WO2015032414A2

WO2015032414A2 - Methods and tools for predicting liver fibrosis

Info

Publication number: WO2015032414A2
Application number: PCT/DK2014/050274
Authority: WO
Inventors: Henning GRØNBÆK; Konstantin KAZANKOV; Holger Jon MØLLER; Bo Martin BIBBY; Jacob George
Original assignee: Aarhus Universitet; Region Midtjylland; The University Of Sydney; Westmead Hospital
Priority date: 2013-09-05
Filing date: 2014-09-05
Publication date: 2015-03-12
Also published as: WO2015032414A3

Abstract

The present application relates to methods and tools for assessing disease progression of liver fibrosis. The methods are based on the demonstration that sCD163 levels correlate significantly with Scheuer fibrosis scores, and that consequently the use of sCD163 serum level can be used to improve existing fibrosis diagnostic scores. In other aspects there is provided novel non-invasive fibrosis scores based on assessment of a few easily detectable markers, including sCD163, age, AST, platelets and optionally fasting insulin and fasting glucose.

Description

Methods and tools for predicting liver fibrosis Field of invention The present application relates to methods and tools for assessing liver fibrosis, in particular in HCV and HBV patients.

Background Worldwide chronic viral hepatitis is a disease of major importance with increased risk of progression to cirrhosis and for the development of primary liver cancer. The WHO has estimated the prevalence of chronic viral hepatitis C infection (HCV) to be -170 million, however, the prevalence rates vary widely: From <2.5% in North America, Europe, Australia and Far East to 2.5-10% in some Mediterranean countries, South America, Africa and the Middle East; and >10% in e.g. Bolivia, Burundi, Cameroon, Egypt, Gabon, Guinea, Mongolia and Rwanda.

The prevalence of chronic viral hepatitis B infection (HBV) is highly variable, ranging from 0.1 % in the United States to 20-30% in some Pacific Island nations. There are an estimated 360 million people who are chronically infected.

Liver fibrosis is the hallmark of progression of many chronic liver diseases (e.g.

hepatitis B, C, alcoholic- and non-alcoholic fatty liver disease) and the reason for increased morbidity and mortality in these patients. The gold standard to assess liver fibrosis and cirrhosis has to date been to perform a liver biopsy, which is an invasive and potentially dangerous procedure.

There is therefore a need for new diagnostic markers for the prediction of liver fibrosis, especially in patients with chronic viral hepatitis. Significant fibrosis is a key factor for selecting patients at risk of developing more advanced liver disease and therefore also to initiate anti-viral treatment.

A number of non-invasive methods for assessing status of liver disease in patients with Hepatitis B or C are reviewed by Castera (18). According to Castera, the clinically relevant end points are detection of significant fibrosis (METAVIR, F≥ 2, or Ishak≥ 3), which indicates that patients with hepatitis B or C should receive antiviral treatment and detection of cirrhosis (METAVIR, F4 or Ishak 5-6) which indicates that the patients should be monitored for complications related to portal hypertension and hepatocellular carcinoma (HCC).

In order to apply the appropriate treatment at an early state of disease progression, there is a need in the field for methods displaying improved sensitivity and specificity in particular for diagnosing significant fibrosis. Summary of invention

The present inventors have developed novel non-invasive fibrosis scores that can identify patients with chronic viral hepatitis with significant fibrosis and the need for antiviral therapy.

The fibrosis scores are based on the biomarker soluble CD163 (sCD163) and selections of other markers. The novel fibrosis scores have been tested in a large cohort of patients with chronic viral hepatitis B and C. In the study underlying the present application the inventors assessed macrophage activation by means of soluble CD163 (sCD163) and related the findings to

biochemical and histological parameters of the severity of liver disease.

The inventors demonstrated that sCD163 levels were elevated in chronic viral hepatitis and that the levels correlated with the severity of liver disease and thus that sCD163 serves as an independent marker of advanced disease.

Moreover, the inventors examined sCD163 in HCV and HBV separately and in a combined cohort of patients for their association with fibrosis stage.

The inventors furthermore examined different combinations of parameters to develop the novel fibrosis score. It was demonstrated that the combination of sCD163, age, aspartate transaminase (AST), and platelet count had superior performance in terms of area under ROC (AUROC) in relation to prior art fibrosis scores such as APRI and FIB- 4 in the prediction of fibrosis stage. The new score based on these markers is as good as or better than the known scores at diagnosing late stage fibrosis and cirrhosis and superior in diagnosing significant fibrosis (Scheuer score≥ F2). This means that the method of diagnosis will result in a higher number of correct diagnoses than the prior art methods and algorithms.

A further advantage of the fibrosis algorithms of the present patent application is that they have been established for both hepatitis B and C patients. This indicates that the fibrosis algorithms are generally applicable for diagnosing liver fibrosis and thus can be used when the cause of the fibrosis is different from HCV and HBV infection.

The algorithm/score based on sCD163, age, AST, and platelet count can be further refined by additionally including the marker HOMA-IR which comprises the product of fasting insulin and fasting glucose divided by 405. In further embodiments, the inventors have calculated new fibrosis scores for subjects diagnosed with HCV and HBV separately. The new HCV fibrosis score is based on a combination of sCD163, age, AST, platelet count and HOMA-IR and is superior to known non-invasive fibrosis scores when it comes to distinguishing significant, advanced fibrosis and cirrhosis from lesser degrees of fibrosis. An even better HCV fibrosis score can be provided by additionally using INR (International Normalised

Ratio). The HCV specific fibrosis score is superior to known scores APRI and FIB-4 in predicting significant and advanced fibrosis and cirrhosis.

Also provided is a novel HBV fibrosis score based on sCD163, gender and BMI.

The new algorithms can be used to establish cutoff values for different fibrosis and cirrhosis stages wherein any cutoff value is associated with a sensitivity, a specificity, and positive and negative predictive values. In one embodiment, a single cut-off is used to discriminate between patients with and without significant fibrosis. In other embodiments, two cut-off values are used to discriminate between patients without significant fibrosis, patients with significant fibrosis, and patients for which a statistically reliable diagnosis cannot be determined. Alternatively, the calculated fibrosis scores can be used to establish a relationship between the calculated fibrosis score and the clinical fibrosis score (Scheuer,

METAVIR, or lshak). Thus, the present inventors hereby provide novel sCD163 based fibrosis scores enabling clinicians to more accurately assess disease stage and progression of liver fibrosis, thus improving implementation of the appropriate treatment regime.

Accordingly, in one aspect the application concerns a method of diagnosing the presence or severity of liver fibrosis in an individual, comprising the steps of:

a. determining the marker sCD163 in a sample from said individual;

b. determining the further marker platelet number in a sample from said individual;

c. determining the further marker AST in a sample from said individual; and d. determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163, and said further markers.

Depending on the clinical setting and infrastructure available, it may in certain aspects be advantageous to combine the biomarker sCD163 with one or more other markers of fibrosis.

Thus in one aspect the present application concerns a method of diagnosing the presence or severity of liver fibrosis in an individual, comprising the steps of:

a. determining the marker sCD163 in a sample from said individual;

b. determining at least one further marker selected from the group consisting of platelet number, insulin, glucose, AST, ALT, age, hyaluronate, bilirubin, alpha-2- macroglobulin, alkaline phosphatase, gamma-globulin, albumin, prothrombin- index, INR, gammaGT, age, urea, uric acid, ferritin, cholesterol, alcohol use, gender, TIMP-1 , MMP1 , PIIINP, HOMA-IR, BMI, waist circumference, CRP, cytokeratin 18; and

c. determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163 and said at least one further marker.

The present inventors have demonstrated that the present application is suitable for diagnosing liver fibrosis in patients suffering from Hepatitis C or B infections. Thus in one aspect the application concerns a method of diagnosing liver fibrosis caused by HCV or HBV infection, said method comprising performing a diagnosis as defined herein above, and comparing a fibrosis score to at least one cutoff value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score). The present methods are useful both for binary diagnosis of whether a patient has liver fibrosis or not, and additionally to determine disease progression of liver fibrosis.

Accordingly, in one aspect the application concerns a method of differentiating between no or mild fibrosis (<F2, Scheuer score) from significant fibrosis (≥F2, Scheuer score), said method comprising calculating a fibrosis score as defined herein, and comparing a fibrosis score to a cutoff value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score).

Similarly, in another aspect the application concerns a method of assessing the stage of a liver disease said method comprising performing a diagnosis as defined herein above and assessing the stage of fibrosis based on said diagnosis.

Based on the present application it is possible to determine the optimal time for administration of an antiviral drug. Thus in one aspect the application concerns a method of deciding to provide or defer antiviral therapy, said method comprising performing a diagnosis as defined herein above, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score), and deferring antiviral therapy if the individual is diagnosed to have no or mild fibrosis (<F2, Scheuer score). In an alternative aspect, the application concerns a method of treatment of HBV or HCV, said method comprising performing a diagnosis as defined herein above, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score) based on one of the fibrosis scores of the present application. The method may involve diagnostic steps for patients, who are predicted not to have significant fibrosis and for patients where significant fibrosis cannot be excluded.

When performing a treatment, it is advantageous to monitor the effect of said treatment in order to provide the accurate type and dosage of the appropriate medication. Thus in one aspect the application concerns a method of monitoring treatment response in an individual, said method comprising performing a diagnosis as defined herein above, calculating a fibrosis score, treating said individual, repeating said diagnosis and calculating the fibrosis score again, and comparing said scores to determine whether said treatment is effective. A disease like liver fibrosis has, like several other diseases and disorders, a degree of progression thus worsening the condition of the patient suffering there from over time. It is thus desirable to be able to monitor disease progression over time. The present application in one aspect provides a method of monitoring disease progression, wherein said method comprises performing a diagnosis as defined herein above, calculating a fibrosis score, repeating said diagnosis and calculation of fibrosis score and comparing said scores to determine whether the disease progresses.

Under certain conditions, it is advantageous to perform the methods of the present application using computer assisted measurement and computing. Thus in one aspect the application concerns a computer-implemented method for diagnosing liver fibrosis, said method comprising entering the level of sCD163, AST, platelet number, and optionally fasting glucose and fasting insulin of a sample from a subject and age of the subject to a computer having an input device, a processor and an output device, the processor comprising software for computing a fibrosis score as herein defined, the method further comprising outputting said fibrosis score to an output device.

As mentioned above, the computer assisted methods for diagnosing liver fibrosis can be used separately or be built into a system suitable for the intended purpose. In one aspect the application thus concerns a system for diagnosing the presence or severity of liver fibrosis in an individual, comprising

a) An input device for entering data including levels of sCD163 concentration, age, AST activity, number of platelets, and optinally fasting glucose and fasting insulin;

b) A processor in data communication with said input device, the processor comprising software for computing a fibrosis score as herein defined; and

c) An output device for displaying or printing said fibrosis score.

The system may further comprise an algorithm for comparing the fibrosis score to one or more pre-determined cut-off values and means for displaying a diagnosis associated with the fibrosis score. The present application provides an improved fibrosis score for non-invasive diagnosis and assessment of disease progression of liver fibrosis. The fibrosis score can be used for generating a resulting report. Accordingly in one aspect the application concerns a fibrosis diagnosis report comprising:

a. Information regarding the identity and age of a patient;

b. Information regarding the level of sCD163, AST, platelets, and optionally fasting glucose and fasting insulin in a sample from said subject;

c. a fibrosis score calculated using the algorithm as herein defined; and d. a comparison of said fibrosis score to at least one cut-off value to

diagnose the presence or extent of fibrosis.

The diagnostic methods of the present application may be used as part of a diagnostic procedure or as part of a Clinical decision support system. That is to say that the medical doctor making the diagnosis may use the present diagnostic methods as one of several steps to reach a diagnostic conclusion. Further, as the present application relates to diagnosis of living beings, there is variation in the results. There is for all measured markers used herein outliers with unusually high or low values for one or more markers. This means that a diagnosis cannot always be made with 100% certainty. Any diagnostic prediction is associated with some level of uncertainty typically reflected in the sensitivity, specificity, accuracy, negative and positive predictive values of a prediction. As a consequence there will often be false positives and false negatives as described herein in further detail.

Description of Drawings

Figure 1 : Soluble CD163 in histological scores of inflammatory activity and fibrosis in patients with HBV and HCV infection. A) sCD163 and Scheuer Lobular Inflammation score (0-4). Spearman's rho=0.29; p<0.001 (unadjusted). B) sCD163 and Scheuer Portal Inflammation score (0-4). Spearman's rho=0.44; p<0.001 (unadjusted). C) sCD163 and Scheuer Fibrosis score (0-4). Spearman's rho=0.45; p<0.001

(unadjusted). Boxes represent interquartile ranges with medians; whiskers show adjacent values (the highest value lower or equal to: 75% quartile +1.5 x interquartile range; the lowest value higher or equal to: 25% quartile -1.5 x interquartile range). Punctured lines represent reference interval (0.89-3.95 mg/L)

*p<0.05 between patients with HBV and HCV infection; **p<0.01 between patients with HBV and HCV infection. Figure 2: A) Receiver Operating Characteristics (ROC) analysis showing the predictive value of the sCD163-based fibrosis score (CD163-FS) for significant fibrosis (F≥2). Circles mark cut-off values of CD613-FS (1 : cut-off at 2.75; 2: cut-off at 4.75). Area Under the ROC Curve (AUROC) = 0.78 (95% CI: 0.74-0.81)

B) Receiver Operating Characteristics (ROC) analysis showing the predictive value of CD163-HOMA-FS for significant fibrosis (F≥2). Circles mark cut-off values of FS- CD163 (1 : cut-off at 2.9; 2: cut-off at 5.1). Area Under the ROC Curve (AUROC) = 0.78 (95% CI: 0.74 - 0.82).

Figure 3: A) Median levels of sCD163 for different stages of Scheuer fibrosis in the combined group of patients with chronic viral hepatitis B and C. Boxes represent interquartile ranges with medians; whiskers show adjacent values (the highest value lower or equal to: 75% quartile +1.5 x interquartile range; the lowest value higher or equal to: 25% quartile -1.5 x interquartile range). Punctured lines represent reference interval (0.89-3.95 mg/L).

Figure 4: Receiver Operating Characteristics (ROC) analysis showing the predictive value of CD163-FS for significant fibrosis (F≥2). A: Patients with HCV infection, AUROC = 0.77 (95% CI: 0.74 - 0.83); B: Patients with HBV infection, Area Under the ROC Curve (AUROC) = 0.74 (95% CI: 0.65 - 0.83).

Figure 5.

A) Association between CD163-FS and the Scheuer fibrosis score (0-4). Boxes represent interquartile ranges with medians; whiskers show adjacent values (the highest value lower or equal to: 75% quartile +1.5 x interquartile range; the lowest value higher or equal to: 25% quartile -1.5 x interquartile range). Spearman's rho=0.53, pO.001.

B) Association between CD163-HOMA-FS levels and the Scheuer fibrosis score (0-4). Boxes represent interquartile ranges with medians; whiskers show adjacent values (the highest value lower or equal to: 75% quartile +1.5 x interquartile range; the lowest value higher or equal to: 25% quartile -1.5 x interquartile range). Spearman's rho=0.55, pO.001. Figure 6. Soluble CD163 in histological scores of inflammatory activity and fibrosis in patients with HCV and HBV infection. A sCD163 and Scheuer Lobular Inflammation score (0-4). HCV: Spearman's rho=0.31 , p<0.001 ; HBV: rho=0.31 , pO.001 B sCD163 and Scheuer Portal Inflammation score (0-4). HCV: rho=0.39, p<0.001 ; HBV:

rho=0.42, p<0.001 C sCD163 and Scheuer Fibrosis score (0-4). HCV: rho=0.45, pO.001 ; HBV: rho=0.32, pO.001.

Boxes represent interquartile ranges with medians; whiskers show adjacent values (the highest value lower or equal to: 75% quartile +1.5 x interquartile range; the lowest value higher or equal to: 25% quartile -1.5 x interquartile range). Punctured lines represent reference interval (0.89-3.95 mg/L)

*p<0.05 between patients with HCV and HBV infection; **p<0.01 between patients with HCV and HBV infection

Figure 7. Receiver Operating Characteristics (ROC) analysis showing the predictive value of the sCD163-based Fibrosis Scores (CD163-HCV-FS and CD163-HBV-FS) for significant fibrosis (F≥2) in patients with HCV and HBV infection.

A ROC for CD163-HCV-FS. Circles mark cut-off values of CD613-FS (1 : cut-off at 1.55; 2: cut-off at 3.50; 3: cut-off at 2.60). Area Under the ROC Curve (AUROC) = 0.79 (95% CI: 0.74-0.83)

B ROC for CD163-HBV-FS. Circles mark cut-off values of CD613-FS (1 : cut-off at 5.0; 2: cut-off at 6.50; 3: cut-off at 5.80). AUROC = 0.71 (95% CI: 0.62-0.79)

Figure 8. INR distribution in patients with HCV and HBV infection.

Definitions

Biological sample: The term 'biological sample' used herein refers to any sample selected from the group, but not limited to, serum, plasma, whole blood, saliva, urine, lymph, a biopsy, semen, faeces, tears, sweat, milk, cerebrospinal fluid, ascites fluid, synovial fluid.

Binding assay: The term 'binding assay' used herein refers to any biological or chemical assay in which any two or more molecules bind, covalently or noncovalently, to each other thereby enabling measuring the concentration of one of the molecules . CD163: The term CD 163 as used herein is an abbreviation for Cluster of Differentiation 163 which is the polypeptide encoded by the CD163 gene. While CD163 is a type-1 membrane protein the term CD163 as used herein refers to both soluble (CD163 lacking its transmembrane anchor) and membrane-bound forms. CD163 is also known as Hemoglobin receptor, Haptoglobin-Hemoglobin receptor, Hemoglobin scavenger receptor, HbSR and M130.

Chromatographic method: The term 'chromatographic method' used herein refers to a collective term for the process of separating mixtures. It involves passing a mixture dissolved in a "mobile phase" through a stationary phase, which separates the analyte to be measured from other molecules in the mixture and allows it to be isolated.

Detection moiety: The term 'detection moiety' used herein refers to a specific part of a molecule, preferably but not limited to be a protein, able to bind and detect another molecule.

Disorder: The term 'disorder' used herein refers to a disease or medical problem, and is an abnormal condition of an organism that impairs bodily functions, associated with specific symptoms and signs. It may be caused by external factors, such as invading organisms, or it may be caused by internal dysfunctions.

