally, but in the context of the global food crisis, attention needs to be paid to malnutrition in low SDI regions, especially among young pregnant women, and corresponding measures need to be taken to effectively reduce the burden of disease.

Associations of Trimester-Specific Exposure to Perchlorate, Thiocyanate, and Nitrate with Childhood Neurodevelopment: A Birth Cohort Study in China.

Studies about the impacts of maternal exposure to perchlorate, thiocyanate, and nitrate on offspring neurodevelopment are scarce. Based on a birth cohort in China, 1,028 mothers provided urine samples at three trimesters for determination of the three target analytes, and their offspring neurodevelopment was evaluated at 2 years old. Associations of maternal exposure to the three chemicals with offspring neurodevelopment were estimated using three statistical methods. Trimester-specific analyses using generalized estimating equation models showed that double increment of thiocyanate and nitrate during the first trimester was associated with 1.56 (95% CI: -2.82, -0.30) and 1.22 (-2.40, -0.03) point decreases in the offspring mental development index (MDI), respectively. Weighted quantile sum (WQS) regression analyses showed that the mixture exposure at the first and second trimesters was negatively associated with the offspring MDI (ß = -2.39, 95% CI: -3.85, -0.93; ß = -1.75, 95% CI: -3.04, -0.47, respectively) and thiocyanate contributed the most to the association (65.0 and 91.6%, respectively). Bayesian kernel machine regression analyses suggested an inverted U-shape relationship of maternal urinary thiocyanate with the offspring MDI. These findings suggested that prenatal exposure to the three chemicals (at current levels), especially thiocyanate and nitrate, may impair neurodevelopment. Early pregnancy seems to be the sensitive window.

Associations of plasma manganese with adverse pregnancy outcomes: Nested case-control studies in a Chinese birth cohort.

Both insufficient and excessive manganese exposure are associated with adverse pregnancy outcomes. However, no systematic research has established a standardized reference range for manganese levels with the consideration of the associated health risks during pregnancy. To verify the associations of prenatal manganese exposure with adverse outcomes and to identify a proper reference range for manganese based on health risks, we designed three nested case-control studies on three adverse outcomes including hypertensive disorders of pregnancy (HDP), preterm birth (PTB), and low birth weight (LBW) to investigate the associations with manganese levels. Plasma manganese concentrations in early pregnancy were measured. Conditional logistic regression analyses were used to estimate the associations of manganese levels with adverse outcomes. Restricted cubic spline (RCS) models were used to characterize the dose-response relationship of manganese and each outcome. Nonlinear associations were observed for manganese and adverse outcomes. Compared with women in the middle tertile of plasma manganese, we found that those in the highest tertile had a significantly higher risk of HDP (OR = 1.72, 95% CI: 1.02 to 2.89), that women in the lowest tertile had almost a tripled risk of delivering LBW infants (OR = 2.93, 95% CI: 1.67 to 5.17), and that women both in the lowest and the highest tertiles had significantly higher risks of PTB [OR = 1.83 (95% CI: 1.14 to 2.95); OR = 1.70 (95% CI: 1.05 to 2.76)]. U-shaped associations were found between plasma manganese and risk of PTB and infant LBW. Based on the results of RCS models, we identified a proper plasma manganese range of 1.72-3.18 µg/L, with relatively lower risks of adverse pregnancy outcomes. In conclusion, our study found U-shaped associations between manganese exposure and adverse pregnancy outcomes, and provided an optimal range of manganese concentration for pregnant women, based on health risk considerations.

Low level of urinary cotinine in pregnant women also matters: variability, exposure characteristics, and association with oxidative stress markers.

