ESPE Abstracts (2018) 89 FC1.5

Untargeted Plasma Metabolomics in Subjects with Differences in Tissue Glucocorticoid Sensitivity Identifies a Novel metabolic Signature

Nicolas C. Nicolaidesa,b, Maria-Konstantina Ioannidic,d, Eleni Koniaria, Amalia Sertedakia, Maria I. Klapac, George P. Chrousosa,b & Evangelia Charmandaria,b

aDivision of Endocrinology, Metabolism and Diabetes, First Department of Pediatrics, National and Kapodistrian University of Athens Medical School, ‘Aghia Sophia’ Children’s Hospital, Athens, Greece; bDivision of Endocrinology and Metabolism, Biomedical Research Foundation of the Academy of Athens, Athens, Greece; cMetabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece; dDepartment of Biology, University of Patras, Patras, Greece

Background: Tissue glucocorticoid sensitivity is characterized by a considerable variation in terms of therapeutic response and side effects to synthetic glucocorticoids. The multi-metabolite concentration profile measured by untargeted plasma metabolomics provides a comprehensive metabolic signature that might be used in clinical practice.

Objective and Hypotheses: To investigate the usefulness of plasma metabolomics in identifying a metabolic signature that could distinguish glucocorticoid resistant from glucocorticoid sensitive subjects and provide clues of the underlying physiological differences.

Methods and Results: One hundred healthy volunteers were given a low-dose (0.25 mg) dexamethasone at midnight, and polarized into the 10% most sensitive (S) and 10% most resistant (R) according to the serum cortisol concentrations in the following morning. One month later, DNA was isolated from peripheral blood mononuclear cells and plasma samples were collected. Sequencing analysis did not reveal any mutations or polymorphisms in the human glucocorticoid receptor (NR3C1) gene. Subsequently, we determined the metabolic profile of plasma samples, using Gas Chromatography - Mass Spectrometry (GC-MS). After appropriate data normalization and filtering, 51 metabolite profiles were used to extract biological conclusions. Multivariate significance analysis (SAM) identified 20 metabolites with significantly lower abundance in the sensitive compared to the resistant groups, including fatty acids and intermediates of serine/threonine metabolism. These results combined with the higher (but not statistically significant) glucose and lactate abundance, indicate higher lipid oxidation, aerobic glycolysis and serine/threonine metabolism rates in the sensitive compared to the resistant individuals.

Conclusions: A metabolic profile indicating oxidative stress conditions was observed in the sensitive compared to the resistant group. This is a significant result in providing a signature to differentiate the glucocorticoid resistant from glucocorticoid sensitive subjects to be useful in clinical practice, while providing clues of the underlying molecular mechanisms of the physiological differentiation.

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