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Identification of glucocorticoid-related molecular signature by whole blood methylome analysis

  • Roberta Armignacco
  • , Anne Jouinot
  • , Lucas Bouys
  • , Amandine Septier
  • , Thomas Lartigue
  • , Mario Neou
  • , Cassandra Gaspar
  • , Karine Perlemoine
  • , Leah Braun
  • , Anna Riester
  • , Fidéline Bonnet-Serrano
  • , Anne Blanchard
  • , Laurence Amar
  • , Carla Scaroni
  • , Filippo Ceccato
  • , Gian Paolo Rossi
  • , Tracy Ann Williams
  • , Casper K. Larsen
  • , Stéphanie Allassonnière
  • , Maria Christina Zennaro
  • Felix Beuschlein, Martin Reincke, Jérôme Bertherat, Guillaume Assié
  • Laboratoire de Probabilités et Modèles Aléatoires
  • INRIA Institut National de Recherche en Informatique et en Automatique
  • CNRS
  • Sorbonne Université
  • University of Munich
  • Pôle Gérontologie
  • Pôle de Biologie
  • University of Padova
  • University of Torino Medical School
  • University Hospital

Résultats de recherche: Contribution à un journalArticleRevue par des pairs

Résumé

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 ins ufficiency. 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 (~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 sample s 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 speci fic 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.

langue originaleAnglais
Pages (de - à)297-308
Nombre de pages12
journalEuropean Journal of Endocrinology
Volume186
Numéro de publication2
Les DOIs
étatPublié - 1 févr. 2022
Modification externeOui

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