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Armignacco, Roberta; Jouinot, Anne; Bouys, Lucas; Septier, Amandine; Lartigue, Thomas; Neou, Mario; Gaspar, Cassandra; Perlemoine, Karine; Braun, Leah; Riester, Anna; Bonnet-Serrano, Fideline; Blanchard, Anne; Amar, Laurence; Scaroni, Carla; Ceccato, Filippo; Rossi, Gian Paolo; Williams, Tracy Ann; Larsen, Casper K.; Allassonniere, Stephanie; Zennaro, Maria-Christina; Beuschlein, Felix; Reincke, Martin; Bertherat, Jerome and Assie, Guillaume (2022): Identification of glucocorticoid-related molecular signature by whole blood methylome analysis. In: European Journal of Endocrinology, Vol. 186, No. 2: pp. 297-308

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Objective Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing's syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. Design We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing's syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. Methods Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (similar to 850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. Results Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing's syndrome correction. In Cushing's syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. Conclusions Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays.

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