China has relatively high indoor contamination of nicotine, according to recent publications. Therefore, nicotine exposure risks for sensitive populations such as pregnant women in China are of concern. The variability of its internal exposure levels across three trimesters among pregnant women is not well documented. Factors related with nicotine exposure across pregnancy and its associations with oxidative stress markers are also understudied. Based on a birth cohort, we measured concentrations of cotinine (a major metabolite of nicotine) and oxidative stress markers including 8-OHdG, 8-OHG, and HNE-MA in urine samples collected at three trimesters from 1,155 pregnant women enrolled between January 2014 and June 2017 in Wuhan, China. The variability of urinary cotinine across the trimesters, potential factors associated with it, as well as the relationships between urinary cotinine and oxidative stress markers were assessed in pregnant women with cotinine concentrations of < 50 ng/mL (the cutoff value to distinguish smokers and non-smokers). Urinary specific gravity adjusted median concentrations of cotinine (ng/mL) in the entire pregnancy, first, second, and third trimester were 3.04, 3.32, 3.36, and 2.50, respectively, which exhibited fair reliability (intraclass correlation coefficient: 0.47) across pregnancy. Most participants had an estimated daily intake of nicotine higher than the acceptable value (100 ng/kg-bw/day) recommended by the UK and the USA. Maternal age, education level, pre-pregnancy body mass index, and sampling seasons were related to urinary concentrations of cotinine. After adjusting for confounding factors, significant positive relationships (ß; 95% confidence interval) were observed between urinary cotinine concentrations and 8-OHdG (0.28; 0.25, 0.30), 8-OHG (0.27; 0.25, 0.29), and HNE-MA (0.27; 0.21, 0.32), respectively (p < 0.01). These results lend insight into the major factors associated with nicotine exposure of pregnant women at environmentally relevant levels and its potential effect on oxidative stress with a large sample size, and warrant the necessity of reducing the exposure in sensitive populations.

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.

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.

Exposure to metal mixtures and hypertensive disorders of pregnancy: A nested case-control study in China.

Exposure to metals has been linked with the risk of hypertensive disorders of pregnancy (HDP), but little is known about the potential effects of exposure to metal mixtures. Thus, our study aimed to investigated the impact of a complex mixture of metals on HDP, especially the interactions among metal mixtures. We did a population-based nested case-control study from October 2013 to October 2016 in Wuhan, China, including 146 HDP cases and 292 controls. Plasma concentrations of Aluminum (Al), Barium (Ba), Cobalt (Co), Copper (Cu), Lead (Pb), Mercury (Hg), Molybdenum (Mo), Nickel (Ni), Selenium (Se), Strontium (Sr), Thallium (Tl), and Vanadium (V) were measured and collected between 10 and 16 gestational weeks. We employed quantile g-computation, conditional logistic regression models, and Bayesian Kernel Machine Regression (BKMR) to assess the association of individual metals and metal mixtures with HDP risk. In the quantile g-computation, the OR for a joint tertile increase in plasma concentrations was 3.67 (95% CI: 1.70, 7.91). Hg contributed the largest positive weights and followed by Al, Ni, and V. In conditional logistic regression models, concentrations of Hg, Al, Ni, and V were significantly associated with the risk of HDP (p-FDR < 0.05). Compared to the lowest tertiles, the ORs (95% CI) for the highest tertiles of these four metals were 2.67 (1.44, 4.95), 3.09 (1.70, 5.64), 5.31 (2.68, 10.53), and 4.52 (2.26, 9.01), respectively. In the BKMR analysis, we observed a linear positive association between Hg, Al, V, and HDP, and a nonlinear relationship between Ni and HDP. A potential interaction between Al and V was also identified. We found that exposure to metal mixtures in early pregnancy, both individually and as a mixture, was associated with the risk of HDP. Potential interaction effects of Al and V on the risk of HDP may exist.

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.

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.

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.

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.

The Association of Metabolic Syndrome with the development of cardiovascular disease among Kazakhs in remote rural areas of Xinjiang, China: a cohort study.

BACKGROUND: Metabolic syndrome (MS) can promote the development of cardiovascular disease (CVD). The objective of this study was to examine the association of MS and its components with CVD, to further prevent and control CVD in Kazakhs. METHODS: In the cohort study, a total of 2644 participants completed the baseline survey between April 2010 and December 2012.The follow-up survey was conducted from April 2016 to December 2016 and was completed by 2286 participants (86.46% follow-up rate). Cox regression was used to evaluate the association of each component and the number of combinations of MS components on the development of CVD. RESULTS: A total of 278 CVD patients were enrolled from rural residents of Xinjiang. The average age of the MS and non-MS groups was 46.33 and 38.71 years, respectively. Independent associations with CVD were found for elevated blood pressure (BP) (adjusted hazard ratio (HR) [aHR] = 1.50,95%confidence interval [CI]: 1.08-2.08), elevated waist circumference (WC) (aHR = 1.60, 95%CI: 1.19-2.15), and elevated triglycerides (TG) (aHR = 1.44, 95%CI: 1.04-2.01). Participants with one to 5 MS components had an increased HR for developing CVD, from 1.82to 8.59 (P for trend < 0.001), compared with those with no MS components. The risk of developing CVD increased when TG and WC coexisted (aHR = 2.16, 95%CI: 1.54-3.04)), when TG and BP coexisted ((aHR = 1.92, 95%CI: 1.32-2.79), and when WC and BP coexisted (aHR = 1.93, 95%CI: 1.33-2.82)). However, no significant interactions were found between BP, WC, and TG. CONCLUSIONS: Elevations of BP, WC, and TG were independent risk factors for CVD in Kazakhs. Control of these factors is important to prevent CVD in this population.

