WO2025008464A2

WO2025008464A2 - Use of a marker or a marker set for determining the risk of an individual to have ascites

Info

Publication number: WO2025008464A2
Application number: PCT/EP2024/068881
Authority: WO
Inventors: Sebastian DE JEL; Frank STÄMMLER; Sebastian RÖTZER; Andrew Robertson; Johannes EIGLSPERGER
Original assignee: Numares AG
Current assignee: Numares AG
Priority date: 2023-07-06
Filing date: 2024-07-04
Publication date: 2025-01-09
Anticipated expiration: 2026-01-06
Also published as: WO2025008464A3

Abstract

The present invention relates, amongst others, to the use of a marker or a marker set in an in vitro method for determining the risk of an individual to have ascites. The marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1, valine, and pyruvate; the marker set comprises at least two substances chosen from a second group consisting of high-density lipoprotein, apolipoprotein A1, valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

Description

Use of a marker or a marker set for determining the risk of an individual to have ascites

Description

The present invention relates to the in-vitro use of a marker or a marker set for determining the risk of an individual to have ascites according to the preamble of claim 1 , to the further medical use of such a marker or a marker set according to the preamble of claim 13, and to an analysis method for determining the risk of an individual to have ascites according to the preamble of claim 14.

Ascites is defined as the accumulation of fluid in the peritoneal cavity and is the most common complication of cirrhosis. Cirrhosis is the most common cause of ascites in the United States, accounting for approximately 85% of ascites cases. 50% of cirrhotic patients within 10 years after the diagnosis of compensated cirrhosis will develop ascites [1]. The other most common causes of ascites include malignancy-related ascites and ascites secondary to heart failure. Less common etiologies include hepatic veno-occlusive disease, constrictive pericarditis, hemodialysis-associated ascites, hypoalbuminemia due to the nephrotic syndrome, and peritoneal diseases. Less than 5% of cases will have a mixed etiology. Successful treatment of ascites depends upon an accurate diagnosis of its cause [2],

Development of ascites is the final step in an intricate pathology of anatomic, pathophysiologic, and biochemical abnormalities which primarily stem from the development of portal hypertension secondary to the densely fibrotic liver in cirrhosis [3]. Patients with cirrhosis but without portal hypertension do not develop ascites, and a portal pressure > 12 mmHg appears to be the critical pressure in the development of fluid retention. This causes prominent arterial vasodilation, leading to a significant reduction in systemic vascular resistance and mean arterial pressure, resulting in a hyperdynamic circulation, and particularly splanchnic vasodilation [4, 5].

Literature suggests that the most plausible cause of vasodilation as a result of portal hypertension is a combination of the opening of porto-systemic collaterals and increased synthesis of circulating vasodilators, chiefly nitric oxide (NO) [6]. The consequence of such vasodilation is activation of endogenous vasoconstrictor agents, in an attempt to restore perfusion pressure, proximal tubular sodium and water retention, and renal vasoconstriction. These mechanisms give rise to a general fluid overload which, combined with splanchnic vasodilation and a low decreased colloid osmotic pressure, effectively push fluid into the peritoneal cavity, with ascites resulting [7, 8]. Moreover, renal vasoconstriction can lead to a decreased glomerular filtration rate which is often masked clinically - the so-called hepatorenal syndrome. Creatinine production can be impaired in liver disease and when muscle mass is decreased, with a net effect of serum creatinine concentrations that appear to be within the normal range [9].

The onset of ascites in cirrhosis is considered a major complication of cirrhosis and is thus diagnostic of decompensated liver disease. Ascites is associated with poor prognostic outcomes for this patient group [10]. The presence of ascites also constitutes a crucial component of the 15-point Child-Pugh score, a global measure of hepatic function and mortality in cirrhosis, with the scale of ascites corresponding to the following point score [11]:

• Absent: +1

• Slight: +2 (detected on investigation with ultrasound as opposed to patient- reported)

• Moderate and above: +3 (obvious to both clinician and patient)

Therefore, an accurate diagnosis is paramount not only to determine the etiology, which determines the treatment algorithm, but also as a component of mortality scores.

Clinically, patients are likely to report progressive abdominal distension, weight gain, shortness of breath, and early fullness. Other stigmata of cirrhosis is likely to be present, such as spider angioma, palmar erythema, and abdominal wall collaterals. Additionally, other features of decompensated cirrhosis may be present, such as variceal bleeding or hepatic encephalopathy [12], Laboratory investigations typically include liver function tests (aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, bilirubin, albumin, and prothrombin time) alongside renal function, electrolyte counts, and complete blood count [13]. Ultrasound imaging is routinely involved for evaluation of ascites, however, has a lowest detection limit down to 50 mL [14], A definitive diagnosis is made upon paracentesis and subsequent investigation of the abdominal fluid, namely the total protein concentration, LDH levels, serum-to-ascites albumin gradient, bacteriological and cytological (leucocytes, erythrocytes, tumor cells) parameters. The grade of ascites is purely determined by the amount of fluid in the peritoneal cavity, and is scaled as [15]:

• Grade 1 : Mild ascites detectable only by ultrasound examination (equivalent to Child-Pugh ‘Slight’)

• Grade 2: Moderate ascites manifested by moderate symmetrical distension of the abdomen (equivalent to Child-Pugh ‘Moderate’)

• Grade 3: Large or gross ascites with marked abdominal distension (also equivalent to Child-Pugh ‘Moderate’)

As mentioned, an accurate diagnosis of ascites at present is comprised on patient-reported symptoms, clinical examination, laboratory investigations, ultrasound, and paracentesis, all of which can be cumbersome, are labor intensive and require a significant time commitment on the patient’s behalf. Moreover, patients with only slight/mild ascites may be asymptomatic and subsequently under-diagnosed, despite the presence of only even slight ascites contributing to one’s Child-Pugh mortality score.

