Saturday, 12 October 2013

Scientific Summary of my PhD thesis

The index of this PhD thesis is available here

This PhD thesis contains a report of research aimed at improving the diagnosis of cardiovascular disease in an early stage by investigating lipoprotein biology. Lipoproteins are the particles that carry cholesterol and triglycerides through the bloodstream. Especially low-density lipoprotein (LDL) cholesterol and LDL particle concentrations are known to be positively associated with cardiovascular disease risk. Technological advances allow the full size spectrum of  lipoproteins to be measured in increasing detail. Although such a detailed measurement contains much information, it does not lead to diagnostic values that both use all relevant information and that doctors can easily use.
         We have developed a mathematical model to aid the interpretation of detailed lipoprotein measurements. This model is able to derive information on the status of lipoprotein metabolism from a single lipoprotein measurement. This PhD thesis reports on the development of the model and model-based diagnostics, their application in a nutritional and a pharmacological human intervention study, and their application as predictors for cardiovascular disease.

Part I: Model development

In chapter 2, we introduced the novel model framework (Particle Profiler) and evaluated its feasibility. The framework was tested using existing stable isotope flux data. The model framework implementation we presented reproduced the flux 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 chapter 3, we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness, starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore, we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. The VLDL metabolic ratios outperformed each of the classical diagnostics separately, and also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics.

Part II: Applications

In chapter 4, we investigated how dietary MCFA and linoleic acid (C18:2n-6) supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. Dietary MCFA supplementation led to a less favorable lipoprotein profile than C18:2n-6 supplementation. Surprisingly, these differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates. The expected higher VLDL production after MCFA intervention was diminished due to chain elongation and metabolic control in the liver. Observed changes in lipolysis and uptake rates are consistent with the mechanism of PPAR activation by linoleic acid. Differences in body fat distribution only correlated with differences in LDL cholesterol values.
         In chapter 5, we investigated the response of lipoprotein profiles to fibrate therapy in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. Fibrates but show heterogeneous treatment response. Using k-means clustering, we divided the baseline NMR lipoprotein profiles of 775 participants in the GOLDN study into three subgroups. The subjects in each subgroup showed differences in conventional lipid characteristics and in presence/absence of cardiovascular risk factors at baseline. Modeling analysis using Particle Profiler revealed differences in VLDL performance between the subgroups. This parameter was high in the first ‘healthy’ subgroup, lower in the second subgroup, and lowest in the third subgroup, with a progressively larger influence of extrahepatic lipolysis dysfunction versus liver uptake dysfunction. The second and third subgroups showed a positive, yet distinct lipid response to fenofibrate treatment. Therefore, using lipoprotein profile clustering, we identified two subgroups of responders to fenofibrate therapy with different underlying disturbances in lipoprotein metabolism. The total responder subgroup is larger than responder subgroups identified with baseline triglyceride and HDL cholesterol cutoffs.
         In chapter 6, we used Particle Profiler to calculate lipoprotein metabolic ratios from NMR lipoprotein profiles measured in the Framingham Heart Study. We tested whether lipoprotein metabolic ratios can improve cardiovascular risk prediction. Two ratios, that reflect VLDL metabolism, significantly improve cardiovascular risk prediction above the classical Framingham Risk Score. The first ratio is called the ‘extrahepatic lipolysis ratio’. This ratio proves to be especially important when estimating the risk for people who take blood pressure medication: when study participants take this medication and the lipolysis of VLDL particles is low, the risk of cardiovascular disease dramatically rises. The second ratio is called the ‘hepatic turnover ratio’. This ratio is especially important for people that are on the border between low and medium risk, judging from their LDL particle number. If in that case, the liver turnover of VLDL particles is good, risk is low; if the liver turnover of VLDL particles is impaired, the risk is in the medium category. So in this chapter we identified two lipoprotein metabolic ratios that significantly improve cardiovascular risk prediction. For clinical application, further validation is required.

Part III: Reflections

In chapter7, we included methodological and technical considerations useful for researchers starting to develop computational model-based diagnostics using clinical chemistry data. We discussed the issues that a modeler has to take into account during the design, construction and evaluation phases of model development. We use the example of Particle Profiler development as a case study to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring, and quality control.

         The general discussion includes additional methodological considerations related to Particle Profiler, a comparison of Particle Profiler with other modeling studies, future research recommendations and a general conclusion. 

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