Liver fibrosis, NASH, and “risk” NASH biomarkers and methods

Abstract

This specification provides biomarkers for diagnosing the fibrotic stage in patients with non-alcoholic fatty liver disease (NFC) or patients at risk of developing NFC. Methods for treating fibrosis are also provided herein. The concentrations of at least two protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule 1 (ICAM), and platelet glycoprotein V (GP5) are evaluated in a blood sample.

Description

Technical Field

[0001] Related Applications This application claims the benefit of priority of U.S. Provisional Patent Application No. 63 / 522,282, filed on Jun. 21, 2023, the content of which is incorporated herein by reference in its entirety.

Background Art

[0002] Field and Background of the Invention Methods are provided herein for diagnosing the stage of fibrosis, NASH, and / or “at risk” NASH in a patient based on the abundance of certain biomarkers in the blood.

[0003] Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver disease worldwide, with a global prevalence of up to 30%. It ranges from simple steatosis to non-alcoholic steatohepatitis (NASH) with inflammation and hepatocyte injury. The increasing global incidence of NAFLD is closely associated with obesity, type 2 diabetes (T2D), and metabolic syndrome. The main complication of NASH is liver fibrosis, which is caused by chronic inflammation. Fibrosis ultimately leads to irreversible scarring of liver tissue and thus significant liver-related risks including advanced fibrosis, cirrhosis, and hepatocellular carcinoma. Lifestyle improvements, pharmacological treatments, and regular monitoring are important for NAFLD management to address the impact on liver health and overall health.

[0004] The gold standard for diagnosing NAFLD and assessing liver fibrosis is liver biopsy. However, liver biopsy is invasive and thus risky. Therefore, there is a need in the art for non-invasive methods for diagnosing NAFLD and staging liver fibrosis.

[0005] Furthermore, this technology also requires methods to differentiate between non-alcoholic steatohepatitis, non-alcoholic steatohepatitis (NASH), and "at-risk" NASH (a combined scale of liver fibrosis and NAFLD activity score), the latter being a severe and rapidly progressive form of NAFLD that typically leads to cirrhosis and liver transplantation. Early detection of NAFLD, accurate staging of fibrosis and NASH, and management and treatment of these diseases are essential.

[0006] Currently, there are several methods for non-invasive diagnosis of liver fibrosis stages based on serological markers and / or imaging tests. The latter are typically costly and require extensive training of health professionals, making them unsuitable for screening entire populations. Developing cost-effective, non-invasive, and accurate methods for precisely identifying liver fibrosis stages, NASH, and NAFLD activity scores, specifically identifying NASH patients who are "at risk" and may benefit from future NASH treatment, is a critical unmet need.

[0007] Finally, identifying reliable markers for identifying liver fibrosis stages and NASH is essential, as these conditions provide prognostics for liver and cardiovascular complications. Accurate risk stratification enables targeted interventions and monitoring of high-risk individuals, ensuring timely and appropriate management. Addressing these unmet needs in NAFLD diagnosis will improve early disease detection, risk stratification, and monitoring, leading to more effective management strategies and improved patient outcomes. [Overview of the Initiative] [Means for solving the problem]

[0008] Summary of the Invention According to one aspect of several embodiments of the present invention, the following is provided: A method for diagnosing liver disease or liver condition selected from a group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis combined with non-alcoholic steatohepatitis (NASH), and "at-risk" NASH in subjects with an alcohol intake of less than 30g per day, (a) A step of measuring the amount of at least two protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule 1 (ICAM), and platelet glycoprotein V (GP5) in the target blood sample, (b) A step of ruling in liver disease or liver condition based on the amounts of at least two protein biomarkers Methods that include...

[0009] According to some embodiments of the present invention, five or fewer proteins are analyzed.

[0010] According to some embodiments of the present invention, the liver condition is significant fibrosis.

[0011] According to some embodiments of the present invention, the liver condition is significant fibrosis combined with NASH.

[0012] According to some embodiments of the present invention, the liver condition is advanced fibrosis.

[0013] According to some embodiments of the present invention, the liver condition is cirrhosis.

[0014] According to some embodiments of the present invention, at least two protein biomarkers include C7 and QSOX1.

[0015] According to some embodiments of the present invention, at least two protein biomarkers include C7 and ICAM.

[0016] According to some embodiments of the present invention, at least two protein biomarkers include C7 and GP5.

[0017] According to some embodiments of the present invention, an increase in the amount of C7, ICAM, or QSOX1 above a predetermined level compared to the amount in a control sample indicates significant hepatic fibrosis, and / or a decrease in the amount of GP5 below a predetermined level compared to the amount in a control sample indicates significant hepatic fibrosis.

[0018] According to some embodiments of the present invention, amounts of C7, ICAM, or QSOX1 below a predetermined level indicate non-significant hepatic fibrosis, and / or amounts of GP5 above a predetermined level indicate non-significant hepatic fibrosis.

[0019] According to some embodiments of the present invention, at least two protein biomarkers include C7, QSOX1, and GP5.

[0020] According to some embodiments of the present invention, at least two protein biomarkers include C7, QSOX1, and ICAM.

[0021] According to some embodiments of the present invention, at least two protein biomarkers include C7, QSOX1, ICAM, and GP5.

[0022] According to some embodiments of the present invention, the method further comprises measuring the amount of at least one additional protein selected from the group consisting of: collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, serglycin (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), and vascular cell adhesion protein 1 (VCAM1).

[0023] According to some embodiments of the present invention, the subject is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

[0024] According to some embodiments of the present invention, the subject is pre-diagnosed as having NASH.

[0025] According to some embodiments of the present invention, the subject has type 2 diabetes.

[0026] According to some embodiments of the present invention, the subject has at least one metabolic syndrome risk factor.

[0027] According to some embodiments of the present invention, the subject has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

[0028] According to some embodiments of the present invention, the measuring step is performed at the protein level.

[0029] According to some embodiments of the present invention, the measuring step is performed at the RNA level.

[0030] According to some embodiments of the present invention, the ruling-in step takes into account the clinical parameters of the subject.

[0031] According to some embodiments of the present invention, the clinical parameters are selected from the group consisting of body weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HbA1c level, waist circumference, blood lipid level, and blood cholesterol level.

[0032] According to some embodiments of the present invention, diagnosis can be made. (a) Apply a predetermined mathematical function to the amounts of at least two proteins and calculate a score, (b) Comparing the score to a predetermined baseline value, including:

[0033] According to some embodiments of the present invention, the mathematical function includes a weight of C7 that is heavier compared to the weights of QSOX1, ICAM, and / or GP5.

[0034] According to some embodiments of the present invention, a predetermined mathematical function is derived from a machine learning algorithm.

[0035] According to some embodiments of the present invention, the following is provided: A method for treating subjects with liver disease or liver condition, (a) A step of defining a disease or condition in a subject in accordance with the method described herein, (b) A step of treating the subject with at least one treatment selected from the group consisting of semaglutide, ranifibrano, ocaliba, resmethylome, saroglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, and effluxifermin, and Methods that include...

[0036] According to some embodiments of the present invention, the following is provided: A method for identifying the stage of fibrosis in a patient or the stage of fibrosis in combination with NASH, or for diagnosing NASH or diagnosing NASH patients who are "at risk," (a) A step of determining the concentrations of at least two biomarkers in patient-derived serum, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (b) A step of determining the stage of fibrosis in a patient, or diagnosing NASH, or diagnosing “at risk” NASH, or diagnosing the stage of fibrosis in combination with NASH in a patient, based on the concentrations of at least two biomarkers. Methods that include...

[0037] According to some embodiments of the present invention, the stages of fibrosis are Significant fibrosis (F≧2), Advanced fibrosis (F≧3), Cirrhosis (F=4) One of the following will be selected.

[0038] According to some embodiments of the present invention, five or fewer proteins are analyzed.

[0039] According to some embodiments of the present invention, the subject is male and has an alcohol intake of less than 30g per day, or the subject is female and has an alcohol intake of less than 20g per day.

[0040] According to some embodiments of the present invention, the step of determining the stage of fibrosis in a patient is: (i) Input the concentration from process (a) into the algorithm to generate a score for each stage of fibrosis, or a stage of fibrosis combined with NASH, or a score for rule-in of NASH or rule-in of "at risk" NASH, (ii) Comparing the score for the stage of fibrosis, or the stage of fibrosis combined with NASH, or for NASH or "at risk" NASH, to a predetermined cutoff value for the stage of fibrosis, NASH, or "at risk" NASH, (iii) Determining the stage of fibrosis, or diagnosing NASH, or diagnosing “at risk” NASH, or diagnosing the stage of fibrosis in combination with NASH, based on a comparison of the score with a predetermined cutoff value. Includes.

[0041] According to some embodiments of the present invention, the method includes the step of determining the concentrations of C7 and QSOX1.

[0042] According to some embodiments of the present invention, the method includes the step of determining the concentrations of C7 and GP5.

[0043] According to some embodiments of the present invention, the method includes the step of determining the concentrations of C7 and ICAM1.

[0044] According to some embodiments of the present invention, the method includes the step of determining the concentrations of C7, QSOX1, and GP5.

[0045] According to some embodiments of the present invention, the method includes the step of determining the concentrations of C7, QSOX1, and ICAM1.

[0046] According to some embodiments of the present invention, a patient is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

[0047] According to some embodiments of the present invention, the patient is pre-diagnosed as having "at-risk" NASH.

[0048] According to some embodiments of the present invention, the patient has type 2 diabetes.

[0049] According to some embodiments of the present invention, the patient has at least one metabolic syndrome risk factor.

[0050] According to some embodiments of the present invention, the patient has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

[0051] According to some embodiments of the present invention, the method includes determining the concentration of the biomarker by mass spectrometry, immunoassay, or aptamer-based assay.

[0052] According to some embodiments of the present invention, the algorithm is a machine learning algorithm.

[0053] According to some embodiments of the present invention, the machine learning algorithm is selected from the group consisting of neural networks, random forests, k-nearest neighbors, naive Bayes classifiers, k-means clustering, decision trees, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machines.

[0054] According to some embodiments of the present invention, the method includes administering to the patient at least one treatment for fibrosis and / or fatty liver and / or liver inflammation.

[0055] According to some embodiments of the present invention, at least one treatment is selected from one or more of the following: semaglutide, ranifibranol, ocaliba, resmethylome, saroglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, and effluxifermin.

[0056] According to some embodiments of the present invention, the following is provided: A method for monitoring the effectiveness of treatment for liver fibrosis in combination with or without NASH, (a) A step of administering the treatment to the subject, (b) A step of measuring the levels of at least two biomarkers in a blood sample derived from a subject, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (c) A method comprising the step of comparing the levels of at least two biomarkers with the levels of at least two biomarkers obtained prior to the administration step, wherein the effectiveness of the treatment is monitored by changes in the levels of at least two biomarkers.

[0057] According to some embodiments of the present invention, the following is provided: A method for monitoring the progression of liver fibrosis in the target patient, (a) A step of measuring the levels of at least two biomarkers in a blood sample derived from a subject at a first time point, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (b) A step of measuring the levels of at least two biomarkers in a blood sample derived from the subject at a later point in time, (c) A method comprising the step of comparing the levels of at least two biomarkers at a first time point and a second time point, wherein a change in the levels of at least two biomarkers indicates progression or regression of the liver fibrosis in question.

[0058] According to some embodiments of the present invention, at least two protein biomarkers include C7 and QSOX1.

[0059] According to some embodiments of the present invention, at least two protein biomarkers include C7 and ICAM.

[0060] According to some embodiments of the present invention, at least two protein biomarkers include C7 and GP5.

[0061] According to some embodiments of the present invention, at least one of the protein biomarkers comprises C7, QSOX1, and GP5.

[0062] According to some embodiments of the present invention, at least one of the protein biomarkers comprises C7, QSOX1, and ICAM.

[0063] According to some embodiments of the present invention, the at least two protein biomarkers include C7, QSOX1, ICAM, and GP5.

[0064] According to another aspect of the present invention, a method for identifying the stage of fibrosis in a patient and determining whether the patient has non-alcoholic steatohepatitis (NASH) or is at risk of NASH, (a) A step of determining the concentrations of at least two biomarkers in patient-derived serum, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), and vascular cell adhesion protein 1 (VCAM1), (b) Determining the stage of fibrosis in the patient and determining whether the patient has NASH or is at risk of NASH based on the concentrations of at least two biomarkers, A method is provided for selecting the stage of fibrosis from one of the following: significant fibrosis (F≧2), advanced fibrosis (F≧3), cirrhosis (F=4), NASH, or NASH at risk (F≧2 and NAFLD activity score (NAS)>4).

[0065] According to another aspect of the present invention, a method for determining the stage of a patient's fibrosis based on concentration, (i) Input the concentrations from process (a) into the algorithm to generate scores for each stage of fibrosis, NASH, and "at-risk" NASH (F≧2 and NAFLD activity score (NAS)>4), (ii) Comparing the scores for fibrosis stage, NASH and "at risk" NASH to predetermined cutoff values ​​for fibrosis stage, NASH and "at risk" NASH, (iii) A method is provided for determining the stage of fibrosis and determining NASH and “at risk” NASH based on a comparison of the score with a predetermined cutoff value, wherein the predetermined cutoff value is a value between 0 and 1.

[0066] According to some embodiments of the present invention, the patient has NAS ≥ 4.

[0067] According to some embodiments of the present invention, the patient has NAS < 4.

[0068] According to some embodiments of the present invention, the method includes determining the concentrations of C7 and QSOX1.

[0069] According to some embodiments of the present invention, the method includes determining the concentrations of C7 and GP5.

[0070] According to some embodiments of the present invention, the method includes determining the concentrations of C7, QSOX1, and GP5.

