Metabolomics Core Facility
The metabolomic core facility mainly covers
- Method development (targeted and untargeted)
- Food- and Nutritional metabolomics
- Clinical metabolomics.
Together these fields offer a great potential to investigate metabolic changes. The primary research aim is to elucidate biological mechanisms in nutrition and to develop novel biomarkers for single foods or whole diets by the use of explorative metabolomics.
- to find specific intake biomarkers for a sufficient number of food items to potentially cover common dietary habits in Denmark.
- To understand the link between diet and gut microbiome and the interaction between metabolites from the microbes digestion on the human physiology.
- To contribute with mechanistic understanding and biomarkers of health (defined as the ability to cope with nutritional or exercise challenges) and nutritionally related disease.
- To better understand and contribute biomarkers of disease susceptibility and risk, particularly related to cardiovascular disease, diabetes and cancers of the breast and colon.
- To collaborate broadly within NEXS and with external partners by providing efficient metabolic profiling and biomarker identification in all biological sample types.
Combined urinary biomarkers to assess coffee intake using untargeted metabolomics: Discovery in three pilot human intervention studies and validation in cross-sectional studies
The anserine to carnosine ratio: an excellent discriminator between white and red meats consumed by free-living overweight participants of the PREVIEW study
Biomarkers of intake for tropical fruits
Urine Metabolome Profiling Reveals Imprints of Food Heating Processes after Dietary Intervention with Differently Cooked Potatoes. J. Agric. Food Chem, 2020.
Biomarkers of meat and seafood intake: an extensive literature review
Combined markers to assess meat intake - human metabolomic studies of discovery and validation
Biomarkers of food intake for Alliumvegetables
Food intake biomarkers for apple, pear, and stone fruit
Validation of biomarkers of food intake − critical assessment of candidate biomarkers
A scheme for a flexible classification of dietary and health biomarkers. Genes Nutr. 2017
Dietary and health biomarkers - time for an update
Detecting beer intake by unique metabolite patterns
Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics
Effect of cheese and butter intake on metabolites in urine using an untargeted metabolomics approach
Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern
A LC-MS metabolomics approach to investigate the effect of raw apple intake in the rat plasma metabolome. Metabolomics, 2013.
Discovery of exposure markers in urine for Brassica-containing meals served with different protein sources by UPLC-qTOF-MS untargeted metabolomics, Metabolomics, 2013.
UPLC-QTOF/MS Metabolic Profiling Unveils Urinary Changes in Humans after a Whole Grain Rye versus Refined Wheat Bread Intervention. Mol. Nutr. Food Res, 2013.
Biomarkers of meat intake and the application of nutrigenomics. Meat Sci, 2010.
An exploratory NMR nutri-metabonomic investigation reveals dimethylsulfone as a dietary biomarker for onion intake. Analyst, 2009.
Comparison of bi- and tri-linear PLS models for variable selection in metabolomic time-series experiments
The metaRbolomics toolbox in bioconductor and beyond. Metabolites, 2019.
Dried urine swabs as a tool for monitoring metabolite excretion
PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems. Anal. Chem., 2015.
Metabolite profiling and beyond: Approaches for the rapid processing and annotation of human blood serum mass spectrometry data. Anal Bioanal Chem, 2013 (accepted).
UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Analyt. Chim. Acta, 2013 (accepted).
Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics. International Journal of Knowledge Discovery in Bioinformatics. IEEE 12th International Conference on Data Mining Workshops. 2012.
Metabolic fingerprinting of high-fat plasma samples processed by centrifugation- and filtration-based protein precipitation delineates significant differences in metabolite information coverage. Analytica Chimica Acta, 2012.
Standardization of factors that influence human urine metabolomics, Metabolomics, 2011.
The Effect of LC-MS Data Preprocessing Methods on the Selection of Plasma Biomarkers in Fed vs. Fasted Rats. Metabolites, 2011.
NMR and iPLS are reliable methods for determination of cholesterol in rodent lipoprotein fractions. Metabolomics, 2009.
Meslier, Laiola, Roager et al. 2020; Gut; https://gut.bmj.com/content/69/7/1258
Roager et al. 2019; Gut; https://gut.bmj.com/content/68/1/83
Roager and Dragsted, 2018; Nutrition Bulletin; https://onlinelibrary.wiley.com/doi/full/10.1111/nbu.12396
Hansen, Roager et al. 2018; Nature Communications; https://www.nature.com/articles/s41467-018-07019-x
Roager and Licht 2018; Nature Communications; https://www.nature.com/articles/s41467-018-05470-4
Roager et al. 2016; Nature Microbiology; https://www.nature.com/articles/nmicrobiol201693
Effects of brown seaweeds on postprandial glucose, insulin and appetite in humans - A randomized, 3-way, blinded, cross-over meal study
Pre-meal protein intake alters postprandial plasma metabolome in subjects with metabolic syndrome
Progressive changes in the plasma metabolome during malnutrition in juvenile pigs
Intakes of whey protein hydrolysate and whole whey proteins are discriminated by LC-MS metabolomics
Patterns of time since last meal revealed by sparse PCA in an observational LC-MS based metabolomics study.Metabolomics, 2013.
LC-QTOF/MS metabolomic profiles in human plasma after a five-week high dietary fiber intake. Anal Bioanal Chem, 2013.
Assessment of dietary exposure related to dietary GI and fibre intake in a nutritional metabolomic study of human urine. Genes Nutr, 2012.
Assessment of the effect of high or low protein diet on the human urine metabolome by NMR. Nutrients, 2012.
LC–MS metabolomics top-down approach reveals new exposure and effect biomarkers of apple and apple-pectin intake. Metabolomics, 2012..
Sucrose, glucose and fructose have similar genotoxicity in the rat colon and affect the metabolome. Fd. Chem. Toxicol, 2008.
Involved in Metabolomics Core Facility
| Name | Title | Phone | |
|---|---|---|---|
| Catalina Sinziana Cuparencu | Assistant Professor | ||
| Giorgia La Barbera | Assistant Professor | ||
| Henrik Munch Roager | Associate Professor - Promotion Programme | +4535324928 | |
| Jan Stanstrup | Postdoc | +4535332859 | |
| Lars Ove Dragsted | Professor | +4535332694 |
More information
For more information about current research look at specific group