In summary, there is a clear need for a more rapid, less invasive testing methodology, such as non-invasive biomarkers, which enable a quantitative test that estimates the presence of ascites, obliviating the need for a deluge of laboratory tests and ultrasound imaging, and also identifying those patients with non-clinical ascites.

US 2020/0378991 A1 describes biomarkers and biomarker panels useful for diagnostic methods evaluating liver disease status in a subject, monitoring liver disease, distinguishing between liver diseases, treating subjects evaluated by diagnostic methods of the invention, providing diagnostic tests for evaluating liver disease status in a subject, and kits therefor. The biomarkers are chosen from bile acids, free fatty acids, amino acids, and carbohydrates listed in Table 1 of this U.S. patent application. A specific example relates to a biomarker panel comprising palmitic acid (C16:0), palmitic acid (016:0)/palmitoleic acid (C16:1 n7) ratio, tyrosine, fructose, fructose/glucose ratio, glycochenodeoxycholic acid (GCDCA), and glycocholic acid (GCA).

US 2017/0370954 A1 describes biomarkers of nonalcoholic steatohepatitis (NASH), nonalcoholic fatty liver disease (NAFLD), and fibrosis and methods for diagnosis (or aiding in the diagnosis) of NAFLD, NASH and/or fibrosis. Additionally, methods of distinguishing between NAFLD and NASH, methods of classifying the stage of fibrosis, methods of determining the severity of liver disease, methods of determining the severity of liver disease or fibrosis, and methods of monitoring progression/regression of NASH, NAFLD, and/or fibrosis are described. In this context, this U.S. patent application lists scores of various substances. A specific example is a biomarker or biomarker set selected from the group consisting of 5-methylthioadenosine (5-MTA), glycine, serine, leucine, 4-methyl-2- oxopentanoate, 3-methyl-2-oxovalerate, valine, 3-methyl-2-oxobutyrate, 2-hydroxybutyrate, prolylproline, lanosterol, tauro-beta-muricholate, and deoxycholate.

US 2017/0032099 A1 describes an in vitro prognostic method for assessing the risk of death or of liver-related event in a subject, the method including the following steps: a) obtaining at least one of the following variables from the subject: i. biomarkers measured in a sample from the subject; ii. clinical data; ill. binary markers; iv. blood test results; b) optionally obtaining at least one blood test result by univariate combination, preferably with a binary logistic regression, of the at least one variable obtained in step a), the blood test not being a Fibrotest, c) obtaining at least one physical data from medical imaging or clinical measurement, from elastometry, or Vibration Controlled Transient Elastography, and d) mathematically combining in a multivariate time-dependent model the variable obtained in step a) and/or the at least one blood test result obtained in step b); and the at least one physical data, obtained in step c) thereby obtaining a prognostic score. The biomarkers are selected from the group comprising glycemia, total cholesterol, HDL cholesterol (HDL), LDL cholesterol (LDL), AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, ferritin, platelets (PLT), AST/PLT, prothrombin time (PT) or prothrombin index (PI), hyaluronic acid (HA or hyaluronate), hemoglobin, triglycerides, alpha-2 macroglobulin (A2M), gamma-glutamyl transpeptidase (GGT), urea, bilirubin, apolipoprotein A1 (ApoA1 ), type III procollagen N- terminal propeptide (P3NP), gamma-globulins (GBL), sodium (Na), albumin (ALB), glucose (Glu), alkaline phosphatases (ALP), YKL-40 (human cartilage glycoprotein 39), tissue inhibitor of matrix metalloproteinase 1 (TIMP-1 ), TGF, cytokeratin 18 and matrix metalloproteinase 2 (MMP-2) to 9 (MMP-9), ratios and mathematical combinations thereof.

It is an object of the present invention to provide novel methods and biomarkers for the diagnosis of ascites as well as for determining the risk of an individual to have ascites.

This object is achieved with the in-vitro use of a marker or a marker set having the claim elements of claim 1. The marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate. The marker set comprises or consists of at least two (e.g., 2, 3, 4, 5, 6, 7) substances that are chosen from a second group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

For this purpose, the concentration of the marker or of the substances contained in the marker set is determined in a body fluid obtained from a patient. This concentration determination can be carried out by any appropriate measuring or analysis method, such as nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, high-performance liquid chromatography (HPLC), infrared spectroscopy such as Fourier-transform infrared (FT-IR) spectroscopy, clinical chemistry, and immunodiagnostics.

