Evaluation of chronic liver disease

Abstract

The present invention relates to a method for assessing chronic liver disease in a subject, the method comprising the steps of: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (b) measuring the amount of a biomarker, gamma-glutamyltransferase (GGT), in the sample; (c) comparing the amount of the biomarker measured in steps (a) and (b) with a standard for the biomarker and / or calculating a score for assessing chronic liver disease; and (d) assessing chronic liver disease in the subject based on the comparison and / or calculation performed in step (c). The present invention further relates to computer-implemented methods, databases, devices, and uses related thereto.

Description

[Technical Field]

[0001] The present invention relates to a method for assessing chronic liver disease in a subject, the method comprising the steps of: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (b) measuring the amount of a biomarker, gamma-glutamyltransferase (GGT), in the sample; (c) comparing the amount of the biomarker measured in steps (a) and (b) with a standard for the biomarker and / or calculating a score for assessing chronic liver disease; and (d) assessing chronic liver disease in the subject based on the comparison and / or calculation performed in step (c). The present invention further relates to computer-implemented methods, databases, devices, and uses related thereto. [Background technology]

[0002] Chronic liver disease (CLD) is a major cause of mortality and morbidity worldwide. Regardless of its cause, CLD follows a common pathway in which the liver is repeatedly or continuously injured, resulting in the formation of scar tissue and fibrosis. Patients with CLD are at increased risk of developing liver fibrosis, cirrhosis, and liver failure. Furthermore, these patients are at significant risk of developing primary liver cancer, particularly hepatocellular carcinoma (HCC). Liver fibrosis is caused by various factors that cause the death of liver cells, notably viral infections (hepatitis B virus (HBV) and hepatitis C virus (HCV)), alcohol, and diet (nonalcoholic fatty liver disease (NAFLD)). These factors lead to the activation of hepatic stellate cells (HSCs), which is the primary mechanism leading to liver fibrosis (Tsukada et al., Clin Chim Acta 2006;364:33-60). Symptoms and diagnosis of liver fibrosis are known in the art, for example, from medical textbooks and Bataller & Brenner (2005), J Clin Invest 115:209, and Guo & Lu (2020), J Clin transl Hepatol 8(3):304. Typically, liver fibrosis is accompanied by excessive accumulation of extracellular matrix proteins, such as collagen, in the liver. There are several staging systems for staging liver fibrosis, such as the Ishak, METAVIR, and Batts-Ludwig scoring systems, as outlined in Chowdhury and Mehta (2022), Clin Exp Med, doi.org / 10.1007 / s10238-022-00799-z.

[0003] As a result of vaccination programs and therapeutic developments, HBV- and HCV-induced CLD is declining. However, nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are becoming increasingly common and represent a significant risk for CLD (Estes et al. (2018), J Hepatol. 69(4):896).

[0004] Choosing the optimal diagnosis and management for patients suffering from liver disease is essential and depends on accurate assessment and monitoring of the stage of fibrosis. Liver biopsy is currently the gold standard for assessing fibrosis. Liver biopsy has several associated drawbacks and disadvantages, such as sampling error caused by small sample size (Bravo et al., N Engl J Med, 2001;344:495-500), inter-observer variability in assessing biopsies, and complications associated with percutaneous liver biopsy. Therefore, the number of biopsies performed has been rapidly decreasing in the past 20 years.

[0005] Instead, noninvasive diagnostic scores, biomarkers, and imaging modalities, such as the aspartate aminotransferase-to-platelet index (ASTI), FIB-4, FibroTest, and hyaluronan, have gained popularity because they are less expensive, better tolerated, safer, and more acceptable to patients than liver biopsies (Lurie et al., World J Gastroenterol, 2015;21(41):11567-11583). Collagen and its fragments, which form the main components of fibrotic scars, are now being validated as biomarkers for assessing fibrosis. For example, testing of the amino-terminal propeptide of procollagen type III, PIIINP, and PRO-C3 has been performed (Karsdal et al., Liver Int 2020;40:736-750). The Enhanced Liver Fibrosis (ELF™) test, which includes hyaluronic acid, PIIINP, and TIMP-1, has good sensitivity and specificity for severe liver fibrosis (Xie et al., PLoS One 2014, doi.org / 10.1371 / journal.pone.0092772). Insulin-like growth factor binding protein 3 (IGFBP3) has been proposed as a fibrosis biomarker, either independently or in conjunction with IGF1 (Correa et al., World J Hepatol 2016;8(17):739-748; Volzke et al., European Journal of Endocrinology 2009;161(5):705-713). Gamma-glutamyltransferase (GGT) activity is an established biomarker of liver function, but is less widely used than other liver function tests (LFTs), such as bilirubin, albumin, alanine aminotransferase (ALT), and alkaline phosphatase (Dillon et al., Annals of Clinical Biochemistry 2016, Vol. 53(6)629-631). GGT is part of several diagnostic panels, such as ALFI, Fibrotest, or HepaScore.IL-8 is a strong predictor of increased fibrotic liver injury compared with established markers of liver fibrosis (Glass et al., Hepatol Commun. 2018;2:1344-1355).

[0006] However, currently available biomarkers are not accurate enough to diagnose fibrosis by themselves, so they are usually combined to form predictive scores. Among these, the Fibrosis-4 Index (FIB-4) index, NAFLD Fibrosis Score (NFS), BARD score, FibroTest, HepatoScore, Hepamet Fibrosis Score (HFS), and AST / Platelet Ratio Index (APRI) score are the most widely used. However, the European Association for the Study of the Liver (EASL) guidelines recommend that serum biomarkers / scores and / or transient elastography (TE) are not very accurate for identifying advanced fibrosis or cirrhosis, and that it is important to confirm the progression of these stages with liver biopsy (European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), and European Association for the Study of Obesity (EASO)). EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease.J Hepatol 2016;64:1388-1402).

[0007] Over the past two decades, transient elastography (TE) has emerged as a quantitative imaging approach for noninvasively assessing liver fibrosis. Elastography can be performed using ultrasound (US) or magnetic resonance imaging (MRI). The underlying principle is that liver tissue stiffness and other tissue mechanical properties can be quantitatively estimated by analyzing the propagation of shear waves introduced into those tissues (Ophir et al., Ultrasound Imaging 1991;13:111-134). However, TE suffers from several technical and patient-related limitations, including frequent recalibration, a significant proportion of unreliable measurements, and a higher rate of technical failure in the presence of confounding factors such as acute inflammation, narrow intercostal spaces, ascites, and obesity (Castera et al., Hepatology 2010;51:828-835).

[0008] Although several non-invasive tools are available, they function suboptimally and result in numerous unnecessary invasive procedures, such as biopsies. Consequently, new biomarkers for CLD patients are urgently needed to aid in patient management and facilitate the evaluation of new drugs. Summary of the Invention

[0009] It is therefore an object of the present invention to provide improved means and methods for assessing chronic liver disease that at least partly avoid the drawbacks of the prior art.

[0010] This problem is solved by the means and method of the present invention using the features of the independent claims. Preferred embodiments, which can be realized in either a stand-alone manner or in any arbitrary combination, are set forth in the dependent claims.

[0011] Accordingly, the present invention provides a method for assessing chronic liver disease in a subject, comprising: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (b) measuring the amount of the biomarker glutamyltransferase (GGT) in said sample; (c) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (d) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c). DETAILED DESCRIPTION OF THE INVENTION

[0012] Generally, terms used herein should be given their ordinary and customary meanings to those skilled in the art and should not be limited to special or customized meanings unless otherwise specified. When used hereinafter, the terms "having," "comprising," or "including," or any grammatical variants thereof, are used in a non-exclusive manner. Thus, these terms can refer to both a situation in which, apart from the features introduced by these terms, no further features are present in the entity described in this context, and a situation in which one or more further features are present. For example, the expressions "A has B," "A comprises B," and "A includes B" can both refer to a situation in which, apart from B, no other elements are present in A (i.e., a situation in which A consists solely and exclusively of B), and a situation in which, apart from B, one or more further elements are present in entity A, such as element C, elements C and D, or further elements. Also, as will be appreciated by those skilled in the art, in one embodiment, the terms "comprising a" and "comprising an" refer to "comprising one or more," i.e., equivalent to "comprising at least one." Thus, unless otherwise indicated, a reference to an item of a plurality of items in one embodiment relates to at least one such item, and in further embodiments, to a plurality thereof; thus, for example, identifying a "cell" relates to identifying at least one cell, and in one embodiment, to identifying a multiplicity of cells.

[0013] Therefore, as used herein, the term "at least one" means that one or more of the items referenced following this term may be used or may be present. For example, if this term indicates that at least one sampling unit should be used, this may be understood as one sampling unit or two or more sampling units, i.e., two, three, four, five, or any other number of sampling units. Depending on the item to which this term refers, a person skilled in the art will understand what upper limit, if any, the term may refer to.

[0014] Furthermore, when used hereinafter, the terms "preferably," "more preferably," "most preferably," "particularly," "more particularly," "specifically," "more specifically," or similar terms are used in conjunction with optional features without further limiting the possibilities. Features introduced by these terms are therefore optional features and are not intended to limit the scope of the claims in any way. The present invention can be implemented by using alternative features, as those skilled in the art will recognize. Similarly, features introduced by "in one embodiment" or similar expressions are intended to be optional features, without any limitations on further embodiments of the invention, without any limitations on the scope of the invention, and without any limitations on the possibility of combining the feature so introduced with other optional or non-optional features.

[0015] In one embodiment, the method described hereinafter is an in vitro method. The method steps may, in principle, be performed in any order deemed appropriate by one of ordinary skill in the art, but in one embodiment, are performed in the specified order, and one or more, in one embodiment, all, of the steps may be assisted or performed by automated equipment. Furthermore, the method may include steps in addition to those explicitly described above. Furthermore, terms such as "first," "second," and "third" in this specification and claims are used to distinguish between similar elements and not necessarily to describe a sequential or chronological order.

[0016] As used herein, unless otherwise indicated, the term "about" refers to the indicated value with a technical precision generally accepted in the relevant field, and in one embodiment, refers to the indicated value ±20%, in a further embodiment, ±10%, and in a further embodiment, ±5%. Furthermore, the term "essentially" indicates that there is no variation affecting the indicated result or use, i.e., potential variations do not cause the indicated result to deviate by more than ±20%, in a further embodiment, ±10%, and in a further embodiment, ±5%. Thus, "consisting essentially of" means including the specified components but excluding other components, excluding materials present as impurities, unavoidable materials present as a result of the process used to provide the components, and components added for purposes other than achieving the technical effect of the present invention. For example, a composition defined using the phrase "consisting essentially of" encompasses any known and acceptable additives, additives, diluents, carriers, and the like. In one embodiment, a composition consisting essentially of a set of components contains less than 5% by weight, in a further embodiment, less than 3% by weight, in a further embodiment, less than 1% by weight, and in a further embodiment, less than 0.1% by weight of an unspecified component.

