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The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

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 ( 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: . Graphical Abstract: [Figure not available: see fulltext.]

Publication metadata

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


DOI: 10.1007/s00125-023-06019-x


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