An alteration of concentration of the individual marker or of at least two substances of the marker set with respect to the concentration in a control group or an alteration of a concentration ratio between at least two substances with respect to the concentration ratio of the same substances in a control group was correlated in a statistically significant way with the presence of ascites at the time of analysis (i.e., enabling a distinction between individuals not having ascites and individuals having ascites).

Already upon testing individual biomarkers, significant results could be obtained for distinguishing the two groups (individuals not having ascites versus ascites patients), provided that high-density lipoprotein, apolipoprotein A1 , valine, or pyruvate was used as biomarker. The area under the curve (AUC) values of receiver operating characteristic (ROC) plots showed values lying around but mainly above 0.75. The AUC value of ROC plots is an aggregated metric that evaluates how well a logistic regression model classifies positive and negative outcomes at all possible cut-offs. It can range from 0 to 1 .0. An AUC value of 0 represents a prediction of the opposite of the trained correlation. An AUC value of 0.5 represents a random prediction. An AUC value of higher than 0.5 represents a classification of an event as fulfilling the trained correlation, wherein higher values represent better classification.

Upon testing a combination of at least two biomarkers, the resulting AUC values were significantly above 0.75, in most cases 0.8 or higher or even 0.85 or higher.

The individual biomarkers and the marker sets were tested against a training dataset and a test dataset. After all training and test processes, high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate turned out to be valid biomarkers for the underlying question (i.e., distinguishing patients having ascites from individuals not having ascites). In this context, these biomarkers were very well appropriate biomarkers if used individually. In addition, high-density lipoprotein, apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol turned out to be particularly valid biomarkers for the underlying question, provided that the concentration of at least two of these biomarkers was determined at the same time (i.e., in one or more body fluid samples from the same patient obtained at the same time point).

The concentration determination can be made with a method being able to determine the concentration of the substances by a single measurement or by a method requiring more than one measurement for such determination. NMR spectroscopy is particularly appropriate for such a concentration determination since it enables a highly accurate concentration determination of multiple substances in a body fluid by a single measurement in a very short measuring time.

In an embodiment, the concentration of the substances is standardized to the concentration of another substance that is naturally present in the sample. This other substance may also be listed in the first group or the second group of substances. In an embodiment, this other substance does not belong to the first or the second group as defined above.

In an embodiment, the marker set comprises at least one substance chosen from the second group and being different from high-density lipoprotein and apolipoprotein A1 if the marker set comprises any of high-density lipoprotein and apolipoprotein A1 as a first of the at least two substances. The concentrations of HDL and apolipoprotein A1 in a body fluid are typically closely interrelated with each other so that both substances can be exchanged against each other for many applications. Therefore, the marker set is particularly appropriate for determining the risk of an individual of having ascites if HDL and apolipoprotein A1 are not used as the only substances in the marker set.

In an embodiment, the marker is HDL or apolipoprotein A1 . HDL as biomarker showed an AUC value of 0.79 in the training dataset (confer Figure 1A) and of 0.76 in the test dataset (confer Figure 1 B). Thus, HDL and consequently the closely related apolipoprotein A1 are a particularly appropriate single biomarker for determining the risk of an individual of having ascites. The accuracy of such risk determination can even be increased if HDL is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, the marker is valine. Such a biomarker showed an AUC value of 0.73 in the training dataset (confer Figure 2A) and of 0.76 in the test dataset (confer Figure 2B). Thus, also valine is a particularly appropriate single biomarker for determining the risk of an individual of having ascites. The accuracy of such risk determination can even be increased if valine is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, the marker is pyruvate. Such a biomarker showed an ALIC value of 0.70 in the training dataset (confer Figure 3A) and of 0.73 in the test dataset (confer Figure 3B). Thus, also pyruvate is a particularly appropriate single biomarker for determining the risk of an individual of having ascites. The accuracy of such risk determination can even be increased if pyruvate is combined with at least one other biomarker, as will be explained in later sections of the present disclosure.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and anhydrosorbitol as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.80 in the training dataset (confer Figure 4A) and of 0.76 in the test dataset (confer Figure 4B). Thus, supplementing HDL with anhydrosorbitol as further biomarker significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using HDL as individual biomarker.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and valine as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.80 in the training dataset (confer Figure 5A) and of 0.78 in the test dataset (confer Figure 5B). Thus, combining HDL and valine as biomarkers in the marker set significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using any of these substances as individual biomarkers.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and pyruvate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.81 in the training dataset (confer Figure 6A) and of 0.79 in the test dataset (confer Figure 6B). Thus, combining HDL and pyruvate as biomarkers in the marker set significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using any of these substances as individual biomarkers.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and lactate as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.80 in the training dataset (confer Figure 7A) and of 0.76 in the test dataset (confer Figure 7B). Thus, supplementing HDL with lactate as further biomarker significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using HDL as individual biomarker.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and myo-inositol as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.81 in the training dataset (confer Figure 8A) and of 0.80 in the test dataset (confer Figure 8B). Thus, supplementing HDL with myo-inositol as further biomarker significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using HDL as individual biomarker.

