10.6084/M9.FIGSHARE.C.6282439
Roberta Armignacco
Roberta
Armignacco
Institut Cochin
Parminder S. Reel
Parminder S.
Reel
University of Dundee
Smarti Reel
Smarti
Reel
University of Dundee
Anne Jouinot
Anne
Jouinot
PSL Research University
Institut Cochin
Amandine Septier
Amandine
Septier
Institut Cochin
Cassandra Gaspar
Cassandra
Gaspar
Karine Perlemoine
Karine
Perlemoine
Institut Cochin
Casper K. Larsen
Casper K.
Larsen
Paris Cardiovascular Research Center
Lucas Bouys
Lucas
Bouys
Institut Cochin
Leah Braun
Leah
Braun
LMU Klinikum
Anna Riester
Anna
Riester
LMU Klinikum
Matthias Kroiss
Matthias
Kroiss
LMU Klinikum
Fidéline Bonnet-Serrano
Fidéline
Bonnet-Serrano
Hôpital Cochin
Institut Cochin
Laurence Amar
Laurence
Amar
Hôpital Européen Georges-Pompidou
Paris Cardiovascular Research Center
Anne Blanchard
Anne
Blanchard
Hôpital Européen Georges-Pompidou
Anne-Paule Gimenez-Roqueplo
Anne-Paule
Gimenez-Roqueplo
Hôpital Européen Georges-Pompidou
Paris Cardiovascular Research Center
Aleksander Prejbisz
Aleksander
Prejbisz
Institute of Cardiology
Andrzej Januszewicz
Andrzej
Januszewicz
Institute of Cardiology
Piotr Dobrowolski
Piotr
Dobrowolski
Institute of Cardiology
Eleanor Davies
Eleanor
Davies
University of Glasgow
Scott M. MacKenzie
Scott M.
MacKenzie
University of Glasgow
Gian Paolo Rossi
Gian Paolo
Rossi
Livia Lenzini
Livia
Lenzini
Filippo Ceccato
Filippo
Ceccato
Azienda Ospedaliera di Padova
Carla Scaroni
Carla
Scaroni
Azienda Ospedaliera di Padova
Paolo Mulatero
Paolo
Mulatero
University of Turin
Tracy A. Williams
Tracy A.
Williams
University of Turin
Alessio Pecori
Alessio
Pecori
University of Turin
Silvia Monticone
Silvia
Monticone
University of Turin
Felix Beuschlein
Felix
Beuschlein
LMU Klinikum
University Hospital of Zurich
Martin Reincke
Martin
Reincke
LMU Klinikum
Ludwig-Maximilians-Universität München
Maria-Christina Zennaro
Maria-Christina
Zennaro
Hôpital Européen Georges-Pompidou
Paris Cardiovascular Research Center
Jérôme Bertherat
Jérôme
Bertherat
Hôpital Cochin
Institut Cochin
Emily Jefferson
Emily
Jefferson
University of Dundee
University of Glasgow
Guillaume Assié
Guillaume
Assié
Hôpital Cochin
Institut Cochin
Whole blood methylome-derived features to discriminate endocrine hypertension
Abstract 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. Conclusions The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.
Medicine
Cell Biology
Genetics
69999 Biological Sciences not elsewhere classified
19999 Mathematical Sciences not elsewhere classified
figshare
2022
2022-11-04
2022-11-04
Collection
10.1186/s13148-022-01347-y
CC BY 4.0