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RESEARCH PRODUCT

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

Alex Sánchez-plaJordi Salas-salvadóRafael LlorachRafael LlorachDolores CorellaRamon EstruchCristina Andres-lacuevaCristina Andres-lacuevaRosa Vázquez-fresnoFrancesc CarmonaEnrique Almanza-aguileraEnrique Almanza-aguileraJosé V. SorlíJosé V. SorlíMireia Urpi-sardaMireia Urpi-sarda

subject

Male0301 basic medicineOncologyNon targetedendocrine system diseasesCross-sectional studyEndocrinology Diabetes and MetabolismType 2 diabetesDiet MediterraneanLogistic regressionDiabetis no-insulinodependentEndocrinologyRisk FactorsNon-insulin-dependent diabetesDietoteràpiaeducation.field_of_studyFactors de risc en les malaltiesBiochemical markersGeneral MedicineMiddle AgedMetabolismeMetabolòmicaCardiovascular DiseasesMarcadors bioquímicsMetabolomeFemalemedicine.medical_specialtyRisk factors in diseasesPopulationUrinalysis03 medical and health sciencesMedicina preventivaMetabolomicsInternal medicineInternal MedicinemedicineHumansMetabolomicseducationAgedPreventive medicineReceiver operating characteristicbusiness.industryDiet therapynutritional and metabolic diseasesmedicine.diseasePredimedMetabolismCross-Sectional Studies030104 developmental biologyDiabetes Mellitus Type 2businesshuman activitiesBiomarkersDiabetic Angiopathies

description

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 1 H-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

yearjournalcountryeditionlanguage
2019-04-01Diabetes &amp; Metabolism
https://doi.org/10.1016/j.diabet.2018.02.006