In an embodiment, the marker set comprises or consists of pyruvate as a first of the at least two substances and valine as a second of the at least two substances. Such a biomarker set showed an AUC value of 0.78 in the training dataset (confer Figure 9A) and of 0.81 in the test dataset (confer Figure 9B). Thus, combining pyruvate and valine as biomarkers in the marker set significantly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using any of these substances as individual biomarkers.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and valine and myo-inositol as further substances of the at least two substances. Such a biomarker set showed an AUC value of 0.82 in the training dataset (confer Figure 10A) and of 0.81 in the test dataset (confer Figure 10B). Thus, using a combination of the three substances HDL, valine, and myo-inositol still slightly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using only two of these substances.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and valine and pyruvate as further substances of the at least two substances. Such a biomarker set showed an AUC value of 0.82 in the training dataset (confer Figure 11 A) and of 0.81 in the test dataset (confer Figure 11 B). Thus, using a combination of the three substances HDL, valine, and pyruvate still slightly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using only two of these substances.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and myo-inositol and anhydrosorbitol as further substances of the at least two substances. Such a biomarker set showed an AUC value of 0.82 in the training dataset (confer Figure 12A) and of 0.80 in the test dataset (confer Figure 12B). Thus, using a combination of the three substances HDL, myo-inositol, and anhydrosorbitol still slightly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using only two of these substances.

In an embodiment, the marker set comprises or consists of HDL or apolipoprotein A1 as a first of the at least two substances and lactate and myo-inositol as further substances of the at least two substances. Such a biomarker set showed an AUC value of 0.81 in the training dataset (confer Figure 13A) and of 0.77 in the test dataset (confer Figure 13B). Thus, using a combination of the three substances HDL, lactate, and myo-inositol still slightly increases the sensitivity of determination of the risk of an individual to have ascites with respect to using only two of these substances.

In an aspect, the present invention relates to the further medical use of a marker or a defined marker set for in-vivo diagnostics of ascites. The marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate. The marker set comprises or consists of at least two (e.g., 2, 3, 4, 5, 6, 7) substances that are chosen from a second group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

In an aspect, the present invention relates to a method for analyzing an isolated body fluid sample in vitro, comprising the steps explained in the following. This method is carried out on an isolated body fluid sample originating from an individual.

In a first step, the concentration of a single substance or of at least two substances is determined by analyzing the body fluid sample with a suited measuring technique. The substance is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate. The at least two (e.g., 2, 3, 4, 5, 6, 7) substances are chosen from a second group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

Afterwards, a score is calculated from the determined concentrations, wherein the score is indicative for the risk of the individual to have ascites.

The score can be calculated by taking into consideration the concentrations measured or expected in a body fluid sample from a control group. To give a simple example, the score can be the median of the concentration ratios of the at least two substances between the body fluid test sample of the patient and corresponding control values of a body fluid control sample that have been measured in the past. If the score is above or below a predetermined threshold value, a significant increase or decrease of the marker substances is present in the body fluid test sample that is indicative for ascites. It should be noted that other calculation methods as well as a weighting of individual marker concentrations with respect to other marker concentrations can also be performed in an embodiment.

A suited way to calculate the score is disclosed on pages 25 to 27 of WO 2012/045773 A9. Another suited way to calculate the score is the following:

wherein n a) = a + > b_x • I_x

X=1 a = const. b_x = substance specific coefficient

I = parameter being indicative for the concentration of substance x

Thereby, the individual factors a, b need to be adjusted according to the underlying model and can vary in dependence on the specific substances considered in the marker set. Parameter “I” can be, e.g., the signal intensity or signal integral of an according signal observed in the evaluated measuring result. To give an example, “I” can be the signal intensity or signal integral of an NMR signal in an NMR spectrum if NMR spectroscopy is used as measuring technique.

In an embodiment, “I” is a ratio between two signal intensities or two signal integrals. In such a case, it is, e.g., possible to standardize the concentration of a first substance (or a plurality of substances) by the concentration of a second substance.

The score is a (semi-)quantitative measure for the likelihood that the individual has developed ascites. Thus, the score serves for (semi-)quantitatively determining the risk of presence of ascites.

Calculating the score comprises multiplying each of the concentrations of the substances by a substance-specific weighting factor to provide a plurality of weighted values and combining the weighted values into a risk equation. Afterwards, an output of the risk equation is compared to a predefined threshold. If the score is above the threshold, there is a likelihood that the individual has developed ascites. In an embodiment, the likelihood and/or the risk is higher, the higher the score is (i.e., the likelihood and/or the risk increases with increasing distance of the score from the threshold).

In an embodiment, the calculated score is output and presented to the individual and/or to a third person such as a physician or medical staff. The output can be performed on a display (i.e., in an electronic way) or in printed form. Thereby, it is also possible to generate a report indicating the score, optionally in combination with a comparative scale of possible scores and their meaning with respect to the risk of having ascites.

