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

Clustering by Plasma Lipoprotein Profile Reveals Two Distinct Subgroups with Positive Lipid Response to Fenofibrate Therapy

<div><p>Fibrates lower triglycerides and raise HDL cholesterol in dyslipidemic patients, but show heterogeneous treatment response. We used k-means clustering to identify three representative NMR lipoprotein profiles for 775 subjects from the GOLDN population, and study the response to fenofibrate in corresponding subgroups. The subjects in each subgroup showed differences in conventional lipid characteristics and in presence/absence of cardiovascular risk factors at baseline; there were subgroups with a low, medium and high degree of dyslipidemia. Modeling analysis suggests that the difference between the subgroups with low and medium dyslipidemia is influenced mainly by hepatic uptake dysfunction, while the difference between subgroups with medium and high dyslipidemia is influenced mainly by extrahepatic lipolysis disfunction. The medium and high dyslipidemia subgroups showed a positive, yet distinct lipid response to fenofibrate treatment. When comparing our subgroups to known subgrouping methods, we identified an additional 33% of the population with favorable lipid response to fenofibrate compared to a standard baseline triglyceride cutoff method. Compared to a standard HDL cholesterol cutoff method, the addition was 18%. In conclusion, by using constructing subgroups based on representative lipoprotein profiles, we have identified two subgroups of subjects with positive lipid response to fenofibrate therapy and with different underlying disturbances in lipoprotein metabolism. The total subgroup with positive lipid response to fenofibrate is larger than subgroups identified with baseline triglyceride and HDL cholesterol cutoffs.</p> </div

Overview of the data analysis approach presented in this paper.

<p>Clustering was carried out to identify representative lipoprotein profiles. The computational model analyzed those representative lipoprotein profiles. In the corresponding subgroups baseline characteristics and the lipid response to fenofibrate intervention was studied. The results of the subgroup studies were compared to the baseline characteristics and lipid response to fenofibrate in subgroups identified using triglyceride or HDL cholesterol cut-off methods.</p

Subject overlaps between different subgroup identification methods. A:

<p>Subject overlap between the low HDLc subgroup (dark circle) and the sum of lipoprotein profile-based cluster 2 and 3 (light circle). Figures indicate the number of subjects in each group, in absolute numbers and as percentage of the total study population. <b>B:</b> Subject overlap between the high baseline-triglyceride subgroup (dark circle) and lipoprotein profile-based cluster 3 (light circle). Figures indicate the number of subjects in each group, in absolute numbers and as percentage of the total study population. <b>C:</b> Subject overlap between the medium baseline-triglyceride subgroup (dark circle) and lipoprotein profile-based cluster 2 (light circle). Figures indicate the number of subjects in each group, in absolute numbers and as percentage of the total study population. Lipoprotein cluster 2 clearly identifies a larger group of fibrate responders than the medium baseline-TG group.</p

Percent changes after fenofibrate intervention in high TG subgroup versus lipoprotein cluster 3.

†<p>indicates significantly different with respect to cluster 3 and high TG subgroup, p<0.01.</p>‡<p>indicates significantly different with respect to cluster 3 and not high TG subgroup, p<0.01.</p>*<p>LDL/HDL particle number (measured by NMR).</p

Mean standardized particle concentrations (unitless) of NMR lipoprotein subclasses in three subgroups based on K-means clustering.

<p>Particle sizes of the various subclasses were the same as described in Freedman, <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038072#pone.0038072-Freedman1" target="_blank">[38]</a>.</p

Percent changes after fenofibrate intervention in medium TG subgroup versus lipoprotein cluster 2.

†<p>indicates significantly different with respect to cluster 2 and medium TG subgroup, p<0.01.</p>‡<p>indicates significantly different with respect to cluster 2 and not medium TG subgroup, p<0.01.</p>*<p>LDL/HDL particle number (measured by NMR).</p

Percent changes after fenofibrate intervention, grouped by NMR clustering.

†<p>indicates significantly different with respect to cluster 1, p<0.01.</p>‡<p>indicates significantly different with respect to cluster 2, p<0.01.</p>*<p>LDL/HDL particle number (measured by NMR).</p

Baseline characteristics of subjects in clustering on lipoprotein profile.

†<p>indicates significantly different with respect to cluster 1, p<0.01.</p>‡<p>indicates significantly different with respect to cluster 2, p<0.01.</p>*<p>LDL/HDL particle number (measured by NMR).</p