Biomarkers for the diagnosis and treatment of secretory hypertension and methods for identifying same - Patent Application 20070122997

Abstract

The present disclosure relates to a combination of biomarkers comprising at least: (i) one biomarker selected from each of the following groups of biomarkers: patient age, plasma steroids, and urinary steroids; and at least one biomarker selected from at least one of the following groups of biomarkers: O-methylated catecholamines, small molecule metabolites, and miRNAs; or (ii) one biomarker selected from each of the following groups of biomarkers: plasma steroids, urinary steroids, and small molecule metabolites; and at least one biomarker selected from at least one of the following groups of biomarkers: patient age, O-methylated catecholamines, and miRNAs. The combination of biomarkers can be used to stratify hypertensive patients among different hypertensive disorders, including endocrine hypertension (EHT), primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

Description

[Technical Field]

[0001] The present disclosure relates to the field of hypertension, particularly endocrine or secondary hypertension (EHT). In particular, the present disclosure relates to biomarker combinations or molecular signatures for identifying hypertensive disorders, particularly for stratifying hypertensive patients into EHT or primary hypertension (PHT). [Background technology]

[0002] Arterial hypertension affects up to 25% of the general population and is responsible for 10.4 million deaths annually worldwide (Lancet, 2017; 389(10064):37-55). Although numerous treatments exist, up to two-thirds of patients have suboptimal blood pressure control. Even small increases in blood pressure are associated with increased cardiovascular risk, with 62% of cerebrovascular disease and 49% of ischemic heart disease attributable to hypertension.

[0003] While measuring blood pressure and detecting hypertension in an individual can be easily accomplished with a sphygmomanometer, diagnosing the specific type of hypertension is much more difficult.

[0004] The detection and identification of secondary forms of hypertension, also known as endocrine hypertension (EHT), is important for targeted management of the underlying disease and prevention of cardiovascular complications. Endocrine hypertension includes a group of adrenal gland disorders that result in increased production of hormones that affect blood pressure regulation: primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), and Cushing's syndrome (CS). These disorders are associated with increased cardiovascular and metabolic risks and reduced quality of life (https: / / cordis.europa.eu / project / id / 633983).

[0005] The diagnosis of EHT is complex and requires referral to a specialized center. Because of the complex workup, the diagnosis of adrenal hypertension is often overlooked, resulting in non- or delayed treatment of the condition until 3 to 5 years after the onset of hypertension, when cardiovascular and metabolic complications have become established. As a result, EHT patients remain at increased risk for renal and cardiovascular complications, including stroke, coronary artery disease, and fatal or debilitating cardiovascular and cerebrovascular events, and undergo costly and often futile antihypertensive treatment throughout their lives, resulting in reduced quality of life (QoL) (Savard et al., Hypertension. 2013; 62(2): 331-336; Mulatero et al., J Clin Endocrinol Metab. 2013; 98(12): 4826-4833; Rossi et al., Hypertension. 2013; 62(1):62-69; Prejbisz et al., J Hypertens. 2011; 29(11): 2049-2060; Dekkers et al., J Clin Endocrinol Metab. 2013; 98(6): 2277-2284; Kunzel et al., J Psychiatr Res 2012; 46: 1650-1654).

[0006] A practical approach to diagnosing endocrine causes of hypertension has recently been reviewed by Yang et al. (Nephrology, 22 (2017) 663-677).

[0007] In the absence of clear symptoms or signs other than hypertension, biochemical screening to diagnose primary aldosteronism is necessary. Patients should be adequately prepared to ensure the accuracy of the results and eliminate potential bias due to drug or dietary therapy (Funder et al., J Clin Endocrinol Metab. 2016;101(5):1889-1916). If confirmatory testing is required, a specialist will be involved. General practitioners are the first port of call for early detection of PA. However, they usually lack sufficient knowledge about PA and its diagnostic guidelines. As a result, PA is often underdiagnosed. The aldosterone-to-renin ratio (ARR) is the recommended test for PA. However, the accuracy and reproducibility of this test are such that the recommended cutoff values vary widely. In addition, this test is available in various units of measurement, making comparison between different results difficult. Furthermore, the test itself has been shown to be particularly sensitive to several factors that can affect plasma aldosterone and renin, such as diet, posture, and medication.

[0008] The diagnosis of Cushing's syndrome is based on a combination of tests, including 24-hour urinary free cortisol (UFC) excretion, an overnight 1 mg dexamethasone suppression test (DST), and overnight salivary cortisol. Abnormal results on two of these tests constitute a positive diagnosis. Nevertheless, the UFC test has some limitations, as it may give false-negative results in patients with moderate to severe chronic renal dysfunction or mild cases of Cushing's syndrome and false-positive results in patients with pseudo-Cushing's syndrome. Therefore, it is recommended that the test be repeated at least twice.

[0009] Diagnosis of PPGL involves measurement of plasma-free O-methylated catecholamines or urinary fractionated O-methylated catecholamines. Measurement of plasma O-methylated catecholamines may consist of administering plasma metabolites of catecholamines collected in the supine position (Lenders et al., J Clin Endocrinol Metab, 2014; 99(6): 1915-1942). Nevertheless, false-positive results are common, occurring in 19–21% of cases for both plasma-free and urinary fractionated O-methylated catecholamines. False-positive results may be due to certain medications, stress, illness, or improper sampling.

[0010] Several tests are available to diagnose EHT, distinguish between EHT and PHT, or even to distinguish between the different EHT, PA, CS, and PPGGL. Unfortunately, as demonstrated herein, these tests are complex, time-consuming, and have limitations that may affect their reliability.

[0011] Therefore, there is a need for methods to stratify hypertensive patients between different types of hypertensive patients, for example, between EHT and PHT or PA patients, between CS and PPGL patients.

[0012] Recent technological and methodological developments have given rise to a research field now known as omics, which includes genomics and epigenomics, transcriptomics, proteomics, and metabolomics. Omics as a whole promises to provide a complete and more accurate picture of the molecular makeup of an organism or species. A clearer understanding of the structure and function of molecules downstream of genomic processes can be crucial to understanding those processes. Rather than simply relying on specific genetic variants in candidate genes in known hypertension pathways, omics approaches provide a comprehensive picture of all genetic factors affecting hypertension. For complex traits such as hypertension, which involve multiple pathways and organs, omics approaches offer the advantage of enabling the identification of novel hypertension mechanisms to further dissect and characterize hypertension pathophysiology (Arnett et al., Circ Res, 2018; 122: 1409-1419).

[0013] On the other hand, especially with the recent availability of high-throughput computational technologies, an integrative approach to combine these various omics would be advantageous to improve the accuracy of predicting prognosis and disease phenotypes. Machine learning provides an efficient way to extract valuable biological knowledge from heterogeneous data with underlying mechanisms (Reel et al., 2021).

[0014] Recently, steroid profiling combined with machine learning has been used to identify and subtype patients with primary aldosteronism (Eisenhofer et al., JAMA Netw Open. 2020).

[0015] There is a need for methods to stratify hypertensive patients among endocrine hypertension (EHT), primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0016] Methods for stratifying hypertensive patients with improved accuracy are needed.

[0017] There is a need for methods for stratifying hypertensive patients with improved sensitivity and specificity.

[0018] Biomarkers are needed to stratify hypertensive patients among endocrine hypertension (EHT), primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0019] There is a need for biomarkers for use in methods for stratifying hypertensive patients with improved accuracy.

[0020] There is a need for a kit for stratifying hypertensive patients among multiple hypertensive disorders.

[0021] There is a need for a computer program product for stratifying hypertensive patients among multiple hypertensive disorders.

[0022] There is a need for combinations of biomarkers for use in methods for treating hypertensive disorders in patients in need thereof.

[0023] There is a need for methods to stratify and treat hypertensive patients.

[0024] There is a need to develop sensitive and specific omics-based methods for stratifying hypertensive patients using machine learning-based techniques.

[0025] There is a need to develop sensitive and specific omics-based methods to stratify hypertensive patients between endocrine hypertension (EHT) and primary hypertension (PHT) using machine learning-based techniques.

[0026] There is a need to develop sensitive and specific omics-based methods to stratify hypertensive patients among endocrine hypertension (EHT) using machine learning-based techniques.

[0027] There is a need to develop sensitive and specific omics-based methods to stratify hypertensive patients among endocrine hypertension (EHT), primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT) using machine learning-based techniques.

[0028] A combination of biomarkers to distinguish between EHT and PHT is needed.

[0029] A combination of biomarkers is needed to distinguish between primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), and Cushing's syndrome (CS).

[0030] A combination of biomarkers is needed to distinguish between primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0031] The present disclosure aims to meet these needs in whole or in part. Summary of the Invention

[0032] According to one of its objectives, the present disclosure provides: (ia) one biomarker selected from each of the following groups of biomarkers: patient age, plasma steroids, and urinary steroids, and at least one biomarker selected from at least one of the groups of biomarkers: O-methylated catecholamines, small molecule metabolites, and miRNAs; or (ib) one biomarker selected from each of the following groups of biomarkers: plasma steroids, urinary steroids, and small molecule metabolites; and at least one biomarker selected from at least one of the groups of biomarkers: patient age, O-methylated catecholamines, and miRNAs; (ic) one biomarker selected in each of the following groups of biomarkers: urinary steroids, and at least one biomarker selected in at least one of the groups of biomarkers: plasma steroids, small molecule metabolites, and miRNAs; (id) one biomarker selected from each of the following groups of biomarkers: patient age, plasma steroids, urinary steroids, and small molecule metabolites, and at least one biomarker selected from at least one of the groups of biomarkers: O-methylated catecholamines and miRNAs; or (ie) one biomarker selected in each of the following groups of biomarkers: O-methylated catecholamines; and at least one biomarker selected in at least one of the groups of biomarkers: patient age, urinary steroids, and small molecule metabolites. The present invention relates to a combination of biomarkers comprising at least:

[0033] In some embodiments, the combination of biomarkers includes: (ia) age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urinary 18-hydroxycortisol (18-OHF), urinary α-cortol (α-cortol), urinary pregnanediol (PD), 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS); and plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p, plasma hsa-miR-485-3p, plasma 3-methoxytyramine, plasma metanephrine, plasma normetanephrine, plasma 11-dehydrocorticosterone, plasma cortisol, plasma cortisone, urinary 11-β-hydroxyandrosterone (11-β-OHAn), urinary 17-OH-pregnanolone (17-HP), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), urinary 5α-tetrahydrocortisol (5αTHF), urinary androsterone (An), urinary cortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA) ), urinary etiocholanolone (Etio), urinary pregnentriol (PT), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), urinary α-cortolone (A-cortolone), urinary β-cortolone (B-cortolone), urinary β-cortolone (B-cortolone), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma glutamic acid, plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae at least one further biomarker selected from the group comprising or consisting in C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (Serotonin / Trp), plasma spermidine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (Total DMA / Arg) and combinations thereof; or (ib) Plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PC aa C32:1, plasma PC aa C34:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), In addition, age, plasma hsa-let-7g-5p, plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma normetanephrine, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urinary pregnanediol (PD), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma citrulline / arginine ratio (Cit / Arg), plasma creatinine, plasma glutamic acid (Glu), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae at least one further biomarker selected from the group comprising or consisting in C36:3, plasma taurine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (Total DMA / Arg) and combinations thereof; (ic) urinary α-cortol (α-cortol) and urinary tetrahydro-11-deoxycortisol (THS); and at least one of plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma dehydroepiandrosterone (DHEA), plasma dehydroepiandrosterone sulfate (DHEAS), plasma hsa-miR-19a-3p, plasma methionine sulfoxide / methionine ratio (Met-SO / Met), plasma tryptophan, urinary 5-pregnenetriol (5-PT), urinary androsterone (An), urinary cortisol, urinary etiocholanolone (Etio), and combinations thereof; (id) Age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydroaldosterone (THAldo). rhodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysophosphoric acid (PC) a C17:0, plasma PC a C32:1, plasma PC a C32:2, plasma PC a C32:3, plasma PC a C34:2, plasma PC a C34:3, plasma PC a C36:3, plasma serotonin and plasma serotonin / tryptophan ratio (serotonin / Trp). and at least one of plasma creatinine, plasma dehydroepiandrosterone sulfate (DHEAS), plasma dodecanoylcarnitine (C12), plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma PC aa C34:1, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma taurine, urinary dehydroepiandrosterone (DHEA), and combinations thereof; or (ie) methoxytyramine, plasma metanephrine, and plasma normetanephrine; and at least one of age, plasma acetylornithine (Ac-Orn), urinary androsterone (An), urinary etiocholanolone (Etio), and combinations thereof. It may include at least:

[0034] As shown in the Examples section, the inventors observed that it was possible to define specific combinations of biomarkers, including omics-based biomarkers, for stratifying hypertensive patients between PHT and EHT. Furthermore, it was possible to define specific combinations of biomarkers for stratifying hypertensive patients between PA vs. PPGL vs. CS vs. PHT (also designated ALL-ALL or ALL vs. ALL in the description or examples). It was also possible to define specific combinations of biomarkers for stratifying hypertensive patients between PA vs. PPGL. It was also possible to define specific combinations of biomarkers for stratifying hypertensive patients between PA vs. CS. It was also possible to define specific combinations of biomarkers for stratifying hypertensive patients between CS vs. PPGL.

[0035] Advantageously, the combination of biomarkers has enabled the development of specific and sensitive methods for stratifying hypertensive patients among hypertensive diseases. The combination of biomarkers can be used in conjunction with trained classifiers to improve the sensitivity and specificity of the method for stratifying hypertensive patients among hypertensive diseases.

[0036] In embodiments, the at least one additional biomarker in (ia) can be selected from plasma metanephrines, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and combinations thereof.

[0037] In an embodiment, the at least one additional biomarker in (ia) is a combination comprising or consisting of at least plasma metanephrines, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisone (THE), and urinary tetrahydrocortisol (THF).

[0038] In an embodiment, the at least one additional biomarker in (ib) is plasma normetanephrine.

[0039] In an embodiment, the at least one additional biomarker in (ic) is urinary androsterone (An).

[0040] The combination of biomarkers can further be used in methods for stratifying hypertensive patients among multiple hypertensive diseases, for screening antihypertensive treatments, or for selecting antihypertensive treatments for a given hypertensive patient.

[0041] Another object of the present disclosure relates to the use of a combination of biomarkers to stratify hypertensive patients among multiple hypertensive diseases.

[0042] (ia) When the multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ia) defined above or elsewhere herein.

[0043] (ib) When the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ib) defined above or elsewhere herein.

[0044] (ic) When the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ic) defined above or elsewhere herein.

[0045] (id) When the multiple hypertensive disorders are primary aldosteronism (PA) and primary hypertension (PHT), the biomarker combination may be a biomarker combination (id) as defined above or elsewhere herein.

[0046] (i.e.) When the multiple hypertensive disorders are pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers defined above or elsewhere herein (i.e.).

[0047] Another object of the present disclosure is to provide a method for the stratification of hypertensive patients among multiple hypertensive diseases, comprising the steps of: (ia) if the multiple hypertensive disorders are primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ia) defined above or elsewhere herein; or (ib) when the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ib) defined above or elsewhere herein; or (ic) when the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ic) defined above or elsewhere herein; or (id) If the multiple hypertensive disorders are primary aldosteronism (PA) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (id) defined above or elsewhere herein; or (i.e.) when the multiple hypertensive disorders are pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers defined above or elsewhere herein (i.e.); wherein the use - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with a hypertensive disease, in order to stratify the hypertensive patient among the plurality of types of hypertensive disease according to (ia) or according to (ib) or according to (ic) or according to (id) or according to (ie); the trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. Regarding use.

[0048] According to another object, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: (ia) the multiple types of hypertensive disorders may include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), or (ib) the multiple types of hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), or (ic) the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT), or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT), or (ie) the multiple hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), The method may include using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers in a suitable biological sample previously isolated from said patient; (wherein for multiple hypertensive disorders according to (ia), the combination of biomarkers may be the combination of biomarkers (ia) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ib), the combination of biomarkers may be the biomarker combination (ib) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ic), the combination of biomarkers is the biomarker combination (ic) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (id), the combination of biomarkers is the biomarker combination (id) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ie), the combination of biomarkers is a combination of biomarkers (ie) defined above or elsewhere herein. b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the multiple types of hypertensive disease according to (ia) or according to (ib), Regarding the method.

[0049] The method further comprises the step of obtaining, for each type of hypertensive disorder, a probability of associating a hypertensive patient with said hypertensive disorder.

[0050] According to another object, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: (ia) Multiple types of hypertensive disorders may include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT); or (ib) the multiple types of hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT); or (ic) multiple hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT); or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT); or (i.e.) multiple hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT); The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter to compare at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo; wherein for multiple hypertensive disorders according to (ia), the combination of biomarkers is the combination of biomarkers (ia) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ib), the combination of biomarkers is the biomarker combination (ib) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ic), the combination of biomarkers is the biomarker combination (ic) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (id), the combination of biomarkers is the biomarker combination (id) defined above or elsewhere herein; or wherein for multiple hypertensive disorders according to (ie), the combination of biomarkers is a combination of biomarkers (ie) defined above or elsewhere herein. b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with a hypertensive disease, in order to stratify the hypertensive patient among the multiple types of hypertensive disease according to (ia) or according to (ib) or according to (ic) or according to (id) or according to (ie); Regarding the method.

[0051] The trained classifier can be selected from decision trees (J48), Naive Bayes (NB), K-nearest neighbors (IBk), Logit Boost (LB), support vector machines (SVM), logic model trees (LMT), bagging, simple logistic (SL), random forests (RF) and sequential minimal optimization (SMO).

[0052] The trained classifier can be selected from LogitBoost (LB), Simple Logistic (SL) and Random Forest (RF).

[0053] The classifier is a) for at least one predetermined comparison between the at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in the group of hypertensive diseases, the first and second types of hypertensive disease being different, using the classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter; b) selecting a combination of biomarkers for stratifying hypertensive patients among the plurality of types of hypertensive diseases based on the calculated evaluation parameters. It may have been trained using at least one predefined input dataset according to at least one method including:

[0054] The evaluation parameters can be selected from accuracy, sensitivity, specificity, AUC, F1, kappa score, and combinations thereof.

[0055] According to another object, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: (ia) the multiple hypertensive disorders may comprise primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), and the kit may comprise at least means for measuring the combination of biomarkers (ia) as defined above or elsewhere herein; or (ib) the multiple hypertensive disorders may comprise endocrine hypertension (EHT) and primary hypertension (PHT), and the kit may comprise at least a means for measuring the combination of biomarkers (ib) as defined above or elsewhere herein; or (ic) the plurality of hypertensive disorders comprises Cushing's syndrome (CS) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers (ic) as defined above or elsewhere herein; or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT), and the kit comprises at least a means for measuring a combination of biomarkers (id) as defined above or elsewhere herein; or (i) the plurality of hypertensive diseases comprises pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the kit comprises at least a means for measuring a combination of biomarkers (i.e., as defined above or elsewhere herein); Regarding the kit.

[0056] According to another object, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, the computer program product comprising: (ia) the multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT); or (ib) the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT); or (ic) the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT); or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT); or (ie) the multiple hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT); The computer program (1) a method for detecting a plurality of combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, the method comprising: (ia) Primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT) or (ib) Endocrine hypertension (EHT) and primary hypertension (PHT) or (ic) Cushing's syndrome (CS) and primary hypertension (PHT) or (id) Primary aldosteronism (PA) and primary hypertension (PHT) or (ie) at least one classifier for comparing at least first and second types of hypertensive diseases (wherein the first and second types of hypertensive diseases are different) selected from the group (ia), (ib), (ic), (id) and (ie) of hypertensive diseases comprising pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT) multiple times, wherein the classifier is a) using said classifier to rank a plurality of combinations of biomarkers for at least one predetermined comparison between said at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said groups of hypertensive diseases (ia), (ib), (ic), (id) and (ie), and wherein said first and second types of hypertensive disease are different, based on calculation of at least one evaluation parameter; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases (ia), (ib), (ic), (id) and (ie) based on the calculated assessment parameters; (2) at least one input of a measured biomarker; wherein said input of measured biomarkers is obtained by measuring, in a suitable biological sample previously isolated from said patient, combination of biomarkers (ia) as defined above or elsewhere herein (wherein a plurality of hypertensive disorders is according to (ia)) or combination of biomarkers (ib) as defined above or elsewhere herein (wherein a plurality of hypertensive disorders is according to (ib)) or combination of biomarkers (ic) (wherein a plurality of hypertensive disorders is according to (ic)) or combination of biomarkers (id) (wherein a plurality of hypertensive disorders is according to (id)) or combination of biomarkers (ie) (wherein a plurality of hypertensive disorders is according to (ie)); - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each hypertensive disorder of the plurality of hypertensive disorders (ia), (ib), (ic), (id) or (ie) with a probability of associating the hypertensive patient with the hypertensive disorder in order to stratify the hypertensive patient among the plurality of hypertensive disorders (ia), (ib), (ic), (id) and (ie) for the at least one input of measured biomarkers. Related to computer program products.

[0057] According to another object, the present disclosure provides a method for stratifying and treating a hypertensive disorder in a patient in need thereof, the method comprising the steps of: - Hypertensive disorders (ia) primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (ia) defined above or elsewhere herein); or (ib) endocrine hypertension (EHT) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (ib) defined above or elsewhere herein); or (ic) Multiple hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), wherein the biomarker combination is a biomarker combination (ic) as defined above or elsewhere herein; or (id) Multiple hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), where the biomarker combination is a biomarker combination (id) as defined above or elsewhere herein; or (i.e.) multiple hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), wherein the biomarker combination is a biomarker combination (i.e.) defined above or elsewhere herein. Selected from among Regarding use.

[0058] According to another object, the present disclosure provides a combination of biomarkers for use in a method for treating a hypertensive disorder in a patient in need thereof, comprising: - Hypertensive disorders (ia) primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (ia) defined above or elsewhere herein); or (ib) endocrine hypertension (EHT) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (ib) defined above or elsewhere herein); or (ic) Cushing's syndrome (CS) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (ic) defined above or elsewhere herein); or (id) primary aldosteronism (PA) and primary hypertension (PHT) (wherein the biomarker combination is the biomarker combination (id) defined above or elsewhere herein); or (i.e.) pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT) (wherein the biomarker combination is a biomarker combination (i.e.) defined above or elsewhere herein). is selected from The method of treatment comprises stratifying a hypertensive patient between hypertensive disorders (ia) and (ib), said stratifying step comprising: - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with a hypertensive disease, in order to stratify the hypertensive patient among the multiple types of hypertensive diseases according to (ia) or according to (ib), the trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. Regarding biomarker combinations.

