title: A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Computational Engineering, Systems Biology and Bioinformation Technology, Helsinki University of Technology, Finland. aki.vehtari@hut.fi
authors.forename: Aki
authors.initials: A
authors.lastname: Vehtari
authors.forename: Ville-Petteri
authors.initials: VP
authors.lastname: Mäkinen
authors.forename: Pasi
authors.initials: P
authors.lastname: Soininen
authors.forename: Petri
authors.initials: P
authors.lastname: Ingman
authors.forename: Sanna M
authors.initials: SM
authors.lastname: Mäkelä
authors.forename: Markku J
authors.initials: MJ
authors.lastname: Savolainen
authors.forename: Minna L
authors.initials: ML
authors.lastname: Hannuksela
authors.forename: Kimmo
authors.initials: K
authors.lastname: Kaski
authors.forename: Mika
authors.initials: M
authors.lastname: Ala-Korpela
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood; D001774:Blood Chemical Analysis / Q000379:methods*; D003327:Coronary Disease / Q000097:blood* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008074:Lipoproteins / Q000097:blood*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011522:Protons; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D008074:Lipoproteins; D011522:Protons