Liver fibrosis, nash and "at-risk" nash biomarkers and methods

Abstract

Provided herein are biomarkers for diagnosing fibrosis stage in patients with or "at-risk" of non-alcoholic fatty liver disease. Also provided herein are methods for treating fibrosis.

Description

[0001] LIVER FIBROSIS, NASH AND “AT-RISK” NASH BIOMARKERS AND METHODS

[0002] RELATED APPLICATIONS SECTION

[0003] This application claims the benefit of priority of U.S. Provisional Patent Application No. 63 / 522,282, filed on June 21, 2023, the contents of which are incorporated herein by reference in their entirety.

[0004] FIELD AND BACKGROUND OF THE INVENTION

[0005] Provided herein are methods for diagnosing fibrosis stage, NASH, and / or “at-risk” NASH in patients based on the abundance of specific biomarkers in blood.

[0006] Non-alcoholic fatty liver disease (NAFLD) is a major cause of liver illness worldwide, with a global prevalence of up to 30%. It ranges from simple steatosis to non-alcoholic steatohepatitis (NASH), involving inflammation and liver cell damage. The increasing global incidence of NAFLD is closely linked to obesity, type 2 diabetes mellitus (T2D), and metabolic syndrome. A major complication of NASH is liver fibrosis, which is caused by chronic inflammation. Fibrosis ultimately leads to irreversible scarring of the liver tissue, thus poses significant liver-related risks, including advanced fibrosis, cirrhosis, and hepatocellular carcinoma. Lifestyle modifications, pharmacological therapies, and regular monitoring are key to NAFLD management, addressing its impact on liver health and overall well-being.

[0007] The gold standard for diagnosing NAFLD and assessing liver fibrosis is liver biopsy. However, liver biopsy is invasive, and, thus, is risky. Thus, there is a need in the art for non- invasive methods of diagnosing NAFLD and staging of liver fibrosis.

[0008] There is also a need in the art for methods of differentiating non-alcoholic steatohepatitis, non-alcoholic steatohepatitis (NASH), and “at-risk” NASH (a combined measure of liver fibrosis and NAFLD activity score); the latter is a severe and rapidly progressive form of NAFLD, typically leading to cirrhosis and liver transplantation. It is essential to detect NAFLD early, accurately stage fibrosis and NASH effective management and treatment of the disease.

[0009] Currently, there are several methods for non-invasive diagnosis of liver fibrosis stage, based on serological markers and / or imaging tests. The latter, are typically costly and demand extensive training of a health specialist, thus cannot be implemented for population-wide screening. Developing cost-effective, non-invasive, accurate methods to accurately identify liver fibrosis stage, NASH, and NAFLD activity score, is a significant unmet need; specifically to identify patients with "at-risk" NASH, who may benefit from upcoming NASH treatments. Finally, it is essential to identify reliable markers to identify liver fibrosis stage and NASH, as these conditions provide prognostics for liver- and cardiovascular-related complications. Accurate risk stratification would enable targeted interventions and monitoring of high-risk individuals, ensuring timely and appropriate management. Addressing these unmet needs in NAFLD diagnosis would improve the early detection, risk stratification, and monitoring of the disease, leading to more effective management strategies and improved patient outcomes.

[0010] SUMMARY OF THE INVENTION

[0011] According to an aspect of some embodiments of the present invention there is provided a method of diagnosing a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with nonalcoholic steatohepatitis (NASH) and “at-risk” NASH of a subject having an alcohol consumption below 30 g a day comprising:

[0012] (a) measuring in a blood sample of the subject an 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); and

[0013] (b) ruling-in the liver disease or condition on the basis of the amount of the at least two protein biomarkers.

[0014] According to some embodiments of the invention, no more than 5 proteins are analyzed.

[0015] According to some embodiments of the invention, the liver condition is significant fibrosis.

[0016] According to some embodiments of the invention, the liver condition is significant fibrosis in combination with NASH.

[0017] According to some embodiments of the invention, the liver condition is advanced fibrosis.

[0018] According to some embodiments of the invention, the liver condition is cirrhosis.

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

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

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

[0022] According to some embodiments of the invention, the increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

[0023] According to some embodiments of the invention, the amount of C7, ICAM or QSOX1 below a predetermined level is indicative of non- significant liver fibrosis and / or the amount of GP5 above a predetermined level is indicative of non- significant liver fibrosis.

[0024] According to some embodiments of the invention, the at least two protein biomarkers comprises C7, QSOX1 and GP5.

[0025] According to some embodiments of the invention, the at least two protein biomarkers comprises C7, QSOX1 and ICAM.

[0026] According to some embodiments of the invention, the at least two protein biomarkers comprises C7, QSOX1, ICAM and GP5.

[0027] According to some embodiments of the invention, the method further comprises measuring an amount of at least one additional protein selected from the group consisting of: Collectin-10 (COLIO), collectin-11 (COLECI 1) 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).

[0028] According to some embodiments of the invention, the subject is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

[0029] According to some embodiments of the invention, the subject is pre-diagnosed as having NASH.

[0030] According to some embodiments of the invention, the subject has type 2 Diabetes.

[0031] According to some embodiments of the invention, the subject has at least one metabolic syndrome risk factor.

[0032] According to some embodiments of the invention, the subject has an intermediate FIB- 4 score (1.30 < FIB-4 < 2.67).

[0033] According to some embodiments of the invention, the measuring is effected on the protein level.

[0034] According to some embodiments of the invention, the measuring is effected on the RNA level.

[0035] According to some embodiments of the invention, the ruling-in takes into account a clinical parameter of the subject. According to some embodiments of the invention, the clinical parameter is 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.

[0036] According to some embodiments of the invention, the diagnosing comprises:

[0037] (a) applying a pre-determined mathematical function on the amount of the at least two proteins to compute a score; and

[0038] (b) comparing the score to a predetermined reference value.

[0039] According to some embodiments of the invention, the mathematical function comprises a heavier weight of C7 as compared to a weight of QSOX1, ICAM and / or GP5.

[0040] According to some embodiments of the invention, the pre-determined mathematical function is derived from a machine learning algorithm.

[0041] According to an aspect of some embodiments of the present invention there is provided a method of treating a subject having a liver disease or condition comprising:

[0042] (a) ruling in the disease or condition in the subject according to the method described herein; and

[0043] (b) treating the subject with at least one treatment selected from the group consisting of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Ternl01+501 combo, GSK4532990, HepaStem, ALN-HSD and Efruxifermin, thereby treating the subject.

[0044] According to an aspect of some embodiments of the present invention there is provided a method of identifying a stage of fibrosis in a patient or stage of fibrosis in combination with NASH or diagnosing NASH or diagnosing “at-risk” NASH patients, the method comprising:

[0045] (a) determining the concentration of at least two biomarkers in serum from the patient, wherein the at least two biomarkers are selected from the group consisting of: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VC AMI); and (b) determining the stage of fibrosis in the patient or diagnose NASH or diagnosing “at- risk” NASH or diagnosing stage of fibrosis in combination with NASH, patients based on the concentration of the at least two biomarkers.

[0046] According to some embodiments of the invention, the stage of fibrosis is selected from any one of: significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4),

[0047] According to some embodiments of the invention, no more than 5 proteins are analyzed.

[0048] According to some embodiments of the invention, the subject is male and has an alcohol consumption below 30 g a day or the subject is female and has an alcohol consumption below 20 g a day.

[0049] According to some embodiments of the invention, the determining the stage of fibrosis in the patient comprises:

[0050] (i) inputting the concentration from step (a) into an algorithm to generate a score for each stage of fibrosis, or stage of fibrosis in combination with NASH, or for ruling in NASH or for ruling in “at-risk” NASH

[0051] (ii) comparing the score for a stage of fibrosis or stage of fibrosis in combination with NASH or NASH or for “at-risk” NASH to a predetermined cutoff value for the stage of fibrosis, NASH or for “at-risk” NASH; and

[0052] (iii) determining the stage of fibrosis or diagnose NASH or diagnose “at-risk” NASH or diagnose stage of fibrosis in combination with NASH based on comparison between the score and a predetermined cutoff value.

[0053] According to some embodiments of the invention, the method comprises determining the concentration of C7 and QSOX1.

[0054] According to some embodiments of the invention, the method comprises determining the concentration of C7 and GP5.

[0055] According to some embodiments of the invention, the method comprises determining the concentration of C7 and ICAM1.

[0056] According to some embodiments of the invention, the method comprises determining the concentration of C7, QSOX1, and GP5.

[0057] According to some embodiments of the invention, the method comprises determining the concentration of C7, QSOX1, and ICAM1. According to some embodiments of the invention, the patient is prediagnosed as having nonalcoholic fatty liver disease (NAFLD).

[0058] According to some embodiments of the invention, the patient is pre-diagnosed as being “at-risk” NASH.

[0059] According to some embodiments of the invention, the patient has Type 2 Diabetes.

[0060] According to some embodiments of the invention, the patient has at least one metabolic syndrome risk factor.

[0061] According to some embodiments of the invention, the patient has an intermediate FIB- 4 score (1.30 < FIB-4 < 2.67).

[0062] According to some embodiments of the invention, the method comprises determining the biomarker concentrations by mass spectrometry, immunoassay or aptamer-based assay.

[0063] According to some embodiments of the invention, the algorithm is a machine learning algorithm.

[0064] According to some embodiments of the invention, the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine.

[0065] According to some embodiments of the invention, the method comprises administering at least one treatment for liver fibrosis and / or liver steatosis and / or liver inflammation to the patient.

[0066] According to some embodiments of the invention, the at least one treatment is selected from one or more of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Teml01+501 combo, GSK4532990, HepaStem, ALN-HSD, Efruxifermin.

[0067] According to an aspect of some embodiments of the present invention there is provided a method for monitoring the effectiveness of treatment for liver fibrosis with or without combination of NASH of a subject comprising

[0068] (a) administering the treatment to the subject;

[0069] (b) measuring the level of at least two biomarkers in a blood sample from the patient, wherein the at least two biomarkers are selected from the group consisting of: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VC AMI); and

[0070] (c) comparing the level of the at least two biomarkers to a level of the at least two biomarkers obtained prior to the administering, wherein the effectiveness of the treatment is monitored by a change in the level of the at least two biomarkers.

[0071] According to an aspect of some embodiments of the present invention there is provided a method for monitoring progression of a liver fibrosis of a subject comprising:

[0072] (a) measuring, at a first time point, the level of at least two biomarkers in a blood sample 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 (COLIO), collectin- 11 (COLECI 1) 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

[0073] (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 (VC AMI);

[0074] (b) measuring, at a later time point, the level of at least two biomarkers in a blood sample from the subject, and

[0075] (c) comparing the level of the at least two biomarkers at the first time point and at the second time point, wherein a change in the level of the at least two biomarkers is indicative of the progression or regression of the liver fibrosis of the subject.

[0076] According to some embodiments of the invention, the at least two protein biomarkers comprise C7 and QSOX1.

[0077] According to some embodiments of the invention, the at least two protein biomarkers comprise C7 and ICAM.

