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Lookup NU author(s): Professor Roy Taylor
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© 2023, The Author(s).Aims/hypothesis: High-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes. Methods: We analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels. Results: Decreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission. Conclusions/interpretation: We have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes. Data availability: The data used for analysis are available on a research data repository (https://researchdata.gla.ac.uk/) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct . Graphical Abstract: [Figure not available: see fulltext.]
Author(s): Corbin LJ, Hughes DA, Bull CJ, Vincent EE, Smith ML, McConnachie A, Messow C-M, Welsh P, Taylor R, Lean MEJ, Sattar N, Timpson NJ
Publication type: Article
Publication status: Published
Journal: Diabetologia
Year: 2024
Volume: 67
Pages: 74-87
Online publication date: 25/10/2023
Acceptance date: 04/08/2023
ISSN (print): 0012-186X
ISSN (electronic): 1432-0428
Publisher: Springer Science and Business Media Deutschland GmbH
URL: https://doi.org/10.1007/s00125-023-06019-x
DOI: 10.1007/s00125-023-06019-x
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