Key result
A machine learning model utilizing five serum microRNAs differentiated primary aldosteronism from primary hypertension with 100% accuracy, and identified unilateral primary aldosteronism with 94% accuracy.
Why the study?
Primary aldosteronism is underdetected partly due to complex diagnostic procedures, highlighting a need for more straightforward diagnostic methods.
Does serum microRNA profiling differentiate between primary hypertension, bilateral primary aldosteronism, and unilateral primary aldosteronism?
Observational (n=39)
Yes
Does serum microRNA profiling differentiate between primary hypertension, bilateral primary aldosteronism, and unilateral primary aldosteronism?
Serum microRNA profiles combined with machine learning can differentiate between primary hypertension and primary aldosteronism subtypes, offering a potential non-invasive diagnostic tool.
Background: Primary aldosteronism (PA) is the principal cause of secondary hypertension; it leads to significantly elevated cardiovascular morbidity and mortality, but only a fraction of its cases ever get detected, partially due to diagnostic procedures that are difficult to perform and to interpret. More straightforward diagnostic methods are needed. Lateralized, or unilateral PA (uPA), is best treated by surgery. Bilateral PA (bPA) is treated medically. Aim: The aim of our study was to explore microRNA (miRNA) in peripheral blood as markers of PA, uPA and bPA. Methods: In groups of subjects with primary hypertension (HT, n = 11), bPA (n = 12), and uPA (n = 16), peripheral serum was used for isolation of total RNA, library preparation, and NGS sequencing to achieve a comparative analysis of miRNA expression. Five-fold cross-validation support vector machine learning (ML) models were employed to search for miRNA that could be used as markers of PA and its forms. Results: In our cohort of patients, the discovered combinations of miRNAs could, with a high level of accuracy, sensitivity, and specificity, characterize the difference between HT and PA, as well as between a combined group of HT + bPA vs. uPA. The differentiating parameters were moderately good for comparison of bPA vs. uPA. Conclusion: Within our patient cohort, and using ML, the study identified distinctly different miRNA profiles between HT and PA, as well as between bPA and uPA. Further validation studies may lead to the emergence of a new tool for clinical diagnostics of PA.
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Makhnov et al. (2025) conducted an observational in Primary aldosteronism and primary hypertension (n=39). Serum microRNA expression profiles vs. Primary hypertension or bilateral primary aldosteronism was evaluated on Diagnostic accuracy of machine learning models based on serum miRNA expression to differentiate PA from HT, and uPA from bPA/HT. A machine learning model utilizing five serum microRNAs differentiated primary aldosteronism from primary hypertension with 100% accuracy, and identified unilateral primary aldosteronism with 94% accuracy.
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