Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants - 20/03/18

Doi : 10.1016/j.diabet.2018.02.006

M. Urpi-Sarda ^a,^b,^{^{1
These authors contributed equally to this work.},}^⁎ , E. Almanza-Aguilera ^a,^b,^{^{1
These authors contributed equally to this work.}}, R. Llorach ^a,^b, R. Vázquez-Fresno ^a,^c, R. Estruch ^d,^e, D. Corella ^e,^f, J.V. Sorli ^e,^f, F. Carmona ^g, A. Sanchez-Pla ^g, J. Salas-Salvadó ^e,^h, C. Andres-Lacueva ^a,^b,^⁎
^a Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA-UB, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
^b CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
^c Department of Computing Science and Biological Sciences, University of Alberta, Edmonton, Canada
^d Department of Internal Medicine, Hospital Clinic, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
^e CIBER Pathophysiology of Obesity and Nutrition (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
^f Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
^g Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain
^h Human Nutrition Unit, Biochemistry and Biotechnology Department. Hospital Universitari de Sant Joan de Reus, Institut d‘Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain

^⁎Corresponding authors at: Av. Joan XXIII 27-31, 08028 Barcelona, Spain.

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Abstract

Aim

To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D.

Methods

A metabolomics analysis using the ¹H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm.

Results

A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose.

Conclusion

The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine.

Trial registration at www.controlled-trials.com/: ISRCTN35739639.

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Abbreviations : T2D, CVD, PREDIMED, FID, OSC, PLS-DA, VIP, AA, ROC, AUROC, PAG