[0017] As used herein, the term "evaluating" refers to establishing information regarding the state of the indicated disease or condition, particularly its severity, symptoms, localization, prognosis, and / or other relevant information. In one embodiment, the evaluating step aids in diagnosing the indicated disease. As will be understood by those skilled in the art, establishing a diagnosis can be based on the aforementioned evaluation, but in further embodiments, it is based on the aforementioned evaluation in combination with additional diagnostic information, such as medical history data, general physical and mental examination findings, and / or additional metabolic data. Thus, evaluating chronic liver disease can involve assessing whether a subject has chronic liver disease, whether they are at risk for chronic liver disease, whether they exhibit a worsening medical condition associated with chronic liver disease, establishing the stage of the subject's chronic liver disease, and / or establishing a prognosis for chronic liver disease. Thus, as used herein, evaluating includes diagnosing chronic liver disease, predicting the risk of developing chronic liver disease, and / or predicting any deterioration in the subject's health, particularly with regard to signs and symptoms associated with chronic liver disease. The evaluation referred to herein can also be an assessment of the risk of developing chronic liver disease. In a further embodiment, the assessment can be a prediction of the risk of a deterioration in the subject's (health) condition. Furthermore, it will be appreciated that when the risk of developing a chronic liver disease or the risk of a deterioration in health condition is predicted, the prediction is typically performed within a prediction window. More typically, the prediction window is between 1 day and 6 months, and in a further embodiment, between 1 week and 2 months.

[0018] The evaluation referred to herein can be related to rule-in evaluation, that is, to identify the subject that belongs to a group of subjects that share common characteristics, for example, suffer from chronic liver disease.However, evaluation can also be related to rule-out evaluation, that is, to identify the subject that shares common characteristics, for example, does not suffer from chronic liver disease.Therefore, evaluation can be useful for establishing diagnosis, but evaluation can also be useful for excluding diagnosis.Therefore, certain method can be particularly included in the method for monitoring chronic liver disease.

[0019] In view of the above, assessing chronic liver disease, particularly in specialist or tertiary care settings with access to a laboratory environment capable of performing automated immunoassays, may include or assist in diagnosing the stage of liver fibrosis and / or, in one embodiment, in conjunction with other laboratory findings and clinical evaluations, may assist in diagnosing and assessing the severity of liver fibrosis in patients with signs and symptoms of chronic liver disease. Also, in one embodiment, assessing chronic liver disease may specifically include or assist in diagnosing liver fibrosis, and in one embodiment, may diagnose progressive liver fibrosis, stage chronic liver disease, stage liver fibrosis, and / or distinguish between non-progressive and progressive liver fibrosis. However, assessing chronic liver disease may include or assist in ruling out one or more specific stages of liver fibrosis and / or, in one embodiment, in conjunction with other laboratory findings and clinical evaluations, may assist in ruling out severe liver fibrosis in patients with signs and symptoms of chronic liver disease. Also, in one embodiment, assessing specifically chronic liver disease may be to include or exclude liver fibrosis, and in one embodiment, to exclude advanced liver fibrosis.

[0020] As will be understood by those skilled in the art, the evaluation performed according to the present invention is usually correct for 100% of the subjects studied, but this may not be the case. However, this term typically requires that the statistically significant portion of the subjects can be accurately assessed. Whether a portion is statistically significant can be easily determined by those skilled in the art using various well-known statistical evaluation tools, such as confidence interval measurement, p-value measurement, Student's t-test, Mann-Whitney test, etc. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983. Typically, the assumed confidence interval is at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%. The p-value is typically 0.2, 0.1, or 0.05.

[0021] As used herein, the term "chronic liver disease" refers to a chronic disease affecting liver function. Chronic liver disease may be accompanied by steatosis (such as NASH), or in one embodiment, may not be accompanied by steatosis. In one embodiment, chronic liver disease is caused by viral infection, bacterial infection, parasitic infection, drug or chemical poisoning, fatty liver disease, autoimmune hepatitis, and / or environmental pollution. In one embodiment, viral infections that cause chronic liver disease are infections caused by HAV (hepatitis A virus), HBV (hepatitis B virus), HCV (hepatitis C virus), HDV (hepatitis D virus), HEV (hepatitis E virus), CMV (cytomegalovirus), and / or EBV (Epstein-Barr virus). Drugs or chemicals that cause chronic liver disease are well known in the art. Well-known drugs and / or chemicals include alcohol, particularly alcohol abuse, carbon tetrachloride, amethopterin, tetracycline, acetaminophen, fenoprofen, cyclopeptide, monomethylhydrazine, sulfamethizole, urorcosyl, sulfacetamide, and silver sulfadiazine. Thus, the method of the present invention allows for the diagnosis of chronic liver disease in a subject after contact with one of the aforementioned biological or chemical agents. The term "fatty liver disease" is well known in the art. In one embodiment, this term refers to liver damage caused by an excess of triacylglycerides, which accumulate in the liver and form large vacuoles. Fatty liver disease can be caused by, for example, alcohol abuse, diabetes mellitus, malnutrition and improper diet, drug toxicity, or genetic predisposition. Fatty liver disease as referred to herein also includes its more severe forms, particularly steatosis, nonalcoholic steatohepatitis (NASH), and nonalcoholic fatty liver disease (NAFLD).

[0022] In one embodiment, the chronic liver disease is liver fibrosis or has liver fibrosis as a symptom. The term "liver fibrosis" is known in the art, as described above. In one embodiment, liver fibrosis involves excessive accumulation of extracellular matrix proteins, such as collagen, in the liver. The accumulation of extracellular matrix proteins can be confirmed, for example, by biopsy, as reviewed in, for example, Chowdhury and Mehta (2022), Clin Exp Med, doi.org / 10.1007 / s10238-022-00799-z. In one embodiment, liver fibrosis is associated with a decline in liver function. As referred to herein, liver fibrosis can be a symptom of cirrhosis, the latter term relating to loss of liver function due to excessive inflammation and / or scarring. Thus, in one embodiment, the chronic liver disease is progressive liver fibrosis, in one embodiment, having a fibrosis level corresponding to a METAVIR score stage F3 or F4, and in one embodiment, severe / progressive fibrosis or cirrhosis. In one embodiment, the liver fibrosis is a symptom of NASH and / or NAFLD. In one embodiment, the liver fibrosis is a symptom of viral hepatitis, particularly HAV, HBV, HCV, HDV, and / or HEV hepatitis, all of which are described herein above.

[0023] As used herein, the term "subject" refers to a vertebrate, preferably a mammal, more typically a human. In one embodiment, the subject is known to suffer from or suspected to suffer from a chronic liver disease, in one embodiment, liver fibrosis. In one embodiment, the subject is suspected to suffer from NASH and / or NAFLD and / or exhibits signs and symptoms of NASH and / or NAFLD. Thus, in one embodiment, the subject suffers from a non-viral chronic liver disease, in one embodiment, alcoholic steatohepatitis (ASH), non-alcoholic steatohepatitis (NASH) or non-alcoholic fatty liver disease (NAFLD). Such suspicion of suffering from a chronic liver disease arises, in particular, from previous diagnostic measures such as medical history, physical examination, ultrasound, radiography, MRT diagnosis, clinical chemistry diagnosis, etc. However, the subject, in particular the subject known or suspected to suffer from a chronic liver disease, may also be infected with a virus as described herein above, in particular a hepatitis virus, in one embodiment, HBV and / or HCV. Thus, in one embodiment, the subject is afflicted with a viral chronic liver disease, in one embodiment, hepatitis C virus hepatitis and / or hepatitis B virus hepatitis. However, in a further embodiment, the subject does not suffer from or is not known to suffer from liver fibrosis, and in one embodiment, does not suffer from or is not known to suffer from chronic liver disease caused by or associated with alcohol abuse, a viral infection as described herein above, and / or autoimmune hepatitis as described herein above, and in a further embodiment, the patient does not suffer from a hepatic viral etiology, alcoholic steatohepatitis, and / or a hepatic autoimmune disease.

[0024] As used herein, the term "biomarker" refers to a molecular species that functions as an indicator of a disease or physiological condition referred to herein. The molecular species may be a chemical compound detectable in a subject's sample, particularly a metabolite of the subject's metabolism. Furthermore, the biomarker may be a molecular species derived from the metabolite. In such cases, the actual metabolite is chemically modified in the sample or during the measurement process, resulting in a chemically distinct molecular species, i.e., the molecular species measured as the analyte. For example, if the biomarker is a polypeptide or protein, the analyte may be a derivative of the polypeptide or protein, a fragment of the polypeptide or protein, or a complex of polypeptides, such as an immunocomplex of polypeptides, or a protein comprising two or more polypeptides. Also, if the biomarker has activity, e.g., catalytic activity and / or activation activity, for example, on target cells, the biomarker may be measured via the activity, e.g., in an enzymatic assay. In the aforementioned cases, it should be understood that the analyte may be equivalent to the actual biomarker and have the same potential as an indicator of the respective medical condition that the biomarker may have. Preferred measurement methods and analytes for the biomarkers herein are described in the context of each biomarker below. Furthermore, as will be understood by one of skill in the art, biomarkers according to the present invention need not necessarily correspond to a single molecular species. Rather, biomarkers may include stereoisomers or enantiomers of a compound and / or, for example, if the biomarker is a polypeptide, may include variant molecular species translated from, for example, splice variants, glycosylation variants, peptidase processing variants, etc. In one embodiment, the variants share at least one measurable characteristic, e.g., epitope or activity.

[0025] As used herein, the term "amount" includes any and all measures of quantity deemed appropriate by those skilled in the art, and in particular includes the absolute amount of a compound referred to herein, the relative amount of a compound, or the concentration of a compound, as well as any value or parameter that correlates thereto or can be derived therefrom in embodiments by standard mathematical operations. Such values ​​or parameters include intensity signal values ​​derived from any specific physical or chemical property obtained from the compound by direct measurement, such as intensity values ​​in a mass spectrum or NMR spectrum. Furthermore, all values ​​or parameters obtained by indirect measurement described elsewhere herein are encompassed, such as the amount of response measured by a biological readout system in response to a compound, or the intensity signal obtained from a specifically bound ligand, such as a detection compound. It should be understood that values ​​that correlate with the above-mentioned amounts or parameters can also be obtained by any standard mathematical operations. When the biomarker is an enzyme such as glutamyltransferase (GGT), the term "amount" also encompasses or can be the activity of the enzyme. Thus, in certain embodiments, the amount of glutamyltransferase (GGT) is related to GGT activity.

[0026] As used herein, the term "measuring" refers to a semi-quantitative or quantitative measurement of the biomarkers referred to herein. Measuring the amount of a biomarker can be done by any technique that allows establishing a measure of the amount of the biomarker in a semi-quantitative or quantitative way. Suitable techniques depend on the nature of the molecule and the properties of the biomarker and are discussed in more detail elsewhere herein.

[0027] Typically, the amount of a biomarker can be measured by measuring the complex between the analyte and a detection compound, in particular an antibody or a fragment thereof, i.e., in an immunoassay. The measurement of the analyte complex can be performed in any format deemed appropriate by a person skilled in the art, in particular sandwich, competitive, or other assay formats. The assay generates a signal indicative of the amount of the biomarker. Thus, measuring includes microplate ELISA-based methods, fully automated or robotic immunoassays (e.g., available from Roche). Suitable measurement methods may also include precipitation (in particular immunoprecipitation), electrochemiluminescence (electrogenerated chemiluminescence), RIA (radioimmunoassay), ELISA (enzyme-linked immunosorbent assay), fluorescent immunoassay (FIA), electrochemiluminescence sandwich immunoassay (ECLIA), dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA), scintillation proximity assay (SPA), nephelometry, nephelometry, latex-enhanced nephelometry or nephelometry, or solid-phase immunoassays. Further methods are known in the art, such as gel electrophoresis, 2D gel electrophoresis, SDS polyacrylamide gel electrophoresis (SDS-PAGE) or Western blotting. More typically, techniques specifically envisaged for measuring the biomarkers referred to herein are described herein below.