[0071] According to some embodiments of the present invention, a predetermined cutoff value for “risky” NASH is about 0.1 to about 0.95, including all values ​​and partial ranges in between; a predetermined cutoff value for significant fibrosis is about 0.1 to about 0.95, including all values ​​and partial ranges in between; a predetermined cutoff value for advanced fibrosis is about 0.1 to about 0.95, including all values ​​and partial ranges in between; and a predetermined cutoff value for cirrhosis is about 0.1 to about 0.95, including all values ​​and partial ranges in between.

[0072] According to some embodiments of the present invention, a predetermined cutoff value for "risky" NASH is 0.25.

[0073] According to some embodiments of the present invention, a predetermined cutoff value for significant fibrosis is 0.21, a predetermined cutoff value for advanced fibrosis is 0.25, and a predetermined cutoff value for cirrhosis is 0.52.

[0074] According to some embodiments of the present invention, a predetermined cutoff value for significant fibrosis is 0.13, a predetermined cutoff value for advanced fibrosis is 0.17, and a predetermined cutoff value for liver cirrhosis is 0.43.

[0075] According to some embodiments of the present invention, the patient has non-alcoholic fatty liver disease (NAFLD).

[0076] According to some embodiments of the present invention, a patient is considered to have high NAS and is deemed to have "at-risk" NASH.

[0077] According to some embodiments of the present invention, the patient has type 2 diabetes.

[0078] According to some embodiments of the present invention, the patient has an intermediate FIB-4 score.

[0079] According to some embodiments of the present invention, the biomarker is a protein.

[0080] According to some embodiments of the present invention, the method includes determining the concentration of a biomarker by mass spectrometry, immunoassay, or aptamer-based assay.

[0081] According to some embodiments of the present invention, the algorithm is a machine learning algorithm.

[0082] According to some embodiments of the present invention, the machine learning algorithm is selected from the group consisting of neural networks, random forests, k-nearest neighbors, naive Bayes classifiers, k-means clustering, decision trees, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machines.

[0083] According to some embodiments of the present invention, the method comprises administering to a patient at least one treatment for fibrosis.

[0084] According to some embodiments of the present invention, at least one treatment for fibrosis and “at-risk” NASH is selected from semaglutide, ranifibranol, ocaliba, resmethirome, saloglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, effluxifermin, or any other drugs currently approved for other indications, as well as / or any other drugs under development for treating fibrosis and / or inflammation.

[0085] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present invention pertains. Similar or equivalent methods and materials to those described herein may be used in carrying out or testing embodiments of the present invention, but exemplary methods and / or materials are described below. In case of any conflict, the patent specification, including definitions, shall prevail. Furthermore, materials, methods, and examples are illustrative and not necessarily intended to limit the scope. [Brief explanation of the drawing]

[0086] [Figure 1-1] Figures 1A-G show the protein intensities of biomarkers measured by mass spectrometry: COL10 (Figure 1A), C7 (Figure 1B), QSOX1 (Figure 1C), VCAM1 (Figure 1D), A2M (Figure 1E), GP5 (Figure 1F), and ICAM1 (Figure 1G), according to the stage of fibrosis. Figure 1H shows the stage of fibrosis according to the patient's age.

[0087] [Figure 1-2] Same as above. [Figure 1-3] Same as above. [Figure 1-4] Same as above. [Figure 1-5] Same as above. [Figure 1-6] Same as above. [Figure 1-7] Same as above. [Figure 1-8] Same as above.

[0088] [Figure 2-1]Figures 2A–J show that the diagnosis of “at risk” NASH using two-component biomarker signatures consisting of (a) C7 and QSOX1 and (b) C7 and ICAM1 was superior to FIB-4 scores in the discovery cohort (training and test sets, as well as the entire cohort) and validation cohorts 1 and 2. Figures 2A–E show the superiority of the C7 and QSOX1 model for diagnosing “at risk” NASH in the discovery cohort (training and test sets, as well as the entire cohort) and validation cohorts 1 and 2. Figures 2F–J show the superiority of the C7 and ICAM1 model for diagnosing “at risk” NASH in the discovery cohort (training and test sets, as well as the entire cohort) and validation cohorts 1 and 2. [Figure 2-2] Same as above. [Figure 2-3] Same as above. [Figure 2-4] Same as above. [Figure 2-5] Same as above.

[0089] [Figure 3-1] Figures 3A–G show that the determination of fibrosis stage using two-component biomarker signatures consisting of (a) C7 and GP5, and (b) C7 and ICAM (referred to as the “MS-LFS model” or “model”) was superior to the determination of fibrosis stage by FIB-4 score in the discovery cohort (test set and all cohorts) and validation cohorts 1–3. Figures 3A–3B show the superiority of the models for diagnosing significant fibrosis. Figures 3C–3D show the superiority of the models for diagnosing advanced fibrosis. Figures 3E–3F show the superiority of the models for diagnosing cirrhosis. Figure 3G shows the superiority of the C7 and GP5 models for diagnosing likely NASH cirrhosis in a real-world patient cohort. [Figure 3-2] Same as above. [Figure 3-3] Same as above. [Figure 3-4] Same as above. [Figure 3-5] Same as above. [Figure 3-6] Same as above. [Figure 3-7] Same as above.

[0090] [Figure 4-1] Figures 4A–K show that the three-component biomarker signature consisting of (a) C7, QSOX1, and GP5, and (b) C7, QSOX1, and ICAM1 (referred to as the “MS-LFS model” or “the model”) was superior to the determination of fibrosis stage by FIB-4 score in the discovery cohort (test set and complete cohort) and validation cohorts 1–3. Figures 4A–4B show the superiority of the MS-LFS model for diagnosing “risky” NASH. Figures 4C–4D show the superiority of the MS-LFS model for diagnosing “risky” NASH NIMBLE. Figures 4E–4F show the superiority of the MS-LFS model for diagnosing significant fibrosis. Figures 4G–4H show the superiority of the MS-LFS model for diagnosing advanced fibrosis. Figures 4I–4J show the superiority of the MS-LFS model for diagnosing cirrhosis. Figure 4K demonstrates the superiority of the C7, QSOX1, and GP5 models for diagnosing likely NASH cirrhosis in a real-world patient cohort. [Figure 4-2] Same as above. [Figure 4-3] Same as above. [Figure 4-4] Same as above. [Figure 4-5] Same as above. [Figure 4-6] Same as above. [Figure 4-7] Same as above. [Figure 4-8] Same as above. [Figure 4-9] Same as above. [Figure 4-10] Same as above. [Figure 4-11] Same as above.

[0091] [Figure 5-1]Figures 5A and 5B show that the two-component biomarker signatures described in Example 2, consisting of (a) C7 and QSOX1, and (b) C7 and ICAM1, perform better than other commonly used clinical scores (FIB-4, BARD, NFS) and as well as / better than Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+, and Agile 4) for diagnosing “at risk” NASH. Figure 5A shows that the model performs better than other scoring systems for diagnosing patients with intermediate FIB-4 scores. Figure 5B shows the superiority of the model for diagnosing “at risk” NASH in patients with type 2 diabetes (T2D). [Figure 5-2] Same as above. [Figure 5-3] Same as above.

[0092] [Figure 6-1] Figures 6A and 6B show that the MS-LFS model described in Example 4 is significantly superior to the FIB-4, BARD, and NFS scoring systems for diagnosing fibrotic stages. Figure 6A shows that the MS-LFS model is superior to other scoring systems for diagnosing patients with intermediate FIB-4 scores. Figure 6B shows the superiority of the model for diagnosing fibrotic stages in patients with type 2 diabetes (T2D). [Figure 6-2] Same as above. [Figure 6-3] Same as above. [Figure 6-4] Same as above. [Figure 6-5] Same as above.

[0093] [Figure 7-1]Figures 7A and 7B demonstrate that the MS-LFS model described in Example 4 is superior to other commonly used clinical scores (FIB-4, BARD, and NFS) and performs as well as / better than its combination with Fibroscan® and other clinical parameters (FAST, Agile 3+, and Agile 4) in diagnosing at-risk "NASH" and fibrotic stages. Figure 7A shows that the MS-LFS model is superior to other scoring systems for diagnosing patients with intermediate FIB-4 scores. Figure 7B shows the superiority of the model for diagnosing fibrotic stages in patients with type 2 diabetes (T2D). [Figure 7-2] Same as above. [Figure 7-3] Same as above. [Figure 7-4] Same as above. [Figure 7-5] Same as above. [Figure 7-6] Same as above.

[0094] [Figure 8] Figure 8 shows that the MS-LFS model, C7, QSOX1, and ICAM1 described in Example 4 are superior to other commonly used clinical scores (FIB-4, ELF, and FibroTest) for monitoring the patient's fibrotic stage. [Modes for carrying out the invention]

[0095] Detailed description of the invention This disclosure provides methods for diagnosing patients with fibrotic stages, NASH, and “at risk” NASH. The methods provided herein are more sensitive and specific than known methods for diagnosing patients with fibrotic stages (see Figures 3A–3G, 4E–4K, 6A–6B, 7A–7B, and Tables 5 and 6a) and “at risk” NASH (Figure 2, 4A–4D and 5A–5B, 7A–7B, and Tables 4 and 6B). These methods are also non-invasive and therefore less risky to patients. These methods enable the identification of patients with fibrotic stages, NASH, and “at risk” NASH, allowing for timely intervention and improving the clinical management of patients.

[0096] I. Definition The indefinite articles "a" and "an," as well as the definite article "the," are intended to include both singular and plural nouns unless the context in which they are used clearly indicates otherwise.

[0097] "At least one" and "one or more" are used interchangeably to mean that an item may contain one or more of the enumerated elements.

[0098] As used herein, the term “about” means plus or minus 10% of the number being referenced, unless otherwise specified or evident from the context, and unless such range exceeds 100% of the possible value or falls below 0% of the possible value.

[0099] The term "biomarker" refers to a protein, metabolite, or lipid that functions as an indicator of a disease or condition.

[0100] The term "cutoff value" refers to a numerical value used to distinguish between stages 2 and above of fibrosis and to diagnose NASH and "at-risk" NASH.

[0101] The term "FIB-4 score" refers to a score calculated according to the following formula: (Age × AST level) / ((Platelet count (10 9 ( / L)) × (square root of ALT level)). In the formula, "age" refers to the patient's age in years. AST level refers to the level of aspartate aminotransferase in serum U / L. ALT level refers to the level of alanine transaminase in serum U / L. Patients in the "intermediate range of FIB-4" have an FIB-4 score greater than 1.3 and less than 2.67.

[0102] The term "NAFLD Activity Score (NAS)" refers to a score calculated by adding up the individual scores for steatosis (score 0-3), lobular inflammation (score 0-3), and hepatocyte ballooning (score 0-2). The NAS score ranges from 0 to 8. The NAS score is described in its entirety in the following reference, which is incorporated herein by reference: Kleiner DE et al., (2005) Hepatology 41:1313-1321.

[0103] II. Biomarkers for diagnosing fibrotic stage, NASH, and “at-risk” NASH patients Biomarkers for diagnosing fibrosis stages, NASH, and “risk” NASH are provided herein. In embodiments, the biomarkers are used to diagnose fibrosis stages and identify patients with NASH and “risk” NASH in patients with NAFLD and / or high-risk NAFLD (e.g., intermediate range of FIB-4, T2D). In embodiments, the biomarkers are used to diagnose fibrosis with / without a high NAFLD activity score (≧4) or NASH. Fibrosis stages indicate the degree of hepatic scarring. The classification of fibrosis stages in non-alcoholic fatty liver disease (NAFLD) is as follows: Fibrosis stages may be staging according to scoring systems of the METAVIR, Ishak, or NASH Clinical Research Network (CRN) classification system, which classify fibrosis levels into several stages to indicate the degree of hepatic scarring.

[0104] Unless otherwise specified, the specific fibrosis stages described herein refer to stages identified by the METAVIR scoring system or the NASH Clinical Research Network (CRN) classification system. According to this system, fibrosis stage 0 (also called "F0") ("F") indicates no fibrosis or scarring. Fibrosis stage 1 (also called "F1") represents minimal fibrosis confined to the portal area. Fibrosis stage 2 (also called "F2") indicates increased fibrosis extending beyond the portal area. Fibrosis stage 3 (also called "F3") indicates significant fibrosis with multiple septa but without cirrhosis. Fibrosis stage 4 (also called "F4") represents cirrhosis with extensive scarring and liver dysfunction.

[0105] In NAFLD, most individuals have little to no fibrosis (F0-F1) or mild fibrosis (F1-F2). These early stages are generally more common and are often present in a significant proportion of NAFLD patients. As the disease progresses, a smaller proportion of individuals may develop significant fibrosis (>=F2), showing increased liver scarring. Advanced fibrosis (>=F3) and cirrhosis (F4) are considered more severe stages of fibrosis in NAFLD. A smaller proportion of individuals with NAFLD may progress to advanced fibrosis, but the risk is increased by certain factors such as advanced age, obesity, diabetes, and the presence of NASH. The progression of fibrosis in NAFLD is not linear and can be influenced by a variety of factors, including lifestyle modifications, treatment interventions, and management of underlying metabolic status.

[0106] In the embodiments, biomarkers are used to diagnose fibrotic stages and / or identify NASH and “at-risk” NASH patients with non-alcoholic fatty liver disease. In the embodiments, biomarkers for diagnosing NASH and / or fibrotic stages can be used to identify individuals at higher risk of fibrotic progression, with or without high NAFLD activity scores, and to provide appropriate care and interventions.

[0107] In embodiments, the biomarker is selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, cerglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), and vascular cell adhesion protein 1 (VCAM1). Additionally or alternatively, the biomarker may be intercellular adhesion molecule-1 (ICAM-1). In the embodiment, fibrosis stage, NASH, and “at risk” NASH are diagnosed using 2 to 10 biomarkers, 3 to 10 biomarkers, 4 to 10 biomarkers, 5 to 10 biomarkers, 6 to 10 biomarkers, 7 to 10 biomarkers, 2 to 5 biomarkers, 3 to 5 biomarkers, or 4 to 5 biomarkers. In the embodiment, fibrosis stage, NASH, and “at risk” NASH are diagnosed using 1 or more, 2 or more, or 3 or more biomarkers. In the embodiment, “at risk” NASH patients are diagnosed using C7 and QSOX1, C7 and ICAM1, and fibrosis stage is diagnosed using C7 and GP5, as well as C7 and ICAM1. In the embodiment, “at risk” NASH patients and / or fibrosis stage are determined using C7, QSOX1 and GP5, as well as C7, QSOX1 and ICAM1.

