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J Proteome Res. 2018 Jul 6;17(7):2307-2317. doi: 10.1021/acs.jproteome.7b00855. Epub 2018 Jun 15.

Untargeted Profiling of Concordant/Discordant Phenotypes of High Insulin Resistance and Obesity To Predict the Risk of Developing Diabetes.

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Biomedical Research Institute [IBIMA], Service of Endocrinology and Nutrition , Malaga Hospital Complex [Virgen de la Victoria] , Campus de Teatinos s/n , Malaga 29010 , Spain.
Scientific and Technological Centres of the University of Barcelona (CCIT-UB), Barcelona 08028 , Spain.
Statistics and Bioinformatics Unit , Vall d'Hebron Institut de Recerca [VHIR] , Barcelona 08035 , Spain.


This study explores the metabolic profiles of concordant/discordant phenotypes of high insulin resistance (IR) and obesity. Through untargeted metabolomics (LC-ESI-QTOF-MS), we analyzed the fasting serum of subjects with high IR and/or obesity ( n = 64). An partial least-squares discriminant analysis with orthogonal signal correction followed by univariate statistics and enrichment analysis allowed exploration of these metabolic profiles. A multivariate regression method (LASSO) was used for variable selection and a predictive biomarker model to identify subjects with high IR regardless of obesity was built. Adrenic acid and a dyglyceride (DG) were shared by high IR and obesity. Uric and margaric acids, 14 DGs, ketocholesterol, and hydroxycorticosterone were unique to high IR, while arachidonic, hydroxyeicosatetraenoic (HETE), palmitoleic, triHETE, and glycocholic acids, HETE lactone, leukotriene B4, and two glutamyl-peptides to obesity. DGs and adrenic acid differed in concordant/discordant phenotypes, thereby revealing protective mechanisms against high IR also in obesity. A biomarker model formed by DGs, uric and adrenic acids presented a high predictive power to identify subjects with high IR [AUC 80.1% (68.9-91.4)]. These findings could become relevant for diabetes risk detection and unveil new potential targets in therapeutic treatments of IR, diabetes, and obesity. An independent validated cohort is needed to confirm these results.


ROC curves; adrenic acid; diglycerides; insulin resistance; metabolic profiles; metabolomics; obesity; observational study; predictive model; uric acid

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