Abstract
Assessment of dietary intake in humans is central in nutrition research and it is a prerequisite to investigate associations between diet and health. The existing methods to estimate dietary intake are largely based on self-reporting. It is well-known that these methods are very uncertain. Biomarkers measured in biological samples are objective measures and therefore advantageous to determine what a person has consumed. Unfortunately, only few well evidenced biomarkers are commonly applied in nutrition studies. Therefore, there is a need to find new biomarkers, in particular for intake of individual foods. In this thesis, untargeted metabolomics, a relatively new method within nutrition research, has been applied to find new potential food exposure markers in urine for intake of a range of foods. In addition, it has been investigated, if it is possible to distinguish two dietary patterns, a New Nordic Diet (NND) and an Average Danish Diet (ADD), in urine samples from a controlled intervention study.
Data from three studies are included in the thesis: A cross-over meal study with nine different meals, a range of small meal studies with individual foods and a six month parallel intervention study with NND and ADD.
By application of LC-MS based untargeted metabolomics, 35 markers related to intake of specific foods were found such as cabbage, citrus, beetroot, walnuts and fish. Some of the markers were found consistently in all studies and were therefore very promising new urinary markers. Several of the markers have also been found in other metabolomics studies. It was possible to distinguish NND and ADD in 24 h urine samples by a multivariate model based on measures of 52 metabolites. The metabolites in the model reflected intake of characteristic foods from both diets –however, mainly the ADD. In a validation of the model, 81% of 139 samples were classified to the correct dietary pattern. There was a higher number of misclassified NND samples compared to ADD samples. A comparison of subjects with correctly classified and misclassified NND samples in the model for different known parameters related to compliance suggests that some of the misclassified NND subjects were non-compliant to the intervention diet.
Overall, the results in this thesis substantiate untargeted metabolomics as a promising tool to develop new dietary compliance measures and exposure markers.
Data from three studies are included in the thesis: A cross-over meal study with nine different meals, a range of small meal studies with individual foods and a six month parallel intervention study with NND and ADD.
By application of LC-MS based untargeted metabolomics, 35 markers related to intake of specific foods were found such as cabbage, citrus, beetroot, walnuts and fish. Some of the markers were found consistently in all studies and were therefore very promising new urinary markers. Several of the markers have also been found in other metabolomics studies. It was possible to distinguish NND and ADD in 24 h urine samples by a multivariate model based on measures of 52 metabolites. The metabolites in the model reflected intake of characteristic foods from both diets –however, mainly the ADD. In a validation of the model, 81% of 139 samples were classified to the correct dietary pattern. There was a higher number of misclassified NND samples compared to ADD samples. A comparison of subjects with correctly classified and misclassified NND samples in the model for different known parameters related to compliance suggests that some of the misclassified NND subjects were non-compliant to the intervention diet.
Overall, the results in this thesis substantiate untargeted metabolomics as a promising tool to develop new dietary compliance measures and exposure markers.
| Original language | English |
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| Place of Publication | Copenhagen |
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| Publisher | Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen |
| Number of pages | 230 |
| ISBN (Print) | 978-87-7611-686-6 |
| Publication status | Published - 2014 |