[0078] According to some embodiments of the invention, the at least two protein biomarkers comprise C7 and GP5.

[0079] According to some embodiments of the invention, the at least one protein biomarker comprises C7, QSOX1 and GP5.

[0080] According to some embodiments of the invention, the at least one protein biomarker comprises C7, QSOX1 and ICAM.

[0081] According to some embodiments of the invention, the at least two protein biomarkers comprises C7, QSOX1, ICAM and GP5. According to another aspect of the invention there is provided a method of identifying a stage of fibrosis in a patient and determining if the patient has nonalcoholic steatohepatitis

[0082] (NASH) or at-risk NASH, the method comprising:

[0083] (a) determining the concentration of at least two biomarkers in serum from the patient, wherein the at least two biomarkers are selected from the group consisting of: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), and vascular cell adhesion protein 1 (VCAM1); and

[0084] (b) determining the stage of fibrosis in the patient and determining if a patient has NASH or is at-risk NASH based on the concentration of the at least two biomarkers; wherein the stage of fibrosis is selected from any one of significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4), NASH, or at-risk NASH (F> 2 and a NAFLD Activity Score (NAS) > 4).

[0085] According to another aspect of the invention there is provided a method of determining the stage of fibrosis in the patient based on the concentration comprises:

[0086] (i) inputting the concentration from step (a) into an algorithm to generate a score for each stage of fibrosis, NASH, and for “at-risk” NASH (F> 2 and a NAFLD Activity Score (NAS) > 4);

[0087] (ii) comparing the score for a stage of fibrosis, NASH, and for “at-risk” NASH) to a predetermined cutoff value for the stage of fibrosis, NASH, and for “at-risk” NASH; and

[0088] (iii) determining the stage of fibrosis and determining NASH and “at-risk” NASH based on comparison between the score and a predetermined cutoff value, wherein the predetermined cutoff value is a value from 0 to 1.

[0089] According to some embodiments of the invention, the patient has a NAS > 4.

[0090] According to some embodiments of the invention, the patient has a NAS < 4.

[0091] According to some embodiments of the invention, the method comprises determining the concentration of C7 and QSOXL

[0092] According to some embodiments of the invention, the method comprises determining the concentration of C7 and GP5.

[0093] According to some embodiments of the invention, the method comprises determining the concentration of C7, QSOX1, and GP5. According to some embodiments of the invention, the pre-determined cutoff value for “at-risk” NASH is from about 0.1 to about 0.95, including all values and subranges therebetween, the pre-determined cutoff value for significant fibrosis is from about 0.1 to about 0.95, including all values and subranges therebetween, the pre-determined cutoff value for advanced fibrosis is about 0.1 to about 0.95, including all values and subranges therebetween, and wherein the pre-determined cutoff value for cirrhosis is about 0.1 to about 0.95, including all values and subranges therebetween.

[0094] According to some embodiments of the invention, the pre-determined cutoff value for “at-risk” NASH is 0.25.

[0095] According to some embodiments of the invention, the pre-determined cutoff value for significant fibrosis is 0.21, the pre-determined cutoff value for advanced fibrosis is 0.25, and the pre-determined cutoff value for cirrhosis is 0.52.

[0096] According to some embodiments of the invention, the pre-determined cutoff value for significant fibrosis is 0.13, the pre-determined cutoff value for advanced fibrosis is 0.17, and the pre-determined cutoff value for cirrhosis is 0.43.

[0097] According to some embodiments of the invention, the patient has nonalcoholic fatty liver disease (NAFLD).

[0098] According to some embodiments of the invention, the patient is with high NAS and considered “at-risk” NASH.

[0099] According to some embodiments of the invention, the patient has Type 2 Diabetes.

[0100] According to some embodiments of the invention, the patient has an intermediate FIB- 4 score.

[0101] According to some embodiments of the invention, the biomarker is protein.

[0102] According to some embodiments of the invention, the method comprises determining the biomarker concentrations by mass spectrometry, immunoassay or aptamer-based assay. According to some embodiments of the invention, the algorithm is a machine learning algorithm.

[0103] According to some embodiments of the invention, the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine.

[0104] According to some embodiments of the invention, the method comprises administering at least one treatment for fibrosis to the patient. According to some embodiments of the invention, the at least one treatment for fibrosis and “at-risk” NASH is selected from one or more of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Teml01+501 combo, GSK4532990, HepaStem, ALN-HSD, Efruxifermin or any other drug currently approved for other indication and / or in development for treating fibrosis and / or inflammation.

[0105] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0106] BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0107] Figs. 1A-G show the protein intensity of the biomarkers as measured by Mass spectrometry, COLIO (Fig. 1A), C7 (Fig. IB), QSOX1 (Fig. 1C), VCAM1 (Fig. ID), A2M (Fig. IE), GP5 (Fig. IF), and ICAM1 (Fig. 1G) by fibrosis stage.

[0108] Fig. 1H shows fibrosis stage by patient age.

[0109] Figs. 2A-J shows that diagnosing "at-risk” NASH by a two component biomarker signatures comprising of (a) C7 and QSOX1 and (b) C7 and ICAM1 were superior to FIB -4 score, in the discovery cohort (train and test set, and full cohort) and in validation cohorts 1 and 2. Fig. 2A-E shows superiority of C7 and QSOX1 model for diagnosing "at-risk” NASH in the discovery cohort (train and test set, and full cohort) and in validation cohorts 1 and 2. Fig. 2F-J shows superiority of C7 and ICAM1 model for diagnosing "at-risk” NASH in the discovery cohort (train and test set, and full cohort) and in validation cohorts 1 and 2.

[0110] Figs. 3A-G shows that determining fibrosis stage by a two component biomarker signatures comprising of (a) C7, and GP5, and (b) C7 and ICAM (referred to as the “MS-LFS model” or “Model”) were superior to determining fibrosis stage by the FIB-4 score, In the discovery cohort (test set and full cohort) and in validation cohorts 1-3. Fig. 3A-3B shows superiority of the models for diagnosing significant fibrosis. Fig. 3C-3D shows superiority of the models for diagnosing advanced fibrosis. Fig. 3E-3F shows superiority of the models for diagnosing cirrhosis. Fig. 3G shows superiority of the C7 and GP5 model for diagnosing probable NASH cirrhosis in the real- world patient cohort Figs. 4A-K shows that diagnosing "at-risk” and determining fibrosis stage by a three component biomarker signatures comprising of (a) C7, QSOX1, and GP5, and (b) C7, QSOX1 and ICAM1 (referred to as the “MS-LFS model” or “Model”) were superior to determining fibrosis stage by the FIB-4 score. In the discovery cohort (test set and full cohort) and in validation cohorts 1-3. Fig. 4A-4B shows superiority of the MS-LFS models for diagnosing “at-risk” NASH. Fig. 4C-4D shows superiority of the MS-LFS models for diagnosing “at-risk” NASH NIMBLE. Fig. 4E-4F shows superiority of the MS-LFS models for diagnosing significant fibrosis. Fig. 4G-4H shows superiority of the MS-LFS models for diagnosing advanced fibrosis. Fig. 4L4J shows superiority of the MS-LFS models for diagnosing cirrhosis. Fig. 4K shows superiority of the C7, QSOX1, and GP5 model for diagnosing probable NASH cirrhosis cirrhosis in the real- world patient cohort.

[0111] Figs. 5A-B show that a two component biomarker signatures comprising of (a) C7 and QSOX1, and (b) C7 and ICAM1, described in Example 2 outperforms other commonly used clinical scores (FIB-4, BARD, and NFS) and perform similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4), for diagnosing “at- risk” NASH. Fig. 5A shows that the models are superior to other scoring systems for diagnosing patients with an intermediate FIB-4 score. Fig. 5B shows the superiority of the models for diagnosing “at-risk” NASH in patients with Type 2 Diabetes (T2D).

[0112] Figs. 6A-B show that the MS-LFS model described in Example 4 is significant superior to FIB-4, BARD, and NFS scoring systems for diagnosing fibrosis stage. Fig. 6A shows that the MS-LFS model is superior to other scoring systems for diagnosing patients with an intermediate FIB-4 score. Fig. 6B shows the superiority of the model for diagnosing fibrosis stage in patients with Type 2 Diabetes (T2D).

[0113] Figs. 7A-B show that the MS-LFS models described in Example 4 outperforms other commonly used clinical scores (FIB-4, BARD and NFS) and perform similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4), for diagnosing at risk” NASH” and fibrosis stages. Fig. 7A shows that the MS-LFS models are superior to other scoring systems for diagnosing patients with an intermediate FIB-4 score. Fig. 7B shows the superiority of the models for diagnosing fibrosis stage in patients with Type 2 Diabetes (T2D).

[0114] Fig. 8 show that the MS-LFS model described in Example 4, C7, QSOX1 and ICAM1, outperforms other commonly used clinical scores (FIB-4, ELF and FibroTest) for monitoring patients’ fibrosis stage. DETAILED DESCRIPTION OF THE INVENTION

[0115] The present disclosure provides methods of diagnosing a patient with a fibrosis stage, NASH and “at-risk” NASH. The methods provided herein are more sensitive and specific than known methods of diagnosing patients with a stage of fibrosis (see Figs. 3A-3G, 4E-4K, 6A- 6B, 7A-7B and Tables 5 and 6a) and “at-risk” NASH (Figs. 2, 4A-4D and 5A-5B, 7A-7B and Table 4 and 6B). These methods are also noninvasive and thus less risky to the patient. The methods allow for identification of fibrosis, NASH and “at-risk” NASH patients, enabling timely treatment and improve patient clinical management.

[0116] I. Definitions

[0117] The indefinite articles “a” and “an” and the definite article “the” are intended to include both the singular and the plural, unless the context in which they are used clearly indicates otherwise.

[0118] “At least one” and “one or more” are used interchangeably to mean that the article may include one or more than one of the listed elements.

[0119] As used herein, the term “about” refers to plus or minus 10% of the referenced number unless otherwise stated or otherwise evident by the context, and except where such a range would exceed 100 % of a possible value, or fall below 0 % of a possible value.

[0120] The term “biomarker” refers to a protein, metabolite, or a lipid that serves as an indicator for a disease / condition.

[0121] The term "cutoff value" refers to a numerical value used to distinguish between two or more stages of fibrosis, diagnose NASH and “at-risk” NASH.

[0122] The term “FIB-4” score refers to a score calculated according to the following formula: (Age x AST Level) / ((Platelet Count (109 / L)) x (the square root of ALT Level)). “Age” in the formula is the patient’ s age in years. AST Level refers to the level of aspartate aminotransferase in serum of U / L. ALT Level refers to the level of alanine transaminase in serum of U / L. Patients who are in an “intermediate range of FIB-4” exhibit a FIB-4 score that is greater than 1.3 and less than 2.67.