[0028] The amount of a biomarker may, in one embodiment, be measured in an activity assay, particularly if the biomarker has catalytic activity, e.g., enzymatic activity, or signal transduction activity. Enzyme assays for measuring the activity of clinically relevant enzymes are known in the art. Assays for measuring signal transduction, such as IL-8 reporter gene assays, are also known in the art.

[0029] In further embodiments, the amount of a biomarker can be measured by detecting the amount of a molecular species of the biomarker or a fragment thereof. For example, small molecule biomarkers can be detected per se or as their ions in mass spectrometry (MS). In the case of polypeptide biomarkers, detecting their fragments may be technically easier to implement. However, other methods for detecting the amount of a molecular species of an analyte are also available, such as chromatographic separation techniques, e.g., liquid chromatography (LC), high-performance liquid chromatography (HPLC), gas chromatography (GC), thin-layer chromatography, and / or size-exclusion or affinity chromatography, coupled to a suitable detection device. Such a detection device may be, for example, a photometer, e.g., a UV / VIS photometer, or an MS device. Suitable devices and methods are known in the art. Further suitable methods include measuring a physical or chemical property specific to the biomarker, such as its exact molecular weight or NMR spectrum. The method preferably involves an analytical device, such as a biosensor, an optical device coupled to an immunoassay, a biochip, a mass spectrometer, an NMR analyzer, a surface plasmon resonance instrument, or a chromatographic device.

[0030] The biomarkers measured according to the present invention are known in the art as such. Furthermore, methods for measuring the amount of biomarkers are also known to those skilled in the art. For example, biomarkers can be measured as described in the Examples section.

[0031] The biomarker of the present invention is insulin-like growth factor binding protein 3 (IGFBP3). IGFBP3 is a member of the insulin-like growth factor binding protein family known to those skilled in the art, and the amino acid sequence of human IGFBP3 is shown, for example, in Genbank Accession No: EAL23801.1. The non-glycosylated form of IGFBP3 has a molecular mass of 29 kDa, and in biological samples, IGFBP3 may form a complex with insulin-like growth factor I (IGF-I) and / or an acid-labile subunit (ALS). In one embodiment, IGFBP3 is measured by immunoassay, and in a further embodiment, by sandwich immunoassay. In one embodiment, IGFBP3 is measured by electrochemiluminescence immunoassay (ECLIA) using a capture anti-IGFBP3 antibody (e.g., biotinylated to mediate binding to a streptavidin-coated solid surface) and a detection anti-IGFBP3 antibody (which may be ruthenium-conjugated). The capture anti-IGFBP3 antibody and the detection anti-IGFBP3 antibody may comprise the same antibody or a fragment thereof, such as a monoclonal antibody, a Fab fragment, etc. In one embodiment, IGFBP3 is measured by the Elecsys IGFBP-3 Cobas® immunoassay manufactured by Roche Diagnostics GmbH, Mannheim. However, IGFBP3 may also be measured by any other method deemed appropriate by a person skilled in the art, in particular the methods described herein above.

[0032] Another biomarker of the present invention is gamma-glutamyltransferase (GGT), a known liver enzyme marker. Standard clinical chemistry tests measure the enzymatic activity of GGT. In one embodiment, the substrate used for enzymatically measuring the amount of GGT is a nitroanilide conjugate of gamma-glutamyl-peptide, particularly gamma-glutamyl-p-nitroanilide or L-gamma-glutamyl-3-carboxy-4-nitroanilide, and the co-substrate (gamma-glutamyl acceptor) may be, in particular, glycylglycine. In one embodiment, GGT is measured by the method recommended by the International Federation of Clinical Chemistry (IFCC). In a further embodiment, GGT is measured by the GGT-2 Cobas® enzyme assay manufactured by Roche Diagnostics GmbH, Mannheim. However, GGT can be measured by any other method deemed appropriate by a person skilled in the art, particularly the method described above. The amino acid sequence of human GGT is shown, for example, in Genbank Accession No. NP_1275762.1.

[0033] An optional additional biomarker of the present invention is interleukin-8 (IL-8). IL-8 is a member of the interleukin family of proteins known to those skilled in the art, and the amino acid sequence of human IL-8 is shown, for example, in Genbank Accession No: AAH13615.1. In one embodiment, IL-8 is measured by immunoassay, and in a further embodiment, by sandwich immunoassay. In one embodiment, IL-8 is measured by fluorescence immunoassay (FIA), for example, using a capture anti-IL-8 antibody that can be bound to a solid surface and a detection anti-IL-8 antibody that can be conjugated to digoxigenin; in such cases, detection can be based on fluorochrome-conjugated latex beads further conjugated to anti-digoxigenin antibodies. In a further embodiment, IL-8 is measured by the IMPACT IL-8 immunoassay manufactured by Roche Diagnostics GmbH, Mannheim (Claudon et al. (2008), Clinical Chemistry 54(9):1463).

[0034] Further optional biomarkers of the present invention, one or more of which may be measured in addition to IGFBP3, GGT, and optionally IL-8, are aspartate aminotransferase, alanine aminotransferase, platelet count, haptoglobin, alpha2-macroglobulin, apolipoprotein A1, bilirubin, cholesterol, hyaluronan, prothrombin index, hepatocyte growth factor (HGF), tissue inhibitor of metalloproteinases (TIMPs), and / or urea. All of these markers are known in the art, and those skilled in the art know how to select appropriate measurement methods, especially from those described herein above and / or standard methods. As will be understood from the description herein, the evaluation may include additional measurement steps and / or other diagnostic means, for example, the measurement of one or more additional biomarkers not specifically mentioned herein.

[0035] Aspartate aminotransferase (AST or ASAT) catalyzes the transamination of L-aspartate to α-ketoglutarate, resulting in the formation of L-glutamate and oxaloacetate. The oxaloacetate formed is reduced to malate by malate dehydrogenase (MDH) with the simultaneous oxidation of reduced nicotinamide adenine dinucleotide (NADH). The change in absorbance over time resulting from the conversion of NADH to NAD is directly proportional to AST activity and can be measured, for example, using a bichromatic (340, 700 nm) rate technique. Alanine aminotransferase (ALAT) catalyzes the transamination of L-alanine to α-ketoglutarate (α-KG), resulting in the formation of L-glutamate and pyruvate. The pyruvate formed is reduced to lactate by lactate dehydrogenase (LDH) with the simultaneous oxidation of reduced nicotinamide adenine dinucleotide (NADH). The change in absorbance is directly proportional to alanine aminotransferase activity and can be measured, for example, using a dichroic (340, 700 nm) rate technique. Platelet count is the number of platelets per volume in a sample, typically a blood or plasma sample. Haptoglobin is a soluble plasma protein and can be measured, for example, by immunoassay; the amino acid sequence of human haptoglobin is provided, for example, in Genbank Acc. No. NP_001119574.1. Alpha2-macroglobulin is a soluble plasma protein and can be measured, for example, by immunoassay; the amino acid sequence of human alpha2-macroglobulin is provided, for example, in Genbank Acc. No. NP_000005.3. Apolipoprotein A1 is a plasma protein and can be measured, for example, by immunoassay; the amino acid sequence of human apolipoprotein A1 is provided, for example, in Genbank Acc. No. NP_000030.1. HGF is a secreted paracrine cell growth, motility and morphogenesis factor that can be measured in blood-derived samples, for example, particularly in immunoassays; the amino acid sequence of human HGF is provided, for example, in Genbank Acc. No: NP_000592.3.

[0036] TIMPs are a family of inhibitors of metalloprotease activity involved in regulating the deposition and degradation of extracellular matrix (ECM), and can be measured, for example, by immunoassay; exemplary amino acid sequences of human TIMPs are provided, for example, in Genbank Acc. No: CAA00898.1. Bilirubin, cholesterol, hyaluronan, prothrombin index, and urea are classic clinical chemistry markers, and methods for measuring them are known to those skilled in the art.

[0037] As used herein, the term "sample" refers to a biological sample derived from a bodily fluid, in one embodiment from blood, plasma, serum, saliva, or urine, or a sample derived from a cell, tissue, or organ, particularly the liver, e.g., by biopsy. In a further embodiment, the sample is a blood, plasma, or serum sample, and in a further embodiment, a serum or plasma sample. Biological samples can be obtained from a subject by techniques known in the art. For example, a blood sample can be obtained by blood collection, while a tissue or organ sample can be obtained, e.g., by biopsy. In one embodiment, the sample is known or suspected to contain the biomarkers referred to herein. Such samples may be pre-treated before being used in accordance with the present invention. Said pre-treatment may include treatments necessary to release or separate biomarkers and / or analytes, or to remove excess material or waste. Suitable techniques include centrifugation, extraction, fractionation, ultrafiltration, protein precipitation followed by filtration and purification, and / or concentration of compounds. Additionally, other pre-treatments may be performed to provide biomarkers and / or analytes in a form or concentration suitable for the intended measurement. The appropriate and necessary pretreatment depends on the means used to carry out the method of the present invention and is well known to those skilled in the art. Such pretreated samples are also included in the term "sample" as used in accordance with the present invention.

[0038] As used herein, the term "reference" refers to a value, e.g., an amount, or any value derived therefrom, e.g., a score, which can be correlated with a medical condition and, in one embodiment, allows for the assessment of the present invention to be performed. In a further embodiment, it allows for the assignment of a subject to either a group of subjects suffering from or at risk of developing a disease or condition, or a group of subjects not suffering from or at risk of developing said disease or condition. Such a reference can be a threshold, e.g., a threshold amount, which separates these groups from each other. Thus, the reference can be a value that allows for the assignment of a subject to a group of subjects suffering from or at risk of developing a disease or condition, or to a group of subjects not suffering from said disease or condition. For example, the reference can be a value that allows for the assignment of a subject to a group of subjects suffering from chronic liver disease, or to a group of subjects not at risk of developing chronic liver disease. However, the reference can also be a reference range, e.g., a range of values ​​that, in one embodiment, can exclude chronic liver disease. Furthermore, in one embodiment, the reference can be a value calculated from the aforementioned values, e.g., from the amounts of two or more biomarkers, to provide a score. Appropriate criteria for separating the two groups can be easily provided, for example, by the statistical tests mentioned elsewhere herein, based on the values ​​of biomarkers from the appropriate reference groups described herein below. As those skilled in the art will understand, it is not always possible to provide criteria that unambiguously assign every possible value of a biomarker to one of the aforementioned groups, although this is specifically envisioned, and therefore there may be a range of values ​​that cannot provide a clear assessment. However, in one embodiment, as shown above, the criteria allow assessment to be performed for every possible value of a biomarker or set of biomarkers that can be measured. As those skilled in the art will understand, the specific values ​​of the criteria may depend on the intended assessment and its parameters, and therefore, the reference value for assessing chronic liver disease may typically be different from the reference value for assessing, for example, severe liver fibrosis. Relevant parameters that affect the criteria may be, in particular, the sensitivity and specificity of the assessment, as shown in the examples.

