Circulating BMP10 (bone morphogenic protein 10) in the assessment of congestion and pulmonary hypertension

Abstract

The present invention concerns the field of diagnostics. Specifically, it relates to a method for assessing congestion in a subject comprising the steps of determining in a sample of the subject the amount of a BMP 10-type peptide (Bone Morphogenic Protein 10-type peptide) and comparing the amount of the BMP 10-type peptide to a reference for the BMP 10-type peptide, whereby congestion is to be assessed. The present invention also contemplates a method of differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension. Further, the invention concerns computer-implemented methods of the aforementioned methods as well as devices and kits for carrying out said methods. Yet, the invention in general relates to the use of a BMP 10-type peptide or a detection agent therefor in a sample of a subject for assessing congestion or the use of a BMP 10-type peptide or a detection agent therefor in a sample of a subject for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

Description

[0001] Circulating BMP10 (Bone Morphogenic Protein 10) in the assessment of congestion and pulmonary hypertension

[0002] The present invention concerns the field of diagnostics. Specifically, it relates to a method for assessing congestion in a subject comprising the steps of determining in a sample of the subject the amount of a BMPlO-type peptide (Bone Morphogenic Protein 10-type peptide) and comparing the amount of the BMP 10-type peptide to a reference for the BMP 10-type peptide, whereby congestion is to be assessed. The present invention also contemplates a method of differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension. Further, the invention concerns computer-implemented methods of the aforementioned methods as well as devices and kits for carrying out said methods. Yet, the invention in general relates to the use of a BMP 10-type peptide or a detection agent therefor in a sample of a subject for assessing congestion or the use of a BMP 10-type peptide or a detection agent therefor in a sample of a subject for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0003] Pulmonary hypertension (PH) is a severe and complex disease characterized by abnormally high pressure in the pulmonary arteries that eventually leads to right ventricular dysfunction and right heart failure (Humbert 2022, Eur Heart J. 43:3618-3731). In turn, PH is found as a common and severe complication of heart failure (HF). The cardinal symptom of every form of pulmonary hypertension is progressive exercise dyspnea. Pulmonary arterial hypertension (PAH) and pulmonary venous hypertension (PVH) develop from distinctly different etiologies: PAH is a precapillary PH that arises idiopathically or as the result of a divergent array of causes, including connective tissue disease. PVH primarily manifests as a postcapillary PH in the setting of left heart failure or valvular disease. Differentiation of PAH and PVH is required for appropriate treatment selection, but is complex and mostly requires invasive right heart catheterization (RHC). (Dadfarmay 2010, Congest Heart Fail; 16:287-91). In PH, both elevation of left atrial filling pressure (post-capillary component) and an increase in pulmonary vascular resistance (pre-capillary component) contribute to the elevation of the mean pulmonary arterial pressure (mPAP). Accordingly, pulmonary hypertension is typically associated with improper circulation and hemodynamics resulting in congestion causing an increase of pressure in the vascular system. The underlying pathophysiology is rather complex. At least three major pathogenic factors are known, i.e. vasoconstriction, thrombosis and pathological vascular remodeling.

[0004] Echocardiography may detect right ventricular (RV) pressure overload and dysfunction in HF and PH, but lacks sufficient sensitivity to diagnose PH. Another important shortcoming of echocardiography is its inability to identify and distinguish specific hemodynamic PH profiles, the pre-capillary component of PH, in particular. Therefore, invasive hemodynamic assessment by right heart catheterization (RCH) is mandatory to confirm the diagnosis of PH and to disentangle the respective contributions of pre- and post-capillary hemodynamic components.

[0005] Blood-based biomarkers might help to better phenotype PH and have the potential to function as a non-invasive tool in addition to echocardiography for the diagnosis and management of PH. To date, N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) is the only biomarker recommended by international guidelines in the setting of PH. In PH, NT-proBNP serum levels correlate with right ventricular function and hemodynamic parameters (Lewis 2020, Eur Respir Rev. 29). For hemodynamic phenotyping, however, NT- proBNP is of limited use because it reflects both left atrial filling pressure [which can be assessed by the pulmonary arterial wedge pressure (PAWP)], and pulmonary vascular resistance (PVR). Therefore, this biomarker lacks discriminative ability between the post-capillary and the pre-capillary component in PH. For this purpose, a discriminative biomarker specifically reflecting PVR, but not left atrial filling pressure (PAWP) would be most suitable.

[0006] Recently, Bone morphogenetic protein 10 (BMP 10) was shown to be associated with ischemic stroke and major adverse cardiovascular events in patients with atrial fibrillation (AF) (Hijazi 2023, Eur Heart J 44(3):208-18; Hennings 2023, J Am Heart Assoc. 12(6):e028255. BMP10, a member of the transforming growth factor P (TGF- P) superfamily, is a cardiac-specific biomarker predominantly expressed in the right atrium (Mikryukov 2021, Cell Stem Cell. 28(1):96- 11 l.e7; Reyat 2020, JCI Insight 5(16)). Ligands of this fam- ily bind various TGF-beta receptors leading to recruitment and activation of certain transcription factors that regulate gene expression. BMP 10 binds to the activin receptor-like kinase 1 (ALK1) and has been shown to be a functional activator of this kinase in endothelial cells (David 2007, Blood, 109(5): 1953-61). BMP10 plays a central role in cardiac embryogenesis, vascular smooth muscle cell (VSMC) contractility and maintenance, as well as playing a cardioprotective and anti-fibrotic function after cardiac injury (Wang 2021, Circulation, 143(14): 1394-410; Qu 2019, J Biol Chem. 294(52): 19877-88; Sun 2014, Cell Bio- chem, 115(11): 1868-76). Nevertheless, the clinical value of BMP10 is still not revealed entirely, also because its measurements in blood were not feasible until recently.

[0007] There is a need to identify biomarkers associated with objective quantification of congestion (in both HF and PH) and biomarkers associated with different etiologies of PH. Of particular interest in this regard are biomarkers with a strong association with mPAP and PVR but little to no association with left atrial filling pressure (measured by PAWP).

[0008] Thus, the technical problem underlying the present invention may be seen as the provision of means and methods complying the aforementioned needs. The technical problem is solved by the embodiments characterized in the claims and herein below.

[0009] Therefore, the present invention relates to a method for assessing congestion in a subject comprising the steps of a) determining in a sample of the subject the amount of a BMPlO-type peptide (Bone Morphogenic Protein 10-type peptide); and b) comparing the amount of the BMP 10-type peptide to a reference for the BMP 10-type peptide, whereby congestion is to be assessed.

[0010] As used in the following, the terms “have”, “comprise” or “include” are meant to have a non-limiting meaning or a limiting meaning. Thus, having a limiting meaning these terms may refer to a situation in which, besides the feature introduced by these terms, no other features are present in an embodiment described, i.e. the terms have a limiting meaning in the sense of “consisting of’ or “essentially consisting of’. Having a non-limiting meaning, the terms refer to a situation where besides the feature introduced by these terms, one or more other features are present in an embodiment described. Further, as used in the following, the terms “preferably”, “more preferably”, “most preferably”, "particularly", "more particularly", “typically”, and “more typically” are used in conjunction with features in order to indicate that these features are preferred features, i.e. the terms shall indicate that the recited features are pivotally envisaged in accordance with the invention but alternative features may yet also be envisaged.

[0011] Further, it will be understood that the term “at least one” as used herein means that one or more of the items referred to following the term may be used in accordance with the invention. For example, if the term indicates that at least one item shall be used this may be understood as one item or more than one item, i.e. two, three, four, five or any other number. Depending on the item the term refers to the skilled person understands as to what upper limit the term may refer, if any.

[0012] The method as referred to in accordance with the present invention includes a method which essentially consists of the aforementioned steps or a method which includes further steps. Moreover, the method of the present invention, preferably, is an ex vivo method, i.e. not practiced on the human or animal body, and, more preferably, an in vitro method. Moreover, it may comprise steps in addition to those explicitly mentioned above. For example, further steps may relate to the determination of further markers and / or to sample pre-treatments or evaluation of the results obtained by the method. The method may be carried out manually or assisted by automation. Preferably, step (a) and / or (b) may in total or in part be assisted by automation, e.g., by a suitable robotic and sensory equipment for the determination in step (a) or a computer-implemented calculation in step (b).

[0013] The term “assessing” as used herein refers to (i) diagnosing the presence or absence of a medical condition or disease referred to herein, (ii) diagnosing the stage of a disease or medical condition or disease referred to herein, (iii) differentiating a medical condition or disease referred to herein from another medical condition or disease referred to herein, (iv) predicting the occurrence of a medical condition or disease referred to herein in a given predictive time window, (v) monitoring the development of a medical condition or disease referred to herein, (vi) identifying a subject which shall be subjected to further diagnostics measures for diagnosing the medical condition or disease referred to herein, and / or (vii) identifying a subject as being susceptible for a therapy against a medical condition or diseases referred to herein. As will be understood by those skilled in the art, the assessment of the present invention is usually not intended to be correct for 100% of the subjects to be tested. However, the term requires that a correct assessment (such as the diagnosis, differentiation, prediction, identification or assessment of a therapy as referred to herein) can be made for a relevant portion of subjects within a given cohort and, preferably, for a statistically significant portion of subjects within a given cohort. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools, e.g., determination of confidence intervals, p-value determination, Student's t-test, Mann- Whitney test etc. Details are found in Dowdy and Wearden, Statis-tics for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. The p-values are, preferably, 0.4, 0.1, 0.05, 0.01, 0.005, or 0.0001.

[0014] The term “congestion” as used herein refers to vascular congestions, i.e. a swelling of body tissues caused by increased vascular blood flow or localized increase in blood pressure, thus leading to an abnormal accumulation of blood-derived fluid outside the blood vessels in extracellular spaces. Congestion (or “fluid overload”) in the lungs and periphery is a very frequent and often severe clinical symptom of patients presenting with heart failure to physicians. Congestion can comprise pulmonary congestion and dyspnea (“shortness of breath”), as well as systemic, peripheral water accumulation (“edema”). Congestion can also occur in patients presenting with pulmonary hypertension. Typically, the blood-derived fluid in congestion is a watery plasma. Congestion may, typically, resulting from vascular overload and, thus, increased blood pressure. Thus, congestion is often associated with high pulmonary vascular resistance (PVR) and can be used as one of the main indicators for assessing congestion. Pulmonary vascular resistance (PVR) is the resistance against blood flow from the four pulmonary veins of the lung to the left atrium. However, congestion may alternatively or in addition be result from pathological vascular remodeling, in particular, of smaller vessels such as arterioles, venules or capillaries. Congestion can be broadly classified into cardiogenic and non-cardiogenic pulmonary congestion. Cardiogenic congestion arises due to a rapid elevation in the hydrostatic pressure of the pulmonary capillaries. This is typically seen in disorders involving left ventricular systolic and diastolic function (acute myocarditis including other etiologies of non-ischemic cardiomyopathy, acute myocardial infarction), valvular function (aortic / mitral regurgitation and stenosis in the moderate to the severe range), rhythm (atrial fibrillation with a rapid ventricular response, ventricular tachycardia, high degree, and third-degree heart block). Non-cardiogenic pulmonary congestion is caused by lung injury with a resultant increase in pulmonary vascular permeability leading to the movement of fluid, rich in proteins, to the alveolar and interstitial compartments. Congestion as referred to herein is extracellular fluid accumulation in the lung and / or periphery, typically instigated by an increase in right- and left-sided cardiac filling pressure, respectively. Congestion as referred to herein, preferably, pulmonary congestion, is leading in the lungs to diminished gas exchange at the alveolar level, leading to shortness of breath in rest or under exercise (e.g., decrease in the patient’s 6-minute walk distance), progressing to potentially causing respiratory failure. Congestion as referred to herein may also preferably be peripheral congestion.

[0015] Congestion in accordance with the present invention may be associated with altered cardiac pressure, altered pulmonary pressure, and / or pathological vascular remodeling. Changes in pulmonary pressure may for example occur due to pulmonary hypertension (PH), i.e. a progressive disorder characterized by abnormally high blood pressure (hypertension) in the pulmonary artery. The most common mechanism of pulmonary hypertension is pulmonary vascular congestion due to increased pulmonary venous pressure resulting from chronically increased left-heart filling pressure (LHFP). The second mechanism is pulmonary arterial vasoconstriction and remodeling associated with increased pulmonary vascular resistances (VPR). To overcome the increased resistance, blood pressure increases in the pulmonary artery and in the right ventricle of the heart. Ultimately, the increased blood pressure leads to damage of the right ventricle of the heart or even heart failure (HF). In addition, HF of many other origins is also mostly associated with congestion such as emphasized and integrated into the Universal Definition of HF .

