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

Abstract

BackgroundA 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.ResultsA 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.ConclusionThe 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.

Metabolic Phenotypes, Vascular Complications, and Premature Deaths in a Population of 4,197 Patients With Type 1 Diabetes

“…Moreover, gradually developing conditions, such as cardiovascular disease, do not present a physiologically clear border between health and disease, so quantitative risk assessment tools are needed to augment and even replace discrete differential categorizations (11). Hence, we are developing new approaches to attain more accurate phenotypes without excessive cost, both through highthroughput analytics (12,13) and computational methods (14,15).…”

“…Moreover, gradually developing conditions, such as cardiovascular disease, do not present a physiologically clear border between health and disease, so quantitative risk assessment tools are needed to augment and even replace discrete differential categorizations (11). Hence, we are developing new approaches to attain more accurate phenotypes without excessive cost, both through highthroughput analytics (12,13) and computational methods (14,15).In this work, we develop an analysis framework based on the self-organizing map and statistically verified visualizations (13,16,17) for a large clinical study. Our goal is to characterize typical phenotypes (or metabolic profiles) that can be associated with high or low mortality during several years of follow-up.…”

NMR spectroscopy – a modern analytical tool for serum analytics of lipoproteins and metabolites

“…Similarly good values were obtained from other statistical algorithms such as MCMC methods (Markov chain Monte Carlo) [21] .…”

NMR metabolomics and drug discovery

“…[54] Nevertheless, assigning 1D 1 H NMR spectra for metabolomic samples is still www.interscience.wiley.com/journal/mrc considerably challenging because of significant peak overlap and the presence of uncharacterized metabolites. [77] Instead, the use of 2D NMR techniques is commonly used to analyze the composition of metabolomic samples. The fast metabolite quantification (FMQ) by NMR method described by Lewis et al [69] uses a series of 2D 1 H-13 C heteronuclear single quantum coherence (HSQC) spectra collected for mixtures of standard metabolites over a range of concentrations.…”