[0039] As indicated hereinbefore, the standard can be derived from at least one reference group, and the term "reference group" refers to a group of subjects whose status is known for evaluation. Thus, the reference group can be, for example, a group of subjects who are known to be suffering from chronic liver disease. In one embodiment, the group of subjects in the reference group comprises a plurality of subjects, for example, at least 5, 10, 50, 100, 1,000 or 10,000 subjects. Typically, the subject to be diagnosed and the subject in the reference group are of the same species. The standard applicable to each subject can vary according to various physiological parameters, such as age, sex, or subpopulation. As will be understood by those skilled in the art, the prevalence of chronic liver disease in the population is low, in one embodiment, less than 2%, and therefore the standard can also be derived from the average population. Assuming that the contribution of actually affected subjects is low, such an average population reference group can be treated as a reference group of known not affected by chronic liver disease, and in one embodiment, in such a case, the size of the reference group is sufficiently large, for example, at least 100, in a further embodiment, at least 1000, and in a further embodiment, at least 10,000 subjects. In view of the description herein, those skilled in the art will understand that the reference group can, in principle, also be a mixed population of subjects with chronic liver disease, provided that the status of each member of the mixed population with respect to chronic liver disease is known or becomes known before deriving a reference from such a group.

[0040] Reference amounts can, in principle, be calculated for a cohort of subjects based on the mean or median value of a given parameter, such as a biomarker amount, by applying standard statistical methods. The accuracy of a test, particularly one intended to diagnose the presence or absence of an event, is best described by its receiver operating characteristic (ROC) (see, in particular, Zweig 1993, Clin. Chem. 39:561-577). An ROC graph is a plot of all sensitivity / specificity pairs resulting from continuously varying the decision threshold over the entire range of observed data. The clinical performance of a diagnostic method depends on its accuracy, i.e., its ability to accurately assign subjects to a particular prognosis or diagnosis. An ROC plot shows the overlap between two distributions by plotting sensitivity versus 1-specificity for the entire range of thresholds suitable for making a distinction. On the y-axis is sensitivity, or the true positive rate, which is defined as the ratio of the number of true-positive test results to the product of the number of true-positive test results and the number of false-negative test results. This is also referred to as positivity in the presence of a disease or condition. It is calculated from the affected subgroup only. On the x-axis is the false positive rate, or 1-specificity, which is defined as the ratio of the number of false positive results to the product of the number of true negative results and the number of false positive results. It is a measure of specificity and is calculated entirely from the unaffected subgroup. Because the true positive rate and false positive rate are calculated completely separately by using test results from two different subgroups, the ROC plot is independent of the prevalence of the event in the cohort. Each point on the ROC plot represents a sensitivity / specificity pair corresponding to a particular decision threshold. A perfectly discriminant test (no overlap between the two result distributions) would have an ROC plot passing through the upper left corner, where the true positive rate is 1.0, or 100% (perfect sensitivity), and the false positive rate is 0 (perfect specificity). The theoretical plot for an indistinguishable test (where the distributions of results for the two groups are identical) would be a 45° diagonal line from the lower left corner to the upper right corner. Most plots fall between these two extremes. If the ROC plot falls completely below the 45° diagonal, this is easily remedied by changing the criterion for "positive" from "greater than" to "less than," or vice versa.Qualitatively, the closer the plot is to the upper left corner, the higher the accuracy of the entire test.According to the desired confidence interval, a threshold value can be derived from the ROC curve, thereby enabling the diagnosis or prediction of a given event with an appropriate balance of sensitivity and specificity.Therefore, the criterion used in the above-mentioned method of the present invention, i.e., the threshold value that allows distinguishing between at-risk and non-at-risk subjects, can usually be generated by establishing the ROC of the cohort as described above and deriving a threshold amount therefrom.According to the desired sensitivity and specificity of the diagnostic method, the ROC plot allows deriving an appropriate threshold value.Of course, optimal sensitivity may be desired to exclude (i.e., rule out) subjects at high risk, while optimal specificity may be assumed for subjects that are assessed as at high risk (i.e., rule in).

[0041] The term "comparing" as used herein encompasses comparing the measured amount of a biomarker referred to herein with a reference. It should be understood that comparison, as used herein, refers to any type of comparison between an amount value and a reference. However, in one embodiment, values ​​of the same type are compared with each other; for example, if an absolute amount is measured, the reference shall also be the absolute amount; if a relative amount is measured, the reference shall also be the relative amount, etc. The term "comparing" also encompasses comparing a calculated score with an appropriate reference score. The comparison may be performed manually or computer-assisted. The amount and reference values ​​can, for example, be compared with each other, or the comparison may be performed automatically by a computer program executing a comparison algorithm. The computer program performing the evaluation provides the desired assessment in an appropriate output format. As mentioned above, it is also envisioned to calculate a score, in particular a single score, based on the amount of biomarkers and compare this score with a reference score. In one embodiment, the calculated score combines information about the amount of biomarkers. Furthermore, in the score, biomarkers can be weighted according to their contribution to establishing discrimination, and the weighting coefficients of individual biomarkers may be different. The score can be considered a classification parameter for the evaluation described herein. In particular, it allows providing an evaluation based on a single score. Therefore, a person skilled in the art does not need to interpret the entire information regarding the amount of each biomarker. Using the scoring system described herein, values ​​of different dimensions or units of biomarkers may be used, since the values ​​are mathematically converted into scores. Thus, for example, absolute concentration values ​​may be combined with peak area ratios and / or enzyme activity values ​​in a score. The applied standard score may be selected based on the desired sensitivity and / or specificity. Methods for selecting a suitable standard score are well known in the art.

[0042] A method for assessing chronic liver disease comprises a step (a) of measuring the amount of the biomarker insulin-like growth factor binding protein 3 (IGFBP3) in a sample from the subject. The biomarker IGFBP3 and methods for measuring its amount have already been described herein above. In one embodiment, the amount of IGFBP3 is measured in a blood-derived sample, and in a further embodiment, in a serum or plasma sample. In one embodiment, the amount of IGFBP3 is measured as a concentration, and standard mathematical and statistical operations such as standardization, normalization, logarithmic transformation, e.g., log10 transformation, may be applied to the obtained value. IGFBP3 concentration is typically decreased in subjects suffering from chronic liver disease, particularly fibrosis and cirrhosis at METAVIS stages F3 and F4. In one embodiment, IGFBP3 can be used regardless of disease etiology.

[0043] The method for assessing chronic liver disease comprises step (b) measuring the amount of the biomarker gamma-glutamyltransferase (GGT) in a sample. The biomarker GGT and methods for measuring its amount have already been described herein above. In one embodiment, the amount of GGT is measured in a blood-derived sample, and in a further embodiment, in a serum or plasma sample. In one embodiment, the amount of GGT is measured as enzyme activity as described herein above, and standard mathematical and statistical operations such as standardization, normalization, logarithmic transformation, e.g., log10 transformation, may be applied to the obtained value. GGT activity is typically increased in subjects suffering from chronic liver disease, particularly fibrosis and cirrhosis at METAVIS stages F3 and F4.

[0044] The method for assessing chronic liver disease comprises a step (c) of comparing the amounts of biomarkers measured in steps (a) and (b) with a standard for said biomarkers and / or calculating a score for assessing chronic liver disease. Standards have already been described herein above. As will be understood by those skilled in the art, if one or more additional biomarkers are measured, they are in one embodiment compared with a standard and / or are also included in the calculation of the score. As will also be understood by those skilled in the art, the measured parameters for the amounts of biomarkers are typically compared with a corresponding standard, thus, for example, if a concentration is measured in step (a), said concentration is compared with a standard concentration, and / or, for example, if an activity is measured in step (b), said activity is compared with a standard activity. In a further embodiment, if a score is calculated from the amounts measured in steps (a) and (b), said score is compared with a standard score calculated from standard amounts of the biomarkers IGFBP3 and GGT by the same mathematical operation. In one embodiment, to calculate the score, the measured amounts of biomarkers are multiplied, preferably by a corresponding coefficient (

number

number

number

number

number

[0045] The particular comparison made in an embodiment will depend on the particular criterion used: if the criterion is a threshold, the comparison may involve determining whether the value of the sample in question exceeds said threshold, and if the criterion is a reference range, the comparison may involve ascertaining whether the value of the sample in question is within the reference range.

[0046] The method for assessing chronic liver disease includes a step (d) of assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c). The term "assessing" has already been described herein above. Typically, the assessment in step (d) may be based, inter alia, on the status of a reference group and a standard derived therefrom. As will be understood by those skilled in the art, biomarkers may be decreased or increased in diseased subjects compared to a healthy standard. Thus, when a standard is derived from a group of subjects known to be suffering from a disease or condition, in one embodiment, a biomarker value essentially identical to the standard will result in the assessment that the subject under investigation is suffering from the disease or condition, while in a further embodiment, a value different from the standard, in one embodiment, a significantly different value, will result in the assessment that the subject under investigation is not suffering from the disease or condition. Conversely, when a standard is derived from a group of subjects known not to be suffering from a disease or condition, in one embodiment, a biomarker value essentially identical to the standard will result in the assessment that the subject under investigation is not suffering from the disease or condition, while in a further embodiment, a value different from the standard, in one embodiment, a significantly different value, will result in the assessment that the subject under investigation is suffering from the disease or condition. Furthermore, as indicated above, the reference may be a threshold value, and such threshold value may be derived from a reference group known to be afflicted with the disease or condition, from a reference group known not to be afflicted with the disease or condition, or from a reference group known to be afflicted with the disease or condition and a reference group known not to be afflicted with the disease or condition. Typically, if the biomarker value of the subject under investigation exceeds the aforementioned threshold criterion relative to the value of the reference group known to be afflicted with the disease or condition, the subject is assumed to be afflicted with the disease or condition, and if the biomarker value of the subject under investigation exceeds the threshold relative to the value of the reference group known not to be afflicted with the disease or condition, the subject is assumed not to be afflicted with the disease or condition.Thus, depending on the particular biomarker and its correlation with the disease or symptom, a value equal to or greater than the threshold value found in the sample may indicate the presence of a medical condition, while a lower value may indicate the absence of the medical condition; alternatively, a value lower than or equal to the threshold value found in the sample being investigated may indicate the presence of a medical condition, while a higher value may indicate the absence of the medical condition. As those skilled in the art will understand in light of the description herein, the foregoing applies mutatis mutandis to scores, which may incorporate the amounts of one or more biomarkers, in one embodiment, all biomarkers. As described above, a reference score may be calculated, in particular, by the same mathematical operations used to calculate the score, but using values ​​from one or more reference groups. A reference score may also be, for example, a threshold score or a reference score range. As those skilled in the art will also understand, the evaluation following the comparison of a score to a reference score depends on the specificity of the score calculation; therefore, whether a score above or below the threshold score indicates chronic liver disease depends on the specific method of calculating the score. For example, in the exemplary scores calculated according to the examples, a score higher than the cutoff score indicates chronic liver disease. In one embodiment, the assessing distinguishes NASH fibrosis from the absence of NAFLD and / or chronic liver disease.

[0047] In one embodiment, the result of the evaluation in step (d) is a statement regarding the subject's status with respect to chronic liver disease. The result can be implicit, for example, by comparing the value measured in the sample with one or more criteria, which can be further evaluated, for example, by a physician. However, the result can also be explicit, for example, by indicating that the comparison suggests a specific status with respect to chronic liver disease. Thus, the method can further comprise a step of displaying the result of the evaluation in step (d). Alternatively or additionally, the result of the evaluation in step (d) can also be used for further evaluation, for example, by including or combining it with the results of further diagnostic procedures, such as ultrasound, magnetic resonance imaging, radiology, transient elastography, and / or determining the subject's age and / or gender.

