Identification of hypertension subtypes using microRNA profiles and machine learning.

IF 5.3 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Smarti Reel, Parminder S Reel, Josie Van Kralingen, Casper K Larsen, Stacy Robertson, Scott M MacKenzie, Alexandra Riddell, John D McClure, Stelios Lamprou, John M C Connell, Laurence Amar, Alessio Pecori, Martina Tetti, Christina Pamporaki, Marek Kabat, Filippo Ceccato, Matthias Kroiss, Michael C Dennedy, Anthony Stell, Jaap Deinum, Paolo Mulatero, Martin Reincke, Anne-Paule Gimenez-Roqueplo, Guillaume Assié, Anne Blanchard, Felix Beuschlein, Gian Paolo Rossi, Graeme Eisenhofer, Maria-Christina Zennaro, Emily Jefferson, Eleanor Davies
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引用次数: 0

Abstract

Objective: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases and is caused by underlying conditions such as primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or paraganglioma (PPGL). EHT is often misdiagnosed as PHT leading to delays in treatment for the underlying condition, reduced quality of life and costly, often ineffective, antihypertensive treatment. MicroRNA (miRNA) circulating in the plasma is emerging as an attractive potential biomarker for various clinical conditions due to its ease of sampling, the accuracy of its measurement and the correlation of particular disease states with circulating levels of specific miRNAs.

Methods: This study systematically presents the most discriminating circulating miRNA features responsible for classifying and distinguishing EHT and its subtypes (PA, PPGL, and CS) from PHT using 8 different supervised machine learning (ML) methods for the prediction.

Results: The trained models successfully classified PPGL, CS, and EHT from PHT with area under the curve (AUC) of 0.9 and PA from PHT with AUC 0.8 from the test set. The most prominent circulating miRNA features for hypertension identification of different disease combinations were hsa-miR-15a-5p and hsa-miR-32-5p.

Conclusions: This study confirms the potential of circulating miRNAs to serve as diagnostic biomarkers for EHT and the viability of ML as a tool for identifying the most informative miRNA species.

利用microRNA谱和机器学习识别高血压亚型。
目的:高血压是影响约1 / 3成年人的主要心血管危险因素。虽然大多数高血压病例(约90%)被归类为“原发性高血压”(PHT),但内分泌高血压(EHT)占病例的约10%,由原发性醛固酮增多症(PA)、库欣综合征(CS)、嗜铬细胞瘤或副神经节瘤(PPGL)等潜在疾病引起。EHT经常被误诊为PHT,导致对基础疾病的治疗延误,生活质量下降,抗高血压治疗费用昂贵,往往无效。血浆中循环的MicroRNA由于其易于采样,测量的准确性以及特定疾病状态与特定MicroRNA循环水平的相关性,正成为各种临床条件下有吸引力的潜在生物标志物。方法:本研究使用8种不同的监督式机器学习(ML)方法进行预测,系统地展示了负责分类和区分EHT及其亚型(PA, PPGL, CS)与PHT的最具鉴别性的循环microRNA特征。结果:训练的模型成功地从AUC为0.9的PHT中分类出PPGL、CS和EHT,从AUC为0.8的PHT中分类出PA。在高血压不同疾病组合识别中最突出的循环microRNA特征是hsa-miR-15a-5p和hsa-miR-32-5p。结论:本研究证实了循环microRNA作为EHT诊断生物标志物的潜力,以及机器学习作为鉴定信息量最大的microRNA物种的工具的可行性。
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来源期刊
European Journal of Endocrinology
European Journal of Endocrinology 医学-内分泌学与代谢
CiteScore
9.80
自引率
3.40%
发文量
354
审稿时长
1 months
期刊介绍: European Journal of Endocrinology is the official journal of the European Society of Endocrinology. Its predecessor journal is Acta Endocrinologica. The journal publishes high-quality original clinical and translational research papers and reviews in paediatric and adult endocrinology, as well as clinical practice guidelines, position statements and debates. Case reports will only be considered if they represent exceptional insights or advances in clinical endocrinology. Topics covered include, but are not limited to, Adrenal and Steroid, Bone and Mineral Metabolism, Hormones and Cancer, Pituitary and Hypothalamus, Thyroid and Reproduction. In the field of Diabetes, Obesity and Metabolism we welcome manuscripts addressing endocrine mechanisms of disease and its complications, management of obesity/diabetes in the context of other endocrine conditions, or aspects of complex disease management. Reports may encompass natural history studies, mechanistic studies, or clinical trials. Equal consideration is given to all manuscripts in English from any country.
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