[0108] III. Diagnostic Methods for Fibrosis Stage, NASH, and "At-Risk" NASH In embodiments, provided herein is a method for identifying a stage of fibrosis in a patient with or without a NASH and / or NAFLD activity score ≥ 4, the method comprising (a) determining the concentrations of at least two biomarkers in the serum of the patient, the at least two biomarkers being selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), platelet glycoprotein V (GP5), and intercellular adhesion molecule-1 (ICAM1), and (b) determining the stage of fibrosis in the patient based on the concentrations of the at least two biomarkers, thereby identifying NASH and “at risk” NASH patients. In embodiments, the stages of fibrosis are F0, F1, F2, F3, or F4. In embodiments, “at risk” NASH patients are defined as having biopsy-confirmed NASH, a NAFLD activity score (NAS) ≥ 4, having at least 1 in each of the NAS components, and excluding patients with cirrhosis, F ≥ 2. The definition of "at-risk" NASH nimble includes the exclusion of patients with F0-F1 and NAS≧4, F≧2 and NAS<4, and patients with cirrhosis. The stages of fibrosis are described in Section II of this document.

[0109] The subjects being diagnosed are typically human.

[0110] In one embodiment, the subject is male.

[0111] In another embodiment, the subject is female.

[0112] In one embodiment, the subject is an adult male.

[0113] Typically, the subjects have a daily alcohol intake of less than 30g.

[0114] In another embodiment, the subjects are women who consume less than 20g of alcohol per day.

[0115] According to this embodiment of the present invention, the subject is pre-diagnosed with non-alcoholic fatty liver disease (NAFLD).

[0116] According to another embodiment of this aspect of the present invention, the subject is pre-diagnosed to have NASH.

[0117] Additionally or alternatively, participants are pre-diagnosed with type 2 diabetes.

[0118] Alternatively, the subject may have at least one metabolic syndrome risk factor.

[0119] Exemplary metabolic syndrome risk factors include, but are not limited to, obesity and / or large waist circumference (e.g., BMI > 30, and / or waist circumference ≥ 40 inches for men and ≥ 35 inches for women), elevated blood glucose levels and / or T2D (e.g., fasting blood glucose ≥ 100 mg / dL or ≥ 125, and / or HbA1C ≥ 6% or HbA1C ≥ 6.5%), hypertriglyceridemia and / or low levels of HDL cholesterol (e.g., triglycerides ≥ 150 mg / dl, or HDL: < 40 mg / dl for men and < 50 mg / dl for women), and hypertension (e.g., systolic blood pressure ≥ 130 mmHg and / or diastolic blood pressure ≥ 85 mmHg).

[0120] The target age range could be 40 to 80 years old.

[0121] In this embodiment, the method is used to rule in significant fibrosis based on the levels of at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM), and platelet glycoprotein V (GP5).

[0122] An increase in the levels of C7, ICAM, or QSOX1 above a predetermined level compared to the control sample indicates significant hepatic fibrosis, and / or a decrease in the level of GP5 below a predetermined level compared to the control sample indicates significant hepatic fibrosis.

[0123] Additionally, when the amount of C7, ICAM, or QSOX1 falls below a predetermined level, it indicates non-significant hepatic fibrosis, and / or when the amount of GP5 exceeds a predetermined level, it indicates non-significant hepatic fibrosis.

[0124] In this embodiment, the method is intended to rule in significant fibrosis in combination with NASH based on the levels of at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM), and platelet glycoprotein V (GP5).

[0125] An increase in the amount of C7, ICAM, or QSOX1 above a predetermined level compared to the amount in the control sample indicates significant hepatic fibrosis in combination with NASH, and / or a decrease in the amount of GP5 below a predetermined level compared to the amount in the control sample indicates significant hepatic fibrosis in combination with NASH.

[0126] In this embodiment, the method is used to rule in “risk” NASH based on the levels of at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM), and platelet glycoprotein V (GP5).

[0127] An increase in the amount of C7, ICAM, or QSOX1 above a predetermined level compared to the amount in the control sample indicates “risky” NASH, and / or a decrease in the amount of GP5 below a predetermined level compared to the amount in the control sample indicates “risky” NASH.

[0128] In this embodiment, the method is for ruling out advanced fibrosis based on the levels of at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM), and platelet glycoprotein V (GP5).

[0129] An increase in the levels of C7, ICAM, or QSOX1 above a predetermined level compared to the control sample indicates advanced hepatic fibrosis, and / or a decrease in the level of GP5 below a predetermined level compared to the control sample also indicates advanced hepatic fibrosis.

[0130] In this embodiment, the method is used to rule out cirrhosis based on the levels of at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM), and platelet glycoprotein V (GP5).

[0131] An increase in the amount of C7, ICAM, or QSOX1 above a predetermined level compared to the amount in the control sample indicates cirrhosis, and / or a decrease in the amount of GP5 below a predetermined level compared to the amount in the control sample indicates cirrhosis.

[0132] Additional protein markers may be used to stage fibrosis (e.g., rule in significant fibrosis), including but not limited to the following:

[0133] Collectin-10 (COL10), Collectin-11 (COLEC11) isoform 10, cerglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-unstable subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), and vascular cell adhesion protein 1 (VCAM1).

[0134] In one embodiment, a predetermined level (i.e., reference value) is the amount (i.e., level) of a biomarker in a control sample derived from one or more subjects (e.g., healthy individuals) who do not have and / or are not suspected of having hepatic fibrosis. In a further embodiment, such subjects are monitored and / or periodically retested over a period relevant to the diagnosis after such testing to verify the continued absence of fibrosis ("longitudinal study"). Such a period may be one week, two weeks, two to five months, five months, five to ten months, ten months, or more than ten months from the date of the initial testing to determine the reference value. Furthermore, these reference values ​​may be established using retrospective measurements of biomarkers in appropriately preserved historical control samples, thus reducing the required study time.

[0135] A predetermined level may also include the amount of biomarkers derived from subjects showing improvement as a result of treatment and / or therapy for fibrosis. A predetermined level may also include the amount of biomarkers derived from subjects in whom fibrosis was confirmed to be insignificant by known techniques.

[0136] Examples of index values ​​for fibrosis criteria include the mean or median concentration of the biomarker in a statistically significant number of subjects diagnosed with non-significant fibrosis.

[0137] In another embodiment, the predetermined level is an index value or baseline value. The index value or baseline value is a composite sample of effective amounts of biomarkers from one or more subjects who do not have significant fibrosis. The baseline value may also include the amount of biomarkers in a sample derived from subjects who have shown improvement in treatment or management of fibrosis. In this embodiment, the amount of biomarkers is similarly calculated and compared to the index value for comparison with the subject-derived sample. If necessary, subjects identified as having significant fibrosis are selected to receive treatment regimens to slow progression or eliminate fibrosis.

[0138] Furthermore, the amount of biomarkers is measured in the test sample and compared to a “normal control level” using techniques such as reference limits, discrimination limits, or risk-defining thresholds to define cutoff points and outliers. The “normal control level” refers to the level of a biomarker or combined biomarker index of 1 or greater that is typically seen in subjects without significant fibrosis. Such normal control levels and cutoff points may vary depending on whether the biomarker is used alone or in an index formula in combination with other biomarkers. Alternatively, the normal control level may be a database of biomarker patterns derived from previously tested subjects.

[0139] Some protein biomarkers may exhibit age-dependent tendencies in patients (e.g., the baseline of a population may rise or fall as a function of age). To adjust for age-related differences, an "age-dependent normalization or stratification" scheme can be used. Performing age-dependent normalization, stratification, or a specific formula can be used to improve the accuracy of biomarkers for distinguishing different types of infections. For example, a person skilled in the art could generate a function that fits the population mean level of each biomarker as a function of age and use it to normalize the individual subject levels of biomarkers across different age groups. Another example is to stratify subjects according to their age and independently determine age-specific cutoff or index values ​​for each age group.

[0140] Subjects may be stratified according to additional parameters, including but not limited to weight (e.g., BMI), age, HDL cholesterol levels, LDL cholesterol levels, liver enzymes (e.g., ALT levels, AST levels), blood glucose levels, blood pressure, HbA1c levels, waist circumference, blood lipid levels, and blood cholesterol levels.

[0141] In a specific embodiment, a subject is ruled to have significant fibrosis when the QSOX1 concentration level is approximately 1.19 times higher than a predetermined level (e.g., the average level of patients with mild fibrosis).

[0142] In a specific embodiment, a subject is ruled to have advanced fibrosis when the concentration level of QSOX1 is approximately 1.23 times higher than a predetermined level (e.g., the average level of patients with mild fibrosis).

[0143] In a specific embodiment, a subject is ruled to have cirrhosis if the QSOX1 concentration level is approximately 1.28 times higher than a predetermined level (for example, the average level of patients with mild fibrosis).

[0144] In a specific embodiment, a subject is ruled to have significant fibrosis when the concentration level of ICAM1 is approximately 1.23 times higher than a predetermined level (e.g., the average level of patients with mild fibrosis).

[0145] In a specific embodiment, a subject is ruled to have advanced fibrosis when the concentration level of ICAM1 is approximately 1.24 times higher than a predetermined level (for example, the average level of patients with mild fibrosis).

[0146] In a specific embodiment, a subject is ruled to have cirrhosis if the concentration level of ICAM1 is approximately 1.27 times higher than a predetermined level (for example, the average level of patients with mild fibrosis).

[0147] In a specific embodiment, a subject is ruled to have significant fibrosis when the concentration level of C7 is approximately 1.57 times higher than a predetermined level (for example, the average level of patients with mild fibrosis).

[0148] In a specific embodiment, a subject is ruled to have advanced fibrosis when the concentration level of C7 is approximately 1.7 times higher than a predetermined level (for example, the average level of patients with mild fibrosis).

[0149] In a specific embodiment, a subject is ruled to have cirrhosis if the concentration level of C7 is approximately twice as high as a predetermined level (for example, the average level of patients with mild fibrosis).

[0150] In a specific embodiment, a subject is ruled to have significant fibrosis when the concentration level of GP5 is approximately 0.81 times a predetermined level (e.g., the average level of patients with mild fibrosis).

[0151] In a specific embodiment, a subject is ruled to have advanced fibrosis when the concentration level of GP5 is approximately 0.74 times a predetermined level (e.g., the average level of patients with mild fibrosis).

[0152] In a specific embodiment, a subject is ruled to have cirrhosis if the concentration level of GP5 is approximately 0.54 times a predetermined level (for example, the average level of patients with mild fibrosis).

[0153] In one embodiment, ruling in significant fibrosis indicates that the fibrosis in question is at a stage beyond mild fibrosis (F0-F1).

[0154] In another embodiment, ruling in significant fibrosis excludes cases where fibrosis is beyond mild fibrosis but has not reached the stage of advanced fibrosis.

[0155] In yet another embodiment, defining significant fibrosis excludes cases where the fibrosis is more severe than mild fibrosis but has not reached the stage of cirrhosis.

[0156] In one embodiment, a subject is ruled to be “at risk” NASH when the QSOX1 concentration is 1.22 times higher than a predetermined level (e.g., the average level for patients with mild fibrosis).

[0157] In one embodiment, a subject is ruled to be “at risk” NASH when the ICAM concentration is 1.33 times higher than a predetermined level (e.g., the average level of patients with mild fibrosis).

[0158] In one embodiment, a subject is ruled to be “at risk” NASH when the concentration of C7 is 1.35 times higher than a predetermined level (e.g., the average level of patients with mild fibrosis).

[0159] According to a particular embodiment, if the concentration level of GP5 is approximately 0.84 times a predetermined level (e.g., the average level in patients with mild fibrosis), then the subject is considered to be "at-risk" NASH.

[0160] In the embodiments, the concentration of each biomarker is determined by immunoassay or mass spectrometry. In the embodiments, the concentration of each biomarker is measured in arbitrary units compared to a standardized control sample. In the embodiments, the concentration of each biomarker is measured in absolute concentration.

[0161] Blood samples can be obtained under standard conditions. In the embodiment, serum is stored for approximately 1 day to approximately 5 years before measuring the concentration of each biomarker.

[0162] In one embodiment, the blood sample includes serum.

[0163] In another embodiment, the blood sample includes plasma.

[0164] In yet another embodiment, the blood sample is whole blood.

[0165] In the embodiment, the serum is stored at approximately -80°C for up to 5 years, and at temperatures in the range of approximately 30°C for up to 2 days, or even 7 days.

[0166] In embodiments, the method includes (i) inputting the concentration from step (a) into an algorithm to generate a score, (ii) comparing the score with a predetermined cutoff value, and (iii) determining the stage of fibrosis based on the comparison of the score with the predetermined cutoff value. In embodiments, the algorithm generates scores from 0 to 1 for each of the following: significant fibrosis (F≧2), advanced fibrosis (F≧3), cirrhosis (F=4), and risk of NASH or non-alcoholic steatohepatitis (NASH) (F≧2, NAS>4 (e.g., excluding patients with cirrhosis, and excluding / not excluding patients with F0~F1 and NAS≧4, and patients with F≧2 and NAS<4)).

[0167] In one embodiment, a patient is diagnosed with a fibrotic stage if the score generated by the algorithm is higher than a predetermined cutoff value. For example, if the predetermined cutoff value for cirrhosis is 0.8, patients with a score greater than 0.8 are diagnosed with cirrhosis. In one embodiment, patients in the late fibrotic stage also have an early fibrotic stage. For example, a patient with advanced fibrosis (F3) also has significant fibrosis (F2). In one embodiment, the algorithm uses the concentration of C7 as well as at least one of QSOX1, ICAM, and / or GP5 to determine the score.