[0048] The results of the assessment in step (d) can form the basis for or a decision regarding treatment of the subject. Thus, in embodiments in which the subject is assessed as suffering from chronic liver disease, the subject can be treated for the chronic liver disease. Suitable treatments are known in the art and include, in particular, treatment of underlying diseases, such as viral infections, metabolic diseases and / or diabetes, obesity, hemochromatosis, and / or Wilson's disease. Thus, treatment can, in particular, include recommending lifestyle changes, and in one embodiment, can include weight loss, improving nutrition, and / or avoiding or reducing addictive substances. Treatment can also include antiviral treatment, such as administering at least one nucleotide analog, interferon, and / or entecavir, particularly in the case of HBV infection, and / or a direct-acting antiviral drug (DAA), particularly in the case of HCV infection. In one embodiment, the treatment comprises administering at least one compound independently selected from the group consisting of an FXR agonist, a PPAR agonist, a dual CCR2 and CCR5 antagonist, an FGF19 analog, an FGF21 analog, an apoptosis signal-regulating kinase 1 inhibitor, a pan-caspase inhibitor, a TGF-β inhibitor, an anti-LOXL2 antibody, an angiotensin receptor blocker, and a combined angiotensin receptor and ACE enzyme inhibitor. Corresponding compounds are known in the art and are reviewed, for example, in Guo and Lu (2020), J Cin Transl Hepatol 8:304.

[0049] Advantageously, in the research underlying the present invention, it was found that the readily accessible biomarkers described herein allow for the diagnosis of chronic liver disease and various physiological conditions associated therewith with improved reliability, allowing for improved monitoring of the disease and allocation of patient treatment options.

[0050] The above definitions apply mutatis mutandis below. The following further additional definitions and explanations also apply mutatis mutandis to all embodiments described herein.

[0051] The present invention further provides, in one embodiment, a computer-implemented method for assessing chronic liver disease in a subject, comprising: (A) obtaining the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (B) obtaining the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (C) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (D) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c).

[0052] The aforementioned method for assessing chronic liver disease is an in vitro method in one embodiment, and an in silico method in a further embodiment. Therefore, the aforementioned method may be, in particular, a computer-implemented method including the steps described above. Furthermore, the method may also include steps in addition to those explicitly described above. For example, the additional steps may relate to, for example, in the embodiment described above, measuring the amount of biomarkers for step (A) and / or (B), and / or displaying and / or otherwise further using the results of the assessment after step (D). Furthermore, one or more of the steps may be assisted or performed by an automated device.

[0053] As used herein, the term "obtaining" relates to obtaining indicated information, in particular the value of a parameter such as the amount of a biomarker, in a form that allows an evaluation based on said information. Thus, in one embodiment, obtaining refers to reading information from a data carrier, e.g., in the form of a data sheet, the output of an analytical device, e.g., the results of an immunoassay, mass spectrum, etc., or from a database containing at least the relevant information. In one embodiment, the obtained information comprises the amount of the measured biomarker as described herein above. The data carrier and / or database may be local, i.e., physically connected to the device used to perform the steps of the method, or they may be remote and accessible via a network connection or via the Internet; thus, the data carrier and / or database may be, for example, a cloud storage. The method may also be implemented as a service provided by a network connection, e.g., via the Internet, whereby the values ​​of the biomarkers or one or more scores derived therefrom are obtained, e.g., from a measuring device or a medical practitioner, and the results of the comparison are output.

[0054] As used herein, the term "computer-implemented" means that the method is performed in an automated manner on a data processing unit, typically contained in a computer or similar data processing device. The data processing device receives values ​​for the amounts of the biomarkers. Such values ​​may be amounts, relative amounts, or any other calculated values ​​reflecting the amounts described in detail elsewhere herein. Thus, it should be understood that the above-described methods do not necessarily require measurement of the amounts of the biomarkers, but rather may use pre-determined amount values.

[0055] The present invention also relates to a database containing archived standards for the biomarkers IGFBP3 and GGT for assessing chronic liver disease.

[0056] As used herein, the term "database" refers to a collection of data that may be physically and / or logically grouped. Accordingly, in one embodiment, the database includes the assignment of criteria to evaluation results. As those skilled in the art will understand from the above description, "stored criteria for the biomarkers IGFBP3 and GGT" may be one or more scores derived from said criteria. In one embodiment, the database includes additional data, such as upper and / or lower detection limits, criteria for additional biomarkers, particularly those described above, data related to validation checks, etc. In a further embodiment, the database includes data related to one or more assay methods used, such as lot-specific data for calibrator samples. In one embodiment, the database may be implemented on a single data storage medium or on physically separate data storage media operatively linked to each other. In one embodiment, the database includes a tangible collection of data on, in one embodiment, embedded in, a suitable storage medium. Furthermore, in one embodiment, the database further comprises a database management system. In one embodiment, the database management system is a network-based, hierarchical, or object-oriented database management system. Furthermore, the database may be a federated or integrated database. In a further embodiment, the database is implemented as a distributed (federal) system, e.g., a client-server system. In a further embodiment, the database is configured such that a search algorithm can compare the test dataset with datasets contained in the dataset, in particular with a reference standard. Specifically, such an algorithm can be used to search the database for similar or identical datasets indicative of the above-mentioned medical condition or effect (e.g., a query search). Thus, in one embodiment, if a dataset that meets the comparison criteria detailed elsewhere herein can be identified in the database, the test dataset is associated with said medical condition or effect. As a result, information obtained from the database can be used, for example, as a reference for the methods described elsewhere herein.

[0057] The present invention further comprises: (a) at least one measuring unit for measuring the amount of a first biomarker, which is IGFBP3, and the amount of a second biomarker, which is GGT, in a sample from a subject, the at least one measuring unit comprising at least one detection means for the first biomarker and the second biomarker; (b) a device comprising: an evaluation unit operably linked to the measurement unit, the evaluation unit comprising a data processor including instructions for performing a comparison of the amounts of the first biomarker and the second biomarker with a reference, and / or instructions for performing calculation of a score for evaluation based on the amounts of the biomarkers.

[0058] The term "device" as used herein relates to a combination of means comprising the above-mentioned units operatively linked to each other to allow the measurement of the amount of a biomarker and its evaluation by the methods described herein above so that an evaluation can be provided. The device comprises at least one measuring unit and at least one evaluation unit.

[0059] The analytical unit typically includes at least one reaction zone having a first detection agent for a first biomarker and a second detection agent for a second biomarker. The device may also include one or more additional detection agents for one or more optional additional biomarkers. The detection agents may be included in immobilized form on a solid support or carrier that is contacted with the sample. However, the detection agents may also be included in the device in liquid form, e.g., as a stock solution, particularly when the biomarkers are measured in an activity assay. Furthermore, in one embodiment, the reaction zone can apply conditions that allow specific binding of the detection agent to the biomarkers contained in the sample and / or the occurrence of a catalytic reaction. The reaction zone can either allow direct sample application or be connected to a loading zone where the sample is applied. In the latter case, the sample can be actively or passively transported to the reaction zone by a connection between the loading zone and the reaction zone. Furthermore, the reaction zone is also connected to a detector. The connection must be such that the detector can detect the result of a detection reaction, such as the binding of biomarkers to their detection agents or an enzymatic reaction. The suitable detector depends on the technology used to measure the presence or amount of the biomarker. For example, optical detection may require light transmission between the detector and the reaction zone, while electrochemical determination may require a fluid connection between the reaction zone and an electrode, for example. The detector must be adapted to allow measurement of the amount of biomarker. The measured amount can then be transmitted to an evaluation unit.

[0060] The evaluation unit includes at least one data processor, which may also be referred to as a data processing unit, such as a computer, having an implemented algorithm for determining the amount present in the sample. Suitable data processing units are known in the art and include, among others, central processing devices (CPUs), graphics processing devices (GPUs), application-specific integrated circuits (ASICs), tensor processing devices (TPUs), field-programmable gate arrays (FPGAs), and other data processing units known in the art. The data processor may be, for example, a general-purpose computer or a portable computing device. It should also be understood that multiple computing devices may be used together, such as via a network or other method of transferring data, to perform one or more steps of the methods disclosed herein. Exemplary computing devices include desktop computers, laptop computers, personal data assistants ("PDAs"), cellular devices, smart or mobile devices, tablet computers, servers, and the like. Generally, a data processing unit includes a processor capable of executing multiple instructions (e.g., software programs).

[0061] The evaluation unit typically includes a memory or has access to a memory. The memory is a computer-readable information storage medium and may include, for example, a single storage device or multiple storage devices located locally on the computing device or accessible to the computing device over a network. The computer-readable medium may be any available medium accessible by the computing device and includes both volatile and non-volatile media. Furthermore, the computer-readable medium may be one or both of removable and non-removable media. By way of example and without limitation, the computer-readable medium may include a computer storage medium. Exemplary computer storage media include, but are 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 cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other medium accessible by a computing device and usable to store instructions executable by the processor of the computing device. In one embodiment, the evaluation unit further includes a database as described herein above.

[0062] According to embodiments of the present disclosure, the software may include instructions that, when executed by a processor of a computing device, may perform one or more steps of the methods disclosed herein. Some of the instructions may be adapted to generate signals that control the operation of other units of the device, such as measurement units and / or other devices, and may in turn act via those control signals to transform substances remote from the device itself. These descriptions and representations are the means used by those skilled in the art of data processing to most effectively convey the substance of their work to others skilled in the art, for example.

[0063] Instructions may also include an algorithm, which is generally conceived to be a self-consistent sequence of steps leading to a desired result. These steps may include those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic pulses or signals capable of being stored, transferred, transformed, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as values, characters, representations, numbers, etc., in reference to the physical items or representations that such signals embody or represent. It should be noted, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.

[0064] The evaluation unit may also include or have access to an output device. Exemplary output devices include displays, printers, file and fax devices, telecommunications devices such as data servers, etc. According to some embodiments, a computing device may perform one or more steps of the methods disclosed herein and then provide output regarding the evaluation results, indications, ratios, or other factors of the methods via the output device.

[0065] As used herein, the term "detection agent," which may also be used as a "detection compound," refers, in one embodiment, to any agent that allows for the measurement of the amount of at least one biomarker. Thus, the detection agent may be a reaction substrate, particularly, for example, if the biomarker has catalytic activity, e.g., an enzyme, or the detection agent may, in one embodiment, be an agent that specifically binds to the biomarker or analyte.

[0066] Those skilled in the art will select an appropriate detection substrate depending on the biomarker, i.e., catalytic activity, to be detected, based on information available in the art. For certain biomarkers, exemplary substrates are provided herein above.

[0067] Binding agents for specific antigens, as well as methods for providing them, are also known in the art. As noted above, the detection agent, which in one embodiment is a binding agent, specifically binds to the biomarker, i.e., does not cross-react with other components present in the sample. Typically, the detection agent specifically binding to the biomarker referred to herein may be an antibody, an antibody fragment or derivative, an aptamer, a ligand of the biomarker, a receptor of the biomarker, an enzyme known to bind to and / or convert the biomarker, or a small molecule known to specifically bind to the biomarker. For example, the antibodies referred to herein as detection agents include both polyclonal and monoclonal antibodies, as well as fragments thereof, such as Fv, Fab, and F(ab)2 fragments, capable of binding to the antigen or hapten. Aptamer detection agents may be, for example, nucleic acid or peptide aptamers. Methods for preparing such aptamers are well known in the art. The detection agent may be permanently or reversibly fused or linked to a detectable label. Suitable labels are well known to those skilled in the art. A suitable detectable label is any label that can be detected by an appropriate detection method. Typical labels include gold particles, latex beads, acridan esters, luminol, ruthenium, enzymatically active labels, radioactive labels, magnetic labels (including "e.g., magnetic beads," paramagnetic and superparamagnetic labels), and fluorescent labels.