[0016] It follows from the above that the term “assessing congestion” as used herein, preferably, refers to:

[0017] (i) diagnosing the presence or absence of congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance),

[0018] (ii) diagnosing the stage of congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance),

[0019] (iii) differentiating congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling from each other or another medical condition or disease,

[0020] (iv) predicting in a given predictive time window the occurrence of congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance),

[0021] (v) monitoring the development of congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance), (vi) identifying a subject which shall be subjected to further diagnostics measures for diagnosing the congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance), and / or ii) identifying a subject as being susceptible for a therapy against congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance).

[0022] Diagnosing as mentioned above also encompasses providing an aid for diagnosis for an attending physician who will establish the clinical diagnosis for a given subject.

[0023] Differentiating congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance) from each other or another medical condition or disease as mentioned above, preferably, relates to differentiating between pulmonary congestion and peripheral congestion in a subject.

[0024] Predicting in a given predictive time window as referred to above means that the likelihood for developing congestion, altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling (including increased vascular resistance) is determined. The said likelihood shall be determined for a predefined predictive time window, e.g., for a time window of 3 months, 6 months, 1 year, two years, 3 years, 4 years, 5 years or 10 years or for the entire remaining lifetime of the subject. Predicting as used herein also encompasses predicting the risk of an adverse event associated with congestion. Preferably, said adverse event is selected from the group consisting of: Hypoxemia, shortness of breath, pulmonary hypertension, respiratory failure, cardiac arrest and congestive heart failure.

[0025] Further diagnostic measures as mentioned above that may be applied are, typically, selected from the group consisting of: X-ray, lung ultrasound, radiographic testing, pulmonary artery catheterization, transpulmonary thermodilution, bioimpedance and electrocardiography (ECG).

[0026] A therapy against congestion as mentioned herein above is, typically, a decongestive therapy. Preferably, said therapy is selected from the group consisting of: de-congestive therapy, preferably, diuretics, BMPlO-type peptide modifying agents, BMPlO-receptor modifying agents. More preferably, said BMPlO-type peptide modifying agents or said BMPlO-receptor modifying agents are antibodies specifically binding to the BMPlO-type peptide or the BMP10 receptor, nucleic acids which are capable of binding tFigo the transcripts of the BMPlO-type peptide or the BMP 10 receptor or small molecule drugs specifically binding to the BMPlO-type peptide or the BMP 10 receptor.

[0027] The “subject” as referred to herein is, preferably, a mammal. Mammals include, but are not limited to, domesticated animals (e.g., cows, sheep, cats, dogs, and horses), primates (e.g., humans and non-human primates such as monkeys), rabbits, and rodents (e.g., mice and rats). Preferably, the subject is a human subject. Preferably, the subject to be tested is of any age. However, more preferably the subject is of adult or of an elderly age, more preferably, the subject to be tested is 50 years of age or older, more preferably 60 years of age or older, and most preferably 65 years of age or older. Further, it is envisaged that the subject to be tested is 70 years of age or older. Moreover, it is envisaged that the subject to be tested is 75 years of age or older. The subject may be between 50 and 90 years. Preferably, the subject referred to herein is a subject suffering from heart failure.

[0028] The subject to be tested, preferably, shall suffer from congestion. Accordingly, the subject shall have a known history of congestion. Thus, the subject shall have experiences episodes of congestion prior to obtaining the test sample. For example, it is envisaged that the subject suffers from congestion, if the assessment of congestion is the differentiation between pulmonary or peripheral congestion, or if the assessment of congestion is the prediction of a risk of an adverse event associated with congestion, or if the assessment of congestion is the assessment of a therapy for congestion.

[0029] Yet, the subject to be tested is, preferably, suspected to suffer from congestion, i.e. is a subject that has no confirmed diagnosis of congestions. Such a subject, typically, shall assessed by diagnosing congestion or identifying the subject which shall be subjected to further diagnostics measures for diagnosing the congestion such as imaging techniques, e.g., X-ray, lung ultrasound, or radiographic testing, pulmonary artery catheterization, transpulmonary thermodilution or electrocardiography (ECG). Preferably, a subject who is suspected to suffer from congestion is a subject who has shown at least one symptom of congestion prior to carrying out the method for assessing congestion. Depending on the type of pulmonary congestion, said symptoms can be acute or chronic. Acute symptoms of congestion include dyspnea, coughing that produces frothy sputum that may contain blood, heart palpitations, excessive sweating, anxiety and pale skin. Chronic symptoms of congestion include paroxysmal nocturnal dyspnea, fatigue, swelling in the legs and feet, and orthopnea. Preferably, the subject has shown at least one symptom of congestion within six months prior to obtaining the sample. Preferably, said subject is a human, preferably, suspected to suffer or being at risk of suffering from congestion. The subject who is suspected to suffer from congestion shall have, preferably, no known history of congestion. In accordance with the present invention, a subject having no known history of congestion is, preferably, a subject who has not been diagnosed to suffer from congestion previously, i.e. before carrying out the method of the present invention (in particular before obtaining the sample from the subject). However, the subject may or may not have had previous undiagnosed episodes of congestion.

[0030] The subject to be tested may or may not experience clinically apparent episodes of congestion when the sample is obtained.

[0031] In a further setting, the subject to be tested shall be at risk of developing congestion. Accordingly, the subject shall have a known and stable history of heart failure. The subject might have none or only few episodes of congestion known prior to obtaining the test sample. It is envisaged that the subject is stable, but at potentially at risk for developing congestion in the next hours and days, if the assessment of BMP10 shows a value above healthy reference range population; or if the serial change of BMP 10 indicates a disease progression, congestion progression, therapeutic urgency and / or progression risk to acutely decompensated heart failure, and urgency for hospital admission.

[0032] The subject according to the present invention, preferably, shall suffer from heart failure (HF), pulmonary arterial hypertension (PAH) and / or pulmonary venous hypertension (PVH).

[0033] The term “sample” refers to a sample of a body fluid, to a sample of cells, tissue or an organ. Samples of body fluids can be obtained by well-known techniques and include, samples of blood, plasma, serum, urine, lymphatic fluid, interstitial fluids or derivatives thereof. Tissue or organ samples may be obtained from any tissue or organ by, e.g., biopsy. Cell-, tissue- or organ samples may be obtained from those cells, tissues or organs, which express or produce the biomarker. The sample may be frozen, fresh, fixed (e.g. formalin fixed), centrifuged, and / or embedded (e.g. paraffin embedded), etc. The sample can, of course, be subjected to a variety of well-known post-collection preparative and storage techniques (e.g., nucleic acid and / or protein extraction, fixation, storage, freezing, ultrafiltration, concentration, evaporation, centrifugation, etc.) prior to assessing the amount of the biomarker(s) in the sample. Preferably, the sample is a blood (i.e. whole blood), serum or plasma sample. Serum is the liquid fraction of whole blood that is obtained after the blood is allowed to clot. For obtaining the serum, the clot is removed by centrifugation and the supernatant is collected. Plasma is the acellular fluid portion of blood. For obtaining a plasma sample, whole blood is collected in anticoagulant-treated tubes (e.g. citrate-treated or EDTA-treated tubes). Cells are removed from the sample by centrifugation and the supernatant (i.e. the plasma sample) is obtained.

[0034] The term “Bone Morphogenic Protein 10-type peptide” or “BMPlO-type peptide” as used herein refers to peptides being or being derived from the Bone Morphogenetic Protein 10 (BMP10). BMP10 belongs to the TGF-P superfamily of proteins and is required, e.g., for maintaining the proliferative activity of embryonic cardiomyocytes by preventing premature activation of the negative cell cycle regulator CDKNlC / p57KIP and maintaining the required expression levels of cardiogenic factors such as MEF2C and NKX2-5. BMP10 also inhibits endothelial cell migration and growth. Several orthologues of BMP 10 have been reported in various animal species. Preferably, BMP 10 as referred to herein is human BMP 10 having an amino acid sequence as deposited under UniProt accession number 095393. It will be understood that the term also relates to variants of said proteins. It is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs by at least one amino acid substitution, deletion and / or addition wherein their amino acid sequence of the variant is still, preferably, at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the human BMP 10 protein, preferably, over its entire length. Such variants shall have at least the same essential biological and immunological properties as human BMP 10, preferably, those detectable by the specific assays referred to in this specification.

[0035] Typically, unprocessed BMP10 (i.e. preproBMPIO) (UniProt accession number 095393-1) comprises a short signal peptide (amino acids 1 to 21) which is enzymatically cleaved off to release proBMPlO. Accordingly, human proBMPIO comprises amino acids 22 to 424 of human preproBMPIO. Human proBMPIO is further cleaved into an N-terminal prosegment of BMP10 and (non-glycosylated) BMP10, which is the mature, active form. The N-terminal prosegment of BMP10 comprises amino acids 22 to 316 of the polypeptide. Since proBMPIO is cleaved into BMP 10 and the N-terminal prosegment in equimolar proportions, the amount of BMP 10 reflects the amount of the N-terminal prosegment. Thus, the amount of BMP 10 can be determined by determining the amount of the N-terminal prosegment and vice versa.

[0036] BMP 10-type peptides are well known in the art. Preferred BMPlO-types peptide are e.g. disclosed in Susan-Resiga et al. (J Biol Chem. 2011 Jul l;286(26):22785-94) which herewith is incorporated by reference in its entirety (Susan-Resiga et al., US 2012 / 0213782). Preferably, the BMP 10 of the present invention refers to a peptide that is selected from the group consisting of: BMP 10, N-terminal prosegment of BMP 10 (N-terminal proBMP 10), proBMP 10, and preproBMPlO. More preferably, the BMPlO-type peptide according to the present invention is selected from the group consisting of N-terminal prosegment of BMP 10 (N-terminal proBMPIO), proBMPIO, and preproBMPlO. Even more preferably, the BMPlO-type peptide is proBMPIO and / or N-terminal proBMPIO. Most preferably, the BMPlO-type peptide is NT-proBMPlO.

[0037] The BMPlO-type peptide according to the present invention may be part of a homo- or heterodimeric complex. In a heterodimeric complex, the BMPlO-type peptide may occur together with other BMPs and, preferably, together with BMP9.

[0038] In accordance with the present invention, the amount of BMPlO-type peptide shall be determined. The determination is, preferably, carried out with at least one detection agent for a BMPlO-type peptide. Such a detection agent, typically, binds NT-proBMPlO or specific sub-regions thereof. Thereby, the amount (i.e. the combined) of all BMPlO-type peptides can be determined that comprise the NT-proBMPlO peptide sequence, such as proBMPIO, preproBMPlO and NT-proBMPlO. Thus, when determining the amount of a BMPlO-type peptide according to the present invention the combined amounts (i.e. the sum of the amounts) of peptides is determined which have an amino acid sequence comprising NT- proBMPlO amino acid sequence, in particular, the combined amounts (i.e. the sum of the amounts) of proBMPIO, preproBMPlO and NT-proBMPlO. Since preproBMPlO is, in principle, not present in many samples, the expression may also mean that the sum of NT- proBMPlO and proBMPIO is determined. Thus, the amount of NT-proBMPlO and / or BMP 10 is determined. Preferably, the amount of the BMPlO-type peptide is determined by using one or more antibodies (or antigen-binding fragments thereof) which specifically bind to the BMPlO-type peptide as detection agent(s).

[0039] The term “amount” as used herein refers to absolute as well as relative amounts of the BMPlO-type peptide. Thus, the BMPlO-type peptide shall be quantitatively determined. However, the term also encompasses the determination of the amount relative to a reference value, such as a reference volume, such as the sample volume or a predefined volume, an amount of a predetermined peptide or protein used as reference value, the total amount of peptide and proteins, the total sample mass, the amount of cells, and the like.

[0040] The term “reference” as used herein refers to a parameter or value and, preferably, to a reference amount that allows for assessing congestion as specified elsewhere herein. Thus, based on the said reference for the BMPlO-type peptide and the determined amount for the BMPlO-type peptide in the sample of the subject, the said subject shall be allocated into either the group of subjects suffering from congestion or the group of subjects not suffering from congestion.

[0041] Preferably, said reference may be an amount for the BMPlO-type peptide derived from a subject or group of subjects known to exhibit congestion. In such a case, preferably, an amount for the BMP 10-type peptide determined in the sample of the subj ect being essentially identical or increased compared to the reference is indicative for a subject suffering from congestion, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being decreased compared to the reference is indicative for a subject which is not suffering from congestion.

