Zi-Hui Tang

Associations between the severity of metabolic syndrome and cardiovascular autonomic function in a Chinese population.

Background: The purpose of this study was to evaluate associations between the severity of metabolic syndrome (MetS) and cardiovascular autonomic function (CAF) in a Chinese population. Methods and materials: We conducted a large-scale community-based cross-sectional study to estimate associations between MetS and CAF in a Chinese population. The study included 2119 subjects. MetS was defined in accordance with the criteria published by the International Diabetes Federation. CAF was assessed via heart rate variability (HRV) and baroreflex sensitivity (BRS). Univariate and multivariate linear regression (MLR) analyses were undertaken to evaluate the statistical significance of the associations investigated. Results: Three HRV components differed significantly between the groups with regard to MetS severity scores; total power (TP), low frequency power (LF), and high frequency power (HF), and BRS components also differed significantly between the groups (p<0.05 for each component). Univariate and MLR analysis revealed that MetS severity and insulin resistance (IR) were significantly associated with HRV and BRS components (β=-0.08~−0.16, p<0.05 for both). Fasting plasma glucose (FPG) and blood pressure (BP) were also negatively correlated with favorable outcomes (β=−0.042~−0.119, p<0.05 for both). Conclusions: In this study, MetS and IR were each significantly and independently negatively associated with CAF. Two MetS components, FPG and BP, were negatively correlated with CAF. Also, decreased HRV and BRS components were associated with increased MetS severity scores.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.

Association and Predictive Value Analysis for Resting Heart Rate and Diabetes Mellitus on Cardiovascular Autonomic Neuropathy in General Population

Background. The purpose of this study was to evaluate the predictive value of DM and resting HR on CAN in a large sample derived from a Chinese population. Materials and Methods. We conducted a large-scale, population-based, cross-sectional study to explore the relationships of CAN with DM and resting HR. A total of 387 subjects were diagnosed with CAN in our dataset. The associations of CAN with DM and resting HR were assessed by a multivariate logistic regression (MLR) analysis (using subjects without CAN as a reference group) after controlling for potential confounding factors. The area under the receiver-operating characteristic curve (AUC) was used to evaluate the predictive performance of resting HR and DM. Results. A tendency toward increased CAN prevalence with increasing resting HR was reported (P for trend <0.001). MLR analysis showed that DM and resting HR were very significantly and independently associated with CAN (P < 0.001 for both). Resting HR alone or combined with DM (DM-HR) both strongly predicted CAN (AUC = 0.719, 95% CI 0.690–0.748 for resting HR and AUC = 0.738, 95% CI 0.710–0.766 for DM-HR). Conclusion. Our findings signify that resting HR and DM-HR have a high value in predicting CAN in the general population.

Bayesian estimation of cardiovascular autonomic neuropathy diagnostic test based on short-term heart rate variability without a gold standard.

Objective To evaluate the reference values for short-term heart rate variability (HRV), estimate the performance of cardiovascular autonomic neuropathy (CAN) diagnostic tests in the absence of a gold standard, and assess CAN prevalence in our dataset. Setting Community and hospital health centre. Participants Of 2092 subjects available for data analysis, 371 healthy subjects were selected so the reference values for the short-term HRV test could be evaluated. An external dataset contained 88 subjects who completed both the short-term HRV test and Ewings test. Intervention Collection of information on clinical outcome. Primary and second outcome measures Cardiovascular autonomic function evaluated by using the short-term HRV test and/or Ewings test. Results Cut-off points of 356.13, 55.45 and 36.64 ms2 were set for total power, low frequency and high frequency (HF), respectively. The diagnostic test for CAN based on the mentioned reference value was created. The HRV test had a high sensitivity (80.01–85.09%) and specificity (82.30–85.20%) for CAN. In addition, the non-inferiority test rejected the null hypothesis that the performance of the HRV test was inferior to that of Ewings test (p<0.05). The estimated CAN prevalence was 14.92% and 29.17% in the total sample and patients with diabetes, respectively. Conclusions Our findings provided reference values for short-term HRV, which were used for the CAN diagnostic test with high sensitivity and specificity. The estimated CAN prevalence was high in the Chinese population.

The association and predictive value analysis of metabolic syndrome on diastolic heart failure in patients at high risk for coronary artery disease

BackgroundThe purpose of the present study was to evaluate the effect and predictive value of metabolic syndrome (MetS) and its components on diastolic heart failure (DHF) in patients at high risk for coronary artery disease (CAD).Materials and methodsWe enrolled 261 patients with normal left ventricular ejection fraction (≥50%) who were scheduled to undergo coronary angiography for suspected myocardial ischemia. They were categorized into three groups (non-MetS, pre-MetS and MetS) based on the number of MetS criteria. Echocardiography was used to assess left ventricular (LV) diastolic function. The association between MetS and DHF was assessed by multivariate logistic regression (MLR) analysis (non-DHF patients as reference group) after controlling for confounders. The predictive performance of the MetS severity score (MSS) was evaluated using the area under the receiver-operating characteristic curve (AUC).ResultsA tendency toward increased DHF prevalence with increasing MSS was found (p < 0.001). MLR analysis showed that in patients with an MSS of 1, the odds ratio (OR) of DHF was 1.60 (95% confidence interval-CI, 1.19–2.16; p = 0.02) compared to non-DHF patients; in patients with MSS ≥4, the OR was 6.61 (95% CI, 4.90–8.90; p < 0.001) compared to non-DHF patients. MSSs strongly predicted DHF (AUC = 0.73, 95% CI, 0.66–0.78, p < 0.001). MLR with MetS components as binary variables showed that blood pressure (BP) and triglycerides (TGs) were significantly associated with DHF (P = 0.001 and 0.043, respectively).ConclusionOur findings signify that MetS and its components of BP or TG were associated with DHF in high-risk CAD patients. DHF prevalence tends to increase with increasing MSS that has a high value in predicting DHF in high-risk CAD patients.

