Background: The carotid artery plaque score (PS) is an independent predictor of Coronary Heart Disease (CHD). This study aims to evaluate the combination of PS and carotid hemodynamics to predict CHD. Methods: A total of 476 patients who underwent carotid ultrasonography and coronary angiography were divided into two groups depending on the presence of CHD. PS, carotid intima-media thickness, and carotid blood flow were measured. Receiver operating characteristic curve analysis was performed to establish the best prediction model for CHD presence. Results: Age, sex, carotid intima-media thickness of internal carotid artery and carotid bifurcation, PS, peak systolic velocity (PSA) of right internal carotid artery (RICA), and most resistance index data were significantly related with the presence of CHD. The area under the curve for a collective model, which included factors of the PS, carotid hemodynamics and age, was significantly higher than the other model. Age, PS, and PSA of RICA were significant contributors for predicting CHD presence. Conclusions: The model of PS and PSA of RICA has greater predictive value for CHD than PS alone. Adding age to PS and PSA of RICA further improves predictive value over PS alone.
ObjectiveThis study was aimed at investigating the relationship between neuron-specific enolase (NSE) and components of metabolic syndrome (MS).DesignCross-sectional study.SettingChinese health check-up population.Participants40 684 health check-up people were enrolled in this study from year 2014 to 2016.Main outcome measuresOR and coefficient for MS.ResultsThe percentage of abnormal NSE and MS was 26.85% and 8.85%, respectively. There were significant differences in sex, body mass index, drinking habit, triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), blood pressure and MS between low-NSE and high-NSE groups. In logistic regression analysis, elevated NSE was present in MS, higher body mass index, hypertriglyceridaemia, hypertension and low-HDL groups. Stepwise linear analysis showed a negative correlation between NSE and fasting blood glucose (FBG) (<6.0 mmol/L), and a positive correlation between NSE and TGs (<20 mmol/L), systolic blood pressure (75–200 mm Hg), HDL-C (0.75–2.50 mmol/L), diastolic blood pressure (<70 mm Hg) and FBG (6.00–20.00 mmol/L). Furthermore, MS was positively correlated with NSE within the range of 2.00–7.50 ng/mL, but had a negative correlation with NSE within the range of 7.50–23.00 ng/mL.ConclusionThere are associations between NSE with MS and its components. The result suggests that NSE may be a potential predictor of MS. Further research could be conducted in discussing the potential mechanism involved.
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