Prediction of fat oxidation capacity using 1H-NMR and LC-MS lipid metabolomic data combined with phenotypic data
Abstract
With the aim of assessing prediction of fat oxidation capacity in an obese population, a metabolomics study, using 1H-NMR and LC-MS platforms, was performed on plasma samples obtained from obese women before and after a high fat test meal. These subjects were selected based on having a high (n=50) or low (n=50) increase in fat oxidation following the test-meal, representing the extremes of fat oxidizing capacity.
An accurate prediction in terms of classification according to fat oxidation group can lead to candidate biomarkers of fat oxidation capacity, especially if they could be identified in the fasting samples, which would make the biomarker practical applicable. Subject characteristics and clinical data were recorded into a phenotypic data set.
For the spectral data sets, filtering by orthogonal signal correction, variable reduction by spectra segmentation, Mann-Whitney U tests and genetic algorithms were applied together with partial least squares regression models. Our findings suggested that only a small fraction of the between-subject variation in metabolomic profiles are related to differences in fat oxidation capacity.
Variable reduction methods improved predictability. The LC-MS data set led to higher specificity (fasting: 86%; postprandial: 73%) and sensitivity (fasting: 75%; postprandial: 75%) than classification using the 1H-NMR data set (specificity fasting: 50%; specificity postprandial: 60%; sensitivity fasting: 67%; sensitivity postprandial: 62%).
Including phenotypic variables increased specificity and sensitivity values in both fasting and postprandial time points. However, the moderate specificity and sensitivity values indicated that fat oxidation capacity may only be reflected in subtle differences in the metabolomic profile analysed here.
Our data suggested, that identification of candidate biomarkers of fat oxidation capacity using metabolomic profiles needs more refined data acquisition techniques like Noesy NMR or GC-MS.