A simple Chinese risk score model for screening cardiovascular autonomic neuropathy.

Background The purpose of the present study was to develop and evaluate a risk score to predict people at high risk of cardiovascular autonomic dysfunction neuropathy (CAN) in Chinese population. Methods and Materials A population-based sample of 2,092 individuals aged 30–80 years, without previously diagnosed CAN, was surveyed between 2011 and 2012. All participants underwent short-term HRV test. The risk score was derived from an exploratory set. The risk score was developed by stepwise backward multiple logistic regression. The coefficients from this model were transformed into components of a CAN score. This score was tested in a validation and entire sample. Results The final risk score included age, body mass index, hypertension, resting hear rate, items independently and significantly (P<0.05) associated with the presence of previously undiagnosed CAN. The area under the receiver operating curve was 0.726 (95% CI 0.686–0.766) for exploratory set, 0.784 (95% CI 0.749–0.818) for validation set, and 0.756 (95% CI 0.729–0.782) for entire sample. In validation set, at optimal cutoff score of 5 of 10, the risk score system has the sensitivity, specificity, and percentage that needed subsequent testing were 69, 78, and 30%, respectively. Conclusion We developed a CAN risk score system based on a set of variables not requiring laboratory tests. The score system is simple fast, inexpensive, noninvasive, and reliable tool that can be applied to early intervention to delay or prevent the disease in China.

A risk score of cardiovascular autonomic dysfunction for targeted screening in the Chinese population

Cardiovascular autonomic (CA) dysfunction has become a major health concern in China following rapid lifestyle changes [1,2]. Individuals with previously undiagnosed CA dysfunction have an unfavorable cardiovascular risk profile [3,4]. Delay and lack of detection of the disease mostly results from patients being asymptomatic during its early stages [5,6]; therefore, the development of a simple and accurate screening tool to identify those at high risk of developing CA dysfunction will be of great value. The aim of this study was to develop and evaluate a simple, noninvasive and informative scoring system to characterize individuals according to their future risk of CA dysfunction. This study is a CA dysfunction factor survey carried out in a random sample of Chinese population [7]. Survey participants with undiagnosed CA dysfunction, aged 30–80 years, were included in this study. Subjects were excluded from the study to eliminate potential confounding factors thatmayhave influenced theirCA function [7].Of these subjects, complete baseline datawere obtained for 2092 (69.46%) of the participantswithout prior CA dysfunction history. Written consent was obtained from all patients before the study. The present study was approved by the Ethics Committee of the Huashan Hospital, Shanghai, China. The subjects were interviewed for the documentation of medical histories and medication, laboratory assessment of cardiovascular disease risk factors, and standardized examination for HRV. All study subjects underwent a complete CA function evaluation after an eighthour fast. CAdysfunctionwas diagnosed based on at least two abnormal CA reflex test results [5]. The potential risk factors for CA dysfunction were age (categorized into three groups: ≤50, 51–60, and N60 years; code 0, 1 and 2), gender, BMI, abdominal obesity (WC ≥ °90 inmen and≥80 cm inwomen; code 0 and 1), current smoking, resting HR (categorized into four groups: ≤70, 71–80, 81–90, and N90 bpm; code 0, 1, 2and 3), diabetes, hypertension (HT, code 0 and 1), blood glucose and lipid profile. Univariate analyses were performed to estimate significant predictors of CA dysfunction.Multiple logistic regression (MLR)was used to compute β-coefficients for known risk factors. Only parameters that are easy to assess without any laboratory tests were entered into the model. Variables significant at 5% were included in the MLR using stepwise elimination, with CA dysfunction as the dependent variable. For each significant variable in theMLR analysis, a risk scorewas calculated by the regression coefficients (β) dividing by a common factor (0.10) and rounding to the nearest integer. A sum score was calculated for each participant by adding the score for each variable in the risk model. A receiver-operating characteristic (ROC) curve and area under the curve (AUC)wereproduced. Sensitivityandspecificitywere calculated foreach cutoff score. The cutoff score that gave the maximum sum of sensitivity and specificity was taken as the optimum. The performance of the risk scorewas evaluatedbyusing theAUC inROC curve, sensitivity, specificity, the positive predictive value (PPV), and the negative predictive value (NPV) in the three different sets. Furthermore, the proportion of individuals who needed subsequent testing (NST) was compared. The baseline characteristics of the 2092 subjects were listed in Table 1. The CA dysfunction prevalence was 18.51% in entire sample. A total of 1066 individuals and 1026 individuals were randomly selected to be the exploratory set and validation set, respectively. The baseline characteristics were similar between the exploratory and validation set (p b 0.05, Table 1). Univariate association analysis to include potential risk factors showed that resting HR, DM, SBP, DBP, HT, BMI, WC, age, TG, and IRwere significantly associatedwith CAdysfunction. After stepwise elimination of the non-significant variables, the final MLR model included risk factors of age, WC, HT, and HR. The risk score was calculatedusing the formula of 4*age + 3*WC+ 5*HT+7*HR. The total score ranged from 0 to 37. In exploratory set, the cutoff score of 16 was optimum (sensitivity =69.78%, specificity = 69.30%, Youden index = 39.08%,