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Whole blood methylome-derived features to discriminate endocrine hypertension
Roberta Armignacco1, Parminder S Reel2, Smarti Reel2
1Université Paris Cité, CNRS, INSERM, Institut Cochin, F-75014, Paris, France. roberta.armignacco@inserm.fr.
Insights
Blood DNA methylation patterns can distinguish between different types of endocrine hypertension. This discovery offers potential for new diagnostic biomarkers to identify specific hormonal causes of high blood pressure.
Area of Science:
- Epigenetics
- Endocrinology
- Cardiovascular Medicine
Background:
- Arterial hypertension is a global health issue, a major risk factor for cardiovascular disease.
- Hypertension can be primary or secondary to endocrine disorders like Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL).
- Current diagnosis of endocrine hypertension relies on hormone assays, posing challenges for efficient detection and patient management.
Purpose of the Study:
- To investigate if blood DNA methylation signatures can differentiate between primary hypertension (PHT) and various types of endocrine hypertension (EHT).
- To identify specific DNA methylation markers for discriminating between CS, PA, and PPGL.
- To explore the utility of machine learning approaches for analyzing methylome profiles and building predictive models.
Main Methods:
- Exploration of methylome profiles in a cohort of 255 hypertensive patients (42 PHT, 213 EHT).
- Unsupervised and supervised analyses to identify differentially methylated CpG sites and gene-specific signatures.
- Application of machine learning algorithms (Lasso, Logistic Regression, Random Forest, Support Vector Machine) to build predictive models.
Main Results:
- Unsupervised classification successfully discriminated between PHT and EHT, with CS patients clustering separately.
- Differentially methylated CpG sites were identified for each EHT type, with FKBP5 highlighted in the CS group.
- Machine learning models achieved high balanced accuracies in predicting specific endocrine hypertension types, ranging from 0.85-0.95 for CS.
Conclusions:
- Blood DNA methylome analysis can effectively discriminate endocrine hypertension.
- Specific methylation signatures are associated with distinct endocrine disorders causing hypertension.
- These findings suggest potential for novel epigenetic biomarkers in the diagnosis of endocrine hypertension.
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.
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