Cholesterol Subfraction Analysis in Patients with Acute Coronary Syndrome

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Relationship between subclasses low-density lipoprotein and carotid plaque

Backgound

Low-density lipoprotein (LDL) cholesterol can lead to the occurrence of atherosclerotic plaques, but patients with normal LDL still have atherosclerotic plaques in clinical practice. With the proposal of LDL subclass, this experiment investigated the relationship between the LDL content of different subclasses and the stability of carotid plaques.

Methods

Plaque stability was suggested by carotid ultrasound results. 37 patients with stable plaques were classified into one group and 41 patients with unstable plaques were classified into another group. The data of age, glycosylated hemoglobin (Ghb), and homocysteine (Hcy) were collected. The contents of LDL subclasses were measured by LIPOPRINT system. The data of total cholesterol (TC), LDL cholesterol, and triglyceride (TG) were collected. The plaque stability was assessed by carotid artery color Doppler ultrasound and the intima-media thickness (IMT) was measured.

Results

The levels of LDL-1 subclass 19.00 (13.00, 27.50) and LDL-2 subclass (21.62 ± 7.24) in the stable plaque group were higher than those in the unstable plaque group (p < 0.05). The levels of LDL-3 subclass (12.24 ± 4.58), LDL-4 subclass 5.00 (2.00, 9.00), and sd-LDL 0 (0.00, 3.00) in the unstable plaque group were higher than those in the stable plaque group (p < 0.05). LDL-1 subclass (adjusted OR = 0.923 and p < 0.05), and LDL-3 subclass (adjusted OR = 1.176 and p < 0.05) were independent risk factors for plaque stability.

Conclusion

Elevated LDL1 is associated with stable plaques whereas LDL3 was found associated with unstable plaques.

High-Density Lipoprotein Subfractions: Much Ado about Nothing or Clinically Important?

High-density lipoproteins (HDL) are a heterogenous group of plasma molecules with a large variety in composition. There is a wide specter in lipid content and the number of different proteins that has been associated with HDL is approaching 100. Given this heterogeneity and the fact that the total amount of HDL is inversely related to the risk of coronary heart disease (CHD), there has been increasing interest in the function of specific HDL subgroups and in what way measuring and quantifying these subgroups could be of clinical importance in determining individual CHD risk. If certain subgroups appear to be more protective than others, it may also in the future be possible to pharmacologically increase beneficial and decrease harmful subgroups in order to reduce CHD risk. In this review we give a short historical perspective, summarize some of the recent clinical findings regarding HDL subclassifications and discuss why such classification may or may not be of clinical relevance.

Subpopulations of High-Density Lipoprotein: Friends or Foes in Cardiovascular Disease Risk in Chronic Kidney Disease?

Dyslipidemia is a major traditional risk factor for cardiovascular disease (CVD) in chronic kidney disease (CKD) patients, although the altered lipid profile does not explain the number and severity of CVD events. High-density lipoprotein (HDL) is a heterogeneous (size, composition, and functionality) population of particles with different atherogenic or atheroprotective properties. HDL-cholesterol concentrations per se may not entirely reflect a beneficial or a risk profile for CVD. Large HDL in CKD patients may have a unique proteome and lipid composition, impairing their cholesterol efflux capacity. This lack of HDL functionality may contribute to the paradoxical coexistence of increased large HDL and enhanced risk for CVD events. Moreover, CKD is associated with inflammation, oxidative stress, diabetes, and/or hypertension that are able to interfere with the anti-inflammatory, antioxidative, and antithrombotic properties of HDL subpopulations. How these changes interfere with HDL functions in CKD is still poorly understood. Further studies are warranted to fully clarify if different HDL subpopulations present different functionalities and/or atheroprotective effects. To achieve this goal, the standardization of techniques would be valuable.

Lipid transfer to high-density lipoproteins in coronary artery disease patients with and without previous cerebrovascular ischemic events

BACKGROUND

Patients with coronary artery disease (CAD) and previous ischemic cerebrovascular events (ICVE, ischemic stroke, or transitory ischemic attack) constitute a high-risk subgroup for cardiovascular outcomes. High-density lipoprotein cholesterol (HDL-C) levels are correlated with cardiovascular events. Lipid transfer to HDL affects structure size and HDL subclass profile. Impairment of this transfer could influence ischemic risk seen in patients with CAD + ICVE. The objective was to evaluate the HDL ability to receive the lipids in patients with CAD with or without ICVE.

METHODS

Patients with CAD + ICVE (n = 60) and patients with CAD only (n = 60) were matched by age, sex, acute coronary syndromes (ACS) event type, and time elapsed between the ACS event and inclusion in the study. Lipid transfer to HDL was evaluated by incubating donor lipid nanoparticles labeled with radioactive unesterified cholesterol (UC) and esterified cholesterol (EC), phospholipid (PL), and triglyceride (TG) with whole plasma. After the chemical precipitation of non-HDL fractions and nanoparticles, the supernatant was counted for HDL radioactivity.