[0059] According to another object, the present disclosure provides a method for stratifying and treating hypertensive patients, said method comprising stratifying said hypertensive patients among a plurality of hypertensive disorders; (ia) the multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (ia) defined above or elsewhere herein; or (ib) the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT) and the combination of biomarkers is the combination of biomarkers (ib) defined above or elsewhere herein; or (ic) the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT) and the combination of biomarkers is the combination of biomarkers (ic) defined above or elsewhere herein; or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT) and the combination of biomarkers is the biomarker combination (id) defined above or elsewhere herein; or (i.e.) the multiple hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers defined above or elsewhere herein (i.e.); ), and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders (ia), (ib), (ic), (id) and (ie) according to the method of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0060] According to another object, the present disclosure provides a method for treating a hypertensive patient, said method comprising: a) stratifying the hypertensive patient among a plurality of hypertensive disorders; and (ia) the multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (ia) defined above or elsewhere herein; or (ib) the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT) and the combination of biomarkers is the combination of biomarkers (ib) defined above or elsewhere herein; or (ic) the multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT) and the combination of biomarkers is the combination of biomarkers (ic) defined above or elsewhere herein; or (id) the multiple hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT) and the combination of biomarkers is the biomarker combination (id) defined above or elsewhere herein; or (i.e.) the multiple hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers defined above or elsewhere herein (i.e.), stratifying said hypertensive patient among said plurality of hypertensive disorders (ia), (ib), (ic), (id) and (ie) according to the method defined above or elsewhere herein for associating a hypertensive disorder with said patient. ), b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) treating the patient by administering to the patient the therapeutic treatment selected in step b); Regarding the method. [Brief explanation of the drawings]

[0061] [Figure 1] An overview of sample and omics availability is provided. [Figure 2] A table of plasma metanephrines tested is shown. [Figure 3] A table of the plasma steroids examined is shown. [Figure 4] A table of tested urinary steroids is shown. [Figure 5] A table of the plasma small molecule metabolites tested, their ratios and combinations is shown. [Figure 6]A table of plasma miRNAs tested is shown. [Figure 7] 1 shows a block diagram of some steps in a second example of the method of the present invention. [Figure 8] Details of randomly split training and test datasets are shown for Cushing's syndrome (CS), primary aldosteronism (PA), pheochromocytoma or paraganglioma (PPGL), and primary hypertension (PHT). [Figure 9] Using a training set of multi-omics data, we present classification results to evaluate the best classifier and best feature selection method for ALL-ALL disease combinations. [Figure 10] The classification indices of the top-performing classifiers on the test set of five disease combinations trained using multi-omics and five mono-omics are shown. [Figure 11] Predictive performance of the top performing classifiers (on the test set) is shown for the combinations ALL-ALL, EHT-PHT, PA-PHT, PPGL-PHT, and CS-PHT. [Figure 12] Confusion matrices of the top performing classifiers on the test set are shown for the disease combinations ALL-ALL, EHT-PHT, PA-PHT, PPGL-PHT, and CS-PHT. [Figure 13] The ROC curves and their respective AUC values for EHT-PHT, PA-PHT, PPGL-PHT, and CS-PHT using the top-performing classifiers are shown. [Figure 14] For the ALL-ALL disease combination, classification results are shown for the training and test sets using the top performing classifiers trained with and without balanced data. [Figure 15] For the EHT-PHT disease combination, classification results are shown for the training and test sets using the top performing classifiers trained with and without balanced data. [Figure 16]For the PA-PHT disease combination, classification results are shown for the training and test sets using the top-performing classifiers trained. The training dataset was balanced so no synthetic samples or downsampling approaches were used. [Figure 17] For the PPGL-PHT disease combination, classification results are shown for the training and test sets using the top performing classifiers trained with and without balanced data. [Figure 18] For the CS-PHT disease combination, classification results are shown for the training and test sets using the top performing classifiers trained with and without balanced data. [Figure 19] The number and contribution of different omics in the overall multi-omics dataset are shown. [Figure 20] The number and contribution of features selected for multi-omics classification in each of the five disease combinations are shown. [Figure 21] The top common features of multi-omics disease combinations are shown as a Venn diagram. [Figure 22] A table detailing unique biomarkers for overlapping disease combinations is provided. [Figure 23] For the ALL-ALL disease combination, the top features common to multi-omics and mono-omics are shown. [Figure 24] The top features common to multi-omics and mono-omics for the EHT-PHT disease combination are shown. [Figure 25] The top features common to multi-omics and mono-omics for the PA-PHT disease combination are shown. [Figure 26] The top features common to multi-omics and mono-omics for the PPGL-PHT disease combination are shown. [Figure 27] The top features common to multi-omics and mono-omics for the CS-PHT disease combination are shown. [Figure 28] Violin plot of the most discriminatory PmiRNA, PMeta, PSteroid and USteroid features selected for the ALL-ALL disease combination in the multi-omics classifier and the corresponding values for NV. [Figure 29] Principal component analysis using top features of the training data is shown for ALL-ALL, EHT-PHT, PA-PHT, PPGL-PHT and CS-PHT disease combinations with NV samples. [Figure 30] Heatmaps of the average classification performance metrics (over 100 random repeats) using multi-omics and five individual omics with the three best classifiers for five disease combinations in Scenario 1 (Sets A and B), Scenario 2 (Sets C and D), and Scenario 3 (Sets E and F). [Figure 31] For sets A–F, a joint heatmap for the five disease combinations is shown showing the list of top features selected in classification using PMeta, PSteroid, USteroid, PmiRNA, and PSmallMB data, truncated at a repeat frequency cutoff of 50 (≥100 random repeats). [Figure 32] For sets A–F, a joint heatmap for the five disease combinations is shown showing the list of top features selected for classification using MOmics data, truncated at a repeat frequency cutoff of 50 (≥100 random repeats). [Figure 33] Schematic showing the ML pipeline within WP1 and outcome probabilities for the validation set. [Figure 34] (5) Map showing the multi-omics features used by the top performing models for the ALL-ALL, EHT-PHT, PA-PHT, PPGL-PHT and CS-PHT disease combinations. DETAILED DESCRIPTION OF THE INVENTION

[0062] Detailed Description definition Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings commonly understood by those skilled in the art. For example, the Concise Dictionary of Biomedicine and Molecular Biology, Juo, Pei-Show, 2nd ed., 2002, CRC Press; The Dictionary of Cell and Molecular Biology, 3rd ed., 1999, Academic Press; and the Oxford Dictionary of Biochemistry and Molecular Biology, Revised, 2000, Oxford University Press can provide those skilled in the art with a general dictionary of many of the terms used in this disclosure. Exemplary methods and materials are described below; however, methods and materials similar or equivalent to those described herein can also be used in practicing or testing this disclosure. In the event of any conflict, the present specification, including definitions, will control. Generally, the nomenclatures and techniques used in connection with cell and tissue culture, molecular biology, virology, immunology, microbiology, genetics, analytical chemistry, synthetic organic chemistry, medicinal and pharmaceutical chemistry, and protein and nucleic acid chemistry and hybridization described herein are those well known and commonly used in the art. Methods are performed according to kit manufacturer's specifications, as commonly accomplished in the art or as described herein. Furthermore, unless otherwise specified, singular terms shall include plurals and plural terms shall include the singular.

[0063] Units, prefixes, and symbols are denoted in the format accepted by the International System of Units (SI). Numerical ranges are inclusive of the numbers defining the range. Unless otherwise specified, amino acid sequences are written left to right in amino to carboxy orientation. The headings provided herein are not intended to limit the various aspects of this disclosure. Accordingly, the terms defined immediately below are more fully defined by reference to the specification in its entirety.

[0064] All publications and other references mentioned herein are incorporated by reference in their entirety. Although a number of documents are cited in this specification, this citation should not be construed as an acknowledgement that any of these documents form part of the general knowledge in the art.

[0065] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly indicates otherwise. Thus, for example, reference to "a biomarker" includes a plurality of such biomarkers, and so forth.

[0066] It is understood that aspects and embodiments of the present disclosure described herein include "having," "including," "consisting of," and "consisting essentially of" aspects and embodiments. The words "having" and "comprising," or variations such as "has," "having," "comprises," or "comprising," will be understood to imply the inclusion of the recited elements (e.g., compositions of matter or method steps) but not the exclusion of any other elements. The term "consisting of" means including the recited elements and excluding any additional elements. The term "consisting essentially of" means including the recited elements and possibly other elements, where such other elements do not materially affect the basic and novel characteristics of the disclosure. Various embodiments of the present disclosure that use the term "comprising" or equivalent terms are understood to cover embodiments in which this term is replaced with "consisting of" or "essentially consisting of."

[0067] Furthermore, as used herein, "and / or" is deemed to specifically disclose each of the two specified features or components, regardless of the presence or absence of the other. Thus, the term "and / or" used herein in phrases such as "A and / or B" is intended to include "A and B," "A or B," "A alone," and "B alone." Similarly, the term "and / or" used in phrases such as "A, B, and / or C" is intended to encompass each of the following embodiments: A, B, and C; A, B, or C; A or C; A or B; B, or C; A and C; A and B; B, and C; A alone; B alone, and C alone.

[0068] The terms "approximately" or "about" are used herein to mean approximately, in the region of, or in the region of. When the term "about" is used in conjunction with a numerical range, it modifies that range by extending the boundaries above and below the stated numerical values. Generally, the term "about" can modify a numerical value above and below the stated value by, for example, a variance of up to or below 10 percent (higher or lower). In some embodiments, the term indicates a deviation from the stated numerical value of ±10%, ±5%, ±4%, ±3%, ±2%, ±1%, ±0.9%, ±0.8%, ±0.7%, ±0.6%, ±0.5%, ±0.4%, ±0.3%, ±0.2%, ±0.1%, ±0.05%, or ±0.01%.

[0069] In this disclosure, the term "significantly" as used in reference to a change is intended to mean that the change observed is noticeable and / or has statistical significance.

[0070] In this disclosure, the term "substantially" as used in connection with a feature of the disclosure is intended to define a range of embodiments related to this feature that are nearly similar to this feature, but not completely similar.

[0071] As used herein, the terms "stratify" or "stratification" when used with respect to a patient is intended to refer to the process by which a patient is assigned a state or condition defined as a particular hypertensive disease.

[0072] As used herein, the terms "patient," "individual," or "subject" are used interchangeably and are intended to refer to a human.

[0073] In the sense of the present disclosure, "endocrine hypertension" (EHT) refers to secondary hypertension caused by excessive hormone production from the adrenal glands. Endocrine hypertension includes primary aldosteronism (PA) caused by autonomous production of aldosterone from aldosterone-producing adenoma or bilateral adrenal hyperplasia, pheochromocytoma / functional paraganglioma (PPGL) caused by excessive production of catecholamines from the adrenal glands or functional paraganglioma, and Cushing's syndrome (CS) caused by autonomous production of cortisol by adrenal or pituitary tumors.

[0074] For purposes of this disclosure, "primary hypertension" (PHT) refers, by definition, to a form of hypertension with no identifiable cause (not a secondary phenomenon). It is the most common type of hypertension, affecting approximately 85-95% of hypertensive patients. It is likely the result of an interaction between genetic and environmental factors.

[0075] In the sense of the present disclosure, "biomarker" is intended to refer to a quantifiable biological characteristic that is objectively measured and evaluated as an indicator of a normal or pathogenic biological process or a pharmacological response to a therapeutic intervention. A biomarker can be any substance, structure, or process that can be measured in the body and that can be affected by or predict an outcome or disease incidence, the effect of a treatment, or an intervention. A biomarker can be a biological molecule, such as a nucleic acid, peptide, protein, hormone, etc., or can be a physical parameter of a patient, such as age, height, weight, BMI, or gender.

[0076] The biomarkers can be selected from at least O-methylated catecholamines, miRNAs, steroids, small molecule metabolites, and patient status, such as age or sex. O-methylated catecholamines, miRNAs, and small molecule metabolites can be measured in plasma, and steroids can be measured in plasma or urine.

[0077] As used herein, a "combination of biomarkers" is intended to refer to a set of biomarkers measured in a suitable biological sample previously collected from a patient, which together can be used as a molecular signature of a hypertensive disease affecting the patient.

[0078] The terms "measure," "measuring," "measured," or any equivalent thereof, as used in this disclosure in connection with a biomarker are intended to mean the quantification or qualification of the biomarker.

[0079] Quantitation is a measure of the amount of a biomarker and can be expressed as volume, moles, weight, weight per weight, or weight per volume of a matrix containing the biomarker, e.g., concentration, particularly molar concentration. For example, biomarker quantitation can be expressed in ng / ml or pg / ml. Biomarker quantitation can also be expressed relative to the quantitation of another biomarker or to a reference (or standard). In such cases, the biomarker quantitation can be expressed as a ratio, such as a weight:weight ratio or a molar ratio. The patient's age is the quantitative value of the biomarker "age," obtained as a biomarker of the patient's condition.

[0080] A qualification for a biomarker is a determination of the presence or absence of the biomarker. A qualification can also be a non-numerical value of a patient's condition, such as gender (male / female) or menopausal status (pre-menopausal / post-menopausal).

[0081] Measurement of a biomarker, eg, quantification or qualification, can be performed by any technique known in the art that is applicable to that biomarker.

[0082] Measuring a combination of biomarkers ex vivo. In the present disclosure, the phrase "measuring a combination of biomarkers ex vivo" is intended to refer to a process performed outside the patient's body, for example, on a suitable biological sample previously isolated from the patient. In the case of a patient's age, the measurement can be determined based on the patient's date of birth or a previously obtained bone mineral density measurement. "Measuring a combination of biomarkers ex vivo" is used interchangeably with the phrase "measuring a combination of biomarkers in a suitable biological sample previously isolated from said patient." For simplicity, "measuring a combination of biomarkers in a suitable biological sample previously isolated from said patient" may, depending on the context, include the determination of the patient's age if the latter is itself performed on an isolated biological sample or based on the patient's date of birth or a previously obtained bone mineral density measurement.

[0083] In the context of this disclosure, the term "omics" or "omics" refers to the characterization and quantification of pools of biomolecules that translate into the structure, function, and behavior of one or more organisms. This covers fields such as genomics, transcriptomics, proteomics, or metabolomics.

[0084] As used herein, "small molecule metabolite" is intended to refer to a broad range of low molecular weight organic compounds, ranging in molecular weight from about 50 to about 1500 daltons (Da), that participate in biological processes as substrates or products. Metabolites are products and intermediates of cellular metabolism. The Human Metabolome Database is accessible at https: / / hmdb.ca / . Metabolites may have many functions, including energy conversion, signal transduction, epigenetic effects, and coenzyme activity. In metabolomics, "metabolites" or "small molecule metabolites" are the subject of study. Metabolomics is the study of metabolic product profiles. In metabolomics, many different metabolites in a biological sample are analyzed using mass spectrometry or nuclear magnetic resonance spectrometry. Metabolites or small molecule metabolites include, for example, uric acid, lipids and derivatives such as palmitoleic acid (c16:1), palmitic acid (c16:0), 1,2-diglyceride (c36:2), amino acids and derivatives such as proline, isoleucine or 4-hydroxyproline, sugars and derivatives such as arabitol, ribitol or xylitol, mannose or galactose, etc. Small molecule metabolites are typically used as biomarkers of biological processes. "Small molecule metabolites" are well known in the art, as exemplified by Qiu S, Cai Y, Yao H, et al. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther. 2023;8(1):132. Published 2023 Mar 20. doi:10.1038 / s41392-023-01399-3 or Toepfer N, Kleessen S, Nikoloski Z. Integration of metabolomics data into metabolic networks. Front Plant Sci. 2015;6:49. Published 2015 Feb 17. doi:10.3389 / fpls.2015.00049.

[0085] As used herein, "metabolites" or "small molecule metabolites" do not include O-methylated catecholamines and steroids (or even miRNAs) as determined elsewhere.

[0086] In the sense of the present disclosure, "certain hypertensive patients" is intended to refer to patients suffering from EHT or PHT, or PA, CS or PPGL. Hereinafter, for example, a hypertensive patient suffering from EHT will be referred to as an EHT patient.

[0087] "Certain hypertensive disorders" refers to hypertension selected from EHT or PHT, or PA, CS or PPGL.

[0088] As used herein, the terms "prevent," "preventing," or "delay progression" (and grammatical variations thereof) in reference to a disease or disorder refer to prophylactic treatment of the disease or disorder, for example, in a patient suspected of having the disease or at risk of developing the disease. Prevention can include, but is not limited to, preventing or delaying the onset or progression of the disease and / or maintaining one or more symptoms of the disease or disorder below a desired or pathological level. The term "prevent" does not require 100% elimination of the possibility or likelihood of an event occurring. Rather, it indicates a reduced likelihood of an event occurring in the presence of a composition or method described herein.

[0089] As used herein, in the context of eliciting an immune response, the terms "treat," "treatment," "therapy," and the like refer to the administration or consumption of a composition disclosed herein with the purpose of curing, ameliorating, alleviating, mitigating, altering, relieving, ameliorating, improving, or affecting the symptoms of a disease, disorder, or condition, or preventing or delaying the onset of symptoms, complications, or otherwise arresting or inhibiting further development of the disorder in a statistically significant manner. Also as used herein, in the context of this disclosure, the terms "treat," "treatment," and the like refer to relieving or alleviating the pathological process of the disorder. In the context of this disclosure, to the extent that it relates to any of the other conditions described herein, the terms "treat," "treatment," and the like refer to alleviating or alleviating one or more symptoms associated with such condition.

[0090] In the sense of the present disclosure, "antihypertensive therapeutic treatment" is intended to refer to any agent or intervention intended to provide a therapeutic effect and that can be used to prevent and / or treat hypertensive disorders in patients in need thereof. The selection of such agents and interventions and their use depending on the specificity of the patient is within the common practice and general knowledge of one of ordinary skill in the art. The nature of the antihypertensive agent or intervention can be selected depending on the type of hypertensive patient being treated. Thus, an antihypertensive agent or intervention may not be used to treat EHT or PHT patients or PA, PPGL, or CS patients as well.

[0091] It will be appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure. All publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited.

[0093] The lists of sources, ingredients and components set forth below are listed so that combinations and mixtures thereof are also contemplated and within the scope of this specification.

[0094] It should be understood that every maximum numerical limitation given throughout this specification will include every lower numerical limitation, as if such lower numerical limitations were expressly written herein. Every minimum numerical limitation given throughout this specification will include every higher numerical limitation, as if such higher numerical limitations were expressly written herein. Every numerical range given throughout this specification will include every narrower numerical range that falls within such broader numerical range, as if such narrower numerical ranges were all expressly written herein.

[0095] All lists of items, e.g., lists of ingredients, etc., are intended to be and should be construed as Markush groups. Thus, all lists can be read and construed as lists of "items" and combinations and mixtures thereof, with items "selected from the group consisting of."

[0096] Trade names of components, including various ingredients, utilized in this disclosure may be referred to herein. The inventors herein do not intend to be limited to materials with any particular trade name. Materials equivalent to the materials referenced by trade name (e.g., materials obtained from different suppliers under different names or reference numbers) may be substituted and utilized herein.

[0097] In describing various embodiments of the present disclosure, various embodiments or individual features are disclosed. As will be apparent to one skilled in the art, all combinations of such embodiments and features are possible and can result in preferred implementations of the present disclosure. While various embodiments and individual features of the present invention have been illustrated and described, various other changes and modifications can be made without departing from the spirit and scope of the present invention. It will also be apparent that all combinations of the embodiments and features taught in this disclosure are possible and can result in preferred implementations of the present invention.

[0098] Biomarkers and their combinations The present disclosure relates to biomarker combinations that can be used to stratify hypertensive patients among different types of hypertensive disorders, such as EHT, PHT, PA, CS, and PPGL.

[0099] The combination of biomarkers may include at least one biomarker selected from each biomarker group of a set of at least two biomarker groups, wherein the at least two biomarker groups are selected from patient age, miRNA, O-methylated catecholamines, steroids, and small molecule metabolites.

[0100] The biomarkers miRNA, O-methylated catecholamines, steroids, and small molecule metabolites can be measured in one or more isolated biological samples taken from a patient. Measurement of the biomarkers is performed in vitro.

[0101] The uses and methods disclosed herein may be in vitro uses and methods.

[0102] Suitable biological samples obtained from a patient may be blood, urine, stool, sweat, saliva or tissue samples, e.g., skin samples. If a blood sample is taken for analysis, whole blood, serum or plasma samples may be used.

[0103] Suitable isolated biological samples may be plasma and / or urine samples.

[0104] Depending on the combination of biomarkers, two or more biological samples can be used. For example, multiple samples of the same nature can be used, such as multiple blood samples. Alternatively, multiple samples of different nature can be used, such as a blood sample and a urine sample, or multiple blood samples and multiple urine samples.

[0105] The measurement of a biomarker or a combination of biomarkers in a sample can be carried out by several possible analytical methods, the choice of which depends on the nature of the biomarkers to be analyzed and can be chosen by those skilled in the art based on their general knowledge.

[0106] Suitable analytical methods can be, for example, a prior separation step, e.g. mass spectrometry coupled to chromatography, immunodetection, in particular quantitative immunoassays, e.g. Western blot or ELISA, multi-analyte biochips, Biochip Array Technology Systems (BAT), PCR, multiplex PCR, RT-PCR, nucleic acid-based chips or microarrays, HPLC or liquid chromatography tandem mass spectrometry coupled with a coulometric detector, chromatography / mass spectrometry, UHPLC-ESI-QTOF-MS / MS or LC-MS / MS, NMR, LC / GC-FID, direct flow injection MS / MS, LC ESI-MS / MS, MS / MS, isothermal amplification, next generation sequencing, hybridization chain reaction or near-infrared techniques.

[0107] For example, steroid profiling can be performed by ultra-high performance liquid chromatography-tandem mass spectrometry (uHPLC-MS / MS or abbreviated as LC-MS / MS), gas chromatography-mass spectrometry (GC-MS) or supercritical fluid chromatography-tandem mass spectrometry (SFC-MS / MS).

[0108] age A biomarker that can be used in the biomarker combinations disclosed herein may be the age of the patient.

[0109] The patient's age is a clinical parameter of the patient, which can be determined based on the patient's date of birth or bone density.

[0110] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least patient age.

[0111] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may not include patient age. Alternatively, a combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include patient age.

[0112] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may not include patient age.

[0113] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least the patient's age.

[0114] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include patient age. Alternatively, a combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include patient age.

[0115] Omics biomarkers The biomarkers used in the biomarker combinations disclosed herein may be omics biomarkers. The omics biomarkers may be metabolomics- or genomics-based biomarkers. The omics biomarkers may be O-methylated catecholamines, steroids, small molecule metabolites, or miRNAs.