[0123] The term “NAFLD Activity Score (NAS)” refers to a score calculated by adding together the individual scores of 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 the following document, which is incorporated by reference herein in its entirety: Kleiner DE et al. (2005) Hepatology 41:1313-1321. II. Biomarkers for Diagnosing Fibrosis Stage, NASH and “at-risk” NASH patients

[0124] Provided herein are biomarkers for diagnosing fibrosis stage, NASH, and “at-risk” NASH. In embodiments, the biomarkers are used to diagnose fibrosis stage and identify patients with NASH and “at-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 high NAFLD activity score (> 4) or NASH. The fibrosis stage indicates the extent of liver scarring. The classification of fibrosis stages in non-alcoholic fatty liver disease (NAFLD) is as follows. The fibrosis stage may be staged according to the METAVIR or Ishak or NASH Clinical Research Network (CRN) classification system scoring systems, which categorize fibrosis levels into several stages to indicate the extent of liver scarring. Unless otherwise stated, particular fibrosis stages described herein refer to the stages identified by the METAVIR scoring system or NASH Clinical Research Network (CRN) classification system. According to this system, a fibrosis stage (“F”) of 0 (also referred to as “FO”) indicates no fibrosis or scarring. A fibrosis stage of 1 (also referred to as “Fl”) represents minimal fibrosis limited to the portal areas. A fibrosis stage of 2 (also referred to as “F2”) indicates increased fibrosis extending beyond the portal areas. A fibrosis stage of 3 (also referred to as “F3”) denotes significant fibrosis with numerous septa but without cirrhosis. A fibrosis stage of 4 (also referred to as “F4”) represents cirrhosis with extensive scarring and liver dysfunction. In NAFLD, most individuals have little to no fibrosis (F0-F1) or mild fibrosis (F1-F2). These early stages are generally more common and often present in a significant proportion of NAFLD patients. As the disease progresses, a smaller percentage of individuals may develop significant fibrosis (>=F2), indicating increasing liver scarring. Advanced fibrosis (>=F3) and cirrhosis (F4) are considered the more severe stages of fibrosis in NAFLD. While a smaller proportion of individuals with NAFLD may progress to advanced fibrosis, the risk increases with certain factors such as older age, obesity, diabetes, and presence of NASH. Fibrosis progression in NAFLD is not linear and can be influenced by various factors, including lifestyle modifications, treatment interventions, and management of underlying metabolic conditions.

[0125] In embodiments, the biomarkers are used to diagnose fibrosis stage and / or identify NASH and “at-risk” NASH patients with non-alcoholic fatty liver disease. In embodiments, the biomarkers for diagnosing NASH and / or fibrosis stage can be utilized to identify individuals at higher risk of fibrosis progression, with / without high NAFLD activity score, and to provide appropriate care and intervention. In embodiments, the biomarkers are selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1). Additionally, or alternatively, the biomarker may be Intercellular adhesion molecule- 1 (ICAM-1). In embodiments, from 2 to 10 biomarkers, from 3 to 10 biomarkers, from 4 to 10 biomarkers, from 5 to 10 biomarkers, from 6 to 10 biomarkers, from 7 to 10 biomarkers, from 2 to 5 biomarkers, from 3 to 5 biomarkers, or from 4 to 5 biomarkers are used to diagnose fibrosis stage, NASH and “at-risk” NASH. In embodiments, one or more, two or more, or three or more biomarkers are used to diagnose fibrosis stage, NASH and “at-risk” NASH. In embodiments, C7 and QSOX1, C7 and ICAM1, are used to diagnose “at-risk” NASH patients, C7 and GP5, and C7 and ICAM1 are used to diagnose fibrosis stage. In embodiments, C7, QSOX1, and GP5, and C7, QSOX1 and ICAM1 are used to diagnose “at-risk” NASH patients and / or determine fibrosis stage.

[0126] III. Methods of Diagnosing Fibrosis Stage, NASH and “at-risk” NASH

[0127] In embodiments, provided herein are methods of identifying a stage of fibrosis with / without NASH and / or NAFLD activity score > 4 in a patient, the method comprising: (a) determining the concentration of at least two biomarkers in serum from the patient, wherein the at least two biomarkers are 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 concentration of the at least two biomarkers, and identify NASH and “at-risk” NASH patients. In embodiments, the stage of fibrosis is F0, Fl, F2, F3, or F4. In embodiments, “at- risk” NASH patients are defined as having biopsy-confirmed NASH, a NAFLD activity score (NAS) > 4 with at least 1 point in each one of the NAS components, and F > 2 with exclusion of cirrhotic patients. “At risk” NASH NIMBLE definition includes also exclusion of patients with F0-F1 and NAS > 4, F > 2 and NAS < 4, and cirrhotic patients. The stages of fibrosis are described in Section II of this document.

[0128] The subject being diagnosed is typically a human subject.

[0129] In one embodiment, the subject is male. In another embodiment the subject is female.

[0130] In one embodiment, the subject is an adult male.

[0131] Typically, the subject has an alcohol consumption below 30 g a day.

[0132] In another embodiment, the subject is a female subject and has an alcohol consumption below 20 g a day.

[0133] According to embodiments of this aspect of the invention, the subject is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

[0134] According to other embodiments of this aspect of the invention, the subject is prediagnosed as having NASH.

[0135] Alternatively, or additionally, the subject is pre-diagnosed as having type 2 Diabetes.

[0136] Alternatively, the subject may have at least one metabolic syndrome risk factor.

[0137] Exemplary metabolic syndrome risk factors include, but are not limited to obesity and / or high waist circumference (e.g. BMI>30, and / or of waist circumference > 40 inches in men and > 35 inches in women), elevated blood glucose levels and / or T2D (e.g. fasting blood glucose > 100 mg / dL or >125 and / or HbAlC>6% or HbAlC>6.5%), hypertriglyceridemia and / or low levels of HDL cholesterol (e.g. either: Triglycerides>150 mg / dl or HDL: < 40mg / dl in men and <50mg / dl in women), hypertension (e.g. systolic blood pressure > 130 mmHg and / or diastolic blood pressure > 85 mmHg).

[0138] The age of the subject may be between 40-80 years old.

[0139] In embodiments, the method is for ruling in significant fibrosis based on the level 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).

[0140] An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

[0141] Alternatively, or additionally, when the amount of C7, ICAM or QSOX1 is below a predetermined level, it is indicative of non- significant liver fibrosis and / or when the amount of GP5 is above a predetermined level, it is indicative of non- significant liver fibrosis.

[0142] In embodiments, the method is for ruling in significant fibrosis in combination with NASH based on the level 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). An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis in combination with NASH and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis in combination with NASH.

[0143] In embodiments, the method is for ruling in “at-risk” NASH based on the level 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).

[0144] An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of “at-risk “NASH and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of “at-risk” NASH.

[0145] In embodiments, the method is for ruling in advanced fibrosis based on the level 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).

[0146] An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of advanced liver fibrosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of advanced liver fibrosis.

[0147] In embodiments, the method is for ruling in cirrhosis based on the level 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).

[0148] An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of cirrhosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of cirrhosis.

[0149] Additional protein markers may be used in order to stage the fibrosis (e.g. rule in significant fibrosis) including but not limited to:

[0150] Collectin- 10 (COLIO), collectin- 11 (COLECI 1) 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).

[0151] In one embodiment, the predetermined level (i.e. reference value is the amount (i.e. level) of biomarkers in a control sample derived from one or more subjects who do not have liver fibrosis and / or not suspected of having a liver fibrosis (e.g., healthy individuals). In a further embodiment, such subjects are monitored and / or periodically retested for a diagnostically relevant period of time (“longitudinal studies”) following such test to verify continued absence of fibrosis. Such period of time may be one week, two weeks, two to five months, five months, five to ten months, ten months, or ten or more months from the initial testing date for determination of the reference value. Furthermore, retrospective measurement of biomarkers in properly banked historical subject samples may be used in establishing these reference values, thus shortening the study time required.

[0152] A predetermined level can also comprise the amounts of biomarkers derived from subjects who show an improvement as a result of treatments and / or therapies for the fibrosis. A predetermined level can also comprise the amounts of biomarkers derived from subjects who have confirmed non- significant fibrosis by known techniques.

[0153] An example of a fibrosis reference value index value is the mean or median concentrations of that biomarker in a statistically significant number of subjects having been diagnosed as having non-significant fibrosis.

[0154] In another embodiment, the predetermined level is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of biomarkers from one or more subjects who do not have significant fibrosis. A baseline value can also comprise the amounts of biomarkers in a sample derived from a subject who has shown an improvement in treatments or therapies for the fibrosis. In this embodiment, to make comparisons to the subject-derived sample, the amounts of biomarkers are similarly calculated and compared to the index value. Optionally, subjects identified as having a significant fibrosis, are chosen to receive a therapeutic regimen to slow the progression or eliminate the fibrosis.

[0155] Additionally, the amount of the biomarker can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values. The “normal control level” means the level of one or more biomarkers or combined biomarker indices typically found in a subject not suffering from significant fibrosis. Such normal control level and cutoff points may vary based on whether a biomarker is used alone or in a formula combining with other biomarkers into an index. Alternatively, the normal control level can be a database of biomarker patterns from previously tested subjects.

[0156] Some protein biomarkers may exhibit trends that depends on the patient age (e.g. the population baseline may rise or fall as a function of age). One can use an 'Age dependent normalization or stratification' scheme to adjust for age related differences. Performing age dependent normalization, stratification or distinct mathematical formulas can be used to improve the accuracy of biomarkers for differentiating between different types of infections. For example, one skilled in the art can generate a function that fits the population mean levels of each biomarker as function of age and use it to normalize the biomarker of individual subjects levels across different ages. Another example is to stratify subjects according to their age and determine age specific cutoff values or index values for each age group independently.

[0157] Subjects may be stratified according to additional parameters including but not limited to weight (e.g., BMI), age, HDL cholesterol level, LDL cholesterol level, liver enzymes (e.g. ALT level, AST level), blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level.

[0158] According to a specific embodiment, when the concentration level of QSOX1 is about 1.19 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0159] According to a specific embodiment, when the concentration level of QSOX1 is about

[0160] 1.23 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0161] According to a specific embodiment, when the concentration level of QSOX1 is about 1.28 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0162] According to a specific embodiment, when the concentration level of ICAM1 is about

[0163] 1.23 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0164] According to a specific embodiment, when the concentration level of ICAM1 is about

[0165] 1.24 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0166] According to a specific embodiment, when the concentration level of ICAM1 is about 1.27 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis. According to a specific embodiment, when the concentration level of C7 is about 1.57 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0167] According to a specific embodiment, when the concentration level of C7 is about 1.7 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0168] According to a specific embodiment, when the concentration level of C7 is about 2 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0169] According to a specific embodiment, when the concentration level of GP5 is about 0.81 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0170] According to a specific embodiment, when the concentration level of GP5 is about 0.74 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0171] According to a specific embodiment, when the concentration level of GP5 is about 0.54 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0172] In one embodiment, ruling in significant fibrosis indicates that the fibrosis of the subject is at a stage beyond mild fibrosis (F0-F1).

[0173] In another embodiment, ruling in significant fibrosis rules out that the fibrosis is beyond mild fibrosis but has not reached the stage of advanced fibrosis.

