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Identification of metabolomics biomarkers for type 2 diabetes: triangulating evidence from longitudinal and Mendelian randomization analyses

By Eleonora Porcu, Federica Gilardi, Liza Darrous, Loic Yengo, Nasim Bararpour, Marie Gasser, Pedro Marques Vidal, Philippe Froguel, Gerard Waeber, Aurelien Thomas, Zoltan Kutalik

Posted 03 Nov 2020
medRxiv DOI: 10.1101/2020.10.30.20222836

The number of people affected by Type 2 Diabetes Mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. As the case for many other complex diseases, early diagnosis is key to prevent irreversible end-organ damages. However, given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than five years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM, and also confirmed to be causal by 2-sample Mendelian randomisation (based on independent data). Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. These findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.

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