[0116] In an embodiment, the omics biomarkers can be measured in plasma or urine samples.

[0117] O-methylated catecholamines, small molecule metabolites, and miRNAs can be measured in plasma samples.

[0118] Steroids can be measured in plasma and / or urine samples.

[0119] O-methylated catecholamines, steroids, and small-molecule metabolites are metabolomics-based biomarkers. Metabolomics is the study of endogenous and exogenous small molecules (typically 50–1500 Da), including substrates and products of metabolic processes. The metabolome is the collection of all metabolic products in a living system. Examples of such metabolites include amino acids and fatty acids, lipids, sugars, and phenolic compounds. The Human Metabolome Database46 currently contains over 114,100 entries. Like the transcriptome and proteome, the metabolome is cell- and tissue-specific. Multiple laboratory analytical approaches, including mass spectrometry and nuclear magnetic resonance spectroscopy, are used to characterize the metabolome. Some methods involve chemical separation (e.g., by gas chromatography or high-performance liquid chromatography) before detection, while others (shotgun metabolomics) do not. Global (or untargeted) metabolomics methods can provide data on over 1,000 metabolites, while targeted methods typically assay specific classes of molecules (e.g., lipids) (Arnett et al., Circ Res. 2018;122(10):1409-1419.).

[0120] miRNA The biomarkers used in the biomarker combinations of the present disclosure may be miRNA biomarkers.

[0121] Plasma miRNAs are transcriptomics-based biomarkers. Transcriptomics seeks to identify and quantify all RNA transcripts (potentially including messenger, transfer, ribosomal, and non-coding regulatory RNAs) produced by a cell or organism under specific conditions (Arnett et al., 2018). Transcriptomics provides a snapshot of the genes that a cell or tissue is actively expressing at a given time. Two commonly used laboratory methods for transcriptomics are microarrays and RNA-Seq.

[0122] Transcriptomes vary even among tissues from the same organism. The choice of sample matrix is determined by factors such as availability and the purpose of the study. Transcriptomics studies typically compare gene expression profiles under two or more different experimental conditions, such as different environmental exposures or different disease states. Multiple methods exist for analyzing transcriptomics data. Heat maps provide a simple way to visually display expression differences between experimental conditions. Gene co-expression network analysis can characterize regulatory programs and link genes of unknown function to metabolic processes. Pathway analysis uses information cataloged in functional gene annotation databases to identify metabolic, signaling, and gene regulatory pathways that may be associated with a given gene expression pattern.

[0123] One method for analyzing transcriptomic biomarkers, particularly miRNAs, is heat maps.

[0124] Plasma miRNAs can be measured or quantified in plasma samples isolated from patients.

[0125] Prior to measurement, miRNAs can be extracted from biological samples, in particular plasma samples, according to the methods described by Sourvinou et al. (Journal of Molecular Diagnostics, Vol. 15, No. 6, November 2013) or Moody et al. (Clin Epigenetics. 2017 Oct 24; 9: 119).

[0126] Measurement or quantification of miRNA, particularly plasma miRNA, can be carried out according to any known technique in the art. For example, useful analytical methods may be digital PCR, quantitative RT-PCR, microarray, isothermal amplification, next-generation sequencing, hybridization chain reaction or near-infrared technology (Sourvinou et al., Journal of Molecular Diagnostics, Vol. 15, No. 6, November 2013; Ma, Jie et al., Biomarker insights vol. 8 127-36. 14 November 2013; Moody et al., Clin Epigenetics. 2017 Oct 24; 9: 119; Wright et al., Sci Rep 10, 825 (2020)).

[0127] In some embodiments, miRNAs can be determined using real-time RT-PCR, which can generate a cycle threshold (Ct) for each miRNA, which can be calibrated and normalized. This allows for comparison between plates and samples. Ct is a relative value that is inversely proportional to the transcript amount and is affected by various factors, including the detection chemistry and the miRNA normalization / calibration combination used. One method for normalizing Ct depends on the number of spike-ins and endogenous miRNAs used for the control.

[0128] The amount of miRNA can be expressed as the number of copies of miRNA per μL of sample volume, or as the weight of miRNA per unit of sample volume, eg, ng / μL.

[0129] The miRNA biomarkers considered in the methods disclosed herein are known in the art, and further information can be obtained from the miRBase: microRNA database (http: / / www.mirbase.org / index.shtml).

[0130] miRNA biomarkers suitable for the present disclosure can be selected from hsa-let-7b-5p, hsa-let-7d-3p, hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-103a-3p, hsa-miR-106b-3p, hsa-miR-107, hsa-miR-130a-3p, hsa-miR-130b-3p, hsa-miR-140-5p, hsa-miR-144-3p, hsa-miR-146a-5p, hsa-miR-148b-3p, hsa-miR-150-5p, hsa-miR-151a-3p, hsa-miR-152-3p, hsa-miR-155-5p, hsa-miR-15a-5p, hsa-miR-15b-3p, hsa-miR-16-2-3p, hsa-miR-16-5p, hsa-miR-181a-5p, hsa-miR-195-5p, hsa-miR-199a-5p, hsa-miR-19a-3p, hsa-miR-19b-3p, hsa-miR-210-3p, hsa-miR-222-3p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-23b-3p, hsa-miR-24-3p, hsa-miR-25-3p, hsa-miR-26b-5p, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-301a-3p, hsa-miR-30c-5p, hsa-miR-30d-5p, hsa-miR-324-5p, hsa-miR-32-5p, hsa-miR-328-3p, hsa-miR-335-5p, hsa-miR-33a-5p, hsa-miR-342-3p, hsa-miR-363-3p, hsa-miR-423-5p, hsa-miR-451a, hsa-miR-485-3p, hsa-miR-486-5p, hsa-miR-495-3p, hsa-miR-497-5p, hsa-miR-502-3p, hsa-miR-629-5p and hsa-miR-92a-3p.

[0131] Suitable miRNA biomarkers for the present disclosure may be selected from plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p, plasma hsa-miR-485-3p, plasma hsa-miR-19a-3p, and combinations thereof.

[0132] Suitable miRNA biomarkers for the present disclosure may be selected from plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p, plasma hsa-miR-485-3p, and combinations thereof.

[0133] A suitable miRNA biomarker for the present disclosure may be plasma hsa-miR-106b-3p.

[0134] A suitable miRNA biomarker for the present disclosure may be plasma hsa-let-7g-5p.

[0135] A suitable miRNA biomarker for the present disclosure may be plasma hsa-miR-19a-3p.

[0136] A miRNA biomarker combination suitable for the present disclosure may comprise or consist of at least plasma hsa-miR-106b-3p. The combination may further comprise plasma hsa-let-7g-5p. The combination may also further comprise plasma hsa-miR-301a-3p and plasma hsa-miR-485-3p.

[0137] A miRNA biomarker combination suitable for the present disclosure may include or consist at least in the combination of plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p and plasma hsa-miR-485-3p.

[0138] A combination of miRNA biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may comprise or consist of at least plasma hsa-miR-106b-3p. The combination may further comprise plasma hsa-let-7g-5p. The combination may also further comprise plasma hsa-miR-301a-3p and plasma hsa-miR-485-3p.

[0139] A combination of miRNA biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include or consist of at least plasma hsa-let-7g-5p.

[0140] A combination of miRNA biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include or consist of at least plasma hsa-miR-19a-3p.

[0141] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may not include any miRNA biomarkers.

[0142] A biomarker combination for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least plasma hsa-miR-106b-3p as a miRNA biomarker. The combination may further include plasma hsa-let-7g-5p. The combination may also further include plasma hsa-miR-301a-3p and plasma hsa-miR-485-3p.

[0143] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may not include any miRNA.

[0144] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least plasma hsa-let-7g-5p as a miRNA biomarker.

[0145] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may not include any miRNA.

[0146] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least plasma hsa-miR-19a-3p as a miRNA biomarker.

[0147] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may not include any miRNA.

[0148] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include any miRNA.

[0149] O-methylated catecholamines The biomarkers used in the present disclosure may be O-methylated catecholamines.

[0150] O-methylated catecholamines are metabolites resulting from the O-methylation of catecholamines, such as dopamine, epinephrine, and norepinephrine, by catechol O-methyltransferase (COMT). As used herein, O-methylated catecholamines is also intended to include 3-methyldopa.

[0151] The combination of biomarkers of the present disclosure may include an O-methylated catecholamine or a combination of O-methylated catecholamines.

[0152] O-methylated catecholamines can be measured, and in particular quantified, in plasma samples isolated from patients.

[0153] Measurement or quantification of O-methylated catecholamines can be carried out according to any known technique in the art.For example, useful analytical methods may be HPLC or liquid chromatography-tandem mass spectrometry (LC-MS / MS) coupled with coulometric detection.These analytical methods can be used to quantify O-methylated catecholamines (Niec et al., J Chromatogr B Analyt Technol Biomed Life Sci., 2015;Lee et al., Ann Lab Med. 2015 Sep;35(5):519-522;Osinga et al., Clin Biochem. 2016 Sep;49(13-14):983-988).

[0154] In some embodiments, measurement or quantification of O-methylated catecholamines can be measured by ultra-performance liquid chromatography-tandem mass spectrometry as disclosed in Peitzsch et al. (Ann Clin Biochem. 2013 Mar;50(Pt 2):147-55).

[0155] The amount of O-methylated catecholamine can be expressed in weight / volume units of the sample, for example, ng / ml or pg / ml plasma.

[0156] The intervals of the reference ranges for plasma O-methylated catecholamines may vary depending on age and sex and the analytical method used.Nevertheless, reference ranges are known in the art, for example, as disclosed in Peitzsch et al., Ann Clin Biochem. 2013 Mar;50(Pt 2):147-55 or Eisenhofer et al., Ann Clin Biochem. 2013;50(Pt 1):62-69.

[0157] The O-methylated catecholamines suitable for the present disclosure may be plasma O-methylated catecholamines selected from the group including or consisting at least of normetanephrine, metanephrine, 3-methoxytyramine, 3-O-methyldopa, and combinations thereof.

[0158] The plasma O-methylated catecholamines can be selected from the group including or consisting at least of normetanephrine, metanephrine, 3-methoxytyramine, and combinations thereof.

[0159] The plasma O-methylated catecholamines can be selected from the group consisting of or including at least normetanephrine, metanephrine, and combinations thereof.

[0160] The plasma O-methylated catecholamine can be normetanephrine.

[0161] The combination of plasma O-methylated catecholamines may include or consist of at least a combination of normetanephrine and metanephrine, which may further include at least 3-methoxytyramine.

[0162] A combination of plasma O-methylated catecholamines for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include or consist of at least normetanephrine and metanephrine. The combination may further include 3-methoxytyramine.

[0163] A plasma O-methylated catecholamine for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may be normetanephrine.

[0164] A combination of plasma O-methylated catecholamines for stratifying hypertensive patients among multiple types of hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include or consist of at least 3-methoxytyramine, normetanephrine, and metanephrine.

[0165] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may not include any plasma O-methylated catecholamines.

[0166] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least plasma normetanephrine and plasma metanephrine as plasma O-methylated catecholamines. The combination may further include plasma 3-methoxytyramine.

[0167] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may not include any plasma O-methylated catecholamines.

[0168] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least plasma normetanephrine as a plasma O-methylated catecholamine.

[0169] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may not include any plasma O-methylated catecholamines.

[0170] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may not include any plasma O-methylated catecholamines.

[0171] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least plasma 3-methoxytyramine, plasma metanephrine, and plasma normetanephrine as plasma O-methylated catecholamines.

[0172] steroid The biomarkers used in the present disclosure may be steroids. The steroids may be plasma and / or urinary steroids.

[0173] Steroids can be measured, and in particular quantified, in plasma and / or urine samples isolated from patients.

[0174] Measurement or quantification of steroids can be carried out according to any technique known in the art. For example, useful analytical methods may be ELISA, liquid column chromatography, gas chromatography / mass spectrometry, UHPLC-ESI-QTOF-MS / MS or LC-MS / MS (Allende et al., Chromatographia 77, 637-642 (2014); van der Veenet et al., Clin Biochem. 2019; 68:15-23 and Renterghem et al., Journal of Chromatography B, Volume 1141, 2020, 122026; Andrew et al., Best Pract Res Clin Endocrinol Metab. 2001; 15(1):1-16).

[0175] In some embodiments, measurement or quantification of steroids can be performed by liquid chromatography coupled to tandem mass spectrometry (LC-MS / MS) as disclosed in Eisenhofer et al., JAMA Netw Open. 2020 Sep 1;3(9); Peitzsch et al., J Steroid Biochem Mol Biol. 2014 Jan;145:75-84 or Bancos et al., Lancet Diabetes Endocrinol. 2020 Sep;8(9):773-781.

[0176] In some embodiments, urinary steroids can be quantified in urine samples collected over a 24 hour period, in timed urine samples collected over a period of less than 24 hours, in first morning urine, or in spontaneously collected urine.

[0177] The amount of steroid can be expressed in weight / volume units of the sample, for example mol / L, pmol / L, nmol / L, ng / ml or pg / ml of plasma or urine, in particular ng / ml or mol / L.

[0178] The intervals for reference ranges for plasma and urinary steroids may vary depending on age and sex and the analytical method used. Nevertheless, reference ranges are known in the art, for example, as disclosed in Eisenhofer et al. (Clin Chim Acta. 2017 Jul;470:115-124) or Van Renterghem et al. (Steroids. 2010 Feb;75(2):154-63).

[0179] Steroids can be measured in plasma samples.

[0180] Steroids can be measured in urine samples.

[0181] Steroids suitable for this disclosure include plasma aldosterone, plasma androstenedione, plasma corticosterone, plasma cortisol, plasma cortisone, plasma dehydroepiandrosterone (DHEA), plasma dehydroepiandrosterone sulfate (DHEAS), plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 17OH-progesterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, and the like. sol, plasma 18OH-corticosterone, plasma 11-dehydrocorticosterone, urinary α-cortol (A-cortol), urinary α-cortol (A-cortol), urinary androsterone (An), urinary β-cortol (B-cortol), urinary β-cortol, urinary cortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA), urinary etiocholanolone (Etio), urinary pregnanediol (PD), urinary pregnentriol (PT), urinary 3α,5β-tetrahydrocortisone Tetrahydroaldosterone (THAldo), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), urinary tetrahydro-11-deoxycortisol (THS), urinary 11-β-hydroxy-androsterone (11-β-OHAn ... The urinary 11-oxo-ethiocholanolone (11-βOHEt), urinary 11-oxo-ethiocholanolone (11-oxoEt), urinary 17-OH-pregnanolone (17-HP), urinary 18-hydroxycortisol (18-OHF), urinary 5α-tetrahydrocorticosterone (5-αTHB), urinary 5α-tetrahydrocortisol (5αTHF), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), and combinations thereof.

[0182] Steroids suitable for the present disclosure include plasma 11-dehydrocorticosterone, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma cortisol, plasma cortisone, plasma dehydroepiandrosterone (DHEA), plasma dehydroepiandrosterone sulfate (DHEAS), urinary 11-β-hydroxy-androsterone (11-β-OHAn), urinary 17-OH-pregnanolone (17-HP), urinary 18-hydroxycortisol (18-OHF), urinary 5α-tetrahydrocortisol (5αTHF), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), urinary α- The test substance may be selected from cortol (A-cortol), urinary α-cortol (A-cortol), urinary androsterone (An), urinary β-cortol (B-cortol), urinary β-cortol (B-cortol), urinary cortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA), urinary etiocholanolone (Etio), urinary pregnanediol (PD), urinary pregnenetriol (PT), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), urinary tetrahydro-11-deoxycortisol (THS), and combinations thereof.

[0183] Plasma steroids The plasma steroids may be selected from the group comprising or consisting at least of aldosterone, androstenedione, corticosterone, cortisol, cortisone, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS), 11-deoxycorticosterone, 11-deoxycortisol, 17OH-progesterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, 18OH-corticosterone and combinations thereof.

[0184] Plasma steroids suitable for the present disclosure may be selected from 11-dehydrocorticosterone, 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18oxo-cortisol, 21-deoxycortisol, aldosterone, corticosterone, cortisol, cortisone, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS).

[0185] The plasma steroids can be selected from the group consisting of or at least 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, and combinations thereof. This group may further include corticosterone. This group may further include dehydroepiandrosterone sulfate (DHEAS). This group may further include 11-dehydrocorticosterone. This group may further include cortisone. This group may further include cortisol.

[0186] The plasma steroids can be selected from the group consisting of or at least 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, corticosterone, dehydroepiandrosterone sulfate (DHEAS), and combinations thereof. This group may further include cortisone. This group may also include cortisol. This group may also include 11-dehydrocorticosterone.

[0187] The plasma steroids can be selected from the group consisting of or at least 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, and combinations thereof. This group may further include corticosterone. This group may also include dehydroepiandrosterone sulfate (DHEAS).

[0188] The plasma steroids may be selected from the group consisting of at least 11-deoxycortisol, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS), and combinations thereof. This group may further comprise 11-deoxycorticosterone.

[0189] The plasma steroids may be selected from the group consisting of or at least consisting of 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, and combinations thereof. This group may further comprise dehydroepiandrosterone sulfate (DHEAS).

[0190] The combination of plasma steroids may include or consist of at least 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, and combinations thereof. The combination may further include corticosterone. The combination may further include dehydroepiandrosterone sulfate (DHEAS). The combination may further include 11-dehydrocorticosterone. The combination may further include cortisone. The combination may further include cortisol.

[0191] The combination of plasma steroids may comprise or consist of at least a combination of 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, aldosterone, corticosterone, and dehydroepiandrosterone sulfate (DHEAS). The combination may further comprise 11-dehydrocorticosterone. The combination may also comprise cortisone. The combination may also comprise cortisol.

[0192] The combination of plasma steroids may comprise or consist of at least 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, and aldosterone. The combination may further comprise corticosterone. The combination may also further comprise dehydroepiandrosterone sulfate (DHEAS).

[0193] The combination of plasma steroids may comprise or consist at least in a combination of 11-deoxycortisol, dehydroepiandrosterone (DHEA), and dehydroepiandrosterone sulfate (DHEAS). The combination may further comprise 11-deoxycorticosterone.

[0194] The combination of plasma steroids may comprise or consist at least in a combination of 11-deoxycorticosterone, 11-deoxycortisol, 18OH-corticosterone, 18OH-cortisol, 18-oxo-cortisol, 21-deoxycortisol, and aldosterone. The combination may further comprise dehydroepiandrosterone sulfate (DHEAS).

[0195] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids. The combination of biomarkers may further include at least one plasma steroid selected from the group consisting of or consisting of plasma 11-dehydrocorticosterone, plasma cortisol, and plasma cortisone as plasma steroids.

[0196] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least the following plasma steroids: plasma 11-dehydrocorticosterone, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma cortisol, plasma cortisone, and plasma dehydroepiandrosterone sulfate (DHEAS).

[0197] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, and plasma aldosterone as plasma steroids. The combination of biomarkers may further include plasma corticosterone as a plasma steroid. The combination of biomarkers may further include plasma dehydroepiandrosterone sulfate (DHEAS) as a plasma steroid. The combination of biomarkers may further include plasma corticosterone and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids.

[0198] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least the following plasma steroids: plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, and plasma dehydroepiandrosterone sulfate (DHEAS).

[0199] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may be free of any plasma steroids.

[0200] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least plasma 11-deoxycortisol, plasma dehydroepiandrosterone (DHEA), and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids. The combination of biomarkers may further include plasma 11-deoxycorticosterone as a plasma steroid.

[0201] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, and plasma aldosterone as plasma steroids. The combination of biomarkers may further include plasma dehydroepiandrosterone sulfate (DHEAS) as a plasma steroid.

[0202] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include any plasma steroids.

[0203] Urinary steroids Urinary steroids were analyzed using α-cortol (A-cortol), α-cortolone (A-cortol), androsterone (An), β-cortol (B-cortol), β-cortol, cortisol, cortisone, dehydroepiandrosterone (DHEA), etiocholanolone (Etio), pregnanediol (PD), pregnenetriol (PT), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydro-11-dehydrocorticosterone (THAs), tetrahydrocorticosterone (THB), tetrahydrodeoxycorticosterone (THDOC), tetrahydrocortisone (THE), tetrahydrocortisol (THF), and tetrahydroaldosterone (THDOC). The corticosteroid may be selected from the group comprising or present at least 11-deoxycortisol (THS), 11-β-hydroxy-androsterone (11-β-OHAn), 11-β-hydroxy-etiocholanolone (11-OHEt), urinary 11-oxo-etiocholanolone (11-oxoEt), 17-OH-pregnanolone (17-HP), 18-hydroxycortisol (18-OHF), 5α-tetrahydrocorticosterone (5-αTHB), 5α-tetrahydrocortisol (5αTHF), 5-pregnanediol (PD), 5-pregnenetriol (5-PT), plasma 11-dehydrocorticosterone, and combinations thereof.

[0204] Urinary steroids were analyzed using 11-β-hydroxyandrosterone (11-β-OHAn), 17-OH-pregnanolone (17-HP), 18-hydroxycortisol (18-OHF), 5α-tetrahydrocortisol (5αTHF), 5-pregnanediol (PD), 5-pregnenetriol (5-PT), α-cortol (A-cortol), α-cortolone (A-cortol), androsterone (An), β-cortol (B-cortol), β-cortolone (B-cortol), cortisol, cortisone, and dehydroepiandrosterone (DHEA). , etiocholanolone (Etio), pregnanediol (PD), pregnenetriol (PT), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydro-11-dehydrocorticosterone (THAs), tetrahydrocorticosterone (THB), tetrahydrodeoxycorticosterone (THDOC), tetrahydrocortisone (THE), tetrahydrocortisol (THF), tetrahydro-11-deoxycortisol (THS), and combinations thereof.

[0205] The urinary steroids may be selected from the group consisting of at least 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), tetrahydro-11-deoxycortisol (THS), and combinations thereof. This group may also include α-cortol (α-cortol). This group may also include pregnanediol (PD).

[0206] The urinary steroids may be selected from the group consisting of or including at least 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), tetrahydro-11-deoxycortisol (THS), α-cortol (α-cortol), pregnanediol (PD), and combinations thereof. The group may further include at least one of 11-β-hydroxy-androsterone (11-β-OHAn), 17-OH-pregnanolone (17-HP), 5-pregnanediol (PD), 5-pregnenetriol (5-PT), 5α-tetrahydrocortisol (5αTHF), androsterone (An), cortisol, cortisone, dehydroepiandrosterone (DHEA), etiocholanolone (Etio), pregnenetriol (PT), tetrahydro-11-dehydrocorticosterone (THAs), tetrahydrocorticosterone (THB), tetrahydrocortisol (THF), tetrahydrocortisone (THE), α-cortolone (A-cortolone), β-cortol (B-cortolone), β-cortolone (B-cortolone), and combinations thereof.