International Normalised Ratio (INR): The result (in seconds) for a prothrombin time performed on a normal individual will vary according to the type of analytical system employed. This is due to the variations between different batches of manufacturer's tissue factor used in the reagent to perform the test. The INR was devised to standardize the results. Each manufacturer assigns an ISI value (International Sensitivity Index) for any tissue factor they manufacture. The ISI value indicates how a particular batch of tissue factor compares to an international reference tissue factor. The ISI is usually between 1.0 and 2.0. The INR is the ratio of a patient's prothrombin the power of the ISI value

Log: as used herein represents the natural logarithm. Prognostic marker: The term 'prognostic marker' used herein refers to the

characteristic of a compound, such as a protein, that can be used to estimate the chance of contracting a disease over a period of time in the absence of therapy. Protein: The term 'protein' used herein refers to an organic compound, also known as a polypeptide, which is a peptide having at least, and preferably more than two amino acids. The generic term amino acid comprises both natural and non-natural amino acids any of which may be in the 'D' or 'L' isomeric form. Risk factor: The term 'risk factor' used herein refers to a variable associated with an increased risk of disease or infection. Risk factors are correlational and not necessarily causal, because correlation does not imply causation.

SCD163: The term soluble CD163 = shed CD163 = plasma CD163 = serum CD163 = circulating CD163.

Soluble: The term 'soluble' used herein refers to the property of a solid, liquid, or gaseous chemical substance to dissolve in a liquid solvent to form a homogeneous solution. Further it refers to a compound, such as a protein, being in liquid solution as not being attached to a membrane or other anchoring or attaching moeities.

Statistical parameters: The clinical parameters of sensitivity, specificity, negative predictive value, positive predictive value and accuracy are calculated using true positives, false positives, true negatives and false negatives.

A "true positive" sample is a sample positive for the indicated stage of fibrosis according to clinical biopsy, which is also diagnosed positive according to a method of the application. A "false positive" sample is a sample negative for the indicated stage of fibrosis by biopsy, which is diagnosed positive according to a method of the application. Similarly, a "false negative" is a sample positive for the indicated stage of fibrosis by biopsy, which is diagnosed negative according to a method of the application. A "true negative" is a sample negative for the indicated stage of fibrosis by biopsy, and also negative for fibrosis according to a method of the application. As used herein, the term "sensitivity" means the probability that a diagnostic method of the application gives a positive result when the sample is positive, for example, fibrotic with a Scheuer score of F2-F4. Sensitivity is calculated as the number of true positive results divided by the sum of the true positives and false negatives. Sensitivity essentially is a measure of how well a method correctly identifies those with fibrotic disease. In a method of the application, the cutoff values can be selected such that the sensitivity of diagnosing an individual is at least about 70%, and can be, for example, at least 75%, 80%, 85%, 90% or 95%. This can be done using the ROC (receiver operating curves) herein disclosed. As used herein, the term "specificity" means the probability that a diagnostic method of the application gives a negative result when the sample is not positive, for example, not of Scheuer fibrosis stage F2-F4. Specificity is calculated as the number of true negative results divided by the sum of the true negatives and false positives. Specificity essentially is a measure of how well a method excludes those who do not have fibrosis. In a method of the application, the cut-off values can be selected such that the specificity of diagnosing an individual is in the range of 70-100%, for example, at least 75%, 80%, 85%, 90% or 95%. This can be done using the ROC (receiver operating curves) herein disclosed. The term "negative predictive value," as used herein, is synonymous with "NPV" and means the probability that an individual diagnosed as not having fibrosis actually does not have the disease. Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. In a method of the application, the lower cut-off values can be selected such that the negative predictive value is in the range of 70-99%, such as 75-99% and can be, for example, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95%.

The term "positive predictive value," as used herein, is synonymous with "PPV" and means the probability that an individual diagnosed as having fibrosis actually has the condition. Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. Positive predictive value is determined by the characteristics of the diagnostic method as well as the prevalence of fibrosis in the population analyzed. In a method of the application, the higher cut-off values can be selected such that, the positive predictive value of the method is at least about 70%, and can be at least about 75%, at least 80%, at least 85%, at least 90% or at least 95%

Detailed description Liver fibrosis is the hallmark of progression of many chronic liver diseases (e.g.

hepatitis B, C, alcoholic- and non-alcoholic fatty liver disease) and the reason for increased morbidity and mortality in these patients. Since the obtaining of liver biopsies is a both painful and potentially dangerous procedure, non-invasive methods for diagnosing stages of liver fibrosis has been developed. The present inventors have found that when the biomarker sCD163 is included along with a number of other key parameters, improved specificity and sensitivity is obtained allowing a more accurate diagnosis to be performed at an early stage of the progressing disease.

Liver and other fibrotic disorders

The methods of the application can be useful for diagnosing the presence or severity of liver fibrosis in a variety of individuals including those at risk for, or having one or more symptoms of, a liver disorder characterized by fibrosis. The methods of the application can be used to diagnose liver fibrosis in an individual having, for example, viral hepatitis such as hepatitis A, B or C virus; chronic persistent hepatitis or chronic active hepatitis; autoimmune liver disease such as autoimmune hepatitis; alcoholic liver disease; fatty liver disease; non-alcoholic liver disease including non-alcoholic fatty liver disease and non-alcoholic steatohepatitis (NASH) ; primary biliary cirrhosis;

primary sclerosing cholangitis, biliary atresia; liver disease resulting from medical treatment (drug-induced liver disease); or a congenital liver disease. The methods of the application can also be extremely useful in alleviating concerns of potential liver damage due to medical treatment (e.g. methotrexate treatment). Periodic monitoring of liver fibrosis in individuals treated with methotrexate or other drugs associated with risk of liver damage can be conveniently performed using the non-invasive methods of the application, without the risks associated with liver biopsy.

In one embodiment, the methods of the application are useful for differentiating individuals having a Scheuer fibrosis score of F0 or Fl from individuals having a Scheuer fibrosis score of F2, F3 or F4. Scheuer scoring is a well-accepted system for grading liver biopsy specimens. F0 is equivalent to the absence of fibrosis; Fl signifies enlarged fibrotic portal tracts. F2 signifies portal fibrosis. F3 signifies distortion without cirrhosis. F4 signifies cirrhosis.

In another embodiment, the methods of the application are useful for differentiating individuals having a Metavir score of FO or Fl from individuals having a Metavir score of F2, F3 or F4. Metavir scoring is a well-accepted system for grading liver biopsy specimens and is described in Knodell, 1981. FO is equivalent to the absence of fibrosis; Fl signifies portal fibrosis without septa. F2 signifies portal fibrosis with a few septa. F3 signifies numerous septa without cirrhosis. F4 signifies cirrhosis.

It is understood that the methods of the application are useful for diagnosing the presence or severity of fibrosis associated with a variety of fibrotic disorders, including but not limited to liver fibrosis, pulmonary fibrosis, kidney fibrosis, prostate fibrosis and breast fibrosis. The methods of the application can be applied, without limitation, to diagnosing the presence or severity of pulmonary fibrosis, for example, idiopathic pulmonary fibrosis or emphysema; kidney fibrosis; bladder fibrosis; periureteric fibrosis or retroperitoneal fibrosis; endomyocardial fibrosis, aortic aneurysm disease;

rheumatoid diseases such as rheumatoid arthritis or systemic lupus erythematosus; or another fibrotic disorder such as Alzheimer's disease. It is understood that algorithms and other combinations of markers disclosed herein as useful for diagnosing the presence or severity of liver fibrosis also can be used to diagnose the presence or severity of fibrosis in another disorder.

It is understood that the diagnostic methods of the application are applicable to a variety of individuals including individuals with chronic or active disease, individuals with one or more symptoms of fibrotic disease, asymptomatic or healthy individuals and individuals at risk for one or more fibrotic disease. It further is clear to the skilled person that the methods of the application can be useful, for example, to corroborate an initial diagnosis of disease or to gauge the progression of fibrosis in an individual with a previous definitive diagnosis of fibrotic disease. The methods of the application can be used to monitor the status of fibrotic disease over a period of time and further can be used, if desired, to monitor the efficacy of therapeutic treatment. If desired, the results obtained from a sample from an individual undergoing therapy can be compared, for example, to the individual's baseline results prior to treatment, to results earlier during treatment, or to a historic or reference value. The methods of the application are useful for diagnosing the severity of liver or other fibrosis in an individual. Thus, the methods of the application can be useful for determining the "stage" or extent of liver or other fibrosis. In one embodiment, a method of the application is used to determine the Fibrosis score, e.g. Scheuer, Metavir or Knodell (Ishak), of an individual, for example, an individual with viral hepatitis C. As indicated above, Scheuer or Metavir scoring is a well-established fibrosis scoring system using values of F0, Fl, F2, F3 and F4. In other embodiments, a method of the application is used to determine the Knodell score (histological activity index), or Ishak score (modified histological activity index) of an individual, for example, an individual with viral hepatitis C or hepatitis B. It is understood that, where the severity of liver or other fibrosis is determined according to a method of the application, any of the above or other art-accepted or clearly defined scoring systems can be useful in reporting results indicating the severity of fibrosis .

Disclosed below is a table with different state-of-the-art fibrosis scoring systems.

Common for these scoring systems is that they are based on invasive diagnosis, i.e. they all include taking of biopsies as opposed to the methods of the present application which are all non-invasive (with respect to the liver) methods, i.e. rely on a blood sample.

Table A. Scoring systems for chronic hepatitis.

A. Scheuer Score

Portal activity Lobular activity (grade) Fibrosis (stage)

0 None/minimal none none

1 mild Inflammation/no necrosis Enlarged fibrotic portal tracts

2 Mild interface hepatitis Focal apoptotic bodies Portal-portal linkage

(IH)

3 Moderate interface Severe focal cell damage Distortion, nor cirrhotic

hepatitis cirrhotic

4 Severe interface hepatitis Bridging necrosis Cirrhosis (or probable)

B METAIVR score

0 none No or mild AO: no PN or lobular activity

1 Focal PN some tracts At least 1 focus per lobule A1 : mild PN (grade 1) OR lobular grade 1 2 Diffuse PN some tracts Multiple foci per lobule OR A2: moderate PN (grade2) OR focal PN all tracts bridging necrosis OR lobular grade 2

3 Diffuse PN all tracts A3: PN grade 2 & lobular grade 2 OR severe PN (grade 3)

Fibrosis

F0 No fibrosis

F1 Portal fibrosis without

septa

F2 Portal fibrosis with rare

septa

F3 Numerous septa without

cirrhosis

F4 Cirrhosis

C. Ishak (modified Knodell) score

Necroinflammatory score

A 0-4 Periportal or periseptal interface hepatitis (piecemeal necrosis)

B 0-6 Confluent necrosis

C 0-4 Focal (spotty) lytic necrosis, apoptosis, focal inflammation

D 0-4 Portal inflammation

Fibrosis stage

0 No fibrosis

1 Fibrous expansion of some portal areas (with or without spurs)

2 Fibrous expansion of most portal areas (with or without spurs)

3 Fibrous expansion of most portal areas with occasional portal-portal linkage

4 Fibrous expansion of portal areas with marked portal-portal and some portal-central linkage

5 Marked bridging (P-P and P-C) with occasional nodules (incomplete cirrhosis)

6 Cirrhosis

Non-invasive fibrosis scores

A number of non-invasive fibrosis scores are known in the art. Examples of these are disclosed in Castera (18) and are listed below.

Non-invasive fibrosis scores for subjects with HCV • Fibrotest (Biopredictive, Paris, France) patented formula combining a-2- macroglobulin, vGT, apolipoprotein A1 , haptoglobin, total bilirubin, age, and gender.

• Forn index = 7.811 - 3.131 x In platelet count + 0.781 x In (GGT) + 3.467 x In (ag,e) - 0.014x(cholesterol)

• AST to platelet ratio (APRI) = AST(ULN)/platelet(10⁹/L)x100

• FibroSpectll (Promotheus Laboratory Inc, San Diego, CA) patented formula

combining a2-macroglobulin, hyaluronate, and TIMP-1

• MP3 = 0.5903 x log PIIINP (ngfnL) - 0.1749 x logMMP-1 (ngfnL)

· Enhanced liver fibrosis score (ELF) (iQur Ltd, Southampton, UK) patented formula combining age, hyaluronate, MMP-3, and TIMP-1

• Fibrosis probability index (FPI) = 10.929 + (1.827 x InAST) + (0.081 x age) +

(0.768 x past alcohol use³) + (0.385 x HOMA-IR) - (0.447 x cholesterol)

• Hepascore (PathWest, University of Western Australia, Perth, Australia) patented formula combining y=exp(-4.185818-0.0249*age) + (0.7464*gender) + (1.0039 a₂- macroglobulin) + (0.0302*hyaluronic acid) + (0.0691*bilirubin) - (0.0012*GGT)) wherein, age is provided in years; male gender=1 , female gender=0, . a₂- macroglobulin as reported in g/L; hyaluronic acid as reported in μg/L; bilirubin as reported in . μΓΤΐοΙ/L; and GGT as reported in U/L.

· Fibrometers (BioLiveScale, Angers, France) patented formula combining platelet count, prothrombin index, AST, α-2-macroglobulin, hyaluronate, urea, and age

• Lok index = -5.56 - 0.0089 x platelet (10/frim³) + 1.26 x AST/ALT ratio + 5.27 x INR

• Goteborg University cirrhosis index (GUCI) = AST x prothrombin-INR x

100/platelets

· Virahep - c model = 5.17 + 0.20 x race + 0.07 x age (years) + 1.19 In (AST [IU/L]) - 1.76 x In (platelet count [10³/mL]) + 1.38 x In (alkaline phosphatase [IU/L])

• Fibroindex = 1.738 - 0.064 x (platelets [10⁴/mm³]) + 0.005 x (AST [I U/L]) + 0.463 x (γ-globulin [g/dL])

• FIB-4 = age (years) + AST [U/L] / platelets [10⁹/L] x ALT [U/L]

· HALT-C model = - 3.66 - 0.00995 x (platelets [10³/mL]) + 0.008 x serum x TIMP-1 + 1.42 x log (hyaluronate)

Non-invasive fibrosis scores for subjects with HBV

• Hui score = 3.148 + 0.167 x BMI + 0.088 x bilirubin - 0.151 x albumin - 0.019 x platelet • Zeng score = 13.995 + 3.220 log (α-2-macroglobulin) + 3.096 (age) + 2.254 log (GGT) + 2.437 log (hyaluronate)

In aspects of the present application, the known serum-based non-invasive scores may be improved by adding to the score the assessment of a sCD163 value. If sCD163 value is added to any of the known non-invasive fibrosis scores this requires performance of a new trial comparable to the trial performed by the present inventors and calculation of new parameters. This can e.g. be done by using a multiple ordered logistic regression analysis as described in the examples of the present application.

Novel fibrosis scores

The present inventors have developed novel non-invasive fibrosis scores that can identify patients with chronic viral hepatitis with significant fibrosis, and the need for anti-viral therapy.

The fibrosis scores are based on the biomarker CD163 and a number of other markers. The novel fibrosis scores have been tested in a large cohort of patients with chronic viral hepatitis B and C.

In the study underlying the present application the inventors assessed macrophage activation by means of soluble CD163 (sCD163) and related the findings to

biochemical and histological parameters of the severity of liver disease. The inventors demonstrated that sCD163 levels were elevated in chronic viral hepatitis and that the levels correlated with the severity of liver disease in a multiple regression model and thus that sCD163 serves as an independent marker of advanced disease.

The inventors furthermore examined different combinations of parameters to develop novel fibrosis scores. It was demonstrated that a score based on the combination of sCD163, age, AST, and platelet count had superior performance in terms of area under ROC (AUROC) in relation to prior art fibrosis scores such as APRI and FIB-4 in the prediction of fibrosis stage. The new score (designated CD163-FS) is as good as or better than the known scores at diagnosing late stage fibrosis and cirrhosis and superior in diagnosing significant fibrosis (Scheuer score≥ F2). This means that the method of diagnosis will result in a higher number of correct diagnoses than the prior art methods and algorithms.

A further advantage of the fibrosis scores of the present patent application is that they have been established for both hepatitis B and C patients. This indicates that the fibrosis algorithms are generally applicable for diagnosing liver fibrosis and thus can be used when the cause of the fibrosis is different from HCV and HBV infection.

The score based on sCD163, age, AST, and platelet count can be further refined by additionally including the marker HOMA-IR which comprises the product of fasting insulin and fasting glucose divided by 405. This score is designated CD163-HOMA-FS

Also provided are novel scores specific for HCV and HBV patients respectively.

Preferably, the HCV and HBV specific scores are used for subjects that suffer from only HCV or HBV respectively and not from both types of hepatitis. Further it is preferred that the subjects do not suffer from HIV, such as that the subjects are diagnosed to be free of HIV. The presence and absence of HCV, HBV, and HIV can be determined by using state-of-the-art diagnostic methods.

Two different scores have been developed for subjects diagnosed with or being infected with HCV. The two scores are based on a mathematical combination of serum sCD163, age, AST, HOMA-IR, and Platelets. One of the scores further include INR.

Both scores are superior to the known scores APRI and FIB-4 in predicting significant and advanced fibrosis and cirrhosis (Tables 16 and 17a). The simplest HCV specific score is designated CD163-HCV-FS. The novel HBV specific score for subjects infected with or diagnosed with HBV is based on a mathematical combination of serum sCD163, gender (female=0; male=1), and BMI. This score, CD163-HBV-FS is also superior to APRI and FIB-4. The new scores can be used to establish cut-off values for different fibrosis and cirrhosis stages wherein any cut-off value is associated with a sensitivity, specificity, and positive and negative predictive values. Alternatively, the calculated fibrosis scores can be used to establish a relationship between the calculated fibrosis score and the histological fibrosis score (Scheuer, METAVIR, or lshak).

Prediction of fibrosis using non-invasive fibrosis scores

The present application concerns new and improved methods for non-invasive prediction of liver fibrosis. In one aspect it is based on the observation that sCD163 correlates strongly with Scheuer Lobular Inflammation Score, Scheuer Portal

Inflammation Score, and with Scheuer Fibrosis Score (Figure 1A, B, and C; Figure 6A, 6B, and 6C) for both HCV and HBV patients. A similar correlation is observed for the pooled material of HCV and HBV patients (Figure 3).

The current inventors therefore believe that the level of sCD163 can be used in combination with one or more other markers that can be assessed non-invasively. Examples of such markers include: platelet number, insulin, glucose, AST, ALT, age, hyaluronate, bilirubin, alpha-2-macroglobulin, alkaline phosphatase, gamma-globulin, albumin, prothrombin-index, INR (international normalised ratio), gammaGT, age, urea, uric acid, ferritin, cholesterol, alcohol use, gender, TIMP-1 , MMP1 , PIINP, HOMA-IR, BMI, waist circumference, CRP, and cytokeratin 18.

Using statistical models of the type used in the appended examples new and improved fibrosis scores can be developed using these markers.