[0174] In still another embodiment, ruling in significant fibrosis rules out that the fibrosis is beyond mild fibrosis but has not reached the stage of cirrhosis.

[0175] In one embodiment, when the concentration of QSOX1 is 1.22 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at-risk” NASH.

[0176] In one embodiment, when the concentration of ICAM is 1.33 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at-risk” NASH.

[0177] In one embodiment, when the concentration of C7 is 1.35 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at-risk” NASH. According to a specific embodiment, when the concentration level of GP5 is about 0.84 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is being “at risk” NASH.

[0178] In embodiments, the concentration of each biomarker is determined by immunoassay or mass spectrometry. In embodiments, the concentration of each biomarker is measured in arbitrary units as compared to a standardized control sample. In embodiments, the concentration of each biomarker is measured in absolute concentrations.

[0179] Blood samples can be obtained under standard conditions. In embodiments, the serum is stored for from about 1 day to about 5 years before the concentration of each biomarker is measured.

[0180] In one embodiment, the blood sample comprises serum.

[0181] In another embodiment, the blood sample comprises plasma.

[0182] In still another embodiment, the blood sample is whole blood.

[0183] In embodiments, the serum is stored at a temperature ranging from about -80 °C for up to 5 years and about 30 °C for up to 2 days or even 7 days.

[0184] In embodiments, the method comprises (i) inputting the concentration from step (a) into an algorithm to generate a score; (ii) comparing the score to a predetermined cutoff value; and (iii) determining the stage of fibrosis based on comparison between the score and a predetermined cutoff value. In embodiments, the algorithm produces a score from 0 to 1 for each of significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4), and NASH or at-risk of nonalcoholic steatohepatitis (NASH) (F> 2 with NAS > 4, (e.g. with exclusion of cirrhotic patients, and, with / without exclusion of patients with F0-F1 and NAS > 4, and F > 2 and NAS < 4).

[0185] In embodiments, if the score generated by the algorithm is higher than the predetermined cutoff value, the patient is diagnosed with the fibrosis stage. For example, if the predetermined cutoff value for cirrhosis is 0.8, a patient with a score of greater than 0.8 is diagnosed with cirrhosis. In embodiments, a patient with a later fibrosis stage also has an earlier fibrosis stage. For example, a patient with advanced fibrosis (F3) also has significant fibrosis (F2). In one embodiment, the algorithm uses the concentration of C7 and at least one of QSOX1, ICAM and / or GP5 to determine a score.

[0186] According to a particular embodiment, the weight of C7 in the algorithm is heavier than the weight of QSOX1, ICAM and / or GP5.

[0187] According to a particular embodiment, the following algorithm is used to generate a score for ruling in “at-risk” NASH. P=(l-exp(F)) / exp(F), where (a) F = 0.849*C7 + 0.745*QSOX1 - 0.124; or (b) F = 0.974*C7

[0188] + 0.513*ICAMl - 0.13.

[0189] According to a particular embodiment, the following algorithm is used to generate a score for ruling in significant fibrosis, advanced fibrosis and cirrhosis respectively.

[0190] P=(l-exp(F)) / exp(F), where (a) F = 1.039*C7 - 0.467*GP5 - 0.161, and (b) F = 0.974*C7 + 0.513*ICAMl - 0.130.

[0191] According to a particular embodiment, the following algorithm is used to generate a score for ruling in “at-risk” NASH, significant fibrosis, advanced fibrosis and cirrhosis. P=(l-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*ICAMl + 0.657*QSOX1 - 0.101.

[0192] The predetermined cutoff for the predictor may be set according to the required sensitivity and specificity of the predictor. In embodiments, the pre-determined cutoff value for at risk of NASH is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for significant fibrosis is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for advanced fibrosis is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for cirrhosis is from about 0. 1 to about 0.95, including all values and subranges therebetween.

[0193] In embodiments, the algorithm is a machine learning algorithm. In embodiments, the machine learning algorithm splits patient data into test and train sets, scales measured quantities, trains on a train set, tuning the algorithm and biomarker selection through cross validation. In embodiments, the resulting algorithm may be applied to the hold-out test set.

[0194] In embodiments, the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine. In embodiments, the algorithm may be applied to another independent validation cohort of patients, obtained in similar or different means to test the validity of the algorithm.

[0195] In embodiments, the method, which includes measurement of differential concentration of the at least two (e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) protein biomarkers detect fibrosis stages, NASH and “at-risk” NASH with a sensitivity of at least 75%, at least 90%, at least 95%, at least 99%; and a specificity of at least 75%, at least 90%, at least 95% or at least 99% (Table 7a-b). In embodiments, the method includes measurement of no more than 10, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5 or no more than 4 protein or no more than 3 biomarkers or no more than 2 biomarkers.

[0196] According to a particular embodiment, the concentration of no more than 10, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5 or no more than 4 protein or no more than 3 biomarkers or no more than 2 biomarkers are taken into account when staging the fibrosis.

[0197] In embodiments, the method can be applied to patient previously diagnosed with fibrosis to reassess their current fibrosis stage. In embodiments, the method is applied to determine the fibrosis state of a patient with an unknown fibrosis stage. In embodiments, the method is used to determine (i.e. rule-in) if a patient has NASH or “at-risk” NASH or significant fibrosis or significant fibrosis in combination with NASH or advanced fibrosis or cirrhosis. In embodiments, the method is used to monitor the progression of a patient’s fibrosis. A score may be obtained at two different time points and the difference between the later score and the earlier score is indicative of the progression or regression of the disease. In one embodiment, the first time point is carried out when the patient is healthy. In another embodiment, the first time point is carried out when the patient has already been diagnosed as having or suspected of having liver fibrosis. In embodiments, the method is used to monitor patients with a fibrosis stage of less than 4 who are later identified as cirrhotic. In embodiments, the method is used to confirm the fibrosis stage that has already been determined by biopsy, the analysis of electronic health records, or any other means. In embodiments, the method can be applied to patient has an intermediate FIB -4 score (1.30 < FIB -4 < 2.67).

[0198] In embodiments, the methods include taking into account a patient’s age, gender, HbAlC, diabetic state, bilirubin, liver biopsy, or any other clinically relevant feature when determining fibrosis stage or ruling in NASH or “at-risk” NASH or significant fibrosis.

[0199] In embodiments, the clinical features are measured simultaneously (within one day) of determining the concentration of a biomarker. In embodiments, the clinical features are measured before determining the concentration of a biomarker. In embodiments, the clinical features are measured after determining the concentration of a biomarker. In embodiments, the clinical features are measured within six months (before or after) of determining the concentration of a biomarker.

[0200] In embodiments, the methods described herein are superior to other methods for determining fibrosis stage and “at-risk” NASH. In embodiments, the methods are superior to clinical scores (e.g., FIB-4, BARD and NFS) and performs similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4). Moreover, MS- LFS scores outperforms commercially available protein biomarkers-based tests, ELF® and FibroTest®. In some indications the test performs significantly better than alternatives and, in some indications, significance cannot be shown (but the test’s mean estimator of performance is higher for the diagnostic test).

[0201] In embodiments, the methods of diagnosing fibrosis stage and “at-risk” NASH are superior to alternative methods for diagnosing fibrosis stage and “at-risk” NASH in patients with Type 2 Diabetes.

[0202] In embodiments, the methods of diagnosing fibrosis stage and “at-risk” NASH are superior to alternative methods for diagnosing fibrosis stage and “at-risk” NASH in patients with an intermediate range of FIB -4.

[0203] IV. Methods of Treating “at-risk” NASH and Fibrosis

[0204] In embodiments, provided herein are methods of treating fibrosis (e.g. significant fibrosis) and “at-risk” NASH comprising diagnosing a patient with fibrosis according to the methods described in Section III and administering to the patient one or more treatments for fibrosis and “at-risk” NASH. In embodiments, the one or more treatment for fibrosis and “at-risk” NASH is an FXR agonists, cyclophylin inhibitor, Berberine / UDCA, FGF21 agonists, GLP-1 receptor agonists PPAR agonists, THR-beta agonists, FASN inhibitor, mithochondrial pyruvate carrier, JNK inhibitor, structurally engeineered fatty acid, DGAT2 inhibitor, FGF19 agonist, SCD1 modulator, or any other mechanism of action for treating fibrosis and / or inflammation.

[0205] In embodiments, the treatment for fibrosis is selected from one or more of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Ternl01+501 combo, GSK4532990, HepaStem, ALN- HSD, Efruxifermin, or any other drug currently approved for other indication and / or in development for treating fibrosis and / or inflammation.

[0206] In embodiments, provided herein are follow-up test and / or procedures for monitoring patients and diagnose possible outcomes comprising diagnosing a patient with fibrosis, NASH or “at-risk” NASH according to the methods described in Section III, referring patient to a specialist doctor (e.g. hepatologist, endourologist) who will refer the patient to follow-up test / procedures. In embodiments, the one or more test / procedures for fibrosis, NASH and “at- risk” NASH is biopsy, abdominal imaging (e.g. CT, US, Fibroscan, MRE), endoscopy, blood tests (e.g. alpha fetoprotein, hepatitis B Ag / Ab, hepatitis C Ag / Ab). V. Kits for Diagnosing Fibrosis, NASH and “at-risk” NASH

[0207] In embodiments, kits are provided for diagnosing fibrosis, NASH and “at-risk” NASH using the biomarkers described herein (see Section II). In embodiments, the kits comprise 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 10 biomarkers.

[0208] In embodiments, the kits comprise reagents for detecting no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 biomarkers.

[0209] The protein biomarkers can be detected in any suitable manner but are typically detected by contacting a sample from the subject with an antibody, which binds the biomarker and then detecting the presence or absence of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay.

[0210] In one embodiment, the antibody which specifically binds the protein biomarker is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label.

[0211] Immunoassays carried out in accordance with some embodiments of the present invention may be homogeneous assays or heterogeneous assays. In a homogeneous assay the immunological reaction usually involves the specific antibody (e.g., anti- biomarker antibody), a labeled analyte, and the sample of interest. The signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte. Both the immunological reaction and detection of the extent thereof can be carried out in a homogeneous solution. Immunochemical labels, which may be employed, include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

[0212] In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and means for producing a detectable signal. Samples as described above may be used. The antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen suspected of containing the antigen in a 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 employing means for producing such signal. The signal is related to the presence of the analyte in the sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if the antigen to be detected contains a second binding site, an antibody which 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 the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays are oligonucleotides, immunoblotting, immunofluorescence methods, immunoprecipitation, chemiluminescence methods, electrochemiluminescence (ECL) or enzyme-linked immunoassays.

[0213] Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which may be useful for carrying out the method disclosed herein. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al., titled “Methods for Modulating Ligand-Receptor Interactions and their Application,” U.S. Pat. No. 4,659,678 to Forrest et al., titled “Immunoassay of Antigens,” U.S. Pat. No. 4,376,110 to David et al., titled “Immunometric Assays Using Monoclonal Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled “Macromolecular Environment Control in Specific Receptor Assays,” U.S. Pat. No. 4,233,402 to Maggio et al., titled “Reagents and Method Employing Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled “Heterogenous Specific Binding Assay Employing a Coenzyme as Label. ’’The biomarker can also be detected with antibodies using flow cytometry. Those skilled in the art will be familiar with flow cytometric techniques which may be useful in carrying out the methods disclosed herein(Shapiro 2005). These include, without limitation, Cytokine Bead Array (Becton Dickinson) and Luminex technology.