[0207] The urinary steroids may be selected from the group consisting of at least or consisting of 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), tetrahydro-11-deoxycortisol (THS), and combinations thereof, and may further include pregnanediol (PD).

[0208] The urinary steroid may be selected from the group consisting of at least alpha-cortol (α-cortol), tetrahydro-11-deoxycortisol (THS), and combinations thereof. This group may further include 5-pregnenetriol (5-PT). This group may further include androsterone (An). This group may further include cortisol. This group may further include etiocholanolone (Etio).

[0209] The urinary steroids may be selected from the group consisting of at least or consisting of 18-hydroxycortisol (18-OHF), pregnanediol (PD), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), tetrahydro-11-deoxycortisol (THS), and combinations thereof, and may further include dehydroepiandrosterone (DHEA).

[0210] The urinary steroid may be selected from the group consisting of or including at least androsterone (An), etiocholanolone (Etio), and combinations thereof.

[0211] The combination of urinary steroids may include or consist of at least 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), and tetrahydro-11-deoxycortisol (THS). The combination may also include α-cortol (α-cortol). The combination may also include pregnanediol (PD).

[0212] The combination of urinary steroids may include or consist of at least 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), tetrahydro-11-deoxycortisol (THS), α-cortol (α-cortol) and pregnanediol (PD). The combination may further comprise at least one of 11-β-hydroxy-androsterone (11-β-OHAn), 17-OH-pregnanolone (17-HP), 5-pregnanediol (PD), 5-pregnenetriol (5-PT), 5α-tetrahydrocortisol (5αTHF), androsterone (An), cortisol, cortisone, dehydroepiandrosterone (DHEA), etiocholanolone (Etio), pregnenetriol (PT), tetrahydro-11-dehydrocorticosterone (THAs), tetrahydrocorticosterone (THB), tetrahydrocortisol (THF), tetrahydrocortisone (THE), α-cortolone (A-cortolone), β-cortol (B-cortolone), β-cortolone (B-cortolone), and combinations thereof.

[0213] The combination of urinary steroids may include or consist of at least 18-hydroxycortisol (18-OHF), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), and tetrahydro-11-deoxycortisol (THS). The combination may also include pregnanediol (PD).

[0214] The combination of urinary steroids may include or consist of a combination of alpha-cortol (α-cortol) and tetrahydro-11-deoxycortisol (THS). This group may further include 5-pregnenetriol (5-PT). This combination may also include androsterone (An). This combination may also include cortisol. This combination may also include etiocholanolone (Etio).

[0215] The combination of urinary steroids may include or consist of at least 18-hydroxycortisol (18-OHF), pregnanediol (PD), 3α,5β-tetrahydroaldosterone (THAldo), tetrahydrodeoxycorticosterone (THDOC), and tetrahydro-11-deoxycortisol (THS), and may further include dehydroepiandrosterone (DHEA).

[0216] The combination of urinary steroids may at least comprise or consist in a combination of androsterone (An) and etiocholanolone (Etio).

[0217] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least the following urinary steroids: urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), urinary α-cortol (α-cortol), and urinary pregnanediol (PD). The combination of biomarkers may further include at least one urinary steroid, urinary 11-β-hydroxy-androsterone (11-β-OHAn), urinary 17-OH-pregnanolone (17-HP), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), urinary 5α-tetrahydrocortisol (5αTHF), urinary androsterone (An), urinary cortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA), urinary etiocholanolone (Etio), urinary pregnenetriol (PT), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), urinary α-cortolone (A-cortolone), urinary β-cortol (B-cortolone), urinary β-cortolone (B-cortolone), and combinations thereof.

[0218] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least the following urinary steroids: urinary 18-hydroxycortisol (18-OHF), urinary alpha-cortol (α-cortol), urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and urinary tetrahydro-11-deoxycortisol (THS).

[0219] A biomarker combination for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least urinary steroids urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS). The biomarker combination may also include urinary steroids urinary pregnanediol (PD).

[0220] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least the following urinary steroids: urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS).

[0221] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include urinary steroids, such as urinary alpha-cortol (α-cortol) and urinary tetrahydro-11-deoxycortisol (THS). The combination may further include urinary 5-pregnenetriol (5-PT). The combination may also include urinary steroids, such as urinary androsterone (An). The combination may also include urinary steroids, such as urinary cortisol. The combination may also include urinary etiocholanolone (Etio).

[0222] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include the urinary steroids urinary alpha-cortol (α-cortol), urinary androsterone (An), and urinary tetrahydro-11-deoxycortisol (THS).

[0223] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least the following urinary steroids: urinary 18-hydroxycortisol (18-OHF), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS). The combination may also include urinary dehydroepiandrosterone (DHEA).

[0224] A combination of biomarkers to stratify hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include any urinary steroids.

[0225] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least urinary etiocholanolone (Etio) as a urinary steroid. The combination may further include urinary androsterone (An).

[0226] A combination of biomarkers to stratify hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), should be considered, including plasma steroids such as at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, and plasma 21-deoxycortisol. The biomarker combination may include cortisol, plasma aldosterone, plasma corticosterone, and plasma dehydroepiandrosterone sulfate (DHEAS), and at least urinary steroids including urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), urinary α-cortol (α-cortol), and urinary pregnanediol (PD). The biomarker combination may further include at least one plasma steroid selected from the group consisting of or consisting of plasma 11-dehydrocorticosterone, plasma cortisol, and plasma cortisone. The combination of biomarkers may further include urinary steroids, such as urinary 11-β-hydroxy-androsterone (11-β-OHAn), urinary 17-OH-pregnanolone (17-HP), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), urinary 5α-tetrahydrocortisol (5αTHF), urinary androsterone (An), urinary cortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA), urinary etiocholanolone (Etio), urinary pregnenetriol (PT), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), urinary α-cortolone (A-cortolone), urinary β-cortol (B-cortolone), urinary β-cortolone (B-cortolone), and combinations thereof.

[0227] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), includes plasma steroids such as at least plasma 11-dehydrocorticosterone, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma cortisol, plasma cortisone, and plasma It may contain dehydroepiandrosterone sulfate (DHEAS) and, as urinary steroids, at least urinary 18-hydroxycortisol (18-OHF), urinary alpha-cortol (α-cortol), urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and urinary tetrahydro-11-deoxycortisol (THS).

[0228] A biomarker combination for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include plasma steroids including at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, and plasma aldosterone, and urinary steroids including at least urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS). The biomarker combination may further include plasma corticosterone as a plasma steroid. The biomarker combination may further include plasma dehydroepiandrosterone sulfate (DHEAS) as a plasma steroid. The combination of biomarkers may further include plasma corticosterone and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids, and may further include urinary pregnanediol (PD) as a urinary steroid.

[0229] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids, and at least urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS) as urinary steroids.

[0230] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may be free of any plasma steroids and may include urinary alpha-cortol (α-cortol) and urinary tetrahydro-11-deoxycortisol (THS) as urinary steroids. The combination may further include urinary 5-pregnenetriol (5-PT) as urinary steroids. The combination may also include urinary androsterone (An) as urinary steroids. The combination may also include urinary cortisol as urinary steroids. The combination may also include urinary etiocholanolone (Etio).

[0231] A biomarker combination for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least plasma 11-deoxycortisol, plasma dehydroepiandrosterone (DHEA), and plasma dehydroepiandrosterone sulfate (DHEAS) as plasma steroids, and urinary alpha-cortol (α-cortol), urinary androsterone (An), and urinary tetrahydro-11-deoxycortisol (THS) as urinary steroids. The biomarker combination may further include plasma 11-deoxycorticosterone as a plasma steroid.

[0232] A biomarker combination for stratifying hypertensive patients among multiple types of hypertensive disorders, including primary aldosteronism (PA) and primary hypertension (PHT), may include plasma steroids including at least plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, and plasma aldosterone, and urinary steroids including at least urinary 18-hydroxycortisol (18-OHF), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS). The biomarker combination may further include dehydroepiandrosterone sulfate (DHEAS) as a plasma steroid. The combination may also further comprise urinary dehydroepiandrosterone (DHEA) as a urinary steroid.

[0233] The combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include any plasma steroids and may not include any urinary steroids.

[0234] The combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may be free of any plasma steroids and may include at least urinary etiocholanolone (Etio) as a urinary steroid. The combination may further include urinary androsterone (An).

[0235] small molecule metabolites Biomarkers used in the present disclosure may be small molecule metabolites. Small molecule metabolites are organic compounds with molecular weights ranging from about 50 to about 1500 Daltons (Da) that are identified as products and / or intermediates of cellular metabolism. Small molecule metabolites do not include O-methylated catecholamines, steroids, or miRNAs. The Human Metabolome Database is accessible at https: / / hmdb.ca / .

[0236] The small molecule metabolites considered in the methods disclosed herein are known in the art and information such as detailed structures or reference ranges can be obtained from the Human Metabolome Database (https: / / hmdb.ca / ), in particular the metaP-server (http: / / metap.helmholtz-muenchen.de / metap2 / ).

[0237] The small molecule metabolites disclosed herein are further described in the Human Metabolome Database (https: / / hmdb.ca / ).

[0238] Small molecule metabolites can be measured, and in particular quantified, in plasma samples isolated from patients.

[0239] Measurement or quantification of small molecule metabolites can be carried out according to any known technique in the art. For example, useful analytical methods may be gas chromatography-mass spectrometry [GC-MS], liquid chromatography-MS, NMR, LC / GC-FID, direct flow injection MS / MS, LC ESI-MS / MS, MS / MS, which can be used to profile and quantify small molecule metabolites (Fiehn et al. Methods Mol Biol. 2007;358:3-17; Psychogios et al. (2011) The Human Serum Metabolome. PLOS ONE 6(2): e16957; Ando et al., Magn Reson Med Sci. 2013;12(2):129-135).

[0240] In an exemplary embodiment, the presence and quantification of small molecule metabolites can be determined by LC ESI-MS / MS as disclosed in Romisch-Margl et al. (Metabolomics 2012; 8:133-142) or Zukunft et al. (Chromatographia 2013; 76:1295-1305).

[0241] Depending on the type of small molecule metabolites to be measured, such as lipids and amino acids, various methods can be used.For example, NMR can be used particularly for amino acids, while GC-MS can be used particularly for fatty acids.Those skilled in the art can select an appropriate measurement method depending on the small molecule metabolite or set of small molecule metabolites to be measured, particularly quantified.

[0242] The amount of small molecule metabolites can be expressed in weight / volume units of the sample, e.g., ng / ml or pg / ml of plasma. Alternatively, some metabolites, such as amino acids, e.g., citrulline and arginine, or spermidine and putrescine, can be expressed by weight or by molar ratio with other small molecule metabolites. Therefore, instead of individual small molecule metabolites, the spermidine / putrescine ratio or the citrulline / arginine ratio can be used as a small molecule metabolite biomarker.

[0243] In the context of the present disclosure, the acronym "PC" used in connection with small molecule metabolites is intended to mean phosphatidylcholine. The letter C followed by a number, e.g., 17:0 or 16:1, e.g., C17:0 and C16:1, is intended to mean the total number of carbon atoms in the fatty chain of the phosphatidylcholine and the total number of unsaturated bonds in the fatty chain.

[0244] SM is intended to refer to sphingomyelin. MUFA is intended to refer to monounsaturated fatty acids. PUFA is intended to refer to polyunsaturated fatty acids. SFA is intended to refer to saturated fatty acids. Hereinafter, the indicated amino acids will be referred to using the standard three-letter code. For example, tryptophan is Trp, methionine is Met, tyrosine is Tyr, arginine is Arg, etc. LysoPC means lysophosphatidylcholine.

[0245] The full names of the abbreviated metabolites disclosed herein are provided in the table below.

[0246] Small molecule metabolites suitable for the methods disclosed herein can be any small molecule metabolite selected from Table 1 below, or any combination thereof.

[0247] [Table 1] TIFF2025526455000002.tif241161 TIFF2025526455000003.tif245161 TIFF2025526455000004.tif245161 TIFF2025526455000005.tif241161 TIFF2025526455000006.tif160161

[0248] In some embodiments, small molecule metabolites suitable for the present disclosure are plasma acetylcarnitine (C2), plasma acetylornithine (Ac-Orn), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma citrulline / arginine ratio (Cit / Arg), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma glutamic acid (Glu), plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma methionine sulfoxide / methionine ratio (Met-SO / Met), plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC a C32:1, plasma PC a C32:2, plasma PC a C32:3, plasma PC The present invention may be applied to a mammalian cell line comprising: aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (Serotonin / Trp), plasma spermidine, plasma taurine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (Total DMA / Arg), plasma tryptophan, and combinations thereof.

[0249] In some embodiments, small molecule metabolites suitable for the present disclosure are plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:2, plasma PC aa The group may be selected from the group consisting of or consisting of C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (Serotonin / Trp), plasma tetradecenoylcarnitine (C14:1), and combinations thereof. This group may further include at least plasma acetylornithine (Ac-Orn). This group may further include at least plasma citrulline / arginine ratio (Cit / Arg). This group may further include at least plasma glutamic acid (Glu). This group may further include at least plasma methionine sulfoxide / methionine ratio (Met-SO / Met). This group may further include at least plasma PC aa C34:4. This group may further include at least plasma PCa C36:1. This group may further include at least plasma PC ae C36:3. This group may further include at least plasma spermidine. This group may further include at least plasma taurine. This group may further include at least plasma total dimethylarginine / arginine ratio (Total DMA / Arg). This group may further include at least plasma tryptophan.

[0250] A suitable small molecule metabolite for the present disclosure may be plasma acetylornithine (Ac-Orn).

[0251] In some embodiments, small molecule metabolites suitable for the present disclosure can be selected from the group comprising or consisting of plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma tetradecenoylcarnitine (C14:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma glutamic acid, plasma H1 (sum of hexoses), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), and combinations thereof. This group may further include at least plasma octadecenoylcarnitine (C18:1). This group may further include at least plasma creatinine. This group may further include at least plasma LysoPC a C16:0. This group may further include at least plasma PC aa C34:1. This group may further include at least plasma PC aa C34:4. This group may further include at least plasma PC aa C36:1. This group may further include at least plasma PC aa C36:2. This group may further include at least plasma PC ae C36:3. This group may further include at least plasma spermidine. This group may further include at least plasma total dimethylarginine / arginine ratio (Total DMA / Arg).

[0252] In some embodiments, small molecule metabolites suitable for the present disclosure can be selected from the group consisting of or consisting of plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PC aa C32:1, plasma PC aa C34:3, plasma serotonin, plasma serotonin / tryptophan ratio (Serotonin / Trp), and combinations thereof. This group may further include at least plasma lysoPC a C16:0. This group may further include at least plasma lysoPC a C17:0. This group may further include at least plasma acetylcarnitine (C2). This group may further include at least plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)). This group may further include at least plasma citrulline / arginine ratio (Cit / Arg). This group may further include at least plasma creatinine. This group may further include at least plasma glutamic acid (Glu). This group may further include at least plasma octadecenoylcarnitine (C18:1). This group may further include at least plasma PC aa C32:2. This group may further include at least plasma PC aa C32:3. This group may further include at least plasma PC aa C34:1. This group may further include at least plasma PC aa C34:2. This group may further include at least plasma PC aa C36:2. This group may further include at least plasma PC aa C36:3. This group may further include at least plasma PC ae C36:3. This group may further include at least plasma taurine. This group may further include at least plasma tetradecenoylcarnitine (C14:1). This group may further include at least plasma total dimethylarginine / arginine ratio (Total DMA / Arg).

[0253] In some embodiments, small molecule metabolites suitable for the present disclosure can be selected from the group comprising or consisting of plasma methionine sulfoxide / methionine ratio (Met-SO / Met), plasma tryptophan, and combinations thereof.

[0254] In some embodiments, small molecule metabolites suitable for the present disclosure can be selected from the group consisting of plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), and combinations thereof. This group may further include at least plasma creatinine. This group may further include at least plasma dodecanoylcarnitine (C12). This group may further include at least plasma H1 (sum of hexoses). This group may further include at least plasma lysoPC a C16:0. This group may further include at least plasma octadecenoylcarnitine (C18:1). This group may further include at least plasma PC aa C34:1. This group may further include at least plasma PC aa C34:4. This group may further include at least plasma PC aa C36:1. This group may further include at least plasma PC aa C36:2. This group may further include at least plasma taurine.

[0255] In some embodiments, a small molecule metabolite suitable for the present disclosure may be plasma acetylornithine (Ac-Orn).

[0256] Suitable small molecule metabolite combinations for the present disclosure include plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:2, plasma PC aa The combination may comprise or consist of at least a combination of C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (Serotonin / Trp), and plasma tetradecenoylcarnitine (C14:1). This combination may further comprise at least plasma acetylornithine (Ac-Orn). This combination may further comprise at least plasma citrulline / arginine ratio (Cit / Arg). This combination may further comprise at least plasma glutamic acid (Glu). This combination may further comprise at least plasma methionine sulfoxide / methionine ratio (Met-SO / Met). This combination may further comprise at least plasma PC aa C34:4. This combination may further comprise at least plasma PCa C36:1. This combination may further comprise at least plasma PC ae C36:3. This combination may further comprise at least plasma spermidine. This combination may further comprise at least plasma taurine. The combination may further comprise at least a plasma total dimethylarginine / arginine ratio (Total DMA / Arg). The combination may further comprise at least plasma tryptophan.

[0257] Suitable small molecule metabolite combinations for the present disclosure may include or consist in at least the following combinations: plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma tetradecenoylcarnitine (C14:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma glutamic acid, plasma H1 (sum of hexoses), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). This combination may further comprise at least plasma octadecenoylcarnitine (C18:1). This combination may further comprise at least plasma creatinine. This combination may further comprise at least plasma lysoPC a C16:0. This combination may further comprise at least plasma PC aa C34:1. This combination may further comprise at least plasma PC aa C34:4. This combination may further comprise at least plasma PC aa C36:1. This combination may further comprise at least plasma PC aa C36:2. This combination may further comprise at least plasma PC ae C36:3. This combination may further comprise at least plasma spermidine. This combination may further comprise at least plasma total dimethylarginine / arginine ratio (total DMA / Arg).

[0258] A combination of small molecule metabolites suitable for the present disclosure may include or consist of at least a combination of plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PC aa C32:1, plasma PC aa C34:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). This combination may further include at least plasma lysoPC a C16:0. This combination may further include at least plasma lysoPC a C17:0. This combination may further include at least plasma acetylcarnitine (C2). This combination may further include at least plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)). This combination may further comprise at least a plasma citrulline / arginine ratio (Cit / Arg). This combination may further comprise at least a plasma creatinine. This combination may further comprise at least a plasma glutamic acid (Glu). This combination may further comprise at least a plasma octadecenoylcarnitine (C18:1). This combination may further comprise at least a plasma PC aa C32:2. This combination may further comprise at least a plasma PC aa C32:3. This combination may further comprise at least a plasma PC aa C34:1. This combination may further comprise at least a plasma PC aa C34:2. This combination may further comprise at least a plasma PC aa C36:2. This combination may further comprise at least a plasma PC aa C36:3. This combination may further comprise at least a plasma PC ae C36:3. This combination may further comprise at least a plasma taurine. The combination may further include at least plasma tetradecenoylcarnitine (C14:1). The combination may further include at least plasma total dimethylarginine / arginine ratio (Total DMA / Arg).

[0259] A suitable small molecule metabolite combination for the present disclosure may include or consist in at least a combination of plasma methionine sulfoxide / methionine ratio (Met-SO / Met) and plasma tryptophan.

[0260] Suitable small molecule metabolite combinations for the present disclosure may include or consist of at least plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC a C32:1, plasma PC a C32:2, plasma PC a C32:3, plasma PC a C34:2, plasma PC a C34:3, plasma PC a C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). This combination may further include at least plasma creatinine. This combination may further include at least plasma dodecanoylcarnitine (C12). This combination may further include at least plasma H1 (sum of hexoses). This combination may further include at least plasma lysoPC a C16:0. This combination may further include at least plasma octadecenoylcarnitine (C18:1). This combination may further include at least plasma PC aa C34:1. This combination may further include at least plasma PC aa C34:4. This combination may further include at least plasma PC aa C36:1. This combination may further include at least plasma PC aa C36:2. This combination may further include at least plasma taurine.

[0261] Suitable small molecule metabolite combinations for the present disclosure may include or consist at least in acetylornithine (Ac-Orn).

[0262] Combinations of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may not include any small molecule metabolites.

[0263] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), include plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma tetradecenoylcarnitine (C14:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma glutamate, plasma H1 (sum of hexoses), plasma lysozyme PC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, and plasma PC aa The combination may include at least C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). This combination may further include at least plasma octadecenoylcarnitine (C18:1). This combination may further include at least plasma creatinine. This combination may further include at least plasma lysoPC a C16:0. This combination may further include at least plasma PC aa C34:1. This combination may further include at least plasma PC aa C34:4. This combination may further include at least plasma PC aa C36:1. This combination may further include at least plasma PC aa C36:2. This combination may further include at least plasma PC ae C36:3. This combination may further include at least plasma spermidine. This combination may further include at least plasma total dimethylarginine / arginine ratio (total DMA / Arg).

[0264] A combination of biomarkers for stratifying hypertensive patients into multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PCaa C32:1, plasma PCaa C34:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp) as small molecule metabolites. This combination may further include at least plasma lysoPCa C16:0. This combination may further include at least plasma lysoPCa C17:0. This combination may further include at least plasma acetylcarnitine (C2). This combination may further comprise at least plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)). This combination may further comprise at least plasma citrulline / arginine ratio (Cit / Arg). This combination may further comprise at least plasma creatinine. This combination may further comprise at least plasma glutamic acid (Glu). This combination may further comprise at least plasma octadecenoylcarnitine (C18:1). This combination may further comprise at least plasma PC aa C32:2. This combination may further comprise at least plasma PC aa C32:3. This combination may further comprise at least plasma PC aa C34:1. This combination may further comprise at least plasma PC aa C34:2. This combination may further comprise at least plasma PC aa C36:2. This combination may further comprise at least plasma PC aa C36:3. This combination may further include at least plasma PC ae C36:3. This combination may further include at least plasma taurine. This combination may further include at least plasma tetradecenoylcarnitine (C14:1). This combination may further include at least plasma total dimethylarginine / arginine ratio (total DMA / Arg).

