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Endocrine Abstracts (2022) 86 OC4.4 | DOI: 10.1530/endoabs.86.OC4.4

1Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom; 2Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom; 3Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom; 4School of Computer Science, University of Birmingham, Birmingham, United Kingdom; 5Division of Endocrinology, Metabolism, Diabetes and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United state of America; 6Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands; 7Department of Endocrinology, Diabetes and Metabolism, Evangelismos Hospital, Athens, Greece; 8Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland; 9Department of Endocrinology, University Hospital Centre Zagreb, Zagreb, Zagreb, Croatia; 10Division of Internal Medicine, University of Turin, San Luigi Hospital, Turin, Italy; 11Department for Obesity, Reproductive and Metabolic Disorders, Clinic for Endocrinology, Diabetes and Metabolic Diseases, University Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 12Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany; 13Medicover Oldenburg MVZ, Oldenburg, Germany; 14Department of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland; 15Department of Medicine III and Institute of Clinical Chemistry and Laboratory Medicine, Technische Universität Dresden, Dresden, Germany; 16Endocrinology in Charlottenburg, Berlin, Germany; 17Department of Endocrinology, Haukeland University Hospital, Bergen, Norway; 18Department of Endocrinology, University Hospital Galway, Newcastle, Galway, Ireland; 19Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitäts-Spital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland; 20Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-Universität München, Munich, Germany; 21Service d’Endocrinologie, Centre Hospitalier Universitaire, Hopital du Haut Leveque, Pessac, France; 22Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom; 23NIHR Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom; 24Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrated Biology, University of Liverpool, Liverpool, United Kingdom


Background: Benign adrenal tumours are found in 3-10% of adults and can be non-functioning (NFAT) or associated with adrenal hormone excess. Analysing 1305 prospectively recruited patients with benign adrenal tumours, we recently demonstrated that 45% had mild autonomous cortisol secretion (MACS), i.e. biochemical cortisol excess without signs of Cushing’s syndrome (CS). MACS increases the prevalence and severity of hypertension and type 2 diabetes (Ann Int Med. 2022 Doi:10.7326/M21-1737). Here we analysed the cohort’s steroid metabolome and global metabolome to reveal underlying metabolic processes.

Methods: We analysed 24-h urines from 1305 patients (649 NFAT, 591 MACS, 65 CS) using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-steroid profiling assay. In addition, we performed untargeted serum metabolome analysis in a representative sub-cohort (104 NFAT, 140 MACS, 47 CS) employing two complementary LC-MS assays, HILIC and C18-lipidomics. Steroid and global metabolome data were analysed by prototype-based supervised machine learning (generalized matrix learning vector quantization and ordinal regression).

Results: Urinary glucocorticoid excretion increased from NFAT over MACS to CS, whereas androgen excretion decreased. Machine learning analysis identified increased excretion of the 11β-hydroxyandrostenedione metabolite 11β-hydroxyandrosterone as the key marker in MACS patients with hypertension and type 2 diabetes. Lipidome analysis identified glycerophospholipids, lysoglycerophospholipids, triacylglycerides, ceramides, sphingolipids, and acylcarnitines as the most relevant metabolite classes exhibiting progressive changes with increasing cortisol excess (NFAT<MACS<CS). Pathway enrichment analysis revealed distinct patterns of changes in arginine & proline metabolism and histidine metabolism with increasing cortisol excess.

Conclusions: We show a gradual change in the lipidome towards lipotoxicity with increasing cortisol excess. Increased CYP11B1-mediated 11β-hydroxyandrostenedione production in MACS patients with type 2 diabetes and hypertension points towards a causative contribution of 11-oxygenated androgens to increased cardiometabolic risk. Observed changes may hold promise for risk stratification in MACS, a highly relevant and previously largely overlooked metabolic risk condition.

Volume 86

Society for Endocrinology BES 2022

Harrogate, United Kingdom
14 Nov 2022 - 16 Nov 2022

Society for Endocrinology 

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