In an embodiment, the method is a computer-implemented method. In particular, all steps of spectral analysis and concentration determination as well as of score calculation are performed on a computer. Such steps are far too complex to be done in a manual way. The computer- implemented concentration determination is, in an embodiment, based on a spectral analysis, such as an analysis of NMR spectra. The spectral analysis and the further required steps until the score can be output can be done on the same computer that is used for controlling a spectrometer performing the spectral analysis or on a different computer.

In an embodiment, the body fluid sample is a urine sample or a blood sample. In an embodiment, the blood sample is a whole blood sample, a blood serum sample, a blood plasma sample, or any other blood preparation derivable from whole blood or from other blood preparations. Blood serum is a particularly appropriate body fluid for carrying out the method or for the above-mentioned in vitro or in vivo uses of specific biomarkers contained in the marker set. Blood plasma is also an appropriate body fluid. In an embodiment, lactate is present in the marker set if blood plasma is used as body fluid to be analyzed.

In an embodiment, the body fluid sample (and therewith the patient from whom the body fluid sample originates) is grouped into one of at least two predefined groups based on the calculated score. Typically, one group encompasses patients suffering from ascites, wherein the other group encompasses individuals not having ascites. The resulting grouping can also be indicated on an according report. In an embodiment, the grouping encompasses more than two groups (yes/no), namely information on the grade of severity of the detected ascites.

In an embodiment, the individual of whom the body fluid is analyzed, is a healthy individual. In an embodiment, the individual is a risk patient, i.e., an individual belonging to a group that has an increased risk of developing ascites with respect to the risk of a healthy standard population. In an embodiment, the individual suffers from (etiology-independent) cirrhosis. In an embodiment, the individual is a patient having cirrhosis but being asymptomatic. In an embodiment, the individual suffers from a (in particular chronic) viral liver infection and has a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis B and has a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis C and has a cirrhosis. In an embodiment, the individual suffers from non-alcoholic fatty liver disease (NAFLD), in particular from non-alcoholic steatohepatitis (NASH), and has a cirrhosis. In an embodiment, the individual suffers from alcoholic liver disease and has a cirrhosis. In an embodiment, the individual has a cryptogenic cirrhosis. In an embodiment, the individual suffers from a (in particular chronic) viral liver infection without having a cirrhosis. In an embodiment, the individual suffers from chronic hepatitis B without having a cirrhosis. In an embodiment, the individual suffers from grade IV fibrosis (i.e., a fibrosis that led to cirrhosis). All of the precedingly mentioned embodiments can be particularly well combined so that the individual may be chosen from any of the beforementioned groups of patients/healthy individuals.

In an embodiment, the score is calculated by not only considering the concentration of the at least two marker substances but also includes the age and/or the gender of the individual who donated the body fluid sample.

In an embodiment, calculating the score involves calculating a ratio between at least two concentration values. To give an example, a ratio between HDL and valine or between pyruvate and anhydrosorbitol is calculated in an embodiment. Generally, individual ratios of pairs of two of all marker substances the concentrations of which have been determined upon carrying out the method can be calculated for calculating the score.

The presently described method is particularly helpful in determining the risk of the individual of having ascites if an ultrasound examination is not available. The presently described method can be particularly well used for calculating the ascites component of the Child-Pugh score, in particular in cirrhotic patients preoperatively or for a prognostic scoring.

In an aspect, the present invention relates to a medical method for diagnosing ascites in an individual. The method comprises the steps explained in the following.

In a first step, a body fluid sample is gathered from an individual. In a second step, the concentration of a single substance or of at least two substances is determined by analyzing the body fluid sample with a suited measuring technique. The marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate. The marker set comprises or consists of at least two (e.g., 2, 3, 4, 5, 6, 7) substances that are chosen from a second group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

Afterwards, a score is calculated from the determined concentrations, wherein the score is indicative for determining the risk of the individual to have ascites.

In a further aspect, the present invention relates to a decision support system for analyzing an isolated body fluid sample in vitro, the decision support system comprising: a) a unit for providing a body fluid sample from an individual; b) a unit for determining the concentration of a single substance or of at least two substances by analyzing the body fluid sample with a suited measuring technique. The marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate. The marker set comprises or consists of at least two (e.g., 2, 3, 4, 5, 6, 7) substances that are chosen from a second group consisting of high-density lipoprotein (HDL), apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol; and c) a unit for calculating a score from the determined concentrations, the score being indicative for determining the risk of the individual to have ascites.

In an embodiment, the unit for determining the concentration of the single substance or of the at least two substances is configured to determine the concentration of any exemplary substance or substance combination of the embodiments explained above.

While some of the explained uses and methods are described as in vitro uses and methods and some of the explained uses and methods are described as in vivo uses and methods, it should be noted that each in vitro use or method can also be carried out as in vivo use or method, and vice versa.

All embodiments of the use of the marker or the marker set can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the further medical use of the marker or the marker set as well as to the different methods and to the decision support system. Likewise, all embodiments of the further medical use of the marker or the marker set can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the use of the marker or the marker set, to the different methods, and to the decision support system. Finally, all embodiments of the different methods can be combined in any desired way and can be transferred either individually or in any arbitrary combination to the use of the marker or the marker set, to the further medical use of the marker or the marker set, to any other of the described methods, and to the decision support system.

