Metabolomics

We profile samples from dietary, clinical, animal or plant studies, primarily designed to investigate specific health outcomes, e.g. markers of disease risk or to unravel metabolic pathways responsible for disease onset or prevention. We are conducting some of the trials in our own facility or we analyze samples collected by others.

The analytical and statistical methods used in metabolomics are diverse; we therefore also develop new and improved techniques and methodologies.

See also the CUBE biobank, the Squidr datahub, and PredRet

Changes in metabolism associated with health risks or disease are not always well known and metabolomics can therefore help to better understand biological mechanisms, and/or identify biomarkers of exposure, risk, diagnosis or prognosis.

Metabolite profiling may be based on almost any biological sample e.g. blood, urine, feces, hair etc. The profile contains large data that are quite demanding on computer resources.

Following essential steps in data pre-processing and data analysis, we are selecting sets of metabolite features (represented by retention time and m/z) that are associated with health or disease,

The identification of these features is a major bottleneck in metabolomics. In order to facilitate this work, we have created tools such as quality control procedures, spectral databases, metabolite profile databases, and a biobank.

Some of the bioinformatics work is internationally coordinated through ELIXIR. Upon identification, we form hypotheses that explain the biological effects under study.

Andersen IKL, Dragsted LO, Rasmussen J, Fomsgaard I (2023) Intercropping of Hordeum vulgare L. and Lupinus angustifolius L. causes the generation of prenylated flavonoids in Lupinus angustifolius L. Journal of Plant Interactions 18:1 https://doi.org/10.1080/17429145.2023.2255039

Pigsborg K, Stentoft-Larsen V, Demharter S, Aldubayan MA, Trimigno A, Khakimov B, Engelsen SB, Astrup A, Hjorth MF, Dragsted LO, Magkos F (2023) Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach. Frontiers Nutr. 10.

Trimigno A, Khakimov B, Rasmussen MA, Dragsted LO, Larsen TM, Astrup A, Engelsen S (2023) Human blood plasma biomarkers of diet and weight loss among  centrally obese subjects in a New Nordic Diet intervention. Frontiers Nutr. (accepted) ID 1198531.

Kurmaeva D, Ye Y, Bakhytkyzy I, Aru V, Dalimova D, Turdikulova S, Dragsted LO, Engelsen S, Khakimov B (2023) Associations between sheep meat intake frequency and blood plasma levels of metabolites and lipoproteins in healthy Uzbek adults. Metabolomics 19:43 https://doi.org/10.1007/s11306-023-02005-x

Wilkens TL, Dragsted LO, Ziegler Z, Aru V, Overgaard SL, Khakimov B, Engelsen SB (2022) 1-2 drinks per day affect lipoprotein composition after 3 weeks - results from a cross-over intervention trial in healthy adults using nuclear magnetic resonance-measured lipoproteins and apolipoproteins. Nutrients 14 (23), 5043

Balech B, Brennan L, Carrillo de Santa Pau E, Cavalieri D, Coort S, D’Elia D, Dragsted LO, Eftimov T, Evelo CT, Ferk P, Finglas P, Gori A, Hancock J, Kalaš M, Seljak BK, Lachat C, Leskošek B, Pasolli E, Pesole G, Presser K, Sandionigi A, Santamaria M, Şener DD, Traka M, Vergères G, Zimmermann K, Bouwman J (2022) The future of food and nutrition in ELIXIR F1000Research 11(ELIXIR):978 (online for open peer review: https://doi.org/10.12688/f1000research.51747.1)

Gürdeniz G, Uusitupa M, Hermansen K, Savolainen MJ, Schwab U, Kolehmainen M, Brader L, Cloetens L, Herzig K-H, Hukkanen J, Rosqvist F, Ulven SM, Gunnarsdóttir I, Thorsdottir I, Oresic M, Poutanen KS, Riserus  U, Åkesson B, Dragsted LO (2022) Analysis of the SYSDIET Healthy Nordic Diet randomized trial based on metabolic profiling reveals beneficial effects on glucose metabolism and blood lipids. Clin Nutr. 41 441-451. https://doi.org/10.1016/j.clnu.2021.12.031

Rainer J, Vicini A, Salzer L, Stanstrup J, Badia JM, Neumann S, Stravs MA, Verri Hernandes V, Gatto L, Gibb S, et al. (2022) A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites  2022; 12(2):173. https://doi.org/10.3390/metabo12020173

