Université Paris Cité, CNRS, INSERM, Institut Cochin, F-75014, Paris, France.
Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK.
Clin Epigenetics. 2022 Nov 3;14(1):142. doi: 10.1186/s13148-022-01347-y.
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.
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.
The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.
动脉高血压是全球范围内的健康负担，也是心血管发病率和死亡率的主要危险因素。高血压可分为原发性（原发性高血压，PHT）或继发于内分泌紊乱（内分泌性高血压，EHT），如库欣综合征（CS）、原发性醛固酮增多症（PA）和嗜铬细胞瘤/副神经节瘤（PPGL）。EHT 的诊断目前基于激素检测。尽管高效检测仍然具有挑战性，但对于正确引导患者进行诊断确认和特定治疗至关重要。更准确的生物标志物将有助于诊断途径。我们假设，每种内分泌性高血压都可能与特定的血液 DNA 甲基化特征相关，这些特征可用于疾病鉴别。为了确定这些标记物，我们旨在探索 255 名高血压患者（PHT [n=42] 或 EHT [n=213]）的甲基组谱，并使用机器学习方法确定特定的鉴别特征。
无监督分类样本显示 PHT 与 EHT 的区分。CS 患者与所有其他患者聚类分开，而 PA 和 PPGL 总体上重叠。与 PHT 相比，CS 组的全局甲基化水平降低。与 PHT 进行有监督比较，确定了每种内分泌性高血压的差异甲基化 CpG 位点，显示出弥漫性基因组位置。在差异甲基化基因中，FKBP5 在 CS 组中被鉴定出来。使用四种不同的机器学习方法——最小绝对收缩和选择算子（Lasso）、逻辑回归、随机森林和支持向量机——在训练队列（每种高血压类型的 80%样本）上构建了每种内分泌性高血压的预测模型，并在验证队列（每种高血压类型的 20%样本）上进行了估计。平衡准确性范围为 0.55 到 0.74 用于预测 EHT，0.85 到 0.95 用于预测 CS，0.66 到 0.88 用于预测 PA，0.70 到 0.83 用于预测 PPGL。
血液 DNA 甲基组可以区分内分泌性高血压，每种内分泌紊乱类型都有特定的甲基化特征。
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