Peking University First Hospital
Beijing, Beijing Municipality, 100034, China
Location status: Recruiting
NCT Number: NCT07111351
Primary aldosteronism is a prevalent yet underdiagnosed cause of secondary hypertension, contributing to significant cardiovascular morbidity and renal dysfunction. Despite affecting up to 20% of hypertensive patients, PA is frequently missed because it lacks distinctive clinical features and often presents with nonspecific symptoms like resistant hypertension or subtle electrolyte imbalances. The diagnostic pathway involves a stepwise approach: initial screening via the aldosterone-to-renin ratio, confirmatory testing (e.g., saline suppression or captopril challenge), and subtype differentiation using adrenal venous sampling to distinguish unilateral adenoma from bilateral hyperplasia. This complexity, combined with clinician unfamiliarity and variable access to specialized centers, perpetuates underdiagnosis. Early identification and tailored treatment are paramount in improving outcomes for patients with primary aldosteronism.
In this study, we will conduct a comprehensive multi-omics analysis on three sample types: 1) blood and urine samples from patients with primary aldosteronism, primary hypertension, and healthy controls; and 2) adrenal tissue samples from patients undergoing adrenalectomy for aldosterone-producing adenomas. We aim to systematically identify differentially expressed biomarkers that could serve as potential early diagnostic markers for primary aldosteronism. The findings may provide new insights into disease pathogenesis and contribute to improving early detection and personalized treatment strategies for this condition.
Interested in participating?
Request Info18 year–80 year
All sexes
Observational
Beijing, Beijing Municipality, 100034, China
Location status: Recruiting
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Without intervention
Time frame: May 2025 to May 2029
Collect various types of biological samples and utilize advanced proteomics and metabolomics technologies to obtain comprehensive omics data. Based on this, combine machine learning algorithms to deeply mine multi-omics data and clinical information, aiming to screen novel biomarkers for the prediction, classification, and diagnosis of primary aldosteronism, and construct a high-precision prediction model.
Contact information is provided by the study sponsor or research team.
Peking University First Hospital
Other
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