[0068] "Specific binding" of a detection agent means that it does not substantially bind, i.e., does not cross-react, with other peptides, polypeptides, or substances present in the sample being analyzed. Preferably, a specifically bound biomarker should bind with an affinity that is at least 3-fold higher, more preferably at least 10-fold higher, and even more preferably at least 50-fold higher than any other component of the sample. Non-specific binding may be acceptable if it can still be clearly distinguished and measured, for example, according to its size on a Western blot or by its relatively high abundance in the sample.

[0069] The present invention also relates to a kit for assessing chronic liver disease in a subject, comprising a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT.

[0070] As used herein, the term "kit" refers to a collection of the above-mentioned components, typically provided as separate compounds or mixtures of compounds. These means, in one embodiment, are provided in a single container (i.e., housing) and, in further embodiments, allow for the joint transfer, e.g., shipping, of the components. The container also typically contains instructions for carrying out the methods of the invention. These instructions may be in manual form or may be provided by computer program code that, when executed on a computer or data processing device, can perform or assist in the measurement of the biomarkers referred to in the methods of the invention. 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 the computer or data processing device, or may be provided in downloadable form, such as a link to an accessible server or cloud. Furthermore, kits typically include standards for reference amounts of biomarkers for calibration purposes. The kits may also include additional components necessary for carrying out one of the methods described herein, which may aid in the implementation or provide additional functionality; in one embodiment, the additional components are solvents, buffers, diluents, washing solutions, and / or one or more reagents necessary for the detection of biomarkers. Additionally, the kit may include, in part or in whole, a device of the present invention.

[0071] The present invention also relates to a method for assessing and treating chronic liver disease, comprising the steps of the method for assessing according to the present invention and the further step of treating said chronic liver disease in a subject identified as suffering from chronic liver disease.

[0072] The terms "treat" and "treatment" refer to the improvement of a disease or disorder referred to herein or symptoms associated therewith to a significant degree. As used herein, said treatment also includes complete restoration of health with respect to the disease or disorder referred to herein. When this term is used herein, it should be understood that treating may not be effective in all subjects treated. However, this term preferably requires that a statistically significant portion of subjects suffering from the disease or disorder referred to herein can be successfully treated. Whether a portion is statistically significant can be easily determined by those skilled in the art using various well-known statistical evaluation tools, as described above in this specification. Exemplary treatments envisioned have already been described herein above.

[0073] As referred to herein, treatment also includes the prevention of disease progression, i.e., the prevention of exacerbation. The term "preventing exacerbation" refers to maintaining a given state of health in a subject with respect to a disease or disorder referred to herein for a certain period of time. Of course, the period may depend on the type and amount of treatment administered and the individual factors of the subject, as discussed elsewhere herein. It should be understood that the prevention of exacerbation may not be effective in all subjects treated. However, in one embodiment, the term requires that a statistically significant portion of subjects in a cohort or population is effectively prevented from worsening the disease or disorder referred to herein or its associated symptoms. In one embodiment, this context contemplates a cohort or population of subjects that would normally, i.e., without preventative measures, develop an exacerbation of the disease or disorder referred to herein. Whether a portion is statistically significant can be easily determined by those skilled in the art using various well-known statistical evaluation tools, as discussed elsewhere herein.

[0074] The present invention further relates to the use of (i) a first biomarker which is IGFBP3 and a second biomarker which is GGT, and / or (ii) a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT, for assessing chronic liver disease, and to the use of a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT for producing a diagnostic agent for assessing chronic liver disease.

[0075] The present invention further discloses and proposes a computer program comprising computer-executable instructions for performing the method according to the present invention in one or more embodiments contained herein, when the program is executed on a computer or computer network or device as described hereinabove. In particular, the computer program may be stored on a computer-readable data carrier. In particular, one, more than one, or all of the method steps a) to d) as described above may thus be performed using a computer or computer network, in one embodiment, using a computer program. The present invention therefore particularly proposes a computer program comprising instructions that, when executed by a computer, cause the computer to perform the method as described hereinabove, as well as a computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method as described hereinabove, a computer-readable data carrier having stored thereon the computer program as described hereinabove, and a data carrier signal carrying the computer program as described hereinabove. The present invention also relates to a data processing apparatus, device, or system comprising means for performing the method according to the present invention, and to a data processing apparatus, device, or system comprising a processor configured to perform the method according to the present invention.

[0076] The present invention further discloses and proposes a computer program product having program code means for carrying out the method according to the present invention in one or more embodiments contained herein, when the program is run on a computer or a computer network. In particular, the program code means may be stored on a computer readable data carrier.

[0077] Furthermore, the present invention discloses and proposes a data carrier storing a data structure that, after being loaded into a computer or computer network, such as the working memory or main memory of the computer or computer network, is capable of carrying out the method according to one or more of the embodiments disclosed herein.

[0078] The present invention further proposes and discloses a computer program product having program code means stored on a machine-readable carrier for performing a method according to one or more of the embodiments disclosed herein when the program is run on a computer or a computer network. As used herein, a computer program product refers to a program as a tradeable product. The product may generally exist in any form, such as in paper form, or may exist on a computer-readable data carrier. In particular, the computer program product may be distributed over a data network.

[0079] Finally, the present invention proposes and discloses a modulated data signal containing instructions readable by a computer system or computer network for carrying out a method according to one or more of the embodiments disclosed herein.

[0080] In one embodiment, with reference to computer-implemented aspects of the invention, one or more or all of the method steps of the methods according to one or more of the embodiments disclosed herein may be performed using a computer or a computer network. Thus, generally, any method step, such as providing and / or manipulating data, may be performed using a computer or a computer network. Generally, these method steps may include any method step, except for those that typically require manual intervention, such as providing a sample and / or certain aspects of performing the actual measurement.

[0081] In particular, the present invention further discloses: a computer or computer network comprising at least one processor, the processor being configured to execute a method according to one of the embodiments described herein; a computer-loadable data structure configured to perform a method according to one of the embodiments described herein when executed on a computer; a computer program configured, when being run on a computer, to carry out a method according to one of the embodiments described herein; a computer program comprising program means for carrying out a method according to one of the embodiments described herein when said computer program is run on a computer or a computer network; a computer program comprising program means according to any preceding embodiment, the program means being stored on a computer-readable storage medium; a storage medium on which a data structure is stored and adapted to perform a method according to one of the embodiments described herein after the data structure has been loaded into a primary and / or working storage device of a computer or computer network; and A computer program product having program code means, which may be or is stored on a storage medium for performing a method according to one of the embodiments described in this specification when the program code means is executed on a computer or a computer network.

[0082] In summary of the present invention's findings, the following embodiments are specifically envisaged.

[0083] Embodiment 1: A method of assessing chronic liver disease in a subject, comprising: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (b) measuring the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (c) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (d) assessing the subject for chronic liver disease based on the comparison and / or calculation performed in step (c).

[0084] Embodiment 2: The method of embodiment 1, wherein the chronic liver disease is liver fibrosis.

[0085] Embodiment 3: The method of embodiment 1 or 2, wherein the chronic liver disease is advanced liver fibrosis, in one embodiment, advanced liver fibrosis corresponding to METAVIR score stage F3 or F4, or cirrhosis.

[0086] Embodiment 4: The method of any one of embodiments 1 to 3, wherein said assessing comprises diagnosing liver fibrosis, and in one embodiment, diagnosing progressive liver fibrosis.

[0087] Embodiment 5: The method of any one of embodiments 1 to 4, wherein said assessing comprises staging chronic liver disease.

[0088] Embodiment 6: The method of any one of embodiments 2 to 5, wherein said assessing comprises staging liver fibrosis.

[0089] Embodiment 7: The method of any one of embodiments 1 to 6, wherein said assessing step comprises distinguishing between non-progressive and progressive liver fibrosis.

[0090] Embodiment 8: The method of any one of embodiments 1 to 7, wherein said assessing step comprises ruling out advanced liver fibrosis, and in one embodiment, ruling out liver fibrosis.

[0091] Embodiment 9: The method of any one of embodiments 1 to 8, wherein said method is comprised in a method for monitoring chronic liver disease.

[0092] Embodiment 10: The method of any one of embodiments 1 to 9, wherein the method further comprises measuring the amount of the biomarker interleukin-8 (IL-8), and step (c) comprises comparing the measured amounts of the three biomarkers with a standard for said biomarkers and / or calculating a score for assessing chronic liver disease.

[0093] Embodiment 11: The method according to any one of embodiments 1 to 10, wherein the amount of the biomarker IGFBP3 and optionally the amount of the biomarker IL-8 are measured by immunoassay.

[0094] Embodiment 12: The method of any one of embodiments 1 to 11, wherein the amount of the biomarker GGT is measured by an activity assay.

[0095] Embodiment 13: The method of any one of embodiments 1 to 12, wherein the subject is known to have or is suspected of having a liver disease, in one embodiment, a chronic liver disease.

[0096] Embodiment 14: The method of any one of embodiments 1 to 13, wherein the subject is suffering from a viral chronic liver disease, in one embodiment, hepatitis C virus hepatitis and / or hepatitis B virus hepatitis.

[0097] Embodiment 15: The method of any one of embodiments 1 to 14, wherein the subject has a non-viral chronic liver disease, in one embodiment, alcoholic steatohepatitis (ASH), non-alcoholic steatohepatitis (NASH), or non-alcoholic fatty liver disease (NAFLD).

[0098] Embodiment 16: The method of any one of embodiments 1 to 15, wherein the subject is a human.

[0099] Embodiment 17: The method of any one of embodiments 1 to 16, wherein the sample is a body fluid sample, in one embodiment a blood sample or a blood-derived sample, in a further embodiment a blood, plasma or serum sample, in a further embodiment a plasma or serum sample.

[0100] Embodiment 18: The method of any one of embodiments 1 to 17, wherein the criteria are for each biomarker derived from at least one subject known to suffer from chronic liver disease, in one embodiment, liver fibrosis, and in a further embodiment, progressive liver fibrosis.

[0101] Embodiment 19: The method of embodiment 18, wherein an amount for each biomarker that is essentially identical or similar to the corresponding reference is indicative of the subject suffering from a chronic liver disease, in one embodiment, liver fibrosis, and in a further embodiment, advanced liver fibrosis, and / or an amount for each biomarker that differs from the corresponding reference is indicative of the subject not suffering from a chronic liver disease, in one embodiment, liver fibrosis, and in a further embodiment, advanced liver fibrosis.

[0102] Embodiment 20: The method of any one of embodiments 1 to 17, wherein the criteria are for each biomarker derived from at least one subject known to be free of chronic liver disease, in one embodiment, liver fibrosis, and in a further embodiment, advanced liver fibrosis.

[0103] Embodiment 21: The method of embodiment 20, wherein an amount for each biomarker that is essentially identical or similar to the corresponding reference is indicative of the subject not suffering from chronic liver disease, in one embodiment liver fibrosis, and in a further embodiment progressive liver fibrosis, and / or an amount for each biomarker that differs from the corresponding reference is indicative of the subject suffering from chronic liver disease, in one embodiment liver fibrosis, and in a further embodiment progressive liver fibrosis.