[0265] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may not include any small molecule metabolites.

[0266] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least the plasma methionine sulfoxide / methionine ratio (Met-SO / Met) and plasma tryptophan as small molecule metabolites.

[0267] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least the following small molecule metabolites: plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). This combination may further include at least plasma creatinine. This combination may further include at least plasma dodecanoylcarnitine (C12). This combination may further include at least plasma H1 (sum of hexoses). This combination may further include at least plasma lysoPC a C16:0. This combination may further include at least plasma octadecenoylcarnitine (C18:1). This combination may further include at least plasma PC aa C34:1. This combination may further include at least plasma PC aa C34:4. This combination may further include at least plasma PC aa C36:1. This combination may further include at least plasma PC aa C36:2. This combination may further include at least plasma taurine.

[0268] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may not include any small molecule metabolites.

[0269] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least acetylornithine (Ac-Orn) as a small molecule metabolite.

[0270] Biomarker combinations The present disclosure relates to various combinations of the different types of biomarkers listed above, which can be used to stratify hypertensive patients among multiple types of hypertensive disorders.

[0271] The multiple types of hypertensive disorders may include at least two of primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0272] The multiple types of hypertensive disorders may include endocrine hypertension (EHT) and primary hypertension (PHT).

[0273] Several types of hypertensive disorders may include Cushing's syndrome (CS) and primary hypertension (PHT).

[0274] Several types of hypertensive disorders may include primary aldosteronism (PA) and primary hypertension (PHT).

[0275] Several types of hypertensive disorders may include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT).

[0276] Several types of hypertensive disorders may include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0277] Several types of hypertensive disorders may include endocrine hypertension (EHT), primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0278] A biomarker combination suitable for the present disclosure may include at least one biomarker selected from at least one, two, three, four, five, or six of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites, and miRNAs.

[0279] The group of biomarkers is as detailed above.

[0280] A combination of biomarkers suitable for the present disclosure may include at least one biomarker selected from at least 1, 2, 3, 4, or 5 of the group of biomarkers.

[0281] A combination of biomarkers selected in a group of biomarkers may include at least three biomarkers selected in said group of biomarkers.

[0282] A combination of biomarkers selected in two groups of biomarkers may include at least three biomarkers selected in one of the two groups of biomarkers.

[0283] A combination of biomarkers selected in a group of three biomarkers may include at least three biomarkers selected in one of said groups of three biomarkers.

[0284] At least one group of biomarkers from among the 1, 2, 3, 4, 5, or 6 groups used in the biomarker combinations of the present disclosure provides at least three biomarkers.

[0285] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, plasma steroids, and urinary steroids, and at least one biomarker selected in at least one of the groups of biomarkers: O-methylated catecholamines, small molecule metabolites, and miRNAs.

[0286] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, and urinary steroids.

[0287] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected from each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, and at least one biomarker selected from at least one of the groups of biomarkers: small molecule metabolites and miRNAs.

[0288] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, and small molecule metabolites.

[0289] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, and miRNAs.

[0290] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites, and miRNAs.

[0291] The combinations of O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites and miRNAs that can be used can be as defined above.

[0292] A suitable biomarker combination for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), is age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, and plasma 21-deoxycortisol. May include at least urinary cortisol, plasma aldosterone, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urinary 18-hydroxycortisol (18-OHF), urinary alpha-cortol (α-cortol), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), and urinary tetrahydro-11-deoxycortisol (THS).

[0293] This combination measured plasma 3-methoxytyramine, plasma metanephrine, plasma normetanephrine, plasma 11-dehydrocorticosterone, plasma cortisol, plasma cortisone, plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p, plasma hsa-miR-485-3p, plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma glutamate, plasma H1 (sum of hexoses), plasma lysosomal protein complex (PC) C16:0, ... C17:0, plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC aa C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), plasma spermidine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (total DMA / Arg), urinary 11-β-hydroxy-androsterone (11-β-OHAn), urinary 17-OH-pregnanolone (17-HP), urinary 5-pregnanediol (PD), urinary 5-pregnenetriol (5-PT), urinary 5α-tetrahydrocortisol (5αTHF), urinary androsterone (An), urinary cortisol The urinary tetrahydrocortisol, urinary cortisone, urinary dehydroepiandrosterone (DHEA), urinary etiocholanolone (Etio), urinary pregnenetriol (PT), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), urinary alpha-cortolone (A-cortolone), urinary beta-cortol (B-cortolone), urinary beta-cortolone (B-cortolone), and combinations thereof.

[0294] The combination may further comprise at least one of plasma metanephrines, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), and combinations thereof.

[0295] The combination may further include combinations including at least plasma metanephrines, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), and combinations thereof.

[0296] The biomarker combinations were age, plasma metanephrine, plasma normetanephrine, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urinary 18-hydroxycortisol (18-OHF), and urinary α-cortisol (α-cortol). These may include or be present in urinary tetrahydrocortisone (THA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and urinary tetrahydro-11-deoxycortisol (THS).

[0297] The biomarker combinations may include or consist in any of the combinations depicted in FIG. 34 and identified by reference numerals A1-A20.

[0298] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), can be selected from combinations A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, and A20 shown in Figure 34 (ALL vs ALL).

[0299] In some embodiments, a biomarker combination for stratifying a hypertensive patient among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), can be selected from combinations A13, A14, and A15 shown in Figure 34 (ALL vs ALL). In some embodiments, the biomarker combination can be combination A13 shown in Figure 34 (ALL vs ALL). In some embodiments, the biomarker combination can be combination A14 shown in Figure 34 (ALL vs ALL). In some embodiments, the biomarker combination can be combination A15 shown in Figure 34 (ALL vs ALL).

[0300] In some embodiments, at least two or more of the biomarker combinations selected from combinations A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, and A20 shown in Figure 34 (ALL vs ALL) can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0301] In some embodiments, a set of three biomarker combinations, the set including combinations A13, A14, and A15 shown in Figure 34 (ALL vs ALL), can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT).

[0302] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: plasma steroids, urinary steroids, and small molecule metabolites, and at least one biomarker selected in at least one of the groups of biomarkers: patient age, O-methylated catecholamines, and miRNAs.

[0303] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: O-methylated catecholamines, plasma steroids, urinary steroids, and small molecule metabolites.

[0304] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: plasma steroids, urinary steroids, small molecule metabolites, and miRNAs.

[0305] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites, and miRNAs.

[0306] A combination of biomarkers suitable for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, O-methylated catecholamines, plasma steroids, urinary steroids, and small molecule metabolites.

[0307] The combinations of O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites and miRNAs that can be used can be as defined above.

[0308] Suitable biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive disorders, including endocrine hypertension (EHT) and primary hypertension (PHT), include plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PC aa C32:1, and plasma PC May contain at least aa C34:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp).

[0309] This combination was evaluated for age, plasma hsa-let-7g-5p, plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma normetanephrine, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urinary pregnanediol (PD), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma citrulline / arginine ratio (Cit / Arg), plasma creatinine, plasma glutamic acid (Glu), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae It may further comprise at least one of C36:3, plasma taurine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (Total DMA / Arg), and combinations thereof.

[0310] The combination may further include at least plasma normetanephrine.

[0311] The biomarker combinations were plasma normetanephrine, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (sum of hexoses), plasma PCaa C32:1, and plasma PCaa May contain or reside in C34:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp).

[0312] The biomarker combinations may include or consist in any of the combinations depicted in FIG. 34 and identified by reference numerals B1-B10.

[0313] Suitable biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT), can be selected from combinations B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10 shown in Figure 34 (EHT vs PHT).

[0314] In some embodiments, at least two or more of the biomarker combinations selected from the combinations B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10 shown in Figure 34 (EHT vs PHT) can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including endocrine hypertension (EHT) and primary hypertension (PHT).

[0315] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least three biomarkers selected in the group of biomarkers: urinary steroids, and at least one biomarker selected in at least one of the groups of biomarkers: plasma steroids, small molecule metabolites, and miRNAs.

[0316] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: urinary steroids and plasma steroids.

[0317] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: urinary steroids and miRNAs.

[0318] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: urinary steroids, plasma steroids, and miRNAs.

[0319] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: urinary steroids, plasma steroids, and small molecule metabolites.

[0320] The combinations of plasma steroids, urinary steroids, small molecule metabolites and miRNA that can be used can be as defined above.

[0321] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive disorders, including Cushing's syndrome (CS) and primary hypertension (PHT), may include at least urinary alpha-cortol (α-cortol) and urinary tetrahydro-11-deoxycortisol (THS).

[0322] The combination may further comprise at least one of plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma dehydroepiandrosterone (DHEA), plasma dehydroepiandrosterone sulfate (DHEAS), plasma hsa-miR-19a-3p, plasma methionine sulfoxide / methionine ratio (Met-SO / Met), plasma tryptophan, urinary 5-pregnenetriol (5-PT), urinary androsterone (An), urinary cortisol, urinary etiocholanolone (Etio), and combinations thereof.

[0323] The combination may further include urinary androsterone (An).

[0324] The combination of biomarkers may include or consist in urinary alpha-cortol (α-cortol), urinary androsterone (An) and urinary tetrahydro-11-deoxycortisol (THS).

[0325] The biomarker combinations may include or consist in any of the combinations depicted in Figure 34 and identified by reference numerals C1 to C20.

[0326] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT), can be selected from combinations C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C12, C13, C14, C15, C16, C17, C18, C19 and C20 shown in Figure 34 (CS vs PHT).

[0327] In some embodiments, at least two or more of the combinations of biomarkers selected from the combinations C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C12, C13, C14, C15, C16, C17, C18, C19 and C20 shown in Figure 34 (CS vs PHT) can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including Cushing's syndrome (CS) and primary hypertension (PHT).

[0328] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, plasma steroids, urinary steroids, and small molecule metabolites, and at least one biomarker selected in at least one of the groups of biomarkers: O-methylated catecholamines and miRNAs.

[0329] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT), may include at least one biomarker selected in each of the following groups of biomarkers: patient age, plasma steroids, urinary steroids, small molecule metabolites, O-methylated catecholamines, and miRNAs.

[0330] The combinations of O-methylated catecholamines, plasma steroids, urinary steroids, small molecule metabolites and miRNA that can be used may be as defined above.

[0331] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT), include age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), urinary pregnanediol (PD), and urinary aldosterone (UR). ), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin and plasma serotonin / tryptophan ratio (Serotonin / Trp).

[0332] The combination may further comprise at least one of plasma creatinine, plasma dehydroepiandrosterone sulfate (DHEAS), plasma dodecanoylcarnitine (C12), plasma H1 (sum of hexoses), plasma lysoPC a C16:0, plasma PC aa C34:1, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma taurine, urinary dehydroepiandrosterone (DHEA), and combinations thereof.

[0333] The combination of biomarkers included age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urinary 18-hydroxycortisol (18-OHF), pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), and urinary These may include or be present in tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysocorticosterone (PC) a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp).

[0334] The biomarker combinations may include or consist in any of the combinations depicted in FIG. 34 and identified by reference numerals D1-D10.

[0335] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT), can be selected from combinations D1, D2, D3, D4, D5, D6, D7, D8, D9 and D10 shown in Figure 34 (PA vs PHT).

[0336] In some embodiments, at least two or more of the combinations of biomarkers selected from the combinations D1, D2, D3, D4, D5, D6, D7, D8, D9 and D10 shown in Figure 34 (PA vs PHT) can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including primary aldosteronism (PA) and primary hypertension (PHT).

[0337] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least three biomarkers selected from the group of biomarkers: O-methylated catecholamines; and at least one biomarker selected from at least one of the group of biomarkers: patient age, urinary steroids, and small molecule metabolites.

[0338] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least three biomarkers selected for O-methylated catecholamines and patient age.

[0339] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least three biomarkers selected for O-methylated catecholamines, at least one biomarker selected for patient age and urinary steroids.

[0340] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least three biomarkers selected for O-methylated catecholamines and at least one biomarker selected for urinary steroids.

[0341] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least three biomarkers selected in O-methylated catecholamines and at least one biomarker selected in each of the following groups of biomarkers: urinary steroids and small molecule metabolites.

[0342] The combinations of O-methylated catecholamines, urinary steroids and small molecule metabolites that can be used can be as defined above.

[0343] A combination of biomarkers for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), may include at least plasma 3-methoxytyramine, plasma metanephrine, and plasma normetanephrine.

[0344] The combination may further include at least one of age, plasma acetylornithine (Ac-Orn), urinary androsterone (An), urinary etiocholanone (Etio), and combinations thereof.

[0345] The combination of biomarkers may include or consist in plasma 3-methoxytyramine, plasma metanephrine, and plasma normetanephrine.

[0346] The biomarker combinations may include or consist in any of the combinations depicted in Figure 34 and identified by reference numerals E1-E20.

[0347] Biomarker combinations for stratifying hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), can be selected from combinations E1, E2, E3, E4, E5, E6, E7, E8, E9, E10, E11, E12, E13, E14, E15, E16, E17, E18, E19, and E20 shown in Figure 34 (PPGL vs PHT).

[0348] In some embodiments, at least two or more of the combinations of biomarkers selected from the combinations E1, E2, E3, E4, E5, E6, E7, E8, E9, E10, E11, E12, E13, E14, E15, E16, E17, E18, E19, and E20 shown in Figure 34 (PPGL vs PHT) can be used to stratify hypertensive patients among multiple types of hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT).

[0349] Methods of using biomarkers and combinations thereof The present disclosure relates to methods and uses for stratifying hypertensive patients among multiple hypertensive disorders, such as EHT, PHT, PA, CS and PPGL.

[0350] The methods and uses include the use of combinations of the biomarkers described above.

[0351] The methods and uses can be used to stratify hypertensive patients between primary aldosteronism (PA) vs. pheochromocytoma / functional paraganglioma (PPGL) vs. Cushing's syndrome (CS) vs. primary hypertension (PHT) or EHT vs. PHT or CS vs. PHT or PA vs. PHT or PPGL vs. PHT.

[0352] The methods and uses can be used for the diagnosis of EHT and / or the diagnosis of PHT and / or the diagnosis of PA and / or the diagnosis of CS and / or PPGL.

[0353] Primary hypertension (PHT), also commonly known as essential hypertension, is a blood pressure disorder with an unknown cause. Its prevalence increases with age in most populations. Due to the fact that the cause is unknown, this type of hypertension is also known as essential hypertension.

[0354] Primary aldosteronism (PA) is recognized as a treatable cause of hypertension, with a prevalence ranging from 4.6% to 13.0% in hypertensive patients and up to 20% in treatment-resistant hypertensive patients (Yang et al., Nephrology, 22(2017) 663-677). Primary aldosteronism (PA) is a heterogeneous condition in the majority of cases, also called idiopathic hyperaldosteronism, due to aldosterone-producing adenomas (40-50%) or bilateral adrenal hyperplasia (50-60%). Unilateral adrenal hyperplasia accounts for less than 2% of cases, and aldosterone-producing adrenocortical carcinoma is extremely rare. Primary aldosteronism may be inherited in familial hyperaldosteronism types I-IV or may occur with other abnormalities in PASNA (PA, seizures, and neurological abnormalities), a rare syndrome characterized by PA and neuromuscular abnormalities. In PA, aldosterone production is autonomous, leading to increased aldosterone levels that cannot be suppressed by sodium loading or volume expansion, along with decreased or suppressed renin (Funder et al., J Clin Endocrinol Metab. 2016; 101(5): 1889-1916). Various genetic abnormalities have been associated with this disease and familial forms of APA (Fernandes-Rosa et al., Trends Mol Med. 2020; Zennaro et al., Nat Rev Endocrinol. 2020 Oct;16(10):578-589).

[0355] Cushing's syndrome (CS) refers to a state of glucocorticoid excess accompanied by multiple adverse symptoms, such as hypertension, obesity, and impaired glucose tolerance, all of which contribute to increased cardiovascular risk. The most common cause is iatrogenic exogenous glucocorticoid use, which must be eliminated first. Cushing's syndrome is considered a rare endocrine disorder, with an incidence of 2-3 cases per million people per year, of which 80% are ACTH-dependent and 20% are ACTH-independent (Nieman et al., J Clin Endocrinol Metab. 2008;93(5):1526-1540). This disease can be associated with various genetic abnormalities, such as mutations in the USP8 gene in Cushing's disease and mutations in PRKAR1A, ARMC5, MEN1, APC, FH, and PRKACA in adrenal Cushing's syndrome (Vaduva et al., J Endocr Soc 2020).

[0356] Pheochromocytoma and functional paraganglioma (PPGL) are rare neuroendocrine tumors associated with hypertension due to the autonomous production of catecholamines, such as adrenaline and noradrenaline. Pheochromocytoma originates from pigment cells in the adrenal medulla, whereas paragangliomas arise from sympathetic ganglia. The estimated annual incidence of PPGL is 0.5–0.8 per 100,000 person-years, accounting for perhaps 0.2–0.6% of hypertensive individuals. Most cases are sporadic with onset in middle age, but approximately 30% of all PPGL patients have a disease-causing germline mutation (Lenders et al., J Clin Endocrinol Metab. 2014;99(6):1915–1942).

[0357] The methods and uses may be performed ex vivo or in vitro. The methods and uses may be performed on a biological sample isolated from a patient.

[0358] The various types of hypertensive patients considered herein are EHT patients, PHT patients, PA patients, CS patients and PPGL patients.

[0359] The present disclosure relates to the use of combinations of biomarkers to stratify hypertensive patients among multiple hypertensive diseases.

[0360] In some embodiments, the present disclosure relates to the use of a combination of biomarkers for stratifying a hypertensive patient among a plurality of hypertensive disorders, wherein the plurality of hypertensive disorders comprises primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0361] In some embodiments, the present disclosure relates to the use of a combination of biomarkers for stratifying a hypertensive patient among a plurality of hypertensive disorders, wherein the plurality of hypertensive disorders comprises endocrine hypertension (EHT) and primary hypertension (PHT), and wherein the combination of biomarkers is a combination of biomarkers as defined above.

[0362] In some embodiments, the present disclosure relates to the use of a combination of biomarkers for stratifying a hypertensive patient among multiple hypertensive diseases, wherein the multiple hypertensive diseases comprise Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0363] In some embodiments, the present disclosure relates to the use of a combination of biomarkers for stratifying a hypertensive patient among multiple hypertensive disorders, wherein the multiple hypertensive disorders comprise primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0364] In some embodiments, the present disclosure relates to the use of a combination of biomarkers for stratifying a hypertensive patient among a plurality of hypertensive diseases, wherein the plurality of hypertensive diseases comprises pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0365] 1. Use of a combination of biomarkers to stratify hypertensive patients among multiple hypertensive diseases, comprising: wherein the use is - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disorder, a probability of associating the hypertensive patient with the hypertensive disorder in order to stratify the hypertensive patient among the plurality of types of hypertensive disorders; The trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. use.

[0366] 1. Use of a combination of biomarkers to stratify hypertensive patients among multiple hypertensive diseases, comprising: (ia) when the multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ia) defined above or elsewhere herein; (ib) when the multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), the combination of biomarkers may be a combination of biomarkers (ib) defined above or elsewhere herein; wherein the use is - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with a hypertensive disease, in order to stratify the hypertensive patient among the multiple types of hypertensive diseases according to (ia) or according to (ib), The trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. use.

[0367] In some embodiments, the method can implement a set of at least two, e.g., three or more, combinations of biomarkers and, optionally, multiple types of classifiers. In such embodiments, a set of probabilities is obtained, each corresponding to a combination of biomarkers and / or classifiers and / or disease predictions. A hypertensive patient can be associated with the hypertensive disease that yields the highest predicted probability associated with the patient. In a variant, a majority voting scheme can be used. Each individual classifier can provide a probability for each disease prediction. This disease prediction can then be converted into a label. The labels from each independent and different classifier can be used to perform a majority voting scheme to determine a single final label.

[0368] The present disclosure relates to combinations of biomarkers as defined above for use in methods for stratifying hypertensive patients among multiple hypertensive diseases.

[0369] In some embodiments, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: Several types of hypertensive disorders exist, including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT). The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo, for example in a suitable biological sample previously isolated from said patient; (wherein, for the plurality of hypertensive diseases, the combination of biomarkers is the combination of biomarkers defined above). b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the plurality of types of hypertensive disease. Regarding the method.

[0370] Step b) may comprise running a trained classifier on the combination of biomarkers obtained in step a) from said hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with the hypertensive disease in order to stratify said hypertensive patient among said plurality of types of hypertensive disease.

[0371] In some embodiments, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: Several types of hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo, for example in a suitable biological sample previously isolated from said patient; (wherein, for the plurality of hypertensive diseases, the combination of biomarkers is the combination of biomarkers defined above). b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the plurality of types of hypertensive disease. Regarding the method.

[0372] Step b) may comprise running a trained classifier on the combination of biomarkers obtained in step a) from said hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with the hypertensive disease in order to stratify said hypertensive patient among said plurality of types of hypertensive disease.

[0373] In some embodiments, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: Several types of hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT), The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo, for example in a suitable biological sample previously isolated from said patient; (wherein, for the plurality of hypertensive diseases, the combination of biomarkers is the combination of biomarkers defined above). b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the plurality of types of hypertensive disease. Regarding the method.

[0374] Step b) may comprise running a trained classifier on the combination of biomarkers obtained in step a) from said hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with the hypertensive disease in order to stratify said hypertensive patient among said plurality of types of hypertensive disease.

[0375] In some embodiments, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: Several types of hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT), The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo, for example in a suitable biological sample previously isolated from said patient; (wherein, for the plurality of hypertensive diseases, the combination of biomarkers is the combination of biomarkers defined above). b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the plurality of types of hypertensive disease. Regarding the method.

[0376] Step b) may comprise running a trained classifier on the combination of biomarkers obtained in step a) from said hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with the hypertensive disease in order to stratify said hypertensive patient among said plurality of types of hypertensive disease.

[0377] In some embodiments, the present disclosure provides a method for stratifying a hypertensive patient among multiple types of hypertensive disorders, comprising: Several types of hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT); The method includes using at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for comparing at least two types of hypertensive patients multiple times; The method comprises at least a) measuring a combination of biomarkers ex vivo, for example in a suitable biological sample previously isolated from said patient; (wherein, for the plurality of hypertensive diseases, the combination of biomarkers is the combination of biomarkers defined above). b) running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient to stratify the hypertensive patient among the plurality of types of hypertensive disease. Regarding the method.

[0378] Step b) may comprise running a trained classifier on the combination of biomarkers obtained in step a) from said hypertensive patient to obtain, for each hypertensive disease, a probability of associating the hypertensive patient with the hypertensive disease in order to stratify said hypertensive patient among said plurality of types of hypertensive disease.