Depending on the clinical setting and infrastructure available, it may in certain aspects be advantageous to combine the biomarker sCD163 with one or more other markers. Thus in one aspect the present application concerns a method of diagnosing the presence or severity of liver fibrosis in an individual, comprising the steps of:

a. determining the marker sCD163 in a sample from said individual;

b. determining at least one further marker selected from the group consisting of platelet number, insulin, glucose, AST, ALT, age, hyaluronate, bilirubin, alpha-2- macroglobulin, alkaline phosphatase, gamma-globulin, albumin, prothrombin- index, INR, gammaGT, age, urea, uric acid, ferritin, cholesterol, alcohol use, gender, TIMP-1 , MMP1 , PIINP, HOMA-IR, BMI, waist circumference, CRP, cytokeratin 18; and

The inventors similarly believe that existing non-invasive fibrosis scores can be improved with respect to specificity and/or selectivity using sCD163. Given the very close correlation between sCD163 and liver fibrosis demonstrated by the present inventors it is surprising that the marker has not yet been used in any algorithm for calculating a liver fibrosis score.

In one aspect the method of diagnosing the presence or severity of liver fibrosis in an individual, comprises the steps of

a. determining the marker sCD163 in a sample from said individual;

b. determining a fibrosis score selected from Fibrotest, Forn index, AST to platelet ratio (APRI), Fibrospectll, MP3, Enhanced Liver Fibrosis score, Fibrosis probability index, Hepascore, Fibrometers, Lok index, Goteborg University cirrhosis index, Virahep - c model, Fibroindex, FIB-4, HALT-C model, Hui score, and Zeng score; and

c. diagnosing the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163 and said fibrosis score. According to a further specific aspect of the application the present inventors have developed two new non-invasive fibrosis scores that are more accurate than known non-invasive fibrosis scores based on a few easily measurable markers.

a. determining the marker sCD163 in a sample from said individual;

c. determining the further marker AST in a sample from said individual; and d. determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163 and said further marker(s).

The method defined herein above may in certain embodiments be supplemented by inclusion of additional markers depending on the clinical utility of choice.

In one embodiment the method further comprises determining the marker fasting insulin in a sample from said individual. The determination of fasting insulin can be performed by any suitable method known by those of skill in the art.

In a further embodiment the method defined herein above further comprises determining the marker fasting glucose in a sample from said individual. The determination of fasting glucose can be performed by any suitable method known by those of skill in the art.

In a further embodiment of the present application, the method additionally includes determining the parameter age of the patient. sCD163 can be determined qualitatively or quantitatively. Preferably sCD163 is determined quantitatively which can be performed by any suitable method known by those of skill in the art. In one embodiment the level of sCD163 is determined using one or more anti-CD163 antibodies.

In a further embodiment of the present application, the method additionally includes determining the level of AST activity.

In one embodiment the fibrosis score, CD163-FS, is log(sCD163 (mg/L) x age (years) x AST (IU/L) /platelets (x10⁹/L)). This score is designated CD163-FS. In certain embodiment the method of the present application furthermore includes comparing the fibrosis score to at least one pre-determined cut-off value that is predictive of the presence of significant fibrosis (≥F2, Scheuer score) in the individual undergoing examination. In one embodiment said at least one cut-off value for the CD163-FS score is 4.75. A fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score). In one embodiment a cutoff value for the CD163-FS score is at least 4.75 and indicates a 79% probability of having significant fibrosis (≥F2, Scheuer score).

In another embodiment a cut-off value for the CD163-FS score is 2.75. A fibrosis score below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score) with a 84% probability.

In another embodiment the fibrosis score comprises the level of sCD163, age, AST activity, HOMA-IR, and platelet number. In one embodiment the fibrosis score, CD163-HOMA-FS is log(sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)). This score is designated CD163-HOMA-FS.

In an embodiment a cut-off value for the CD163-HOMA-FS score is 5.1. A fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score). In another embodiment a cut-off value for the CD163-HOMA-FS score is 2.9. A fibrosis score at or below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score) with a 84% probability. In another embodiment a fibrosis score for the CD163-HOMA-FS score is at least 5.1 and indicates an 80% probability of having significant fibrosis (≥F2, Scheuer score).

In one embodiment the fibrosis score is (0.5*log(sCD163 (mg/L)) + 1.5*log(Age (years)) + los(AST (IU/L))+ 0.25*(logHOMA-IR) - 1.5*log(Platelets (x10⁹/L)). This score is designated CD163-HCV-FS and can be used for subjects diagnosed with or being infected with HCV.

In an embodiment a cut-off value for the CD163-HCV-FS score is 3.5. A fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score). In another embodiment a cut-off value for the CD163-HCV-FS score is 1.55. A fibrosis score at or below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score) with a 82% probability. In another embodiment a fibrosis score for the CD163-HCV-FS score is at least 3.5 and indicates an 82% probability of having significant fibrosis (≥F2, Scheuer score).

If only one cutoff value is used to distinguish between HCV-infected subjects having significant fibrosis (≥F2, Scheuer score) and subjects not having significant fibrosis (<F2, Scheuer score), a value of 2.6 can be used. Subjects having a score of 2.6 or more have a 73% probability of having at least significant fibrosis. Subjects having a score below 2.6 have a 73% probability of not having significant fibrosis. Another HCV-specific fibrosis score is CD163-+HCV-FS1= 0.5*log(sCD163 (mg/L)) + 1.5*log(Age (years)) + log(AST (IU/L)) + 0.5*logHOMA-IR + 5*loglNR - 1.5*log(Platelets (x10⁹/L)). This score predicts fibrosis with improved statistical certainty compared to CD163-HCV-FS. In one embodiment the fibrosis score, CD163-HBV-FS is CD163-HBV-FS=

1.5*log(sCD163 (mg/L)) + 0.8*(gender (female=0, male=1)) - 2*log(BMI (kg/m²)) + 10. This score is designated CD163-HBV-FS and can be used for subjects diagnosed with or being infected with HBV. In an embodiment a cut-off value for the CD163-HBV-FS score is 6.5. A fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score). In another embodiment a cut-off value for the CD163-HCV-FS score is 5. A fibrosis score at or below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score) with a 88% probability. In another embodiment a fibrosis score for the CD163-HOMA-FS score is at least 6.5 and indicates an 74% probability of having significant fibrosis (≥F2, Scheuer score).

If only one cutoff value is used to distinguish between HBV-infected subjects having significant fibrosis (≥F2, Scheuer score) and subjects not having significant fibrosis (<F2, Scheuer score), a value of 5.8 can be used. Subjects having a score of 5.8 or more have a 53% probability of having at least significant fibrosis. Subjects having a score below 5.8 have a 84% probability of not having significant fibrosis.

In preferred embodiments of the application the methods comprise comparing a determined fibrosis score to both a higher and a lower cut-off value to distinguish between subjects not at risk of having fibrosis, subjects at high risk of having fibrosis and subjects about which no statistically reliable prediction can be made.

In another embodiment the method defined herein above further comprises providing at least one statistical parameter relating to the fibrosis score. In one such embodiment, the statistical parameter is a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score. The fibrosis score can e.g. be selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

The samples used for obtaining the marker values or levels required for implementing the present application can be any suitable biological sample from the patient or individual to be diagnosed/investigated. In one embodiment the sample is selected from the group consisting of blood, serum, plasma, urine, and saliva.

The method as defined herein above can be used to distinguish between one or more of: presence of fibrosis, significant fibrosis, advanced fibrosis, and liver cirrhosis.

Cut-off values

As disclosed herein, two or three sets of cut-off values can be used to increase the accuracy of an assay based on the algorithms herein disclosed. A first set of cut-off values can be selected based on optimization for sensitivity in order to first rule out fibrosis (Scheuer score <F2), and a second set of cut-off values optimized for specificity to determine the presence of significant fibrosis (≥F2 Scheuer score).

Further cutoff values can be determined wherein sensitivity and specificity are balanced so that only one cutoff value is used.

For subjects with a fibrosis score as herein defined below the primary cut-off value are predicted to be free of significant fibrosis. Therefore treatment is not indicated but the development of the fibrosis should be followed by taking new samples at regular intervals. For subjects with a Fibrosis score of the present application above the second cut-off value, significant fibrosis is present with high lilkelihood and anti-viral treatment is preferably undertaken. The development of fibrosis can be followed by determining the fibrosis score at regular intervals to observe any improvement.

For subjects with a fibrosis score between the high and the low cut-off value one can verify the level of fibrosis by taking a liver biopsy and analysing according to methods known in the art. The fibrosis scores of the invention can be used to reduce the number of subjects from which a liver biopsy are taken.

The two tables below illustrate for the two of the algorithms disclosed in the present application, the lower cutoff value (in order to rule out fibrosis) in the upper row and the higher cutoff (optimized to determine the presence (rule in) of significant fibrosis). Table B: Algorithm based on Age, sCD163, AST and Platelets (CD163-FS). Cut-off values and corresponding sensitivity, specificity, positive and negative predictive values for significant fibrosis (F≥2), prevalence 43.8 %.

Table C: Algorithm based on Age, sCD163, AST, HOMA-IR and Platelets (CD163- HOMA-FS). Cut-off values and corresponding sensitivity, specificity, positive and negative predictive values for significant fibrosis (F≥2), prevalence 43.8 %.

Table D: CD163-HCV-FS Cut-off values. Cut-off values and corresponding sensitivity, specificity, positive and negative predictive values for significant fibrosis (F≥2), prevalence 48.3 %. Sensitivity Specificity

Cut-off values NPV (%) PPV (%)

(%) (%)

< 1.55 90 42 82 59

> 3.50 34 93 60 82

2.60 71 75 73 73

Table E: CD163-HBV-FS Cut-off values. Cut-off values and corresponding sensitivity, specificity, positive and negative predictive values for significant fibrosis (F≥2), prevalence 30.5 %.

The primary cut-offs were set at 2.9, 2.75, 1.55, and 5, respectively, to achieve a high sensitivity in the primary analysis. Any samples with levels below the primary cut-off values are predicted to be free of significant fibrosis with a negative predictive value of 84%, 84%, 82%, and 88% respectively for the four scores.

Lower and higher primary cut-off values can be used. If lower values are used one is more certain that subjects with a Fibrosis Score below the cut-off value are free of significant fibrosis. On the other hand a lowering will increase the number of subjects for which a liver biosy or antiviral treatment is indicated. If higher primary cut-off values are used this will lower the number of subjects for which a liver biopsy or antiviral treatment is indicated and the number of subjects which are predicted not to have significant fibrosis increases. There will thus be an increased risk that subjects which should have received antiviral treatment are not treated. The primary cut-off of CD163-FS can for example be set at 3.5 or lower such as, 3.4, 3.3, 3.2, 3, 2.9, 2.8, 2.7, 2.6, 2.5 or lower.

The primary cut-off of CD163-HOMA-FS can for example be set at 2.5 or lower such as, 2.4, 2.3, 2.2, 2, 1.9, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3 or lower. The primary cut-off of CD163-HCV-FS can for example be set at 3.5 or lower such as, 3.4, 3.3, 3.2, 3, 2.9, 2.8, 2.7, 2.6, 2.5 or lower.

The primary cut-off of CD163-HBV-FS can for example be set at 5.5 or lower such as, 5, 4.5, 4, 3.5 or lower.

The second set of cut-off values optimized for high specificity is at 5.1 , 4.75, 3.5, and 6.5 respectively. A positive predictive value of about 80%, 79%, 82%, and 54% respectively was observed.

The second cut-off of CD163-FS can for example be set at 4.5 or more, 4.6, 4.7, 4.8, 4.9, 5, 5.1 , 5.2, 5.3 or more.

The second cut-off of CD163-HOMA-FS can for example be set at 4.5 or more, 4.6, 4.7, 4.8, 4.9, 5, 5.1 , 5.2, 5.3 or more.

The second cut-off of CD163-HCV-FS can for example be set at 3 or more, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4 or more. The second cut-off of CD163-HBV-FS can for example be set at 6 or more, such as 6.25, 6.5, 6.75, 7 or more.

The methods of the application based on dual cut-off values for the fibrosis scores can be useful in differentiating no or mild liver fibrosis from significant liver fibrosis in a variety of patient populations. Such methods can be useful, for example, in diagnosing an individual having a liver disease such as viral hepatitis, autoimmune liver disease such as autoimmune hepatitis, alcoholic liver disease, fatty liver disease or drug- induced liver disease. In one embodiment, a method of the application is used to differentiate no or mild liver fibrosis from significant liver fibrosis in an individual infected with hepatitis B or C virus. Samples useful in a method of the application based on dual cut-off values include, but are not limited to, blood, serum, plasma, urine, saliva and liver tissue. In one preferred embodiment, a method of the application is practiced by determining the sCD163, AST, Platelets, and optionally HOMA-IR level in one or more serum samples. In a further embodiment, the present application provides a method of differentiating no or mild liver fibrosis from significant liver fibrosis in an individual, where the

differentiation is based on a lower cut-off value of <2.75 (CD163-FS), <2.9 (CD163- HOMA-FS), <1.55 (CD163-HCV-FS), and <5.0 (CD163-HBV-FS) respectively. Subjects having a value below these cutoff values are predicted not to have significant fibrosis. These cutoff values have been selected with the purpose of optimizing the sensitivity of the method. The sensitivity is approximately 90% for all algorithms. This means that of 100 subjects having a value below the cutoff value, approximately 10 will be wrongly diagnosed as not having significant fibrosis (false negatives). The cutoff values result in a negative predictive value of approximately 84% (CD163-FS and CD163-HOMA-FS) and 82% (CD163-HCV-FS), and 88% (CD163-HBV-FS). Lower cutoff values are possible but will result in a significant lowering of the sensitivity.

In a further embodiment, the present application provides a method of differentiating no or mild liver fibrosis from significant liver fibrosis in an individual, where the

differentiation is based on a high cut-off value of >4.75 (CD163-FS), >5.1 (CD163- HOMA-FS), >3.5 (CD163-HCV-FS), and >6.5 (CD163-HBV-FS) respectively. These cutoff values have been selected with the purpose of optimizing the specificity of the method. The specificity is approximately 93-94% for three of the four algorithms. This means that of 100 subjects having a value above the cutoff value, only approximately 6-7 will be wrongly diagnosed as having significant fibrosis (false positive). Higher cutoff values are possible however this will be at the expense of the specificity.

Subjects having a cutoff value below the lowest value are predicted not to have significant fibrosis. Consequently antiviral therapy may not be indicated. Development of fibrosis can be followed by taking a new blood sample in the course of some months in order to see whether the score increases and at which speed.

Subjects having a score above the high cutoff value may be indicated for antiviral therapy. Advantageously, the stage of fibrosis or cirrhosis can be verified with a liver biopsy.

Subjects having a score between the high and the low cutoff values cannot be predicted to have significant fibrosis, nor can significant fibrosis be ruled out. It would therefore be beneficial to verify the stage of fibrosis by taking a liver biopsy. Alternatively the patient can be followed more closely with new blood samples being taken at more frequent intervals to observe whether the score increases or not.

If only one cut-off value (singular cut-off) is to be used, a value of 2.6 (CD163-HCV-FS) and 5.8 (CD163-HBV-FS) can be used. This cutoff value balances specificity and sensitivity.

These singular cut-off values can also vary. For example the cut-off for CD163-HCV- FS can be 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, or 3. For example the cut-off for CD163-HBV-FS can be 5.1 , 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, or 6.

In all cases it is relevant to compare the fibrosis score to an earlier fibrosis score calculated for the same patient, in order to be able to estimate the speed of

progression of fibrosis.

Diagnosis of fibrosis in Hepatitis B and C patients

The present inventors have demonstrated that the present application is suitable for diagnosing liver fibrosis in patients suffering from Hepatitis C or B infections. Thus in one aspect the application concerns a method of diagnosing liver fibrosis caused by HCV or HBV infection, said method comprising performing a diagnosis as defined herein above, and comparing a fibrosis score to a cutoff value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score).

Assessment of disease progression

The present application is useful both for binary diagnosis of whether a patient has liver fibrosis or not, and additionally it may be used to determine disease progression of liver fibrosis.

Accordingly, in one aspect the application concerns a method of differentiating between no or mild fibrosis (<F2, Scheuer score) from significant fibrosis (≥F2, Scheuer score), said method comprising performing a diagnosis as defined herein above, and comparing a fibrosis score to a cutoff value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score). Similarly, in another aspect the application concerns a method of assessing the stage of a liver disease said method comprising performing a diagnosis as defined herein above and assessing the stage of fibrosis based on said diagnosis. A disease like liver fibrosis has, like several other diseases and disorders, a degree of progression thus worsening the condition of the patient suffering therefrom over time. It is thus desirable to be able to monitor disease progression over time. The present application in one aspect provides a method of monitoring disease progression, wherein said method comprises performing a diagnosis as defined herein above, calculating a fibrosis score, repeating said diagnosis and calculation of fibrosis score and comparing said scores to determine whether the disease progresses.

Theranostic applications

Based on the present application it is possible to determine the optimal time for administration of an antiviral drug. Thus in one aspect the application concerns a method of deciding to provide or defer antiviral therapy, said method comprising performing a diagnosis as defined herein above, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score), and deferring antiviral therapy if the individual is diagnosed to have no or mild fibrosis (<F2, Scheuer score).

The current standard of care treatment for chronic HCV infection is Peg-interferon, ribavirin and protease inhibitors (boceprevir or telaprevir). The future treatment may involve combinations of therapies hitting multiple targets of HCV and host factors in interferon free regimens (40).

The current standard of care treatment for chronic HBV infection is monotherapy using nucleoside/nucleotide analogues (NAs) e.g. entecavir (ETV) or tenofovir (TDF).

However, due to high costs for these newly developed drugs lamivudine (LAM), adefovir (ADV) and telbivudine (LdT) remain the mainstay therapy in many countries with high HBV prevalence (for example, in China) because of a lower cost. (41).

For both viral hepatitis C and B new combinations of protease inhibitors and nucleos(t)ide analogues are currently being tested. Thus, the application also concerns a method of treatment of HBV or HCV, said method comprising performing a diagnosis as defined herein above, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score). The therapy may comprise administration of Peg-interferon, ribavirin and protease inhibitors (boceprevir or telaprevir), the nucleos(t)ide analogues (NAs) e.g. entecavir (ETV) or tenofovir (TDF), lamivudine (LAM), adefovir (ADV) and telbivudine (LdT). Preferably, Peg-interferon, ribavirin and protease inhibitors (boceprevir or telaprevir) are used for treatment of HCV, and entecavir (ETV) or tenofovir (TDF), lamivudine (LAM), adefovir (ADV) and telbivudine (LdT) are used for treatment of HBV.

Monitoring of treatment

When implementing a treatment regime, it is advantageous to monitor the effect of said treatment in order to provide the accurate type and dosage of the appropriate medication. Thus in one aspect the application concerns a method of monitoring treatment response in an individual, said method comprising performing a diagnosis as defined herein above, calculating a fibrosis score, treating said individual, repeating said diagnosis and calculating the fibrosis score again, and comparing said scores to determine whether said treatment is effective. Computer assisted diagnosis

Under certain conditions, it is advantageous to perform the methods of the present application using CPU assisted measurement and computing. Thus in one aspect the application concerns a computer-implemented method for diagnosing liver fibrosis, said method comprising entering the level of sCD163, AST, platelet number, age, and optionally fasting glucose and fasting insulin of a subject to a computer having an input device, a processor and an output device,

the processor comprising software for computing a fibrosis score, the method further comprising outputting said fibrosis score to an output device. In one embodiment, the fibrosis score is the CD163-FS score as herein defined. In other embodiments, the fibrosis score is CD163-HCV-FS as herein defined or the CD163-HCV-FS1 as herein defined.