La Barbera G, Nommesen KD, Cuparencu C, Stanstrup J and Dragsted LO (2022) A Comprehensive Database for DNA Adductomics. Front. Chem. 10:908572. doi: 10.3389/fchem.2022.908572

Bejder, J., Gürdeniz, G., Cuparencu, C., Hall, F., Gybel-Brask, M., Breenfeldt Andersen, A., Dragsted, L. O., Secher, N. H., Johansson, P. I., & Nordsborg, N. B. (2021). An Untargeted Urine Metabolomics Approach for Autologous Blood Transfusion Detection. Medicine and science in sports and exercise, 53(1), 236–243. https://doi.org/10.1249/MSS.0000000000002442

Low DY, Micheau P, Koistinen VM, Hanhineva K, Abrankó L, Rodriguez-Mateos A, da Silva AB, van Poucke C, Almeida C, Andres-Lacueva C, ai DK, Capanoglu E, Barberán FAT, Mattivi F, Schmidt G, Gürdeniz G, Valentová K, Bresciani L, Petrásková L, Dragsted LO, Philo M, Ulaszewska M, Mena P, González-Domínguez R, Garcia-Villalba R, Kamiloglu S, de Pascual-Teresa S, Durand S, Wiczkowski W, Bronze MR, Stanstrup J, Manach C (2021) Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. Food Chemistry 357, 129757. https://doi.org/10.1016/j.foodchem.2021.129757

Dragsted LO (2020) The metabolic nature of individuality. Nature Food (1; June 2020) 327-329. https://doi.org/10.1038/s43016-020-0104-z 

Meslier V, Laiola M, Roager HM, De Filippis F, Roume H, Quinquis B, Giacco R, Mennella I, Ferracane R, Pons N, Pasolli E, Rivellese AA, Dragsted LO, Vitaglione P, Ehrlich DS, Ercolini D (2020) Mediterranean diet intervention in overweight and obese subjects leads to multiple beneficial shifts in gut microbiome and metabolome independently from energy intake. Gut (69, 7) 1258-68. https://doi.org/10.1136/gutjnl-2019-320438

Trošt K, Ahonen L, Suvitaival T, Christiansen N, Nielsen T, Thiele M, Jacobsen S, Krag A, Rossing P, Hansen T, Dragsted LO, Legido-Quigley C (2020) Describing the fecal metabolome in cryogenically collected samples from healthy participants. Scientific Reports 1, 885; https://doi.org/10.1038/s41598-020-57888-w  

Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S (2019) The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 9 (10), 200. https://doi.org/10.3390/metabo9100200

Pekmez CT, Bjørnshave A, Práticó G, Hermansen K, Dragsted LO (2019) Premeal protein intake alters postprandial plasma metabolome in subjects with metabolic syndrome. Eur. J. Nutr. (59,5) 1181-94 https://doi.org/10.1007/s00394-019-02039-9

Baye E, Mark AB, Poulsen MW, Andersen JM, Dragsted LO, Bügel SG, de Courten B (2019) Associations between urinary advanced glycation end products and cardiometabolic parameters in normoglycemic obese women. J. Clin. Med. 8, 1008 (online) https://doi.org/10.3390/jcm8071008    

Maruvada P, Lampe J, Wishart DS, Barupal D, Chester DN, Dodd D, Djoumbou-Feunang Y, Dorrestein PC, Dragsted LO, Draper D, Duffy LC, Dwyer JT, Emenaker NJ, Fiehn O, Gerszten RE, Hu F, Karp RW, Klurfeld DM, Laughlin MR, Little AR, Lynch CJ, Moore SC, Nicastro HL, O’Brien DM, Ordóvas JM, Osganian SK, Playdon M, Prentice R, Raftery D, Reisdorph N, Roche HM, Ross SA, Sang S, Scalbert A, Srinivas PR, Zeisel SH (2019) Perspective: Dietary Biomarkers of Intake and Exposure: Exploration with Omics Approaches. Adv. Nutr. (11, 2) 200-211. https://doi.org/5544358[pii];10.1093/advances/nmz075

Gao Q, Dragsted LO & Ebbels T (2019) Comparison of bi- and tri-linear PLS models for variable selection in metabolomic time-series experiments. Metabolites 9:5. https://doi.org/10.3390/metabo9050092

