Diagnostic markers based on a computational model of lipoprotein metabolism

Abstract Background: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study 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. Results: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. Conclusions: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible

Use of Physiologically Based Biokinetic (PBBK) Modeling to Study Estragole Bioactivation and Detoxification in Humans as Compared with Male Rats

The extent of bioactivation of the herbal constituent estragole to its ultimate carcinogenic metabolite 1′-sulfooxyestragole depends on the relative levels of bioactivation and detoxification pathways. The present study investigated the kinetics of the metabolic reactions of both estragole and its proximate carcinogenic metabolite 1′-hydroxyestragole in humans in incubations with relevant tissue fractions. Based on the kinetic data obtained a physiologically based biokinetic (PBBK) model for estragole in human was defined to predict the relative extent of bioactivation and detoxification at different dose levels of estragole. The outcomes of the model were subsequently compared with those previously predicted by a PBBK model for estragole in male rat to evaluate the occurrence of species differences in metabolic activation. The results obtained reveal that formation of 1′-oxoestragole, which represents a minor metabolic route for 1′-hydroxyestragole in rat, is the main detoxification pathway of 1′-hydroxyestragole in humans. Due to a high level of this 1′-hydroxyestragole oxidation pathway in human liver, the predicted species differences in formation of 1′-sulfooxyestragole remain relatively low, with the predicted formation of 1′-sulfooxyestragole being twofold higher in human compared with male rat, even though the formation of its precursor 1′-hydroxyestragole was predicted to be fourfold higher in human. Overall, it is concluded that in spite of significant differences in the relative extent of different metabolic pathways between human and male rat there is a minor influence of species differences on the ultimate overall bioactivation of estragole to 1′-sulfooxyestragol

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales

Nutritional systems biology modeling: from molecular mechanisms to physiology.

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales

Use of physiologically based biokinetic (PBBK) modeling to study estragole bioactivation and detoxification in huyman as compared to male rat

The extent of bioactivation of the herbal constituent estragole to its ultimate carcinogenic metabolite 1'-sulfooxyestragole depends on the relative levels of bioactivation and detoxification pathways. The present study investigated the kinetics of the metabolic reactions of both estragole and its proximate carcinogenic metabolite 1'-hydroxyestragole in humans in incubations with relevant tissue fractions. Based on the kinetic data obtained a physiologically based biokinetic (PBBK) model for estragole in human was defined to predict the relative extent of bioactivation and detoxification at different dose levels of estragole. The outcomes of the model were subsequently compared with those previously predicted by a PBBK model for estragole in male rat to evaluate the occurrence of species differences in metabolic activation. The results obtained reveal that formation of 1'-oxoestragole, which represents a minor metabolic route for 1'-hydroxyestragole in rat, is the main detoxification pathway of 1'-hydroxyestragole in humans. Due to a high level of this 1'-hydroxyestragole oxidation pathway in human liver, the predicted species differences in formation of 1'-sulfooxyestragole remain relatively low, with the predicted formation of 1'-sulfooxyestragole being twofold higher in human compared with male rat, even though the formation of its precursor 1'-hydroxyestragole was predicted to be fourfold higher in human. Overall, it is concluded that in spite of significant differences in the relative extent of different metabolic pathways between human and male rat there is a minor influence of species differences on the ultimate overall bioactivation of estragole to 1'-sulfooxyestragol

Lipid transport in the body: modeling of apoB100-containing lipoproteins.

<p>(A) apoB100 carrying lipoproteins are synthesized in the liver by stepwise addition of lipids to the growing particle. Once secreted, lipoprotein lipase (LpL) and hepatic lipase (HL) may hydrolyze the triglycerides. Intermediate- and low-density lipoproteins (IDLs and LDLs) may be taken up by the LDL receptor. (B) The outline of compartmental models describing lipoprotein kinetics consists of subsystems of tracer molecules (e.g., leucine and/or glycerol), which can be replaced by forcing functions from sample data. A time delay represents the incorporation of the tracer molecules into proteins and triglycerides and is modeled as a series of compartments. The complexity of the blocks representing VLDL₁, VLDL₂ (and IDL and LDL) varies with the studied individuals, the length of the study, and the infusion (bolus or primed constant).</p