Whole blood methylome-derived features to discriminate endocrine hypertension

Roberta Armignacco, Parminder S. Reel, Smarti Reel, Anne Jouinot, Amandine Septier, Cassandra Gaspar, Karine Perlemoine, Casper K. Larsen, Lucas Bouys, Leah Braun, Anna Riester, Matthias Kroiss, Fidéline Bonnet-Serrano, Laurence Amar, Anne Blanchard, Anne-Paule Gimenez-Roqueplo, Aleksander Prejbisz, Andrzej Januszewicz, Piotr Dobrowolski, Eleanor DaviesScott M. MacKenzie, Gian Paolo Rossi, Livia Lenzini, Filippo Ceccato, Carla Scaroni, Paolo Mulatero, Tracy A Williams, Alessio Pecori, Silvia Monticone, Felix Beuschlein, Martin Reincke, Maria-Christina Zennaro, Jérôme Bertherat, Emily Jefferson, Guillaume Assié (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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Background: Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches.

Results: Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods-Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine-predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL.

Conclusion: The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.

Original languageEnglish
Article number142
Number of pages11
JournalClinical Epigenetics
Issue number1
Publication statusPublished - 3 Nov 2022


  • Humans
  • Epigenome
  • DNA Methylation
  • Pheochromocytoma/complications
  • Hypertension/diagnosis
  • Adrenal Gland Neoplasms/diagnosis
  • Biomarkers
  • Circulating biomarker
  • Whole blood methylome
  • Endocrine hypertension

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics
  • Molecular Biology
  • Developmental Biology


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