[0042] Yet preferably, said reference may be an amount for the BMPlO-type peptide derived from a subject or group of subjects known not to exhibit congestion. In such a case, preferably, an amount for the BMP 10-type peptide determined in the sample of the subj ect being essentially identical or decreased compared to the reference is indicative for a subject which is not suffering from congestion, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being increased compared to the reference is indicative for a subject suffering from congestion.

[0043] Moreover, the reference amount as referred to herein may be a predetermined reference amount value. Said predetermined value shall allow for assessing congestion as described above. In principle, preferred reference amount values can be also calculated for a cohort of subjects as specified above based on the average or mean values for a BMPlO-type peptide by applying standard methods of statistics. In particular, accuracy of a test such as a method aiming to diagnose an event, or not, is best described by its receiver-operating characteristics (ROC) (see Zweig 1993, Clin. Chem. 1993;39:561-577).

[0044] It is to be understood that the reference may differ based on the type of assessment. For example, the reference for the BMPlO-type peptide for the differentiation of congestion will be usually higher than the reference amount for the diagnosis of congestion.

[0045] In accordance with the present invention, the amount of the BMPlO-type peptide shall be compared to a reference as specified above. It is to be understood that comparing as used herein usually refers to a comparison of corresponding parameters or values, e.g., an absolute amount is compared to an absolute reference amount while a concentration is compared to a reference concentration or an intensity signal obtained from the BMPlO-type peptides in a sample is compared to the same type of intensity signal. The comparison may be carried out manually or computer-assisted. Thus, the comparison may be carried out by a computing device. The value of the determined or detected amount of BMPlO-type peptides in the sample from the subject and the reference amount can be, e.g., compared to each other and the said comparison can be automatically carried out by a computer program executing an algorithm for the comparison. The computer program carrying out the said evaluation will provide the de-sired assessment in a suitable output format. For a computer-assisted comparison, the value of the determined amount may be compared to values corresponding to suitable references, which are stored in a database by a computer program. The computer program may further evaluate the result of the comparison, i.e. automatically provide the desired assessment in a suitable output format. For a computer-assisted comparison, the value of the determined amount may be compared to values corresponding to suitable references, which are stored in a database by a computer program. The computer program may further evaluate the result of the comparison, i.e. automatically provides the desired assessment in a suitable output format. Thus, step b) of the method of the present invention is, preferably, computer- implemented.

[0046] The method for assessing congestion may further comprise determining the amount of at least one further biomarkers selected from the group consisting of: a natriuretic peptide, preferably, N-terminal propeptide of B-type natriuretic peptide (NT-proBNP), myosin-binding protein C cardiac-type (MyBPC3), fibroblast growth factor 23 (FGF23), endothelial cell specific molecule 1 (ESMI), insulin like growth factor binding protein 7 (IGFBP7), growth differentiation factor 15 (GDF15), interleukin 6 (IL-6), angiopoietin 2 (ANG2), C-terminal fragment of type VI (a3) collagen (PRO-C6), and

[0047] - N-terminal type III collagen propeptide (PRO-C3).

[0048] These biomarkers are also suitable for assessing conditions associated with congestion, such as pulmonary hypertension, heart failure, vascular remodeling, and vascular injury and inflammation. The term “MyBPC3” as used herein refers to the myosin-binding protein C, cardiac-type encoded in humans by the MYBPC3 gene. It is crucial for sarcomere organization and maintenance of normal cardia function. In humans, two isoforms have been reported. Moreover, several orthologues have been reported in various animal species. The MyBPC3 protein referred to in accordance with the present invention is preferably human MyBPC3 having an amino acid sequence as deposited under UniProt accession number Q14896. It will be understood that the term MyBPC3 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the MyBPC3 protein, preferably over the entire length of the said MyBPC3 proteins, respectively.

[0049] The term “FGF23” as used herein refers to the fibroblast growth factor 23 encoded in humans by the FGF23 gene. It is essential in the regulation of phosphate homeostasis in plasma and vitamin D metabolism and decreases reabsorption of phosphate in the kidney. Several orthologues have been reported in various animal species. The FGF23 protein referred to in accordance with the present invention is preferably human FGF23 having an amino acid sequence as deposited under UniProt accession number Q9GZV9. It will be understood that the term FGF23 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the FGF23 protein, preferably over the entire length of the said FGF23 proteins, respectively.

[0050] The term “ESM1” as used herein refers to the endothelial cell-specific molecule 1 encoded in humans by the ESMI gene. It is mainly expressed in the endothelial cells in human lung and kidney tissue and may play a role in endothelium-dependent pathological disorders. Several orthologues have been reported in various animal species. The ESMI protein referred to in accordance with the present invention is preferably human ESMI having an amino acid sequence as deposited under UniProt accession number Q9NQ30. It will be understood that the term ESMI also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the ESMI protein, preferably over the entire length of the said ESMI proteins, respectively.

[0051] The term “IGFBP7” as used herein refers to insulin like growth factor binding protein 7 encoded in humans by the IGFBP7 gene. Its major function is the regulation of availability of insulin-like growth factors (IGFs) in tissue as well as in modulating IGF binding to its receptors. It also stimulates cell adhesion. In humans, two isoforms have been reported. Moreover, several orthologues have been reported in various animal species. The IGFBP7 protein referred to in accordance with the present invention is preferably human IGFBP7 having an amino acid sequence as deposited under UniProt accession number QI 6270. It will be understood that the term IGFBP7 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the IGFBP7 protein, preferably over the entire length of the said IGFBP7 proteins, respectively.

[0052] The term “GDF15” as used herein refers to growth differentiation factor 15 encoded in humans by the GDF15 gene. It is implicated in the regulation of inflammatory pathways, apoptosis, angiogenesis, cell repair and cell growth. Several orthologues have been reported in various animal species. The GDF15 referred to in accordance with the present invention is preferably human GDF15 having an amino acid sequence as deposited under UniProt accession number Q99988. It will be understood that the term GDF15 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the GDF15, preferably over the entire length of the said GDF15 proteins, respectively.

[0053] The term “IL-6” as used herein refers to interleukin 6 encoded in humans by the IL6 gene. IL-6 has a wide variety of biological functions in immunity, tissue regeneration and metabolism. Several orthologues have been reported in various animal species. The IL-6 referred to in accordance with the present invention is preferably human IL-6 having an amino acid sequence as deposited under UniProt accession number P05231. It will be understood that the term IL-6 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the IL-6, preferably over the entire length of the said IL-6 proteins, respectively.

[0054] The term “ANG2” (also designated as ANGP2) as used herein refers to angiopoietin 2 encoded in humans by the ANGPT2 gene. It is upregulated in multiple inflammatory diseases and is implicated in the direct control of inflammation-related signaling pathways. In humans, three isoforms have been reported. Moreover, several orthologues have been reported in various animal species. The ANG2referred to in accordance with the present invention is preferably human ANG2 having an amino acid sequence as deposited under UniProt accession number 015123. It will be understood that the term ANG2 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the ANG2, preferably over the entire length of the said ANG2, respectively. The term “C-terminal type VI (a3) collagen (PRO-C6)” as used herein refers to a collagen VI (Col6a3) fragment also called endotrophin. Preferably, Col6a3 in human has an amino acid sequence as shown in UniProt accession number P 12111. It will be understood that the term PRO-C6 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the PRO-C6, preferably over the entire length of the said PRO-C6, respectively.

[0055] The term “N-terminal type III collagen propeptide (PRO-C3)” as used herein refers to a fragment of collagen type III (Col3al). Preferably, Col3al in human has an amino acid sequence as shown in UniProt accession number P02461. It will be understood that the term PRO-C3 also relates to variants of said proteins. Such variants have at least the same essential biological and immunological properties if they are detectable by the same specific assays referred to in this specification. Moreover, it is to be understood that a variant as referred to in accordance with the present invention shall have an amino acid sequence which differs due to at least one amino acid substitution, deletion and / or addition wherein the amino acid sequence of the variant is still, preferably at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, or 99% identical with the specific amino sequence of the PRO-C3, preferably over the entire length of the said PRO-C3, respectively.

[0056] Preferably, elevated amounts relative the physiological amounts of biomarkers NT-proBNP, MyBPC3, ANG2, and FGF23 are associated with peripheral vascular resistance in descending order while ANG2, NT-proBNP, MyBPC3, IGFBP7, and FGF23 in descending order are associated with pulmonary arterial wedge pressure or left ventricular filling pressure. In contrast to these further biomarkers, BMP10 is, preferably, associated with strong peripheral vascular resistance but independent from pulmonary arterial wedge pressure.

[0057] The physiological amount of a biomarker as referred to herein refers to the amount of the biomarker that can be found in a subject know to not suffer from congestion or a medical condition or disease referred to herein. An elevated amount for the biomarker in comparison to the physiological amount may be determined by comparing the determined amount to either a reference and, preferably, a reference amount for the biomarker, derived from a subject or group of subjects known not to suffer from the medical condition or disease referred to herein or by comparing the determined amount to either a reference and, preferably, a reference amount for the biomarker, derived from a subject or group of subjects known to suffer from the medical condition or disease referred to herein. In the latter case an identical or increased amount is indicative for an elevated amount of the biomarker while in the first case, an increased amount is indicative for an elevated amount of the biomarker. Vice versa, in the latter case a reduced amount is indicative for a non-elevated amount and in the first case for the aforementioned reference, an identical or decreased amount is also indicative for a non-elevated amount.

[0058] Advantageously, it has been found in the studies underlying the present invention that BMP 10 is a biomarker of atrial stress (such as caused by increased filling pressure, atrial dilation or stretch) and congestion in patients with heart failure. Further, BMP 10 is an independent predictor of mortality and / or hospitalization in patients with heart failure and provides additional prognostic value to established risk models. More specifically, BMP 10 can help in identifying patients at risk for atrial fibrillation, undergoing pathophysiological atrial remodeling or suffer from congestion including congestion, which is not yet clinically apparent. Consequently, BMP 10 improve the identification of patients who benefit from timely treatment against atrial remodeling and / or congestion.

[0059] Moreover, it was found in the studies underlying the present invention that BMP 10 in patients suffering from pulmonary hypertension and congestion was strongly associated with peripheral vascular resistance, but not with pulmonary arterial wedge pressure, thereby reflecting the pre-capillary component of pulmonary hypertension. Measurement of BMP 10 is, thus, useful in the diagnosis and risk stratification of patients with suspected or established pulmonary hypertension in addition to other biomarkers, e.g., NT-proBNP. The improved differentiation provided by the invention influences the choice of therapy.

[0060] Taken together, these findings suggest that BMP 10 is a biomarker for congestion and medical conditions and disease accompanying it. Moreover, BMP 10 was suggested to play a role in governing cell proliferation and tonic quiescence of endothelial cells as well as affecting vascular endothelia integrity and function (Desroches-Castan 2022, Dev Dyn. 251 : 178-197; David 2007, Blood 109: 1953-1961; Morrell 2019, Eur Respir J. 53). Accordingly, conges- tion, including pulmonary and peripheral congestion, as well as associated medical conditions and diseases such as altered cardiac pressure or altered pulmonary pressure may be related to pathological vascular remodeling, vascular porosity and vascular resistance.

[0061] The definitions of the terms and explanations made before apply mutatis mutandis to the following embodiments of the invention.

[0062] The present invention also relates to a computer-implemented method for assessing congestion in a subject comprising the steps of a) receiving data for the amount of a BMPlO-type peptide in a sample of a subject in a processor; b) comparing said data using the processor to reference data, preferably, stored on a storage medium; and c) assessing congestion in the subject based on the comparison.

[0063] Preferably, all steps of the computer-implemented method are performed by one or more processing units of a computer or computer network. Thus, the assessment in step (c) is carried out by a processing unit. Preferably, said assessment is based on the results of step (b).

[0064] The data for the amount of a BMPlO-type peptide received in step (a) shall be derived from the determination of the amount of a BMPlO-type peptide from a sample of a subject suffering from or being suspected to suffer from congestion or a medical condition or disease associated therewith as described elsewhere herein. Preferably, the data comprise a value for the total amount or concentration of a BMPlO-type peptide in the sample. Typically, the value will be received by the processing unit by uploading or sending the data to the processing unit. Alternatively, the data can be received by the processing unit by inputting the data manually via a user interface.