[0168] According to a particular embodiment, the weight of C7 in the algorithm is heavier than the weights of QSOX1, ICAM, and / or GP5.

[0169] According to a particular embodiment, the following algorithm is used to generate a score for ruling in “risky” NASH. P = (1 - exp(F)) / exp(F) (where (a) F = 0.849 * C7 + 0.745 * QSOX1 - 0.124; or (b) F = 0.974 * C7 + 0.513 * ICAM1 - 0.13).

[0170] According to a particular embodiment, the following algorithm is used to generate scores for ruling in significant fibrosis, advanced fibrosis, and cirrhosis, respectively. P = (1 - exp(F)) / exp(F) (where (a) F = 1.039 * C7 - 0.467 * GP5 - 0.161, and (b) F = 0.974 * C7 + 0.513 * ICAM1 - 0.130).

[0171] According to a particular embodiment, the following algorithm is used to generate scores for ruling in “risk” NASH, significant fibrosis, advanced fibrosis, and cirrhosis. P = (1 - exp(F)) / exp(F) (where (a) F = 0.775 * C7 - 0.356 * GP5 + 0.658 * QSOX1 - 0.130, and (b) F = 0.743 * C7 - 0.398 * ICAM1 + 0.657 * QSOX1 - 0.101).

[0172] A predetermined cutoff for a predictor may be set according to the required sensitivity and specificity of the predictor. In an embodiment, a predetermined cutoff value for NASH at risk is approximately 0.1 to approximately 0.95 (including all values ​​and partial ranges in between). In an embodiment, a predetermined cutoff value for significant fibrosis is approximately 0.1 to approximately 0.95 (including all values ​​and partial ranges in between). In an embodiment, a predetermined cutoff value for advanced fibrosis is approximately 0.1 to approximately 0.95 (including all values ​​and partial ranges in between). In an embodiment, a predetermined cutoff value for cirrhosis is approximately 0.1 to approximately 0.95 (including all values ​​and partial ranges in between).

[0173] In the embodiment, the algorithm is a machine learning algorithm. In the embodiment, the machine learning algorithm divides patient data into test and training sets, scales the measurements, trains on the training set, and adjusts the algorithm and biomarker selection through cross-validation. In the embodiment, the resulting algorithm can be applied to a holdout test set.

[0174] In the embodiments, the machine learning algorithm is selected from the group consisting of: neural networks, random forests, k-nearest neighbors, naive Bayes classifiers, k-means clustering, decision trees, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machines. In the embodiments, the algorithm may be applied to a separate, independent validation cohort of patients obtained by similar or different means to test the effectiveness of the algorithm.

[0175] In embodiments, a method comprising measuring differential concentrations of at least two (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten) protein biomarkers can detect fibrotic stages, NASH, and “at-risk” NASH with sensitivity of at least 75%, at least 90%, at least 95%, and at least 99% and specificity of at least 75%, at least 90%, at least 95%, or at least 99% (Tables 7a-b).

[0176] In this embodiment, the method includes measuring 10 or fewer proteins, 9 or fewer proteins, 8 or fewer proteins, 7 or fewer proteins, 6 or fewer proteins, 5 or fewer proteins, or 4 or fewer proteins, or 3 or fewer proteins, or 2 or fewer biomarkers.

[0177] According to a particular embodiment, when classifying fibrosis into disease stages, the concentrations of 10 or fewer proteins, 9 or fewer proteins, 8 or fewer proteins, 7 or fewer proteins, 6 or fewer proteins, 5 or fewer proteins, or 4 or fewer proteins, or 3 or fewer biomarkers, or 2 or fewer biomarkers are taken into consideration.

[0178] In an embodiment, the method can be applied to patients previously diagnosed with fibrosis to re-evaluate the current fibrosis stage. In an embodiment, the method is applied to determine the fibrotic status of patients with an unknown fibrosis stage. In an embodiment, the method is used to determine (i.e., rule in) whether a patient has NASH, or "at-risk" NASH, or significant fibrosis, or significant fibrosis in combination with NASH, or advanced fibrosis, or cirrhosis. In an embodiment, the method is used to monitor the progression of a patient's fibrosis. Scores may be obtained at two different time points, and the difference between the later score and the earlier score indicates the progression or regression of the disease. In one embodiment, the first time point is performed when the patient is healthy. In another embodiment, the first time point is performed if the patient has already been diagnosed as having or suspected of having liver fibrosis. In an embodiment, the method is used to monitor patients with a fibrosis stage less than 4 that is later identified as cirrhosis. In an embodiment, the method is used to confirm a fibrosis stage already determined by biopsy, analysis of electronic health records, or any other means. In an embodiment, the method can be applied to patients having an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

[0179] In an embodiment, the method includes taking into account the patient's age, gender, HbA1C, diabetes status, bilirubin, liver biopsy, or any other clinically relevant feature when determining the fibrosis stage or ruling in NASH, or "at-risk" NASH, or significant fibrosis.

[0180] In an embodiment, the clinical features are measured simultaneously (within 1 day) with determining the concentration of the biomarker. In an embodiment, the clinical features are measured before determining the concentration of the biomarker. In an embodiment, the clinical features are measured after determining the concentration of the biomarker. In an embodiment, the clinical features are measured within 6 months (before or after) the time point of determining the concentration of the biomarker.

[0181] In embodiments, the method described herein is superior to other methods for determining fibrosis stage and “at risk” NASH. In embodiments, the method is superior to clinical scores (e.g., FIB-4, BARD, and NFS) and performs as well as / better than its combination with Fibroscan® and other clinical parameters (FAST, Agile 3+, and Agile 4). Furthermore, the MS-LFS score is superior to commercially available protein biomarker-based tests, ELF® and FibroTest®. In some indications, the test performs significantly better than alternatives, and in some indications, it does not show significant improvement (although the mean estimate of the test's performance for this diagnostic test is higher).

[0182] In this embodiment, the method for diagnosing fibrotic stages and “at risk” NASH is superior to alternative methods for diagnosing fibrotic stages and “at risk” NASH in patients with type 2 diabetes.

[0183] In this embodiment, the method for diagnosing fibrotic stages and “at risk” NASH is superior to alternative methods for diagnosing fibrotic stages and “at risk” NASH in patients with intermediate FIB-4 scores.

[0184] IV. How to treat NASH and fibrosis that are at risk In embodiments, methods for treating fibrosis (e.g., significant fibrosis) and “risky” NASH are provided herein, comprising diagnosing a patient having fibrosis according to the method described in Section III and administering one or more treatments to the patient for fibrosis and “risky” NASH. In embodiments, one or more treatments for fibrosis and “risky” NASH are FXR agonists, cyclophylline inhibitors, berberine / UDCA, FGF21 agonists, GLP-1 receptor agonists, PPAR agonists, THR-β agonists, FASN inhibitors, mitochondrial pyruvate carriers, JNK inhibitors, structurally engineered fatty acids, DGAT2 inhibitors, FGF19 agonists, SCD1 modulators, or any other mechanism of action for treating fibrosis and / or inflammation.

[0185] In embodiments, the treatment for fibrosis is selected from semaglutide, ranifibranol, ocaliba, resmethirome, saroglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, effluxifermin, or one or more of any other drugs currently approved for other indications and / or under development to treat fibrosis and / or inflammation.

[0186] In embodiments, follow-up studies and / or procedures for monitoring a patient and diagnosing possible outcomes are provided herein, including diagnosing a patient with fibrosis, NASH, or “at risk” NASH according to the method described in Section III, and referring the patient to a specialist (e.g., a hepatologist, a urologist) who will refer the patient to a follow-up study / procedure. In embodiments, one or more studies / procedures for fibrosis, NASH, and “at risk” NASH are biopsy, abdominal imaging (e.g., CT, US, FibroScan, MRE), endoscopy, and blood tests (e.g., alpha-fetoprotein, hepatitis B Ag / Ab, hepatitis C Ag / Ab).

[0187] V. Kits for diagnosing fibrosis, NASH, and "at-risk" NASH In embodiments, kits are provided for diagnosing fibrosis, NASH, and “at-risk” NASH using the biomarkers described herein (see Section II). In embodiments, the kit comprises reagents for detecting at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten biomarkers.

[0188] In the embodiment, the kit includes reagents for detecting two or fewer, three or fewer, four or fewer, five or fewer, six or fewer, seven or fewer, eight or fewer, nine or fewer, or ten or fewer biomarkers.

[0189] Protein biomarkers can be detected by any suitable method, but typically they are detected by contacting a sample from the target with an antibody that binds to the biomarker, and then detecting the presence or absence of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or the aforementioned fragments, as discussed in detail above, and the step of detecting the reaction product can be carried out using any suitable immunoassay.

[0190] In one embodiment, an antibody that specifically binds to a protein biomarker is (directly or indirectly) attached to a signal-generating label, including, but not limited to, radiolabels, enzyme labels, haptens, reporter dyes, or fluorescent labels.

[0191] Immunoassays performed according to some embodiments of the present invention may be homogeneous or heterogeneous assays. In a homogeneous assay, the immunological reaction typically involves a specific antibody (e.g., an anti-biomarker antibody), a labeled analyte, and a sample of interest. The signal resulting from the labeling is modified directly or indirectly when the antibody binds to the labeled analyte. Both the immunological reaction and its degree of detection can be performed in a homogeneous solution. Possible immunochemical labels include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

[0192] In heterogeneous assay approaches, reagents typically consist of the sample, antibody, and means of generating a detectable signal. Samples such as those described above may be used. The antibody can be immobilized on a support such as beads (e.g., protein A and protein G agarose beads), a plate, or a slide, and brought into contact with a sample suspected of containing an antigen in the liquid phase. The support is then separated from the liquid phase, and either the support phase or the liquid phase is examined for a detectable signal using means of generating such a signal. The signal is related to the presence of the analyte in the sample. Means of generating a detectable signal include the use of radiolabeling, fluorescent labeling, or enzymatic labeling. For example, if the antigen to be detected contains a second binding site, an antibody that binds to that site can be conjugated to a detectable group and added to the liquid-phase reaction solution before the separation step. The presence of a detectable group on a solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays include oligonucleotides, immunoblotting, immunofluorescence, immunoprecipitation, chemiluminescence, electrochemiluminescence (ECL), or enzyme-conjugated immunoassays.

[0193] Those skilled in the art are familiar with numerous specific immunoassay formats and variations thereof that may be useful for carrying out the methods disclosed herein. See, in general, E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); U.S. Patent No. 4,727,022 to Skold titled “Methods for Modulating Ligand-Receptor Interactions and their Application”, U.S. Patent No. 4,659,678 to Forrest titled “Immunoassay of Antigens”, U.S. Patent No. 4,376,110 to David et al. titled “Immunometric Assays Using Monoclonal Antibodies”, U.S. Patent No. 4,275,149 to Litman et al. titled “Macromolecular Environment Control in Specific Receptor Assays”, U.S. Patent No. 4,233,402 to Maggio titled “Reagents and Method Employing Channeling”, and “Heterogenous Specific Binding” to Boguslaski et al. See also U.S. Patent No. 4,230,767, entitled “Assay Employing a Coenzyme as Label”. Biomarkers can also be detected with antibodies using flow cytometry. Those skilled in the art will be familiar with flow cytometry techniques (Shapiro 2005) that may be useful when carrying out the methods disclosed herein. These include, but are not limited to, Cytokine Bead Array (Becton Dickinson) and Luminex techniques.

[0194] Antibodies can be conjugated to solid supports suitable for diagnostic assays (e.g., Protein A or Protein G agarose, microspheres, plates, slides, or beads formed from materials such as latex or polystyrene) according to known techniques such as passive conjugation. Antibodies described herein can also be radiolabeled (e.g., according to known techniques) 35 S, 125 I, 131 I) It can be conjugated with a detectable label or base such as an enzyme label (e.g., horseradish peroxidase, alkaline phosphatase) and a fluorescent label (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine).

[0195] Antibodies may also be useful for detecting post-translational modifications of biomarker proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, and glycosylation (e.g., O-GlcNAc). Such antibodies can specifically detect phosphorylated amino acids in one or more proteins of interest and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well known to those skilled in the art and are commercially available. Post-translational modifications can also be determined using metastable ions in reflective matrix-assisted laser desorption / ionization-time-of-flight mass spectrometry (MALDI-TOF) (Wirth U. and Muller D. 2002).

[0196] For biomarker proteins, polypeptides, mutations, and polymorphisms known to possess enzymatic activity, their activity can be determined in vitro using enzyme assays known in the art. Such assays include, but are not limited to, numerous kinase assays, phosphatase assays, and reductase assays. The dynamics of enzyme activity can be regulated using linear regression plots such as Hill plots, Michaelis-Menten equations, and Lineweaver-Burk analyses, and known algorithms such as Scatchard plots, by determining the rate constant K MThis can be determined by measuring it.

[0197] In certain embodiments, the antibody of the present invention is a monoclonal antibody.

[0198] In some embodiments, the immunoassay kit includes antibodies specific to at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten biomarkers.

[0199] In the embodiment, the kit contains two or fewer, three or fewer, four or fewer, five or fewer, six or fewer, seven or fewer, eight or fewer, nine or fewer, or ten or fewer antibodies.

[0200] In embodiments, the kit includes a labeled secondary antibody that can bind to an antibody that binds to a biomarker described herein (and shown as an example in Table 1). The disclosed immunoassay measurement kit may include a diluent, an assay buffer, a substrate solution, and a stop solution for performing the measurement. Table 1 - Exemplary commercially available antibodies [Table 1-1] [Table 1-2]

[0201] In other embodiments, the measurement involves a protein analysis based on mass spectrometry or an aptamer-based protein analysis.