[0104] Embodiment 22: Any one of the methods of embodiment 20 or 21, wherein a decrease in IGFBP3 and / or an increase in GGT compared to said reference is indicative of the subject suffering from a chronic liver disease, in one embodiment, liver fibrosis, and in a further embodiment, progressive liver fibrosis.

[0105] Embodiment 23: The method of any one of embodiments 1 to 22, wherein the method comprises measuring at least one additional biomarker, which in one embodiment comprises measuring aspartate aminotransferase, alanine aminotransferase, platelet count, haptoglobin, alpha2-macroglobulin, apolipoprotein A1, bilirubin, cholesterol, hyaluronan, prothrombin index, hepatocyte growth factor (HGF), tissue inhibitor of metalloproteinases (TIMPs), and / or urea.

[0106] Embodiment 24: The method of any one of embodiments 1 to 23, wherein the method comprises a further diagnostic step, in one embodiment, sonography, magnetic resonance imaging, radiography, transient elastography, and / or determining the age and / or sex of the subject.

[0107] Embodiment 25: A method of assessing chronic liver disease in a subject, comprising: (A) obtaining the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (B) obtaining the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (C) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (D) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c).

[0108] Embodiment 26: The method of embodiment 25, wherein the method is computer-implemented.

[0109] Embodiment 27: The method of embodiment 25 or 26, wherein the method further comprises measuring the amount of the biomarker or having the amount of the biomarker measured in steps (a) and / or (b).

[0110] Embodiment 28: The method of any one of embodiments 25 to 27, further comprising at least one feature of any one of embodiments 1 to 24.

[0111] Embodiment 29: A database comprising archived standards for the biomarkers IGFBP3 and GGT for assessing chronic liver disease.

[0112] Embodiment 30: The database according to embodiment 29, wherein the criteria are the criteria described in any one of embodiments 18 to 22.

[0113] Embodiment 31: The database of embodiment 29 or 30, wherein the database is physically embedded in a storage means.

[0114] Embodiment 32: A device, comprising: (a) at least one measuring unit for measuring the amount of a first biomarker, which is IGFBP3, and the amount of a second biomarker, which is GGT, in a sample from a subject, the at least one measuring unit comprising at least one detection means for the first biomarker and the second biomarker; (b) an evaluation unit operably linked to the measurement unit, the evaluation unit including a data processor including instructions for performing a comparison of the amounts of the first biomarker and the second biomarker with a reference, and / or instructions for performing calculation of a score for evaluation based on the amounts of the biomarkers.

[0115] Embodiment 33: A device according to embodiment 32, wherein said detection means is capable of specifically detecting at least one of said biomarkers, and in one embodiment both of said biomarkers.

[0116] Embodiment 34: A device as described in embodiment 32 or 33, wherein the device further comprises a database as described in any one of embodiments 29 to 31 operably coupled to a data processor.

[0117] Embodiment 35: A device described in any one of embodiments 32 to 34, wherein the device is a device for assessing chronic liver disease in a subject, and the evaluation unit further comprises means for assessing the subject based on the comparison.

[0118] Embodiment 36: A device according to any one of embodiments 32 to 35, wherein the evaluation unit is capable of automatically receiving the value of the amount of the biomarker from the measurement unit.

[0119] Embodiment 37: A device described in any one of embodiments 32 to 36, adapted to perform the method described in any one of embodiments 1 to 28, and in one embodiment including physically embedded instructions that, when executed by a data processor, cause the device to perform the method described in any one of embodiments 1 to 28.

[0120] Embodiment 38: A kit for assessing chronic liver disease in a subject, comprising a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT.

[0121] Embodiment 39: A method for assessing and treating chronic liver disease, comprising the steps of the method of any one of embodiments 1 to 28 and the further step of treating said chronic liver disease in a subject identified as suffering therefrom.

[0122] Embodiment 40: Use of (i) a first biomarker which is IGFBP3 and a second biomarker which is GGT, and / or (ii) a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT, for assessing chronic liver disease.

[0123] Embodiment 41: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from said subject; (b) measuring the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (c) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (d) assessing chronic liver disease in the subject based on the comparison and / or calculation performed in step (c).

[0124] Embodiment 42: (A) obtaining the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from said subject; (B) obtaining the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (C) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (D) assessing chronic liver disease in the subject based on the comparison and / or calculation performed in step (c).

[0125] Embodiment 43: The use according to any one of embodiments 40 to 42, further comprising the features according to any one of embodiments 1 to 39.

[0126] Embodiment 44: Use of a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT for the manufacture of a diagnostic agent for assessing chronic liver disease.

[0127] Embodiment 45: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from said subject; (b) measuring the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (c) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (d) assessing chronic liver disease in the subject based on the comparison and / or calculation performed in step (c).

[0128] Embodiment 46: (A) obtaining the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from said subject; (B) obtaining the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (C) comparing the amounts of biomarkers measured in steps (a) and (b) with standards for said biomarkers and / or calculating a score for assessing chronic liver disease; (D) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c).

[0129] Embodiment 47: The use according to any one of embodiments 44 to 46, further comprising the features according to any one of embodiments 1 to 39.

[0130] All references cited herein are hereby incorporated by reference with respect to their entire disclosure content and the disclosure content specifically mentioned herein. [Brief explanation of the drawings]

[0131] [Figure 1-1] Changes in individual biomarker concentrations (logarithmically transformed) in non-progressive (0) and progressive fibrosis (1). A: IGFBP3 concentration, B: GGT activity, C: IL-8 concentration. 0: control, 1: case. [Figure 1-2] Changes in individual biomarker concentrations (logarithmically transformed) in non-progressive (0) and progressive fibrosis (1). A: IGFBP3 concentration, B: GGT activity, C: IL-8 concentration. 0: control, 1: case. [Figure 2] Box plot (A) and AUROC curve (B) for IGFBP3 as a single biomarker score calculated using the logistic regression model described below in Example 1. Of note, the score is designed to be between 0 and 1 and to increase with decreasing IGFBP3 concentration in the sample. 0: non-progressive fibrosis, 1: progressive fibrosis. [Figure 3] Box plot (A) and AUROC curve (B) of IGFBP3 in combination with GGT, scores calculated using the logistic regression model described below in Example 1. 0: non-progressive fibrosis, 1: progressive fibrosis. [Figure 4] Box plot (A) and AUROC curve (B) of IGFBP3 in combination with GGT and IL-8, scores calculated using the logistic regression model described below in Example 1. 0: non-progressive fibrosis, 1: progressive fibrosis. [Figure 5-1] Cross-validation results for 1 (A), 2 (B), or 3 (C) biomarkers. Black boxes indicate selected variables. The right y-axis indicates the frequency of selection. [Figure 5-2]Cross-validation results for 1 (A), 2 (B), or 3 (C) biomarkers. Black boxes indicate selected variables. The right y-axis indicates the frequency of selection. [Figure 5-3] Cross-validation results for 1 (A), 2 (B), or 3 (C) biomarkers. Black boxes indicate selected variables. The right y-axis indicates the frequency of selection. [Figure 6] Box plot (A) and AUROC curve (B) for GGT values ​​using the dataset from Example 2. [Figure 7] Box plot (A) and AUROC curve (B) for IGFBP3 concentration using the dataset from Example 2. [Figure 8] Box plot (A) and AUROC curve (B) of IGFBP3+GGT using the score model trained on the dataset of Example 1 against the dataset of Example 2. [Figure 9] Box plot (A) and AUROC curve (B) of IGFBP3+GGT using a score model trained on the dataset of Example 2 against the dataset of Example 2. [Figure 10] Box plot (A) and AUROC curve (B) for the ELF assay using the dataset from Example 2.

[0132] The following examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention in any way.

[0133] Example 1: IGFBP3 and GGT as biomarkers for chronic liver disease The biomarkers insulin-like growth factor binding protein 3 (IGFBP3, Uniprot P17936), gamma-glutamyltransferase (GGT, activity assay), and interleukin-8 (IL-8, Uniprot P10145) were evaluated as markers of chronic liver disease in a sample panel consisting of: 284 serum samples from patients with non-progressive liver fibrosis corresponding to METAVIR score stages F0-F2 330 serum samples from patients with advanced liver fibrosis, corresponding to METAVIR score stages F3, F4, and cirrhosis · Etiology: HBV (majority), HCV, NASH, ASH, HBV approximately 66%, HCV 15%, and other etiologies approximately 19%.

[0134] IGFBP3 and GGT were measured with Roche IVD assays. For IL-8 measurement, the Roche prototype platform IMPACT was used (Chandra et al., Arthritis Research & Therapy, 2011, 13:R102). AUCROC was calculated for distinguishing non-progressive fibrosis from progressive fibrosis for individual biomarkers and biomarker combinations and increased with the number of biomarkers combined.

[0135] Scores based on single biomarkers or combinations of biomarkers were calculated as follows: the measured biomarker amounts were log10 transformed and the corresponding coefficients (c1, ..., c for n biomarkers) were calculated. n (where n is the number of biomarkers)) and the intercept (

number

number

number

[0136] Example 1.1: IGFBP3, GGT and IL-8 as single markers The changes in the single markers of the panel are shown in Figure 1 .

[0137] IGFBP3 concentrations in serum / plasma of patients with liver fibrosis are significantly decreased in progressive (F3, F4, cirrhosis) disease compared with non-progressive fibrosis (F0, F1, and F2). The AUROC for IGFBP3 as a single marker using the single-biomarker score described in Example 1 above to distinguish between non-progressive and progressive fibrosis in the measured cohort was 0.73 (0.69-0.77) (Figure 2). Exemplary cutoff values ​​for a score based on the single biomarker IGFBP3, along with the resulting specificity and sensitivity values, are shown in Table 1 below. [Table 1]

[0138] The AUROC of GGT as a single marker to distinguish between non-progressive and progressive fibrosis in the measured cohort was 0.68 (0.64-0.72), whereas the performance of IL-8 as a single biomarker to distinguish progressive from non-progressive fibrosis was considerably weaker, with an AUROC of 0.51 (0.47-0.56).

[0139] Example 1.2: Two Biomarker Panels: IGFBP3 + GGT The performance of two biomarker panels: IGFBP3 + GGT (combined into the score described in Example 1) is summarized in Figure 3. Exemplary cutoff values, along with the resulting specificity and sensitivity values, are shown in Table 2 below. The AUROC for GGT in combination with IGFBP3 was 0.76 (0.72-0.79) (Figure 3). Exemplary cutoff values, along with the resulting specificity and sensitivity values, are shown in Table 2 below. [Table 2]

[0140] Example 1.3: Three-biomarker panel: IGFBP3 + GGT + IL-8 The performance of the three biomarker panels: IGFBP3 + GGT + IL-8 (combined in the model that calculates the score described in Example 1) is summarized in Figure 4. Inclusion of IL-8 increased the AUROC for the combination of IGFBP3 and GGT to 0.78 (range, 0.74-0.82). Exemplary cutoff values, along with the resulting specificity and sensitivity values, are shown in Table 3 below. [Table 3]

[0141] Example 1.4: Comparison of the biomarker panels of Examples 1.2 and 1.3 with known biomarkers and their combinations The biomarker panels of Examples 1.2 and 1.3 were compared to known biomarkers of chronic liver disease and combinations of two or three of these biomarkers, and the biomarkers evaluated were: IL-10, IL-8, BMP7, COMP, DKK1, OPG, ALB, GGT, BILT, AFU, GLDH, ALT, AST, GP73, HGF, TIMP-1, AXL, Midkine, CES1, Ang-2, OPN, GPC3, MMP3, CEA, Cyfra 21-1, ferritin, HE4, IL-6, NSE, CA 125, CA 15.3, CA 19-9, CA72.4, IGF1, IGFBP3, IGFBP7, MMP2, sialyltransferase, AFP-L3, AFP, PIVKA-II, PRO-C3, PRO-C3X, Seprase, age, and gender.

