Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Cholesterol efflux capacity is associated with lipoprotein size and vascular health in mild to moderate psoriasis

Background and objective

Psoriasis is a systemic inflammatory condition with poor cholesterol transport measured by cholesterol efflux capacity (CEC) that is associated with a heightened risk of cardiovascular disease (CVD). In psoriasis patients, we sought to characterize the lipoprotein profile by size using a novel nuclear magnetic resonance algorithm in patients with low CEC compared to normal CEC.

Methods

Lipoprotein profile was assessed using the novel nuclear magnetic resonance LipoProfile-4 deconvolution algorithm. Aortic vascular inflammation (VI) and non-calcified burden (NCB) were characterized via positron emission tomography-computed tomography and coronary computed tomography angiography. To understand the relationship between lipoprotein size and markers of subclinical atherosclerosis, linear regression models controlling for confounders were constructed.

Results

Psoriasis patients with low CEC had higher more severe psoriasis (p = 0.04), VI (p = 0.04) and NCB (p = 0.001), concomitant with smaller high-density lipoprotein (HDL) (p < 0.001) and low-density lipoprotein (LDL) particles (p < 0.001). In adjusted models HDL size (β = -0.19; p = 0.02) and LDL size (β = -0.31; p < 0.001) associated with VI and NCB. Lastly, HDL size strongly associated with LDL size in fully adjusted models (β = -0.27; p < 0.001).

Conclusion

These findings demonstrate that in psoriasis, low CEC associates with a lipoprotein profile comprised of smaller HDL and LDL particles which correlates with vascular health and may be driving early onset atherogenesis. Further, these results demonstrate a relationship between HDL and LDL size and provide novel insights into the complexities of HDL and LDL as biomarkers of vascular health.

CYP2C19 metabolizer phenotypes may affect the efficacy of statins on lowering small dense low-density lipoprotein cholesterol of patients with coronary artery disease

Background

Dyslipidemia is a major cause of arteriosclerotic cardiovascular disease (ASCVD), and low-density lipoprotein cholesterol (LDL-C) is the profile to be reduced to prevent disease progression. Small dense low-density lipoprotein cholesterol (sdLDL-C) has been proven to be a more effective biomarker than LDL-C for ASCVD primary and secondary prevention. CYP2C19 is an important drug metabolism gene. This study aimed to investigate the relationship between sdLDL-C and coronary artery disease (CAD) risk factors and explore the influence of CYP2C19 metabolizer phenotypes on the sdLDL-C lowering efficacy of statins.

Methods

This study recruited 182 patients with CAD and 200 non-CAD controls. Baseline laboratory indices of fasting blood were detected, including blood lipids, glucose, and creatinine. In addition, LDL-C subfractions were separated and quantified. Gene polymorphisms of SLCO1B1 and CYP2C19 were detected in patients with CAD. The LDL-C subfractions levels of patients with CAD were followed up after statin drug treatment.

Results

Total cholesterol, LDL-C, LDLC-2, LDLC-3, LDLC-4, LDLC-5, LDLC-6, LDLC-7, and sdLDL-C levels of patients with CAD were significantly higher than those in non-CAD controls. Meanwhile, sdLDL-C (AUC = 0.838) and LDLC-4 (AUC = 0.835) performed outstandingly in distinguishing patients with CAD from controls. Based on CYP2C19 metabolizer phenotypes, 113 patients with CAD were divided into the extensive metabolizer (EM, n = 49), intermediate metabolizer (IM, n = 52), and poor metabolizer (PM, n = 12) groups. The patients with IM and PM metabolizer phenotypes had better sdLDL-C lowering efficacy after taking statin drugs than patients with EM phenotype (P = 0.0268, FDR = 0.0536). The SLCO1B1 genotype had no significant impact on the efficacy of statins (P = 0.1611, FDR = 0.1611).

Conclusion

sdLDL-C and LDLC-4 outperformed other blood lipids such as LDL-C for CAD risk screening. CYP2C19 metabolizer phenotypes had the potential to predict the efficacy of statins in lowering sdLDL-C.

Patterns of Dyslipidemia Among Acute Coronary Syndrome (ACS) Patients at a Tertiary Care Hospital in Lahore, Pakistan

INTRODUCTION

Dyslipidemia refers to the presence of abnormalities in lipid parameters. It has become a global issue with a high risk of cardiovascular diseases (CVDs). The aim of the investigation was to find out the pattern and prevalence of dyslipidemia among patients with the acute coronary syndrome (ACS).

METHODOLOGY

A cross-sectional study design was used. Data were collected using convenient sampling from 101 patients presenting with ACS, admitted at the critical care unit (CCU) / Rasheeda Begum Cardiac Centre (RBCC) of Shalamar Hospital, during a 12-month period from January 2020 to December 2021. Dyslipidemia is diagnosed by testing the lipid profile when there are one or more abnormal readings of the lipid profile.

RESULTS

Nearly 43 (42.6%) had ST-segment elevation myocardial infarction (STEMI), 27 (26.7%) had non-ST segment elevation myocardial infarction (NSTEMI), and 31 (30.7%) were categorized as unstable angina (USA). Overall dyslipidemia was present in 84 (83.2%) patients. The prevalence of dyslipidemia was 55 (65%) in male patients and 29 (34.5%) in female patients. Dyslipidemia was present in 39 (90.7%) patients with STEMI, 25 (80.6%) in the USA, and 20 (74.1%) with NSTEMI.

CONCLUSION

The prevalence of dyslipidemia was quite high among ACS patients. The proportion of obese patients was also high in our study. However, dyslipidemia was more frequent in overweight patients.

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

Purpose. Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results. While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions. In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

Lipoprofiling Assessed by NMR Spectroscopy in Patients with Acute Coronary Syndromes: Is There a Need for Fasting Prior to Sampling?

Most patients presenting in an emergency unit with acute coronary syndromes (ACS) (which include non-ST-elevation myocardial infarction (NSTEMI), ST-elevation MI (STEMI), and unstable angina) usually meet at least two cardiovascular risk factors, such as dyslipidemia, arterial hypertension, diabetes mellitus type 2, obesity, history of or current smoking, etc. Most ACS patients suffer from a type of dyslipidemia, and in addition to this there are ACS patients rushed into the emergency units for which the feeding status is unknown. Thus, we set out to evaluate the effect of fasting on 16 blood metabolite concentrations and 114 lipoprotein parameters on one control group and a group of statin-treated ACS patients hospitalized in a cardiovascular emergency unit, using Nuclear Magnetic Resonance (NMR) spectroscopy. The results indicated trends (in terms of number of cases, but not necessarily in terms of the magnitude of the effect) for as many as four metabolites and 48 lipoproteins. The effect was defined as a trend for results showing over 70% of the cases from either one or both groups that experienced parameter changes in the same direction (i.e., either increased or decreased). In terms of magnitude, the effect is rather low, leading to the overall conclusion that in cardiovascular (CV) emergency units, the blood samples analyzed in any feeding status would provide close results and very valuable information regarding prognosis and for fast decisions on patient’s proper management.

Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile

The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.