[0202] In embodiments, a kit for aptamer-based measurement comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten biomarker-specific aptamers that selectively bind to the biomarkers described herein. The disclosed kit for aptamer-based protein measurement may further comprise an assay buffer solution and a substrate solution.

[0203] In one embodiment, the kit for protein measurement based on mass spectrometry includes at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten synthetic proteins for the quantification of biomarkers.

[0204] In the embodiment, the kit includes two or fewer, three or fewer, four or fewer, five or fewer, six or fewer, seven or fewer, eight or fewer, nine or fewer, or ten or fewer synthetic proteins for the quantification of a biomarker.

[0205] The disclosed kit for protein measurement based on mass spectrometry may further include all standards and reagents for monitoring system performance, alkylation solution, digestion solution, analytical column, dilution buffer, running buffer, and stop solution for performing the measurement.

[0206] In embodiments, the antibody and / or aptamer is mounted on an array such as a biochip, lateral flow device, or dipstick. In embodiments, the array includes other aptamers or antibodies that function as negative or positive controls. In embodiments, the kit includes antibodies and / or aptamers and / or primers, probes, or antibodies that recognize at least two biomarkers described herein.

[0207] In embodiments, the kit includes aptamers that specifically bind to biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, cerglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule-1 (ICAM1). In embodiments, the kit includes aptamers that specifically bind to C7 and QSOX1. In embodiments, the kit includes aptamers that specifically bind to C7 and GP5. In the embodiment, the kit includes aptamers that specifically bind to C7 and ICAM1. In the embodiment, the kit includes aptamers that specifically bind to C7, QSOX1, and GP5. In the embodiment, the kit includes aptamers that specifically bind to C7, QSOX1, and ICAM1.

[0208] In embodiments, the kit includes antibodies that bind to one or more of the following: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, cerglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-unstable subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule-1 (ICAM1). In embodiments, the kit includes antibodies that bind to C7 and QSOX1. In embodiments, the kit includes antibodies that bind to C7 and ICAM1. In one embodiment, the kit includes antibodies that bind to C7 and GP5. In another embodiment, the kit includes antibodies that bind to C7, QSOX1, and GP5. In yet another embodiment, the kit includes aptamers that specifically bind to C7, QSOX1, and ICAM1.

[0209] In embodiments, the kit includes synthetic proteins for measuring the absolute concentration of one or more of the following: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, cerglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule-1 (ICAM1). In embodiments, the kit includes synthetic proteins for measuring the absolute concentration of C7 and QSOX1. In embodiments, the kit includes aptamers that specifically bind to C7 and ICAM1. In one embodiment, the kit includes synthetic proteins for measuring the absolute concentrations of C7 and GP5. In another embodiment, the kit includes synthetic proteins for measuring the absolute concentrations of C7, QSOX1, and GP5. In yet another embodiment, the kit includes aptamers that specifically bind to C7, QSOX1, and ICAM1.

[0210] The machine-readable storage medium may include data storage material encoded in machine-readable data or data arrays that can be used for a variety of purposes when using a machine programmed with instructions for using the data. The measurement of effective amounts of the biomarkers of the present invention and / or the assessment of risks derived from those biomarkers can be carried out, among other things, by a computer program running on a programmable computer that includes a processor, a data storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The program code can be applied to input data to perform the functions described above and generate output information. The output information can be applied to one or more output devices according to methods known in the art. The computer may be, for example, a conventionally designed personal computer, a microcomputer, or a workstation.

[0211] Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program may be implemented in assembly language or machine language as needed. The language may be a compiled language or an interpreted language. Each such computer program may be stored in a storage medium or device (e.g., ROM or magnetic diskette, or other as defined elsewhere in this disclosure) that is read by a general-purpose or dedicated programmable computer, and which configures and operates the computer when the storage medium or device is read by the computer and performs the procedures described herein. A health-related data management system used in some aspects of the present invention may also be considered to be implemented as a computer-readable storage medium configured with a computer program, which in turn operates the computer in a specific predetermined manner to perform the various functions described herein.

[0212] In some embodiments, the protein markers of the present invention can be used to generate a “reference biomarker profile” of a subject without fibrosis (e.g., significant fibrosis). The biomarkers disclosed herein can also be used to generate a “target biomarker profile” taken from a subject having fibrosis (e.g., significant fibrosis). By comparing the target biomarker profile with the reference biomarker profile, subjects having fibrosis can be diagnosed or identified. By comparing target biomarker profiles of different fibrosis stages, the stage of fibrosis can be diagnosed or identified, or NASH or NASH at risk can be diagnosed. The reference biomarker profile and target biomarker profile of the present invention can, in some embodiments, but not limited to, be contained in a machine-readable medium such as VCR-readable analog tape, CD-ROM, DVD-ROM, USB flash drive, etc. Such a machine-readable medium can also, but not limited to, include additional test results such as clinical parameters and measurements of conventional laboratory risk factors. Additionally or alternatively, the machine-readable medium can also include subject information such as medical history and any relevant family history. The machine-readable medium can also include information on other disease risk algorithms and calculated indices such as those described herein.

[0213] A measure of performance and accuracy for the present invention. The performance of the present invention, and therefore its absolute and relative clinical utility, can be evaluated in several ways as described above. Among the various evaluations of performance, some aspects of the present invention are intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method relates to the ability of a test, assay, or method to rule in significant fibrosis, NASH or NASH at risk, or to staging liver fibrosis, based on whether the subject has a “significant change” (e.g., clinically significant and diagnostically significant) in the level of a biomarker. “Effective dose” means the measurement of a suitable number of biomarkers (one or more) to produce a “significant change” (e.g., level of biomarker expression or activity) that differs from a predetermined cutoff value (or threshold) for that biomarker(s), and thus indicates that the subject has a particular level of liver fibrosis for which that biomarker(s) is an indicator. The difference in biomarker levels is preferably statistically significant. As described below, but without limiting the scope of the present invention, achieving statistical significance and therefore preferred analysis, diagnosis, and clinical accuracy may require using a panel of several biomarkers together and combining them with mathematical algorithms to achieve statistically significant biomarker indices.

[0214] In the categorical diagnosis of disease states, changing the cutoff point or threshold of a test (or assay) typically alters sensitivity and specificity, though qualitatively the opposite relationship exists. Therefore, when evaluating the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject's state, both sensitivity and specificity should always be considered, and it should be noted what the reported cutoff point is, as sensitivity and specificity can vary significantly across the range of the cutoff point. One way to achieve this is to use the Matthews correlation coefficient (MCC) metric, which depends on both sensitivity and specificity. The use of statistics such as the area under the ROC curve (AUC), which encompasses all potential cutoff point values, is preferred for most categorical risk measures when using some aspects of the present invention, but for continuous risk measures, goodness-of-fit statistics and calibration against observed results or other gold standards are preferred.

[0215] A given level of predictability means that the Method provides an acceptable level of clinical or diagnostic accuracy. Using such statistics, “acceptable diagnostic accuracy” is defined herein as a test or assay (e.g., a test used in some aspects of the Invention to determine the clinically significant presence of a biomarker, thereby indicating the presence of an infectious type) having an AUC (Area Under the ROC curve of the test or assay) of at least 0.60, preferably at least 0.65, more preferably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

[0216] "Very high diagnostic accuracy" means a test or assay in which the AUC (Area Under the ROC Curve of the test or assay) is at least 0.75, 0.80, preferably at least 0.85, more preferably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.

[0217] Alternatively, this method predicts the presence or absence of fibrosis, the stage of fibrosis, or the response to treatment with an overall accuracy of at least 75%, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99%, or higher.

[0218] Alternatively, this method predicts the presence or stage of fibrosis or the response to treatment with a sensitivity of at least 75%, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99%, or higher.

[0219] Alternatively, this method predicts the presence of “at-risk” NASH or response to NASH therapy with a specificity of at least 75%, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99%, or higher.

[0220] Alternatively, this method may be used to rule in significant fibrosis by an NPV of at least 75%, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99%, or higher. Alternatively, this method may be used to rule in NASH or “at risk” NASH by a PPV of at least 50%, more preferably 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or higher.

[0221] Alternatively, this method rules in significant fibrosis, NASH, or "at-risk" NASH, or a response to treatment, based on MCC values ​​greater than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0.

[0222] Generally, alternative methods for determining diagnostic accuracy are commonly used on continuous measures when the disease category is not yet clearly defined by the relevant medical community and medical practice, when thresholds for therapeutic use have not yet been established, or when there is no existing gold standard for diagnosing predisease. For continuous measures of risk, measures of diagnostic accuracy of the calculated index are typically based on curve fitting and calibration between predicted continuous values ​​and actual observed values ​​(or historical index calculations), utilizing measures such as R-squared, Hosmer-Lemeshow p-value statistics, and confidence intervals. It is not uncommon to see predictive values ​​reported using such algorithms that include confidence intervals (usually 90% or 95% CI) based on predictions from historically observed cohorts, as in the trial of the risk of future breast cancer recurrence marketed by Genomic Health, Inc. (Redwood City, California).

[0223] In general, by defining the degree of diagnostic accuracy, i.e., the cut point on the ROC curve, defining acceptable AUC values, and determining the acceptable range of relative concentrations of what constitutes the effective amount of the biomarker of the present invention, those skilled in the art will be able to use the biomarker to identify, diagnose, or prognose subjects with a predetermined level of predictability and performance.

[0224] Furthermore, other unlisted biomarkers are highly correlated with the biomarker (for the purposes of this application, any two variables have a coefficient of determination (R) greater than 0.5). 2 (A biomarker is considered "highly correlated" if it has the following characteristics). Some aspects of the present invention encompass such functional and statistical equivalents to the biomarkers described above. Furthermore, the statistical utility of such additional biomarkers depends substantially on the cross-correlations between multiple biomarkers, and any new biomarkers are often needed to work within the panel in order to elaborate on the underlying biological implications. In the context of this invention, the following statistical terms may be used.

[0225] "TP" stands for True Positive, meaning a positive test result that accurately reflects the tested-for activity. For example, in the context of this invention, TP is, for instance, a true classification of a bacterial infection as such.

[0226] "TN" stands for true negative, meaning a negative test result that accurately reflects the tested activity. For example, in the context of this invention, TN means, for example, but not limited to, a true classification of the viral infection itself.

[0227] "FN" stands for false negative, meaning a result that appears negative but fails to clarify the situation. For example, in the context of this invention, FN could be, but is not limited to, misclassifying a bacterial infection as a viral infection.

[0228] "FP" stands for false positive, meaning a test result that is incorrectly classified into the positive category. For example, in the context of this invention, FP is, for example, a misclassification of a viral infection as a bacterial infection, but is not limited to this.

[0229] Sensitivity is calculated using TP / (TP+FN) or the true positive rate for the disease target.

[0230] "Specificity" is calculated using TN / (TN+FP) or the true negative rate for non-disease subjects or normal subjects.

[0231] "Total accuracy" is calculated as (TN+TP) / (TN+FP+TP+FN).

[0232] The "positive predictive value" or "PPV" is calculated as TP / (TP+FP) or the true positive rate of all positive test results. It is inherently influenced by the prevalence of the disease and the pre-test probability of the population intended to be tested.

[0233] The "negative predictive value" or "NPV" is calculated using TN / (TN+FN) or the true negative rate for all negative test results. It is also inherently influenced by the prevalence of the disease and the pre-test probability of the population intended to be tested. See, for example, O'Marcaigh AS, Jacobson RM, “Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results”, Clin. Ped. 1993, 32(8):485-491 (which discusses the specificity, sensitivity, and positive and negative predictive values ​​of a test, e.g., a clinical diagnostic test).

[0234] The Matthews correlation coefficient (MCC) is calculated as follows: MCC = (TP*TN - FP*FN) / {(TP+FN)*(TP+FP)*(TN+FP)*(TN+FN)}^0.5 (wherein TP, FP, TN, and FN are true positive, false positive, true negative, and false negative, respectively). Note that the MCC value ranges from -1 to +1, representing completely wrong classification and perfect classification, respectively. An MCC of 0 indicates random classification. The MCC has been shown to be useful for combining sensitivity and specificity into a single metric (Baldi, Brunak et al., 2000). It is also useful for measuring and optimizing classification accuracy in the case of imbalanced class sizes (Baldi, Brunak et al., 2000).

[0235] In many cases, for binary disease status classification approaches using sequential diagnostic test measurements, sensitivity and specificity are summarized by a patient operating characteristic (ROC) curve, as per Pepe et al., “Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker”, Am.J.Epidemiol 2004, 159(9):882-890, and by the area under the curve (AUC) or c-statistic, which is an index that allows the sensitivity and specificity of a test, assay, or method across the entire range of cutpoints of the test (or assay) to be expressed by a single numerical value. For example, Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4 th Chapter 14 of "Clinical Interpretation Of Laboratory Procedures" in the 1996 edition, Shultz, WBSaunders Company, pp. 192-199; and Zweig et al., "ROC Curve Analysis: An Example Showing The Relationships Among Serum Lipid And Apolipoprotein Concentrations In Identifying Subjects With Coronory Artery Disease," Clin.Chem, 1992, 38(8):1425-1428. Alternative approaches using likelihood functions, odds ratios, information theory, predictors, calibration (including goodness-of-fit), and reclassification measures are summarized according to Cook, "Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction," Circulation 2007, 115:928-935.

[0236] "Accuracy" refers to the degree to which a measured or calculated quantity (test-reported value) fits its actual (or true) value. Clinical accuracy is related to the ratio of true outcomes (true positive (TP) or true negative (TN)) to misclassified outcomes (false positive (FP) or false negative (FN)) and may be described as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), Matthews correlation coefficient (MCC), or likelihood, odds ratio, receiver operating characteristic (ROC) curve, or area under the curve (AUC), among other measures.

