Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites.

An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 % of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 μM were quantified with average absolute relative error less than 10 % when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.