In another aspect the application concerns a computer-implemented method for diagnosing liver fibrosis, said method comprising entering the level of sCD163 from a blood or serum sample, gender, height and weight or BMI of a subject to a computer having an input device, a processor and an output device, the processor comprising software for computing a fibrosis score, the method further comprising outputting said fibrosis score to an output device. Preferably, the score is CD163-HBV-FS as herein defined.

In one embodiment the computer implemented method further comprises entering information about the identity of a patient into the system and means for linking the identity of a patient to the input levels and score.

In one embodiment the marker levels are entered from different input devices. For example, one input device can be located at a laboratory and another input device can be located at a hospital or clinic. In a further embodiment the computer implemented method comprises entering the level of fasting insulin and fasting glucose, calculating the HOMA-IR value, and computing a fibrosis score (CD163-HOMA-FS) using an algorithm wherein the algorithm is log (sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)).

The method may further comprise providing at least one statistical parameter relating to the fibrosis score, such as wherein the statistical parameter is a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score. The fibrosis score can be selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

Systems for implementing diagnostic methods

As mentioned above, the computer assisted methods for diagnosing liver fibrosis can be used separately or be built into a system suitable for the intended purpose. In one aspect the application thus concerns a system for of diagnosing the presence or severity of liver fibrosis in an individual, comprising:

a) An input device for entering data including levels of sCD163 concentration, age, AST activity, number of platelets and optionally fasting glucose and fasting insulin; b) A processor in data communication with said input device, the processor comprising software for computing a fibrosis score; and

c) An output device for displaying or printing said fibrosis score. The fibrosis score may be the CD163-FS score as herein defined, the CD163-HOMA- FS score as herein defined, or the CD163-HCV-FS or CD163-HCV-FS1 as herein defined.

In one embodiment the system defined herein above, further comprises software for comparing said fibrosis score to at least one cut-off value to diagnose the presence of significant fibrosis and presenting said diagnosis on the output device.

In a further embodiment the system comprises entering the level of fasting insulin and fasting glucose, calculating the HOMA-IR value, and computing a fibrosis score (CD163-HOMA-FS) using an algorithm wherein the algorithm is log (sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)).

In another aspect the application concerns a system for of diagnosing the presence or severity of liver fibrosis in an individual, comprising:

a) An input device for entering data including levels of sCD163 concentration, gender (female=0; male=1) and BMI (or height and weight);

b) A processor in data communication with said input device, the processor comprising software for computing a fibrosis score; and

c) An output device for displaying or printing said fibrosis score.

Preferably the fibrosis score for this aspect is CD163-HBV-FS as herein defined. In one embodiment the input device, processor, and output device are connected via a wide area network or a local area network. This may be implemented by e.g. locating the input device or output device on a client and the processor and software on a server. In one embodiment the system defined herein above comprises more than one input device allowing entry of data from the more than one input device.

In one embodiment the system defined herein above comprises means for linking the data to a patient. In a further embodiment the system defined herein above comprises providing at least one statistical parameter relating to the fibrosis score.

In one embodiment the statistical parameter is a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score. The fibrosis score can e.g. be selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

Reports

The present application provides an improved fibrosis score for non-invasive diagnosis and assessment of disease progression of liver fibrosis. The fibrosis score can be used for generating a resulting report. Accordingly in one aspect the application concerns a fibrosis diagnosis report comprising:

a. Information regarding the identity of a patient;

b. Information regarding the level of sCD163, AST, age, platelets, and optionally fasting glucose and fasting insulin in a sample from said subject;

c. a fibrosis score; and

d. a comparison of said fibrosis score to a cut-off value to diagnose the presence or extent of fibrosis.

The fibrosis score may be the CD163-FS score as herein defined, the CD163-HOMA- FS score as herein defined, or the CD163-HCV-FS or CD163-HCV-FS1 as herein defined.

The report may be in paper format or in electronic format, and may further comprise an illustration of the correlation between said fibrosis score and a fibrosis or cirrhosis score selected from Scheuer score, METAVIR fibrosis score, and Ishak (modified Knodell) score.

The report may also comprise information regarding the level of fasting insulin and fasting glucose, optionally a HOMA-IR value, and a fibrosis score calculated using an algorithm wherein the algorithm is log (sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)). In another aspect the application concerns a fibrosis diagnosis report comprising: a. Information regarding the identity of a patient;

b. Information regarding the level of sCD163 in an isolated sample, gender (female=0; male=1) and BMI of said subject;

c. a fibrosis score; and

The fibrosis score may be the CD163-HBV-FS score as herein defined. In one embodiment the method of the present application further comprises providing at least one statistical parameter relating to the fibrosis score. The statistical parameter is typically a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score. The fibrosis score can be selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

Kit for sampling blood samples

In blood, the serum is the component that is neither a blood cell (serum does not contain white or red blood cells) nor a clotting factor; it is the blood plasma with the fibrinogens removed. Serum includes all proteins not used in blood clotting

(coagulation) and all the electrolytes, antibodies, antigens, hormones, and any exogenous substances (e.g., drugs and microorganisms). When sampling serum, the blood is normally poured into a glass without additives. After coagulation and centrifugation, the serum can be pipetted off.

Blood plasma is the straw-colored/pale-yellow liquid component of blood that normally holds the blood cells in whole blood in suspension. It makes up about 55% of total blood volume. It is mostly water (92% by volume), and contains dissolved proteins (i.e.— albumins, globulins, and fibrinogen), glucose, clotting factors, electrolytes (Na+, Ca2+, Mg2+, HC03- CI- etc.), hormones and carbon dioxide. Plasma is collected in tubes, which contain an anticoagulant, e.g. EDTA, Li-heparin, citrate, or oxalate. After centrifugation, the plasma can be pipetted off.

For assessment of blood cells, e.g. platelets, one needs full blood collected in a tube which contains an anticoagulant, e.g. EDTA, Li-heparin, citrate, or oxalate. 1) sCD163 - can be detected in serum, and various plasma (EDTA-plasma, heparin- plasma, citrat-plasma)

2) AST - is typically determined in Li-heparin-plasma or in serum

3) Insulin - is typically determined in serum (and can also be determined in plasma)

4) fasting glucose - is typically determined in special tubes in order to avoid a degradation of glucose, e.g. Na-fluoride-Citrate-K2-EDTA-tubes or Na-fluoride-K2- oxalate-tubes

5) Platelets (thrombocytes, platelets) - are typically determined in EDTA-blood (full- blood) but can also be determined in e.g. heparin-blood

In order to determine the markers sCD163, AST and platelets, it is preferred to use at least two tubes for sampling a blood sample: a dry glass and an anticoagulant coated glass, preferably an EDTA-coated glass.

In order to determine the markers sCD163, AST, platelets, insulin and glucose, it is preferred to use at least four tubes for sampling a blood sample: a dry glass and an anticoagulant coated glass, preferably an EDTA-coated glass; and a specially coated glass for the glucose sample, preferably Na-fluoride-Citrate-K2-EDTA-tubes or Na- fluoride-K2-oxalate-tubes.

A kit for diagnosing liver fibrosis, said kit comprising

a) One non-coated blood tube for analysing: sCD163, and optionally for insulin determination.

b) One anticoagulant coated blood tube for platelet count determination c) One anti-coagulant coated blood tube for AST determination.

In one embodiment, the kit further comprises a specialised blood tube for analysing fasting glucose, the tube being coated with Na-fluoride-Citrate-K2-EDTA-tubes or Na- fluoride-K2-oxalate-tubes.

Samples

A variety of samples can be useful in practicing the methods of the application including, for example, blood, serum, plasma, urine, saliva and liver tissue. In one embodiment, a single venous blood sample is obtained from the individual to be diagnosed. Such a blood sample can be collected into, for example, a tube for serum collection and a tube for plasma collection. As used herein, the term "sample" means a biological specimen that contains one or more fibrotic markers such as sCD163, AST, insulin, glucose, platelets. A sample can be, for example, a fluid sample such as whole blood, plasma, saliva, urine, synovial fluid or other bodily fluid, or a tissue sample such as a lung, liver, kidney, prostate or breast tissue sample. One skilled in the art understands that fluid samples can be diluted, if desired, prior to analysis.

One skilled in the art understands that a single sample can be obtained from the individual to be diagnosed and can be subdivided prior to detecting sCD163, AST, platelets, and optionally insulin and glucose. One skilled in the art also understands that, if desired, two or more samples can be obtained from the individual to be diagnosed and that the samples can be of the same or a different type. In one embodiment, the markers each are detected in venous blood samples. In another embodiment, a single blood sample is obtained from an individual and subdivided prior to detecting the markers.

Means and methods for analysing level and characteristics of individual markers

As mentioned elsewhere, means and methods for quantitative and/or qualitative analysis of the marker parameters of the method of the present application can be any suitable means and methods for quantitative and/or qualitative analysis known by those of skill in the art.

CD 163

CD163 is a transmembrane haptoglobin-hemoglobin receptor, mainly expressed on macrophages and monocytes, particularly in adipose tissue and the liver, and is closely associated with macrophage activation. The amino acid sequence of CD163 is presented in figure 1 of WO 2011/044904 (Uniprot Q86VB7). The extracellular part of CD163 or fragments hereof, may be shed to the blood and is hereby present in a soluble form (sCD163). The soluble form comprises all or part of the extracellular domain (amino acids 42-1050 of Uniprot Q86VB7). All aspects of CD163 measurements herein and all detection methods refer to any form of CD163, membrane-bound or soluble or both. In a preferred embodiment, the measured CD163 is sCD163.

The function of sCD163 is largely unknown, and there is no data to suggest a direct role of sCD163 in the pathogenesis of liver disease (10). However, levels of sCD163 have previously been reported to be increased in various diseases with enhanced load of monocytes/macrophages and inflammatory components, as rheumatoid arthrititis, Gaucher's disease, liver diseases, and coronary heart disease (10, 17, 38, 39).

Methods for determining CD163

CD163 can be determined using a variety of different methods, mainly immunological methods. According to one embodiment, a Point of Care test is used, such as a lateral flow tests (also known as lateral flow immunochromatographic assays). Semiquantitative lateral flow tests can operate as either competitive or sandwich assays.

In another preferred embodiment, the level of CD163 is detected by nephelometry where an antibody and the antigen are mixed in concentrations such that only small aggregates are formed. These aggregates will scatter light (usually a laser) passed through it rather than simply absorbing it. The fraction of scattered light is determined by collecting the light at an angle where it is measured and compared to the fraction of scattered light from known mixtures. Scattered light from the sample is determined by using a standard curve.

In another preferred embodiment, the sample moves from the application site where it, for example, is mixed with antibody-coated nanoparticles in lateral flow/diffusion through a (e.g. nitrocellulose-) membrane. At one point on the way another CD163 antibody is fixed in the membrane making the CD163-primary antibody complex to halt. The nano-particle (preferably colloidal gold/dyed latex) will give a visual line.

In another embodiment, the sample is applied through a (e.g. nitrocellulose-) membrane coated with a primary CD163 antibody. The sample CD163 is then recognised and bound by the primary CD163 antibody. The immobilised CD163 on the membrane may then be recognised by (preferably colloidal gold/dyed latex) particles conjugated with another CD163 antibody, and the complex will develop a colour reaction, which intensity corresponds to the amount of CD163 in the sample.

For large-scale detection and more precise quantitative measurement of CD163 in a sample, several methods may be applied:

In another preferred embodiment, the level of CD163 is detected by radioimmunoassay (RIA). RIA is a very sensitive technique used to measure concentrations of antigens without the need to use a bioassay. To perform a radioimmunoassay, a known quantity of an antigen is made radioactive, frequently by labeling it with gamma-radioactive isotopes of iodine attached to tyrosine. This radio labeled antigen is then mixed with a known amount of antibody for that antigen, and as a result, the two chemically bind to one another. Then, a sample of serum from a patient containing an unknown quantity of that same antigen is added. This causes the unlabeled (or "cold") antigen from the serum to compete with the radio labeled antigen for antibody binding sites. As the concentration of "cold" antigen is increased, more of it binds to the antibody, displacing the radio labeled variant, and reducing the ratio of antibody-bound radio labeled antigen to free radio labeled antigen. The bound antigens are then separated from the unbound ones, and the radioactivity of the free antigen remaining in the supernatant is measured. Using known standards, a binding curve can then be generated which allows the amount of antigen in the patient's serum to be derived. In this assay, the binding between antibody and antigen may be substituted by any protein-protein or protein-peptide interaction, such as ligand-receptor interaction, for example CD163- haemoglobin or CD163-haemoglobin/haptoglobin binding.

In a preferred embodiment, the level of CD163 is detected by enzyme-linked immunosorbent assay (ELISA). ELISA is a quantitative technique used to detect the presence of protein, or any other antigen, in a sample. In ELISA an unknown amount of antigen is affixed to a surface, and then a specific antibody is washed over the surface so that it can bind to the antigen. This antibody is linked to an enzyme, and in the final step a substance is added that the enzyme can convert to some detectable signal. Several types of ELISA exist and include e.g. Indirect ELISA, Sandwich ELISA,

Competitive ELISA and Reverse ELISA. Other immuno-based assays may also be used to detect CD163 in a sample, such as chemiluminescent immunometric assays and Dissociation-Enhanced Lanthinide Immunoassays. The absolute values of sCD163 in the current application have been determined using the ELISA-assay described in the examples. Using this assay, the level of sCD163 has been measured 275 times in NFKK reference serum X, yelding a mean sCD163 concentration of 1.73 mg/L (SD 0.13). NFKK-reference serum X, which is commercially available from NOBIDA, Nordic Reference Interval Project Bio-bank and Database

(Scand J Clin Lab Invest. 2004;64(4):431-8. Nordic Reference Interval Project Bio-bank and Database (NOBIDA): a source for future estimation and retrospective evaluation of reference intervals. Rustad P, Simonsson P, Felding P, Pedersen M.). It is conceivable that other analytical methods will yield different concentrations of sCD163 in absolute numbers. The cutoff values provided in the present application can be converted to cutoff values for other analytical methods. If the concentration of sCD163 in NFKK-X using a different analytical method is found to be "C mg/L", then all reported cut-off values for sCD163 using the methods herein described should be multiplied by the constant "C/1.73".

This conversion can also be used when computing the fibrosis scores of the present application. As an example, the CD163-FS score when CD163 is detected using a different detection method from the ELISA-method of the examples would be: CD163-FS = log (sCD163 (mg/L)x C/1.73 x age (years) x AST (IU/L) /platelets (x 10⁹/L)),

CD163-HOMA-FS = log (sCD163 (mg/L) x C/1.73 x age (years) x AST (IU/L) x (HOMA- IRA0.25)/platelets (x 10⁹/L)), CD163-HBV-FS, is 1.5*log (sCD163 (mg/L) x C/1.73) + 0.8*gender (female=0; male=1) - 2*logBMI +10,

CD163-HCV-FS is (0.5*log(sCD163 (mg/L) x C/1.73) + 1.5*log(Age (years)) + los(AST (IU/L))+ 0.25*(logHOMA-IR) - 1.5*log(Platelets (x10⁹/L)),

CD163-HCV-FS1 is 0.5*log(sCD163 (mg/L) x C/1.73) + 1.5*log(Age (years)) + log(AST (IU/L)) + 0.5*logHOMA-IR + 5*loglNR - 1.5*log(Platelets (x10⁹/L)), wherein C is the concentration of sCD163 in NFKK-X determined using the same detection method. Using these versions of the fibrosis scores of the application, the cut-off values provided herein can be applied to scores calculated using different methods for detecting sCD163.

In a preferred embodiment, the level of CD163 is detected by chromatography-based methods, more specifically liquid chromatography. Therefore, in a more preferred embodiment, the level of CD163 is detected by affinity chromatography, which is based on selective non-covalent interaction between an analyte and specific molecules.

In another preferred embodiment, the level of CD163 is detected by ion exchange chromatography, which uses ion exchange mechanisms to separate analytes. Ion exchange chromatography uses a charged stationary phase to separate charged compounds. In conventional methods the stationary phase is an ion exchange resin that carries charged functional groups, which interact with oppositely charged groups of the compound to be retained.

In yet another preferred embodiment, the level of CD163 is detected by size exclusion chromatography (SEC) which is also known as gel permeation chromatography (GPC) or gel filtration chromatography. SEC is used to separate molecules according to their size (or more accurately according to their hydrodynamic diameter or hydrodynamic volume). Smaller molecules are able to enter the pores of the media and, therefore, take longer to elute, whereas larger molecules are excluded from the pores and elute faster.

In yet another preferred embodiment, the level of CD163 is detected by reversed- phase chromatography which is an elution procedure in which the mobile phase is significantly more polar than the stationary phase. Hence, polar compounds are eluted first while non-polar compounds are retained.

In a preferred embodiment, the level of CD 163 is detected by electrophoresis.

Electrophoresis utilizes the motion of dispersed particles relative to a fluid under the influence of an electric field. Particles then move with a speed according to their relative charge. More specifically, the following electrophoretic methods may be used for detection of CD163: Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Rocket Immunoelectrophoresis, Affinity Immunoelectrophoresis and Isoelectric focusing.

In a preferred embodiment, the level of CD163 is detected by flow cytometry. In flow cytometry a beam of light of a single wavelength is directed onto a hydrodynamically- focused stream of fluid. A number of detectors (some fluorescent) are aimed at the point where the stream passes through the light beam: one in line with the light beam and several detectors perpendicular to it. Each suspended particle from 0.2 to 150 micrometers passing through the beam scatters the light in some way, and fluorescent chemicals found in the particle or attached to the particle may be excited into emitting light at a longer wavelength than the light source. This combination of scattered and fluorescent light is picked up by the detectors, and, by analysing fluctuations in brightness at each detector, it is then possible to derive various types of information about the physical and chemical structure of each individual particle. In a preferred embodiment, the level of CD163 is detected by Luminex technology, which is based on a technique where microspheres are coated with reagents specific to capture a specific antigen from a sample.

In a preferred embodiment, the level of CD163 is detected by mass spectrometry (MS). MS is an analytical technique for the determination of the elemental composition of a sample or molecule. It is also used for elucidating the chemical structures of molecules, such as proteins and other chemical compounds. The MS principle consists of ionizing chemical compounds to generate charged molecules or molecule fragments and measurement of their mass-to-charge ratios. The methods described for CD163 are generally applicable for determining the amount of protein in a sample with suitable modifications, such as obviously using antibodies directed against the protein in question.