External validation of three atherosclerotic cardiovascular disease risk equations in rural areas of Xinjiang, China.

BACKGROUND: To externally validate the Prediction for ASCVD Risk in China (PAR) risk equation for predicting the 5-year atherosclerotic cardiovascular disease (ASCVD) risk in the Uyghur and Kazakh populations from rural areas in northwestern China and compare its performance with those of the pooled cohort equations (PCE) and Framingham risk score (FRS). METHODS: The final analysis included 3347 subjects aged 40-74 years without CVD at baseline. The 5-year ASCVD risk was calculated using the PAR, PCE, and FRS. Discrimination, calibration, and clinical usefulness of the three equations in predicting the 5-year ASCVD risk were assessed before and after recalibration. RESULTS: Of 3347 included subjects, 1839 were female. We observed 286 ASCVD events in within 5-year follow-up. All three risk equations had moderate discrimination in both men and women. C-indices of PAR, PCE, and FRS were 0.727 (95% CI, 0.725-0.729), 0.727 (95% CI, 0.725-0.729), and 0.740 (95% CI, 0.738-0.742), respectively, in men; the corresponding C-indices were 0.738 (95% CI, 0.737-0.739), 0.731 (95% CI, 0.730-0.732), and 0.761 (95% CI, 0.760-0.762), respectively, in women. PCE, PAR and FRS substantially underestimated the 5-year ASCVD risk in women by 70, 23 and 51%, respectively. However, PAR and FRS fairly predicted the risk in men and PAR was well calibrated. The calibrations of the three risk equations could be changed by recalibration. The decision curve analyses demonstrated that at the threshold risk of 5%, PCE was the most clinically useful in both men and women after recalibration. CONCLUSIONS: All three risk equations underestimated the 5-year ASCVD risk in women, while PAR and FRS fairly predicted that in men. However, the results of predictive performances for three risk equations are inconsistent, more accurate risk equations are required in the primary prevention of ASCVD aiming to this Uyghur and Kazakh populations.

Association of total bilirubin and indirect bilirubin content with metabolic syndrome among Kazakhs in Xinjiang.

BACKGROUND: Some studies have shown that a high level of bilirubin is a protective factor against metabolic syndrome (MS), while a high level of transaminase is a risk factor for MS. However, the existing results are inconsistent and few cohort studies have been published. METHODS: Using an ambispective cohort study, 565 Kazakhs from Xinjiang, China were selected as the study subjects. The baseline serum bilirubin and transaminase levels of the subjects were divided into quartiles and the relationship between these values and the incidence of MS was analyzed. The definition of MS was based on the Joint Interim Statement (JIS) diagnostic criteria. RESULTS: The average follow-up time for the subjects was 5.72 years. The cumulative incidence of MS was 36.11% (204 of the 565 subjects), and the incidence density was 63.10/1000 person-years. Multivariate Cox regression analysis showed that the levels of total bilirubin (TBIL) and indirect bilirubin (IBIL) were negatively correlated with the occurrence of MS, Compared to the lowest quartile level (Q1), the hazard ratios of MS the TBIL levels at the Q2-Q4 quartiles were: 0.47 (0.31-0.71), 0.53 (0.35-0.79), and 0.48 (0.32-0.72), respectively, while IBIL levels at the Q2-Q4 quartiles showed an MS hazard ratio of 0.48 (0.32-0.72), 0.54(0.36-0.81), and 0.52 (0.35-0.77), respectively, all at a 95% confidence level. However, no relationship was found between transaminase levels and the incidence of MS. CONCLUSION: Serum TBIL and IBIL levels were negatively correlated with the incidence of MS in a Kazakh population in China.

Microarray analysis of long non-coding RNA expression profiles in low high-density lipoprotein cholesterol disease.