[0379] The method may comprise the step of quantifying or detecting the presence of a biomarker of the considered combination of biomarkers. Quantifying or detecting the biomarkers can be performed according to any suitable technique known in the art.

[0380] The method of the present disclosure may further comprise the step of obtaining, for each hypertensive disorder type, a probability of associating a hypertensive patient with said hypertensive disorder.

[0381] The trained classifier can be selected from decision trees (J48), Naive Bayes (NB), K-nearest neighbors (IBk), Logit Boost (LB), support vector machines (SVM), logic model trees (LMT), bagging, simple logistic (SL), random forests (RF) and sequential minimal optimization (SMO).

[0382] In some embodiments, the trained classifier can be selected from LogitBoost (LB), Simple Logistic (SL), and Random Forest (RF).

[0383] The classifier is a) using said classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for at least one predetermined comparison between at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said group of hypertensive diseases, wherein the first and second types of hypertensive disease are different; b) selecting a combination of biomarkers for stratifying hypertensive patients among the plurality of types of hypertensive diseases based on the calculated evaluation parameters. It may have been trained using at least one predefined input dataset according to at least one method including:

[0384] The evaluation parameters can be selected from accuracy, sensitivity, specificity, AUC, F1, kappa score, and combinations thereof.

[0385] The method may include communicating to the patient or medical professional the output of the trained classifier, e.g., a probability of which type of hypertensive patient the patient is, or multiple probabilities each corresponding to one type of hypertensive patient.

[0386] The probability is in the form of a number, for example a value between 0 and 1. In a variant, the probability is in the form of a letter, in particular a letter indicating that the patient is considered to belong to a certain type of hypertensive patient, for example group A, group B, group C, etc.

[0387] The probabilities indicate the risk that the patient will be stratified into one of the following hypertensive disorders: "EHT", "PHT", "PPGL", "CS" or "PA".

[0388] The probabilities may be communicated to the user by any suitable means, for example by being displayed on the screen of an electronic device, printed, or by voice synthesis.

[0389] The probabilities can be used as inputs to another program and / or can be combined with other information, for example, clinical and / or biological data.

[0390] In some embodiments, the method can implement a set of at least two, e.g., three or more, combinations of biomarkers and, optionally, multiple types of classifiers. In such embodiments, a set of probabilities corresponding to each combination of biomarkers and / or classifiers and / or disease predictions is obtained. A hypertensive patient can be associated with the hypertensive disease that yields the highest predicted probability associated with the patient. In a variant, a majority voting scheme can be used. Each individual classifier can provide a probability for each disease prediction. This disease prediction can then be converted into a label. The labels from different independent classifiers can be used to perform a majority voting scheme to determine a single final label.

[0391] The steps of the method of the present invention can preferably be carried out in an electronic system, in particular a personal computer, a computing server or a medical imaging device, comprising at least a microcontroller and a memory.

[0392] In some embodiments, in the method, the classifier can be pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, for multiple comparisons of at least first and second types of hypertensive disorders selected in a group of hypertensive disorders including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), primary hypertension (PHT), and endocrine hypertension (EHT).

[0393] A method for training a classifier to learn multiple combinations of biomarkers to stratify hypertensive patients suspected of having hypertension among multiple hypertensive diseases can use at least one calculated assessment parameter, multiple comparisons of at least two types of hypertensive patients, and multiple predefined input data sets. The method includes at least: selecting, for each predefined input data set and for each comparison between at least two types of hypertensive patients, at least one combination of biomarkers based on the calculation of said at least one evaluation parameter; training a classifier to learn the selected combinations of biomarkers associated with comparisons between the hypertensive patient types; May include:

[0394] The method for learning multiple combinations of biomarkers to stratify hypertensive patients suspected of having hypertension among multiple hypertensive diseases can use at least one classifier having at least one predefined input dataset; using the classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for the at least one predefined input data set and at least one predetermined comparison between at least two types of hypertensive patients; selecting a combination of biomarkers to stratify the hypertensive patient among the plurality of hypertensive diseases based on the calculated evaluation parameters; May include:

[0395] In the method for learning multiple combinations of biomarkers, the at least one predetermined comparison is preferably selected from among all types of hypertensive patients vs. all types (ALL-ALL), EHT vs. PHT, PPGL vs. PHT, CS vs. PHT, and PA vs. PHT. The at least one predetermined comparison can also be selected from among PPGL vs. CS, PPGL vs. PA, and CS vs. PA.

[0396] The method for learning multiple combinations of biomarkers is advantageously a computer-implemented method.

[0397] The method for stratifying hypertensive patients according to the present disclosure is advantageously a computer-implemented method.

[0398] At least part of the at least one predefined input data set is advantageously extracted from at least one biological sample previously isolated from said patient.

[0399] The input dataset may include at least one omics measurement in a biological sample obtained from the patient and / or the patient's age. The omics measured may be selected from O-methylated catecholamines, steroids, small molecule metabolites, or miRNAs. The steroids may be plasma and / or urinary steroids.

[0400] The evaluation parameters can be selected from accuracy, sensitivity, specificity, AUC, F1, kappa score, and combinations thereof.

[0401] A "classifier" should be understood to be a learning model with an associated learning algorithm that analyzes data used for classification and regression analysis.

[0402] At least one classifier can be selected from decision trees (J48), naive Bayes (NB), K-nearest neighbors (IBk), logit boost (LB), support vector machines (SVM), logic model trees (LMT), bagging, simple logistic (SL), random forests (RF), and sequential minimal optimization (SMO). In a variant, the classifier is a neural network. However, this list is not exhaustive, and the present invention is not limited to a particular type of classifier. The combination of biomarkers can be divided into a training set and a test set.

[0403] In some embodiments, the classifier may be selected from LogitBoost (LB), Simple Logistic (SL), and Random Forest (RF).

[0404] The predefined input data set includes parameters for comparison between at least two types of hypertensive patients and / or for selecting at least one biomarker or a combination of at least one biomarker.

[0405] At least one feature selection method, particularly wrapper-based and filter-based methods, can be used during the step of selecting biomarker combinations, although this list is not exhaustive and the present invention is not limited to any particular type of feature selection method.

[0406] In a variant, no feature selection method is used, so all combinations of biomarkers are ranked and no feature reduction is applied.

[0407] The predefined input dataset may or may not include outlier biomarkers. Thus, the predefined input dataset may include or exclude outliers. Extreme outliers can be removed by applying quartile methods. Removing outliers can provide better biomarker identification, which in turn can provide better performance for stratifying patients suspected of having hypertensive disease among multiple types of hypertensive disease.

[0408] Multiple classifiers can be used independently, with at least two, and in particular three, classifiers being selected based on the calculated evaluation parameters.

[0409] When feature reduction is applied, the step of selecting a combination of biomarkers can be performed using multiple feature selection methods in succession, with at least one feature selection method being selected based on the calculated evaluation parameters. In this way, the classifier and / or feature selection method with the best evaluation can be selected, thereby enabling efficient and reliable identification.

[0410] In some embodiments, the present disclosure provides a combination of biomarkers for use in a method for treating a hypertensive disorder in a patient in need thereof, comprising: the hypertensive disorder is selected from a plurality of hypertensive disorders; The method of treatment includes stratifying a hypertensive patient among a plurality of hypertensive disorders, the stratification step comprising: - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disorder, a probability of associating the hypertensive patient with the hypertensive disorder in order to stratify the hypertensive patient among the plurality of types of hypertensive disorders; the trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. Regarding biomarker combinations.

[0411] In some embodiments, the present disclosure relates to the use of a combination of biomarkers in a method for stratifying and treating a hypertensive disorder in a patient in need thereof, wherein the hypertensive disorder is selected from among primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers defined above.

[0412] In some embodiments, the present disclosure relates to the use of a combination of biomarkers in a method for stratifying and treating a hypertensive disorder in a patient in need thereof, wherein the hypertensive disorder is selected from among endocrine hypertension (EHT) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0413] In some embodiments, the present disclosure relates to the use of a combination of biomarkers in a method for stratifying and treating a hypertensive disorder in a patient in need thereof, wherein the hypertensive disorder is selected from among Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0414] In some embodiments, the present disclosure relates to the use of a combination of biomarkers in a method for stratifying and treating a hypertensive disorder in a patient in need thereof, wherein the hypertensive disorder is selected from among primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0415] In some embodiments, the present disclosure relates to the use of a combination of biomarkers in a method for stratifying and treating a hypertensive disorder in a patient in need thereof, wherein the hypertensive disorder is selected from among pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the combination of biomarkers is a combination of biomarkers as defined above.

[0416] In some embodiments, the present disclosure provides a method for treating a hypertensive patient, the method comprising: a) stratifying the hypertensive patient among a plurality of hypertensive disorders; and The hypertensive patient is stratified among the plurality of hypertensive disorders by: - measuring said combination of biomarkers ex vivo; - running a trained classifier on the measured combination of biomarkers to obtain, for each hypertensive disorder, a probability of associating the hypertensive patient with the hypertensive disorder in order to stratify the hypertensive patient among the plurality of types of hypertensive disorders; and The trained classifier is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. ) b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) treating the patient by administering to the patient the therapeutic treatment selected in step b); Regarding the method.

[0417] In some embodiments, the present disclosure provides a method for stratifying and treating a hypertensive patient, the method comprising: stratifying the hypertensive patient among a plurality of hypertensive disorders; The multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers defined above. and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders according to the methods of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0418] In some embodiments, the present disclosure provides a method for stratifying and treating a hypertensive patient, the method comprising: stratifying the hypertensive patient among a plurality of hypertensive disorders; The multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers defined above. and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders according to the methods of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0419] In some embodiments, the present disclosure provides a method for stratifying and treating a hypertensive patient, the method comprising: stratifying the hypertensive patient among a plurality of hypertensive disorders; The multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers defined above. and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders according to the methods of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0420] In some embodiments, the present disclosure provides a method for stratifying and treating a hypertensive patient, the method comprising: stratifying the hypertensive patient among a plurality of hypertensive disorders; The multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers defined above. and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders according to the methods of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0421] In some embodiments, the present disclosure provides a method for stratifying and treating a hypertensive patient, the method comprising: stratifying the hypertensive patient among a plurality of hypertensive disorders; The multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers defined above. and treating said patient with a therapeutic treatment suspected to cure or alleviate at least one symptom of the hypertensive disorder, said method comprising: a) stratifying said hypertensive patient among said plurality of hypertensive disorders according to the methods of the present disclosure to associate a hypertensive disorder with said patient; b) selecting a therapeutic treatment predicted to treat or alleviate at least one symptom of a hypertensive disorder associated with said patient; c) administering the therapeutic treatment selected in step b) to the patient. Regarding the method.

[0422] Antihypertensive agents are, in some cases, active agents approved for the treatment of PHT, PA, CS, or PPGL.

[0423] The methods of treatment disclosed herein may also include observing the alleviation, reduction, improvement, amelioration or cure of the symptoms or signs of EHT, particularly blood pressure.

[0424] Treatments for PHT, PA, CS, and PPGL are well known in the art (Williams et al., J Hypertens. 2018;36(10):1953-2041; Funder et al., J Clin Endocrinol Metab. 2016;101(5):1889-1916; Nieman et al., J Clin Endocrinol Metab. 2008;93(5):1526-1540; Lenders et al., J Clin Endocrinol Metab. 2014;99(6):1915-1942; Feelders et al., J Clin Endocrinol Metab. 2013;98(2):425-438). These may include surgery and / or administration of therapeutically effective compounds.

[0425] Among the antihypertensive agents that may be useful in the treatment of PHT, mention may be made of ACE inhibitors, angiotensin receptor antagonists, beta-antagonists, calcium channel antagonists and diuretics (thiazides and thiazide-like diuretics, e.g., chlorthalidone and indapamide).

[0426] By way of example, treatment of PA may involve administering to a patient in need of such treatment at least one mineralocorticoid receptor (MR) antagonist, such as spironolactone or eplerenone, as an active agent. The use of epithelial sodium channel antagonists, such as amiloride, ACE inhibitors, angiotensin receptor antagonists, or calcium channel antagonists may also be considered.

[0427] Treatment of PPGL may involve administering at least one alpha-adrenergic receptor antagonist or one calcium channel antagonist to a patient in need of such treatment.

[0428] Treatment of CS may involve administering to a patient in need thereof at least one steroidogenesis inhibitor or glucocorticoid antagonist. Active agents useful in the treatment of CS may include ketoconazole, mitotane, etomidate, metyrapone, cabergoline, pasireotide or mifepristone.

[0429] The dosage and administration schedule of a therapeutic composition intended to treat or alleviate PA, CS or PPGL or related symptoms is adapted to the patient in need thereof according to age, weight, blood pressure and / or sex. Tailoring treatment to the specifics of the patient is within the knowledge of a person skilled in the art.

[0430] Kits and their uses In some embodiments, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: the plurality of hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers defined above; Regarding the kit.

[0431] In some embodiments, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: the plurality of hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers defined above; Regarding the kit.

[0432] In some embodiments, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: the plurality of hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers defined above; Regarding the kit.

[0433] In some embodiments, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: the plurality of hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers defined above; Regarding the kit.

[0434] In some embodiments, the present disclosure provides a kit for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: the plurality of hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the kit comprises at least a means for measuring the combination of biomarkers defined above; Regarding the kit.

[0435] In some embodiments, the present disclosure relates to a kit for stratifying a hypertensive patient among multiple hypertensive disorders.

[0436] In some embodiments, the kits of the present disclosure may include an instruction leaflet setting out the steps of a method for stratifying a hypertensive patient among multiple hypertensive disorders, as detailed in this disclosure.

[0437] In some embodiments, the kits may comprise means for determining the presence or quantification of the miRNAs disclosed herein, which may be means for performing digital PCR, quantitative RT-PCR, microarray, isothermal amplification, next generation sequencing, hybridization chain reaction, or near-infrared techniques.

[0438] In some embodiments, the kit may include a means for determining the presence or quantitation of the O-methylated catecholamines disclosed herein, which may be a means for performing HPLC or liquid chromatography tandem mass spectrometry coupled with coulometric detection (LC-MS / MS).

[0439] In some embodiments, the kit may comprise means for determining the presence or quantification of the steroids disclosed herein, particularly plasma and / or urinary steroids. Such means may be means for performing ELISA, liquid column chromatography, gas chromatography / mass spectrometry, UHPLC-ESI-QTOF-MS / MS or LC-MS / MS.

[0440] In some embodiments, the kits may include means for determining the presence of or quantifying the small molecule metabolites disclosed herein, which may be means for performing gas chromatography-mass spectrometry [GC-MS], liquid chromatography-MS, NMR, LC / GC-FID, direct flow injection MS / MS, LC ESI-MS / MS, or MS / MS.

[0441] computer program products Such a method of the invention is advantageously implemented automatically by a computer program on any electronic system including a processor, in particular on a computer.

[0442] In some embodiments, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: Multiple hypertensive disorders include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT). The computer program (1) at least one classifier for comparing at least first and second types of hypertensive diseases (the first and second types of hypertensive diseases being different) selected in a group of hypertensive diseases including primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT) multiple times, the classifier being pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter; wherein the classifier is a) using said classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for at least one predetermined comparison between at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said group of hypertensive diseases, wherein the first and second types of hypertensive disease are different; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases based on the calculated evaluation parameters; (2) at least one input of a measured biomarker; said input of measured biomarkers is obtained by measuring a combination of biomarkers as defined above for said plurality of hypertensive disorders in a suitable biological sample previously isolated from said patient; - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use, for the at least one input of measured biomarkers, the classifier trained to associate each hypertensive disorder of the plurality of hypertensive disorders with a probability of associating the hypertensive patient with the hypertensive disorder to stratify the hypertensive patient among the plurality of hypertensive disorders. Related to computer program products.

[0443] In some embodiments, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: Multiple hypertensive disorders include endocrine hypertension (EHT) and primary hypertension (PHT), The computer program (1) at least one classifier for comparing at least first and second types of hypertensive diseases (wherein the first and second types of hypertensive diseases are different) selected in a group of hypertensive diseases including endocrine hypertension (EHT) and primary hypertension (PHT) multiple times, the classifier being pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, wherein the classifier: a) using said classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for at least one predetermined comparison between at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said group of hypertensive diseases, wherein the first and second types of hypertensive disease are different; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases based on the calculated evaluation parameters; (2) at least one input of a measured biomarker; said input of measured biomarkers is obtained by measuring a combination of biomarkers as defined above for said plurality of hypertensive disorders in a suitable biological sample previously isolated from said patient; - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each hypertensive disorder of the plurality of hypertensive disorders with a probability of associating the hypertensive patient with the hypertensive disorder for the at least one input of measured biomarkers in order to stratify the hypertensive patient among the plurality of hypertensive disorders. Related to computer program products.

[0444] In some embodiments, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: Multiple hypertensive disorders include Cushing's syndrome (CS) and primary hypertension (PHT), The computer program (1) at least one classifier for comparing at least first and second types of hypertensive diseases (wherein the first and second types of hypertensive diseases are different) selected in a group of hypertensive diseases including Cushing's syndrome (CS) and primary hypertension (PHT) multiple times, the classifier being pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, wherein the classifier: a) using said classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for at least one predetermined comparison between at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said group of hypertensive diseases, wherein the first and second types of hypertensive disease are different; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases based on the calculated evaluation parameters; (2) at least one input of a measured biomarker; said input of measured biomarkers is obtained by measuring a combination of biomarkers as defined above for said plurality of hypertensive disorders in a suitable biological sample previously isolated from said patient; - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each hypertensive disorder of the plurality of hypertensive disorders with a probability of associating the hypertensive patient with the hypertensive disorder for the at least one input of measured biomarkers in order to stratify the hypertensive patient among the plurality of hypertensive disorders. Related to computer program products.

[0445] In some embodiments, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: Hypertensive disorders include primary aldosteronism (PA) and primary hypertension (PHT), The computer program (1) at least one classifier for comparing at least first and second types of hypertensive diseases (wherein the first and second types of hypertensive diseases are different) selected in a group of hypertensive diseases including primary aldosteronism (PA) and primary hypertension (PHT) multiple times, the classifier being pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, wherein the classifier: a) for at least one predetermined comparison between the at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in the group of hypertensive diseases, the first and second types of hypertensive disease being different, using the classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases based on the calculated evaluation parameters; (2) at least one input of a measured biomarker; said input of measured biomarkers is obtained by measuring a combination of biomarkers as defined above for said plurality of hypertensive disorders in a suitable biological sample previously isolated from said patient; - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each hypertensive disorder of the plurality of hypertensive disorders with a probability of associating the hypertensive patient with the hypertensive disorder for the at least one input of measured biomarkers in order to stratify the hypertensive patient among the plurality of hypertensive disorders. Related to computer program products.

[0446] In some embodiments, the present disclosure provides a computer program product for stratifying a hypertensive patient among multiple hypertensive disorders, comprising: Multiple hypertensive disorders include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT); The computer program (1) at least one classifier for comparing at least first and second types of hypertensive diseases (wherein the first and second types of hypertensive diseases are different) selected in a group of hypertensive diseases including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT) multiple times, the classifier being pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, wherein the classifier is a) using said classifier to rank a plurality of combinations of biomarkers based on calculation of at least one evaluation parameter for at least one predetermined comparison between at least one predefined input dataset and at least first and second hypertensive patients, each patient having first and second types of hypertensive disease selected in said group of hypertensive diseases, wherein the first and second types of hypertensive disease are different; b) selecting a combination of biomarkers to stratify hypertensive patients among the plurality of hypertensive diseases based on the calculated evaluation parameters; (2) at least one input of a measured biomarker; said input of measured biomarkers is obtained by measuring a combination of biomarkers as defined above for said plurality of hypertensive disorders in a suitable biological sample previously isolated from said patient; - the computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each hypertensive disorder of the plurality of hypertensive disorders with a probability of associating the hypertensive patient with the hypertensive disorder for the at least one input of measured biomarkers in order to stratify the hypertensive patient among the plurality of hypertensive disorders. Related to computer program products.

[0447] The features defined above for the methods of the present disclosure apply to computer program products. [Example]

[0448] The following examples illustrate the embodiments of the present disclosure that are best known at present. However, it is to be understood that the following are merely exemplary or illustrative of the application of the principles of the present disclosure. Numerous modifications and alternative compositions, methods, and systems may be devised by those skilled in the art without departing from the spirit and scope of the present disclosure.

[0449] Example 1: Materials and Methods Materials and Methods Patient details and multi-omics data This study included 487 patients with PA, PPGL, CS, and PHT, and normotensive volunteers (NV) (PA = 113, PPGL = 88, CS = 41, PHT = 112, and NV = 133). They were recruited by the Adrenal Disorders Reference Center of the ENS@T-HT Horizon2020 Consortium (ENSAT-HT Project, n.d.). Diagnosis was based on current guidelines for each disease at each specialized center. In the omics study, plasma miRNAs (PmiRNAs: 173), plasma O-methylated catecholamines (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small molecule metabolites (PSmallMB: 189) were measured in biological samples collected from each patient within 24 hours. After completion of omics measurements, quality control, and data cleaning, 408 patients had complete omics sets for further analysis (Figure 1). Patient demographic characteristics are summarized in Table 2.

[0450] Table 2 below shows the redistribution of the number of patients according to four types of hypertensive patients: primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT) for input datasets 2 and 3.

[0451] [Table 2]

[0452] After quality control, omics measurements for all five omics were available for 408 patients (CS = 30, PA = 100, PPGL = 69, PHT = 108, and NV = 101). For a total of 408 patients, MOmics features (PmiRNAs: 173, PMetas: 4, PSteroids: 16, USteroids: 27, and PSmallMB: 189) were exported along with age and sex and used to conduct supervised ML experiments (Table 2). The names of the features were prefixed with "O1_," "O2_," "O3_," "O4_," and "O5_" for PmiRNAs, PMetas, PSteroids, USteroids, and PSmallMB, respectively. The MOmics and mono-omics datasets were randomly divided into a training set (approximately 80%) and a test set (approximately 20%). A complete list of all MOmics features is included in Figures 2-6.

[0453] Biomarker Discovery Using Supervised Machine Learning Biomarker discovery involves selecting disease combinations, detecting outliers, selecting supervised ML classifiers, configuring experimental parameters, and exploring various evaluation scenarios (Figure 7).

[0454] In this second example, biomarker discovery involved three stages: preprocessing (outlier detection and supervised ML classifier selection) in stage 1, feature selection in stage 2, and final training / testing in stage 3 (Figure 7). Classification was performed for ALL-ALL (PPGL vs PA vs CS vs PHT), EHT (PPGL+PA+CS)-PHT, and individual endocrine hypertension (i.e., PPGL / PA / CS)-PHT.