Aspartate transaminase (AST)

The level of AST (Aspartate aminotransferase (EC 2.6.1.1); also known as serum glutamic oxaloacetic transaminase (SGOT) ) is preferably determined using enzymatic methods. In alternative embodiments, immunological methods are used. In that case, a different algorithm should be determined using the methods used to develop the presently disclosed algorithms, e.g. multiple ordered logistic regression analysis.

In common with the methods used for measuring most enzymes for clinical purposes, AST is assayed by measuring its catalytic activity, not its mass.

The activity of AST is determined as the number of IU/L.

The term determining as used herein in one embodiment comprises quantifying the amount or activity of said marker or markers.

Platelet number

In one embodiment, the platelet number is determined using flow cytometry (e.g. using impedance or fluorescence determination), or automated or manual cell counting using microscopic methods. Flow cytometry can include measurement of impedance where the change in conductivity is measured while the platelets pass a capillary opening after

hydrodynamic focusing. Cells have a larger electrical resistance than an electrolyte solution, and the larger the cell the larger the resistance. Both the number and size of the platelets can be detected and the size distribution can be displayed in a histogram. By comparing to a normal thrombycytogram, one can estimate the number and mean cell volume of the platelets.

In an alternative embodiment, determination of the number of thrombocytes is performed by staining the nuclei with a fluorescent dye such as polymetin. Using flow cytometry, the side scatter is determined along the x- and y-axis. Using a normal scattergram, one can derive the number of thrombocytes and the fraction of RNA- containing thrombocytes.

Using manual methods one can count the number of platelets in a counter chamber (e.g. a hemocytometer) preferably using phase contrast optics. Advantageously, the blood sample has been hemolysed using ammonium chloride prior to counting to lyse the erythrocytes.

Microscopic techniques can be combined with image analysis and automated cell counting methods known in the art.

Insulin

In one embodiment, the insulin level is determined using one or more anti-insulin antibodies.

Exemplary immunological methods include the use of ELISA-methods, RIA, turbidometry and nephelometry. Glucose

In one embodiment, the glucose level is measured chemically, enzymatically or using chromatography. E.g. the the chemical method for measuring glucose can be selected from the group consisting of oxidation-reduction reactions, and condensation reactions.

The oxidation-reduction reaction can be a method utilising an alkaline copper reaction such as the Folin-Wu method, the Benedict's method, the Nelson-Somogyi method, the Neocuproine method and/or the Shaeffer-Hartmann-Somogyi method. The oxidation-reduction reaction can be a method utilising an alkaline ferricyanide reaction such as the Hagedorn-Jensen method.

Enzymatic methods for measuring glucose includes but is not limited to glucose oxidase methods such as the Saifer-Gerstenfeld method, the Trinder method, the Kodak method, and/or a glucose oxidase method utilising a glucometer.

Another enzymatic method for measuring glucose is the hexokinase method.

Examples

Example 1 : Clinical study in HBV and HCV patients

Clinical methods The present inventors performed a cross-sectional study in 556 patients with chronic HCV infection and 208 patients with chronic HBV infection who were referred to The Storr Liver Unit, Westmead Hospital, Westmead, Australia, between July 1991 and August 2010 for evaluation of chronic viral hepatitis. The diagnosis of chronic HCV infection was confirmed by the presence of anti-HCV antibodies (Monolisa anti-HCV;

Sanofi Diagnostics Pasteur, Marnes-la-Coquette, France) and viral RNA as detected by polymerase chain reaction (PCR) (Amplicor HCV; Roche Diagnostics, Branchburg, NJ). Hepatitis C virus genotyping was performed with a second generation reverse hybridization line probe assay (Inno-Lipa HCV II; Innogenetics, Zwijndrecht, Belgium). The diagnosis of chronic hepatitis B was confirmed by the presence of hepatitis B surface antigen in the blood for more than 6 months, hepatitis B core antibodies and HBV-DNA detection by signal amplification hybridization microplate assay (Digene HBV Test using Hybrid Capture 2, Digene) with a lower limit of detection of 0.5 pg/ml (1.42 x 10⁵ virus copies/ml), or by real-time PCR.

There were 9 cases of dual infection with HCV and HBV (1.2% of the total cohort). There was only a single patient (0.1 % of the total cohort) with HCV infection who was co-infected with HIV. Exclusion of these subjects from the analysis did not alter the results.

Liver biopsy was performed as part of the workup, for assessment of severity of steatosis, inflammation and fibrosis. The stained biopsies were examined by

experienced pathologists and scored according to the Scheuer scoring system. (19) Steatosis was graded as described by Brunt et al. (20) All of the biopsies had a minimum of 11 portal tracts, and inadequate biopsies were excluded. None of the patients had antiviral treatment prior to inclusion. At the time of the liver biopsy, basic demographic and clinical data were obtained, including gender, age, ethnicity, height, weight and waist circumference. Alcohol consumption was assessed by 2 separate interviews with the patient and close family members. Body Mass Index (BMI) was calculated from height and weight. At the same time, a fasting blood sample was drawn and routine biochemical tests were performed as described below. Additional blood samples were taken and frozen at -80°C for future research. All patients signed an informed consent form in accordance with the Helsinki Declaration; the acquisition, storage and use of the blood samples was approved by the Sydney West Area Health Service Ethics Committee. Biochemical analyses

The concentrations of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), total bilirubin, prothrombin time, international normalized ratio (INR), fasting glucose and insulin, serum albumin, haemoglobin, platelet and leucocyte count, triglycerides and

cholesterol and its components were determined prior to the liver biopsy using standard assays and methods. Homeostatic model assessment (HOMA-IR) was calculated (fasting glucose (mg/dL) x fasting insulin (μΐυ/mL) / 405). (21) The Model for End-stage Liver Disease (MELD) score was calculated using the values of bilirubin, creatinine and I NR. (22) AST to platelet ratio index (APRI) was calculated according to the established formula: (AST (IU/L)/upper normal Iimit)x100/platelet count (10⁹/L).(23) The FIB-4 index was calculated as follows: age (years) x AST (IU/L)/(platelets (10⁹/L) x (ALT (IU/L))½).(24)

The plasma concentration of sCD163 was determined in duplicate in samples that had been frozen at -80°C by an in-house sandwich enzyme-linked immunosorbent assay using a BEP-2000 ELISA-analyser (Dade Behring) as previously described. (25) Control samples and serum standards with concentrations that ranged from 6.25 to 200 g/l were included in each run. The inter-assay coefficient of variation in the current project (n=20) was 3.5-6.0 % at a level of 1.31 mg/l and 6-10% at a level of 3.59 mg/l. The limit of detection (lowest standard) was 6.25 ^g/L. Soluble CD163 is resistant to repeated freezing and thawing. (25)

Statistical calculations

One-way Analysis of Variance (ANOVA) was used for the comparison of multiple groups, and Student's t-test to study differences of normally distributed variables between groups. For the non-normally distributed data, Kruskall-Wallis and Mann- Whitney/Wilcoxon tests, respectively, were used. The relationships between sCD163 and other variables were analysed by simple linear regression (after logarithmic transformation) or Spearman's rank correlation. Spearman's rank test was used to study relationships between sCD163 and histological scores. To study the differences in sCD163 between patients with HBV and HCV infection with the same histological scores of fibrosis and inflammatory activity, the inventors used two-way ANOVA with post-hoc t-tests. To assess differences in proportions, x2-test was used. When appropriate, Fisher's exact test was performed. The present inventors performed multiple ordered logistic regression analysis with Scheuer Fibrosis score as the dependent variable and sCD163 as the explanatory in two different models. These models provided odds ratios (OR) for a given fibrosis score corresponding to specific increases in sCD163. In this study, the inventors chose to report odds ratios for a 25% increase in sCD163. In model 1 , the inventors aimed to determine whether sCD163 is associated with fibrosis score directly or through its relationship with liver inflammation and known risk factors for fibrosis. Age, gender, BMI, ethnicity, alcohol consumption, viral etiology (HBV or HCV), and presence of genotype 1 (HCV only) were identified as risk factors for liver fibrosis in chronic viral hepatitis and included in model 1. Scheuer scores for Lobular and Portal Inflammation were also included in this model.

In model 2, The goal of the inventors was to investigate whether sCD163 is a marker of fibrosis when adjusted for demographic, clinical and biochemical parameters shown to be associated with fibrosis in previous studies. (26) Thus, the inventors included age, gender, BMI, ethnicity, viral etiology, genotype 1 , alcohol consumption, albumin, platelet count, ALT, AST, INR and HOMA-IR in model 2. Variables that showed significant associations with the Scheuer fibrosis score in model 2 were identified as candidates for a new sCD163 based fibrosis score (CD163-FS), and combinations of these parameters were examined. Nonparametric Receiver Operating Characteristics (ROC) analyses for the presence of liver cirrhosis (defined by Scheuer Fibrosis score of 4(27)), as well as advanced (F≥3) and significant fibrosis (F≥2) were performed to evaluate the diagnostic performance of these combinations, and the combination providing the highest areas under the ROC-curves (AUROCs) was chosen for the new score. The new score was then compared with APRI and FIB-4 using the test of equality of ROC areas. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) were determined for appropriate cut-off values of FS-CD163, which were chosen based on the ROC-curve.

All data are expressed as medians with interquartile ranges (IQR) or proportions and P- values≤ 0.05 were considered statistically significant. STATA version 12.0 ©StataCorp LP was used for the data analysis. Results Basic demographic, clinical and biochemical data for the HCV and HBV patients are presented in Table 1. The two groups did not differ significantly in terms of age or gender, the majority of the patients being male. BMI and HOMA-IR were higher in HCV patients, however, with no significant difference in the prevalence of diabetes between the groups. There was a statistically significant difference in the consumption of alcohol between HBV and HCV patients, but the distribution of categories of alcohol intake was similar for the two groups (Table 1).

Table 1 : Demographic, clinical and biochemical parameters in patients with HCV and HBV infection.

HCV HBV

Parameter

n = 556 n = 208

Age (years) 43 (36-49) 41 (33-49) p=0.09

352 (63%) : 204

Gender (Male : Female) 134 (64%) : 74 (36%) p=0.81

(37%)

Alcohol consumption

< 10 gms/day 450 (82.6%) 157 (75.5%)

10-19 gms/dag 41 (7.6%) 28 (13.4%)

20-39 gms/day 35 (6.4%) 17 (8.2%) p=0.02 40-59 gms/day 10 (1 .8%) 6 (2.9%)

>60 gms/day 9 (1 .6%) 0 (%)

Missing record 1 1 0

Fibrosis stage

46 (8.9%) 5 (2.4%)

Cirrhosis (F4) (n)

101 (19.5%) 23 (1 1 .2%)

Advanced fibrosis or higher (F>3) (n)

252 (48.6%) 65 (31 .7%) p=0.001 Significant fibrosis or higher (F>2) (n)

266(51 .4%) 140 (68.3%)

No/mild fibrosis (F0-1)(n)

38 3

Missing histology (n)

MELD 7.5 (6.4-8.4) 8.1 (7.1-8.6) p=0.44

BMI (kg/m²) 26 (23-30) 24 (22-27) p<0.001

Diabetes (n) 26 (4.7%) 4 (2.1 %) p=0.12

ALT (IU/L, males <70 IU/L; females <45

82 (52-144) 49 (29-78) p<0.001 IU/L)

AST (IU/L, <45 IU/L) 62 (43-100) 46 (37-63) p<0.001

Bilirubin (μπιοΙ/L, 5-25 μπιοΙ/L) 1 1 (8-14) 10 (8-14) p=0.26

ALP (IU/L, 35-105 IU/L) 77 (66-96) 80 (67-99) p=0.42

GGT (IU/L, < 1 15 IU/L) 50 (29-93) 28 (20-46) p<0.001

INR 1 (0.9-1) 1 (1-1 .1) p<0.001

Albumin (g/L, 36-48 g/L) 43 (41-45) 45 (43-48) p<0.001

HOMA-IR 2.22 (1 .43-3.78) 1 .43 (0.67-2.35) p<0.001 Cholesterol (mmol/L, <5 mmol/L) 4.5 (3.9-5.1) 4.9 (4.5-5.6) p<0.001

LDL (mmol/L, <3 mmol/L) 2.6 (2-3.2) 2.9 (2.4-3.5) p<0.001

HDL (mmol/L, >1 .2 mmol/L) 1 .3 (1 .1-1 .6) 1 .3 (1 .2-1 .7) p=0.01

Triglycerides (mmol/L, <2 mmol/L) 0.98 (0.75-1 .36) 0.98 (0.73-1 .37) p=0.80

Haemoglobin (g/L, males 134-169 g/L;

150 (140-160) 146 (137-157) p=0.008 females 1 18-153 g/L)

Leucocytes ( x 10⁹/L, 3.5-10 x 10⁹/L) 6.8 (5.6-8.4) 5.4 (4.6-6.5) p<0.001

Platelets ( x 10⁹/L, 165-400 x 10⁹/L) 225 (186-275) 222 (189-257) p=0.06

Parameters are presented in table 1 as medians (interquartile range) for continuous variables, and as total number (%) for categorical variables. Units and normal ranges are in parenthesis. MELD, model for end-stage liver disease; BMI, Body Mass Index; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyltransferase; INR, international normalized ratio; HOMA-IR, homeostatic model assessment; LDL, low-density lipoprotein; HDL, high-density lipoprotein The present inventors observed higher ALT, AST, and GGT levels in HCV patients compared to HBV. Albumin was slightly, but significantly lower in patients with HCV infection. INR was significantly lower in HCV patients, although the two groups had the same median values. A trend toward lower platelets in HCV patients was observed. Levels of cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were significantly higher in HBV patients, while triglyceride levels were similar for the two groups.

Soluble CD163 levels were significantly higher in HCV patients (3.7 (2.5-5.5) mg/L) compared to those with HBV infection (2.4 (1.8-3.6) mg/L), p<0.001. Histological scores of activity and fibrosis in patients with HC V and HB V infection

The patients with HCV infection had more advanced disease with higher scores for Scheuer Fibrosis (p<0.001) compared to HBV patients (Table 1). Cirrhosis was significantly more prevalent in patients with HCV, compared to HBV infection (Table 1). None of the patients with biopsy-verified HCV or HBV cirrhosis had decompensated cirrhosis. Similarly, the proportion of advanced fibrosis and significant fibrosis in patients with HCV infection was significantly higher than in the HBV group (Table 1). In addition, HCV patients had higher scores for Scheuer Portal Inflammation (p<0.001) and Steatosis (p<0.001). There was no significant difference in the score of Scheuer Lobular Inflammation between the two groups (p=0.51). The full distribution of histological scores in patients with HBV and HCV infection is presented in Table 5.

Associations between sCD163 and demographic parameters

Associations between sCD163 and demographic parameters were not significantly different in the groups of HCV and HBV patients, for which reason they are presented for the whole population. In univariate analysis, soluble CD163 correlated weakly with age (Spearman's rho=0.18; p<0.001). Males had slightly, but significantly elevated median levels of sCD163 compared to females (3.3 (2.3 - 5.3) vs. 3.1 (2.1 - 4.5) mg/L; p=0.02).

Associations between sCD163 and clinical and biochemical parameters in HBV and HCV patients

The univariate associations between sCD163 and clinical and biochemical parameters are presented in Table 2.

Table 2. Univariate associations between sCD163 and clinical and biochemical parameters in patients with HBV and HCV infection. Associations are statistically tested by Spearman's test.

Parameter HCV HBV

rho P rho P

BMI (kg/m²) 0.22 <0.001 0.07 0.30

Waist circumference (cm) 0.26 <0.001 0.1 1 0.12

ALT (IU/L) 0.47 <0.001 0.53 <0.001

AST (IU/L) 0.61 <0.001 0.57 <0.001

Albumin (g/L) - 0.21 <0.001 - 0.34 <0.001

Platelets (x 10⁹/L) - 0.37 <0.001 - 0.15 0.04

Haemoglobin (g/L) 0.09 0.051 0.03 0.64

INR 0.21 <0.001 0.30 <0.001

HOMA-IR 0.34 <0.001 0.08 0.28 Cholesterol (mmol/L) - 0.17 <0.001 - 0.05 0.49

Triglycerides (mmol/L) 0.16 <0.001 - 0.01 0.99

BMI, Body Mass Index; HOMA-IR, homeostatic model assessment; ALT, alanine transaminase; AST, aspartate transaminase; INR, international normalized ratio

In patients with HCV infection, sCD163 showed significant associations with more parameters than HBV patients, possibly reflecting the larger number of patients in this group. Soluble CD163 was significantly associated with BMI in patients with HCV, but not HBV infection. However, this association was significant in the pooled population. The association with waist circumference, was significant in HCV patients, but not in the HBV group or the pooled population. In both groups we observed significant associations with ALT, AST and INR, and inverse associations with platelet count and albumin. There was a significant association between sCD163 and HOMA-IR in patients with HCV infection and the pooled population, but not in HBV patients.

Associations with triglycerides and cholesterol were significant in the HCV group alone, and not in the HBV group or the pooled population. Soluble CD163 did not show associations with levels of HCV viral load or HBV DNA titer counts; it did not differ significantly between various HCV genotypes while data on HBV genotypes were unavailable. Soluble sCD163 showed no association with alcohol consumption in patients with HBV or HCV. Associations between sCD163 and histological scores of activity and fibrosis in HB V and HCV patients

In univariate analyses, sCD163 increased in association with rising scores for fibrosis stage and histological inflammatory activity in patients with HBV and HCV infection (Figure 1 A-C). Median sCD163 levels were higher in HCV compared to HBV patients with the same scores for inflammatory activity and fibrosis, reaching statistical significance in a number of cases.

There was a weak association between sCD163 and steatosis score (Spearman's rho=0.27; p<0.001).

Ordered logistic regression analysis for association between sCD163 and fibrosis in the combined group of HCV and HBC patients The inventors combined the HBV and HCV patients and assessed sCD163 levels in relation to fibrosis stage (Figure 3). As described in the methods, the inventors used an ordered logistic regression model with Scheuer Fibrosis score as the dependent variable and sCD163 as explanatory variable, in univariate and two multivariate models. In the simple (univariate) model the OR was 1.47 (95% confidence interval (CI): 1.38 - 1.57), p<0.001. Thus, for two random patients from the cohort, if one of them has 25% higher sCD163 compared with the other, this patient has a 47% greater odds of for example presence of fibrosis (F≥1), than the other patient. It is a property of ordered logistic regression, that this increase in odds is the same for significant fibrosis (F≥2), advanced fibrosis (F≥3) or liver cirrhosis (F=4).

In multiple regression models the inventors adjusted for risk factors and clinical and biochemical parameters. Applying multiple models to the two hypothetical patients described above, the resulting odds ratios estimate the odds of having a higher fibrosis score for the patient with 25% higher sCD163, assuming that the two patients were completely alike in terms of risk factors and parameters the inventors adjusted for. In both models applied, sCD163 showed a significant independent association with fibrosis with ORs of 1.24 (95% CI: 1.14 - 1.34), pO.001 and 1.23 (95% CI: 1.12 - 1.37), p<0.001 for the combined cohort of HCV and HBV patients (Table 3).

