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  • Packard, Chris J.; Boren, Jan; Taskinen, Marja-Riitta (2020)
    Elevations in plasma triglyceride are the result of overproduction and impaired clearance of triglyceride-rich lipoproteins-very low-density lipoproteins (VLDL) and chylomicrons. Hypertriglyceridemia is characterized by an accumulation in the circulation of large VLDL-VLDL1-and its lipolytic products, and throughout the VLDL-LDL delipidation cascade perturbations occur that give rise to increased concentrations of remnant lipoproteins and small, dense low-density lipoprotein (LDL). The elevated risk of atherosclerotic cardiovascular disease in hypertriglyceridemia is believed to result from the exposure of the artery wall to these aberrant lipoprotein species. Key regulators of the metabolism of triglyceride-rich lipoproteins have been identified and a number of these are targets for pharmacological intervention. However, a clear picture is yet to emerge as to how to relate triglyceride lowering to reduced risk of atherosclerosis.
  • Berglund, Martin; Adiels, Martin; Taskinen, Marja-Riitta; Boren, Jan; Wennberg, Bernt (2015)
    Context Mathematical models may help the analysis of biological systems by providing estimates of otherwise un-measurable quantities such as concentrations and fluxes. The variability in such systems makes it difficult to translate individual characteristics to group behavior. Mixed effects models offer a tool to simultaneously assess individual and population behavior from experimental data. Lipoproteins and plasma lipids are key mediators for cardiovascular disease in metabolic disorders such as diabetes mellitus type 2. By the use of mathematical models and tracer experiments fluxes and production rates of lipoproteins may be estimated. Results We developed a mixed effects model to study lipoprotein kinetics in a data set of 15 healthy individuals and 15 patients with type 2 diabetes. We compare the traditional and the mixed effects approach in terms of group estimates at various sample and data set sizes. Conclusion We conclude that the mixed effects approach provided better estimates using the full data set as well as with both sparse and truncated data sets. Sample size estimates showed that to compare lipoprotein secretion the mixed effects approach needed almost half the sample size as the traditional method.