[0455] Combination of diseases The five disease combinations used for classification were ALL-ALL (PPG vs PA vs CS vs PHT), EHT (PPGL+PA+CS)-PHT, and individual endocrine hypertension (i.e., PPGL / PA / CS)-PHT. NV was not included in these combinations, as the key question addressed was: how can hypertensive patients be stratified among themselves? Omics data from NV were used to assess how it differs from different forms of hypertension.

[0456] Outlier detection To investigate the effect of outliers on classification, two sets of results were analyzed, as shown in Stage 1 (Figure 7): 1) using the data including outliers, and 2) applying the 1.5 quadratic rule three times to remove extreme outliers (exclude outliers). Outliers were then filled in using the maximum value.

[0457] Classifiers, feature selection and classification performance metrics A combination set of eight classifiers was used: decision tree (J48) (Breiman, 1998), naive Bayes (NB) (Zhang, 2004), k-nearest neighbor (IBk) (Bentley, 1975), logit boost (LB) (Friedman et al., 1998), logic model tree (LMT) (Landwehr et al., 2005), simple logistic (SL) (Sumner et al., 2005), random forest (RF) (Breiman, 2001), and sequential minimal optimization (SMO) (Platt, 1998) (stage 1 in Figure 7). Classification was implemented using caret (Kuhn, 2008) and RWeka (Hornik et al., 2009) in R (R Core Team, 2013). To ensure reproducibility, a further training validation (80-20%) split was performed on the training set for 100 random repeats (RR) using random seeds for each iteration. For feature selection, a wrapper method (Boruta) (Kursa & Rudnicki, 2010) and a filter method (Correlation-based Feature Selection - CFS) (Hall, 1999) were compared (stage 2 in Figure 7). Classification was evaluated with 100 random repeats (RR) using performance metrics: balanced accuracy, sensitivity, specificity, AUC, F1, and kappa score. Balanced accuracy allows for adjustments for class imbalance problems.

[0458] Evaluation scenario One of the key objectives of the analysis was to identify the list of most discriminatory features for a given disease combination. Potential bias due to patient age or sex was explored in different scenario sets. Table 3 summarizes the three scenarios investigated for each disease combination, along with their justification and set combinations.

[0459] [Table 3]

[0460] The corresponding patient numbers are shown in Figure 8. The top features from Set A, with a feature frequency cutoff of 50, were used for the final training / testing stage (Figure 7). This value was empirically chosen as a tradeoff to find the optimal number of reduced features without affecting classification performance. To minimize the impact of class imbalance issues, additional synthetic samples were generated for CS and PPGL using SMOTE (Chawla et al., 2002). For the combined EHT-PHT disease, a downsampling approach was used for class balancing instead of synthetic samples.

[0461] Stage 3 of the schematic diagram shows the process of the final / test stage using the test data (Figure 7). Omics type and disease combinations were selected and the best three classifiers were subsequently trained. The trained models were then used to classify the test data. The predicted outcomes, various performance metrics, and lists of selected features were then saved and compared at the end of each classification. The properties of the final discriminatory feature set were then evaluated for NV using PCA analysis. All classifiers shown in Figure 7 were used on the MOmics data and then individually on all five mono-omics.

[0462] result Evaluation of data-driven preprocessing and feature selection methods First, we evaluated the classification performance for ALL-ALL using MOmics data with and without outliers (Figure 9). As observed from various performance metrics, excluding outliers provided better classification. SL showed approximately 4%, 5%, and 1% improvements in balanced accuracy, sensitivity, and specificity, respectively, when outliers were excluded. Overall, LB, SL, and RF were the best-performing classifiers. Next, feature selection methods were compared using the LB, RF, and SL classifiers for the ALL-ALL disease combination (Figure 9). Both the filter and wrapper methods provided comparable classification performance. However, the wrapper method was selected for the next stage of analysis to evaluate feature subsets as an exploratory problem to discover significant inter-feature dependencies.

[0463] Overall classification of primary and endocrine hypertension The top three ML models were trained using a reduced training dataset using the top features from the 100 RRs for each disease combination (see Stage 3 in Figure 7). These trained classifiers were then evaluated on the test set. The corresponding performance metrics and associated highly discriminatory features selected by the classifiers were as follows:

[0464] Performance indicators The MOmics classifier outperformed the mono-omics classifiers when considering balanced accuracy, AUC (excluding CS-PHT), F1, and kappa scores (Figure 10). Using the MOmics data for the ALL-ALL combination, the RF classifier (using a balanced dataset) provided better classification performance (approximately 92% balanced accuracy, 0.95 AUC, 88% sensitivity, and 96% specificity) compared to the other five mono-omics. The corresponding call values (prediction probabilities) for each test sample were evaluated (Figure 11). A high call value emphasizes the reliability of the classifier in predicting the test sample. Many correctly classified samples had high call values. This highlights the fact that the MOmics classifier provided better performance compared to the others. In the ALL-ALL combination, the MOmics classifier had seven incorrectly classified samples (Figure 11). In contrast, the best-performing PSteroids-based RF classifier (among the five mono-omics) achieved a balanced accuracy of approximately 81%, an AUC of 0.88, a sensitivity of approximately 72%, and a specificity of approximately 90%. The corresponding call scores showed high confidence, with 18 samples misclassified. These results were also evaluated as a confusion matrix (Figure 12).

[0465] For the EHT-PHT combination, the SL classifier using MOmics (using balanced data) provided an AUC of 0.96 (Figure 13), 90% sensitivity, and approximately 86% specificity (Figure 10). High classification confidence was observed for most correctly classified samples (Figure 11). The PmiRNA- and PMetas-based RF classifiers achieved approximately 86% specificity (same as MOmics), but their AUCs were 0.88 and 0.80, respectively. Notably, both PSmallMB-based RF classifiers provided the highest sensitivity of 95%, but had a lower AUC of 8.2.

[0466] In the PA-PHT combination, the SL classifier using MOmics provided the best balanced accuracy and AUC (approximately 90% and 0.95, respectively), with a sensitivity of 95% and a specificity of approximately 86% (Figures 10 and 13). The PmiRNA-based LB classifier provided the highest AUC (95% sensitivity) of 0.91 among the monoomics, while USteroids achieved the highest specificity of approximately 90%. The decision scores emphasized the high reliability of the MOmics classifier compared to the others (Figure 11). In the PPGL-PHT combination, the LB classifier using MOmics and the RF classifier using PMetas achieved the same balanced accuracy of approximately 96% and AUCs of 0.99 and 0.97, respectively. The comparative performance of the decision scores for these classifiers demonstrates their high reliability (Figure 11). The PmiRNA-based LB classifier also provided an AUC of 0.99 and a specificity of 81%. Furthermore, for the CS-PHT combination, the monoomics-based SL classifier provided 100% specificity and approximately 92% balanced accuracy, but with a lower AUC of 0.93. In contrast, the monoomics-based classifiers using PSteroids and USteroids achieved higher AUCs of 0.98 and 0.97, respectively. Probability values for the test set indicated differences in reliability between the classifiers (Figure 11).

[0467] These classifiers were also tested on the training dataset to understand the effect of overfitting (Figures 14-18). Among the three classifiers (LB, RF, and SL), RF clearly provided better classification results when tested on the training set compared to the test set. This highlighted that the training of the RF classifier was overfitting regardless of whether the training data was balanced or not. On the other hand, the LB and SL classifiers showed less overfitting and demonstrated stable performance on both the training and test sets.

[0468] Highly recognizable features The final selected set of MOmics features used for classifier training contained different omics features for each disease combination. PmiRNA and PSmallMB features represented 88% of the entire MOmics dataset (Figure 19), and similar shares were observed within the final selected set of features (Figure 20). For example, PSmallMB formed a significant portion of all disease combinations except for CS-PHT, where many PmiRNAs were found to be highly discriminatory (approximately 58% of the total selected features). In contrast, very few PmiRNAs were selected for PPGL-PHT (approximately 5.5% of the total features).

[0469] We also investigated the commonality of selected MOmics features among various disease combinations (Figure 21). Two PSmallMB features (O5_PC ae C38:1 and O5_C9) and one PmiRNA (O1_hsa-miR-15a-5p) were present in all five disease combinations (Figure 22). Similarly, 13 PSmallMB features were common to all four disease combinations (i.e., all except CS-PHT). Various unique features were selected for each disease combination. For example, 20 features (15 PmiRNAs, 1 PS steroid, 3 US steroid, and 1 PSmallMB) were selected in CS-PHT alone. Overall, ALL-ALL had more highly discriminatory features (57 in total) than any of the other four disease combinations. Not unexpectedly, age and gender were not selected in any of the five disease combinations.

[0470] We also investigated the highly discriminatory features selected by the mono-omics classifiers (Figures 23-27). Age and gender were selected by most mono-omics classifiers (except PSmallMB) in all diseases except PPGL-PHT. In the case of ALL-ALL and EHT-PHT, more PmiRNAs were selected compared to the other disease combinations. In contrast, in CS-PHT among all disease combinations, only nine features were selected by the PSmallMB classifier.

[0471] We also analyzed the contribution of each omics feature (in terms of number in the entire dataset) to the final selection of omics features. For example, in the ALL-ALL and PPGL-PHT disease combinations, three of four PMetas features (75%) were selected. In the PPGL-PHT disease combination, no PSteroid features were selected. In the PA-PHT and CS-PHT disease combinations, no PMetas features were selected (Figures 25 and 27).

[0472] Inspection of features between MOmics and mono-omics classifiers highlighted that, with the exception of PSmallMB and PmiRNA, all features selected in MOmics were part of the individual omics classifiers (Figures 23-27). In the case of PSmallMB, some features were exclusively selected in the MOmics classifier (e.g., O5_PC aa C34:3 and O5_PC ae C40:2 in PA-PHT). Similarly, in CS-PHT, PmiRNA, O1_hsa-miR-106b-3p, was exclusively selected in the MOmics classifier.

[0473] The selected omics features in the ALL-ALL disease combination were compared with the corresponding omics features of NV in the training set (Figure 28). PCA analysis was also performed for all five disease combinations along with NV (Figure 29). The first components of ALL-ALL and EHT-PHT accounted for approximately 40% and 57% of the explained variance, respectively.

[0474] Detailed analysis of primary and endocrine hypertension The training set of MOmics data was studied for a set of different scenarios (Table 1), including using age and sex as features and understanding the effect of age- and sex-segregated subsets on feature selection in various disease combinations (see stage 2 in Figure 7).

[0475] Scenario 1: Scenarios that include age and gender as features (Set A) vs. scenarios that do not (Set B) In scenario 1, the MOmics data provided better performance for all disease combinations (Figure 30). In intra-set comparisons, MOmics achieved similar performance in sets A and B across all disease combinations. Therefore, excluding age and gender as features (in set B) did not significantly change classification performance. On the other hand, for PMetas, balanced accuracy decreased (except for PPGL-PHT) when age and gender were excluded from the feature set (set B). For example, balanced accuracy decreased by 5% and 7% for ALL-ALL and EHT-PHT, respectively.

[0476] The remaining four mono-omics provided comparable performance regardless of the age and gender features used. For example, for USteroids, a detailed overview of the features selected during 100 RRs shows that almost the same features were selected almost the same number of times for Sets A and B (Figure 31). A similar trend was observed for the other mono-omics (Figure 31). Of note, for MOmics, even though age and gender were included as features (Set A), they were not designated as top features in the 100 RRs because their selection frequencies were below the threshold (Figure 32).

[0477] Scenario 2: Men (Set C) vs Women (Set D) The classification performance of sets C and D could not be compared due to the different numbers of samples used for training and testing. On the other hand, for EHT-PHT and PA-PHT, the female subset of the MOmics data provided better accuracy compared to the male subset (Figure 30). The comparison between sets highlighted the superior performance of the MOmics dataset, regardless of the choice of classifier, compared to the mono-omics dataset for the ALL-ALL and EHT-PHT disease combinations. For PPGL-PHT, PMetas outperformed the MOmics classification for both sets. On the other hand, for CS-PHT, set C could not be performed due to an insufficient number of male CS samples for classifier training (Figure 8).

[0478] From the perspective of feature selection, various features were selected in Sets C and D for the MOmics dataset (Figure 32). For example, in ALL-ALL, O1_hsa-miR-15a-5p, O5_spermidine, and O5_spermidine / putrescine were selected only for the male dataset. In contrast, various other features, such as O5_PC ae C38:1, O3_18 oxo-cortisol, and O4_18-OHF, were selected only for the female dataset. Upon closer inspection, it was clear that the combination of features in Sets C and D was largely intersecting with both Sets A and B. Similar trends were also observed for most disease combinations in the MOmics and mono-omics datasets (Figures 31-32).

[0479] Scenario 3: Older people (Set E) vs. younger people (Set F) Overall, MOmics data provided better classification performance compared to mono-omics (except PPGL-PHT), regardless of cohort age (Figure 30). Considering comparisons between sets, set E (age 50 years or older) provided better results than set F (age less than 50 years) for almost all disease combinations. For all disease combinations, more unique features were selected in both cohorts (Figure 32). A similar trend was observed for the mono-omics dataset (Figure 31).

[0480] Figure 22 shows a diagram illustrating the number of unique biomarkers in a comparison of various overlapping hypertensive conditions.

[0481] Discussion Here, we implement a MOmics ML integrated approach for stratifying arterial hypertension. Results show that the MOmics approach provided improved discriminatory power compared with single omics (mono-omics) data analysis, provided a combination of potential diagnostic biomarkers for diagnosing hypertension subtypes, and was able to correctly distinguish various forms of endocrine hypertension with high sensitivity and specificity.

[0482] With the recent availability of high-throughput experimental and computational techniques, ML-based integration will facilitate the discovery of biomarkers for diagnosis and improve our understanding of complex diseases, such as arterial hypertension. However, acquiring mono-omics data can be logistically challenging when biological samples are sourced from multiple recruitment centers and require multicenter omics measurements. This can result in fewer samples with all omics available for integration. The ENS@T-HT study provided a prime example of how this challenge can be successfully addressed by obtaining complete omics sets for approximately 84% of all patients. While several mono-omics studies on identifying endocrine-type hypertension have been published (Eisenhofer et al., 2020; Erlic et al., 2021), to our knowledge, no other studies have collected and analyzed mono-omics data for hypertension stratification and hypertension subtype prediction.

[0483] In this study, we used a dedicated and customizable ML pipeline to predict EHT subtypes. Class imbalance is a well-known problem in ML, which prevents classifiers from being trained on the minority class. This was corrected for CS and PPGL patients by using Synthetic Minority Over-sampling TEchnique (SMOTE) (Chawla et al., 2002). Evaluating classification performance was one of the key outcomes for this study. The method used also allowed for evaluation of top discriminatory features and comparison of these with NV.

[0484] Despite the robust classification performance, this analysis had several drawbacks. First, CS is a rare disease, and the number of CS patient samples was limited. Second, advanced ML techniques, such as deep learning, could not be used in this analysis because they would require much more samples than were available in this study. Finally, not all samples could be used for MOmics integration due to limitations in sample quantity or specific quality measures. This is a common issue in studies involving multicenter biological samples and multicenter omics measurements. On the other hand, a major strength of this study is the clear diagnosis of the major EHT subtypes according to expert guidelines. In addition, our analysis only explored MOmics data using an ML-based data-driven approach. The top discriminatory features identified require further investigation in terms of biological significance and pathway network analysis.

[0485] Future studies would benefit from including a broader population enrolled prospectively. This would make the classifier more robust and sufficiently trained for formal clinical deployment. The ENS@T-HT study is currently collecting such relevant prospective data with the aim of measuring the most discriminatory features in new samples and conducting independent validation (ClinicalTrials.gov Identifier: NCT02772315). The improved algorithm could be deployed as a web-server-based predictive tool to screen patients in primary care and refer those identified as at risk for endocrine hypertension to centers with appropriate expertise for subsequent evaluation, as needed. The developed ML pipeline is fully customizable and can be deployed in biomarker discovery and analysis studies of other mono / MOmics databases. For example, it could be used to investigate MOmics signatures for other forms of secondary hypertension, such as renal artery stenosis.

[0486] Example 2-A: Materials and Methods Patient details Plasma and 24-hour urine samples from prospective patients with primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT) were provided by seven collaborating clinical centers participating in the ENS@T-HT Horizon2020 consortium (ENSAT-HT Project. ENSAT-HT Project http: / / www.ensat-ht.eu / .) EHT patients were classified as PA, PPGL, and CS. Diagnosis was made according to current guidelines (Funder, JW et al. The Management of Primary Aldosteronism: Case Detection, Diagnosis, and Treatment: An Endocrine Society Clinical Practice Guideline. J. Clin. Endocrinol. Metab. 101, 1889-1916 (2016); Mulatero, P. et al. Genetics, prevalence, screening and confirmation of primary aldosteronism: a position statement and consensus of the Working Group on Endocrine Hypertension of The European Society of Hypertension*. Journal of Hypertension 38, 1919-1928 (2020); Lenders, JWM et al. Pheochromocytoma and Paraganglioma: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab 99, 1915-1942 (2014)).

[0487] Plasma and 24-hour urine samples were collected from 1,093 male and female participants (PA = 513, PPGL = 40, CS = 47, PHT = 493) with one of four hypertension subtypes. Plasma and urine samples were stored at -80°C before shipping and analysis. All study protocols for patient recruitment were approved by the local ethics committees of each participating institution, and all subjects provided written informed consent before study participation.

[0488] Multi-omics data Five omics sets were generated (see Table 4).

[0489] Table 4: Various omics and their features [Table 4]

[0490] All omics sets were cataloged in the Research Data Management Platform (RDMP) (Nind, T. et al. The Research Data Management Platform (RDMP): A novel, process-driven, open-source tool for the management of longitudinal cohorts of clinical data. GigaScience (2018) doi:10.1093 / gigascience / giy060) and hosted at the Health Informatics Centre (HIC) Safe Haven for systematic access. Biological samples were available for 1093 patients, but after quality control, omics measurements for all five omics were available for 961 patients (CS = 37, PA = 454, PPGL = 32, and PHT = 438) (see Table 5).

[0491] Table 5: Omics availability for disease types [Table 5]

[0492] A total of 278 multi-omics features (PmiRNAs: 55, PMetas: 4, PSteroids: 15, USteroids: 27 and PSmallMB: 177) along with age and sex were exported and used to perform supervised machine learning (ML) experiments.

[0493] Table 6 provides a list of all omics feature names. Extreme outliers in the omics features were carefully evaluated and capped. Feature names were prefixed with "O1_," "O2_," "O3_," "O4_," and "O5_" for plasma miRNAs, plasma O-methylated catecholamines (PMetas), plasma steroids, urinary steroids, and plasma small molecule metabolites, respectively.

[0494] Table 6: List of all omics features [Table 6] TIFF2025526455000012.tif245165 TIFF2025526455000013.tif244165 TIFF2025526455000014.tif244165 TIFF2025526455000015.tif69165

[0495] Supervised Machine Learning Pipeline Machine Learning (ML) Training The machine learning (ML) pipeline developed for molecular signature discovery is disclosed in PCT / EP No. 2022 / 053142, filed February 9, 2022, the contents of which are incorporated by reference.

[0496] The ML system was trained as described in Example 1.

[0497] A high level schematic is shown in Figure 33.

[0498] In the subsections below, improvements for retraining and refining molecular signatures are highlighted.

[0499] Machine Learning (ML) Retraining In the subsections below, improvements for retraining and refining molecular signatures (combinations of biomarkers) are highlighted.

[0500] Combination of diseases The five disease combinations used for classification were ALL-ALL (PPGL vs PA vs CS vs PHT), EHT (PPGL + PA + CS)-PHT, and each endocrine hypertension (i.e., PPGL / PA / CS)-PHT.

[0501] Omics combination, data imputation and sampling In addition to single omics and complete multi-omics sets, other intermediate combinations were also investigated. Table 4 lists all 31 omics combinations.

[0502] Table 1: Various omics combinations used in the analysis [Table 7]

[0503] Because very few CS and PPGL samples were available for analysis, we adopted two approaches to minimize the impact of class imbalance issues during model training. First, five CS samples and six PPGL samples (see rows 2 and 4 in Table 2) were imputed for the fifth unavailable omic using the DMwR27 package in R. These 11 samples were then concatenated with 961 samples. The 961 and 972 sample sets are denoted "original" and "imputed," respectively. Next, upsampling and downsampling were performed on the class-imbalanced disease combinations. All five disease combinations were downsampled, while only three (CS-PHT, PPGL-PHT, and ALL-ALL) were upsampled. Upsampling involved generating additional synthetic samples for CS and PPGL using SMOTE8. These sampling approaches are denoted "up" and "down," respectively.

[0504] Classifiers and Feature Selection Three machine learning classifiers were used: Logit Boost (LB) (Friedman, J., Hastie, T. & Tibshirani, R. Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics 28, 2000 (1998)), Simple Logistic (SL) (Sumner, M., Frank, E. & Hall, M. Speeding up logistic model tree induction. In Proceedings of the 9th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 675-683 (Springer-Verlag, 2005). doi:10.1007 / 11564126_72), and Random Forest (RF) (Breiman, L. Random Forests. Machine Learning 45, 5-32 (2001)). Classification was implemented using caret (Kuhn, M. Building Predictive Models in R Using the caret Package. Journal of Statistical Software 28, 1-26 (2008)) and RWeka (Hornik, K., Buchta, C. & Zeileis, A. Open-source machine learning: R meets Weka. Comput Stat 24, 225-232 (2009)) in R (R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2013)).

[0505] For feature selection, we used the wrapper Boruta (Kursa, MB & Rudnicki, WR Feature Selection with the Boruta Package. Journal of Statistical Software 36, 1-13 (2010)) method. One of the key objectives of the analysis was to identify the list of most discriminatory features for a given disease combination. The top features during feature selection were selected over 100 iterations with feature frequency cutoffs of 50, 70, and 90 and used for final training. This value was chosen empirically as a tradeoff to find the optimal number of reduced features without affecting classification performance.

[0506] Evaluation scenario Considering various combinations, a total of 446,400 iterations were evaluated, resulting in 4044 final models, as shown in Figure 33 (some models did not converge because no valuable top features were found). The number of iterations was calculated as follows: 31 omics combinations × 2 imputed sets (original and archived) × [5 disease combinations (downsampling) + 3 disease combinations (upsampling)] × 3 classifiers × 100 random replicates

[0507] Train / Test-Validation Split and Performance Metrics The omics set was divided into a training set and a validation set (80-20%) based on patient ID. These patient IDs were randomly selected, and age and gender were matched between the two sets. The validation set was separated through the ML model training pipeline. Meanwhile, the training set was further divided into training and testing sets using 100 Monte Carlo-based random repeats. Classification results were evaluated across these random repeats using performance metrics: balanced accuracy (to adjust for class imbalance issues), sensitivity, specificity, AUC, F1, and kappa score. All models were ranked for balanced accuracy, and the top five models for each disease combination, two imputation types (original and imputed) and sampling types (up and down). Finally, the configuration from the top five models was used to train on the full training set. The top-trained models were then used to predict disease for the validation set patients. These outcomes were provided in the form of probabilities. The features selected for training the top model were then evaluated to understand the effect of the various parameters selected.