[0214] Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g.,35S,125I,131I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.

[0215] Antibodies can also be useful for detecting post-translational modifications of biomarker proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, glycosylation (e.g., O- GlcNAc). Such antibodies specifically detect the phosphorylated amino acids in a protein or 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 commercially available. Post-translational modifications can also be determined using metastable ions in reflector matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF) (Wirth U. and Muller D. 2002).

[0216] For biomarker-proteins, polypeptides, mutations, and polymorphisms known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art. Such assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis- Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.

[0217] In particular embodiments, the antibodies of the present invention are monoclonal antibodies.

[0218] In embodiments, the kit for immunoassay comprises antibodies specific for 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 10 biomarkers.

[0219] In embodiments, the kits comprise no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 antibodies.

[0220] In embodiments, the kits comprise labeled secondary antibodies, which can bind to antibodies that bind the biomarkers described herein (and as shown as an example in Table 1). The disclosed immunoassay measurement kits may include a dilution solution, assay buffer solution, substrate solution, and stop solution for performing measurements.

[0221] Table 1 - Exemplary commercially available antibodies

[0222] In other embodiments, measurements are performed mass spectrometry-based protein measurements or aptamer-based protein measurements.

[0223] In embodiments, the kit for aptamer-based measurements comprises aptamers specific for 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 10 biomarkers, that selectively bind to the biomarkers described herein. The disclosed kits for aptamer-based protein measurements may further include assay buffer solutions, substrate solutions.

[0224] In embodiments, the kit for mass spectrometry-based protein measurements 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 10 synthetic proteins for quantification of biomarkers.

[0225] In embodiments, the kits comprise no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 synthetic proteins for quantification of biomarkers.

[0226] The disclosed kits for mass spectrometry-based protein measurements may further include all standards and reagents for monitoring of the system performance, alkylation solutions, digesting solutions, analytical columns, dilution buffers, running buffers and stop solutions for performing measurements.

[0227] In embodiments, antibodies and / or aptamers are attached to an array, such as a biochip, lateral flow device, or dipstick. In embodiments, the array includes other aptamers or antibodies that serve 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.

[0228] In embodiments, the kit comprises aptamers that bind specifically to the biomarker selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule-1 (ICAM1). In embodiments, the kit comprises aptamers that bind specifically to C7 and QSOX1. In embodiments, the kit comprises aptamers that bind specifically to C7 and GP5. In embodiments, the kit comprises aptamers that bind specifically to C7 and ICAM1. In embodiments, the kit comprises aptamers that bind specifically to C7, QSOX1, and GP5. In embodiments, the kit comprises aptamers that bind specifically to C7, QSOX1, and ICAM1.

[0229] In embodiments, the kit comprises antibodies that bind to one or more of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule-1 (ICAM1). In embodiments, the kit comprises antibodies that bind to C7 and QSOXL In embodiments, the kit comprises aptamers that bind specifically to C7 and ICAML In embodiments, the kit comprises antibodies that bind to C7 and GP5. In embodiments, the kit comprises antibodies that bind to C7, QSOX1, and GP5. In embodiments, the kit comprises aptamers that bind specifically to C7, QSOX1, and ICAML

[0230] In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of one or more of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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 factorbinding protein complex acid labile 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 comprises synthetic proteins for measuring absolute concentration of C7 and QSOXL In embodiments, the kit comprises aptamers that bind specifically to C7 and ICAML In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of C7 and GP5. In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of C7, QSOX1, and GP5. In embodiments, the kit comprises aptamers that bind specifically to C7, QSOX1, and ICAML A machine-readable storage medium can comprise a data storage material encoded with machine-readable data or data arrays which, when using a machine programmed with instructions for using the data, is capable of use for a variety of purposes. Measurements of effective amounts of the biomarkers of the invention and / or the resulting evaluation of risk from those biomarkers can be implemented in computer programs executing on programmable computers, comprising, inter alia, 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. 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 personal computer, microcomputer, or workstation of conventional design.

[0231] Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language. Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette or others as defined elsewhere in this disclosure) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The health-related data management system used in some aspects of the invention may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform various functions described herein.

[0232] The protein markers of the present invention, in some embodiments thereof, can be used to generate a “reference biomarker profile” of those subjects who do not have fibrosis (e.g. significant fibrosis). The biomarkers disclosed herein can also be used to generate a “subject biomarker profile” taken from subjects who have fibrosis (e.g. significant fibrosis). The subject biomarker profiles can be compared to a reference biomarker profile to diagnose or identify subjects with fibrosis. The subject biomarker profile of different fibrosis stages can be compared to diagnose or identify a stage of fibrosis or to diagnose NASH or at risk-NASH. The reference and subject biomarker profiles of the present invention, in some embodiments thereof, can be contained in a machine-readable medium, such as but not limited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others. Such machine-readable media can also contain additional test results, such as, without limitation, measurements of clinical parameters and traditional laboratory risk factors. Alternatively, or additionally, the machine-readable media can also comprise subject information such as medical history and any relevant family history. The machine-readable media can also contain information relating to other disease-risk algorithms and computed indices such as those described herein.

[0233] Performance and Accuracy Measures of the Invention.

[0234] The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, some aspects of the invention are intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to rule in significant fibrosis, rule in NASH or at-risk NASH or staging of liver fibrosis is based on whether the subjects have, a “significant alteration” (e.g., clinically significant and diagnostically significant) in the levels of a biomarker. By “effective amount” it is meant that the measurement of an appropriate number of biomarkers (which may be one or more) to produce a “significant alteration” (e.g., level of expression or activity of a biomarker) that is different than the predetermined cut-off value (or threshold value) for that biomarker (s) and therefore indicates that the subject has a particular level of liver fibrosis for which the biomarker (s) is an indication. The difference in the level of biomarker is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical, diagnostic, and clinical accuracy, may require that combinations of several biomarkers be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant biomarker index.

[0235] In the categorical diagnosis of a disease state, changing the cut-off point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject’s condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. One way to achieve this is by using the Matthews correlation coefficient (MCC) metric, which depends upon both sensitivity and specificity. Use of statistics such as area under the ROC curve (AUC), encompassing all potential cut point values, is preferred for most categorical risk measures when using some aspects of the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred. By predetermined level of predictability it is meant that the method provides an acceptable level of clinical or diagnostic accuracy. Using such statistics, an “acceptable degree of diagnostic accuracy”, is herein defined as a test or assay (such as the test used in some aspects of the invention for determining the clinically significant presence of biomarkers, which thereby indicates the presence an infection type) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

[0236] By a “very high degree of diagnostic accuracy”, it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.

[0237] Alternatively, the methods predict the presence or absence of fibrosis or stage of fibrosis or response to therapy with at least 75% total accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.

[0238] Alternatively, the methods predict the presence of fibrosis or stage of fibrosis or response to therapy with at least 75% sensitivity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater sensitivity.

[0239] Alternatively, the methods predict the presence of “at-risk” NASH or response to NASH therapy with at least 75% specificity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater specificity.

[0240] Alternatively, the methods may be used to rule in significant fibrosis with at least 75% NPV, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater NPV. Alternatively, the methods rule in NASH or “at risk” NASH with at least 50% PPV, more preferably 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater PPV.

[0241] Alternatively, the methods rule in significant fibrosis, NASH or “at-risk” NASH or response to therapy with an MCC larger than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or 1.0.

[0242] In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer- Lemeshow P-value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% CI) based on a historical observed cohort’s predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City, California).

[0243] In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the biomarkers of the invention allows for one of skill in the art to use the biomarkers to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.

[0244] Furthermore, other unlisted biomarkers will be very highly correlated with the biomarkers (for the purpose of this application, any two variables will be considered to be “very highly correlated” when they have a Coefficient of Determination (R2) of 0.5 or greater). Some aspects of the present invention encompass such functional and statistical equivalents to the aforementioned biomarkers. Furthermore, the statistical utility of such additional biomarkers is substantially dependent on the cross-correlation between multiple biomarkers and any new biomarkers will often be required to operate within a panel in order to elaborate the meaning of the underlying biology.

[0245] In the context of the present invention the following statistical terms may be used:

[0246] “TP” is true positive, means positive test result that accurately reflects the tested-for activity. For example in the context of the present invention a TP, is for example but not limited to, truly classifying a bacterial infection as such.

[0247] “TN” is true negative, means negative test result that accurately reflects the tested-for activity. For example in the context of the present invention a TN, is for example but not limited to, truly classifying a viral infection as such.

[0248] “FN” is false negative, means a result that appears negative but fails to reveal a situation. For example in the context of the present invention a FN, is for example but not limited to, falsely classifying a bacterial infection as a viral infection.

[0249] “FP” is false positive, means test result that is erroneously classified in a positive category. For example in the context of the present invention a FP, is for example but not limited to, falsely classifying a viral infection as a bacterial infection.

[0250] “Sensitivity” is calculated by TP / (TP+FN) or the true positive fraction of disease subjects.

[0251] “Specificity” is calculated by TN / (TN+FP) or the true negative fraction of non-disease or normal subjects. "Total accuracy" is calculated by (TN + TP) / (TN + FP +TP + FN).

[0252] “Positive predictive value” or “PPV” is calculated by TP / (TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

[0253] “Negative predictive value” or “NPV” is calculated by TN / (TN + FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested. See, e.g., 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 specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test.

[0254] "MCC" (Mathwes Correlation coefficient) is calculated as follows: MCC = (TP * TN - FP * FN) / {(TP + FN) * (TP + FP) * (TN + FP) * (TN + FN)}A0.5 where TP, FP, TN, FN are true -positives, false-positives, true-negatives, and false-negatives, respectively. Note that MCC values range between -1 to +1, indicating completely wrong and perfect classification, respectively. An MCC of 0 indicates random classification. MCC has been shown to be a 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 cases of unbalanced class sizes (Baldi, Brunak et al. 2000).

[0255] Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by a Receiver Operating Characteristics (ROC) curve according to 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 summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, “Clinical Interpretation Of Laboratory Procedures,” chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4thedition 1996, W.B. Saunders Company, pages 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. An alternative approach using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction,”

[0256] Circulation 2007, 115: 928-935.

[0257] “Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), Matheus correlation coefficient (MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC) among other measures.

[0258] A “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value”. Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical-biomarkers, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of biomarkers detected in a subject sample and the subject’s probability of having an infection or a certain type of infection. In panel and combination construction, of particular interest are structural and syntactic statistical classification algorithms, and methods of index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a biomarker selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross- validation (10-Fold CV). At various steps, false discovery rates may be estimated by value permutation according to techniques known in the art. A “health economic utility function” is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care. It encompasses estimates of the accuracy, effectiveness and performance characteristics of such intervention, and a cost and / or value measurement (a utility) associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and / or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome. The sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcome’s expected utility is the total health economic utility of a given standard of care. The difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention. This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance. Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost- effective clinical performance characteristics required of a new intervention.

