EYES2022 ESE Young Endocrinologists and Scientists (EYES) 2022 Adrenal and Cardiovascular (12 abstracts)
Combining steroid and global metabolome profiling by mass spectrometry with machine learning to investigate metabolic risk in benign adrenal tumours with mild autonomous cortisol secretion
Prete A 1 , Abdi L 1 , Canducci M 2 , Taylor A. E. 1 , Gilligan L. C. 1 , Albors-Zumel A 3 , van den Brandhof E 3 , Zhang Y 3 , Manolopoulos K. N. 1 , Tino P 2 , Biehl M 3 , Dunn W. B. 4 & Arlt W 1
1University of Birmingham, Institute of Metabolism and Systems Research; 2University of Birmingham, School of Computer Science; 3University of Groningen, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence; 4University of Liverpool, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrated Biology
Background: Benign adrenal tumours are discovered in 3-10% of adults and can be non-functioning (NFAT) or associated with adrenal hormone excess, most frequently mild autonomous cortisol secretion (MACS) defined by the failure to suppress cortisol after 1 mg dexamethasone overnight but lack of distinct signs of Cushing’s syndrome (CS). We found that MACS increases the prevalence and severity of type 2 diabetes and hypertension and primarily affects women (Ann Int Med. 2022 Doi:10.7326/M21-1737).
Objectives: We prospectively recruited 1305 patients with benign adrenal tumours to assess their steroid and global metabolomes and determine links to type 2 diabetes and hypertension.
Methods: We analysed 24-h urine samples from 1305 patients (649 NFAT, 591 MACS, 65 CS) using a 17-steroid liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay. We also performed untargeted serum metabolome analysis in a representative sub-cohort of 290 patients (104 NFAT, 140 MACS, 47 CS) employing HILIC and C18-lipidomics LC-MS assays. The data were analysed by two supervised machine learning approaches, generalized matrix learning vector quantization and ordinal regression, to identify the most relevant metabolic changes.
Results: Urine steroid metabolome analysis revealed increased glucocorticoid metabolite excretion from NFAT over MACS to CS, whereas androgen metabolite excretion decreased. Similarly, increased glucocorticoid metabolites were observed in patients with type 2 diabetes and hypertension. Lipidome analysis revealed gradual progression towards lipotoxicity with increasing cortisol excess. Patients with type 2 diabetes showed additional changes in acylcarnitines, bioactive lipids, and triacylglycerides.
Conclusions: We provide mechanistic insights into the metabolic consequences of cortisol excess. Increased cortisol was linked to a change in the lipidome towards lipotoxicity. Patients with type 2 diabetes and hypertension had increased glucocorticoid output and more adverse changes in the lipidome, indicative of a causative contribution of cortisol excess to their higher cardiometabolic burden. Observed changes may hold promise for risk stratification in MACS, a highly relevant and previously largely overlooked metabolic risk condition.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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