Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.
Keywords: LC-MS; diagnostic biomarker; metabolomics; plasma; primary aldosteronism.
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