[0142] To evaluate the performance of the biomarkers and biomarker panels of Examples 1.1-1.3, cross-validation using univariate and multivariate analyses was performed, allowing us to assess whether a biomarker or combination of biomarkers from Examples 1.1-1.3 provides better discrimination of non-progressive from progressive liver fibrosis compared to any of the other tested biomarkers or any combination thereof.

[0143] Specifically, the diagnostic accuracy of biomarker combinations was estimated by exhaustive search in a nested cross-validation scheme, with the biomarker data randomly divided into a training set (3 / 4) and a test set (1 / 4). The best model was selected (i.e., by optimizing coefficients and intercepts in particular) using exhaustive search with internal cross-validation on the training set (i.e., by evaluating all possible biomarker combinations) and tested on the remaining test data set. This procedure was repeated 100 times. The clinical performance of the best model was evaluated using the outer-loop test data. The nested cross-validation procedure yielded robust estimates (AUCROC) of the frequency of selection of the best marker combination and the performance of each model (avoiding overfitting).

[0144] IGFBP3 was the most frequently selected feature in the one-, two-, and three-biomarker combinations, with frequencies of 85%, 93%, and 82%, respectively. GGT was selected as the second feature with frequencies of 65% and 68%. IL8 was selected as the third feature with a frequency of 54% (Figure 5A). Thus, this cross-validation clearly demonstrates that IGFBP3 is the best single biomarker for distinguishing non-progressive from progressive liver fibrosis in the test cohort. Furthermore, this cross-validation surprisingly demonstrates that the multivariate combination of GGT, among a wide variety of other biomarkers, best improved the distinction of non-progressive from progressive liver fibrosis among all two-biomarker combinations tested (Figure 5B). Notably, many two-biomarker combinations did not improve performance at all over the single biomarkers in distinguishing non-progressive from progressive liver fibrosis. It is a novel and surprising finding that the combination of IGFBP3 and GGT provides such superior differentiation of non-progressive from progressive liver fibrosis compared to all other two-biomarker combinations. Another surprising finding is that adding IL-8 as a third biomarker to the multivariate analysis further improved the differentiation of non-progressive from progressive liver fibrosis, which appeared to be superior to any other three-biomarker combination tested (Figure 5c).

[0145] Overall, this cross-validation analysis shows that the combination of IGFBP3 and GGT (and optionally IL-8) provides superior performance in distinguishing non-progressive from progressive liver fibrosis compared to any other combination of other tested biomarkers, including well-known markers of chronic liver disease.

[0146] The mean AUROCs for the combination of IGFBP3, GGT, and optionally IL8 to distinguish between non-progressive (F0, F1, and F2) fibrosis + cirrhosis and progressive (F3, F4) fibrosis + cirrhosis are described above in Examples 1.1-1.3.

[0147] Example 2: Evaluation of biomarkers in a NASH cohort. Clinical performance and clinical data of biomarkers: Siemens ELF score: HA, TIMP-1, PIIINP, GGT and IGFBP3 were assessed in a sample panel consisting of: 125 serum samples from patients with confirmed NASH fibrosis, · 82 serum samples from controls including NAFLD (n=17) and apparently healthy donors.

[0148] Biomarkers and biomarker panels were evaluated for distinguishing NASH liver fibrosis from control panels (NAFLD + healthy). IGFBP3 and GGT were measured using Roche IVD or Robust Prototype Elecsys assays. ELF panels were measured using a Siemens Atellica analyzer.

[0149] Example 2.1 GGT and IGFBP3 as a single biomarker The results for GGT and IGFBP3 are summarized in Figures 6 and 7, respectively. Exemplary cutoff values, along with the resulting specificity and sensitivity values, are shown in Tables 4 and 5 below. [Table 4] [Table 5]

[0150] Example 2.2: Two Biomarker Panels: IGFBP3 + GGT The performance of two biomarker panels: IGFBP3 + GGT for the data from Example 2 is summarized in Figures 8 and 9. Exemplary cutoff values, along with the resulting specificity and sensitivity values, are shown in Tables 6 and 7 below. Performance was calculated using (i) a scoring model trained on the dataset from Example 1 against the dataset from Example 2 (Figure 8), and (ii) a scoring model trained on the dataset from Example 2 against the dataset from Example 2 (Figure 9), respectively. Both models showed the same performance, demonstrating the robustness of the biomarker panel and its applicability to different liver fibrosis etiologies. [Table 6] [Table 7]

[0151] Example 2.3: Comparative Example: ELF Test For comparison, the performance of a known biomarker panel from the ELF test (Siemens Healthineers International AG) was evaluated in the same cohort described in Example 2. This test is a widely used blood test for fibrosis that includes measurements of hyaluronic acid (HA), amino-terminal propeptide of procollagen type III (PIIINP), and tissue inhibitor of metalloproteinase 1 (TIMP-1). The results obtained are shown in Figure 10, and exemplary cutoff values, along with the obtained specificity and sensitivity values, are shown in Table 8 below. [Table 8]

[0152] Example 2.4: Conclusion As evident from Example 2, the marker panel proposed herein is superior to, or at least comparable to, known biomarker panels in distinguishing NASH fibrosis from NAFLD and healthy cases. Importantly, the performance of the IGFBP3+GGT combination was identical regardless of the model training data used for data evaluation. We first evaluated this dataset using a model for combining IGFBP3 and GGT trained on the data from Example 1, based primarily on viral hepatitis etiology, followed by an independent evaluation using a model trained on the data from Example 2, based solely on non-viral hepatitis. As shown in Figures 8 and 9, the AUCROCs for both evaluations were identical, thus demonstrating the universal usefulness of the IGFBP3+GGT biomarker combination for assessing liver fibrosis, regardless of etiology. It is particularly noteworthy that the IGFBP3 and GGT panel contains only two biomarkers instead of the three used in ELF, the most popular biomarker solution in the field. This is particularly advantageous because it requires fewer resources and is easier to use. Considering the data in Example 1.3, it seems plausible that the combined panel of IGFBP3, GGT and IL-8 also shows better discrimination of the control panel (NAFLD+healthy) from NASH liver fibrosis than ELF.

[0153] literature Bataller & Brenner (2005), J Clin Invest 115:209 Castera et al.,Hepatology 2010;51:828-835 Chowdhury and Mehta(2022), Clin Exp Med, doi.org / 10.1007 / s10238-022-00799-z Claudon et al. (2008), Clinical Chemistry 54(9):1463 Correa et al.,World J Hepatol 2016;8(17):739-748 Dillon et al.,Annals of Clinical Biochemistry 2016,Vol.53(6)629-631 Estes et al.(2018),J Hepatol.69(4):896 EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease.J Hepatol 2016;64:1388-1402 Glass et al.,Hepatol Commun.2018;2:1344-1355 Guo&Lu (2020), J Clin Transl Hepatol 8(3):304 Karsdal et al.,Liver Int 2020;40:736-750 Lurie et al.,World J Gastroenterol 2015;21(41):11567-11583 Ophir et al.,Ultrasound Imaging 1991;13:111-134 Tsukada et al, Clin Chim Acta 2006;364:33-60 Volzke et al.,European Journal of Endocrinology 2009;161(5):705-713 Xie et al,PLoS One 2014,doi.org / 10.1371 / journal.pone.0092772 Zweig 1993,Clin.Chem.39:561-577

Claims

1. 1. A method of assessing chronic liver disease in a subject, comprising: (a) measuring the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (b) measuring the amount of the biomarker gamma-glutamyltransferase (GGT) in the sample; (c) comparing the amounts of the biomarkers measured in steps (a) and (b) with standards for the biomarkers and / or calculating a score for assessing chronic liver disease; (d) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c).

2. 2. The method of claim 1, wherein the chronic liver disease is liver fibrosis, in one embodiment progressive liver fibrosis, and in a further embodiment progressive liver fibrosis corresponding to METAVIR score stage F3 or F4, or liver cirrhosis.

3. 3. The method of claim 1 or 2, wherein the evaluating step comprises diagnosing liver fibrosis, comprising staging chronic liver disease, comprising staging liver fibrosis, comprising distinguishing between non-progressive and progressive liver fibrosis, and / or comprising ruling out progressive liver fibrosis.

4. The method according to any one of claims 1 to 3, wherein said method is included in a method for monitoring chronic liver disease.

5. 5. The method of claim 1, further comprising measuring the amount of a biomarker, interleukin-8 (IL-8), and step (c) comprises comparing the measured amounts of the three biomarkers with a standard for the biomarkers and / or calculating a score for assessing chronic liver disease.

6. The method of any one of claims 1 to 5, wherein the amount of the biomarker IGFBP3 and optionally the amount of the biomarker IL-8 are measured by immunoassay.

7. The method according to any one of claims 1 to 6, wherein the amount of the biomarker GGT is measured by an activity assay.

8. 8. The method of any one of claims 1 to 7, wherein the sample is a body fluid sample, in one embodiment a blood sample or a blood-derived sample, in a further embodiment a blood, plasma or serum sample, in a further embodiment a plasma or serum sample.

9. 9. The method of any one of claims 1 to 8, wherein the method comprises measuring at least one further biomarker, which in one embodiment comprises measuring aspartate aminotransferase, alanine aminotransferase, platelet count, haptoglobin, alpha2-macroglobulin, apolipoprotein A1, bilirubin, cholesterol, hyaluronan, prothrombin index, hepatocyte growth factor (HGF), tissue inhibitor of metalloproteinases (TIMP) and / or urea.

10. 10. The method of any one of claims 1 to 9, wherein the method comprises a further diagnostic step, in one embodiment sonography, magnetic resonance imaging, radiography, transient elastography, and / or determining the age and / or sex of the subject.

11. 1. A computer-implemented method for assessing chronic liver disease in a subject, comprising: (A) obtaining the amount of a biomarker, insulin-like growth factor binding protein 3 (IGFBP3), in a sample from the subject; (B) obtaining the amount of gamma-glutamyltransferase (GGT) as a biomarker in the sample; (C) comparing the amounts of the biomarkers measured in steps (a) and (b) with standards for the biomarkers and / or calculating a score for assessing chronic liver disease; (D) assessing the subject's chronic liver disease based on the comparison and / or calculation performed in step (c).

12. A database containing archived standards for the biomarkers IGFBP3 and GGT for assessing chronic liver disease.

13. A computer program comprising computer executable instructions for performing the method of claim 11 when the program is run on a computer or computer network.

14. A device, (a) at least one measuring unit for measuring the amount of a first biomarker, which is IGFBP3, and the amount of a second biomarker, which is GGT, in a sample from a subject, the at least one measuring unit comprising at least one detection means for the first biomarker and the second biomarker; (b) an evaluation unit operably coupled to the measurement unit, the evaluation unit comprising a data processor including instructions for performing a comparison of the amounts of the first biomarker and the second biomarker with a reference, and / or instructions for performing a calculation of a score for evaluation based on the amounts of the biomarkers.

15. 1. Use of (i) a first biomarker which is IGFBP3 and a second biomarker which is GGT, and / or (ii) a first detection agent for measuring the amount of IGFBP3 and a second detection agent for measuring the amount of GGT, for assessing chronic liver disease.

Images