Type 2 diabetes mellitus (T2DM) includes around 90% of patients with diabetes. Metabolomics has helped in diabetes research and discovering new biomarkers to assist in therapeutical studies and discovering new pathways. However, this technique is yet to be applied to a cohort of patients who remitted from T2DM.

The present study included all patients with a newly diagnosed T2DM at baseline (n = 190). It employed an untargeted metabolomics approach to identify metabolic differences between individuals who remitted (RE) and those who did not (non-RE) from T2DM during a 5-year dietary intervention study. It subjected the data to orthogonal projection on the latent structure discriminant analysis (O-PLS DA), a generalized linear model (GLM), a receiver operating characteristic (ROC), a DeLong test, a Cox regression, and pathway analyses.

The study observed that-

• The non-RE group showed a significant increase in 12 metabolites compared to the RE group.
• Cox proportional hazard models showed that patients in the high-score tercile had significantly higher remission probabilities than those in the lowest tercile.
• The predictive power of these metabolites was additionally studied using GLMs and ROCs.
• The area under the curve (AUC) of the clinical variables alone was 0.61, but this increased to 0.72 on considering the 12 metabolites.
• A DeLong test showed this difference to be statistically significant.

This study identified 12 endogenous metabolites with the potential to predict T2DM remission following a dietary intervention. In clinical practice, these metabolites can be combined with clinical variables to provide more precise treatment.

Mora-Ortiz M, Alcala-Diaz JF, Rangel-Zuñiga OA. et al. Metabolomics analysis of type 2 diabetes remission identifies 12 metabolites with predictive capacity: a CORDIOPREV clinical trial study. BMC Med. 2022;20. https://doi.org/10.1186/s12916-022-02566-z