[0259] For diagnostic (or prognostic) interventions of the invention, as each outcome (which in a disease classifying diagnostic test may be a TP, FP, TN, or FN) bears a different cost, a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures. These different measurements and relative trade-offs generally will converge only in the case of a perfect test, with zero error rate (a.k.a., zero predicted subject outcome misclassifications or FP and FN), which all performance measures will favor over imperfection, but to differing degrees.

[0260] “Analytical accuracy” refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation (CV), Pearson correlation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and / or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.

[0261] “Performance” is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate “performance metrics,” such as AUC and MCC, time to result, shelf life, etc. as relevant.

[0262] By “statistically significant”, it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a “false positive”). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.

[0263] EXAMPLES

[0264] The following examples are given for the purpose of illustrating various embodiments of the disclosure and are not meant to limit the present disclosure in any fashion. Changes therein and other uses which are encompassed within the spirit of the disclosure, as defined by the scope of the claims, will be recognized by those skilled in the art.

[0265] Example 1. Identification of a Biomarker Signature for Fibrosis and “at-risk” NASH

[0266] Purpose: Serum samples from 2,186 patients were used to identify a biomarker signature for fibrosis and “at-risk” NASH.

[0267] Patient Populations: Overall, 357 patients from the NAFLD biopsy-confirmed “Discovery Cohort”, 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 5 cohorts. Table 2- Patient Demographics In the NAFLD biopsy-confirmed discovery cohort, patients are predominantly male, fitting the known literature. Age ranged from 52.3-60.2 years, T2D was presented in 18%-61 % of cohorts’ patients, hypertension in 6%-20% of cohorts’ patients, dyslipidemia in 46%-62 of cohorts’ patients, all correlate with increase in the fibrosis stage. The body-mass index average was overall ~34 kg / m2and hemoglobin Ale (HbAlC) ranged from 5.8 %-7.1%, both with no clear trend between different fibrosis stages. Overall, 161 patients were identified as having significant fibrosis, 106 as having advanced fibrosis, 40 as having cirrhosis, 77 as having “at- risk” NASH, and 63 as having “at-risk” NASH NIMBLE definition. Another 196 patients were at lower fibrosis stage (F stage < 2). Other commonly used clinical scores, FIB -4, and Fibroscan measurements, liver stiffness measurement (LSM) and controlled attenuation parameter (CAP), increase as expected with the progression of the biopsy tested liver fibrosis, while NAFLD activity score (NAS) remains relatively stable above F stage of 0.

[0268] In the validation cohorts 1-3, 51-57% of subjects were women, mean age ranged from 48 to 57 years, and diabetes mellitus was present in 30-59% of patients, having increasing prevalence of age and T2D with fibrosis stage. The BMI average was above 30 kg / m2 and hemoglobin Ale (HbAlC) average ranged from 6.3% to 7.5%. Overall, validation was performed based on -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 “at-risk” NASH, and 59 as having “at-risk” NASH NIMBLE definition. In validation cohort-2 153 subjects were identified as having significant fibrosis, 87 as having advanced fibrosis, 27 as having cirrhosis, 80 as having “at-risk” NASH, and 69 as having “at-risk” NASH NIMBLE definition. In validation cohort-3, 97 subjects were identified as having significant fibrosis, and 122 as having advanced fibrosis. Similar to the discovery cohort, all commonly used clinical scores (FIB-4, LSM, CAP, ELF® and FirboTest®) correlated with the increase in liver fibrosis, while NAS remains relatively stable above F stage of 0, with an overall average of 4.

[0269] In the real-world patient cohort patients are predominantly male, with mean age of 68.8- 69.2 years, in both the NAFLD and probable NASH cirrhosis groups. T2D was presented in 59%, hypertension in 74%, dyslipidemia in 64% of the probable NASH cirrhotic patient and in similar prevalence also in the NAFLD patients (51%, 74% and 57%, respectively). Moreover, BMI and HbAlC mean was similar in both groups. Notably, FIB-4 mean was higher in the probable NASH cirrhotic cases (3.6) compared to the NAFLD patients (1.7).

[0270] The Discovery Cohort contained serum samples from 357 biopsy-proven NAFLD patients from Puerta de Hierro and Marques de Valdecilla hospitals, Spain (> 18 years old). The validation cohort- 1 includes 241 biopsy-confirmed NAFLD patients from Hospital Universitario Virgen del Rocfo (HUVR), Spain (>18 years old). The validation cohort-2 includes 519 biopsy-confirmed NAFLD patients from Antwerp University Hospital, UZA, Belgium (>18 years old). The validation cohort-3 includes 256 biopsy-confirmed NAFLD patients, collected as part of clinical trial cohort done in US and included patients with fibrosis stage F1-F3 and with NAS > 4. In general, biopsy criteria included suspected advanced liver disease by imaging or laboratory tests, or at the time of bariatric surgery. Exclusion criteria included significant alcohol intake (>30 g daily for men and >20 g daily for women) and evidence of concomitant liver disease, including viral or autoimmune hepatitis, human immunodeficiency virus, drug-induced fatty liver, hemochromatosis, or Wilson’s disease. Collected data included anthropometric measurements, blood lab measurements, 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 cohort- 1 and 3 enhanced liver fibrosis (ELF®, by Siemens) test results were collected. Significant fibrosis cases were defined as patients with fibrosis stage F > 2, advanced fibrosis with F > 3 and cirrhosis with F = 4. “At-risk” NASH cases were defined as having biopsy-confirmed NASH, a NAFLD activity score (NAS) > 4 with at least 1 point in each one of the components, and F > 2 with exclusion of cirrhotic patients. “At risk” NASH NIMBLE definition includes exclusion of patients with F0-F1 and NAS > 4, and F > 2 and NAS < 4 and with exclusion of cirrhotic patients, according to recently published work by [Sanyal et al. 2023, Nat Med 29, 2656-2664 (2023). https: / / doi.org / 10.1038 / s41591-023-02539-6].

[0271] Real-world Patient Cohort contained serum samples collected from the general Israeli population between 2021-2022, as part of a clinical trial (IRB). De-identified electronic health records (EHR) were used to identify 184 samples from patients diagnosed with probable NASH, defined as having ICD-9 diagnosis ± 6 months blood collection and at least 2 coexisting or history of metabolic risk factors (https: / / doi.org / 10.1016 / S2468-1253(20)30252-l). All cases were individually validated by a health specialist based on biopsy, imaging and / or clinical assessment data. 629 control samples from NAFLD patients were defined as having ICD-9 diagnosis and documented history of abdominal imaging. Exclusion criteria included no evidence for a competing etiology, including viral hepatitis, chronic Hepatitis, alcoholic liver disease, autoimmune disorders, cryptogenic cirrhosis, and cardiac cirrhosis. Collected data included anthropometric measurements, blood lab measurements and medical history.

[0272] Determining Concentration of Biomarkers: multi-OMICS analysis - All samples were analyzed with a proprietary high-throughput liquid-chromatography mass-spectrometry (LC- MS) based metabolomics (with a coverage of 12,000 ions and 1,500 metabolites), lipidomics (with a coverage of 19,000 ions and 1,600 lipids), proteomics (multiplexing 1,800 proteins), detecting altogether tens of thousands of biomarker ions per sample (RSD < 30%). Biomarker intensities were normalized based on repeated injection of a biological QC sample, repeatedly analyzed every 10th sample. Thus, intensities are in arbitrary relative units and not in absolute concentrations.

[0273] Statistical analysis'. The discovery cohort was randomly split to train and test sets. First, a feature-by-feature correlation to the outcome was applied, and corrected for multiple hypothesis testing. The significant features were normalized to a standard distribution and passed to a pipeline consisting of forward feature selection using a leave one out crossvalidated L2 regularized logistic regression. This pipeline was used to identify a minimal component serum signature on the train set, optimized for the best area under the receiver operating characteristic curve (AUROC) for all indications (significant and advanced fibrosis, cirrhosis, “at-risk” NASH and “at-risk” NASH NIMBLE definition). Optimal signatures consisting of 2 and 3 proteins were identified and then tested on the test set to confirm performance. Finally, signatures were applied to the discovery, validation cohorts 1-3 and real- world patient cohort in leave-one-out cross validation manner, to assess performance in identifying the above-mentioned indications. For validation cohort-3, only the performance of diagnosing significant and advanced fibrosis was determined. For the real- world patient cohort, only the performance of diagnosing cirrhosis was examined. The 95% confidence intervals (CI) for the AUROCs were estimated using Delong method.

[0274] Results: The first correlation testing on the test set identified several candidate biomarkers for the subsequent modeling stage. Among the identifiable proteins and peptides, the following proteins are included: Quiescin sulfhydryl oxidase 1 (QSOX1), Complement component C7 (C7), Collectin-10 (COL10), Alpha-2-macroglobulin (A2M), Vascular cell adhesion protein 1 (VCAM1), Platelet glycoprotein V (GP5). See full list of biomarkers in Table 3. Figs. 1A-1F show the protein intensity of the biomarkers, COL10 (Fig. 1A), C7 (Fig. IB), QSOX1 (Fig. 1C), VCAM1 (Fig. ID), A2M (Fig. IE), GP5 (Fig. IF), and ICAM-1 (Fig. 1G) by fibrosis stage. Fig. 1H shows fibrosis stage by patient age.

[0275] Table 3 - AUCs of top biomarkers on training patients in the discovery cohort

[0276] Example 2. Identification of a Two-Protein Biomarker Signature for “At-risk” NASH

[0277] An optimal two component biomarker signatures were identified using the candidate features identified in Example 1.

[0278] For At-risk NASH, the optimal two component models included (a) the proteins C7 and QSOX1 that gave an AUC on the train set of 0.79 and 0.76 on the test set, and (b) C7 and ICAM1 that gave an AUC on the train set of 0.76 and 0.77 on the test set. The resulting logistic regression model had the following equation in arbitrary units on the scaled proteins: P=(l- exp(F)) / exp(F), where (a) F = 0.849*C7 + 0.745*QSOX1 - 0.124, and (b) F = 0.974*C7 + 0.513HCAM1 - 0.13.

[0279] Applying the C7 and QSOX1 model to the full cohorts in leave one out manner, we find AUCs of 0.78 for the discovery cohort, and 0.78 and 0.71 for validation cohorts 1 and 2, respectively. For “at-risk” NASH NIMBEE definition, we find AUCs of 0.81 for the discovery cohort, and 0.80 and 0.74 for validation cohorts 1 and 2, respectively. Applying the C7 and ICAM1 model to the full cohorts in leave one out manner, we find AUCs of 0.77 for the discovery cohort, and 0.78 and 0.74 for validation cohorts 1 and 2, respectively. For “at-risk” NASH NIMBLE definition, we find AUCs of 0.82 for the discovery cohort, and 0.87 and 0.78 for validation cohorts 1 and 2, respectively.

