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
{"title":"利用microRNA谱和机器学习识别高血压亚型。","authors":"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","doi":"10.1093/ejendo/lvaf052","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":11884,"journal":{"name":"European Journal of Endocrinology","volume":" ","pages":"418-428"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of hypertension subtypes using microRNA profiles and machine learning.\",\"authors\":\"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\",\"doi\":\"10.1093/ejendo/lvaf052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":11884,\"journal\":{\"name\":\"European Journal of Endocrinology\",\"volume\":\" \",\"pages\":\"418-428\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Endocrinology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/ejendo/lvaf052\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Endocrinology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ejendo/lvaf052","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Identification of hypertension subtypes using microRNA profiles and machine learning.
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.
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.