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(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.

Breast milk (BM) is a biofluid that has a fundamental role in early life nutrition and has direct impact on growth, neurodevelopment, and health. Global metabolic profiling is increasingly being utilized to characterize complex metabolic changes in biological samples. However, in order to achieve broad metabolite coverage, it is necessary to employ more than one analytical platform, typically requiring multiple sample preparation protocols. In an effort to improve analytical efficiency and retain comprehensive coverage of the metabolome, a new extraction methodology was developed that successfully retains metabolites from BM in a single-phase using an optimized methyl-tert-butyl ether solvent system. We conducted this single-phase extraction procedure on a representative pool of BM, and characterized the metabolic composition using LC-QTOF-MS and GC-Q-MS for polar and lipidic metabolites. To ensure that the extraction method was reproducible and fit-for-purpose, the analytical procedure was evaluated on both platforms using 18 metabolites selected to cover a range of chromatographic retention times and biochemical classes. Having validated the method, the metabolic signature of BM composition was mapped as a metabolic reaction network highlighting interconnected biological pathways and showing that the LC-MS and GC-MS platforms targeted largely different domains of the network. Subsequently, the same protocol was applied to ascertain compositional differences between BM at week 1 (n = 10) and 4 weeks (n = 9) post-partum. This single-phase approach is more efficient in terms of time, simplicity, cost, and sample volume than the existing two-phase methods and will be suited to high-throughput metabolic profiling studies of BM.

We describe a new multivariate statistical approach to recover metabolite structure information from multiple (1)H NMR spectra in population sample sets. Subset optimization by reference matching (STORM) was developed to select subsets of (1)H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data by using these reduced spectral subsets containing smaller numbers of samples than the number of variables (n ≪ p). We have used statistical shrinkage to limit the number of false positive associations and to simplify the overall interpretation of the autocorrelation matrix. The STORM approach has been applied to findings from an ongoing human metabolome-wide association study on body mass index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (statistical total correlation spectroscopy, STOCSY). STORM is a useful new tool for biomarker discovery in the "omic" sciences that has widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than one dimension (e.g., 2D NMR spectra).