Joint effects of paraoxonase 1 rs662 polymorphism and vitamins C/E intake on coronary artery disease severity (Gensini and SYNTAX scores) and lipid profile in patients undergoing coronary angiography

Modulation of the antioxidant enzyme paraoxonase-1 for protection against cardiovascular diseases

AIM

The enzyme paraoxonase 1 (PON1) bound to high-density lipoprotein has received special attention for its protective role against stress-mediated damage and use as a potential regulatory target in atherosclerosis and related vascular diseases.

DATA SYNTHESIS

We present an overview of the literature on PON1 activity and mRNA levels by investigating its modulation for clinical translations. Specifically, the expression of PON1 and its regulated activity can be modified in different ways with natural substances, drugs, and lifestyle factors thar affect the development of atherosclerosis.

CONCLUSIONS

The endothelial contribution of PON1 to overcome differences considering an individual's disease development risk is supported by polymorphism interaction data and the susceptibility to modify PON1 responses in chronic events composed by biological and environmental factors.

Development and Validation of a Nomogram for Predicting the Severity of Coronary Artery Disease Based on Cardiopulmonary Exercise Testing

As a major global health concern, coronary artery disease (CAD) demands precise, noninvasive diagnostic methods like cardiopulmonary exercise testing (CPET) for effective assessment and management, balancing the need for accurate disease severity evaluation with improved treatment decision-making. Our objective was to develop and validate a nomogram based on CPET parameters for noninvasively predicting the severity of CAD, thereby assisting clinicians in more effectively assessing patient conditions. This study analyzed 525 patients divided into training (367) and validation (183) cohorts, identifying key CAD severity indicators using least absolute shrinkage and selection operator (LASSO) regression. A predictive nomogram was developed, evaluated by average consistency index (C-index), the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), confirming its reliability and clinical applicability. In our study, out of 25 variables, 6 were identified as significant predictors for CAD severity. These included age (OR = 1.053, P < .001), high-density lipoprotein (HDL, OR = 0.440, P = .002), hypertension (OR = 2.050, P = .007), diabetes mellitus (OR = 3.435, P < .001), anaerobic threshold (AT, OR = 0.837, P < .001), and peak kilogram body weight oxygen uptake (VO2/kg, OR = 0.872, P < .001). The nomogram, based on these predictors, demonstrated strong diagnostic accuracy for assessing CAD severity, with AUC values of 0.939 in the training cohort and 0.840 in the validation cohort, and also exhibited significant clinical utility. The nomogram, which is based on CPET parameters, was useful for predicting the severity of CAD and assisted in risk stratification by offering a personalized, noninvasive diagnostic approach for clinicians.