Controlling the thickness of the atherosclerotic plaque by statin medication

A biomathematical model of atherosclerosis in mice

Atherosclerosis is one of the leading causes of death worldwide. Biomathematical modelling of the underlying disease and therapy processes might be a useful aid to develop and improve preventive and treatment concepts of atherosclerosis. We here propose a biomathematical model of murine atherosclerosis under different diet and treatment conditions including lipid modulating compound and antibiotics. The model is derived by translating known biological mechanisms into ordinary differential equations and by assuming appropriate response kinetics to the applied interventions. We explicitly describe the dynamics of relevant immune cells and lipid species in atherosclerotic lesions including the degree of blood vessel occlusion due to growing plaques. Unknown model parameters were determined by fitting the predictions of model simulations to time series data derived from mice experiments. Parameter fittings resulted in a good agreement of model and data for all 13 experimental scenarios considered. The model can be used to predict the outcome of alternative treatment schedules of combined antibiotic, immune modulating, and lipid lowering agents under high fat or normal diet. We conclude that we established a comprehensive biomathematical model of atherosclerosis in mice. We aim to validate the model on the basis of further experimental data.

Effect of Lipid Composition on the Atheroprotective Properties of HDL-Mimicking Micelles

Atherosclerosis progression is driven by an imbalance of cholesterol and unresolved local inflammation in the arteries. The administration of recombinant apolipoprotein A-I (ApoA-I)-based high-density lipoprotein (HDL) nanoparticles has been used to reduce the size of atheroma and rescue inflammatory response in clinical studies. Because of the difficulty in producing large quantities of recombinant ApoA-I, here, we describe the preparation of phospholipid-based, ApoA-I-free micelles that structurally and functionally resemble HDL nanoparticles. Micelles were prepared using various phosphatidylcholine (PC) lipids combined with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[azido(polyethylene glycol)-2000] (DSPE-PEG2k) to form nanoparticles of 15-30 nm in diameter. The impacts of PC composition and PEGylation on the anti-inflammatory activity, cholesterol efflux capacity, and cholesterol crystal dissolution potential of micelles were investigated in vitro. The effects of micelle composition on pharmacokinetics and cholesterol mobilization ability were evaluated in vivo in Sprague Dawley rats. The study shows that the composition of HDL-mimicking micelles impacts their overall atheroprotective properties and supports further investigation of micelles as a therapeutic for the treatment of atherosclerosis.

The Mutual Contribution of 3-NT, IL-18, Albumin, and Phosphate Foreshadows Death of Hemodialyzed Patients in a 2-Year Follow-Up

Patients with chronic kidney disease (CKD), especially those who are hemodialyzed (HD), are at significantly high risk of contracting cardiovascular disease and having increased mortality. This study aimed to find potential death predictors, the measurement of which may reflect increased mortality in HD patients, and then combine the most promising ones in frames of a simple death risk assessment model. For this purpose, HD patients (n=71) with acute myocardial infarction in the last year (HD group) and healthy people (control group) as a comparative group (n=32) were included in the study. Various laboratory determinations and non-invasive cardiovascular tests were performed. Next, patients were followed for two years, and data on cardiovascular (CV) deaths were collected. On this basis, two HD groups were formed: patients who survived (HD-A, n=51) and patients who died (HD-D, n=20). To model HD mortality, 21 out of 90 potential variables collected or calculated from the raw data were selected. The best explanatory power (95.5%) was reached by a general linear model with four variables: interleukin 18, 3-nitrotyrosine, albumin, and phosphate. The interplay between immuno-inflammatory processes, nitrosative and oxidative stress, malnutrition, and calcium-phosphate disorders has been indicated to be essential in predicting CV-related mortality in studied HD patients. ClinicalTrials.gov Identifier: NCT05214872.

Recognized and Potentially New Biomarkers-Their Role in Diagnosis and Prognosis of Cardiovascular Disease

Atherosclerosis and its consequences are the leading cause of mortality in the world. For this reason, we have reviewed atherosclerosis biomarkers and selected the most promising ones for review. We focused mainly on biomarkers related to inflammation and oxidative stress, such as the highly sensitive C-reactive protein (hs-CRP), interleukin 6 (IL-6), and lipoprotein-associated phospholipase A2 (Lp-PLA2). The microRNA (miRNA) and the usefulness of the bone mineralization, glucose, and lipid metabolism marker osteocalcin (OC) were also reviewed. The last biomarker we considered was angiogenin (ANG). Our review shows that due to the multifactorial nature of atherosclerosis, no single marker is known so far, the determination of which would unambiguously assess the severity of atherosclerosis and help without any doubt in the prognosis of cardiovascular risk.

Management of High-Risk Atherosclerotic Patients by Statins May Be Supported by Logistic Model of Intima-Media Thickening

While the use of statins in treating patients with atherosclerosis is an undisputed success, the questions regarding an optimal starting time for treatment and its strength remain open. We proposed in our earlier paper published in Int. J. Mol. Sci. (2019, 20) that the growth of intima-media thickness of the carotid artery follows an S-shape (i.e., logistic) curve. In our subsequent paper in PLoS ONE (2020, 15), we incorporated this feature into a logistic control-theoretic model of atherosclerosis progression and showed that some combinations of patient age and intima-media thickness are better suited than others to start treatment. In this study, we perform a new and comprehensive calibration of our logistic model using a recent clinical database. This allows us to propose a procedure for inferring an optimal age to start statin treatment for a particular group of patients. We argue that a decrease in the slope of the IMT logistic growth curve, induced by statin treatment, is most efficient where the curve is at its steepest, whereby the efficiency means lowering the future IMT levels. Using the procedure on an aggregate group of severely sick men, 38 years of age is observed to correlate with the steepest point of the logistic curve, and, thus, it is the preferred time to start statin treatment. We believe that detecting the logistic curve’s steepest fragment and commencing statin administration on that fragment are courses of action that agree with clinician intuition and may support decision-making processes.