[0237] A “formula,” “algorithm,” or “model” is any mathematical equation, algorithm, analysis or program process, or statistical technique that takes one or more continuous or categorical inputs (referred to herein as “parameters”) and computes an output value, sometimes called an “index” or “index value.” Non-exclusive examples of a “formula” include sums, ratios, and regression operators (such as coefficients and exponents), transformations and normalizations of biomarker values ​​(including, but not limited to, normalization schemes based on clinical biomarkers, e.g., sex, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Linear and nonlinear equations, as well as statistical classification analyses, are particularly used when combining biomarkers to determine the relationship between the level of a biomarker detected in a sample of interest and the probability of an subject having an infection or a particular type of infection. Of particular interest in constructing panels and combinations are structural and syntactic statistical classification algorithms and methods for constructing indicators, as well as other established techniques such as cross-correlation, principal component analysis (PCA), factor rotation, logistic regression (LogReg), linear discriminant analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), support vector machines (SVM), random forests (RF), recursive partition trees (RPART), and other related decision tree classification methods, as well as pattern recognition capabilities including Schrunkun-Centroid (SC), StepAIC, K-nearest neighbors, boosting, decision trees, neural networks, Bayesian networks, and hidden Markov models. Other techniques, including Cox, Weibull, Kaplan-Meier, and Greenwood models, well known to those skilled in the art, may be used for hazard analysis of survival and time to events. Many of these techniques are useful in combination with biomarker selection techniques such as forward selection, reverse selection, or stepwise selection, complete enumeration of all potential panels of a given size, and genetic algorithms, or they themselves may include biomarker selection methodologies in their own techniques.These may be combined with information criteria such as Akaike's Information Criterion (AIC) or Bayes' Information Criterion (BIC) to quantify the trade-off between additional biomarkers and model improvements and to help minimize overfitting. The resulting predictive models may be validated in other studies using techniques such as bootstrapping, leave-one-out (LOO), and 10-fold cross-validation (10-fold CV), or cross-validated in the study in which they were originally trained. At various stages, the false detection rate may be estimated by value permutation according to techniques known in the art. The "health economic utility function" is a formula derived from a combination of expected probabilities of a set of clinical outcomes in an idealized, applicable patient population, both before and after the introduction of diagnostic or therapeutic interventions into standard care. This encompasses estimates of the accuracy, effectiveness, and performance characteristics of such interventions, as well as cost and / or value measures (usefulness) associated with each outcome, which can be derived from the actual healthcare system costs (services, supplies, devices, and drugs, etc.) and / or the estimated tolerance per quality-adjusted life year (QALY) resulting in each outcome. The sum of the products of the predicted population size for a given outcome and the predicted usefulness for each outcome across all predicted outcomes is the total health-economic utility of a given standard treatment. The difference between (i) the total health-economic utility calculated for standard treatments with interventions and (ii) the total health-economic utility for standard treatments without interventions provides an overall measure of the health-economic cost or value of the intervention. This itself can be divided among the entire patient population being analyzed (or only among the intervention groups) to arrive at the cost per unit intervention and guide decisions such as market positioning, pricing, and assumptions of healthcare system acceptance. While such health economic utility functions are commonly used to compare the cost-effectiveness of interventions, they can be transformed to estimate the acceptable value per QALY that a healthcare system is willing to pay, or the acceptable cost-effectiveness of the clinical performance characteristics required for a new intervention.

[0238] In the diagnostic (or prognostic) interventions of the present invention, since each outcome (which may be TP, FP, TN, or FN in a disease classification diagnostic test) has a different cost, the health economic utility function may prioritize sensitivity over specificity, or PPV over NPV, based on the clinical context and the cost and value of individual outcomes, thus providing an alternative measure of the economic performance and value of health that may differ from more direct clinical or analytical performance measures. These different measures and relative trade-offs generally converge only in the case of a complete test with zero error rates (also known as zero misclassification of predicted outcomes for subjects, or FP and FN) that are more favorable than imperfections, but to varying degrees.

[0239] "Analytical accuracy" refers to the reproducibility and predictability of the measurement process itself and can be summarized by measurements such as the coefficient of variation (CV), Pearson correlation, and agreement and calibration tests of the same sample or control using different time periods, users, instruments, and / or reagents. These and other considerations when evaluating novel biomarkers are also summarized in Vasan, 2006.

[0240] "Performance" is a term relating to the overall usefulness and quality of a diagnostic or prognostic trial, and includes, among other things, clinical and analytical accuracy, other analytical and process characteristics, such as usability (e.g., stability, ease of use), economic value to health, and the relative cost of the trial's components. Any of these factors can be a source of good performance, and therefore usefulness, and can be measured by appropriate "performance metrics" such as relevant AUC and MCC, time to results, and shelf life.

[0241] "Statistically significant" means that the change is greater than what could be expected to occur by chance alone (which could be a "false positive"). Statistical significance can be determined by any method known in the art. A commonly used measure of significance is the p-value, which represents the probability of obtaining a result at least as extreme as the given data points, assuming the data points were the result of chance alone. Results are often considered to be highly significant with a p-value of 0.05 or less.

Example

[0242] Example The following examples are provided for the purpose of illustrating various embodiments of the present disclosure and are not meant to limit the present disclosure in any way. Modifications and other uses of the present disclosure that are encompassed within the scope of the spirit of the present disclosure as defined by the claims will be recognized by those skilled in the art.

[0243] Example 1. Identification of Biomarker Signatures for Fibrosis and "At-Risk" NASH Objective: Serum samples from 2,186 patients were used to identify biomarker signatures for fibrosis and "at-risk" NASH.

[0244] Patient Population: Overall, 357 patients from the "Discovery Cohort" confirmed by NAFLD biopsy, 241 patients from "Validation Cohort - 1", 519 patients from "Validation Cohort - 2", 256 patients from "Validation Cohort - 3" and 813 patients from the "Real-World Patient Cohort" were included in the study. Table 2 summarizes the demographic and clinical characteristics of all patients in the five cohorts. Table 2 - Patient Demographics

Table 2

[0245] In the discovery cohort confirmed by NAFLD biopsy, the patients were predominantly male and consistent with known literature. Ages ranged from 52.3 to 60.2 years, T2D was presented in 18% to 61% of patients in the cohort, hypertension in 6% to 20%, and dyslipidemia in 46% to 62%, all of which correlated with an increase in fibrosis stage. The mean body mass index overall was approximately 34 kg / m². 2 Hemoglobin A1c (HbA1C) ranged from 5.8% to 7.1%, and neither showed a clear trend between the various stages of fibrosis. Overall, 161 patients were identified with significant fibrosis, 106 with advanced fibrosis, 40 with cirrhosis, 77 with “risk-pregnancy” NASH, and 63 with “risk-pregnancy” NASH NIMBLE definition. Another 196 patients had lower stages of fibrosis (F stage < 2). Other commonly used clinical scores, such as FIB-4 and Fibroscan measurements, liver stiffness measurement (LSM), and controlled attenuation parameter (CAP), increased as expected with the progression of liver fibrosis tested on biopsy, while the NAFLD activity score (NAS) remained relatively stable above F stage 0.

[0246] In validation cohorts 1-3, 51-57% of the subjects were female, with a mean age ranging from 48-57 years. Diabetes was present in 30-59% of patients, and the prevalence of age with fibrosis and T2D was increasing. The mean BMI was above 30 kg / m2, and the mean hemoglobin A1c (HbA1c) ranged from 6.3% to 7.5%. Overall, validation was performed based on approximately 1,000 samples from patients with liver biopsy data. In validation cohort-1, 133 subjects were identified as having significant fibrosis, 101 as having advanced fibrosis, 32 as having cirrhosis, 80 as having “risk” NASH, and 59 as having “risk” NASH according to the NIMBLE definition. In validation cohort-2, 153 subjects were identified with significant fibrosis, 87 with advanced fibrosis, 27 with cirrhosis, 80 with “risk” NASH, and 69 with “risk” NASH NIMBLE definition. In validation cohort-3, 97 subjects were identified with significant fibrosis, and 122 with advanced fibrosis. Similar to the discovery cohort, all commonly used clinical scores (FIB-4, LSM, CAP, ELF®, and FirboTest®) correlated with increased liver fibrosis, but NAS remained relatively stable above F stage 0, with an overall mean of 4.

[0247] In the real-world patient cohort, patients were predominantly male, with a mean age of 68.8–69.2 years in both the NAFLD and probable NASH cirrhosis groups. T2D was presented in 59% of probable NASH cirrhosis patients, hypertension in 74%, and dyslipidemia in 64%, with similar prevalence rates in NAFLD patients (51%, 74%, and 57%, respectively). Furthermore, mean BMI and HbA1c were similar in both groups. Notably, mean FIB-4 was higher in probable NASH cirrhosis cases (3.6) compared to NAFLD patients (1.7).

[0248] The discovery cohort included serum samples from 357 biopsy-confirmed NAFLD patients (aged 18 and older) from Puerta de Hierro Hospital and Marques de Valdecilla Hospital in Spain. Validation cohort-1 included 241 biopsy-confirmed NAFLD patients (aged 18 and older) from Hospital Universitario Virgen del Rocio (HUVR) in Spain. Validation cohort-2 included 519 biopsy-confirmed NAFLD patients (aged 18 and older) from University Hospital Antwerp (UZA, Belgium). Validation cohort-3 was collected as part of a clinical trial cohort conducted in the United States and included 256 biopsy-confirmed NAFLD patients, including those with fibrosis stages F1-F3 and NAS > 4. Generally, biopsy criteria included suspected advanced liver disease based on imaging or laboratory tests, or during bariatric surgery. Exclusion criteria included significant alcohol consumption (more than 30g per day for men and more than 20g per day for women), and evidence of associated liver disease, including viral or autoimmune hepatitis, human immunodeficiency virus, drug-induced fatty liver, hemochromatosis, or Wilson's disease. Data collected included anthropometric measurements, laboratory blood test results, medical history, Fibroscan® (by Echosense), and biopsy results. For the discovery cohort and validation cohort 3, FibroTest® (by BioPredictive) test results were collected. For validation cohorts 1 and 3, enhanced hepatic fibrosis (ELF®, by Siemens) test results were collected. Significant fibrosis cases were defined as patients with fibrosis stage F≧2, advanced fibrosis (F≧3), and cirrhosis (F=4). A "high-risk" NASH case was defined as having NASH confirmed by biopsy, a NAFLD activity score (NAS) ≥ 4 with at least 1 point in each component, and F ≥ 2 after excluding patients with cirrhosis.According to a recently published study by Sanyal et al. [2023, Nat Med 29, 2656-2664 (2023). https: / / doi.org / 10.1038 / s41591-023-02539-6], the definition of a “risk” NASH nimble includes the exclusion of patients with F0-F1 and NAS≧4, as well as patients with F≧2 and NAS<4, and patients with cirrhosis.

[0249] The real-world patient cohort included serum samples taken from a typical Israeli population between 2021 and 2022 as part of an Investigative Review Board (IRB). Using anonymized electronic health records (EHRs), 184 samples were identified from patients likely diagnosed with NAFLD, defined as having an ICD-9 diagnosis within 6 months prior to or following blood collection and the presence or history of at least two metabolic risk factors (https: / / doi.org / 10.1016 / S2468-1253(20)30252-1). All cases were individually validated by health professionals based on biopsy, imaging, and / or clinical evaluation data. 629 control samples from NAFLD patients were defined as having an ICD-9 diagnosis and a recorded history of abdominal imaging. Exclusion criteria included the absence of evidence of competing etiologies, including viral hepatitis, chronic hepatitis, alcoholic liver disease, autoimmune disorders, idiopathic cirrhosis, and cardiac cirrhosis. The collected data included anthropometric measurements, blood laboratory measurements, and medical history.

[0250] Determination of Biomarker Concentrations: Multi-OMICS Analysis – All samples were analyzed using proprietary high-throughput liquid chromatography-mass spectrometry (LC-MS) based metabolomics (coverage of 12,000 ions and 1,500 metabolites), lipidomics (coverage of 19,000 ions and 1,600 lipids), and proteomics (multiplexing of 1,800 proteins), detecting a total of tens of thousands of biomarker ions per sample (RSD < 30%). Biomarker intensities were normalized based on repeated injections of biological QC samples and analyzed repeatedly every 10 samples. Therefore, intensities are in arbitrary relative units and not absolute concentrations.

[0251] Statistical Analysis: The discovery cohort was randomly divided into training and test sets. First, feature-specific correlations to outcomes were applied and corrected for multiple hypothesis testing. Significant features were normalized to a standard distribution and passed through a pipeline consisting of forward feature selection using leave-one-out cross-validation L2 regularized logistic regression. Using this pipeline, minimal component serum signatures on the training set, optimized for the best area under receiver operating characteristic curves (AUROC) for all indications (definitions of significant and advanced fibrosis, cirrhosis, "risky" NASH, and "risky" NASH NIMBLE). Optimal signatures consisting of two and three proteins were identified and then tested on the test set to confirm performance. Finally, to evaluate performance in identifying the indications described above, the signatures were applied to the discovery cohort, validation cohorts 1-3, and real-world patient cohort using a leave-one-out cross-validation scheme. For validation cohort-3, only performance in diagnosing significant and advanced fibrosis was determined. For real-world patient cohorts, only the diagnostic performance for cirrhosis was examined. The 95% confidence interval (CI) for AUROC was estimated using the Delong method.

[0252] Results: The first correlation study against the test set identified several candidate biomarkers for subsequent modeling phases. Among the identifiable proteins and peptides were the following proteins: kyesin sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), collectin-10 (COL10), alpha-2-macroglobulin (A2M), vascular cell adhesion protein 1 (VCAM1), and platelet glycoprotein V (GP5). See Table 3 for a complete list of biomarkers. Figures 1A–1F show the protein intensities of the biomarkers COL10 (Figure 1A), C7 (Figure 1B), QSOX1 (Figure 1C), VCAM1 (Figure 1D), A2M (Figure 1E), GP5 (Figure 1F), and ICAM-1 (Figure 1G) by fibrosis stage. Figure 1H shows the fibrosis stage by patient age. Table 3 - AUC of top biomarkers in training patients in the discovery cohort [Table 3-1] [Table 3-2] [Table 3-3]

[0253] Example 2. Identification of two protein biomarker signatures for "at-risk" NASH. The optimal two-component biomarker signature was identified using the candidate features identified in Example 1.