[0280] Fig. 2 shows that identifying “at-risk” NASH by a two component biomarker signatures comprising C7 and QSOX1, or C7 and ICAM1 was superior to identifying “at-risk” NASH by the FIB-4 score. In all figures that follow, the proteomic model is referred to as either “MS- LFS”, or “Model”, with composition dependent on context.

[0281] Example 3. Identification of a Two-Protein Biomarker Signature for Fibrosis Stage

[0282] For Fibrotic stage, the optimal two component models included (a) the proteins C7 and GP5 that gave an AUC on the test set of 0.82, 0.86 and 0.96 for significant fibrosis, advanced fibrosis and cirrhosis respectively, and (b) C7 and ICAM1 that gave an AUC on the test set of 0.85, 0.85 and 0.92 for significant fibrosis, advanced fibrosis and cirrhosis respectively. The resulting logistic regression model had the following equation in arbitrary units on the scaled proteins: P=(l-exp(F)) / exp(F), where (a) F = 1.039*C7 - 0.467*GP5 - 0.161, and (b) F = 0.974*C7 + 0.513*ICAMl - 0.130 (as mentioned in above section) Applying the C7 and GP5 model to the full cohorts in leave one out manner, we find AUCs of 0.82, 0.86, 0.96 for significant fibrosis, advanced fibrosis and cirrhosis, respectively, in the discovery cohort. For the validation cohorts, we find AUCs of 0.82, 0.82, 0.85 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in validation cohort- 1, 0.79, 0.87, 0.97 for significant fibrosis, advanced fibrosis and cirrhosis, respectively, in validation cohort-2, and 0.77, 0.83 for significant fibrosis and advanced fibrosis, respectively, in validation cohort- 3. Applying the C7 and ICAM1 model to the full cohorts in leave one out manner, we find AUCs of 0.82, 0.83, 0.93 for significant fibrosis, advanced fibrosis and cirrhosis, respectively, in the discovery cohort. For the validation cohorts, we find AUCs of 0.82, 0.81, 0.82 for significant fibrosis, advanced fibrosis and cirrhosis, respectively, in validation cohort- 1, 0.82, 0.87, 0.95 for significant fibrosis, advanced fibrosis and cirrhosis, respectively, in validation cohort-2, and 0.77, 0.82 for significant fibrosis and advanced fibrosis, respectively, in validation cohort-3.

[0283] Validating the two-component signature, C7 and GP5, on the real- world patient cohort gave an AUC of 0.82, outperforming FIB-4.

[0284] Figs. 3A-3D shows that determining fibrosis stage by a two component biomarker signature comprising of either C7 and GP5, or C7 and ICAM1, was superior to determining fibrosis stage by the FIB-4 score. Fig. 3A-3B shows superiority of the two component biomarker models for diagnosing significant fibrosis. Fig. 3C-3D shows superiority of the two component biomarker models for diagnosing advanced fibrosis. Fig. 3E-3F shows superiority of the two component biomarker models for diagnosing cirrhosis. Fig. 3G shows superiority of the two component biomarker signature C7 and GP5 for diagnosing probable NASH cirrhosis.

[0285] Example 4. Identification of a Three-Protein Biomarker Signature for “at-risk” NASH and Fibrosis Stage

[0286] Two optimal three component biomarker signature for fibrotic stage and “at-risk” NASH were identified using the candidate features identified in Example 1. The optimal 3 component models consisted of (a) C7, QSOX1 and GP5, and (b) C7, QSOX1 and ICAM1.

[0287] The addition of GP5 to the biomarker signature improved the AUC of the model in the test set for “at-risk” NASH to 0.74, “at-risk” NASH NIMBLE to 0.81, significant fibrosis to 0.825, and to 0.873 for advanced fibrosis. Similarly, it improved the performance in validation cohort- 2 and 3. The addition of ICAM1 to the biomarker signature improved the AUC of the model in the test set for “at-risk” NASH to 0.79, “at-risk” NASH NIMBLE to 0.89, significant fibrosis to 0.859, and to 0.865 for advanced fibrosis. Similarly, it improved the performance in validation cohort-2. The resulting logistic regression models had the following equation in arbitrary units on the scaled proteins: P=(l-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*ICAMl + 0.657*QSOX1

[0288] - 0.101.

[0289] Applying the C7, QSOX1 and GP5 model to the full cohorts in leave one out manner, we find AUCs of 0.77, 0.79, 0.827, 0.863, 0.958 for “at-risk” NASH, “at-risk” NASH NIMBLE, significant fibrosis, advanced fibrosis and cirrhosis, respectively, in the discovery cohort. For the validation cohorts, we find AUCs of 0.77, 0.84, 0.82, 0.82, 0.84 for “at-risk” NASH, “at-risk” NASH NIMBLE, significant fibrosis, advanced fibrosis, and cirrhosis, respectively, in validation cohort-1, 0.7, 0.74, 0.8, 0.87, 0.97 for “at-risk” NASH, “at-risk” NASH NIMBLE, significant fibrosis, advanced fibrosis and cirrhosis, respectively, in validation cohort-2, and 0.75, 0.82 for significant fibrosis and advanced fibrosis, respectively, in validation cohort-3. Applying the C7, QSOX1 and ICAM1 model to the full cohorts in leave one out manner, we find AUCs of 0.78, 0.82, 0.824, 0.843, 0.937 for “at-risk” NASH, “at- risk” NASH NIMBLE, significant fibrosis, advanced fibrosis and cirrhosis, respectively, in the discovery cohort. For the validation cohorts, we find AUCs 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, 0.75, 0.79, 0.82, 0.87, 0.96 for “at-risk” NASH, “at-risk” NASH NIMBLE, significant fibrosis, advanced fibrosis and cirrhosis, respectively, in validation cohort-2, and 0.75, 0.82 for significant fibrosis and advanced fibrosis, respectively, in validation cohort-3.

[0290] Validating the three-component signature, C7, QSOX1 and GP5, on the real-world patient cohort gave an AUC of 0.83, outperforming FIB-4.

[0291] Figs. 4A-4K shows that diagnosing “at-risk” NASH and determining fibrosis stage by a three component biomarker signature comprising of either C7, QSOX1 and GP5, or C7, QSOX1 and ICAM1 was superior to determining fibrosis stage by the FIB-4 score. Fig. 4A4B shows superiority of the three component biomarker models for diagnosing “at-risk” NASH. Fig. 4C-4D shows superiority of the three component biomarker models for diagnosing “at- risk” NASH NIMBLE. Fig. 4E-4F shows superiority of the three component biomarker models for diagnosing significant fibrosis. Fig. 4G-4H shows superiority of the three component biomarker models for diagnosing advanced fibrosis. Fig. 4I-4J shows superiority of the three component biomarker models for diagnosing cirrhosis. Fig. 4K shows superiority of the three component biomarker C7, QSOX1 and GP5 signature for diagnosing probable NASH cirrhosis a in real world patient cohort. Example 5. The C7,QSOX1 and C7,ICAM1 Biomarker Signatures are Superior to

[0292] Other Clinical Scores for Diagnosing “at-risk” NASH

[0293] The two component MS-LFS models described in Example 2 outperforms other commonly used clinical scores (i.e., BARD and NFS) and perform similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4) (Fig. 5). Moreover, MS-EFS scores outperforms commercially available protein biomarkers-based tests, EEF® and FibroTest® (Table 4).

[0294] Table 4. C7, QSOX1 and C7, ICAM1 biomarker signatures are superior to other commercially available protein biomarker tests for diagnosing “at-risk” NASH.

[0295] The diagnostic performance of MS-LFS models were compared to additional common clinical predictors of fibrosis state: 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 of each predictor and the 95% CI are show in Fig. 5 for the full cohorts. In addition, the AUC of commercially available protein biomarker-based tests, FibroTest® and ELF®, are shown for the full cohorts in Table 4. The MS-LFS scores outperforms all commonly used clinical scores and protein biomarker-based tests, and perform similarly or better to Fibroscan and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4) (Fig. 5, Table 4).

[0296] Importantly, the same results are shown even when focusing on patients with an intermediate FIB-4 score (1.30 < FIB-4 < 2.67), having indecisive diagnosis and management. MS-LFS scores performs better then or similar to all techniques in all indications, albeit with larger CI due to the smaller sample size (Fig. 5A, Table 4). Focusing on T2D, the two protein MS-LFS significantly outperforms all other common clinical predictors and performs numerically better than Fibroscan and its combination with other clinical parameters (Fig. 5B). Example 6. The C7,ICAM1 and C7,GP5 Biomarker Signatures are Superior to Other Clinical Scores for Diagnosing Fibrosis Stage

[0297] The two component MS-LFS models described in Example 3 outperforms other commonly used clinical scores (i.e., BARD and NFS) and perform similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4). Moreover, MS-LFS scores outperforms commercially available protein biomarkers-based tests, ELF® and

[0298] FibroTest® (Fig. 6, Table 5).

[0299] Table 5. C7, ICAM1 and C7, GP5 biomarker signatures are superior to other commercially available protein biomarker tests for diagnosing fibrosis stage.

[0300] The diagnostic performance of MS-LFS models were compared to additional common clinical predictors of fibrosis state: 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 of each predictor and the 95% CI are show in Fig. 6 for the full cohorts. In addition, the AUC of commercially available protein biomarker-based tests, FibroTest® and ELF®, are shown for the full cohorts in Table 5. The MS-LFS scores outperforms all commonly used clinical scores and protein biomarker-based tests, and perform similarly or better to Fibroscan and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4) (Fig. 6, Table 5).

[0301] Importantly, the same results are shown even when focusing on patients with an intermediate FIB-4 score (1.30 < FIB-4 < 2.67), having indecisive diagnosis and management. MS-LFS scores performs better then or similar to all techniques in all indications, albeit with larger CI due to the smaller sample size (Fig. 6A, Table 5).

[0302] Focusing on T2D, the two protein MS-LFS significantly outperforms all other common clinical predictors and performs numerically better than Fibroscanand its combination with other clinical parameters, beside performing the same as Agile 3+ (Fig. 6B).

[0303] Lastly, the diagnostic performance remains high for diagnosing probable NASH cirrhosis also in a real-world primary care patient cohort, the IMOSS-500K, outperforming FIB-4, BARD and NFS. In addition, MS-LFS significantly outperform FIB-4, BARD and NFS in the cohort, also when focusing on intermediate FIB-4 subpopulations (Fig. 6A).

[0304] Example 7. The Three-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 Stage

[0305] The three component MS-LFS scores described in Example 4 outperforms other commonly used clinical scores (i.e., BARD and NFS) and perform similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4) (Fig. 7). Moreover, MS-LFS scores outperforms commercially available protein biomarkers-based tests, ELF® and FibroTest® (Table 6a-b).

[0306] Table 6a. Three-protein biomarker signatures are superior to other commercially available protein biomarker tests for diagnosing fibrosis stage.

[0307] Table 6b. Three-protein biomarker signatures are superior to other commercially available protein biomarker tests for “at-risk” NASH.

