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.
Study Type
OBSERVATIONAL
Enrollment
95
Metabolomics is a rapidly evolving high-throughput technology that allows the measurement of the entire complement of metabolites generated by biochemical reactions under certain conditions in biological fluids or tissues. This technology has been used extensively to identify biomarkers in various cancers, nervous system diseases, cardiovascular diseases, pituitary diseases, and other diseases. The identification of biomarkers can be clinically useful for a more accurate diagnosis, prognosis, and treatment choice as well as disease monitoring. Among mass spectrometry (MS) methods, liquid chromatography- mass spectrometry (LC-MS) has been recognized as a robust metabolomics tool and has been widely applied in metabolite identification and quantification due to its high sensitivity, peak resolution, and reproducibility.
China Chongqing The third hospital affiliated to the Third Millitary Medical University
Chongqing, Chongqing Municipality, China
The potential biomarkers for primary aldosteronism diagnosis via untargeted metabolomics
The differentially expressed metabolites between primary aldosteronism (PA) and essential hypertension (EH) will be identified by untargeted metabolomics. The differentially expressed metabolites with good discriminative capability for determination of PA from EH can serve as biomarkers for PA diagnosis.
Time frame: 4 months
The potential biomarkers for primary aldosteronism subtype classification via untargeted metabolomics
The differentially expressed metabolites between idiopathic aldosteronism (IHA) and aldosterone-producing adenoma (APA) will be identified by untargeted metabolomics. The differentially expressed metabolites with good discriminative capability for determination of APA from IHA can serve as biomarkers for PA subtype classification.
Time frame: 4 months
The predictive models for PA diagnosis and subtype classification by machine learning
The predictive models will be constructed through the application of machine learning, integrating clinical data with differentially expressed metabolites for the diagnosis and subtype classification of PA
Time frame: 4 months
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