[0508] Example 2-B: Results The results of retraining and refining the molecular signatures (combinations of biomarkers) are described below.

[0509] Pretreatment Preprocessing included standard exploratory data analysis, including evaluation of descriptive statistics, principal component analysis (PCA), followed by outlier detection and removal. Next, we set up various datasets according to the training / validation split and ran the ML pipeline.

[0510] Top ML models and discriminatory features All 4044 trained models were ranked based on balanced accuracy for each disease combination, patient (original, imputed), and sampling type (up, down). The top five models for each setting were then further investigated (80 models). Tables 7-11 show the top models and their details for ALL vs. ALL, EHT-PHT, CS-PHT, PA-PHT, and PPGL-PHT, respectively.

[0511] These tables show the omics combinations, the number of features selected, and the average performance on the training set. Empty black cells highlight omics for which no features were selected.

[0512] Table 7: Top models for All vs. All disease combinations. Black shaded cells highlight omics that were included in the omics combination, but none of their features were selected in the final model.

[0513] [Table 8] TIFF2025526455000018.tif127161

[0514] Table 8: Top models for EHT vs PHT disease combinations. Gray shaded cells highlight omics that were included in the omics combination, but none of their features were selected in the final model.

[0515] [Table 9] TIFF2025526455000020.tif101161

[0516] Table 9: Top models for the CS vs PHT disease combination. Gray shaded cells highlight omics that were included in the omics combination, but none of their features were selected in the final model.

[0517] [Table 10] TIFF2025526455000022.tif130161

[0518] Table 10: Top models for the PA vs PHT disease combination. Gray shaded cells highlight omics that were included in the omics combination, but none of their features were selected in the final model.

[0519] [Table 11] TIFF2025526455000024.tif102161

[0520] Table 11: Top models for the PPGL vs PHT disease combination. Gray shaded cells highlight omics that were included in the omics combination, but none of their features were selected in the final model.

[0521] [Table 12] TIFF2025526455000026.tif134161

[0522] Once the top models were identified and selected through the training phase, they were first applied to the validation set, and predictions by each model were made for all patients in the validation set. Predicted outcomes (in the form of probabilities) for the validation set patients using these top 80 models were selected. Table 12 shows example probability outcomes for the ALL-ALL combination.

[0523] [Table 13]

[0524] A heatmap of the features selected in the top models across various disease combinations was created (Figure 34). Most of these features were common among ALL-ALL, EHT-PHT, and PA-PHT. However, in the case of CS-PHT, unique features were observed. For example, one PSteroid (O3_DHEA) and two PSmallMBs (O5_Met-SO / Met and O5_Trp) were selected only in the CS-PHT combination. Furthermore, no PmiRNA features were selected in PA-PHT or PPGL-PHT.

[0525] conclusion In this study, we report successful retraining of ML models and refinement of molecular signatures (combinations of biomarkers). The top-performing ML models and their corresponding highly discriminatory features were evaluated. Prediction probabilities for patients in the validation set were calculated using the top models.

Claims

1. (i-a) The following biomarker groups: one biomarker selected for each of the patient's age, plasma steroids, and urinary steroids, and the biomarker group: at least one biomarker selected for at least one of O-methylated catecholamines, low molecular weight metabolites, and miRNAs; (i-b) The following biomarker groups: one biomarker selected for each of plasma steroids, urinary steroids, and low molecular weight metabolites, and the biomarker group: at least one biomarker selected for at least one of patient age, O-methylated catecholamines, and miRNA. (i-c) The following biomarker groups: one biomarker selected for each of the urinary steroids, and the biomarker group: at least one biomarker selected for at least one of the plasma steroids, low molecular weight metabolites, and miRNAs; (i-d) Biomarker groups: one biomarker selected for each of the patient's age, plasma steroids, urinary steroids, and low molecular weight metabolites, and biomarker groups: at least one biomarker selected for at least one of O-methylated catecholamines and miRNA, or (i-e) The following biomarker groups: one biomarker selected for each of the O-methylated catecholamines, and the biomarker group: at least one biomarker selected for at least one of the patient's age, urinary steroids, and low molecular weight metabolites. A combination of biomarkers that includes at least [specific element].

2. (i-a) Age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urine 18-hydroxycortisol (18-OHF), urine α-cortol (a-cortol), urine pregnanediol (PD), urine 3α,5β-tetrahydroaldosterone (THAldo), urine tetrahydrodeoxycorticosterone (THDOC), and urine tetrahydro-11-deoxycortisol (THS); Furthermore, plasma hsa-let-7g-5p, plasma hsa-miR-106b-3p, plasma hsa-miR-301a-3p, plasma hsa-miR-485-3p, plasma 3-methoxytyramine, plasma metanephrine, plasma normetanephrine, plasma 11-dehydrocorticosterone, plasma cortisol, plasma cortisone, urine 11-β-hydroxyandrosterone (11-β-OHAn), urine 17-OH-pregnanolon (17-HP), urine 5-pregnanediol (PD), urine 5-pregnentriol (5-PT), urine 5α-tetrahydrocortisol (5αTHF), urine androsterone (An), urine cortisol, urine cortisone, urine dehydroepiandrosterone DHEA, urinary ethiocoranolon (Etio), urinary pregnentriol (PT), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocorticosterone (THB), urinary tetrahydrocortisol (THF), urinary tetrahydrocortisone (THE), urinary α-cortol (A-cortol), urinary β-cortol (B-cortol), urinary β-cortol (B-cortol), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma creatinine, plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma glutamate, plasma H1 (total hexoses), plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma octadecadienylcarnitine (C18:2), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae C36:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), plasma spermidine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (total DMA / Arg), and combinations thereof, including or present in at least one further biomarker selected from the group; or, (i-b) Plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urine 18-hydroxycortisol (18-OHF), urine 3α,5β-tetrahydroaldosterone (THAldo), urine tetrahydrodeoxycorticosterone (THDOC), urine tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (total hexoses), plasma PC aa C32:1, plasma PC aa C34:3 and plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), In addition, age, plasma hsa-let-7g-5p, plasma lysoPC a C16:0, plasma lysoPC a C17:0, plasma normetanephrine, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urine pregnanediol (PD), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine (fumarylcarnitine)), plasma citrulline / arginine ratio (Cit / Arg), plasma creatinine, plasma glutamic acid (Glu), plasma octadecenoylcarnitine (C18:1), plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:1, plasma PC aa C34:2, plasma PC aa C36:2, plasma PC aa C36:3, plasma PC ae C36:3, plasma taurine, plasma tetradecenoylcarnitine (C14:1), plasma total dimethylarginine / arginine ratio (total DMA / Arg), and at least one further biomarker selected from the group including or present in combinations thereof. (i-c) Urinary α-cortol (a-cortol) and urinary tetrahydro-11-deoxycortisol (THS), Furthermore, at least one of the following: plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma dehydroepiandrosterone (DHEA), plasma dehydroepiandrosterone sulfate (DHEAS), plasma hsa-miR-19a-3p, plasma methionine sulfoxide / methionine ratio (Met-SO / Met), plasma tryptophan, urinary 5-pregnentriol (5-PT), urinary androsterone (An), urinary cortisol, urinary etiocholanolone (Etio), and combinations thereof. (i-d) Age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urine 18-hydroxycortisol (18-OHF), urine pregnanediol (PD), urine 3α,5β-tetrahydroaldosterone (THAldo), urine tetrahydro Rodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp). Furthermore, at least one of the following: plasma creatinine, plasma dehydroepiandrosterone sulfate (DHEA), plasma dodecanoylcarnitine (C12), plasma H1 (total hexoses), plasma lysoPC a C16:0, plasma PC aa C34:1, plasma PC aa C34:4, plasma PC aa C36:1, plasma PC aa C36:2, plasma taurine, urinary dehydroepiandrosterone (DHEA), and combinations thereof, or (i-e) Methoxytyramine, plasma metanephrine and plasma normetanephrine, In addition, age, plasma acetylornithine (Ac-Orn), urinary androsterone (An), urinary etiocholanolone (Etio), and at least one of these combinations. A combination of biomarkers according to claim 1, comprising at least the following:

3. (ii-a) At least one further biomarker in (i-a) is selected from plasma metanephrine, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and combinations thereof, or (ii-b) At least one further biomarker in (i-b) is plasma normetanephrine. (ii-c) At least one further biomarker in (i-c) is urinary androsterone (An), The biomarker combination according to claim 1.

4. The biomarker combination according to claim 1, wherein at least one further biomarker in (i-a) comprises at least plasma metanephrine, plasma normetanephrine, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrocortisone (THE), and urinary tetrahydrocortisol (THF), or a combination thereof.

5. The combination of biomarkers (i-a) Age, plasma metanephrine, plasma normetanephrine, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, plasma corticosterone, plasma dehydroepiandrosterone sulfate (DHEAS), urine 18-hydroxycortisol (18-OHF), urine α-cortisol ( α-cortisol, urinary cortisol, urinary dehydroepiandrosterone (DHEA), urinary pregnanediol (PD), urinary 3α,5β-tetrahydroaldosterone (THAldo), urinary tetrahydro-11-dehydrocorticosterone (THAs), urinary tetrahydrodeoxycorticosterone (THDOC), urinary tetrahydrocortisone (THE), urinary tetrahydrocortisol (THF), and urinary tetrahydro-11-deoxycortisol (THS), (i-b) Plasma normetanephrine, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urine 18-hydroxycortisol (18-OHF), urine 3α,5β-tetrahydroaldosterone (THAldo), urine tetrahydrodeoxycorticosterone (THDOC), urine tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma dodecanoylcarnitine (C12), plasma octadecadienylcarnitine (C18:2), plasma H1 (total hexoses), plasma PC aa C32:1, plasma PC aa C34:3, plasma serotonin, plasma serotonin / tryptophan ratio (serotonin / Trp), (i-c) Urinary α-cortol (a-cortol), urinary androsterone (An), and urinary tetrahydro-11-deoxycortisol (THS), (i-d) Age, plasma 11-deoxycorticosterone, plasma 11-deoxycortisol, plasma 18OH-corticosterone, plasma 18OH-cortisol, plasma 18-oxo-cortisol, plasma 21-deoxycortisol, plasma aldosterone, urine 18-hydroxycortisol (18-OHF), urine pregnanediol (PD), urine 3α,5β-tetrahydroaldosterone (THAldo), urine tetrahydro Rodeoxycorticosterone (THDOC), urinary tetrahydro-11-deoxycortisol (THS), plasma decanoylcarnitine (C10), plasma tetradecenoylcarnitine (C14:1), plasma octadecenoylcarnitine (C18:1), plasma octadecadienylcarnitine (C18:2), plasma acetylcarnitine (C2), plasma C6 (C4:1-DC) (hexanoylcarnitine), plasma lysoPC a C17:0, plasma PC aa C32:1, plasma PC aa C32:2, plasma PC aa C32:3, plasma PC aa C34:2, plasma PC aa C34:3, plasma PC aa C36:3, plasma serotonin, and plasma serotonin / tryptophan ratio (serotonin / Trp), or (i-e) Plasma 3-methoxytyramine, plasma metanephrine, and plasma normetanephrine The biomarker combination according to claim 1.

6. The use of a combination of biomarkers to stratify hypertensive patients among multiple hypertensive diseases, (i-a) Multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-a) described in claim 1, or (i-b) Multiple hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-b) described in claim 1, or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-c) described in claim 1, or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-d) described in claim 1, or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the biomarker combination is the biomarker combination (i-e) described in claim 1. This use is, - The combination of biomarkers will be measured using ex vivo, - In order to stratify the hypertensive patients among the multiple types of hypertensive diseases that follow (i-a), (i-b), (i-c), (i-d), or (i-e), a trained classifier is operated on the measured combination of biomarkers to obtain the probability of associating the hypertensive patient with the hypertensive disease for each hypertensive disease. Includes, The trained classifier is a classifier that is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. use.

7. A method for stratifying hypertensive patients among several types of hypertensive diseases, (i-a) Multiple types of hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), or (i-b) Multiple types of hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), The method involves at least one classifier pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and using at least one classifier for comparing at least two types of hypertensive patients multiple times. The method includes at least, a) A step of measuring a combination of biomarkers using ex vivo, For multiple hypertensive diseases according to (i-a), the combination of biomarkers is the combination of biomarkers (i-a) described in claim 1, or For multiple hypertensive diseases according to (i-b), the combination of biomarkers is the combination of biomarkers (i-b) described in claim 1, or For multiple hypertensive diseases according to (i-c), the combination of biomarkers is the combination of biomarkers (i-c) described in claim 1, or For multiple hypertensive diseases according to (i-d), the combination of biomarkers is the combination of biomarkers (i-d) described in claim 1, or For multiple hypertensive diseases according to (i-e), the step is to use a combination of biomarkers which is the combination of biomarkers (i-e) described in claim 1. b) In order to stratify the hypertensive patient among the multiple types of hypertensive diseases according to (i-a), (i-b), (i-c), (i-d), or (i-e), the step of running the trained classifier on the combination of biomarkers obtained in step a) from the hypertensive patient in order to obtain the probability of associating the hypertensive patient with the hypertensive disease for each hypertensive disease. including, method.

8. The use of claim 6, wherein the trained classifier is selected from decision trees (J48), naive Bayes (NB), K-nearest neighbors (IBk), logit boost (LB), support vector machines (SVM), logic model trees (LMT), bagging, simple logistic (SL), random forests (RF), and sequential minimal problem optimization (SMO).

9. The method according to claim 7, wherein the trained classifier is selected from logit boost (LB), simple logistic (SL), and random forest (RF).

10. The classifier performs at least the following steps: a) Using the classifier, rank a plurality of combinations of biomarkers based on the calculation of at least one evaluation parameter for at least one predefined input dataset and for at least one predetermined comparison between at least first and second hypertensive patients, each having first and second types of hypertensive diseases selected in the group of hypertensive diseases, and the first and second types of hypertensive diseases being different; b) A step of selecting a combination of biomarkers to stratify hypertensive patients among the multiple types of hypertensive diseases based on the calculated evaluation parameters (one or more) The use of claim 6, which is trained with at least one predefined input dataset according to a method including the following:

11. The classifier comprises at least the following steps: a) Using the classifier, rank a plurality of combinations of biomarkers based on the calculation of at least one evaluation parameter for at least one predefined input dataset and for at least one predetermined comparison between at least first and second hypertensive patients, each having first and second types of hypertensive diseases selected in the group of hypertensive diseases, and the first and second types of hypertensive diseases being different; b) A step of selecting a combination of biomarkers to stratify hypertensive patients among the multiple types of hypertensive diseases based on the calculated evaluation parameters (one or more) The method according to claim 7, which is trained using at least one predefined input dataset according to a method including the following:

12. The use according to claim 6, wherein the evaluation parameters are selected from accuracy, sensitivity, specificity, AUC, F1, kappa score, and combinations thereof.

13. The method according to claim 7, wherein the evaluation parameters are selected from accuracy, sensitivity, specificity, AUC, F1, kappa score, and combinations thereof.

14. A kit for stratifying hypertensive patients among multiple hypertensive diseases, (i-a) Multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the kit includes means for measuring at least one combination of biomarkers according to any one of claims 1 to 5 (i-a), or (i-b) Multiple hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), and the kit includes means for measuring at least one combination of biomarkers (i-b) according to any one of claims 1 to 3 or 5, or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the kit includes means for measuring at least one combination of biomarkers according to any one of claims 1 to 3 or 5 (i-e), or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the kit includes means for measuring at least one combination of biomarkers (i-d) as described in claim 1, 2, or 5, or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the kit includes means for measuring at least one combination of biomarkers (i-e) according to any one of claims 1, 2, or 5. kit.

15. A computer program product for stratifying hypertensive patients among multiple hypertensive diseases, (i-a) Multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS) and primary hypertension (PHT), or (i-b) Multiple hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), The computer program is (1) At least one classifier that is pre-trained to learn a plurality of combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, (i-a) Primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), (i-b) Endocrine hypertension (EHT) and primary hypertension (PHT) (i-c) Cushing's syndrome (CS) and primary hypertension (PHT), (i-d) Primary aldosteronism (PA) and primary hypertension (PHT), or (i-e) Pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT) A classifier for comparing multiple times at least first and second types of hypertensive diseases selected in groups of hypertensive diseases including (i-a), (i-b), (i-c), (i-d), and (i-e), The first and second types of hypertensive diseases are different, and the classifier comprises at least the following steps: a) For at least one predefined input dataset, and for at least one predetermined comparison between at least first and second hypertensive patients, each having first and second types of hypertensive diseases selected in the groups (i-a), (i-b), (i-c), (i-d), and (i-e) of hypertensive diseases, and the first and second types of hypertensive diseases being different, the classifier is used to rank multiple combinations of biomarkers based on the calculation of at least one evaluation parameter; b) A step of selecting a combination of biomarkers to stratify hypertensive patients among the multiple hypertensive diseases (i-a), (i-b), (i-c), (i-d), and (i-e) based on the calculated evaluation parameters (one or more); Trained using at least one predefined input dataset according to a method including, At least one classifier, (2) at least one input of the measured biomarker, the input of the measured biomarker obtained by measuring, in a suitable biological sample previously isolated from the patient, the combination of biomarkers (i-a) according to any one of claims 1 to 4, wherein multiple hypertensive diseases follow (i-a), or the combination of biomarkers (i-b) according to any one of claims 1 to 3 or 5, wherein multiple hypertensive diseases follow (i-b), or the combination of biomarkers (i-c) according to any one of claims 1 to 3 or 5, wherein multiple hypertensive diseases follow (i-c), or the combination of biomarkers (i-d) according to any one of claims 1, 2 or 5, wherein multiple hypertensive diseases follow (i-d), or the combination of biomarkers (i-e) according to any one of claims 1, 2 or 5, wherein multiple hypertensive diseases follow (i-e), along with at least one input obtained by measuring, Use, - The computer program product includes instructions that, when the program is executed by a computer, cause the computer to use the trained classifier to associate each of the multiple hypertensive diseases (i-a), (i-b), (i-c), (i-d), or (i-e) with respect to the at least one input of measured biomarkers, using the probability of associating the hypertensive patient with the hypertensive disease (i-a), (i-b), (i-c), (i-d), or (i-e). Computer program products.

16. A combination of biomarkers for use in a method for treating hypertensive diseases in patients who require it, - Hypertensive diseases are, (i-a) Primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), or (i-b) Endocrine hypertension (EHT) and primary hypertension (PHT), wherein the combination of biomarkers is the combination of biomarkers (i-b) described in any one of claims 1 to 3 or 5, or (i-c), a combination of biomarkers is the combination of biomarkers (i-c) described in any one of claims 1 to 3 or 5, for a plurality of hypertensive diseases including Cushing's syndrome (CS) and primary hypertension (PHT), or (i-d) A combination of biomarkers is the combination of biomarkers (i-d) described in any one of claims 1, 2, or 5, for a plurality of hypertensive diseases including primary aldosteronism (PA) and primary hypertension (PHT), or (i-e) Multiple hypertensive diseases, including pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), wherein the combination of biomarkers is the combination of biomarkers (i-e) described in any one of claims 1, 2, or 5. Selected from among, The treatment method includes a step of stratifying the hypertensive patient among the hypertensive diseases of (i-a), (i-b), (i-c), (i-d), or (i-e), and the stratification step is - The combination of biomarkers will be measured using ex vivo, - In order to stratify the hypertensive patients among the multiple types of hypertensive diseases that follow (i-a), (i-b), (i-c), (i-d), or (i-e), a trained classifier is operated on the measured combination of biomarkers to obtain the probability of associating the hypertensive patient with the hypertensive disease for each hypertensive disease. Includes, The trained classifier is a classifier that is pre-trained to learn multiple combinations of pre-selected biomarkers based on at least one calculated evaluation parameter, and is a classifier for comparing at least two types of hypertensive patients multiple times. A combination of biomarkers.

17. A method for providing data for treating patients with hypertension, the method is a) (i-a) Multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-a) described in any one of claims 1 to 5, or (i-b) Multiple hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-b) described in any one of claims 1 to 3 or 5, or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-c) described in any one of claims 1 to 3 or 5, or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-d) described in any one of claims 1, 2, or 5, or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the biomarker combination is the biomarker combination (i-e) described in any one of claims 1, 2, or 5. The stratification of the hypertensive patient among the plurality of hypertensive diseases (i-a), (i-b), (i-c), (i-d), and (i-e) is carried out in accordance with the use described in any one of claims 6, 8, and 10 in order to associate the hypertensive disease with the patient. To stratify the hypertensive patients among multiple hypertensive diseases, b) To provide data for selecting a therapeutic measure that is presumed to treat or alleviate at least one symptom of a hypertensive disease associated with the patient. Methods that include...

18. A method for providing data for treating a patient with hypertension, the method being: a) (i-a) Multiple hypertensive diseases include primary aldosteronism (PA), pheochromocytoma / functional paraganglioma (PPGL), Cushing's syndrome (CS), and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-a) described in any one of claims 1 to 5, or (i-b) Multiple hypertensive diseases include endocrine hypertension (EHT) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-b) described in any one of claims 1 to 3 or 5, or (i-c) Multiple hypertensive diseases include Cushing's syndrome (CS) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-c) described in any one of claims 1 to 3 or 5, or (i-d) Multiple hypertensive diseases include primary aldosteronism (PA) and primary hypertension (PHT), and the combination of biomarkers is the combination of biomarkers (i-d) described in any one of claims 1, 2, or 5, or (i-e) Multiple hypertensive diseases include pheochromocytoma / functional paraganglioma (PPGL) and primary hypertension (PHT), and the biomarker combination is the biomarker combination (i-e) described in any one of claims 1, 2, or 5. The stratification of the hypertensive patient among the plurality of hypertensive diseases (i-a), (i-b), (i-c), (i-d), and (i-e) is carried out in accordance with the method described in any one of claims 7, 9, and 11 in order to associate the hypertensive disease with the patient. To stratify the hypertensive patients among multiple hypertensive diseases, b) To provide data for selecting a therapeutic measure that is presumed to treat or alleviate at least one symptom of a hypertensive disease associated with the patient. Methods that include...

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