[0308] Importantly, the same results are shown even when focusing on patients with an intermediate FIB-4 score (1.30 < FIB-4 < 2.67), having indecisive diagnosis and management. MS-LFS scores performs better then or similar to all techniques in all indications, albeit with larger CI due to the smaller sample size (Fig. 7 A, and Table 6a-b)

[0309] Focusing on T2D MS-LFS scores significantly outperforms all other common clinical predictors and performs numerically better than Fibroscan® and its combination with other clinical parameters, beside performing the same as Agile 3+ (Fig. 7B).

[0310] Lastly, the diagnostic performance remains high for diagnosing probable NASH cirrhosis also in a real-world primary care patient cohort, the IMOSS-500K, outperforming FIB-4, BARD and NFS. In addition, MS-LFS significantly outperform FIB-4, BARD and NFS in the cohort, also when focusing on intermediate FIB -4 and T2D subpopulations (Fig. 7A).

[0311] Example 8. The Three-Protein Biomarker Signature, C7, QSOX1 and ICAM, is Superior to Other Clinical Scores for Monitoring Liver Fibrosis Stage

[0312] Focusing on validation cohort-3, we performed an additional post-hoc analysis of serum samples collected at baseline and end-of-trial (EOT) for 220 patients, having consistent biopsy reads by two pathologies at baseline and a 2nd biopsy at EOT. Biopsy data shows fibrosis stage progression in a total of 42 patients, no change in 120, and regression in 58. The three component MS-LFS score, C7, QSOX1 and ICAM described in Example 4, had a statistically significant drop in MASH-fibrosis score (average drop of 8%; p < 0.012), while patients with fibrosis progression had a significant increase in score (average increase of 7%; p < 0.05). In contrast, no statistically significant drop in FIB-4, FibroTest, or ELF scores were detected in patients with a regressed fibrosis stage (Fig. 8).

[0313] Example 9. Sensitivity and specificity of the proteomic predictors in different cutoffs.

[0314] For the developed predictors, the sensitivity and specificity were calculated for different cutoffs. Depending on the required performance the predictors can be tuned to be more specific or more sensitive. Examples of possible sensitivity and specificity pairs are given in Table 7 for the discovery cohort (Table 7a) and the validation cohorts 1-3 (Table 7b-d).

[0315] MS-LFS is novel non-invasive serum test, composed 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, FIB -4, BARD and NFS, and achieves similar diagnostic performance to those of Fibroscan® and its combination with clinical parameters (FAST, Agile 3+ and Agile 4). Moreover, MS-LFS outperforms commertially available protein-based biomarker tests, ELF® and FibroTest®. MS-LFS synergizes with FIB-4, improving diagnosis of fibrosis and “at-risk” NASH in patients with intermediate FIB-4 values.

[0316] Furthermore, MS-LFS outperform other tested scores when applied in patients with T2D, “at- risk” NASH and significant fibrosis. This subpopulation is considered as being at a higher risk for progression and for developing liver- and cardiovascular-related outcomes. Thus, these patients are projected to get instantly treated with the new coming NASH treatments and may benefit the most of having a non-invasive accurate diagnostic serum test.

[0317] The potential of using MS-LFS in population screening was demonstrated with samples from IMOSS-500K study, identifying high-risk NAFLD patients as having probable NASH cirrhosis, in real-world primary care screening setting.

[0318] Example 9.

[0319] Table 7a - Performance of predictors for discovery

[0320] Table 7b - Performance of predictors for validation cohort- 1

[0321] Table 7c - Performance of predictors for validation cohort-2

[0322] Table 7d - Performance of predictors for validation cohort-3

[0323] Table 8 - Minimal Fold Change of Average Protein Levels per Indication

[0324] Table 9 - Minimal Average Score for Models and Indications.

[0325] The model score was calculated for each subject, given the biomarker measurement and the model algorithm. The median reflects the calculated median value of the score for each indication.

[0326] All references, articles, publications, patents, patent publications, and patent applications cited herein are incorporated by reference in their entireties for all purposes. However, mention of any reference, article, publication, patent, patent publication, and patent application cited herein is not, and should not be taken as, an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world. In addition, any priority document(s) of this application is / are hereby incorporated herein by reference in its / their entirety.

Claims

WHAT IS CLAIMED IS:

1. A method of diagnosing a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH of a subject having an alcohol consumption below 30 g a day comprising:(a) measuring in a blood sample of the subject an 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); and(b) ruling-in the liver disease or condition on the basis of said amount of said at least two protein biomarkers.

2. The method of claim 1, wherein no more than 5 proteins are analyzed.

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

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

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

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

7. The method of any one of claims 1-6, wherein said at least two protein biomarkers comprise C7 and QSOX1.

8. The method of any one of claims 1-6, wherein said at least two protein biomarkers comprise C7 and ICAM.

9. The method of any one of claims 1-6, wherein said at least two protein biomarkers comprise C7 and GP5.

10. The method of any one of claims 1-6, wherein an increase in said amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis and / or a decrease in said amount of GP5 below apredetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

11. The method of any one of claims 1-6, wherein said amount of C7, ICAM or QSOX1 below a predetermined level is indicative of non- significant liver fibrosis and / or said amount of GP5 above a predetermined level is indicative of non- significant liver fibrosis.

12. The method of any one of claims 1-11, wherein said at least two protein biomarkers comprises C7, QSOX1 and GP5.

13. The method of any one of claims 1-11, wherein said at least two protein biomarkers comprises C7, QSOX1 and ICAM.

14. The method of any one of claims 1-13, wherein said at least two protein biomarkers comprises C7, QSOX1, ICAM and GP5.

15. The method of any one of claims 1-14, further comprising measuring an amount of at least one additional protein selected from the group consisting of: Collectin- 10 (COL 10), collectin- 11 (COLECI 1) 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).

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

17. The method of any one of claims 1-15, wherein the subject is pre-diagnosed as having NASH.

18. The method of any one of claims 1-17, wherein the subject has type 2 Diabetes.

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

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

21. The method of any one of claims 1-20, wherein the measuring is effected on the protein level.

22. The method of any one of claims 1-20, wherein the measuring is effected on the RNA level.

23. The method of any one of claims 1-20, wherein the ruling-in takes into account a clinical parameter of the subject.

24. The method of claim 23, wherein the clinical parameter is 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 method of claim 24, wherein the diagnosing comprises:(a) applying a pre-determined mathematical function on the amount of said at least two proteins to compute a score; and(b) comparing the score to a predetermined reference value.

26. The method of claim 24, wherein said mathematical function comprises a heavier weight of C7 as compared to a weight of QSOX1, ICAM and / or GP5.

27. The method of claim 24, wherein said pre-determined mathematical function is derived from a machine learning algorithm.

28. A method of treating a subject having a liver disease or condition comprising:(a) ruling in the disease or condition in the subject according to any one of claims 1-25; and(b) treating the subject with at least one treatment selected from the group consisting of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate,PXL065, bio89-100, HM15211, MSDC-0602K, Ternl01+501 combo, GSK4532990, HepaStem, ALN-HSD and Efruxifermin, thereby treating the subject.

29. A method of identifying a stage of fibrosis in a patient or stage of fibrosis in combination with NASH or diagnosing NASH or diagnosing “at-risk” NASH patients, the method comprising:(a) determining the concentration of at least two biomarkers in serum from the patient, wherein the at least two biomarkers are selected from the group consisting of: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VCAM1); and(b) determining the stage of fibrosis in the patient or diagnose NASH or diagnosing “at- risk” NASH or diagnosing stage of fibrosis in combination with NASH, patients based on the concentration of the at least two biomarkers.

30. The method of claim 29, wherein the stage of fibrosis is selected from any one of: significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4).

31. The method of claim 29, wherein no more than 5 proteins are analyzed.

32. The method of claim 29, wherein the subject is male and has an alcohol consumption below 30 g a day or the subject is female and has an alcohol consumption below 20 g a day.

33. The method of claim 29, wherein determining the stage of fibrosis in the patient comprises:(i) inputting the concentration from step (a) into an algorithm to generate a score for each stage of fibrosis, or stage of fibrosis in combination with NASH, or for ruling in NASH or for ruling in “at-risk” NASH(ii) comparing the score for a stage of fibrosis or stage of fibrosis in combination with NASH or NASH or for “at-risk” NASH to a predetermined cutoff value for the stage of fibrosis, NASH or for “at-risk” NASH; and(iii) determining the stage of fibrosis or diagnose NASH or diagnose “at-risk” NASH or diagnose stage of fibrosis in combination with NASH based on comparison between the score and a predetermined cutoff value.

34. The method of claim 28, 29 or 33, comprising determining the concentration of C7 and QSOX1.

35. The method of any one of claims 28, 29 or 33, comprising determining the concentration of C7 and GP5.

36. The method of any one of claims 28, 29 or 33, comprising determining the concentration of C7 and ICAM1.

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

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

39. The method of any one of claims 28-38, wherein the patient is prediagnosed as having nonalcoholic fatty liver disease (NAFLD).

40. The method of any one of claims 28-39, wherein the patient is pre-diagnosed as being “at-risk” NASH.

41. The method of any one of claims 28-40, wherein the patient has Type 2 Diabetes.

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

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

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

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

46. The method of claim 45, wherein the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine.

47. The method of any one of claims 28-46, comprising administering at least one treatment for liver fibrosis and / or liver steatosis and / or liver inflammation to the patient.

48. The method of claim 46, wherein the at least one treatment is selected from one or more of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Ternl01+501 combo, GSK4532990, HepaStem, ALN-HSD, Efruxifermin.

49. A method for monitoring the effectiveness of treatment for liver fibrosis with or without combination of NASH of a subject comprising(a) administering the treatment to the subject;(b) measuring the level of at least two biomarkers in a blood sample 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 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VCAM1); and(c) comparing the level of said at least two biomarkers to a level of said at least two biomarkers obtained prior to the administering, wherein the effectiveness of the treatment is monitored by a change in the level of said at least two biomarkers.

50. A method for monitoring progression of a liver fibrosis of a subject comprising:(a) measuring, at a first time point, the level of at least two biomarkers in a blood sample 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 (COLIO), collectin-11 (COLECI 1) 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), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VC AMI);(b) measuring, at a later time point, the level of at least two biomarkers in a blood sample from the subject, and(c) comparing the level of said at least two biomarkers at said first time point and at said second time point, wherein a change in the level of said at least two biomarkers is indicative of the progression or regression of the liver fibrosis of the subject.

51. The method of claim 49 or 50, wherein said at least two protein biomarkers comprise C7 and QSOX1.

52. The method of claim 49 or 50, wherein said at least two protein biomarkers comprise C7 and ICAM.

53. The method of claim 49 or 50, wherein said at least two protein biomarkers comprise C7 and GP5.

54. The method of claim 49 or 50, wherein said at least one protein biomarker comprises C7, QSOX1 and GP5.

55. The method of claim 49 or 50, wherein said at least one protein biomarker comprises C7, QSOX1 and ICAM.

56. The method of claim 49 or 50, wherein said at least two protein biomarkers comprises C7, QSOX1, ICAM and GP5.

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