BACKGROUND Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limited availability, and often difficult interpretation pose challenges. This study explores the utility of circulating microRNAs (miRNAs) and machine learning in distinguishing between unilateral and bilateral forms of primary aldosteronism. METHODS MiRNA profiling was conducted on plasma samples from 18 patients with primary aldosteronism taken during adrenal venous sampling on an Illumina MiSeq platform. Bioinformatics and machine learning identified 9 miRNAs for validation by reverse transcription real-time quantitative polymerase chain reaction. Validation was performed on a cohort consisting of 108 patients with known subdifferentiation. A 30-patient subset of the validation cohort involved both adrenal venous sampling and peripheral, the rest only peripheral samples. A neural network model was used for feature selection and comparison between adrenal venous sampling and peripheral samples, while a deep-learning model was used for classification. RESULTS Our model identified 10 miRNA combinations achieving >85% accuracy in distinguishing unilateral primary aldosteronism and bilateral adrenal hyperplasia on a 30-sample subset, while also confirming the suitability of peripheral samples for analysis. The best model, involving 6 miRNAs, achieved an area under curve of 87.1%. Deep learning resulted in 100% accuracy on the subset and 90.9% sensitivity and 81.8% specificity on all 108 samples, with an area under curve of 86.7%. CONCLUSIONS Machine learning analysis of circulating miRNAs offers a minimally invasive alternative for primary aldosteronism lateralization. Early identification of bilateral adrenal hyperplasia could expedite treatment initiation without the need for further localization, benefiting both patients and health care providers.

中文翻译：

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循环 miRNA 和机器学习用于偏侧化原发性醛固酮增多症。


背景 由于治疗方法不同，区分单侧和双侧原发性醛固酮增多症是继发性高血压的主要原因，这一点至关重要。虽然肾上腺静脉取样是金标准，但其侵入性、可用性有限且通常难以解释，这带来了挑战。本研究探讨了循环 microRNA （miRNA） 和机器学习在区分单侧和双侧原发性醛固酮增多症方面的效用。方法 在 Illumina MiSeq 平台上对 18 例原发性醛固酮增多症患者的血浆样本进行 MiRNA 分析。生物信息学和机器学习确定了 9 个 miRNA，用于通过逆转录实时定量聚合酶链反应进行验证。对由 108 名已知亚分化患者组成的队列进行了验证。验证队列的 30 名患者子集涉及肾上腺静脉取样和外周血，其余仅涉及外周血样本。神经网络模型用于肾上腺静脉采样和外周样本的特征选择和比较，而深度学习模型用于分类。结果我们的模型在 30 个样本的子集上确定了 10 种 miRNA 组合，在区分单侧原发性醛固酮增多症和双侧肾上腺皮质增生症方面达到 >85% 的准确率，同时也证实了外周样本适合分析。涉及 6 个 miRNAs 的最佳模型实现了 87.1% 的曲线下面积。深度学习对子集的准确率为 100%，对所有 108 个样本的灵敏度为 90.9%，特异性为 81.8%，曲线下面积为 86.7%。 结论 循环 miRNAs 的机器学习分析为原发性醛固酮增多症偏侧化提供了一种微创替代方案。双侧肾上腺皮质增生症的早期识别可以加快治疗开始，而无需进一步定位，使患者和医疗保健提供者都受益。