[0065] More preferably, said data for the amount of a BMPlO-type peptide in a sample are received from an analyzer capable of automatically analyzing the amount of a BMPlO-type peptide present in sample. Preferably, the aforementioned computer-implemented method of the invention further comprises the steps of: d) integrating further biomarkers and clinical information to differentiate the various etiologies of congestion, altered cardiac and / or pulmonary pressures and / or vascular remodeling; and e) estimating the future risk of congestion to guide discharge decisions in patients with congestive heart failure.

[0066] Integrating further biomarkers and clinical information may be made in accordance with the computer-implemented method of the present invention by combining the assessment of congestion made based the BMPlO-type peptide with assessments made on the basis of the said further biomarkers and clinical information. Typically, the different assessments are weighted by, e.g., allocating scores to the assessments and integrated by combining the different scores in a scoring system.

[0067] Estimating the future risk may be made in accordance with the computer-implemented method of the present invention by comparing the weighted assessments, e.g. the scores in the scoring system, with pre-defined references, e.g. reference scores or threshold values in the scoring system, associated with various etiologies of congestion, altered cardiac and / or pulmonary pressures and / or vascular remodeling which are related to potential future risks. Based on the comparison, which allows for identifying a future risk by matching with a predefined reference, the future risk may be estimated.

[0068] The present invention also relates to a method of recommending a therapy against congestion (also referred to as “decongestion”) comprising the steps of the method of the aforementioned present invention and the further step of recommending the said therapy if the subject is assessed to suffer from congestion.

[0069] Preferably, said therapy is selected from the group consisting of: decongestive therapy, preferably, diuretics, BMPlO-type peptide modifying agents, BMPlO-receptor modifying agents.

[0070] Decongestive therapy may include manual lymphatic drainage (MLD), compression therapy, ultrafiltration and the use of diuretics or a combination thereof. The term “diurectics” refers to any substance that increases the amount of water and salt expelled from the body as urine. There are three types of diuretic medications: i) thiazide diuretics, ii) loop diuretics and iii) potassium- sparing diuretics. Thiazides are the most commonly prescribed diuretics and used to treat high blood pressure. Examples of thiazides include chlorthalidone, hodro- chlorothi azide, metolazone and indapamide. Loop diuretics are often used to treat heart failure. Examples of loop diuretics include torsemide, furosemide and bumetanide. Potassium- sparing diuretics refer to medication that reduces fluid levels in the body without loss of potassium. Examples of potassium- sparing diuretics include amiloride, triamterene, spironolactone and eplerenone.

[0071] More preferably, said BMPlO-type peptide modifying agents or said BMPlO-receptor modifying agents are antibodies specifically binding to the BMPlO-type peptide or the BMP 10 receptor, nucleic acids, which are capable of binding to the transcripts of the BMPlO-type peptide or the BMP 10 receptor or small molecule drugs specifically binding to the BMPlO- type peptide or the BMP 10 receptor. Specific BMP 10 modifying therapies have been recently proposed for use in tumor suppression / anti-angiogenesis in oncology (Castonguay 2011. J Biol Chem. 26;286:30034-46), or as systemic antihypertensive treatment (European Patent Office EP3725804A1), but not use in congestion. In pulmonary hypertension, a series of more generic FLRG polypeptides has been recently invented to inhibit a very broad range of GDF / BMP pathways via ActRII modulation, including most BMPs, but not being specific to a single BMP and not targeting specifically congestion (European Patent Office EP3496739). Preferably, present invention may provide guidance also to such therapies by identification of patients at risk, stratifying the responders, guiding their therapeutic dose, and monitoring the effectiveness of the BMP 10 modification.

[0072] The present invention concerns also a device adapted for carrying out the aforementioned method of the invention comprising: a) a detector for determining the amount of a BMPlO-type peptide in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMPlO- type peptide to a reference and to assess based on the said comparison congestion.

[0073] The term “device” as used herein refers to a system comprising the aforementioned elements operatively linked to each other as to allow the determination of the amount of a BMP 10- type peptide in a sample of a subject and carrying out the comparison as well as establishing the assessment.

[0074] The detector, typically, comprises at least one detection zone being capable of detecting the BMPlO-type peptide present in the sample. The detector may also comprise a reaction zone that allows carrying out a chemical detection reaction. Preferably, prior to introducing the sample into the detection zone, the sample may be contacted to detection agents in order to generated detectable signals, e.g., by allowing the formation of analyte-antibody complexes whereby the antibody as detection agents comprises a detectable label that can be detected by the detector in the detection zone. The detection zone shall be adapted to determine the amount of the biomarkers based on the presence, absence or intensity of detectable signals generated. The determined amount can be subsequently transmitted to the processor for making the comparison and, typically, also making the assessment.

[0075] The processor is adapted for carrying out the comparison of the determined amount of the BMPlO-type peptide in the sample and the reference. Typically, it may run an implemented algorithm for doing so. The processor, typically, comprises a Central Processing Unit (CPU) and / or one or more Graphics Processing Units (GPUs) and / or one or more Application Specific Integrated Circuits (ASICs) and / or one or more Tensor Processing Units (TPUs) and / or one or more field-programmable gate arrays (FPGAs) or the like. A processor may, for example, be or may be included in a general purpose computer or a portable computing device. It should also be understood that multiple computing devices may be used together, e.g., over a network or other methods of transferring data, for performing one or more steps of the methods disclosed herein. Exemplary computing devices include desktop computers, laptop computers, personal data assistants (“PDA”), cellular devices, smart or mobile devices, tablet computers, servers, and the like. In general, a data processing element comprises a processor capable of executing a plurality of instructions (such as a program of software). The processor, typically, comprises or has access to a memory. The memory, typically, comprises ta stored reference to be used for the comparison. A memory is a computer readable medium and may comprise a single storage device or multiple storage devices, located either locally with the computing device or accessible to the computing device across a network, for example. Computer-readable media may be any available media that can be accessed by the computing device and includes both volatile and non-volatile media. Further, computer readable-media may be one or both of removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media. Exemplary computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or any other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used for storing a plurality of instructions capable of being accessed by the computing device and executed by the processor of the computing device. The processor may also comprise or has access to an output device. Exemplary output devices include fax machines, displays, printers, and files, for example. According to some embodiments of the present disclosure, a computing device may perform one or more steps of a method disclosed herein, and thereafter provide an output, via an output device, relating to a result, indication, ratio or other factor of the method.

[0076] Yet the present invention contemplates a kit for carrying out the aforementioned method of the invention comprising a detection agent for a BMPlO-type peptide in a sample of a subject.

[0077] The term “kit” as used herein refers to a collection of the aforementioned components, preferably, provided separately or within a single container. The container also comprises instructions for carrying out the method of the present invention. These instructions may be in the form of a manual or may be provided by a computer program code, which is capable of carrying out the calculations and comparisons referred to in the methods of the present invention and to establish the assessment accordingly when implemented on a computer or a data processing device. The computer program code may be provided on a data storage medium or device such as an optical storage medium (e.g., a Compact Disc) or directly on a computer or data processing device. Moreover, the kit may, preferably, comprise standard amounts for the analyte to be detected for calibration purposes.

[0078] The kit shall comprise at least one detection agent for a BMPlO-type peptide. Yet, the kit may also preferably comprise at least one further detection agent for a biomarker selected from the group consisting of A natriuretic peptide, MyBPC3, FGF23, ESMI, GDF15, IL- 6, ANG2, PR0-CD6 or PRO-C3.

[0079] The term “detection agent” relates to an agent that comprises a binding moiety which specifically binds the analyte to be detected, i.e. the BMPlO-type peptide or any other biomarker referred to herein such as a biomarker selected from the group consisting of A natriuretic peptide, MyBPC3, FGF23, ESMI, GDF15, IL-6, ANG2, PR0-CD6 or PRO-C3. Preferred detection agents referred to herein are antibodies, aptamers or engineered peptides, proteins or peptide nucleic acids (PNAs). Specific binding means that the detection agent has properties that allow for binding of the analyte but not to other molecules present in the sample under the conditions applied for carrying out the method of the invention.

[0080] Preferably, the detection agent in accordance with the present invention is an antibody. Antibodies as referred to herein may be monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments that exhibit the desired antigen-binding activity (i.e. an antigen-binding fragments thereof). Preferably, the antibody is a polyclonal antibody or an antigen-binding fragment thereof. More preferably, the antibody is a monoclonal antibody or an antigen-binding fragment thereof. Moreover, as described elsewhere herein, it is envisaged that two monoclonal antibodies are used that bind at different positions of the BMPlO-type peptide and, thus, can be used in a sandwich- format immunoassay.

[0081] The present invention also relates to a method of differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension comprising a) determining the amount of a BMPlO-type peptide in a sample of the subject; b) determining the amount of ANG2 in a sample of the subject; and c) comparing the amounts of the BMPlO-type peptide and ANG2 to references, whereby it is differentiated between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0082] The term “differentiating” as used herein refers to allocating the investigated subject into either the group of subjects suffering from precapillary pulmonary hypertension or the group of subjects suffering from postcapillary pulmonary hypertension. As will be understood by those skilled in the art, the differentiation of the present invention is usually not intended to be correct for 100% of the subjects to be tested. However, the term requires that a correct differentiation can be made for a statistically significant portion of subjects within a given cohort. Whether a portion is statistically significant can be determined without further ado by the person skilled in the art using various well known statistic evaluation tools referred to elsewhere herein. Differentiation between these two conditions is based on whether pulmonary artery wedge pressure (PAWP) or left ventricular (LV) end-diastolic pressure (LVEDP) is elevated >15 mmHg. The method of the present invention allows for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in a subject based on the determination of the biomarkers BMPlO-type peptide as well as ANG2 specified elsewhere herein in detail. The actual differentiation may comprise further steps such as the confirmation of the differentiation. Thus, differentiation in the context of the present invention also encompasses aiding the physician to differentiate between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0083] The term “precapillary pulmonary hypertension” as used herein refers to pulmonary arterial hypertension caused by pulmonary arterial vascular remodeling leading to increased precapillary pulmonary vascular resistance (PVR).

[0084] The term “postcapillary pulmonary hypertension” as used herein refers to pulmonary venous hypertension due to an increase in pulmonary venous pressure, e.g., in left-sided heart diseases.

[0085] Preferably, the subject to be investigated by the aforementioned method for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension has an amount of NT-proBNP in the sample being increased compared to its physiological amount. Elevated amounts of NT-proBNP are strong indicators for pulmonary hypertension in general.

[0086] It is to be understood that the reference amount shall allow for the herein described differentiation between precapillary pulmonary hypertension and postcapillary pulmonary hypertension. In connection with the method for differentiation between precapillary pulmonary hypertension and postcapillary pulmonary hypertension, the reference amount preferably refers to an amount, which allows for allocation of a subject into either (i) the group of subjects suffering from precapillary pulmonary hypertension, (ii) the group of subjects suffering from postcapillary pulmonary hypertension, or (iii) the group of subjects suffering neither from precapillary pulmonary hypertension nor from postcapillary pulmonary hypertension. A suitable reference amount may be determined from a sample to be analyzed together, i.e. simultaneously or subsequently, with the test sample.

[0087] Preferably, said references may be amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit precapillary pulmonary hypertension. More preferably, an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or increased and an amount of ANG2, which is essentially identical or decreased compared to the reference is indicative for a subject suffering from precapillary pulmonary hypertension, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being decreased and an amount of ANG2 being increased compared to the reference is indicative for a subject, which is suffering from postcapillary pulmonary hypertension.

[0088] Preferably, said references may be amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit postcapillary pulmonary hypertension. More preferably, an amount for the BMPlO-type peptide determined in the sample of the subject being increased and an amount of ANG2, which is decreased compared to the reference is indicative for a subject suffering from precapillary pulmonary hypertension, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or decreased and an amount of ANG2 being essentially identical or increased compared to the reference is indicative for a subject, which is suffering from postcapillary pulmonary hypertension.

[0089] Preferably, step c) of the aforementioned method is computer-implemented.

[0090] In the studies underlying the present invention, it was, inter alia, found that NT-proBNP, ANG2, MyBPC3, BMP10, and FGF23 were strongly associated with mPAP (p <0.001), while ANG2 and NT-proBNP were strongly associated with PAWP (p <0.001), and NT- proBNP and BMP10 were strongly associated with PVR (p <0.001). Therefore, a BMP-10 type peptide according to the invention may be used together with ANG-2 for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension. The said differentiation may be further strengthened in a preferred embodiment by considering one of the other biomarkers in addition, i.e. NT-proBNP, MyBPC3 and / or FGF23, preferably, FGF23.