RESULTS

CAD + ICVE group presented with impaired lipid transfer to HDL for PL (CAD + ICVE: 21.14 ± 2.7% vs CAD: 21.67 ± 3.1%, P = .03), TG (CAD + ICVE: 4.88 ± 0.97% vs CAD: 5.63 ± 0.92%, P = .002), and UC (CAD + ICVE: 5.55 ± 1.19% vs CAD: 6.16 ± 1.14%, P = .009). Lipid transfer to HDL was similar in both groups for EC. Adjusted models showed similar results.

CONCLUSION

Patients with CAD and ICVE have reduced lipid transfer to HDL compared to those with CAD only. Dysfunctional HDL may account for the higher incidence of ischemic outcomes observed in this population.

Associations between the lipid profile and the development of hypertension in young individuals - the preliminary study

INTRODUCTION

Hypertension is the leading direct cause of death in the world and one of the most important risk factors for cardiovascular disease (CVD). Elevated blood pressure (BP) often coexists with lipid disorders and is an additional factor that increases CV risk. Nowadays, we are able to distinguish low density lipoproteins (LDL) and high density lipoproteins (HDL) subfractions. Except LDL also HDL small subfractions can increase the risk of CV events. Therefore, we aimed to investigate the associations between changes of lipoprotein subfractions and the risk of hypertension development.

MATERIAL AND METHODS

In 2-year long study 200 volunteers with normal blood pressure at the age of 19-32 years were included. Each volunteer underwent detailed medical examination, 12-lead electrocardiogram was taken at rest, echocardiogram, lipid subfraction assessment (using Lipoprint®) and two 24-hour BP measurements.

RESULTS

Mean total cholesterol concentration was 189 mg/dl (4.89 mmol/l), with mean LDL concentration of 107 mg/dl (2.77 mmol/l), HDL of 63 mg/dl (1.63 mmo/l), very low-density lipoprotein (VLDL) of 40 mg/dl (1.04 mmol/l) and triglycerides (TG) of 89 mg/dl (1.00 mmol/l). Subfractions LDL 1-3 were most abundant, LDL 4-5 making up a marginal portion and LDL 6-7 were not observed. Whereas, subfractions HDL 4-6 were most abundant, in lower concentration was present HDL 1-3 and HDL 8-10. We showed that increased systolic blood pressure coreclated significantly with HDL cholesterol concentrations (p = 0.0078), HDL intermediate subgractions (p = 0.0451), with HDL-3 subfraction (p = 0.0229), and intermediate density lipoprotein-A (IDL-A) (p = 0.038). A significant correlation between increased diastolic blood pressure and HDL lipoprotein levels (p = 0.0454) was only observed.

CONCLUSIONS

Obtained results indicating correlation between total HDL levels and HDL-3 subfraction concentration (for systolic BP) and the tendency to develop hypertension.

Mon2 predicts poor outcome in ST-elevation myocardial infarction

AIMS

There are limited data on the role of human monocyte subsets in ST-elevation myocardial infarction (STEMI). The study aimed to establish the relationship between monocyte subsets, their phagocytic and nuclear factor κB (NFκB) activity and outcomes in STEMI.

METHODS

Monocyte subsets and their phagocytic activity and intracellular levels of inhibitory κB kinase β (IKKβ, marker of NFκB activity) were measured by flow cytometry in 245 patients with STEMI, median follow-up of 46 months.

RESULTS

Mon2 (CD14++CD16+CCR2+) counts were independently predictive of major adverse cardiovascular events (MACE) [4th quartile HR 3.42 (95% CI 1.43-8.16), P = 0.006 and 3rd quartile HR 2.88 (95% CI 1.19-7.00), P = 0.02 vs. 1st quartile]. Mon2 subset was the only subset associated with higher occurrence of heart failure (4th quartile vs. 1st quartile, sevenfold, P = 0.001 on univariate analysis; fivefold, P = 0.04 on multivariable analysis). On receiver operating characteristic, analysis including of Mon2 improved prognostic value of troponin T and creatine kinase for MACE and heart failure (HF). Higher intracellular Mon2 IKKβ levels were associated with 10-fold lower occurrence of HF on multivariable analysis (4th vs. 1st quartiles, P = 0.03). Abnormal Mon1 and Mon2 phagocytic capacities were related to HF development, but the association was dependent on the infarct size and other prognosticators. High Mon2 levels were associated with lower ejection fraction after STEMI onset (P = 0.001) and at 6-month follow-up (P < 0.001).

CONCLUSIONS

Abnormal Mon2 characteristics have a unique association with poor outcome in patients with STEMI. The relation of Mon2 with occurrence of HF is strongly and independently related to their functional status, which may have potential therapeutic implications.