[0254] For NASH at risk, the optimal two-component model included (a) proteins C7 and QSOX1, which gave AUCs of 0.79 and 0.76 in the test set and in the training set, and (b) C7 and ICAM1, which gave AUCs of 0.76 and 0.77 in the test set and in the training set. The resulting logistic regression model had the following equation for scaled proteins in arbitrary units: P = (1 - exp(F)) / exp(F) (wherein (a) F = 0.849 * C7 + 0.745 * QSOX1 - 0.124, and (b) F = 0.974 * C7 + 0.513 * ICAM1 - 0.13).

[0255] When the C7 and QSOX1 models are applied to all cohorts using a leave-one-out method, the inventors find an AUC of 0.78 for the discovery cohort and AUCs of 0.78 and 0.71 for validation cohorts 1 and 2, respectively. For the NASH NIMBLE definition of "at risk," the inventors find an AUC of 0.81 for the discovery cohort and AUCs of 0.80 and 0.74 for validation cohorts 1 and 2, respectively. When the C7 and ICAM1 models are applied to all cohorts using a leave-one-out method, the inventors find an AUC of 0.77 for the discovery cohort and AUCs of 0.78 and 0.74 for validation cohorts 1 and 2, respectively. For the NASH NIMBLE definition of "at risk," the inventors find an AUC of 0.82 for the discovery cohort and AUCs of 0.87 and 0.78 for validation cohorts 1 and 2, respectively.

[0256] Figure 2 shows that identifying “at-risk” NASH using a two-component biomarker signature including C7 and QSOX1 or C7 and ICAM1 was superior to identifying “at-risk” NASH using the FIB-4 score. In all subsequent figures, proteomics models are referred to as either “MS-LFS” or “model” with contextual composition.

[0257] Example 3. Identification of two protein biomarker signatures for the fibrotic stage. In the fibrotic stage, the optimal two-component models included (a) proteins C7 and GP5, which gave respective AUCs of 0.82, 0.86, and 0.96 in the test set for significant fibrosis, advanced fibrosis, and cirrhosis, and (b) C7 and ICAM1, which gave respective AUCs of 0.85, 0.85, and 0.92 in the test set for significant fibrosis, advanced fibrosis, and cirrhosis. The resulting logistic regression models had the following equations in arbitrary units on the scaled proteins: P = (1 - exp(F)) / exp(F), where (a) F = 1.039 * C7 - 0.467 * GP5 - 0.161, and (b) F = 0.974 * C7 + 0.513 * ICAM1 - 0.130 (as described in the above section).

[0258] When applying the C7 and GP5 models to the entire cohort in a leave-one-out fashion, the inventors found respective AUCs of 0.82, 0.8, and 0.96 in the discovery cohort for significant fibrosis, advanced fibrosis, and cirrhosis. For the validation cohorts, the inventors found respective AUCs of 0.82, 0.82, 0.85 in validation cohort - 1 for significant fibrosis, advanced fibrosis, and cirrhosis, 0.79, 0.87, 0.97 in validation cohort - 2, and 0.77, 0.83 for significant fibrosis and advanced fibrosis in validation cohort - 3. When applying the C7 and ICAM1 models to the entire cohort in a leave-one-out fashion, the inventors found respective AUCs of 0.82, 0.83, 0.93 in the discovery cohort for significant fibrosis, advanced fibrosis, and cirrhosis. For the validation cohorts, the inventors found respective AUCs of 0.82, 0.81, 0.82 in validation cohort - 1 for significant fibrosis, advanced fibrosis, and cirrhosis, 0.82, 0.87, 0.95 in validation cohort - 2, and 0.77, 0.82 for significant fibrosis and advanced fibrosis in validation cohort - 3.

[0259] When the two-component signatures C7 and GP5 were validated against a real-world patient cohort, an AUC of 0.82 was obtained, which was superior to FIB-4.

[0260] Figures 3A–3D show that determining fibrosis stage using a two-component biomarker signature containing either C7 and GP5, or C7 and ICAM1, was superior to determining fibrosis stage using the FIB-4 score. Figures 3A–3B demonstrate the superiority of the two-component biomarker model for diagnosing significant fibrosis. Figures 3C–3D demonstrate the superiority of the two-component biomarker model for diagnosing advanced fibrosis. Figures 3E–3F demonstrate the superiority of the two-component biomarker model for diagnosing cirrhosis. Figure 3G demonstrates the superiority of the two-component biomarker signature C7 and GP5 for diagnosing probable NASH cirrhosis.

[0261] Example 4. Identification of three protein biomarker signatures for “risk” NASH and fibrotic stages. Two optimal three-component biomarker signatures for fibrous stage and “at-risk” NASH were identified using the candidate features identified in Example 1. The optimal three-component models consisted of (a) C7, QSOX1, and GP5, and (b) C7, QSOX1, and ICAM1.

[0262] Adding GP5 to the biomarker signature improved the model's AUC to 0.74, 0.81 for "risk-related" NASH NIMBLE, 0.825 for significant fibrosis, and 0.873 for advanced fibrosis in the trial set of "risk-related" NASH. Similarly, it improved performance in validation cohorts 2 and 3. Adding ICAM1 to the biomarker signature improved the model's AUC to 0.79, 0.89 for "risk-related" NASH NIMBLE, 0.859 for significant fibrosis, and 0.865 for advanced fibrosis in the trial set of "risk-related" NASH. Similarly, it improved performance in validation cohort 2. The resulting logistic regression model had the following equation for any scaled protein: P = (1 - exp(F)) / exp(F) (wherein (a) F = 0.775 * C7 - 0.356 * GP5 + 0.658 * QSOX1 - 0.130, and (b) F = 0.743 * C7 - 0.398 * ICAM1 + 0.657 * QSOX1 - 0.101).

[0263] When the C7, QSOX1, and GP5 models were applied to the entire cohort using a leave-one-out approach, the inventors found AUCs of 0.77, 0.79, 0.827, 0.863, and 0.958 for “risk” NASH, “risk” NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in the discovery cohort. Regarding the validation cohorts, the inventors found AUCs of 0.77, 0.84, 0.82, 0.82, and 0.84 for "risky" NASH, "risky" NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in validation cohort-1; 0.7, 0.74, 0.8, 0.87, and 0.97 for "risky" NASH, "risky" NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in validation cohort-2; and 0.75 and 0.82 for significant fibrosis and advanced fibrosis, respectively, in validation cohort-3. When the C7, QSOX1, and ICAM1 models were applied to the entire cohort using a leave-one-out approach, the inventors found AUCs of 0.78, 0.82, 0.824, 0.843, and 0.937 for “risk” NASH, “risk” NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in the discovery cohort. Regarding the validation cohorts, the inventors found an AUC of 0.78 for "at risk" NASH, 0.9 for "at risk" NASH NIMBLE, and 0.81 for significant fibrosis, advanced fibrosis, and cirrhosis in validation cohort-1. In validation cohort-2, they found AUCs of 0.75, 0.79, 0.82, 0.87, and 0.96 for "at risk" NASH, "at risk" NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively. In validation cohort-3, they found AUCs of 0.75 and 0.82 for significant fibrosis and advanced fibrosis, respectively.

[0264] By validating the three-component signatures, C7, QSOX1, and GP5, against a real-world patient cohort, an AUC of 0.83 was obtained, which was superior to FIB-4.

[0265] Figures 4A–4K show that diagnosing “risk” NASH and determining fibrosis stage by a three-component biomarker signature including either C7, QSOX1, and GP5, or C7, QSOX1, and ICAM1, was superior to determining fibrosis stage by FIB-4 score. Figures 4A and 4B demonstrate the superiority of the three-component biomarker model for diagnosing “risk” NASH. Figures 4C–4D demonstrate the superiority of the three-component biomarker model for diagnosing “risk” NASH NIMBLE. Figures 4E–4F demonstrate the superiority of the three-component biomarker model for diagnosing significant fibrosis. Figures 4G–4H demonstrate the superiority of the three-component biomarker model for diagnosing advanced fibrosis. Figures 4I–4J demonstrate the superiority of the three-component biomarker model for diagnosing cirrhosis. Figure 4K demonstrates the superiority of the three-component biomarkers C7, QSOX1, and GP5 signature for diagnosing probable NASH cirrhosis in a real-world patient cohort.

[0266] Example 5. The C7, QSOX1 and C7, ICAM1 biomarker signatures are superior to other clinical scores for diagnosing “at-risk” NASH. The two-component MS-LFS model described in Example 2 outperforms other commonly used clinical scores (i.e., BARD and NFS) and performs as well as / better than its combination with Fibroscan® and other clinical parameters (FAST, Agile 3+, and Agile 4) (Figure 5). Furthermore, the MS-LFS score outperforms commercially available protein biomarker-based tests, ELF® and FibroTest® (Table 4).

[0267] Table 4. The C7,QSOX1 and C7,ICAM1 biomarker signatures are superior to other commercially available protein biomarker tests for diagnosing “at-risk” NASH. [Table 4]

[0268] The diagnostic performance of the MS-LFS model was compared with additional common clinical predictors of fibrosis status: body mass index, aspartate aminotransferase / alanine aminotransferase ratio, and diabetes (BARD); NAFLD fibrosis score (NFS); FibroScan-AST (FAST); Agile 3+ and Agile 4. The AUC and 95% CI for each predictor are shown in Figure 5 for the entire cohort. Additionally, the AUCs of the commercially available protein biomarker-based tests, FibroTest® and ELF®, are shown in Table 4 for the entire cohort. The MS-LFS score is superior to all commonly used clinical scores and protein biomarker-based tests and functions as well or better than its combination with Fibroscan and other clinical parameters (FAST, Agile 3+, and Agile 4) (Figure 5, Table 4).

[0269] Importantly, the same results are shown when focusing on patients with intermediate FIB-4 scores (1.30 < FIB-4 < 2.67) who have uncertain diagnoses and management. The MS-LFS score functions as well or similarly to all techniques in all indications, despite the large CIs due to the small sample size (Figure 5A and Table 4).

[0270] Focusing on T2D, MS-LFS with two proteins is significantly superior to all other common clinical predictors and shows numerically superior performance to its combination with Fibroscan and other clinical parameters (Figure 5B).

[0271] The C7 and ICAM1 and C7 and GP5 biomarker signatures of Example 6.C7 are superior to other clinical scores for the diagnosis of fibrosis stage The two-component MS-LFS model described in Example 3 outperforms other commonly used clinical scores (i.e., BARD and NFS) and performs as well as / better than its combination with Fibroscan® and other clinical parameters (FAST, Agile 3+, and Agile 4). Furthermore, the MS-LFS score outperforms commercially available protein biomarker-based tests, ELF® and FibroTest® (Figure 6, Table 5).

[0272] Table 5. The C7, ICAM1 and C7, GP5 biomarker signatures are superior to other commercially available protein biomarker tests for diagnosing fibrotic stages. [Table 5]

[0273] The diagnostic performance of the MS-LFS model was compared to additional common clinical predictors of fibrotic status: body mass index, aspartate aminotransferase / alanine aminotransferase ratio, and diabetes mellitus (BARD); NAFLD fibrosis score (NFS); FibroScan-AST (FAST); Agile 3+; and Agile 4. The AUC and 95% CI for each predictor are shown in Figure 6 for all cohorts. Furthermore, the AUC for commercially available protein biomarker-based tests, FibroTest® and ELF®, are shown in Table 5 for all cohorts. The MS-LFS score outperformed all commonly used clinical scores and protein biomarker-based tests, performing similarly to or better than its combination with Fibroscan and other clinical parameters (FAST, Agile 3+, and Agile 4) (Figure 6, Table 5).

[0274] Importantly, the same results are shown even when focusing on patients with intermediate FIB-4 scores (1.30 < FIB-4 < 2.67) with uncertain diagnosis and management. The MS-LFS score functions better or similarly to all techniques in all indications, despite the large CI due to the small sample size (Figure 6A and Table 5).

[0275] Regarding T2D, the MS-LFS by two proteins is significantly superior to all other common clinical predictors, functions similarly to Agile 3+, and shows numerically superior performance compared to combinations with Fibroscan and other clinical parameters (Figure 6B).

[0276] Finally, the diagnostic performance is still high for diagnosing likely NASH cirrhosis in the IMOSS-500K, a real-world primary care patient cohort, and is superior to FIB-4, BARD, and NFS. Furthermore, the MS-LFS is significantly superior to FIB-4, BARD, and NFS in the cohort even when focusing on the intermediate FIB-4 subpopulation (Figure 6A).

[0277] Example 7.3 The protein biomarker signatures C7, QSOX1, and GP5, and C7, QSOX1, and ICAM1 are superior to other clinical scores for diagnosing "at-risk" NASH and fibrosis stages The 3-component MS-LFS score described in Example 4 is superior to other commonly used clinical scores (i.e., BARD and NFS) and functions as well or better than its combinations with Fibroscan® and other clinical parameters (FAST, Agile 3+, and Agile 4) (Figure 7). Furthermore, the MS-LFS score is superior to commercially available protein biomarker-based tests, ELF® and FibroTest® (Tables 6a - b).

[0278] Table 6a. The 3-protein biomarker signature is superior to other commercially available protein biomarker tests for diagnosing the fibrosis stage. [Table 6a]

[0279] Table 6b. The 3-protein biomarker signature is superior to other commercially available protein biomarker tests for diagnosing "at-risk" NASH. [Table 6b-1] [Table 6b-2]

[0280] Importantly, the same results are shown even when focusing on patients with an intermediate FIB-4 score (1.30 < FIB-4 < 2.67) who have an uncertain diagnosis and management. The MS-LFS score functions better or similarly to all techniques in all indications, despite the large CI due to the small sample size (Figure 7A and Tables 6a - b).