[0091] Thus, the present invention, in particular, also relates to a computer-implemented method for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in a subject comprising the steps of a) receiving data for the amount of a BMPlO-type peptide and ANG2 in a sample of a subject in a processor; b) comparing said data using the processor to reference data, preferably, stored on a storage medium; and c) differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in the subject based on the comparison.

[0092] Preferably, said data for the amounts of the BMPlO-type peptide and ANG2 in a sample are received from an analyzer capable of automatically analyzing the amounts of a BMPlO-type peptide and ANG2 present in sample.

[0093] The present invention also concerns a device adapted for carrying out the aforementioned method of the invention comprising: a) a detector for determining the amounts of a BMPlO-type peptide and ANG2 in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMPlO- type peptide to a reference and the amount of ANG2 to a reference and for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0094] The present invention also contemplates a kit for carrying out the aforementioned method of the present invention comprising a detection agent for a BMPlO-type peptide and a detecting agent for ANG2 in a sample of a subject.

[0095] The present invention relates, in general, to the use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for assessing congestion in heart failure and pulmonary hypertension. Preferably, said congestion is pulmonary congestion and / or peripheral congestion. Also preferably, said assessment comprises diagnosing and / or predicting altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling or monitoring of congestion prior, during and after a decongestive therapy. Yet preferably, said altered pulmonary pressure is pulmonary hypertension and, more preferably, said pulmonary hypertension is precapillary pulmonary hypertension or postcapillary pulmonary hypertension.

[0096] The present invention, also in general, relates to the use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for differentiating between precapillary hypertension and postcapillary hypertension. Preferably, ANG2 in the sample is used for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in addition.

[0097] The following embodiments are particular preferred embodiments envisaged in accordance with the present invention.

[0098] Embodiment 1 : A method for assessing congestion in a subject comprising the steps of a) determining in a sample of the subject the amount of a BMPlO-type peptide (Bone Morphogenic Protein 10-type peptide); and b) comparing the amount of the BMP 10-type peptide to a reference for the BMP 10-type peptide, whereby congestion is to be assessed.

[0099] Embodiment 2: The method of embodiment 1, wherein said congestion is pulmonary congestion and / or peripheral congestion.

[0100] Embodiment 3 : The method of embodiment 1 or 2, wherein said congestion is associated with altered cardiac pressure, altered pulmonary pressure, and / or pathological vascular remodeling.

[0101] Embodiment 4: The method of any one of embodiments 1 to 3, wherein said reference is an amount for the BMP 10-type peptide derived from a subject or group of subjects known to exhibit congestion.

[0102] Embodiment 5: The method of embodiment 4, wherein an amount for the BMP 10-type peptide determined in the sample of the subject being essentially identical or increased compared to the reference is indicative for a subject suffering from congestion, whereas an amount for the BMP 10-type peptide determined in the sample of the subject being decreased compared to the reference is indicative for a subject which is not suffering from congestion.

[0103] Embodiment 6: The method of any one of embodiments 1 to 3, wherein said reference is an amount for the BMP 10-type peptide derived from a subject or group of subjects known not to exhibit congestion. Embodiment 7: The method of embodiment 6, wherein an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or decreased compared to the reference is indicative for a subject which is not suffering from congestion, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being increased compared to the reference is indicative for a subject suffering from congestion.

[0104] Embodiment 8: The method of any one of embodiments 1 to 7, wherein said subject is suspected to suffer or being at risk of suffering from congestion and / or suffering from heart failure, pulmonary arterial hypertension and / or pulmonary venous hypertension.

[0105] Embodiment 9: The method of any one of embodiments 1 to 8, wherein said sample is a blood sample, preferably, a whole blood, a serum or a plasma sample.

[0106] Embodiment 10: The method of any one of embodiments 1 to 9, wherein step b) of said method is computer-implemented.

[0107] Embodiment 11 : A computer-implemented method for assessing congestion in a subject comprising the steps of a) receiving data for the amount of a BMPlO-type peptide in a sample of a subject in a processor; b) comparing said data using the processor to reference data, preferably, stored on a storage medium; and c) assessing congestion in the subject based on the comparison.

[0108] Embodiment 12: The method of embodiment 11, wherein said method further comprises the steps of: d) integrating further biomarkers and clinical information to differentiate the various etiologies of congestion, altered cardiac and / or pulmonary pressures and / or vascular remodeling; and e) estimating the future risk of congestion to guide discharge decisions in patients with congestive heart failure.

[0109] Embodiment 13: The method of embodiment 11 or 12, wherein said data for the amount of a BMPlO-type peptide in a sample are received from an analyzer capable of automatically analyzing the amount of a BMPlO-type peptide present in sample. Embodiment 14: The method of any one of embodiments 1 to 13, wherein said congestion is pulmonary congestion and / or peripheral congestion.

[0110] Embodiment 15: The method of any one of embodiments 1 to 14, wherein said assessment comprises diagnosing and / or predicting altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling or monitoring of congestion prior, during and after a decongestive therapy.

[0111] Embodiment 16: The method of embodiment 15, wherein said altered pulmonary pressure is pulmonary hypertension.

[0112] Embodiment 17: The method of embodiment 16, wherein said pulmonary hypertension is precapillary pulmonary hypertension or wherein said pulmonary hypertension is postcapillary pulmonary hypertension.

[0113] Embodiment 18: A method of recommending a therapy against congestion comprising the steps of the method of any one of embodiments 1 to 17 and the further step of recommending the said therapy if the subject is assessed to suffer from congestion.

[0114] Embodiment 19: The method of embodiment 18, wherein said therapy is selected from the group consisting of: de-congestive therapy, preferably, diuretics, BMPlO-type peptide modifying agents, BMPlO-receptor modifying agents.

[0115] Embodiment 20: The method of embodiment 19, wherein said BMPlO-type peptide modifying agents or said BMPlO-receptor modifying agents are antibodies specifically binding to the BMPlO-type peptide or the BMP 10 receptor, nucleic acids which are capable of binding to the transcripts of the BMPlO-type peptide or the BMP 10 receptor or small molecule drugs specifically binding to the BMPlO-type peptide or the BMP 10 receptor.

[0116] Embodiment 21 : A device adapted for carrying out the method of any one of embodiments 1 to 20 comprising: a) a detector for determining the amount of a BMPlO-type peptide in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMP 10- type peptide to a reference and to assess based on the said comparison congestion.

[0117] Embodiment 22: A kit for carrying out the method of any one of embodiments 1 to 20 comprising a detection agent for a BMPlO-type peptide in a sample of a subject.

[0118] Embodiment 23 : A method of differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension comprising a) determining the amount of a BMPlO-type peptide in a sample of the subject; b) determining the amount of ANG2 in a sample of the subject; and c) comparing the amounts of the BMPlO-type peptide and ANG2 to references, whereby it is differentiated between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0119] Embodiment 24: The method of embodiment 23, wherein said subject has an amount of NT- proBNP in the sample being increased compared to its physiological amount.

[0120] Embodiment 25: The method of embodiment 23 or 24, wherein said references are amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit precapillary pulmonary hypertension.

[0121] Embodiment 26: The method of embodiment 25, wherein an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or increased and an amount of ANG2 which is essentially identical or decreased compared to the reference is indicative for a subject suffering from precapillary pulmonary hypertension, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being decreased and an amount of ANG2 being increased compared to the reference is indicative for a subject which is suffering from postcapillary pulmonary hypertension.

[0122] Embodiment 27: The method of embodiment 23 or 24, wherein said references are amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit postcapillary pulmonary hypertension.

[0123] Embodiment 28: The method of embodiment 27, wherein an amount for the BMPlO-type peptide determined in the sample of the subject being increased and an amount of ANG2 which is decreased compared to the reference is indicative for a subject suffering from precapillary pulmonary hypertension, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or decreased and an amount of ANG2 being essentially identical or increased compared to the reference is indicative for a subject which is suffering from postcapillary pulmonary hypertension.

[0124] Embodiment 29: The method of any one of embodiments 23 to 28, wherein step c) of said method is computer-implemented.

[0125] Embodiment 30: A computer-implemented method for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in a subject comprising the steps of a) receiving data for the amount of a BMPlO-type peptide and ANG2 in a sample of a subject in a processor; b) comparing said data using the processor to reference data, preferably, stored on a storage medium; and c) differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in the subject based on the comparison.

[0126] Embodiment 31: The method of embodiment 30, wherein said data for the amounts of the BMPlO-type peptide and ANG2 in a sample are received from an analyzer capable of automatically analyzing the amounts of a BMPlO-type peptide and ANG2 present in sample.

[0127] Embodiment 32: A device adapted for carrying out the method of any one of embodiments 23 to 31 comprising: a) a detector for determining the amounts of a BMPlO-type peptide and ANG2 in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMPlO- type peptide to a reference and the amount of ANG2 to a reference and for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0128] Embodiment 33: A kit for carrying out the method of any one of embodiments 23 to 31 comprising a detection agent for a BMPlO-type peptide and a detecting agent for ANG2 in a sample of a subject. Embodiment 33: Use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for assessing congestion.

[0129] Embodiment 34: The use of embodiment 33, wherein said congestion is pulmonary congestion and / or peripheral congestion.

[0130] Embodiment 35: The use of embodiment 33 or 34, wherein said assessment comprises diagnosing and / or predicting altered cardiac pressure, altered pulmonary pressure and / or pathological vascular remodeling or monitoring of congestion prior, during and after a decongestive therapy.

[0131] Embodiment 36: The use of embodiment 35, wherein said altered pulmonary pressure is pulmonary hypertension.

[0132] Embodiment 37: The method of embodiment 36, wherein said pulmonary hypertension is precapillary pulmonary hypertension.

[0133] Embodiment 38: Use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension.

[0134] Embodiment 39: The use of embodiment 38, wherein ANG2 in the sample is used in addition for differentiating between precapillary pulmonary hypertension and postcapillary pulmonary hypertension in addition.

[0135] All references cited throughout this specification are herewith incorporated by reference with respect to the specifically mentioned disclosure content as well as in their entireties. FIGURES

[0136] Figure 1. Scheme for the exclusion of patients in the derivation and validation cohort. There were 46 patients with duplicate and 2 patients with triplicate BMP 10 values in the derivation (index) cohort. In the validation cohort, there were 4 patients with duplicate values.

[0137] Figure 2. (A) Association of biomarkers and mean pulmonary arterial pressure (mPAP). Linear regression of the respective biomarker model adjusted for age, sex, and body mass index. All biomarkers are log-transformed and standardized. Models are sorted by P coefficients. AIC = Akaike information criterion. (B) Scatterplots of biomarkers and mean pulmonary arterial pressure (mPAP). R indicates the Spearman correlation coefficient. LOESS (locally estimated scatterplot smoothing) method is used for fitting a smooth curve through the points of the scatterplot. Grey line indicates linear regression line of the age-, sex-, and body mass index-adjusted model for an average patient (mean age [66 years], most frequent sex category [female], mean body mass index [28 kg / m2])

[0138] Figure 3. Diagnosis and type of pulmonary hypertension. (A) patients with pulmonary hypertension among the investigated cohort (B) differentiation into pre-, post-, combined and unclassified pulmonary hypertension within the investigated cohort.

[0139] Figure 4. (A) Association of biomarkers and pulmonary hypertension (yes / no). Logistic regression of the respective biomarker model adjusted for age, sex, and body mass index. All biomarkers are log-transformed and standardized. Models are sorted by odds ratios (OR). AIC = Akaike information criterion. (B) Predicted probabilities of pulmonary hypertension. Probabilities calculated based on the age-, sex-, and body mass index-adjusted logistic regression models for an average patient (mean age [66 years], most frequent sex category [female], mean body mass index [28 kg / m2]). PH = pulmonary hypertension. (C) Receiver operating characteristic (ROC) curves for pulmonary hypertension (yes / no). Biomarker models adjusted for age, sex, and body mass index. Biomarker models are sorted by area under the curve (AUC).

[0140] Figure 5. (A) Association of biomarker and pulmonary vascular resistance (PVR). Linear regression of the respective biomarker model adjusted for age, sex, and body mass index. All biomarkers are log-transformed and standardized. Models are sorted by P coefficients. AIC = Akaike information criterion. (B) Association of biomarkers and pulmonary arterial wedge pressure (PAWP) Linear regression of the respective biomarker model adjusted for age, sex, and body mass index. All biomarkers are log-transformed and standardized. Models are sorted by P coefficients. AIC = Akaike information criterion. (C) BMP10 distribution (in tertiles) per edema severity.

