Abstract: The disclosure relates to a combination of biomarkers comprising at least: (i-a) one biomarker selected in each of the following group of biomarkers: Patient's age, Plasma steroids, and Urinary steroids, and at least one biomarker selected in at least one of the group of biomarkers: O-methylated catecholamines, Small metabolites, and miRNA; or (i-b) one biomarker selected in each of the following group of biomarkers: Plasma steroids, Urinary steroids, and Small metabolites, and at least one biomarker selected in at least one of the group of biomarkers: Patient's age, O-methylated catecholamines, and miRNA. The combinations of biomarkers may be used for stratifying a hypertensive patient among different hypertensive diseases comprising Endocrine Hypertension (EHT), Primary Aldosteronism (PA), Pheochromocytoma/Functional Paraganglioma (PPGL), Cushing's Syndrome (CS), and Primary Hypertension (PHT).

Abstract: The disclosed invention relates to a method for identifying biomarkers for the stratification of hypertensive patients among different hypertension diseases: endocrine forms of hypertension and primary hypertension. The method is a machine-learning based method using one trained classifier on a predefined input dataset to rank several combinations of omics biomarkers (miRNA, steroids, metanephrines, small metabolites) on the basis of the computation of at least one evaluation parameter. A combination of biomarkers is selected to stratify the hypertensive patient among said plurality of hypertension diseases. Also, the disclosed invention relates to a method for stratifying hypertensive patients, such as hypertensive patients with endocrine forms of hypertension (EHT).