Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study
(2023) In Metabolites 13(7).- Abstract
- Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed... (More)
- Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/19a08f3a-671b-42a7-bbef-8a45a14ed242
- author
- Vieira, João LU ; Ottosson, Filip LU ; Jujic, Amra LU ; Denisov, Vladimir LU ; Magnusson, Martin LU ; Melander, Olle LU and Duarte, Joao LU
- organization
-
- MultiPark: Multidisciplinary research focused on Parkinson´s disease
- EXODIAB: Excellence of Diabetes Research in Sweden
- Diabetes and Brain Function (research group)
- WCMM-Wallenberg Centre for Molecular Medicine
- Cardiovascular Research - Hypertension (research group)
- LTH Profile Area: Engineering Health
- Hyperpolarised MR (research group)
- Biomedical Engineering, Lund
- EpiHealth: Epidemiology for Health
- LU Profile Area: Proactive Ageing
- publishing date
- 2023-07-23
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- metabolomics, LC-MS, NMR, plasma, biomarkers
- in
- Metabolites
- volume
- 13
- issue
- 7
- article number
- 874
- publisher
- MDPI AG
- external identifiers
-
- scopus:85166284794
- pmid:37512581
- ISSN
- 2218-1989
- DOI
- 10.3390/metabo13070874
- language
- English
- LU publication?
- yes
- id
- 19a08f3a-671b-42a7-bbef-8a45a14ed242
- date added to LUP
- 2023-07-26 16:05:25
- date last changed
- 2024-01-19 01:14:19
@article{19a08f3a-671b-42a7-bbef-8a45a14ed242, abstract = {{Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.}}, author = {{Vieira, João and Ottosson, Filip and Jujic, Amra and Denisov, Vladimir and Magnusson, Martin and Melander, Olle and Duarte, Joao}}, issn = {{2218-1989}}, keywords = {{metabolomics; LC-MS; NMR; plasma; biomarkers}}, language = {{eng}}, month = {{07}}, number = {{7}}, publisher = {{MDPI AG}}, series = {{Metabolites}}, title = {{Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study}}, url = {{http://dx.doi.org/10.3390/metabo13070874}}, doi = {{10.3390/metabo13070874}}, volume = {{13}}, year = {{2023}}, }