[0141] Figure 6. Biomarker position of BMP 10 depicted in hierarchical cluster analysis (A) derivation (B) validation cohort. Hierarchical clustering was performed based on the Spearman p correlation coefficient. The further down the scale a variable is positioned, the higher the correlation coefficient between a cluster of variables. Body mass index (BMI), jugular venous pressure (JVP), estimated glomerular filtration rate (eGFR), high-density lipoprotein (HDL), low-density lipoprotein (LDL), left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD), N-terminal pro-B-type natriuretic peptide (NT-proBNP), biologically active adrenomedullin (bio-ADM), cancer antigen 125 (CA-125), growth differentiation factor 15 (GDF-15).

[0142] Figure 7. Kaplan-Meier survival curves and p-value of log-rank test in the derivation and validation cohort per BMP 10 tertile. (A) All-cause mortality, (B) HF hospitalization and (C) the combined endpoint.

[0143] EXAMPLES

[0144] The following Examples shall illustrate the invention. They shall, however, not be construed as limiting the scope of the invention.

[0145] Example 1: Biomarker measurements

[0146] Study design, setting and participants

[0147] Patients undergoing RHC within a prospective, single-center, cross-sectional study conducted between May 2021 and November 2022 at the University Hospital Basel, Switzerland were analyzed. Patients aged > 18 years were eligible and were consecutively screened. Patients who had experienced atrial fibrillation (AF) in the previous 6 months were excluded because one of the tested biomarkers can be influenced by rhythm (bone morphogenetic protein 10 [BMP10]) (Meyre 2022, Scientific Reports 12(1): 1-11). The study protocol was approved by the Ethics Committee Northwest and Central Switzerland (EKNZ, project-ID 2021-00125), and the study complied with the Declaration of Helsinki. Written informed consent from each patient were obtained.

[0148] In addition, out of the multicentre, multinational, prospective, observational index trial of BIOSTAT-CHF (derivation cohort), 2516 patients from 11 European countries with new- onset or worsening signs of chronic HF receiving suboptimal treatment were included between 2010 and 2012 (Voors 2016, Eur J Heart Fail. 18(6):716-26). Data were validated in the BIOSTAT validation cohort consisting of 1738 patients with worsening HF from six centres in Scotland that were enrolled between 2010 and 2014. Inclusion criteria for both cohorts included but were not limited to; age >18 years; receiving furosemide treatment; anticipated initiation or up-titration of ACE inhibitors / ARBs and / or beta-blockers; and a diagnosis of HF (validation) or objective evidence of cardiac dysfunction (derivation). More details of inclusion criteria have been described elsewhere (Voors 2016, loc. cit.). The primary outcomes of this study were 1) all-cause mortality, 2) HF hospitalization and 3) a combined endpoint of both all-cause mortality and / or HF hospitalization at 2 years.

[0149] Detection and measurement o f biomarkers from blood samples

[0150] Venous blood samples were obtained from study participants during RHC. Blood samples were centrifuged, aliquoted into cryotubes, and stored at -80 °C in a biobank. Biomarker concentrations were measured centrally (Roche Diagnostics, Penzberg, Germany). The following biomarkers were assessed: Angiopoietin 2 (ANG2), BMP10, endothelial cell specific molecule 1 (ESMI, endocan), fibroblast growth factor 23 (FGF23), growth differentiation factor 15 (GDF15), insulin-like growth factor-binding protein 7 (IGFBP7), interleukin 6 (IL6), myosin binding protein C3 (MyBPC3), NT-proBNP, N-terminal type III collagen propeptide (proC3), and fragment of C-terminal type VI (a3) collagen (proC6, endotrophin). These biomarkers were selected based on published literature and biological plausibility. The laboratory personnel were unaware of any clinical information. Standardized questionnaires were used to collect data on the patients’ characteristics, lifestyle, medical history, medication, and echocardiographic data if available. In addition, the body mass index (BMI; kg / m2) was calculated.

[0151] Detection and measurement of BMP 10 from blood samples

[0152] In addition, blood samples were also used to measure plasma concentrations of BMP 10 by a prototype electrochemiluminescence immunoassay developed by Roche Diagnostics. In this assay, a BMP 10 specific monoclonal capture antibody binds to BMP 10, after which a ruthenylated second monoclonal antibody subsequently binds to BMP 10 as a detection antibody. To normalize the measurements across the runs with high accuracy, recombinant BMP 10 was used. The Upper and Lower Limits of Quantification (ULOQ and LLOQ) for BMP10 were 10.10 ng / mL and 0.01 ng / mL, respectively. BMP10 was log-transformed to ensure normality. Hemolytic and lipemic samples were excluded. Furthermore, patients with log-BMPIO measurements >5 log-standard deviations above or below the log-mean were also excluded (Ho et al., J Am Coll Cardiol. 2012). Subsequently, if patients had multiple measurements of BMP10, the average between the measurements was used (Figure 1). This resulted in 2085 and 1487 patients with BMP10 plasma measurements in the derivation and validation cohort, respectively (Figure 1).

[0153] Example 2: Right Heart Catheterization (RHC) and outcome measures

[0154] RHC was performed by experienced cardiologists by a standardized protocol. The mid-thoracic level in supine position of the patient was the zero reference level. Vascular access was gained by the jugular or femoral vein. Pressures in the right atrium, pulmonary artery, and pulmonary artery wedge position were measured by a wedge pressure balloon floating catheter (Braun®). All pressure measurements were performed at end expiration and saturation was taken in the wedge position to confirm an accurate PAWP. Systemic and mixed venous O2 content were determined by blood sampling and cardiac output was calculated using the Fick method.

[0155] Two physicians independently adjudicated the PH diagnosis using the hemodynamic variables measured during RHC. Physicians were not aware of the biomarker concentrations. The primary outcome variables were mPAP, PVR, and PAWP. The secondary outcome variables were the diagnosis of PH (“yes” if mPAP > 20 mmHg, “no” if mPAP < 20 mmHg) and elevated PVR (“yes” if PVR > 2 wood units [WU] equals 160 dyn*s*cm'5, “no” if PVR < 2 WU). PH diagnosis and elevated PVR were defined according to the European Society of Cardiology (ESC) guidelines of 2022 (Humbert 2022, Eur Heart J. 43:3618-3731).

[0156] Example 3: Statistical analysis based on the detection of biomarkers

[0157] The baseline characteristics were presented overall and stratified by PH (yes / no). Categorical variables, indicated as numbers (percentages), were compared using chi-square tests. Continuous variables, indicated as mean ± standard deviation (SD) or median [interquartile range], were compared using t-test or Wilcoxon rank sum test as appropriate.

[0158] Linear regression models were built to determine the association of biomarker concentration with hemodynamic parameters (mPAP, PVR, PAWP). One model was unadjusted. A second model was adjusted for patient age and sex. A third model was adjusted for age, sex, and BMI. The results were presented as P coefficient with corresponding 95% confidence intervals (CI). Assumptions including residuals and homoscedasticity were checked. The R2and Akaike information criterion (AIC) was calculated for each model. A higher R2indicates a higher proportion of variance in the outcome that is explained by the predictors. A lower AIC indicates a better fit for the model, implying that the model is more likely to be the best fit for the data among all models tested. For graphical presentation, we constructed scatterplots and used the locally estimated scatterplot smoothing (LOESS) method to fit a smooth curve through the points of the scatterplot. The linear regression line of the model adjusted for age, sex, and BMI for an average patient (continuous variables were set to the mean and categorical variables to the most frequent category) was added. The Spearman correlation coefficients were calculated and added to the plots.

[0159] Logistic regression models were built to determine the association of biomarker concentration and PH (yes / no) and elevated PVR (yes / no). Models were adjusted for age, sex, and BMI, and results are presented as odds ratio (OR) and 95% CI. The Brier score, AIC, and Nagelkerke’ s R2were calculated. A higher Brier score indicates greater accuracy of the probabilistic predictions. This means that the model's predictions are closer to the actual outcome. A higher Nagelkerke’ s R2indicates a better goodness of fit. For graphical presentation, receiver operating curves (ROC) were constructed and the area under the curve (AUC) was calculated. A higher AUC indicates superior discrimination ability, implying that the model is superior at distinguishing between positive and negative cases. The predicted probabilities were plotted for PH according to the different biomarkers. An interaction test was applied to check whether sex or age had an effect on the diagnosis of PH in the different biomarker models. A further exploratory analysis involved variable selection by backwards-forwards selection to identify the best multibiomarker model for the diagnosis of PH. For the best model, the AIC, AUC, Brier score, and Nagelkerke’ s R2were calculated. A likelihood ratio test was then performed to compare the multibiomarker model to the best univariable model.

[0160] All biomarkers were log-transformed because their distributions were skewed and then standardized using z-scores for the purpose of comparison. Correlation between biomarkers was assessed by the Spearman correlation coefficient, and multicollinearity was assessed by the variance inflation factor (VIF). For the analyses, all cases with complete datasets were used. Because very few missing values were anticipated, patients with missing biomarker concentrations or covariates from the analysis were excluded. Due to the exploratory nature of the analysis, no correction for multiple testing were performed and p-values were interpreted as a continuous variable adding to the evidence against the relevant null hypothesis. The presented p-values are two-sided. All analyses were performed using the statistical software R version 4.2.1 (2022-06-23, R Core Team).

[0161] Example 4: Statistical analysis based on the detection of BMP10

[0162] Statistical Analyses

[0163] Variables were log-transformed to ensure normality when necessary. To maximize the number of patients to compare in the differential expression and GSEA while avoiding overlap, patients were divided into tertiles based on BMP10 concentrations. Normally distributed variables are presented as mean (standard deviation), whereas non-normally distributed variables are presented as median [quartiles] and categorical variables as N (%). Baseline characteristics between BMP10 tertiles were analyzed using: t-test or ANOVA when continuous and normally distributed; Kruskal- Wallis test when continuous non-normally distributed; and %2 or Fisher test when categorical. Linear regression analyses were performed, evaluating cardiovascular risk factors as independent variables of log-transformed BMP 10 concentrations. In addition, bidirectional stepwise regression based on the Akaike information criterion (AIC) was performed to identify the strongest predictors of log-BMPIO, including all variables that had a p<0.10 in univariable regression and had missingness in <40% of patients using the stepAIC function in R. A similarity matrix of clinical parameters, echocardiographic measurements, laboratory values and biomarkers based on the Spearman correlation coefficient was constructed using pairwise complete observations. This similarity matrix was then visualized in a heatmap using the reshape2 and ggplot2 package in both cohorts. Subsequently, hierarchical clustering was used to create a dendrogram based on the similarity matrix using the complete linkage method with the Hmisc package in R.

[0164] The 2-year survival per BMP 10 tertile for each of the three outcomes were evaluated using Kaplan-Meier curves. Then, Cox proportional hazards regression was performed to calculate hazard ratios (HR) of BMP10 with subsequent adjustment by 4 models for each outcome: model A) adjusting for age and sex, B) adjusting for the previously established BIOSTAT risk score C) adjusting for the BIOSTAT risk score, AF and stroke and D) adjusting for NT - proBNP (Voors 2017, Eur J Heart Fail. 19(5):627— 34). The variables in the risk score for all-cause mortality included age (years), log-blood urea (mmol / L), log-NT-proBNP (ng / L), haemoglobin (g / dL), and beta-blocker use at baseline. Variables in the risk score for HF hospitalization included age (years), HF hospitalization in last year, peripheral oedema, systolic blood pressure (SBP) (mmHg), and estimated glomerular filtration rate (eGFR) (mL / min). Variables in the risk score for the combined endpoint included age (years), HF hospitalization in last year, peripheral oedema, SBP (mmHg), log-NT-proBNP, haemoglobin (g / dL), high-density lipoprotein (HDL) (mmol / L), sodium (mmol / mL) and beta-blocker use at baseline. The proportionality assumption was checked for each model using Schoenfeld residuals, and if needed, variables were time transformed. All variables were standardized prior to stepwise and Cox regression. Subsequently, a meta-analysis was performed to compare the results between the derivation and validation cohort by comparing the HR for log-transformed and standardized BMP10 levels in multivariable models, adjusted for model C and D, using random effects modeling. Lastly, to test whether BMP 10 gave incremental value on top of model C and D, the likelihood ratio %2 test was performed. Additionally, continuous net reclassification index (NRI) and integrated discrimination index (IDI) were performed.