Table 3. Simple and multiple ordered logistic regression models with soluble (s)CD163 as the explanatory variable for Scheuer Fibrosis score. Unadjusted and adjusted odds ratios are presented for each 25% increase in sCD163.

OR 95% CI P

Patients with hepat itis B infection

Unadjusted 1.34 1.19-1.51 <0.001

Model 1 1.13 0.97-1.31 0.115

Model 2 1.32 1.06-1.66 0.014

Patients with hepat itis C infection

Unadjusted 1.49 1.38-1.61 <0.001

Model 1 1.28 1.16-1.41 <0.001

Model 2 1.21 1.07-1.37 0.002 Pooled patients

Unadjusted 1.47 1.38-1.57 <0.001

Model 1 1.24 1.14-1.34 <0.001

Model 2 1.24 1.12-1.38 <0.001

Model 1. Age, gender, body mass index, ethnicity, alcohol consumption, viral etiology (hepatitis B or C infection), presence of genotype 1 (hepatitis C only), and Scheuer Lobular and Portal Inflammation scores included in the model.

Model 2. Age, gender, body mass index, ethnicity, alcohol consumption, viral etiology (hepatitis B or C infection), presence of genotype 1 (hepatitis C only), albumin, platelets, alanine transaminase, aspartate transaminase, international standardized ratio and homeostatic model assessment (HOMA-IR) included in the model.

Development of a sCD163-based predictive fibrosis score and comparison to the APRI and FIB-4 scores.

In model 2 of the multiple ordered logistic regression analysis, the following parameters were significantly associated with Scheuer fibrosis score: sCD163 (p<0.001), gender (p=0.038), age (p=0.003), AST (p<0.001), HOMA-IR (p=0.002), platelets (p=0.002) and INR (p=0.001). The inventors examined different combinations of these parameters to develop a novel fibrosis score, and the combination of sCD163, age, AST, HOMA-IR and platelets showed the best AUROCs. However, excluding HOMA-IR did not alter its predictive capability significantly, and the inventors calculated a new score as follows; CD163-FS = log (sCD163 (mg/L) x age (years) x AST (IU/L) /platelets (x 10⁹/L)).

In ROC analysis, CD163-FS had an AUROC of 0.87 (95%CI: 0.82-0.92) for cirrhosis (F=4), 0.84 (95%CI: 0.80-0.88) for advanced fibrosis (F≥3) and 0.78 (95%CI: 0.74- 0.81) for significant fibrosis (F≥2). In the combined cohort, CD613-FS was superior to APRI for cirrhosis (p=0.02), advanced (p=0.02) and significant fibrosis (p<0.001), and was significantly better than FIB-4 for significant fibrosis (p=0.001). If HOMA-IR was included the score was calculated as follows: CD163-HOMA-FS = log (SCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IRA0.25)/platelets (x 10⁹/L)). In ROC analysis, CD163-HOMA-FS had an AUROC of 0.88 (95%CI: 0.83 - 0.92) for cirrhosis (F=4), 0.85 (95%CI: 0.81 - 0.89) for advanced fibrosis (F≥3) and 0.78 (95%CI: 0.74 - 0.82) for significant fibrosis (F≥2). In the combined cohort, CD163-HOMA-FS was superior to APRI for cirrhosis (p=0.008), advanced (p=0.03) and significant fibrosis (p=0.003), and was significantly better than FIB-4 for significant fibrosis (p=0.001) (Table 4).

Table 4. AUROCs for significant fibrosis, advanced fibrosis and cirrhosis for APRI, FIB- 4 and sCD163 in patients with chronic viral hepatitis.

Data are presented as AUROCs (95% CI)

†p<0.01 compared to FIB-4

The present inventors identified cut-off values for CD163-FS that provided the best discrimination for the presence and absence of fibrosis.

The present inventors identified cut-off values for CD163-FS that provided the best discrimination for the presence and absence of fibrosis. The inventors chose to do so only for significant fibrosis, as CD163-FS was superior to both APRI and FIB-4 in prediction of this fibrosis stage. The inventors defined two cut-off values based on the ROC-curve for significant fibrosis (Figure 2A). The first cut-off was defined at CD163- FS = 2.75 (Marked 1 on Figure 2A). The sensitivity was 90%, specificity 40%, PPV 54% and NPV 84%. The other cut-off was at CD163-FS = 4.75 (Marked 2 on Figure 2A); the sensitivity was 33%, specificity 93%, PPV 79% and NPV 64%, with total prevalence of significant fibrosis at 43.8%.

Similarly, the inventors identified cut-off values for CD163-HOMA-FS that provided the best discrimination for the presence and absence of significant fibrosis. The inventors defined two cut-off values based on the ROC-curve for significant fibrosis (Figure 2B). The first cut-off was defined at CD163-HOMA-FS = 2.9 (Marked 1 on Figure 2B). The sensitivity was 90%, specificity 40%, PPV 54% and NPV 84%. The other cut-off was at CD163-HOMA-FS = 5.1 (Marked 2 on Figure 2B); the sensitivity was 31 %, specificity 94%, PPV 80% and NPV 64%, with total prevalence of significant fibrosis at 43.8%. Table 5: Distribution of Scheuer scores for Fibrosis, Portal Inflammation and Lobular Inflammation in patients with HBV and HCV infection.

X²-test was used for comparison

Table 6 AUROCs for significant fibrosis, advanced fibrosis and cirrhosis for (A) APRI, (B) FIB-4 (C) CD163-FS and (D) CD163-HOMA-FS in patients with HBV and HCV infection considered separately. Presented as AUROC (95% CI). *p<0.05 compared with APRI; **p<0.01 compared with APRI APRI

A

HCV HBV

>F2 0.74 (0.69-0.79) 0.74 (0.66-0.82)

>F3 0.82 (0.77-0.87) 0.75 (0.63-0.87)

F4 0.85 (0.79-0.91) 0.79 (0.71-0.86)

FIB-4

B

HCV HBV

>F2 0.75 (0.70-0.79) 0.68 (0.59-0.77)

>F3 0.83 (0.79-0.88) 0.74 (0.61-0.87)

F4 0.89 (0.85-0.94) 0.77 (0.51-1.0)

CD163-FS

C

HCV HBV

>F2 0.77 (0.74-0.83)** 0.74 (0.65-0.83)

>F3 0.84 (0.79-0.88)* 0.78 (0.65-0.91)

F4 0.86 (0.81-0.92) 0.86 (0.79-0.94)*

Conclusion: The present study demonstrated a highly significant association between sCD163 and fibrosis when adjusted for multiple biochemical and clinical parameters. The study furthermore demonstrated a progressive increase in sCD163 in association with the severity of disease. Furthermore, the study demonstrated that the novel sCD163-based fibrosis scores (CD163-FS and CD163-HOMA-FS)) were superior to APRI for the prediction of all fibrosis stages and to FIB-4 for significant fibrosis.

Example 2: Development of CD-163 Fibrosis Score

Material and methods

The present inventors studied sCD163 in 556 patients with chronic hepatitis C virus (HCV) and 208 patients with chronic hepatitis B virus (HBV) before anti-viral treatment. Scheuer histological scores of activity and fibrosis were obtained along with clinical, biochemical, and metabolic parameters.

Variables that showed significant associations with the Scheuer Fibrosis score were identified as candidates for a new sCD163 based fibrosis score (CD163-FS), and combinations of these parameters were examined.

Nonparametric Receiver Operating Characteristics (ROC) analyses for the presence of liver cirrhosis (defined by Scheuer Fibrosis score of 4(26)), as well as advanced (F≥3) and significant fibrosis (F≥2) were performed to evaluate the diagnostic performance of these combinations, and the combination providing the highest areas under the ROC- curves (AUROCs) was chosen for the new score. The new score was compared with APRI and FIB-4 using the test of equality of ROC areas. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) were determined for appropriate cut-off values of CD613-FS, which were chosen based on the ROC-curve.

Development of sCD163-based fibrosis scores (CD163-FS and CD163-HOMA-FS) The following parameters were significantly associated with Scheuer Fibrosis score: SCD163 (p<0.001), gender (p=0.038), age (p=0.003), AST (p<0.001), HOMA-IR (p=0.002), platelets (p=0.002) and INR (p=0.001). The inventors examined different combinations of these parameters to develop a novel fibrosis score, and the combination of sCD163, age, AST and platelets provided the best AUROCs. Including HOMA-IR, gender and INR into the score did not further improve its predictive capability as assessed by AUROCs. The new sCD163-based Fibrosis Score (CD163- FS) was calculated as follows:

CD163-FS = log (sCD163 (mg/L) x age (years) x AST (IU/L) /platelets (x 10⁹/L)).

When the inventors included HOMA-IR the score was calculates as follows:

CD163-HOMA-FS = log (sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA- IRA0.25)/platelets (x 10⁹/L))

Both CD163-FS and CD163-HOMA-FS correlated significantly with the Scheuer fibrosis score (Figures 5A and 5B). Further, the inventors performed two separate ordered logistic regression analyses, with Scheuer fibrosis score as the dependent variable, and fibrosis scores CD163-FS and CD163-HOMA-FS as the explanatory. In this analysis, CD163-FS was associated with fibrosis with OR of 1.25 (95% CI: 1.21- 1.29), p<0.001. CD163-HOMA-FS had odds ratio (OR) of 1.25 (95% CI: 1.21-1.29), pO.001. The inventors performed ROC analyses for the presence of liver cirrhosis (F=4), as well as advanced (F≥3) and significant fibrosis (F≥2) for both fibrosis scores and

determined areas under the ROC-curves (AUROCs). Both CD163-FS and CD163- HOMA-FS were superior to APRI for cirrhosis, advanced and significant fibrosis, and were significantly better than FIB-4 for significant fibrosis (Tables 7 and 9).

The inventors then identified cut-off values of CD163-FS and CD163-HOMA-FS that provided the best discrimination for the presence or absence of fibrosis. The inventors chose to do so only for significant fibrosis, as our fibrosis scores were superior to both APRI and FIB-4 in prediction of this fibrosis stage. The inventors defined two cut-off values for each of the fibrosis scores based on the ROC-curves for significant fibrosis (Figures 2A and 2B). The cut-off values with corresponding sensitivities, specificities, positive and negative predictive values are presented in Tables 8 and 10.

Table 7: Areas under the ROC-curve (AUROCs) for significant fibrosis (F≥2), advanced fibrosis (F≥3) and cirrhosis (F=4) for the AST to platelet ratio index (APRI), FIB-4 and the CD163-FS in patients with chronic viral hepatitis.

Data are presented as AUROCs (95% CI)

†p<0.01 compared to FIB-4

Table 8. Cut-off values of CD613-FS and corresponding sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for significant fibrosis (F≥2), with total prevalence of significant fibrosis at 43.8 %.

Sensitivity Specificity

Cut-off values NPV (%) PPV (%)

(%) (%) < 2.75 90 40 84 54

> 4.75 33 93 64 79

Table 9: Areas under the ROC-curve (AUROCs) for significant fibrosis (F≥2), advanced fibrosis (F≥3) and cirrhosis (F=4) for the AST to platelet ratio index (APRI), FIB-4 and CD163-HOMA-FS in patients with chronic viral hepatitis.

Data are presented as AUROCs (95% CI)

†p<0.01 compared to FIB-4

Table 10. Cut-off values of CD163-HOMA-FS and corresponding sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for significant fibrosis (F≥2), with total prevalence of significant fibrosis at 43.8 %.

Example 3: Development of new sCD163 based Fibrosis Scores for subjects diagnosed with HCV and HBV.

We undertook this study in a large cohort of chronic HCV and HBV infected patients with histological data for disease activity and fibrosis. New sCD163-based Fibrosis Scores, CD163-HCV-FS and CD163-HBV-FS, were developed and compared to the AST to platelet ratio index (APRI) and the FIB-4 score. The study was performed on the same cohort of patients as in Example 1 but patients diagnosed with both HCV and HBV or co-infected with HIV were excluded from the study.

METHODS We performed a cross-sectional study in 551 patients with chronic HCV infection and 203 patients with chronic HBV infection who were referred to The Storr Liver Unit, Westmead Hospital, Westmead, Australia, between July 1991 and August 2010 for evaluation of chronic viral hepatitis. The diagnosis of chronic HCV infection was confirmed by the presence of anti-HCV antibodies (Monolisa anti-HCV; Sanofi

Diagnostics Pasteur, Marnes-la-Coquette, France) and viral RNA as detected by polymerase chain reaction (PCR) (Amplicor HCV; Roche Diagnostics, Branchburg, NJ). Hepatitis C virus genotyping was performed with a second generation reverse hybridization line probe assay (Inno-Lipa HCV II; Innogenetics, Zwijndrecht, Belgium). The diagnosis of chronic hepatitis B was confirmed by the presence of hepatitis B surface antigen in the blood for more than 6 months, hepatitis B core antibodies and HBV-DNA detection by signal amplification hybridization microplate assay (Digene HBV Test using Hybrid Capture 2, Digene) with a lower limit of detection of 0.5 pg/ml (1.42 x 10⁵ virus copies/ml), or by real-time PCR. Dual infection with HCV and HBV (n=9), and coinfection with HIV(n=1) led to exclusion. .

experienced pathologists and scored according to the Scheuer scoring system. (19) Steatosis was graded as described by Brunt et al. (20). All biopsies had a minimum of 1 1 portal tracts, and inadequate biopsies were excluded. Consequently, histological data were missing in 38 patients with HCV and in 3 patients with HBV infection. None of the patients had antiviral treatment prior to inclusion. At the time of the liver biopsy, basic demographic and clinical data were obtained, including gender, age, ethnicity, height, weight and waist circumference. Alcohol consumption was assessed by 2 separate interviews with the patient and close family members. Body Mass Index (BMI) was calculated from height and weight. At the same time, a fasting blood sample was drawn and routine biochemical tests were performed as described below. Additional blood samples were taken and frozen at -80 °C for future research. All patients signed an informed consent form in accordance with the Helsinki Declaration; the acquisition, storage and use of the blood samples was approved by the Sydney West Area Health Service ethics committee.

Biochemical analyses

As in example 1. Statistical methods

Basically as in Example 1.

We performed multiple ordered logistic regression analysis with Scheuer Fibrosis score as the dependent variable and sCD163 as the explanatory in two different models. These models provided odds ratios (OR) for a given fibrosis score corresponding to specific increases in sCD163. In this study, we chose to report odds ratios for a 25% increase in sCD163 based on the median difference in sCD163 between patients who differed in Scheuer fibrosis score by 1 (28%). In model 1 , we aimed to determine whether sCD163 is associated with fibrosis score directly or through its relationship with liver inflammation and known risk factors for fibrosis. Age, gender, BMI, ethnicity, alcohol consumption, viral etiology (HBV or HCV), and presence of genotype 1 (HCV only) were identified as risk factors and included in model 1. Scheuer scores for Lobular and Portal Inflammation were also included in this model.

In model 2, our goal was to investigate whether sCD163 is a marker of fibrosis when adjusted for demographic, clinical and biochemical parameters shown to be associated with fibrosis in previous studies. (26) Thus, we included age, gender, BMI, ethnicity, viral etiology, genotype 1 , alcohol consumption, albumin, platelet count, ALT, AST, INR and HOMA-IR in model 2. All continuous variables were logarithmically transformed. Variables showing significant associations with the Scheuer Fibrosis score in model 2 were identified as candidates for the new sCD163 based Fibrosis Scores (CD163- HCV-FS and CD163-HBV-FS). We performed backward elimination based on the likelihood ratio-test with significance limit of 0.1 to discover possible other predictors of fibrosis. Then, combinations of these variables were examined. Nonparametric

Receiver Operating Characteristics (ROC) analyses for the presence of liver cirrhosis (defined by Scheuer Fibrosis score of 4(27)), as well as advanced (F≥3) and significant fibrosis (F≥2) were performed to evaluate the diagnostic performance of these combinations. The combinations providing the highest areas under the ROC-curves (AUROCs) in patients with HCV and HBV infection were chosen for the new scores. The new scores were compared to APRI and FIB-4 using the test of equality of ROC areas. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) were determined for appropriate cut-off values of CD613-HCV-FS and CD163-HBV-FS, based on the ROC-curves.

All data are expressed as medians with interquartile ranges (IQR) or proportions and P- values≤0.05 were considered statistically significant. STATA version 12.0 ©StataCorp LP was used for the data analysis. RESULTS

Patient characteristics

Basic demographic, clinical and biochemical data for the HCV and HBV patients are presented in Table 1 1. The two groups did not differ significantly in terms of gender, the majority of the patients being male. Patients with HCV infection were slightly, but significantly older than HBV patients. BMI and HOMA-IR were higher in HCV patients, however, with no significant difference in the prevalence of diabetes between the groups. There was a statistically significant difference in the consumption of alcohol between HCV and HBV patients (Table 11). We observed higher ALT, AST, and GGT levels in HCV patients compared to HBV. Albumin was slightly, but significantly lower in patients with HCV infection. INR was significantly lower in HCV patients, although the two groups had the same median values. A trend toward lower platelets in HCV patients was observed. Levels of cholesterol, low-density lipoprotein (LDL) and high- density lipoprotein (HDL) were significantly higher in HBV patients, while triglyceride levels were similar for the two groups.

Soluble CD163 levels were significantly higher in HCV patients (3.6 (2.5-5.4) mg/L) compared to those with HBV infection (2.4 (1.8-3.6) mg/L), p<0.001. Table 11. Demographic, clinical and biochemical parameters in patients with HCV and HBV infection.