[0281] Focusing on T2D, the MS-LFS score is significantly superior to all other common clinical predictors, numerically superior to its combination with Fibroscan® and other clinical parameters, and functions equivalently to Agile 3+ (Figure 7B).

[0282] Finally, the diagnostic performance is still high for diagnosing likely NASH cirrhosis in the IMOSS-500K, a real-world primary care patient cohort, and is superior to FIB-4, BARD, and NFS. Furthermore, MS-LFS is significantly superior to FIB-4, BARD, and NFS in the cohort even when focusing on the intermediate FIB-4 and T2D subpopulations (Figure 7A).

[0283] Example 8.3 Protein biomarker signatures C7, QSOX1, and ICAM are superior to other clinical scores for monitoring liver fibrosis stages. Focusing on validation cohort-3, the inventors performed additional post-hoc analyses of serum samples collected at baseline and end of the study for 220 patients in whom consistent biopsy readings by two pathologists were performed at baseline and a second biopsy was performed at end of study (EOT). Biopsy data showed progression of fibrosis stage in a total of 42 patients, no change in 120 patients, and regression in 58 patients. The three-component MS-LFS score (C7, QSOX1, and ICAM) described in Example 4 showed a statistically significant decrease in the MASH-fibrosis score (mean 8% decrease; p<0.012), while patients with progressive fibrosis showed a significant increase in the score (mean 7% increase; p<0.05). In contrast, no statistically significant decrease in FIB-4, FibroTest, or ELF scores was detected in patients with a regression of fibrosis stage (Figure 8).

[0284] Example 9. Sensitivity and specificity of proteomics predictors at different cutoffs. Sensitivity and specificity were calculated for the developed predictors at different cutoffs. Depending on the required performance, the predictors can be adjusted to be more specific or more sensitive. Examples of possible sensitivity and specificity pairs are shown in Table 7 for the discovery cohort (Table 7a) and validation cohorts 1-3 (Tables 7b-d).

[0285] MS-LFS is a novel, non-invasive serological test consisting of two or three proteins for identifying patients with significant and advanced fibrosis, cirrhosis, and “at risk” NASH. MS-LFS outperforms commonly used clinical scores such as FIB-4, BARD, and NFS, achieving similar diagnostic performance to Fibroscan® and its combination with clinical parameters (FAST, Agile 3+, and Agile 4). Furthermore, MS-LFS outperforms commercially available protein-based biomarker tests, ELF® and FibroTest®. MS-LFS works synergistically with FIB-4 to improve the diagnosis of fibrosis and “at risk” NASH in patients with intermediate FIB-4 values. In addition, MS-LFS outperforms other test scores when applied to patients with T2D, “at risk” NASH, and significant fibrosis. This subpopulation is considered to be at higher risk with respect to progression and to the development of hepatic and cardiovascular outcomes. Therefore, these patients are expected to be treated immediately with new, future NASH treatments, and having non-invasive, accurate diagnostic serological tests may be most beneficial.

[0286] The potential of using MS-LFS in population screening was demonstrated using samples from the IMOSS-500K study, identifying high-risk NAFLD patients, likely those with NASH cirrhosis, in a real-world primary care screening setting.

[0287] Example 9. Table 7a - Performance of predictors for discovery [Table 7a-1] [Table 7a-2]

[0288] Table 7 b - Performance of predictors against validation cohort-1 [Table 7b-1] [Table 7b-2] [Table 7b-3] [Table 7b-4]

[0289] Table 7 c - Performance of predictors against validation cohort-2 [Table 7c-1] [Table 7c-2]

[0290] Table 7d - Performance of predictors against validation cohort 3 [Table 7d-1] [Table 7d-2]

[0291] Table 8 - Minimum fold change in average protein levels per indication. [Table 8-1] [Table 8-2]

[0292] Table 9 - Minimum Mean Scores for Models and Indications A model score was calculated for each subject, taking into account biomarker measurements and model algorithms. The median reflects the median value of the scores calculated for each indication. [Table 9]

[0293] All references, articles, publications, patents, patent gazettes, and patent applications cited herein are incorporated in their entirety for all purposes. However, any reference to any reference, article, publication, patent, patent publication, or patent application cited herein is not, and should not be construed as, an acknowledgment or proposal in any form that they constitute valid prior art or form part of common general knowledge in any country of the world. Furthermore, any priority document(s) of this application are incorporated in their entirety by reference.

Claims

1. A method for diagnosing liver disease or liver condition selected from a group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis combined with non-alcoholic steatohepatitis (NASH), and "at-risk" NASH in subjects with an alcohol intake of less than 30 g per day, (a) A step of measuring the amount of at least two protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule 1 (ICAM), and platelet glycoprotein V (GP5) in the target blood sample, (b) A step of ruling in the liver disease or liver condition based on the amounts of the at least two protein biomarkers Methods that include...

2. The method according to claim 1, wherein five or fewer proteins are analyzed.

3. The method according to claim 1, wherein the liver condition is significant fibrosis.

4. The method according to claim 1, wherein the liver condition is significant fibrosis in combination with NASH.

5. The method according to claim 1, wherein the liver condition is advanced fibrosis.

6. The method according to claim 1, wherein the liver condition is cirrhosis.

7. The method according to any one of claims 1 to 6, wherein the at least two protein biomarkers include C7 and QSOX1.

8. The method according to any one of claims 1 to 6, wherein the at least two protein biomarkers include C7 and ICAM.

9. The method according to any one of claims 1 to 6, wherein the at least two protein biomarkers include C7 and GP5.

10. The method according to any one of claims 1 to 6, wherein an increase in the amount of C7, ICAM, or QSOX1 above a predetermined level compared to the amount in a control sample indicates significant hepatic fibrosis, and / or a decrease in the amount of GP5 below a predetermined level compared to the amount in a control sample indicates significant hepatic fibrosis.

11. The method according to any one of claims 1 to 6, wherein the amount of C7, ICAM, or QSOX1 below a predetermined level indicates non-significant hepatic fibrosis, and / or the amount of GP5 above a predetermined level indicates non-significant hepatic fibrosis.

12. The method according to any one of claims 1 to 11, wherein the at least two protein biomarkers include C7, QSOX1, and GP5.

13. The method according to any one of claims 1 to 11, wherein the at least two protein biomarkers include C7, QSOX1, and ICAM.

14. The method according to any one of claims 1 to 13, wherein the at least two protein biomarkers include C7, QSOX1, ICAM, and GP5.

15. The method according to any one of claims 1 to 14, further comprising the step of measuring the amount of at least one additional protein selected from the group consisting of collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), and vascular cell adhesion protein 1 (VCAM1).

16. The method according to any one of claims 1 to 15, wherein the subject is pre-diagnosed to have non-alcoholic fatty liver disease (NAFLD).

17. The method according to any one of claims 1 to 15, wherein the subject is pre-diagnosed to have NASH.

18. The method according to any one of claims 1 to 17, wherein the subject has type 2 diabetes.

19. The method according to any one of claims 1 to 17, wherein the subject has at least one metabolic syndrome risk factor.

20. The method according to any one of claims 1 to 19, wherein the subject has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

21. The method according to any one of claims 1 to 20, wherein the measurement step is performed at the protein level.

22. The method according to any one of claims 1 to 20, wherein the measurement step is performed at the RNA level.

23. The method according to any one of claims 1 to 20, wherein the rule-in step takes into consideration the clinical parameters of the subject.

24. The method according to claim 23, wherein the clinical parameters are selected from the group consisting of weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HbA1c level, waist circumference, blood lipid level, and blood cholesterol level.

25. The aforementioned diagnosis is (a) Applying a predetermined mathematical function to the amounts of at least two proteins and calculating a score, (b) The method according to claim 24, comprising comparing the score with a predetermined reference value.

26. The method according to claim 24, wherein the mathematical function includes a weight of C7 that is heavier than the weights of QSOX1, ICAM and / or GP5.

27. The method according to claim 24, wherein the predetermined mathematical function is derived from a machine learning algorithm.

28. A method for treating subjects with liver disease or liver condition, (a) A step of defining the disease or condition in the subject in accordance with any one of claims 1 to 25, (b) A step of treating the subject with at least one treatment selected from the group consisting of semaglutide, ranifibranol, ocaliba, resmethylome, saroglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, and effluxifermin, thereby treating the subject Methods that include...

29. A method for identifying the stage of fibrosis in a patient or the stage of fibrosis in combination with NASH, or for diagnosing NASH or diagnosing NASH patients who are "at risk," (a) A step of determining the concentrations of at least two biomarkers in the patient's serum, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (b) A step of determining the stage of fibrosis in the patient, or diagnosing NASH, or diagnosing “risk” NASH, or diagnosing the stage of fibrosis in the patient in combination with NASH, based on the concentrations of the at least two biomarkers. Methods that include...

30. The aforementioned stages of fibrosis are Significant fibrosis (F≧2), Advanced fibrosis (F≧3), The method according to claim 29, wherein one of the following conditions is selected: cirrhosis of the liver (F=4).

31. The method according to claim 29, wherein five or fewer proteins are analyzed.

32. The method according to claim 29, wherein the subject is male and has an alcohol intake of less than 30 g per day, or the subject is female and has an alcohol intake of less than 20 g per day.

33. The step of determining the stage of fibrosis in the aforementioned patient is, (i) Inputting the concentration from step (a) into the algorithm to generate each stage of fibrosis, or the stage of fibrosis combined with NASH, or a score for NASH rule-in or NASH rule-in for "risky" NASH, (ii) Comparing the score for the stage of fibrosis, or the stage of fibrosis combined with NASH, or NASH or "risky" NASH, with a predetermined cutoff value for the stage of fibrosis, NASH, or "risky" NASH, (iii) The method of claim 29, comprising determining the stage of fibrosis or diagnosing NASH or diagnosing “at risk” NASH or diagnosing a stage of fibrosis in combination with NASH, based on a comparison of the score with a predetermined cutoff value.

34. The method according to claim 28, 29, or 33, comprising the step of determining the concentrations of C7 and QSOX1.

35. The method according to any one of claims 28, 29, or 33, comprising the step of determining the concentrations of C7 and GP5.

36. The method according to any one of claims 28, 29, or 33, comprising the step of determining the concentrations of C7 and ICAM1.

37. The method according to any one of claims 28, 29, or 33, comprising the step of determining the concentrations of C7, QSOX1, and GP5.

38. The method according to any one of claims 28, 29, or 33, comprising the step of determining the concentrations of C7, QSOX1, and ICAM1.

39. The method according to any one of claims 28 to 38, wherein the patient is pre-diagnosed to have non-alcoholic fatty liver disease (NAFLD).

40. The method according to any one of claims 28 to 39, wherein the patient is pre-diagnosed as having "at-risk" NASH.

41. The method according to any one of claims 28 to 40, wherein the patient has type 2 diabetes.

42. The method according to any one of claims 28 to 41, wherein the patient has at least one metabolic syndrome risk factor.

43. The method according to any one of claims 28 to 42, wherein the patient has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

44. The method according to any one of claims 28 to 43, comprising the step of determining the concentration of the biomarker by mass spectrometry, immunoassay, or aptamer-based assay.

45. The method according to claim 33, wherein the algorithm is a machine learning algorithm.

46. The method according to claim 45, wherein the machine learning algorithm is selected from the group consisting of neural networks, random forests, k-nearest neighbors, naive Bayes classifiers, k-means clustering, decision trees, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machines.

47. The method according to any one of claims 28 to 46, comprising the step of administering to the patient at least one treatment for hepatic fibrosis and / or fatty liver and / or hepatitis.

48. The method according to claim 46, wherein the at least one treatment is selected from one or more of the following: semaglutide, ranifibranol, ocaliba, resmethylome, saroglitazal, cotadutide, VK2809, icosubtate, PXL065, bio89-100, HM15211, MSDC-0602K, Tern101+501combo, GSK4532990, HepaStem, ALN-HSD, and effluxifermin.

49. A method for monitoring the effectiveness of treatment for liver fibrosis in combination with or without NASH, (a) A step of administering the above treatment to the subject, (b) A step of measuring the levels of at least two biomarkers in a blood sample derived from the subject, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (c) A method comprising the step of comparing the levels of the at least two biomarkers with the levels of the at least two biomarkers obtained before the administration step, wherein the effectiveness of the treatment is monitored by a change in the levels of the at least two biomarkers.

50. A method for monitoring the progression of liver fibrosis in the target patient, (a) A step of measuring the levels of at least two biomarkers in a blood sample derived from the subject at a first time point, wherein the at least two biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), collectin-10 (COL10), collectin-11 (COLEC11) isoform 10, selglycine (SRGN), SPARC, adhesion G protein-coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid-instability subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1), and vascular cell adhesion protein 1 (VCAM1), (b) A step of measuring the levels of at least two biomarkers in the blood sample derived from the subject at a later point in time, (c) A method comprising the step of comparing the levels of the at least two biomarkers at a first time point and a second time point, wherein a change in the levels of the at least two biomarkers indicates progression or regression of the liver fibrosis of the subject.

51. The method according to claim 49 or 50, wherein the at least two protein biomarkers include C7 and QSOX1.

52. The method according to claim 49 or 50, wherein the at least two protein biomarkers include C7 and ICAM.

53. The method according to claim 49 or 50, wherein the at least two protein biomarkers include C7 and GP5.

54. The method according to claim 49 or 50, wherein at least one of the protein biomarkers comprises C7, QSOX1, and GP5.

55. The method according to claim 49 or 50, wherein at least one of the protein biomarkers comprises C7, QSOX1, and ICAM.

56. The method according to claim 49 or 50, wherein the at least two protein biomarkers include C7, QSOX1, ICAM, and GP5.

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