Acar E, Gurdeniz G, Khakimov B, Savorani F, Korndal SK, Larsen TM, Engelsen SB, Astrup A, Dragsted LO (2019) Biomarkers of Individual Foods, and Separation of Diets Using Untargeted LC-MS Based Plasma Metabolomics in a Randomized Controlled Trial. Mol. Nutr. Food Res. 63(1): e1800215 https://doi.org/10.1002/mnfr.201800215

Monošík, R & Dragsted, LO (2018) Dried urine swabs as a tool for monitoring metabolite excretion. Bioanalysis 10:17 1371-1381. https://doi.org/10.4155/bio-2018-0042

Eriksen JN, Prahm AP, Falk MK, Arrigoni E, Jeppesen PB, Larsen M, Dragsted LO (2018) Serum carotenoids and macular pigment optical density in patients with intestinal resections and healthy subjects: an exploratory study. J Nutr Sci 7:e8. https://doi.org/10.1017/jns.2017.71

Jiang P, Trimigno A, Stanstrup J, Khakimov B, Viereck N, Engelsen SB, Sangild PT, Dragsted LO (2017) Antibiotic Treatment Preventing Necrotising Enterocolitis Alters Urinary and Plasma Metabolomes in Preterm Pigs. J. Proteome Res. 16(10) 3547-3557.

Lamichhane S, Sundekilde UK, Blaedel T, Dalsgaard TK, Larsen LH, Dragsted LO, Astrup A, Bertram HC (2017) Optimizing sampling strategies for NMR-based metabolomics of human feces: pooled vs. unpooled analyses. Anal. Methods 9:4476  https://doi.org/10.1039/c7ay01465a

Pinart M, Nimptsch K, Bouwman J, Dragsted LO, Lachat C, Perozzi G, Canali R, Lombardo R, D'Archivio M, Guillaume M, Donneau A-F, Jeran S, Linseisen J, Kleiser C, Nöthlings U, Barbaresko J, Boeing H, Stelmach-Mardas M, Heuer T, Laird E, Walton J, Gasparini P, Robino A, Castaño L , Rojo-Martínez G, Merino J, Masana L, Standl L, Schulz H, Biagi E, Nurk E, Matthys C, Gobbetti M, de Angelis M, Windler E, Zyriax B-C, Tafforeau J, Pischon T (2018) Joint data analysis in nutritional epidemiology: Identification of observational studies and minimal requirements. J. Nutr. 148(2) 285-297

Yang C, Pinat M, Kolsteren P, Van Kamp J, De Cock N, Nimptsch K, Pischon T, Laird E, Perrozzi G, Canali R, Hoge A, Stelmach-Mardas M, Dragsted LO, Palombi SM, Dobre I, Bouwman J, Clarys P, Minervini F, De Angelis M, Gobbetti M, Tafforeau J, Coltell O, Corolla D, De Ruyck H, Walton J, Kohoe L, Matthys C, De Baets B, De Tré G, Bronselaer A, Rivellese A, Giacco R, Lombardo R, De Clerq S, Lachat C (2017). Perspectives: Essential study quality descriptors for data from nutritional epidemiological research. Adv. Nutr. 8,5 639-651 http://advances.nutrition.org/content/8/5/639.full

Acar E, Gurdeniz G, Savorani F, Hansen L, Olsen A, Tjonneland A, Dragsted LO, Bro R (2017) Forecasting Chronic Diseases Using Data Fusion. J.Proteome Res. 16,7 2435-2444.

Khakimov B, Poulsen SK, Savorani F, Acar E, Gurdeniz G, Larsen TM, Astrup A, Dragsted LO, Engelsen SB (2016) New Nordic Diet versus Average Danish Diet: A Randomized Controlled Trial Revealed Healthy Long-Term Effects of the New Nordic Diet by GC-MS Blood Plasma Metabolomics. J.Proteome Res. 15; 1939-54.

Lankinen,M., Schwab,U., Kolehmainen,M., Paananen,J., Nygren,H., Seppänen-Laakso,T., Poutanen,K., Hyötyläinen,T., Risérus,U., Savolainen,M.J., Hukkanen,J., Brader,L., Marklund,M., Rosqvist,F., Hermansen,K., Cloetens,L., Önning,G., Thorsdottir,I., Gunnarsdottir,I., Åkesson,B., Dragsted,L.O., Uusitupa,M., Orešič,M. (2015) A healthy Nordic diet alters the plasma lipidomic profile in adults with features of metabolic syndrome in a multicenter randomized dietary intervention. J. Nutrition 146(4); 662-672.