Further details of aspects of the present invention will be explained in the following making reference to exemplary embodiments and accompanying Figures. In the Figures:

Figures 1 A to 13B show ROC plots illustrating the ability of different markers or marker sets for determining the risk of an individual to have ascites upon evaluating a training dataset (always in Figure A) or upon evaluating a test dataset (always in Figure B); and

Figures 1 C to 13D show confusion matrices illustrating the distributions of calculated scores from the determined concentrations of the individual biomarkers or the biomarkers contained in the respective marker set upon evaluating a training dataset (always in Figure C) or upon evaluating a test dataset (always in Figure D).

All ROC plots shown in Figures 1 A to 13A (in the A Figures) and the corresponding confusion matrices shown in Figures 1 C to 13C (in the C Figures) as well as all ROC plots shown in Figures 1 B to 13B (in the B Figures) and the corresponding confusion matrices shown in Figures 1 D to 13D (in the D Figures) were obtained by analyzing blood serum samples of individuals with or without ascites. The recruiting of patients for the analyzed samples as well as the sample preparation and measuring will be explained in the following in more detail.

Study design

A retrospective case-control design using banked serum samples from individuals with or without ascites was chosen. The samples were either banked or retained samples from routine clinical care collected for other research studies or non-research purposes, or samples collected as part of a clinical study. Serum cohorts were obtained from four different study sites, namely three in Europe and one in the U.S. Thus, this study was a retrospective, multicentric, cross-sectional case-control study.

Group assignment and reference standard The case group comprised patients with diagnosis of mild/moderate or severe ascites by standard techniques (including ultrasound, paracentesis and/or subsequent investigation of the abdominal fluid). Cohort details

Details of the tested patient cohort are listed in the following Table A:

Table A: Details of the tested patient cohort.

Characteristic N Training, N = 564 Test, N = 238

Center, n 1 N (%) 802

Center 1 A 228 / 564 (40%) 95 / 238 (40%)

Center 1 B 38 / 564 (6.7%) 20 / 238 (8.4%)

Center 2 230 / 564 (41 %) 89 / 238 (37%)

Center 4 68 / 564 (12%) 34 / 238 (14%)

Age, Mean (SD) 802 61.09 (10.91) 61.71 (10.30)

Age Group, n 1 N (%) 802

<63 294 / 564 (52%) 117 / 238 (49%)

>=63 270 / 564 (48%) 121 / 238 (51 %)

Sex, n 1 N (%) 802 female 161 / 564 (29%) 67 / 238 (28%) male 403 / 564 (71%) 171 / 238 (72%)

Ascites grade, n 1 N (%) 801 none 353 / 564 (63%) 149 / 237 (63%) mild/moderate 163 / 564 (29%) 67 / 237 (28%) severe 48 / 564 (8.5%) 21 / 237 (8.9%)

(Missing) 0 1

Ascites*: yes, n 1 N (%) 802 211 / 564 (37%) 89 / 238 (37%)

* The target variable used in modelling for this use case.

Sample preparation

Samples were prepared with reagents of the AXINON® serum kit 2.0 offered by numares AG.

During sample preparation, all reagents were used at ambient temperature (15-30°C). a) Calibration samples The AXINON® serum calibrator 2.0 was filled into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Subsequently, the calibration sample was placed in a defined position of an NMR rack. b) Control samples

The AXINON® serum control 2.0 was filled into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Subsequently, two control samples were placed in defined positions of the same NMR rack. c) Analytical samples

The AXINON® serum additives solution 2.0 was combined with the blood serum to be analyzed in a ratio of 1 :10 in a suitable reagent container. The liquid was gently mixed, wherein foaming was avoided. The volume of the mixture required for the NMR measurement was transferred into an NMR tube. A cap for NMR tubes was placed onto the NMR tube. Each analytical sample was placed at a defined position into the same NMR rack according to a rack list (as defined by the AXINON® Sample Wizard, see AXINON® Sample Wizard User manual). Attention was paid to ensure proper positioning of the samples. The analysis results of samples that were not clearly assignable were discarded.

Measurement

All measurements were carried out on a Broker Avance II+ 600MHz NMR spectrometer with an UltraShield 600 Plus NMR Magnet System, or a Broker Avance III HD 600MHz NMR spectrometer with an UltraShield 600 Plus NMR Magnet System, or a Broker Avance III HD 600MHz NMR spectrometer with an Ascend NMR Magnet System using a PATXI 1 H/D- 13C/15N Z-GRD probe. All samples were kept at 5-7°C in the SampleJet and brought to the target temperature in the integrated preheating block before measurement.

Two measurement sequences were used for all samples:

• on the one hand, a standard pulse program with 30-degree excitation pulse and presaturation for water suppression was used (zgpr30);

• on the other hand, a pulse program to measure T2-weighted spectra without J modulation using refocusing pulses between double spin echoes (project = Periodic Refocusing Of J Evolution by Coherence Transfer) was used.

