Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabonomics studies, but optimal recovery of latent biological information requires increasingly sophisticated statistical methods to identify quantitative relationships within these often highly complex data sets. Statistical heterospectroscopy (SHY) extracts latent relationships between NMR and mass spectrometry (MS) data from the same samples. Here we extend this concept to identify novel metabolic correlations between different biofluids and tissues from the same individuals. We acquired NMR data from blood plasma and cerebrospinal fluid (CSF) (N = 19) from HIV-1-infected individuals, who are known to be susceptible to neuropsychological dysfunction. We compared two computational approaches to SHY, namely the Pearson's product moment correlation and the Spearman's rank correlation. High correlations were observed for glutamine, valine, and polyethylene glycol, a drug delivery vehicle. Orthogonal projections to latent structures (OPLS) identified metabolites in blood plasma spectra that predicted the amounts of key CSF metabolites such as lactate, glutamine, and myo-inositol. Finally, brain metabolic data from magnetic resonance spectroscopy (MRS) measurements in vivo were integrated with CSF data to identify an association between 3-hydroxyvalerate and frontal white matter N-acetyl aspartate levels. The results underscore the utility of tools such as SHY and OPLS for coanalysis of high dimensional data sets to recover biological information unobtainable when such data are analyzed in isolation.
