Improved cholesterol phenotype analysis by a model relating lipoprotein life cycle processes to particle size

Abstract

Total cholesterol concentration in plasma has long been known to correlate with cardiovascular disease risk. Subsequent investigations have distinguished more specifi c fractions of plasma cholesterol to attribute this risk to. First, LDL cholesterol was identifi ed as a risk factor and later the size distribution within that fraction was found to be of importance [for a historical review, see Ref.( 1 )]. Based on these fi ndings, an 'atherogenic lipoprotein phenotype' has been defi ned, which takes into account a particle size profi le within the LDL class ( 2 ). In addition to the cholesterol-based risk factors, apolipoprotein (Apo) measurements, such as ApoB or the ApoB/ApoA-I ratio, have been found to indicate atherosclerosis risk (3)(4)(5).Further improvements in risk assessment will primarily result from a more detailed understanding of lipoprotein physiology. To increase quantitative insight, various multi-compartmental models have been developed to analyze experiments with radioactive or stable isotope labeled lipoprotein constituents. The fi rst models describe the fl uxes of ApoB between lipoprotein fractions ( 6-8 ) with subsequent refi nements allowing better data interpretation ( 9-15 ). Other models describe the fl uxes of triglycerides through the lipoprotein fractions (16)(17)(18)(19)(20) Abstract Increased plasma cholesterol is a known risk factor for cardiovascular disease. Lipoprotein particles transport both cholesterol and triglycerides through the blood. It is thought that the size distribution of these particles codetermines cardiovascular disease risk. New types of measurements can determine the concentration of many lipoprotein size-classes but exactly how each small class relates to disease risk is diffi cult to clear up. Because relating physiological process status to disease risk seems promising, we propose investigating how lipoprotein production, lipolysis, and uptake processes depend on particle size. To do this, we introduced a novel model framework (Particle Profi ler) and evaluated its feasibility. The framework was tested using existing stable isotope fl ux data. The model framework implementation we present here reproduced the fl ux data and derived lipoprotein size pattern changes that corresponded to measured changes. It also sensitively indicated changes in lipoprotein metabolism between patient groups that are biologically plausible. Finally, the model was able to reproduce the cholesterol and triglyceride phenotype of known genetic diseases like familial hypercholesterolemia and familial hyperchylomicronemia. In the future, Particle Profi ler can be applied for analyzing detailed lipoprotein size profi le data and deriving rates of various lipolysis and uptake processes if an independent production estimate is given.

Diagnostic markers based on a computational model of lipoprotein metabolism

“…With the new model equation and settings, we examined whether the results are comparible to those in our first paper [13]. The subjects reported by Packard et al [18] were fitted with the new model and the results were compared with those from the first model implementation [13].…”

“…The subjects reported by Packard et al [18] were fitted with the new model and the results were compared with those from the first model implementation [13]. In the first implementation, the model reproduced a shift in 'LDL peak size', which was independently measured.…”

“…The results from the further developed and calibrated model versus the original model from are shown [13]. Only those processes that show a significant difference (p < 0.1) using the nonparametric Kruskal-Wallis test are included.…”

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

“…In silico models have been used for various purposes in the study of cholesterol metabolism, such as in the interpretation of isotope-labeling studies (4)(5)(6)33 ), in the analysis of the regulatory pathway of cholesterol synthesis ( 34 ), or in making predictions of the effect of genetic mutations or food and drug interventions ( 35,36 ). These models, however, could predict the effect of a few genetic mutations only.…”

“…Including a more detailed mechanistic description of lipoprotein metabolism is a highly complex task that will necessitate to, for example, include the link between cholesterol metabolism and triglyceride metabolism. Although signifi cant progress in this fi eld is being made ( 4,38,39 ), this was considered too ambitious for the present stage of model development.…”