Samples were measured in batches of up to 93 analytical samples per run. In addition to the analytical samples, each run included one AXINON® blood serum calibrator sample and two AXINON® blood serum control samples (before and after the analytical blood serum samples, respectively) to assure ideal measurement conditions throughout the run.

Signal analysis a) Spectrum Qualification (Quality Control for measurement)

NMR spectra underwent automatic referencing, phase correction and baseline correction before further analysis.

Subsequently, the NMR spectra underwent an automatic standardization and calibration procedure to minimize between-device, between-day and between-run effects. The quality of each of these spectra was assessed by a custom spectrum qualification algorithm that analyzes general spectral properties, e.g., offset and tilt of the baseline in selected spectral regions, and properties of selected indicator signals, e.g., signal position, shape and width. Spectra that did not meet the predefined quality criteria were excluded from further analysis. b) Bins

Successfully qualified spectra (typically covering a chemical shift from -5 to 14 ppm) were subjected to further modifications. In particular, broad background signals were separated with a suitable algorithm, e.g., background intensities (such as generated from proteins) were subtracted from the spectra, resulting in spectral intensities devoid of such background signals.

The cohort (i.e., the plurality) of modified spectra was checked for regions in which the cohort did not show a significant number of signals. These regions - like the region of the water signal and the regions in which signals are contained that originate from substances contained in AXINON® serum additives - were ignored in the steps explained in the following.

The remaining spectral regions were subject to an adaptive binning, which divides the spectrum in bins of differing size or extent (typically covering 0.01 to 0.05 ppm, but in extreme cases also covering 0.005 to 0.5 ppm). The resulting boundaries for the bins are tailored to represent signals and/or signal structures in the spectrum as good as possible.

Depending on the cohort of modified spectra, the size and thus the number of bins varies. Typical numbers of bins lie in a range of from 100 to 400. c) Quantifier

Quantification of substances was done by fitting a predefined, characteristic set of PseudoVoigt functions, which represent a linear combination of a Gaussian and a Lorentzian function, to the substance specific signal structure(s). The resulting signal fits were checked for goodness of fit and physical plausibility of properties related to fit parameters in order to reject results of insufficient fit quality.

Alternatively, quantification models making use of the previously assigned bins were applied. After substance identification, substance labels have been assigned to these bins. The quantification was then determined by the bin value, which calculates as [(sum of intensities in bin)/(number of data points in bin)]. The standardization by data points is used to compensate for a varying number of data points in the bins. The number of data points in a bin may vary by one data point due to shifts of the applied discretization grid.

In either case, the resulting integral of the quantification is then translated into a substance concentration by applying a conversion factor which has been determined experimentally for each of the substances.

Test of identified marker substances

The identified marker substances were tested in different combinations to assess their suitability for determining the risk of an individual of having ascites. In doing so, the result of the determination based on the marker substances (predicted risk) has been checked against clinical signs of ascitesin the patient who donated the serum sample, as already explained above.

The results are summarized in receiver operating characteristic (ROC) plots. In these plots, the area under the curve (AUC) indicates the fitness of the prediction. If the AUC is 0.5, the prediction is to be considered random and thus not well suited. The higher the AUC, the better is the prediction model.

The obtained results will be explained in the following in more detail making reference to Figures 1 A to 13D.

Figures 1A, 2A, 3A, 4A, 5A, 6A, 7A, 8A, 9A, 10A, 1 1 A, 12A, and 13A show ROC plots of different individual markers or marker combinations upon evaluation of a training dataset. Figures 1 C, 2C, 3C, 4C, 5C, 6C, 7C, 8C, 9C, 10C, 11 C, 12C, and 13C show the corresponding confusion matrices based on the scores assigned to the patient’s health status (i.e., having ascites or not having ascites) confirmed by other tests. Figures 1 B, 2B, 3B, 4B, 5B, 6B, 7B, 8B, 9B, 10B, 1 1 B, 12B, and 13B show ROC plots of different individual markers or marker combinations upon evaluation of a test dataset. Figures 1 D, 2D, 3D, 4D, 5D, 6D, 7D, 8D, 9D, 10D, 11 D, 12D, and 13D show the corresponding confusion matrices based on the scores assigned to the patient’s health status (i.e., having ascites or not having ascites) confirmed by other tests.

The following Table 1 summarizes the results depicted in the Figures.

Table 1 : Summary of biomarkers/biomarker set composition and corresponding AUC values depicted in the Figures.

AUC value in training AUC value in test Figure Biomarker dataset (353 controls; dataset (149

211 cases) controls; 89 cases)

1A and 1 B HDL 0.79 0.76

2A and 2B Valine 0.73 0.76

3A and 3B Pyruvate 0.70 0.73

4A and 4B HDL and anhydrosorbitol 0.80 0.76

5A and 5B HDL and valine 0.80 0.78

6A and 6B HDL and pyruvate 0.81 0.79

7A and 7B HDL and lactate 0.80 0.76

8A and 8B HDL and myo-inositol 0.81 0.80

9A and 9B Pyruvate and valine 0.78 0.81

10A and 10B ^HDL> valine, and myo-

inositol

11 A and 11 B ^HDL’ ^valine’ ^and 0.82 0.81 pyruvate

anhydrosorbitol HDL, lactate, and

13A and 13B ’ ’ . _n 0.81 _n 0.77 anhydrosorbitol

List of references cited in the preceding sections or otherwise deemed to be relevant 1 . Gines, P., et al., Compensated cirrhosis: natural history and prognostic factors. Hepatology,

1987. 7(1 ): p. 122-8. 2. Runyon, B.A., et al., The serum-ascites albumin gradient is superior to the exudate-transudate concept in the differential diagnosis of ascites. Ann Intern Med, 1992. 117(3): p. 215-20.