HCV HBV

Parameter

n = 551 n = 203

Age (years) 43 (36-49) 41 (33-49) p=0.05

348 (63%) : 203 130 (64%) :

Gender (Male : Female) p=0.82

(37%) 73 (36%)

Alcohol consumption

< 10 g/day 446 (82.6%) 152 (74.9%)

10-19 g/day 41 (7.6%) 28 (13.8%)

20-39 g/day 34 (6.3%) 17 (8.3%) p=0.005 40-59 g/day 10 (1.8%) 6 (3.0%)

≥60 g/day 9 (1.7%) 0 (%)

Missing record 1 1 0

Fibrosis stage 265 (51.7%) 139 (69.5%) p=0.001 No/mild fibrosis (F0-1)(n) 248 (48.3%) 61 (30.5%)

Significant fibrosis or higher 99 (19.3%) 21 (10.5%)

(F≥2) (n) 45 (8.8%) 4 (2.0%)

Advanced fibrosis or higher 38 3

(F≥3) (n)

Cirrhosis (F4) (n)

Missing histology (n)

8.5 (6.4-

MELD 7.5 (6.4-8.4) p=0.55

8.8)

BMI (kg/m²) 26 (23-30) 24 (21-27) pO.001

Diabetes (n) 25 (4.6%) 4 (2.2%) p=0.15

ALT (IU/L, males <70 IU/L;

82 (52-144) 49 (29-78) p<0.001 females <45 IU/L)

AST (IU/L, <45 IU/L) 62 (43-100) 46 (37-62) pO.001

Bilirubin (μΓΤΐοΙ/L, 5-25 μΓΤΐοΙ/L) 1 1 (8-14) 10 (8-14) p=0.31

ALP (IU/L, 35-105 IU/L) 77 (66-96) 80 (67-99) p=0.35

GGT (IU/L, < 115 IU/L) 50 (29-93) 26 (19-47) pO.001

INR† 1 (0.9-1) 1 (1-1.1) pO.001

Albumin (g/L, 36-48 g/L) 43 (41-45) 45 (43-48) pO.001

1.43 (0.67-

HOMA-IR 2.22 (1.43-3.78) pO.001

2.35)

Cholesterol (mmol/L, <5 4.9 (4.4-

4.5 (3.9-5.1) pO.001 mmol/L) 5.6)

2.9 (2.4-

LDL (mmol/L, <3 mmol/L) 2.6 (2-3.2) pO.001

3.5)

1.3 (1.2-

HDL (mmol/L, >1.2 mmol/L) 1.3 (1-1.6) p=0.01

1.7)

Triglycerides (mmol/L, <2 0.98 (0.72-

0.98 (0.75-1.36) p=0.70 mmol/L) 1.37)

Haemoglobin (g/L, males

146 (137- 134-169 g/L; females 118-153 151 (140-160) p=0.007

157)

g/L)

Leucocytes ( x 10⁹/L, 3.5-10 x 6.8 (5.6-8.4) 5.4 (4.6- p<0.001 10⁹/L) 6.5)

Platelets ( x 10⁹/L, 165-400 x 220 (190-

225 (186-276) p=0.07 10⁹/L) 257)

Parameters are presented as medians (interquartile range) for continuous variables, and as total number (%) for categorical variables. Units and normal ranges are in parenthesis.

MELD, model for end-stage liver disease; BMI, Body Mass Index; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyltransferase; INR, international normalized ratio; HOMA-IR, homeostatic model assessment; LDL, low-density lipoprotein; HDL, high-density lipoprotein†The distribution of INR in patients with HCV and HBV infection is presented in Figure 8.

Histological scores of activity and fibrosis in patients with HCV and HBV infection The patients with HCV infection had more advanced disease with higher scores for Scheuer Fibrosis (p<0.001) compared to HBV patients (Table 11). Cirrhosis was significantly more prevalent in patients with HCV, compared to HBV infection (Table 1 1). None of the patients with biopsy-verified HCV or HBV cirrhosis had

decompensated cirrhosis. Similarly, the proportion of advanced fibrosis and significant fibrosis in patients with HCV infection was significantly higher than in the HBV group (Table 1 1).

In addition, HCV patients had higher scores for Scheuer Portal Inflammation (p<0.001) and Steatosis (p<0.001). There was no significant difference in the score for Scheuer Lobular Inflammation between the two groups (p=0.45). The full distribution of histological scores in patients with HCV and HBV infection is presented in Table 12.

Table 12. Distribution of Scheuer scores for Fibrosis, Portal Inflammation and Lobular Inflammation in patients with HCV and HBV infection.

X²-test was used for comparison

Scheuer score HCV HBV

Fibrosis

0 65 (12.7%) 42 (21.0%)

1 201 (39.0%) 97 (48.5%) pO.001

2 149 (29.0%) 40 (20.0%)

3 54 (10.5%) 17 (8.5%) 4 45 (8.8%) 4 (2.0%)

Missing histology 38 3

Portal Inflammation

0 10 (1.9%) 20 (10.0%)

1 44 (8.6%) 37 (18.5%)

2 327 (63.7%) 1 15 (57.5%) pO.001

3 126 (24.6%) 21 (10.5%)

4 6 (1.2%) 7 (3.5%)

Missing histology 38 3

Lobular Inflammation

0 13 (2.5%) 4 (2.0%)

1 67 (13.1 %) 19 (9.5%)

2 415 (80.9%) 166 (83.0%) p=0.45

3 12 (2.3%) 6 (3.0%)

4 6 (1.2%) 5 (2.5%)

Missing histology 38 3

Steatosis

0 305 (59.4%) 194 (97%)

1 137 (26.7%) 5 (2.5%)

pO.001

2 46 (9.0%) 1 (0.5%)

3 25 (4.9%) 0 (0%)

Missing histology 38 3

Associations between sCD163 and demographic parameters

Associations between sCD163 and demographic parameters were not significantly different in the groups of HCV and HBV patients, for which reason they are presented for the whole population. In univariate analysis, soluble CD163 correlated weakly with age (Spearman's rho=0.19; p<0.001). Males had slightly, but significantly higher levels of SCD163 compared to females (3.3 (2.3-5.3) vs. 3.1 (2.1^1.5) mg/L; p=0.03).

Associations between sCD163 and clinical and biochemical parameters in HCV and HBV patients

The univariate associations between sCD163 and clinical and biochemical parameters are presented in Table 13. In patients with HCV infection, sCD163 showed significant associations with more parameters than in HBV patients, possibly reflecting the higher number of patients in this group. Soluble CD163 was significantly associated with BMI in patients with HCV, but not HBV infection. The association with waist circumference was significant in HCV patients, but not in the HBV group. In both groups we observed significant associations with ALT, AST and INR, and inverse associations with platelet count and albumin. There was a significant association between sCD163 and HOMA- IR in patients with HCV infection, but not in HBV patients. Associations with

triglycerides and cholesterol were significant in the HCV group alone. Soluble CD163 did not show associations with levels of HCV viral load or HBV DNA titer counts; it did not differ significantly between various HCV genotypes, while data on HBV genotypes were unavailable.

Soluble sCD163 showed no association with alcohol consumption in patients with HCV or HBV infection.

Table 13. Univariate associations between sCD163 and clinical and biochemical parameters in patients with HCV and HBV infection. Associations are statistically tested by Spearman's test.

BMI, Body Mass Index; ALT, alanine transaminase; AST, aspartate transaminase; INR, international normalized ratio; HOMA-IR, homeostatic model assessment Associations between sCD163 and histological scores of activity and fibrosis in HCV and HBV atients

In univariate analyses, sCD163 increased in association with rising scores for histological fibrosis stage and inflammatory activity in patients with HCV and HBV infection (Figure 6 A-C). Median sCD163 levels were higher in HCV compared to HBV patients with the same scores for inflammatory activity and fibrosis, reaching statistical significance in a number of cases.

There was a weak association between sCD163 and steatosis score in patients with HCV (rho=0.21 ; p<0.001), but not HBV infection (p=0.41).

Ordered logistic regression analysis for association between sCD163 and fibrosis in the combined group of HCV and HBC patients

As described in the methods, we used an ordered logistic regression model with Scheuer Fibrosis score as the dependent variable and sCD163 as the explanatory variable, in univariate and two multivariate models. In the simple (univariate) model, the OR for HCV patients was 1.49 (95% confidence interval (CI): 1.38-1.61), pO.001. Thus, for two random HCV patients from the cohort, if one of them has 25% higher sCD163 compared to the other, this patient has 49% greater odds of e.g. presence of fibrosis (F≥1) than the other patient. In patients with HBV infection, this OR was 1.32 (95% CI: 1.17-1.49), p<0.001. It is a property of ordered logistic regression, that this increase in odds is the same for significant fibrosis (F≥2), advanced fibrosis (F≥3) or liver cirrhosis (F=4).

In multiple regression models we adjusted for risk factors and clinical and biochemical parameters. Applying multiple models to the two hypothetical patients described above, the resulting odds ratios estimate the odds of having a higher fibrosis score for the patient with 25% higher sCD163, assuming that the two patients were completely alike in terms of the risk factors and parameters we adjusted for. In patients with HCV infection, sCD163 showed a significant independent association with fibrosis in both multiple models, whereas in HBV patients sCD163 was significantly associated with fibrosis in Model 2, but not in Model 1 (Table 14).

Table 14. Simple and multiple ordered logistic regression models with soluble

(s)CD163 as the explanatory variable for Scheuer Fibrosis score. Unadjusted and adjusted odds ratios are presented for each 25% increase in sCD163.

OR 95% CI P Patients with hepatitis C infection

Unadjusted 1.49 1.38-1.61 <0.001

Model 1 1.28 1.17-1.41 <0.001

Model 2 1.19 1.06-1.35 0.005

Patients with hepatitis B infection

Unadjusted 1.32 1.17-1.49 <0.001

Model 1 1.1 1 0.96-1.29 0.166

Model 2 1.31 1.05-1.64 0.018

Model 2. Age, gender, body mass index, ethnicity, alcohol consumption, viral etiology (hepatitis B or C infection), presence of genotype 1 (hepatitis C only), albumin, platelets, alanine transaminase, aspartate transaminase, international standardized ratio and homeostatic model assessment (HOMA-IR) included in the model. Development of a sCD163-based predictive fibrosis score (CD163-HCV-FS) and comparison to the APR I and FIB-4 scores in HCV patients

As described in the statistical methods, we performed a multiple ordered logistic regression analysis including parameters shown to be associated with fibrosis in previous studies (model 2). In this analysis, the following variables were significantly associated with the fibrosis score: sCD163 (p=0.005), age (p<0.001), platelets

(p=0.007), AST (p<0.001), HOMA-IR (p<0.001) and INR (p<0.001). Applying backward elimination based on the likelihood ratio-test with significance limit of 0.1 on the same regression model did not identify further candidate variables. We then performed a new multiple ordered logistic regression analysis with Scheuer fibrosis as the dependent variable, and the significant variables above as the explanatory. The result of this analysis is presented in Table 15.

Table 15. Multiple ordered logistic regression analysis with Scheuer Fibrosis Scores the dependent variable in patients with HCV infection. Backward elimination based on the likelihood ratio-test with significance limit of 0.1 was applied. _7Q

R, homeostatic model assessment; INR, international normalized ratio

Based on these coefficients (β), we computed a predictive score including all of the parameters:

Score 1 = 0.5*logCD163 + 1.5*logAge + logAST + 0.5*logHOMA-IR + 5*loglNR - 1.5*logPlatelets

Since we wanted to investigate if a simpler score could have just as good predictive value as the above score, we next calculated a second score (the coefficients in the regression equation were the same with the exception of the coefficient for HOMA-IR): Score 2 = 0.5*logCD163 + 1.5*logAge + logAST + 0.25*logHOMA-IR - 1.5*logPlatelets The AUROCs (with 95% CI) of Score 1 and Score 2 for the presence of significant fibrosis (≥F2), advanced fibrosis (≥F3) and cirrhosis (F4) are presented in Table 16. Table 16. Areas under the ROC-curve (AUROCs) for significant fibrosis, advanced fibrosis and cirrhosis for the two candidate predictive models in patients with HCV infection.

Score 2 = 0.5*logCD163 + 1.5*logAge + logAST + 0.25*logHOMA-IR - 1.5*logPlatelets

Score 1 Score 2

≥F2 0.78 (0.73-0.83) 0.79 (0.74-0.83)

≥F3 0.88 (0.84-0.92) 0.86 (0.82-0.90)

F4 0.91 (0.87-0.96) 0.90 (0.85-0.94) sCD163, soluble CD163; AST, aspartate transaminase; HOMA-IR, homeostatic model assessment; INR, international normalized ratio

The presence of significant fibrosis (≥F2) is usually used as a determinant for initiating anti-viral therapy, and Score 2 had the highest AUROC for significant fibrosis (≥F2), for which reason we used Score 2 as the new sCD163-based fibrosis score in HCV patients (CD163-HCV-FS).

CD163-HCV-FS = 0.5*logCD163 (mg/L) + 1.5*logAge (years) + logAST (IU/L) + 0.25*logHOMA-IR - 1.5*logPlatelets (x 10⁹/L)

Next, we compared the AUROCs of the new CD163-HCV-FS score with those of APRI and FIB-4, presented in Table 17A. CD163-HCV-FS was superior to both APRI and FIB-4 for significant fibrosis and to APRI for advanced fibrosis and cirrhosis.

We identified cut-off values for CD163-HCV-FS that provided the best discrimination for the presence or absence of significant fibrosis due to its importance for anti-viral therapy. We defined cut-off values based on the ROC-curve (Figure 7A). The first cutoff was defined at CD163-HCV-FS=1.55 (marked 1 on Figure 7A). The sensitivity was 90%, specificity 42%, PPV 59% and NPV 82%. The other cut-off was at CD163-HCV- FS=3.50 (marked 2 on Figure 7A); the sensitivity was 34%, specificity 93%, PPV 82% and NPV 60%, with total prevalence of significant fibrosis in HCV patients at 48.3%. If only one cut-off value was to be chosen, we identified a value of CD163-HCV-FS=2.60 (marked 3 on Figure 7A), with a sensitivity of 71 %, specificity of 75%, PPV 73% and NPV 73%.

Table 17. Areas under the ROC-curve (AUROCs) for significant fibrosis, advanced fibrosis and cirrhosis for the AST to platelet ratio index (APRI), FIB-4 and the sCD163- based Fibrosis Scores (CD163-HCV-FS and CD163-HBV-FS) in patients with chronic viral hepatitis.

A: Patients with HCV infection; B: Patients with HBV infection

Data are presented as AUROCs (95% CI)

*p<0.01 compared to APRI;†p<0.05 compared to FIB-4

A

APRI FIB-4 CD163-HCV-FS

≥F2 0.74 (0.69-0.79) 0.75 (0.70-0.79) 0.79 (0.74-0.83)*†

≥F3 0.81 (0.76-0.86) 0.82 (0.78-0.87) 0.86 (0.82-0.90)* F4 0.85 (0.79-0.91) 0.89 (0.85-0.93) 0.90 (0.85-0.94)*

B

APRI FIB-4 CD163-HBV-FS

≥F2 0.73 (0.65-0.81) 0.67 (0.58-0.76) 0.71 (0.62-0.79)

≥F3 0.76 (0.63-0.88) 0.72 (0.59-0.85) 0.77 (0.67-0.88)

F4 0.79 (0.72-0.86) 0.75 (0.39-1.0) 0.82 (0.64-1.0)

Development of a sCD163-based predictive fibrosis score (CD163-HBV-FS) and comparison to the APRI and FIB-4 scores in HB V patients

We used the same approach for the development of the predictive score in HBV patients as described above for patients with HBV infection. In the multiple regression analysis (model2), only sCD163 was statistically significant (p=0.018), while gender showed a trend to association (p=0.092). We applied backward elimination based on a likelihood ratio-test with significance limit of 0.1 , the result presented in Table 18.

Table 18. Multiple ordered logistic regression analysis with Scheuer Fibrosis Scores the dependent variable in patients with HBV infection. Backward elimination based on the likelihood ratio-test with significance limit of 0.1 was applied.

In this analysis, BMI was identified as another candidate variable for a predictive score. By using the coefficients from the regression equation, we were able to compute a fibrosis score for HBV patients. However, this score had all negative values, which is inconvenient, and we modified the score by adding 10 to avoid it. CD163-HBV-FS was thus calculated as follows:

CD163-HBV-FS= 1.5*log(sCD163 (mg/L)) + 0.8*(gender (female=0, male=1)) - 2*log(BMI (kg/m²)) + 10 The new score had higher AUROCs than APRI and FIB-4 but the difference did not reach statistical significance (Table 17B). Analogous to patients with HCV infection, we identified cut-off values for CD163-HBV-FS for the presence or absence of significant fibrosis. We defined cut-off values based on the ROC-curve (Figure 7B). The first cut- off was at CD163-HBV-FS=5.0 (marked 1 on Figure 7B). The sensitivity was 89%, specificity 37%, PPV 38% and NPV 88%. The other cut-off was at CD163-HBV- FS=6.50 (marked 2 on Figure 7B); the sensitivity was 30%, specificity 89%, PPV 54% and NPV 74%, with total prevalence of significant fibrosis in HBV patients at 30.5%. If only one cut-off value was to be chosen, we identified a value of CD163-HBV-FS=5.80 (marked 3 on Figure 7B), with a sensitivity of 68%, specificity of 74%, PPV 53% and NPV 84%.

Conclusion

In our patients sCD163 was independently associated with fibrosis and we developed new sCD163-based Fibrosis Scores CD163-HCV-FS and CD163-HBV-FS. CD163- HCV-FS was superior to the existing scores APRI and FIB-4, mainly in predicting significant fibrosis. CD163-HBV-FS had higher AUROCs than APRI and FIB-4 for all fibrosis stages, but the differences did not reach statistical significance, probably due to lower number of patients and prevalence of severe disease in this group. The new scores are simple and include parameters that are readily available.

References

1. Wynn TA, Barron L. Macrophages: master regulators of inflammation and fibrosis. Semin Liver Dis 2010;30:245-257.

2. Ramachandran P, Iredale JP. Macrophages: central regulators of hepatic fibrogenesis and fibrosis resolution. J Hepatol 2012;56: 1417-1419. 3. Hosomura N, Kono H, Tsuchiya M, Ishii K, Ogiku M, Matsuda M, Fujii H. HCV- related proteins activate Kupffer cells isolated from human liver tissues. Dig Dis Sci 201 1 ;56:1057-1064. 4. Tu Z, Pierce RH, Kurtis J, Kuroki Y, Crispe IN, Orloff MS. Hepatitis C virus core protein subverts the antiviral activities of human Kupffer cells. Gastroenterology 2010; 138:305-314. 5. Oyanagi Y, Takahashi T, Matsui S, Takahashi S, Boku S, Takahashi K, Furukawa K et al. Enhanced expression of interleukin-6 in chronic hepatitis C. Liver 1999; 19:464- 472.

6. Sandler NG, Koh C, Roque A, Eccleston JL, Siegel RB, Demino M, Kleiner DE et al. Host response to translocated microbial products predicts outcomes of patients with

HBV or HCV infection. Gastroenterology 2011 ;141 : 1220-30, 1230.

7. Bataller R, Brenner DA. Liver fibrosis. J Clin Invest 2005; 1 15:209-218. 8. Graversen JH, Svendsen P, Dagnaes-Hansen F, Dal J, Anton G, Etzerodt A, Petersen MD et al. Targeting the hemoglobin scavenger receptor CD163 in

macrophages highly increases the anti-inflammatory potency of dexamethasone. Mol Ther 2012;20: 1550-1558. 9. Moller HJ, de FM, Aerts H, Hollak C, Moestrup SK. Plasma level of the macrophage- derived soluble CD163 is increased and positively correlates with severity in Gaucher's disease. Eur J Haematol 2004;72: 135-139.

10. Moestrup SK, Moller HJ. CD163: a regulated hemoglobin scavenger receptor with a role in the anti-inflammatory response. Ann Med 2004;36:347-354.

1 1. Schaer DJ, Schleiffenbaum B, Kurrer M, Imhof A, Bachli E, Fehr J, Moller HJ et al. Soluble hemoglobin-haptoglobin scavenger receptor CD163 as a lineage-specific marker in the reactive hemophagocytic syndrome. Eur J Haematol 2005;74:6-10.

