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.

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.

Plasma PFOA and PFOS Levels, DNA Methylation, and Blood Lipid Levels: A Pilot Study

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) is associated with blood lipids in adults, but the underlying mechanisms remain unclear. This pilot study aimed to investigate the associations between PFOA or PFOS and epigenome-wide DNA methylation and assess the mediating effect of DNA methylation on the PFOA/PFOS-blood lipid association. We measured plasma PFOA/PFOS and leukocyte DNA methylation in 98 patients enrolled from the hospital between October 2018 and August 2019. The median plasma PFOA/PFOS levels were 0.85 and 2.29 ng/mL. Plasma PFOA and PFOS levels were significantly associated with elevated total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels. There were 63/87 CpG positions and 8/11 differentially methylated regions (DMRs) associated with plasma PFOA/PFOS levels, respectively. In addition, 5 CpG positions (annotated to AFF3, CREB5, NRG2, USF2, and intergenic region) and one DMR annotated to IRF6 may mediate the association between plasma PFOA/PFOS and LDL levels (mediated proportion from 7.29 to 46.77%); two CpG positions may mediate the association between plasma PFOA/PFOS and TC levels (annotated to CREB5 and USF2, mediated proportion is around 30%). The data suggest that PFOA/PFOS exposure alters DNA methylation. More importantly, the association of PFOA/PFOS with lipid indicators was partly mediated by DNA methylation changes in lipid metabolism-related genes.

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, standardized 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.

Associations between serum PFOA and PFOS levels and incident chronic kidney disease risk in patients with type 2 diabetes

Chronic kidney disease (CKD) is a common comorbidity among patients with type 2 diabetes. Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been linked to poorer kidney function in general population, but the related studies in individuals with diabetes were very limited. We aimed to examine the longitudinal associations of PFOA and PFOS exposure and CKD incidence among diabetes patients. Baseline levels of PFOA and PFOS were measured in serum in 967 diabetes patients from the Dongfeng-Tongji cohort. Multivariable logistic regression models were used to characterize the relationship between serum PFOA and PFOS levels and incident CKD risk (defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m²). During 10-years follow-up, 267 incident CKD cases were identified. Only PFOS level was significantly associated with lower risk of CKD incidence (adjusted OR: 0.67; 95%CI: 0.51, 0.88). Such inverse association was only observed among participants with lower eGFR levels (< 70 mL/min/1.73 m²), although the interaction did not achieve statistical significance. Notably, an inverted U-shaped relationship between eGFR and serum PFOS level (P_{for nonlinearity} < 0.001) was observed based on the 1825 subjects with available data at baseline. PFOS exposure was negatively associated with CKD incidence in patients with diabetes, especially in those with baseline eGFR levels < 70 mL/min/1.73 m². This may be explained by the implication of baseline kidney function on the serum PFAS concentrations which in turn affect the relationship between PFOS exposure and the incident CKD risk among diabetes.

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.

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.

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.

Clinical Characteristics and Prognosis of 244 Cardiovascular Patients Suffering From Coronavirus Disease in Wuhan, China

Background

The coronavirus disease 2019 (COVID‐19) has developed into a global outbreak. Patients with cardiovascular disease (CVD) with COVID‐19 have different clinical characteristics and prognostic outcomes. This study aimed to summarize the clinical characteristics and laboratory indicators of patients with COVID‐19 with CVD, especially the critically ill patients.

Methods and Results

This study included 244 patients diagnosed with COVID‐19 and CVD (hypertension, coronary heart disease, or heart failure). The patients were categorized into critical (n=36) and noncritical (n=208) groups according to the interim guidance of China’s National Health Commission. Clinical, laboratory, and outcome data were collected from the patients’ medical records and compared between the 2 groups. The average body mass index of patients was significantly higher in the critical group than in the noncritical group. Neutrophil/lymphocyte ratio, and C‐reactive protein, procalcitonin, and fibrinogen, and d‐dimer levels at admission were significantly increased in the critical group. The all‐cause mortality rate among cases of COVID‐19 combined with CVD was 19.26%; the proportion of coronary heart disease and heart failure was significantly higher in deceased patients than in recovered patients. High body mass index, previous history of coronary heart disease, lactic acid accumulation, and a decrease in the partial pressure of oxygen were associated with death.

Conclusions

All‐cause mortality in patients with COVID‐19 with CVD in hospitals is high. The high neutrophil/lymphocyte ratio may be a predictor of critical patients. Overweight/obesity combined with coronary heart disease, severe hypoxia, and lactic acid accumulation resulting from respiratory failure are related to poor outcomes.

Registration

URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2000029865.

Mendelian randomization study of serum uric acid levels and diabetes risk: evidence from the Dongfeng-Tongji cohort

OBJECTIVE

Limited Mendelian randomization (MR) studies have assessed the causal relationship between serum uric acid levels and diabetes risk. Here we investigated causality between the serum uric acid concentration and diabetes risk in Chinese population.

RESEARCH DESIGN AND METHODS

The observational analysis, based on the Dongfeng-Tongji prospective cohort (n=15 195) we tested the association of serum uric acid levels with incident diabetes risk. In the instrumental variable analysis, we examined the association of the genetic risk score (GRS) of serum uric acid with diabetes risk in case-control design (2539 cases and 4595 controls) via MR analysis.

RESULTS

During a mean (SD) follow-up of 4.5 (0.5) years, 1156 incident diabetes cases were identified. Compared with those in the lowest quintile of serum uric acid levels, the HRs of incident diabetes were 1.19 (95% CI 0.96 to 1.48), 1.12 (95% CI 0.90 to 1.40), 1.38 (95% CI 1.12 to 1.70), and 1.51 (95% CI 1.23 to 1.87) for Q2, Q3, Q4 and Q5, respectively (P-trend <0.001). The GRS was strongly associated with serum uric acid levels (β=0.17, 95% CI 0.15 to 0.19; P=2.81×10^-67). However, no significant association was observed between the GRS and diabetes risk (OR=1.01, 95 CI 0.95 to 1.06; P=0.75).

CONCLUSIONS

Even though serum uric acid levels were significantly associated with increased incident diabetes risk, the results did not provide evidence for a causal relationship between them.