Rago, D., Gürdeniz, G., Ravn-Haren, G., and Dragsted,L.O. (2015) An explorative study of the effect of apple and apple products on the human plasma metabolome investigated by LC–MS profiling. Metabolomics 11(1) 27-39

Bro, R., Kamstrup-Nielsen,M.H., Engelsen,S.B., Savorani,F., Rasmussen,M.A., Hansen,L., Olsen,A., Tjønneland,A., Dragsted,L.O. (2015) Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 11(5); 1376-1380 https://doi.org/10.1007/s11306-015-0793-8

Stanstrup J, Neumann S, Vrhovšek U (2015) PredRet: Prediction of retention time by direct mapping between multiple chromatographic systems. Analytical chemistry 87 (18), 9421-9428. https://doi.org/10.1021/acs.analchem.5b02287  

Stanstrup,J., Schou,S.S., Holmer-Jensen,J., Hermansen,K., Dragsted, L.O. (2014) Whey protein delays gastric emptying and suppresses plasma fatty acids and their metabolites compared to casein, gluten and cod protein. J. Proteome Res. (13:5) 2396-2408.

Hjerpsted, J.B., Ritz, C., Schou, S.S., Tholstrup, T., Dragsted, L.O. (2014) Effect of cheese and butter intake on metabolites in urine using a non-targeted metabolomics approach. Metabolomics (10:6) 1176-1185.

Hanhineva, K., Barri, T., Kolehmainen, M., Pekkinen, J., Urban, J.F.,Jr., Solano-Aguilar, G., Mykkänen, H., Dragsted, L.O., Poutanen, K. (2013) Comparative non-targeted metabolite profiling of metabolic changes in tissues and bio-fluids in high-fat diet fed Ossabaw pig. J. Proteome Res. (12,9) 3980-3992 https://doi.org/dx.doi.org/10.1021/pr400257d

Stanstrup, J., Gerlich, M., Dragsted, L.O., Neumann, S. (2013) Metabolite profiling and beyond: Approaches for the rapid processing and annotation of human blood serum mass spectrometry data. Analytical and Bioanalytical Chemistry (405, 15) 5037-48. https://doi.org/10.1007/s00216-013-6954-6.

Gürdeniz, G., Hansen, L., Barri, T., Olsen, A., Christensen, J., Overvad, K., Rasmussen, M.A., Acar, E., Tjønneland, A., Dragsted, L.O. (2013a) Patterns of time since last meal revealed by sparse PCA in an observational LC-MS based metabolomics study. Metabolomics (9,5) 1073-1081. https://doi.org/10.1007/s11306-013-0525-x

Barri, T. and Dragsted, L.O. (2013) UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Analyt. Chim. Acta (768) 118-28. https://doi.org/10.1016/j.aca.2013.01.015

Andersen, J.M., Hjelmgaard, T., Dragsted, L.O. and Nielsen, J. (2012) Convenient synthesis of N-(carboxymethyl)lysine, a key Advanced Glycation Endproduct biomarker. Synlett 23,4 531-534 https://doi.org/10.1055/s-0031-1290348

Gürdeniz,G., Kristensen,M., Skov,T., Dragsted, L.O. (2012) The Effect of LC-MS Data Preprocessing Methods on the Selection of Plasma Biomarkers in Fed vs. Fasted Rats’ Metabolites (2,1) 77-96 https://doi.org/10.3390/metabo2010077

Barri,T., Holmer-Jensen, J., Hermansen, K., Dragsted, L.O. (2012) Metabolomic fingerprinting of high-fat plasma samples processed by centrifugation- or filtration-based protein precipitation delineates significant differences in metabolite information coverage. Analyt. Chim. Acta 718 47-57.

Kristensen, M., Savorani, F., Ravn-Haren, G., Poulsen, M., Markowski, J., Larsen, F.H., Dragsted, L.O. and Engelsen, S.B. (2009) NMR and iPLS are reliable methods for determination of cholesterol in rodent lipoprotein fractions. Analyst 6 129-136.