BACKGROUND: Low high-density lipoprotein cholesterol (HDL-C) disease with unknown etiology has a high prevalence in the Xinjiang Kazak population. In this study, long noncoding RNAs (lncRNAs) that might play a role in low HDL-C disease were identified. METHODS: Plasma samples from 10 eligible individuals with low HDL disease and 10 individuals with normal HDL-C levels were collected. The lncRNA profiles for 20 Xinjiang Kazak individuals were measured using microarray analysis. RESULTS: Differentially expressed lncRNAs and mRNAs with fold-change values not less than 1.5 and FDR-adjusted P-values less than 0.05 were screened. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses, were used to determine relevant signaling pathways and predict potential target genes. In total, 381 lncRNAs and 370 mRNAs were differentially expressed based on microarray analysis. Compared with those in healthy individuals, several lncRNAs were upregulated or downregulated in patients with low HDL-C disease, among which TCONS_00006679 was most significantly upregulated and TCONS_00011823 was most significantly downregulated. GO and KEGG pathway analyses as well as co-expression networks of lncRNAs and mRNAs revealed that the platelet activation pathway and cardiovascular disease were associated with low HDL-C disease. CONCLUSIONS: Potential target genes integrin beta-3 (ITGB3) and thromboxane A2 receptor (TBXA2R) were regulated by the lncRNAs AP001033.3-201 and AC068234.2-202, respectively. Both genes were associated with cardiovascular disease and were involved in the platelet activation pathway. AP001033.3-201 and AC068234.2-202 were associated with low HDL-C disease and could play a role in platelet activation in cardiovascular disease. These results reveal the potential etiology of dyslipidemia in the Xinjiang Kazakh population and lay the foundation for further validation using large sample sizes.

Effect of metabolic syndrome on coronary heart disease in rural minorities of Xinjiang: a retrospective cohort study.

BACKGROUND: Metabolic syndrome is diagnosed by a cluster of risk factors that associated with an increased risk of coronary heart disease (CHD). We aimed to explore the impact of and interactions between individual metabolic syndrome components on the risk of CHD in Xinjiang. METHODS: The baseline population included 7635 participants. The degree to which the components increase the risk of CHD and the multiplicative interactions between them were assessed using hazard ratios (HRs) and 95% confidence intervals (CIs). Additive interactions were appraised by the relative excess risk due to interaction, synergy index (SI), and attributable proportion of interaction. RESULTS: A total of 304 CHD patients were enrolled from rural residents of Xinjiang. Elevated blood pressure (HR 1.81; 95% CI 1.35-2.44) and elevated fasting blood glucose (FBG) (HR 1.82; 95% CI 1.38-2.38) increased the risk of CHD after adjustment for confounding factors. We found a positive additive interaction (SI 1.14; 95% CI 0.51-2.51) between elevated blood pressure and elevated FBG, but none were significant. As the number of components increased, the risk of CHD increased. The combinations of [high triglycerides (TG) + low high-density lipoprotein cholesterol (HDL-C) + elevated FBG + large waistline] (HR 4.26; 95% CI 1.43-12.73) and [elevated blood pressure + elevated FBG + low HDL-C + large waistline] (HR 1.82; 95% CI 1.38-2.38) increased the risk of CHD. CONCLUSIONS: We provide evidence that elevated blood pressure and elevated FBG are independent risk factors for CHD and it might be necessary to maintain the normal waistline for preventing CHD.

Risk of coronary heart disease in the rural population in Xinjiang: A nested case-control study in China.

BACKGROUND AND AIM: Coronary heart disease (CHD) is a chronic complex disease caused by a combination of factors such as lifestyle behaviors and environmental and genetic factors. We conducted this study to evaluate the risk factors affecting the development of CHD in Xinjiang, and to obtain valuable information for formulating appropriate local public health policies. METHOD: We conducted a nested case-control study with 277 confirmed CHD cases and 554 matched controls. The association of the risk factors with the risk of CHD was assessed using the multivariate Cox proportional hazard model. Multiplicative interactions were evaluated by entering interaction terms in the Cox proportional hazard model. The additive interactions among the risk factors were assessed by the index of additive interaction. RESULTS: The risk of CHD increased with frequent high-fat food consumption, dyslipidemia, obesity, and family history of CHD after adjustment for drinking, smoking status, hypertension, diabetes, family history of hypertension, and family history of diabetes. We noted consistent interactions between family history of CHD and frequent high-fat food consumption, family history of CHD and obesity, frequent high-fat food consumption and obesity, frequent high-fat food consumption and dyslipidemia, and obesity and dyslipidemia. The risk of CHD events increased with the presence of the aforementioned interactions. CONCLUSIONS: Frequent high-fat food consumption, family history of CHD, dyslipidemia and obesity were independent risk factors for CHD, and their interactions are important for public health interventions in patients with CHD in Xinjiang.