PhD Scientific Days 2024

Budapest, 9-10 July 2024

Conservative Medicine

Artificial Intelligence Based Differentiation of Uni- and Bilateral Primary Aldosteronism by Circulating microRNA Combinations


Bálint Vékony1,2, Gábor Nyirő1,2,3, Zoltán Herold2, János Fekete4, Filippo Ceccato5, Sven Gruber6, Lydia Kürzinger7, Mirko Parasiliti-Caprino8, Nikolette Szücs1,2, Bálint Szeredás1,2, Siti Khadijah Syed Mohammed Nazri9, Vanessa Fell10, Mohamed Bassiony10, Elena Aisha Azizan9, Irina Bancos10, Felix Beuschlein6, Péter Igaz1,2
1: Department of Endocrinology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
2: Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
3: Department of Laboratory Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
4: Department of Bioinformatics, Faculty of Medicine, Semmelweis University, Budapest, Hungary
5: Endocrinology Unit, Department of Medicine DIMED, University of Padova, Padova, Italy
6: Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ) and University of Zurich (UZH), Zurich, Switzerland
7: Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Germany
8: Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, Italy
9: Endocrine Unit, Department of Medicine, The National University of Malaysia (UKM) Medical Centre, Kuala Lumpur, Malaysia
10: Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA

Text of the abstract

Introduction: Primary aldosteronism (PA) is the most prevalent cause of secondary hypertension. Its two main clinical forms are unilateral adenoma (APA) and bilateral hyperplasia (BAH), which require different medical treatments, so differentiating between the two is of utmost clinical importance. The current gold standard method for this is adrenal vein sampling (AVS), the application of which is hindered by limited availability and high skill requirements.
Aim: Our goal was to identify circulating microRNAs – or their combinations – which enable differentiation between the two most prevalent aetiologies of PA from a peripheral blood sample.
Methods: MicroRNA specific sequencing was performed on an Illumina platform, using adrenal vein blood samples taken during AVS, from 18 patients (10 uni-, and 8 bilateral). Bioinformatical analysis was used to evaluate the differences in expression; along a neural network model, tasked to identify the most fit individual and groups of microRNAs for differentiation. The microRNAs comprising the five best performing models were then validated using RT-qPCR, on 90 samples, including AVS and peripheral plasma samples from 30 patients (15 uni-, and 15 bilateral). The qPCR results were then re-analysed using the same neural network. Finally, a deep learning algorithm was used on a peripheral plasma sample group from 108 patients (54 uni-, and 54 bilateral).
Results: Based on the qPCR results, miRNA abundance shows a non-significant decrease on the periphery compared to the adrenal vein samples. 10 neural network models were able to differentiate BAH and APA using peripheral plasma samples with an accuracy above 85%, with the best model consisting of 6 miRNAs having an AUC value of 87.1% on the subset. The deep learning algorithm had 100% accuracy on the subset, while having an 87% AUC on the 108 sample group.
Conclusion: Circulating microRNA-based differentiation between uni-, and bilateral PA could be a reliable diagnostic method. Should the diagnosis of BAH be established, medical treatment may begin immediately, with no further tests required to establish localization. This could prove beneficial for the health and financial costs of both patients and providers.
Funding: This project has been funded through the National Research, Development and Innovation Office of Hungary K134215 and K146906 to PI and ÚNKP-23-3-1 to BV