Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps

Peer reviewe

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

<p>Abstract</p> <p>Background</p> <p>A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by ¹H NMR spectroscopy of serum.</p> <p>Results</p> <p>A Bayesian methodology, with a biochemical motivation, is presented for a real ¹H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the ¹H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the ¹H NMR spectra.</p> <p>Conclusion</p> <p>The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.</p

Impaired HDL2-mediated cholesterol efflux is associated with metabolic syndrome in families with early onset coronary heart disease and low HDL-cholesterol level

Abstract Objective: The potential of high-density lipoproteins (HDL) to facilitate cholesterol removal from arterial foam cells is a key function of HDL. We studied whether cholesterol efflux to serum and HDL subfractions is impaired in subjects with early coronary heart disease (CHD) or metabolic syndrome (MetS) in families where a low HDL-cholesterol level (HDL-C) predisposes to early CHD. Methods: HDL subfractions were isolated from plasma by sequential ultracentrifugation. THP-1 macrophages loaded with acetyl-LDL were used in the assay of cholesterol efflux to total HDL, HDL2, HDL3 or serum. Results: While cholesterol efflux to serum, total HDL and HDL3 was unchanged, the efflux to HDL2 was 14% lower in subjects with MetS than in subjects without MetS (p&lt;0.001). The efflux to HDL2 was associated with components of MetS such as plasma HDL-C (r = 0.76 in men and r = 0.56 in women, p&lt;0.001 for both). The efflux to HDL2 was reduced in men with early CHD (p&lt;0.01) only in conjunction with their low HDL-C. The phospholipid content of HDL2 particles was a major correlate with the efflux to HDL2 (r = 0.70, p&lt;0.001). A low ratio of HDL2 to total HDL was associated with MetS (p&lt;0.001). Conclusion: Our results indicate that impaired efflux to HDL2 is a functional feature of the low HDL-C state and MetS in families where these risk factors predispose to early CHD. The efflux to HDL2 related to the phospholipid content of HDL2 particles but the phospholipid content did not account for the impaired efflux in cardiometabolic disease, where a combination of low level and poor quality of HDL2 was observed

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in H NMR metabonomic data-0

<p><b>Copyright information:</b></p><p>Taken from "A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in H NMR metabonomic data"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S8</p><p>BMC Bioinformatics 2007;8(Suppl 2):S8-S8.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892077.</p><p></p>lation coefficients (R) shown are between predictions and observations (for predictive R-values see Results and discussion). The straight black lines show a 1:1-relationship and are drawn only to guide the eye

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in H NMR metabonomic data-2

<p><b>Copyright information:</b></p><p>Taken from "A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in H NMR metabonomic data"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S8</p><p>BMC Bioinformatics 2007;8(Suppl 2):S8-S8.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892077.</p><p></p>l kernels for the VLDL-TG (), IDL-C (), LDL-C () and HDL-C (). The assignments for the resonances refer to fatty acids in triglycerides, cholesterol compounds and phospholipids in various lipoprotein particles, the cholesterol backbone -C(18)and the -N(C)groups of surface phospholipids. Thus, it should be noted that all the lipoprotein fractions present in serum contribute to all of these resonances. The insets show the choline -N(C)region and the lipid (-C-)region in mode detail. The highest intensity kernel for each lipoprotein fraction was scaled to 1.0. The dotted horizontal line shows the zero level