Primary aldosteronism (PA), characterized by overt renin-independent aldosterone production, is the most common form endocrine hypertension. Compared with blood pressure-matched cases of essential hypertension (EH), PA is associated with a higher risk of cardiovascular morbidity and mortality. It is estimated that PA affects at least 10% of hypertensive patients and up to 25% of treatment-resistant hypertension. The major subtypes of PA are comprised of bilateral idiopathic hyperaldosteronism (IHA) and unilateral aldosterone-producing adenoma (APA). The screening, confirmatory testing, and subtype differentiation of PA for therapeutic management is a multi-step and complex process, resulting in low screening rates and poor clinical recognition. PA is an independent risk factor for metabolic morbidity. Metabolomic profiling is a relatively new strategy for the diagnosis and prognosis of disease through identification and quantification of various metabolites. In the current study, we aimed to investigate the potential biomakers for discriminating PA from EH, as well as subtype classification for PA, by untargeted metabolomics.
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The potential biomarkers for primary aldosteronism diagnosis via untargeted metabolomics
Timeframe: 4 months
The potential biomarkers for primary aldosteronism subtype classification via untargeted metabolomics
Timeframe: 4 months
The predictive models for PA diagnosis and subtype classification by machine learning
Timeframe: 4 months