[0165] Detected characteristics of patients with PH A total of 127 consecutive patients undergoing RHC because of suspected or established PH were included in the analysis. Table 1 shows the baseline characteristics overall and stratified by PH. Within the study cohort, mean (± SD) pulmonary arterial pressure (mPAP) was 30.8 ± 13.7 mmHg, median pulmonary vascular resistance (PVR) was 215.6 [IQR 135.2, 349.3], and mean pulmonary arterial wedge pressure (PAWP) was 12.5 ± 6.4 mmHg. PH was diagnosed in 93 (73%) patients. Of these PH patients, 54 (43%) patients had pre-capillary PH, while 11 (9%) patients had post-capillary PH, 22 (17%) patients had combined pre- and postcapillary PH, and 6 (5%) patients had unclassified PH (Figure 3). Mean (± SD) age of the study population was 66 ± 13 years, and 47% of the patients were male. Patients with PH had a slightly higher BMI and were more likely to have a history of chronic obstructive pulmonary disease (COPD) or lung emphysema compared to patients without PH. Median left ventricular ejection fraction (LVEF) measured by echocardiography was 60% [IQR 55%, 63%] with no strong difference between groups.

[0166] Table 1 : Baseline characteristics overall and stratified by pulmonary hypertension (yes / no)

[0167] Pulmonary hypertension

[0168] Characteristics Overall

[0169] No Yes p-value

[0170] Number of patients 127 (100) 34 (27) 93 (73)

[0171] Age, years 65.66 ± 12.71 62.47 ± 13.86 66.83 ± 12.13 0.09

[0172] Male sex, n (%) 59 (46.5) 15 (44.1) 44 (47.3) 0.90

[0173] Body mass index, kg / m227.64 ± 5.87 25.77 (4.22) 28.32 ± 6.25 0.03

[0174] Smoking status, n (%) 0.31

[0175] - Never 54 (42.5) 16 (47.1) 38 (40.9)

[0176] - Past 51 (40.2) 15 (44.1) 36 (38.7)

[0177] - Current 22 (17.3) 3 (8.8) 19 (20.4)

[0178] History of atrial fibrillation, n 11 (8.7) 1 (2.9) 10 (10.8) 0.30

[0179] (%)

[0180] History of heart failure, n (%) 45 (35.4) 9 (26.5) 36 (38.7) 0.29

[0181] History of hypertension, n (%) 72 (56.7) 17 (50.0) 55 (59.1) 0.47

[0182] History of diabetes, n (%) 33 (26.0) 8 (23.5) 25 (26.9) 0.88

[0183] History of chronic obstructive 30 (23.6) 3 (8.8) 27 (29.0) 0.03 pulmonary disease, n (%) Lung emphysema, n (%) 31 (24.4) 2 (5.9) 29 (31.2) 0.01

[0184] History of pulmonary embo- 30 (23.6) 6 (17.6) 24 (25.8) 0.47 lism, n (%)

[0185] Hypercholesterolemia, n (%) 38 (29.9) 10 (29.4) 28 (30.1) 1

[0186] Left ventricular ejection frac- 60 [55, 63] 60 [55, 64] 60 [55, 63] 0.92 tion (LVEF)

[0187] Right heart catheterization:

[0188] - Mean pulmonary arterial 30.81 ±13.70 16.32 ± 2.67 36.11 ± 12.18 <0.001 pressure (mPAP), mmHg

[0189] - Pulmonary vascular re- 215.57 117.81 [80.00, 266.67 <0.001 sistance (PVR), dyn*S*cm-5[135.20, 148.45] [187.00,

[0190] 349.32] 482.14]

[0191] - Pulmonary arterial wedge 12.45 ± 6.36 7.88 ± 2.98 14.12 ± 6.45 <0.001 pressure (PAWP), mmHg

[0192] - Cardiac index, L / min / m23.06 ± 0.92 3.30 ± 0.93 2.97 ± 0.90 0.08

[0193] Values are given as mean ± standard deviation, median [interquartile range], or numbers (percentage). Missing value: LVEF (n = 1).

[0194] NT-proBNP exhibits the strongest association with mean pulmonary arterial pressure (mPAP) and best predicts pulmonary hypertension

[0195] First, the association of candidate biomarkers with the hemodynamic parameter mPAP in linear regression models was determined. Results of adjusted models for age, sex, and BMI showed a strong association of NT-proBNP, ANG2, MyBPC3, BMP10 and FGF23 with mPAP in descending order (Figure 2A / B). The highest adjusted R2(0.33) was detected in the NT-proBNP model. This indicates that 33% of the variance in mPAP can be explained by this model.

[0196] If utilized for the association with PH defined as mPAP > 20 mmHg, the models of NT- proBNP, ANG2, FGF23, and MyBPC3 adjusted for age, sex, and BMI showed strongly elevated ORs in descending order (Figure 4A). Predicted probabilities of pulmonary hypertension calculated based on the age-, sex-, and body mass index-adjusted logistic regression models for an average patient (mean age [66 years], most frequent sex category [female] mean body mass index [28 kg / m2]) are shown in Figure 4B. Biomarker concentration overall and stratified by PH is shown in Table 2. Receiver operating characteristics (ROC) analysis identified NT-proBNP as the best predictor of PH with an AUC of 0.76, followed by ANG2 and FGF23 (both AUC 0.74) (Figure 4C). There was no evidence of any interaction of biomarkers with age or sex except for IGFBP7 that revealed an interaction with sex (p = 0.04) (data not shown). For the prediction of PH, a model consisting of ANG2, ESMI, GDF15, and NT-proBNP proved to be the best multibiomarker model (Table 3). A likelihood ratio test between this best multibiomarker model and the model containing only NT-proBNP

[0197] (best univariable model) was significant (p = 0.01) with an increase in AUC of 0.06, a lower AIC (133 vs. 138), and a higher Brier score (0.14 vs. 0.16).

[0198] Table 2: Biomarker concentration overall and stratified by pulmonary hypertension

[0199] Pulmonary hypertension

[0200] Biomarker Overall

[0201] No Yes p-value

[0202] Number of patients 127 (100) 34 (27) 93 (73)

[0203] ANG2, ng / mL 2.38 [1.57, 4.20] 1.61 [1.34, 2.30] 2.80 [1.78, 4.80] <0.001

[0204] BMP10, ng / mL 1.96 [1.68, 2.33] 1.88 [1.62, 2.18] 1.96 [1.69, 2.42] 0.18

[0205] ESMI, pg / mL 2250.54 [1637.19, 3570.30] 2259.21 [1576.63, 3280.98] 2250.54 [1657.00, 3650.74] 0.77

[0206] FGF23, pg / mL 199.35 [121.60, 314.17] 127.10 [103.41, 170.20] 235.76 [156.53, 362.15] <0.001

[0207] GDF15, pg / mL 1593.80 [1083.99, 3058.30] 1419.72 [758.54, 1994.72] 1791.52 [1170.10, 3451.97] 0.03

[0208] IGFBP7, ng / mL 91.58 [79.63, 117.04] 90.17 [71.79, 98.79] 93.28 [81.80, 124.29] 0.06

[0209] IL6, pg / mL 4.13 [1.85, 9.26] 2.38 [1.21, 5.26] 4.57 [2.41, 10.50] 0.03

[0210] MyBPC3, pg / mL 19.10 [9.17, 41.58] 12.92 [5.64, 25.43] 23.63 [9.98, 46.08] 0.02

[0211] NT-proBNP, pg / mL 291.37 [95.21, 740.53] 97.27 [54.11, 304.73] 356.89 [127.93, 981.45] <0.001 proC3, ng / mL 28.86 [24.78, 33.47] 28.36 [24.92, 32.14] 28.86 [24.73, 34.16] 0.34 proC6, ng / mL 14.39 [12.17, 17.20] 12.85 [11.43, 14.83] 14.48 [12.81, 17.55] 0.02

[0212] Values are given as median [interquartile range], ANG2 = Angiopoietin 2, BMP10 = Bone morphogenetic protein 10, ESMI = Endothelial cell specific molecule 1 / endocan, FGF23 = Fibroblast growth factor 23, GDF15 = Growth differentiation factor 15, IGFBP7 = Insulin-like growth

[0213] factor-binding protein 7, IL6 = Interleukin 6, MyBPC3 = Myosin binding protein C3, NT -proBNP = N-terminal prohormone of B-type natriuretic peptide, proC3 = N-terminal type III collagen propeptide, proC6 = fragment of C-terminal type VIa3 collagen / endotrophin.

[0214] Table 3: Variable selection for pulmonary hypertension (yes / no)

[0215] Selected multibiomarker model _

[0216] Logistic regression VIF

[0217] 6OR (95% CI)

[0218] ANG2 1.75 (0.92; 3.33) 1.45

[0219] BMP10

[0220] ESMI 0.49 (0.28; 0.86) 1.51

[0221] FGF23

[0222] GDF15 1.64 (0.95; 2.85) 1.20

[0223] Brier score 0.16

[0224] Nagelkerke's R20.25 n = 127. All biomarkers are log-transformed and standardized. AIC = Akaike information criterion, AUC = area under the curve, OR = odds ratio, 5 VIF = variance inflation factor.

[0225] BMP 10 is the only biomarker associated with high pulmonary vascular resistance (PVR) independent of left ventricular filling pressure

[0226] Pulmonary vascular resistance is one of the main factors associated with congestion, and thus, can be used as an indicator therefor. Because pulmonary arterial pressure depends on both, PVR (pre-capillary component of mPAP) and left atrial filling pressure (post-capillary component of mPAP, measured by PAWP), the association of biomarkers with these hemodynamic parameters was determined. For the parameter PVR, the biomarkers NT-proBNP, BMP 10, MyBPC3, ANG2, and FGF23 demonstrated a strong association in descending order (Figure 5 A). Among these biomarkers, BMP10 was the only biomarker with a significant association for high PVR (> 2 WU equal to >160 dyn*s*cm-5) with an OR of 1.60 (95% CI 1.01; 2.54), p = 0.04 (Table 4. For the hemodynamic parameter PAWP, representing the left ventricular filling pressure, ANG2, NT-proBNP, MyBPC3, IGFBP7, and FGF23 showed the strongest association in descending order (Figure 5B). Notably, no association with PAWP was found for BMP10 (standardized 0 = -0.10 [95% CI -1.27; 1.07], p = 0.86).

[0227] Table 4: Association of biomarker concentration and high pulmonary vascular resistance (PVR) yes / no defined as >2 WU equal to >160 dyn*s*cm-5

[0228] Age-, sex-, and BMI-adjusted model

[0229] Logistic regression

[0230] Standardized OR (95% CI), p-value

[0231] ANG2 1.10 (0.74; 1.63), 0.64

[0232] BMP10 1.60 (1.01 ; 2.54), 0.04

[0233] ESMI 0.73 (0.49; 1.08), 0.11

[0234] FGF23 1.08 (0.73; 1.60), 0.71

[0235] GDF15 0.86 (0.58; 1.28), 0.46

[0236] IGFBP7 0.75 (0.51; 1.12), 0.16

[0237] IL6 0.80 (0.54; 1.19), 0.27

[0238] MyBPC3 1.17 (0.74; 1.84), 0.51

[0239] NT-proBNP 1.27 (0.84; 1.93), 0.25 proC3 0.95 (0.66; 1.38), 0.80 proC6 1.04 (0.70; 1.55), 0.83 n = 127 (PVR high no = 46, yes = 81 = event). All biomarkers are log-transformed and standardized. BMI = body mass index, CI = confidence interval, OR = odds ratio, WU = wood units.

[0240] Detected characteristics of patients with HF In the derivation cohort, patients had a median age of 70 years, and 26.8% were female. Median NT-proBNP and BMP10 concentrations were 2719 pg / mL and 2.56 ng / mL, respectively. Patients in the highest BMP 10 tertile were older, more likely to be female, had a lower body mass index (BMI), and were more likely to have a history of AF, renal disease, or stroke (Table 5). Furthermore, they had worse kidney function reflected by a lower eGFR and higher urea, and more severe HF reflected by higher NT-proBNP, Troponin-T, cancer antigen 125 (CA-125), biologically active adrenomedullin (bio- ADM), growth differentiation factor 15 (GDF-15) and signs / symptoms of congestion. Additionally, patients in higher BMP10 tertiles were less likely to have a history of myocardial infarction (MI) or a primary ischemic etiology of HF and had lower HDL and low-density lipoprotein (LDL). Lastly, they had larger left atrial diameter, lower left ventricular end-diastolic diameter (LVEDD), lower left ventricular ejection fraction (LVEF), and were more likely to have moderate-to-severe mitral valve regurgitation.