12. Weaver LK, Hintz-Goldstein KA, Pioli PA, Wardwell K, Qureshi N, Vogel SN, Guyre PM. Pivotal advance: activation of cell surface Toll-like receptors causes shedding of the hemoglobin scavenger receptor CD163. J Leukoc Biol 2006;80:26-35. 13. Holland-Fischer P, Gronbaek H, Sandahl TD, Moestrup SK, Riggio O, Ridola L, Aagaard NK et al. Kupffer cells are activated in cirrhotic portal hypertension and not normalised by TIPS. Gut 201 1 ;60:1389-1393. 14. Gronbaek H, Sandahl TD, Mortensen C, Vilstrup H, Moller HJ, Moller S. Soluble CD163, a marker of Kupffer cell activation, is related to portal hypertension in patients with liver cirrhosis. Aliment Pharmacol Ther 2012.

15. Hiraoka A, Horiike N, Akbar SM, Michitaka K, Matsuyama T, Onji M. Expression of CD163 in the liver of patients with viral hepatitis. Pathol Res Pract 2005;201 :379-384.

16. Hiraoka A, Horiike N, Akbar SM, Michitaka K, Matsuyama T, Onji M. Soluble CD163 in patients with liver diseases: very high levels of soluble CD163 in patients with fulminant hepatic failure. J Gastroenterol 2005;40:52-56.

17. Moller HJ, Gronbaek H, Schiodt FV, Holland-Fischer P, Schilsky M, Munoz S, Hassanein T et al. Soluble CD163 from activated macrophages predicts mortality in acute liver failure. J Hepatol 2007;47:671-676. 18. Castera L. Noninvasive Methods to Assess Liver Disease in Patients With Hepatitis B or C. Gastroenterology 2012: 142:1293-1302.

19. Scheuer PJ. Classification of chronic viral hepatitis: a need for reassessment. J Hepatol 1991 ; 13:372-374.

20. Brunt EM, Janney CG, Di Bisceglie AM, Neuschwander-Tetri BA, Bacon BR.

Nonalcoholic steatohepatitis: a proposal for grading and staging the histological lesions. Am J Gastroenterol 1999;94:2467-2474. 21. Turner RC, Holman RR, Matthews D, Hockaday TD, Peto J. Insulin deficiency and insulin resistance interaction in diabetes: estimation of their relative contribution by feedback analysis from basal plasma insulin and glucose concentrations. Metabolism 1979;28:1086-1096. 22. Wiesner R, Edwards E, Freeman R, Harper A, Kim R, Kamath P, Kremers W et al. Model for end-stage liver disease (MELD) and allocation of donor livers.

Gastroenterology 2003; 124:91-96. 23. Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, Lok AS. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003;38:518-526.

24. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, Sulkowski S et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006;43:1317-1325.

25. Moller HJ, Hald K, Moestrup SK. Characterization of an enzyme-linked

immunosorbent assay for soluble CD163. Scand J Clin Lab Invest 2002;62:293-299.

26. Castera L. Noninvasive methods to assess liver disease in patients with hepatitis B or C. Gastroenterology 2012;142: 1293-1302.

27. Desmet VJ, Gerber M, Hoofnagle JH, Manns M, Scheuer PJ. Classification of chronic hepatitis: diagnosis, grading and staging. Hepatology 1994; 19: 1513-1520.

28. Moller HJ, Peterslund NA, Graversen JH, Moestrup SK. Identification of the hemoglobin scavenger receptor/CD 163 as a natural soluble protein in plasma. Blood 2002;99:378-380.

29. Dolganiuc A, Norkina O, Kodys K, Catalano D, Bakis G, Marshall C, Mandrekar P et al. Viral and host factors induce macrophage activation and loss of toll-like receptor tolerance in chronic HCV infection. Gastroenterology 2007; 133:1627-1636. 30. Heydtmann M. Macrophages in hepatitis B and hepatitis C virus infections. J Virol 2009;83:2796-2802.

31. Cooper A, Tal G, Lider O, Shaul Y. Cytokine induction by the hepatitis B virus capsid in macrophages is facilitated by membrane heparan sulfate and involves TLR2. J Immunol 2005; 175:3165-3176. 32. Zhang JY, Zou ZS, Huang A, Zhang Z, Fu JL, Xu XS, Chen LM et al. Hyper- activated pro-inflammatory CD16 monocytes correlate with the severity of liver injury and fibrosis in patients with chronic hepatitis B. PLoS One 201 1 ;6:e17484.

33. Chang S, Dolganiuc A, Szabo G. Toll-like receptors 1 and 6 are involved in TLR2- mediated macrophage activation by hepatitis C virus core and NS3 proteins. J Leukoc Biol 2007;82:479-487. 34. Dolganiuc A, Oak S, Kodys K, Golenbock DT, Finberg RW, Kurt-Jones E, Szabo G. Hepatitis C core and nonstructural 3 proteins trigger toll-like receptor 2-mediated pathways and inflammatory activation. Gastroenterology 2004;127: 1513-1524.

35. Caradonna L, Mastronardi ML, Magrone T, Cozzolongo R, Cuppone R, Manghisi OG, Caccavo D et al. Biological and clinical significance of endotoxemia in the course of hepatitis C virus infection. Curr Pharm Des 2002;8:995-1005.

36. Kaser A, Ludwiczek O, Waldenberger P, Jaschke W, Vogel W, Tilg H. Endotoxin and its binding proteins in chronic liver disease: the effect of transjugular intrahepatic portosystemic shunting. Liver 2002;22:380-387.

37. Sozinov AS. Systemic endotoxemia during chronic viral hepatitis. Bull Exp Biol Med 2002; 133: 153-155. 38. Aristoteli LP et al., The monocytic lineage specific soluble CD163 is a plasma marker of coronary atherosclerosis. Atherosclerosis 2006 184 342-7

39. Holger Jon M0ller: Soluble CD163. Scandinavian Journal of Clinical & Laboratory Investigation, 72: 1-13, 2012

40. Imran M, Manzoor S, Khattak NM, Khalid M, Ahmed QL, Parvaiz F, Tariq M, Ashraf J, Ashraf W, Azam S, Ashraf M., Arch Virol. 2013 Aug 25. [Epub ahead of print]).

41. Chen EQ, Tang H. The current status of combination therapy of Chronic Hepatitis B. Eur Rev Med Pharmacol Sci. 2013 Aug;17(15):2023-31).

Claims

A method of diagnosing the presence or severity of liver fibrosis in an individual, comprising the steps of:

a. determining the marker sCD163 in a sample from said individual;

c. determining the further marker AST in a sample from said individual; and

d. determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163, and said further markers.

The method of claim 1 , comprising determining the further marker fasting insuli in a sample from said individual and determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163, and said further markers.

The method of any of the preceding claims, comprising determining the further marker fasting glucose in a sample from said individual and determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163, and said further markers.

The method of any of the preceding claims, comprising determining the further marker age of said individual and determining the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163, and said further markers.

The method of any of the preceding claims, wherein determining comprises quantifying the amount or activity of said marker(s).

The method of any of the preceding claims, further comprising calculating a fibrosis score based on the determined markers.

The method of claim 6, wherein said fibrosis score comprises the level of sCD163, age, AST activity, and platelet number.

8. The method of claim 7, wherein said fibrosis score, CD163-FS, is log (sCD163 (mg/L) x age (years) x AST (IU/L) /platelets (x10⁹/L)).

9. The method of claim 6, wherein said fibrosis score comprises the level of

sCD163, age, AST activity, HOMA-IR, and platelet number.

10. The method of claim 9, wherein said fibrosis score, CD163-HOMA-FS, is log (SCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)).

1 1. The method of claim 9, wherein said fibrosis score, CD163-HCV-FS is

(0.5*log(sCD163 (mg/L)) + 1.5*log(Age (years)) + los(AST (IU/L))+

0.25*(logHOMA-IR) - 1.5*log(Platelets (x10⁹/L)).

12. The method of any of the preceding claims 6 to 10, further comprising

comparing said fibrosis score to at least one pre-determined cut-off value that is predictive of the presence of significant fibrosis (≥F2, Scheuer score) in said individual.

13. The method of claim 12, wherein said fibrosis score is CD163-FS and said cutoff value is 4.75 and wherein a fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score).

14. The method of claim 12, wherein said fibrosis score is CD163-HOMA-FS and said cut-off value is 5.1 or 3.5 and wherein a fibrosis score at or above said value is indicative of significant fibrosis (≥F2, Scheuer score).

15. The method of claim 12, wherein said fibrosis score is CD163-FS and said cutoff value is 2.75 or 2.9 and wherein a fibrosis score below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score).

16. The method of claim 12, wherein said fibrosis score is CD163-HCV-FS and said cut-off value is 1.55 and wherein a fibrosis score below said value is indicative of absence of clinically significant fibrosis (<F2, Scheuer score).

17. The method of claim 12, wherein said fibrosis score is CD163-FS and a fibrosis score of at least 4.75 indicates a 79% probability of having significant fibrosis (≥F2, Scheuer score).

18. The method of claim 12, wherein said fibrosis score is CD163-HOMA-FS and a fibrosis score of at least 5.1 indicates an 80% probability of having significant fibrosis (≥F2, Scheuer score).

19. A method of diagnosing the presence or severity of liver fibrosis in an individual, comprising the steps of:

a. determining the marker sCD163 in a sample from said individual;

b. determining at least one further marker selected from the group

consisting of platelet number, insulin, glucose, AST, ALT, age, hyaluronate, bilirubin, alpha-2-macroglobulin, alkaline phosphatase, gamma-globulin, albumin, prothrombin-index, INR, gammaGT, age, urea, uric acid, ferritin, cholesterol, alcohol use, gender, TIMP-1 , MMP1 , PIINP, HOMA-IR, BMI, waist circumference, CRP, cytokeratin 18, and c. Diagnosing the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163 and said at least one further marker.

20. The method of claim 19, comprising calculating a fibrosis score wherein said fibrosis score, CD163-HBV-FS, is 1.5*log (sCD163 (mg/L)) + 0.8*(gender (female=0; male=1)) - 2*log(BMI (kg/m²)) + 10.

21. The method of claim 20, wherein a fibrosis score of at least 6.5 is indicative of significant fibrosis (≥F2, Scheuer score).

22. The method of claim 20, wherein a fibrosis score of 5 or less is indicative of absence of significant fibrosis (<F2, Scheuer score).

23. The method of claim 20, wherein sais fibrosis score, CD163-HCV-FS1 is

0.5*log(sCD163 (mg/L)) + 1.5*log(Age (years)) + log(AST (IU/L)) +

0.5*logHOMA-IR + 5*loglNR - 1.5*log(Platelets (x10⁹/L)).

24. The method of diagnosing the presence or severity of liver fibrosis in an

individual, comprising the steps of

a. determining the marker sCD163 in a sample from said individual; b. determining a fibrosis score selected from fibrotest, forn index, AST to platelet ratio (APRI), Fibrospectll, MP3, Enhanced Liver Fibrosis score, Fibrosis probability index, Hepascore, Fibrometers, Lok index, Goteborg Univsersity cirrhosis index, Virahep - c model, Fibroindex, FIB-4, HALT- C model, Hui score, and Zeng score, and

c. diagnosing the presence or severity of liver fibrosis in said individual based on the level or presence of sCD163 and said fibrosis score.

25. The method of any of the preceding claims, wherein said individual is

diagnosed with viral hepatitis.

26. The method of any of the preceding claims, wherein said individual is

diagnosed with hepatitis B or C virus.

27. The method of any of the preceding claims, wherein said individual is

diagnosed with a liver disease selected from an autoimmune liver disease, alcoholic liver disease, fatty liver disease and drug-induced liver disease.

28. The method of any of the preceding claims, further comprising providing at least one statistical parameter relating to the fibrosis score.

29. The method of claim 28, wherein the statistical parameter is a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score.

30. The method of claim 28, wherein the estimated fibrosis score is selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

31. The method of any of the preceding claims, wherein said sample is selected from the group consisting of blood, serum, plasma, urine, and saliva.

32. The method of any of the preceding claims, wherein the diagnosis distinguishes between one or more of: presence of fibrosis, significant fibrosis, advanced fibrosis, and liver cirrhosis.

33. A method of diagnosing liver fibrosis caused by HCV or HBV infection, said method comprising performing a diagnosis according to any of claims 6-33, and comparing a fibrosis score to a cut-off value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score)

34. A method of differentiating between no or mild fibrosis (<F2, Scheuer score) from significant fibrosis (≥F2, Scheuer score), said method comprising performing a diagnosis by calculating a fibrosis score according to any of claims 6-33, and comparing a fibrosis score to a cutoff value indicative of the presence or absence of significant fibrosis (≥F2, Scheuer score).

35. A method of assessing the stage of a liver disease said method comprising performing a diagnosis according to any of claims 1-33, and assessing the stage of fibrosis based on said diagnosis.

36. A method of deciding to provide or defer antiviral therapy, said method

comprising performing a diagnosis according to any of claims 1-33, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score), and deferring antiviral therapy if the individual is diagnosed to have no or mild fibrosis (<F2, Scheuer score).

37. A method of treatment of HBV or HCV, said method comprising performing a diagnosis according to any of claims 1-33, and providing antiviral therapy if the individual is diagnosed to have significant fibrosis (≥F2, Scheuer score).

38. The method of claim 37, wherein said antiviral therapy comprises administration of one or more of Peg-interferon, ribavirin and protease inhibitors (boceprevir or telaprevir), the nucleos(t)ide analogues (NAs) e.g. entecavir (ETV) or tenofovir (TDF), lamivudine (LAM), adefovir (ADV) and telbivudine (LdT).

39. The method of claim 37, wherein the subject suffers from HCV.

40. A method of monitoring treatment response, said method comprising

performing a diagnosis according to any of claims 6-33, calculating a fibrosis score, treating said individual, repeating said diagnosis and calculation of fibrosis score and comparing said scores to determine whether said treatment is effective.

41 A method of monitoring disease progression said method comprising

performing a diagnosis according to any of claims 6-33, calculating a fibrosis score, repeating said diagnosis and calculation of fibrosis score and comparing said scores to determine whether the disease progresses.

42. A computer-implemented method for diagnosing liver fibrosis, said method comprising entering the level of sCD163, AST, platelet number, age, and optionally fasting insulin and fasting glucose of a subject to a computer having an input device, a processor and an output device, the processor comprising software for computing a fibrosis score, the method further comprising outputting said fibrosis score to an output device.

43. The method of claim 42, wherein the fibrosis score, CD163-FS, is calculated using an algorithm wherein the algorithm is log (sCD163 (mg/L) x age (years) x AST (IU/L) /platelets (x10⁹/L)).

44. The method of claim 42, wherein the fibrosis score, CD163-HCV-FS, is

calculated using an algorithm wherein the algorithm is (0.5*log(sCD163 (mg/L)) + 1.5*log(Age (years)) + log(AST (IU/L))+ 0.25*(logHOMA-IR) - 1.5*log(Platelets (x10⁹/L)).

45. The method of claim 42, further comprising entering information about the

identity of a patient into the system and means for linking the identity of a patient to the input levels and score.

46. The method of claim 42 or 45, wherein the levels are entered from different input devices.

47. The method of claim 46, wherein one input device is located at a laboratory and another input device is located at a hospital or clinic.

48. The method of any of claims 42 to 47, further comprising calculating the HOMA- IR value, and computing a fibrosis score, CD163-HOMA-FS, using an algorithm wherein the algorithm is log (sCD163 (mg/L) x age (years) x AST (IU/L) x (HOMA-IR^A0.25)/platelets (x10⁹/L)).

49. The method of any of the claims 42 to 48, further comprising providing at least one statistical parameter relating to the fibrosis score.

50. The method of claim 49, wherein the statistical parameter is a probability that the subject suffers from significant fibrosis, a probability that the subject does not suffer from significant fibrosis, or a fibrosis score estimated from the computed fibrosis score.

51. The method of claim 50, wherein the fibrosis score is selected from Scheuer score, METAVIR score, and Ishak (modified Knodell) score.

52. A system for of diagnosing the presence or severity of liver fibrosis in an

individual, comprising

a) An input device for entering data including levels of sCD163 concentration, age, AST activity, number of platelets, and optionally fasting insulin and fasting glucose;

c) An output device for displaying or printing said fibrosis score.

53. The system of claim 52, being adapted to perform the computer-implemented method of any of the claims 42 to 51.

54. The system of claim 52, further comprising software for comparing said fibrosis score to at least one cut-off value to diagnose the presence of significant fibrosis and presenting said diagnosis on the output device.

55. The system of any of claims 52 to 54, wherein the input device, processor, and output device are connected via a wide area network or a local area network.

56. The system of any of claims 52 to 55, wherein the input device or output device are located on a client and the processor and software are located on a server.

57. The system of any of claims 52 to 56, comprising more than one input device allowing entry of data from the more than one input device.

58. A fibrosis diagnosis report comprising:

a. Information regarding the identity of a patient; b. Information regarding the level of sCD163, AST, age, platelets, and optionally fasting glusose and fasting insulin, in a sample from said subject;

c. A fibrosis score according to any of the claims 6 to 18; and

d. A comparison of said fibrosis score to at least one cut-off value to

diagnose the presence or extent of fibrosis.

59. The report of claim 58, in paper format or in electronic format.

60. The report of claim 58 or 59, further comprising an illustration of the correlation between said fibrosis score and a fibrosis or cirrhosis score selected from Scheuer score, METAVIR fibrosis score, and Ishak (modified Knodell) score.

61. The report of any of claims 58 to 60, further comprising a HOMA-IR value.

62. A computer-implemented method for diagnosing liver fibrosis in a subject

diagnosed with HBV, said method comprising entering the level of sCD163, gender, and BMI or height and waist circumference of a subject to a computer having an input device, a processor and an output device, the processor comprising software for computing a fibrosis score, CD163-HBV-FS, is 1.5*log (sCD163 (mg/L)) + 0.8*gender (female=0; male=1) - 2*logBMI + 10, the method further comprising outputting said fibrosis score to an output device.

63. A system for of diagnosing the presence or severity of liver fibrosis in an

individual diagnosed with HBV, the system comprising:

a. An input device for entering data including levels of sCD163

concentration, gender, and BMI or height and waist circumference; b. A processor in data communication with said input device, the processor comprising software for computing a fibrosis score CD163-HBV-FS, is 1.5*log (sCD163 (mg/L)) + 0.8*gender (female=0; male=1) - 2*logBMI +10; and

c. An output device for displaying or printing said fibrosis score.

64. A fibrosis diagnosis report comprising: Information regarding the identity of a patient; Information regarding the level of sCD163 concentration, gender, and BMI or height and waist circumference, in a sample from said subject;

A fibrosis score CD163-HBV-FS, is 1.5*log (sCD163 (mg/L)) +

0.8*gender (female=0; male=1) - 2*logBMI +10; and

A comparison of said fibrosis score to at least one cut-off value to diagnose the presence or extent of fibrosis.