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Essential, primary, or idiopathic hypertension accounts for 90\u201395% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management. Materials and methods: In total, 174 participants were enrolled in the present observational case\u2013control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model. Results: Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p\/361-3p\/362-5p\/378a-5p\/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model. Conclusions: This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension\u2019s pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.<\/jats:p>","DOI":"10.3390\/ncrna9060064","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T04:14:47Z","timestamp":1698207287000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension"],"prefix":"10.3390","volume":"9","author":[{"given":"Amela","family":"Jusic","sequence":"first","affiliation":[{"name":"Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg"},{"name":"HAYA Therapeutics SA, Route De La Corniche 6, SuperLab Suisse\u2014Batiment Serine, 1066 Epalinges, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inela","family":"Junuzovic","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Medical Center \u201cPlava Medical Group\u201d, Mihajla i \u017divka Crnogor\u010devi\u0107a do br. 10, 75000 Tuzla, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Hujdurovic","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Medical Center \u201cPlava Medical Group\u201d, Mihajla i \u017divka Crnogor\u010devi\u0107a do br. 10, 75000 Tuzla, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7141-6730","authenticated-orcid":false,"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bioinformatics Platform, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5261-7729","authenticated-orcid":false,"given":"M\u00e9lanie","family":"Vausort","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5321-8543","authenticated-orcid":false,"given":"Yvan","family":"Devaux","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"ref_1","unstructured":"NCD Risk Factor Collaboration NCD-RisC (2021). 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