Journal article
Prediction of fat oxidation capacity using 1H-NMR and LC-MS lipid metabolomic data combined with phenotypic data
Institute of Preventive Medicine, Copenhagen University Hospital, Center for Health and Society, Copenhagen, Denmark1
The Bioinformatics Centre, Department of Molecular Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark2
BioAnalytical Science Department, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne-26, Switzerland3
Department of Human Nutrition, Faculty of Life Sciences, University of Copenhagen, Denmark4
Department of Sports Medicine, Third Faculty of Medicine, Charles University, Prague, Czech Republic5
Department of Physiology and Nutrition, University of Navarra, Pamplona, Spain6
Department of Human Biology, Nutrition and Toxicology Research Institute NUTRIM, Maastricht University, Maastricht, The Netherlands7
School of Biomedical Sciences, University of Nottingham Medical School, Queen's Medical Centre, UK8
There is evidence from clinical trials that a low capacity to oxidize dietary fats may predispose human individuals to weight gain, obesity, and weight regain following weight loss. These observations have led to a need to identify plasma markers of fat oxidation capacity in order to avoid time consuming direct measurements by indirect calorimetry.
The aim of this study was to investigate whether prediction of fat oxidation capacity in an obese population is possible, using 1H-NMR and LC-MS-based metabolic profiling of blood plasma samples collected before and after a high fat test meal from 100 obese women, who represented the extremes of fat oxidizing capacity.
Subject characteristics (baseline anthropometrics, body composition and dietary records) and clinical data (blood values and derived measures for insulin resistance) were recorded into a phenotypic dataset. Filtering by orthogonal signal correction, variable reduction by spectra segmentation, Mann–Whitney U tests and genetic algorithms were applied to spectral data together with partial least squares regression models for prediction.
Our findings suggested that only a small fraction of subject variation contained in metabolic profiles is related to fat oxidation capacity and variable reduction methods improved fat oxidation capacity predictability. The LC-MS dataset led to higher specificity (fasting: 86%; postprandial: 73%) and sensitivity (fasting: 75%; postprandial: 75%) than classification using the 1H-NMR dataset (specificity: fasting: 50%; postprandial: 60%; sensitivity: fasting: 67%; postprandial: 62%).
Inclusion of 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 metabolic profiles analyzed. In future studies, metabolomics data may be supplemented with gene variation and gene expression data to caption the properties of fat oxidation capacity more precisely.
| Language: | English |
|---|---|
| Year: | 2008 |
| Pages: | 34-42 |
| ISSN: | 18733239 and 01697439 |
| Types: | Journal article |
| DOI: | 10.1016/j.chemolab.2008.03.008 |
| ORCIDs: | Sørensen, T.I.A. , 0000-0001-7094-6801 and 0000-0001-8968-8996 |