[0241] Table 5. Baseline characteristics per BMP 10 tertile in the derivation cohort

[0242] Data are presented as mean (standard deviation), median [quartiles] or N (%). Body mass index (BMI), Percutaneous Coronary Intervention (PCI), coronary artery bypass graft (CABG), Chronic Obstructive Pulmonary Disease (COPD), left ventricular ejection fraction (LVEF), Left Ventricular End Diastolic Diameter (LVEDD), Left Ventricular End Systolic Diameter (LVESD), N-terminal pro-B type natriuretic peptide (NT-proBNP), cancer antigen 125 (CA125), biologically active adrenomedullin (Bio-ADM), growth differentiation factor 15 (GDF-15), bone morphogenetic factor 10 (BMP 10).

[0243] * The data shows that BMP 10 is strongly associated with signs and symptoms of congestion including the presence of peripheral edema, as well as elevated JVP. There is a clear shift from the lower to upper BMP 10 tertiles, when the symptoms and severity of congestion increase (Fig. 5C).

[0244] Clinical and echocardiographic predictors of higher BMP 10

[0245] The strongest clinical predictors of log-BMPIO were a history of AF (standardized beta [P]=0.146, p<0.001), higher log-NT-proBNP (P=0.121, p<0.001), log-UACR (P=0.049, p<0.001), and log-urea (P=0.030, p=0.022), as well as lower log-troponin T (P=-0.029, p=0.024), and lower systolic blood pressure (P=-0.027, p=0.018) (Table 6). The strongest echocardiographic predictors of log-BMPIO were lower LVEDD (P=-0.058, p<0.001), presence of moderate-to- severe mitral valve regurgitation (P=0.046, p=0.042), lower LVEF (P=- 0.039, p=0.002) and higher left atrial diameter (P=0.034, p=0.005). The adjusted R2for the model was 0.380 (Table 6). Univariable regression results can be found in Table 7.

[0246] Table 6. Multivariable model including the strongest clinical and echocardiographic predictors of increased log-BMPIO concentrations in the derivation cohort

[0247] Adjusted R-squared: 0.3798. N-terminal pro-B type natriuretic peptide (NT-; i, left ventricular end-diastolic diameter (LVEDD), urinary albumin / creatinine ratio (UACR), left ventricular ejection fraction (LVEF), systolic blood pressure (SBP), estimated glomerular filtration rate (eGFR), and chronic obstructive pulmonary disease (COPD).

[0248] Table 7: Variable selection for pulmonary hypertension (yes / no)

[0249] T. . . Selected multibiomarker model

[0250] Logistic regressionOR (95» / . CI)

[0251] ANG2 1.75 (0.92; 3.33) 1.45

[0252] BMP10

[0253] ESMI 0.49 (0.28; 0.86) 1.51

[0254] FGF23

[0255] GDF15 1.64 (0.95; 2.85) 1.20

[0256] IGFBP7

[0257] IL6

[0258] MyBPC3

[0259] NT-proBNP 2.24 (1.16; 4.33) 1.45 proC3 proC6

[0260] AIC 133.20

[0261] AUC (95% CI) 0.77 (0.67; 0.85)

[0262] Brier score 0.16

[0263] Nagelkerke's R20.25 n = 127. All biomarkers are log-transformed and standardized. AIC = Akaike information criterion, AUC = area under the curve, OR = odds ratio, VIF = variance inflation factor.

[0264] Associations between circulation markers of HF and BMP 10 In a multivariable model, higher log-NT-proBNP, higher log-GDF-15, higher log-bio- ADM, and lower log-troponin T all independently predicted increased log-BMPIO concentrations (all p<0.001), as can be seen in Table 8. The multivariable model of biomarkers for HF had an adjusted R2of 0.3502.

[0265] Table 8. Multivariable model including circulating markers of heart failure predicting log- BMP10 concentrations in the derivation cohort.

[0266] All markers were log-transformed to ensure normality and subsequently standardized. N- terminal pro-B type natriuretic peptide (NT -proBNP), growth differentiation factor 15 (GDF-15), cancer antigen 125 (CA125), biologically active adrenomedullin (Bio-ADM), bone morphogenetic factor 10 (BMP10). The adjusted R2for the multivariable model was 0.3502.

[0267] Heatmap and Dendrogram showing the position of BMP 10 between biomarkers, clinical parameters and echocardiographic measurements

[0268] In the index cohort, BMP10 correlated most strongly with NT-proBNP (Spearman p=0.51; P0.001), GDF-15 (Spearman p=0.46; P<0.001), bio-ADM (Spearman p=0.38; P<0.001), urinary albumin / creatinine ratio (Spearman p=0.32; P<0.001), CA-125 (Spearman p=0.31; P<0.001), and eGFR (Spearman p=-0.31; P<0.001) (not shown). Similar results were seen in the validation cohort (not shown). BMP 10 clustered with signs and symptoms of congestion including NT-proBNP, CA-125, urinary albumin / creatinine ratio, and bio-ADM, as well as presence of rales / crackles, elevated JVP, hepatomegaly, and orthopnea (Figure 6A). Similar results were found in the validation cohort (Figure 6B).

[0269] Survival analysis During a maximum follow-up time of 2 years, 489 patients died and 485 were hospitalized for HF in the derivation cohort. Higher log-BMPIO was significantly associated with all three outcomes (Figure 4). Cox regression analyses concluded BMP 10 was an independent predictor of all-cause mortality (HR=1.51, Confidence Interval [CI]=1.38-1.65, p<0.001), HF hospitalization (HR [CI]=1.39 [1.28-1.52], p<0.001) and the combined endpoint (HR [CI]=1.42 [1.33-1.53], p=<0.001) when adjusted for age and sex (Table 9). BMP10 remained an independent predictor of all-cause mortality for all three outcomes when corrected for the BIOSTAT risk score (Model B), and preserved significance when additionally adjusted for AF and stroke next to the risk score (Model C) (Table 9). Moreover, the likelihood ratio test showed that BMP 10 significantly improved both Model B and C for all-cause mortality (Model B: p=0.002, Model C: p=0.006), HF hospitalization (Model B: p=0.007, Model C: p=0.011), and the combined endpoint (Model B: p=0.004, Model C: p=0.026) (Table 9). In the validation cohort, BMP 10 remained a significant predictor of all three outcomes when adjusted for by model A, B, and C, in addition to giving incremental value to model B and C (Table 9).

[0270] Table 9. Hazard ratios (HR) of loge(BMP10) when adjusted for the BIOSTAT risk score as well as the incremental value of BMP10 to the references model calculated per likelihood ratio test (LRT) in the derivation cohort and validation cohort.

[0271] ’atients with missing data in any of the included variables were excluded, leading to a final number of 2085 and 1451 patients in the derivation anc

[0272] 5 validation cohort, respectively. Variables were log transformed if necessary, and all variables were standardized prior to analysis. Schoenfeld residuals were checked, and if needed, variables were time transformed. Risk score for all-cause mortality: age (years), log-blood urea (mmol / L), log- NT-proBNP (ng / L), haemoglobin (g / dL), and beta-blocker use at baseline. Risk score HF hospitalization: age (years), HF hospitalization in last year, Peripheral oedema, SBP (mmHg), and eGFR (mL / min). Risk score combined endpoint: age (years), HF hospitalization in last year, peripheral oedema, SBP (mmHg), log-NT-proBNP, haemoglobin (g / dL), HDL (mmol / L), sodium (mmol / mL), and beta-blocker use at baseline.

Claims

Claims1. A method for assessing congestion in a subject comprising the steps of a) determining in a sample of the subject the amount of a BMPlO-type peptide (Bone Morphogenic Protein 10-type peptide); and b) comparing the amount of the BMP 10-type peptide to a reference for the BMP 10-type peptide, whereby congestion is to be assessed.

2. The method of claim 1, wherein said congestion is pulmonary congestion and / or peripheral congestion and / or wherein said congestion is associated with altered cardiac pressure, altered pulmonary pressure, and / or pathological vascular remodeling.

3. The method of claim 1 or 2, wherein said reference is an amount for the BMP 10-type peptide derived from a subject or group of subjects known to exhibit congestion and, preferably, wherein an amount for the BMP 10-type peptide determined in the sample of the subject being essentially identical or increased compared to the reference is indicative for a subject suffering from congestion, whereas an amount for the BMP 10-type peptide determined in the sample of the subject being decreased compared to the reference is indicative for a subject which is not suffering from congestion.

4. The method of claim 1 or 2, wherein said reference is an amount for the BMP 10-type peptide derived from a subject or group of subjects known not to exhibit congestion and, preferably, wherein an amount for the BMP 10-type peptide determined in the sample of the subject being essentially identical or decreased compared to the reference is indicative for a subject which is not suffering from congestion, whereas an amount for the BMP 10-type peptide determined in the sample of the subject being increased compared to the reference is indicative for a subject suffering from congestion.

5. The method of any one of claims 1 to 4, wherein said subject is suspected to suffer or being at risk of suffering from congestion and / or suffering from heart failure, pulmonary arterial hypertension and / or pulmonary venous hypertension.

6. The method of any one of claims 1 to 5, wherein step b) of said method is computer- implemented.

7. The method of any one of claims 1 to 6, wherein said assessment comprises diagnosing and / or predicting altered cardiac pressure, altered pulmonary pressure and, preferably, pulmonary hypertension, and / or pathological vascular remodeling or monitoring of congestion prior, during and after a decongestive therapy.

8. A method of recommending a therapy against congestion comprising the steps of the method of any one of claims 1 to 7 and the further step of recommending the said therapy if the subject is assessed to suffer from congestion.

9. A device adapted for carrying out the method of any one of claims 1 to 7 comprising: a) a detector for determining the amount of a BMPlO-type peptide in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMPlO- type peptide to a reference and to assess based on the said comparison congestion.

10. A kit for carrying out the method of any one of claims 1 to 7 comprising a detection agent for a BMPlO-type peptide in a sample of a subject.

11. A method of differentiating between precapillary hypertension and postcapillary hypertension comprising a) determining the amount of a BMPlO-type peptide in a sample of the subject; b) determining the amount of ANG2 in a sample of the subject; and c) comparing the amounts of the BMPlO-type peptide and ANG2 to references, whereby it is differentiated between precapillary hypertension and postcapillary hypertension.

12. The method of claim 11, wherein said references are amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit precapillary hypertension and, preferably, wherein an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or increased and an amount of ANG2 which is essentially identical or decreased compared to the reference is indicative for a subject suffering from precapillary hypertension, whereasan amount for the BMPlO-type peptide determined in the sample of the subject being decreased and an amount of ANG2 being increased compared to the reference is indicative for a subject which is suffering from postcapillary hypertension.

13. The method of claim 11, wherein said references are amounts for the BMPlO-type peptide and ANG2 derived from a subject or group of subjects known to exhibit postcapillary hypertension and, preferably, wherein an for the BMPlO-type peptide determined in the sample of the subject being increased and an amount of ANG2 which is decreased compared to the reference is indicative for a subject suffering from precapillary hypertension, whereas an amount for the BMPlO-type peptide determined in the sample of the subject being essentially identical or decreased and an amount of ANG2 being essentially identical or increased compared to the reference is indicative for a subject which is suffering from postcapillary hypertension.

14. The method of any one of claims 11 to 13, wherein step c) of said method is computer-implemented.

15. A device adapted for carrying out the method of any one of claims 11 to 14 comprising: a) a detector for determining the amounts of a BMPlO-type peptide and ANG2 in a sample of a subject; and b) a processor which is adapted to compare the detected amount of the BMPlO- type peptide to a reference and the amount of ANG2 to a reference and for differentiating between precapillary hypertension and postcapillary hypertension.

16. A kit for carrying out the method of any one of claims 11 to 14 comprising a detection agent for a BMPlO-type peptide and a detecting agent for ANG2 in a sample of a subject.

17. Use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for assessing congestion and, preferably, congestion is pulmonary congestion and / or peripheral congestion.

18. The use of claim 17, wherein said assessment comprises diagnosing and / or predicting altered cardiac pressure, altered pulmonary pressure and, preferably, pulmonary hypertension, and / or pathological vascular remodeling or monitoring of congestion prior, during and after a decongestive therapy.

19. Use of a BMPlO-type peptide or a detection agent for a BMPlO-type peptide in a sample of a subject for differentiating between precapillary hypertension and postcapillary hypertension and wherein, preferably, ANG2 in the sample is used for differentiating between precapillary hypertension and postcapillary hypertension in addi- tion.

Images