3. Sherlock, S. and S. Shaldon, The aetiology and management of ascites in patients with hepatic cirrhosis: a review. Gut, 1963. 4(2): p. 95-105.

4. Gines, P., et al., Pathogenesis of ascites in cirrhosis. Semin Liver Dis, 1997. 17(3): p. 175-89.

5. Abelmann, W.H., Hyperdynamic circulation in cirrhosis: a historical perspective. Hepatology, 1994. 20(5): p. 1356-8.

6. Iwakiri, Y. and R.J. Groszmann, Vascular endothelial dysfunction in cirrhosis. J Hepatol, 2007. 46(5): p. 927-34.

7. Asbert, M., et al., Circulating levels of endothelin in cirrhosis. Gastroenterology, 1993. 104(5): p. 1485-91.

8. Sacerdoti, D., et al., Renal vasoconstriction in cirrhosis evaluated by duplex Doppler ultrasonography. Hepatology, 1993. 17(2): p. 219-24.

9. Papadakis, M.A. and A.I. Arieff, Unpredictability of clinical evaluation of renal function in cirrhosis. Prospective study. Am J Med, 1987. 82(5): p. 945-52.

10. Mansour, D. and S. McPherson, Management of decompensated cirrhosis. Clin Med (Lond), 2018. 18(Suppl 2): p. s60-s65.

11 . Pugh, R.N., et al., Transection of the oesophagus for bleeding oesophageal varices. Br J Surg, 1973. 60(8): p. 646-9.

12. Cattau, E.L., Jr., et al., The accuracy of the physical examination in the diagnosis of suspected ascites. JAMA, 1982. 247(8): p. 1164-6.

13. Qamar, A. A., et al., Incidence, prevalence, and clinical significance of abnormal hematologic indices in compensated cirrhosis. Clin Gastroenterol Hepatol, 2009. 7(6): p. 689-95.

14. Simonovsky, V., The diagnosis of cirrhosis by high resolution ultrasound of the liver surface. Br J Radiol, 1999. 72(853): p. 29-34.

15. Moore, K.P., et al., The management of ascites in cirrhosis: report on the consensus conference of the International Ascites Club. Hepatology, 2003. 38(1 ): p. 258-66.

16. US 2020/0378991 A1

17. US 2017/0370954 A1

18. US 2017/0032099 A1

Claims

1. Use of a marker or of a marker set in an in vitro method for determining the risk of an individual to have ascites, characterized in that the marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate and in that the marker set comprises at least two substances chosen from a second group consisting of high-density lipoprotein, apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

2. Use according to claim 1 , characterized in that the marker set comprises at least one substance chosen from the second group and being different from high-density lipoprotein and apolipoprotein A1 if the marker set comprises any of high-density lipoprotein and apolipoprotein A1 as a first of the at least two substances.

3. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances and anhydrosorbitol as a second of the at least two substances.

4. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances and valine as a second of the at least two substances.

5. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances and pyruvate as a second of the at least two substances.

6. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances and lactate as a second of the at least two substances.

7. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances and myo-inositol as a second of the at least two substances.

8. Use according to claim 1 or 2, characterized in that the marker set comprises pyruvate and valine.

9. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances as well as valine and myo-inositol as further substances.

10. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances as well as valine and pyruvate as further substances.

1 1 . Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances as well as myoinositol and anhydrosorbitol as further substances.

12. Use according to claim 1 or 2, characterized in that the marker set comprises high-density lipoprotein or apolipoprotein A1 as a first of the at least two substances as well as lactate and anhydrosorbitol as further substances.

13. Marker or marker set for use in in-vivo diagnostics of ascites, characterized in that the marker is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate and in that the marker set comprises at least two substances chosen from a second group consisting of high-density lipoprotein, apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol.

14. Method for analyzing an isolated body fluid sample in vitro, comprising the following steps: a) determining the concentration of a substance or of at least two substances, wherein the substance is chosen from a first group consisting of high-density lipoprotein, apolipoprotein A1 , valine, and pyruvate and wherein the at least two substances are chosen from a second group consisting of high-density lipoprotein, apolipoprotein A1 , valine, pyruvate, lactate, myo-inositol, and anhydrosorbitol in an isolated body fluid sample from an individual by analyzing the body fluid sample with a suited measuring technique, b) calculating a score from the determined concentrations, the score being indicative for determining the risk of the individual to have ascites.

15. Method according to claim 14, characterized in that calculating the score involves calculating a ratio between at least two concentration values.