Proteomic signatures to detect unilateral primary aldosteronism in hypertensive patients.

Abstract

Context: Primary aldosteronism (PA) is a major cause of hypertension and cardiovascular disease; however, diagnosing PA remains challenging.

Objective: We investigated whether deep proteomic analyses could be used to diagnose unilateral PA in hypertensive patients.

Methods: We enrolled 52 patients with unilateral PA and 46 with essential hypertension (EH) and divided them into training and validation cohorts. Plasma samples were collected at baseline from all patients and again from PA patients after adrenalectomy. Deep proteomic analysis was performed to identify peptide signatures used to develop a classification model distinguishing PA from EH in the training cohort. The classification model was subsequently tested in the validation cohort and post-adrenalectomy PA patients.

Results: After proteomic analysis, six peptide features including HBB, FIBA, Complement CO7, ALBU, C4BPA, and A2AP were selected to generate risk scores and develop a classification model for distinguishing unilateral PA from EH. Risk scores were significantly higher in PA patients compared to those with EH. The classification model had a sensitivity and specificity of 80.5% and 83.3%, respectively, for diagnosing unilateral PA in the training cohort, and 81.8% and 80.0% in the validation cohort. The model demonstrated strong performance with an area under the curve of .92 for distinguishing hypertensive patients with or without PA. Post-unilateral adrenalectomy, the risk scores showed a significant decrease.

Conclusions: Proteomic analysis can identify peptide signatures that effectively distinguish unilateral PA from EH. These findings underscore the potential utility of proteomics as an adjunct diagnostic and monitoring tool in the clinical management of PA.