US20120010824A1 - Non-Invasive Method for Assessing Liver Fibrosis Progression - Google Patents

Non-Invasive Method for Assessing Liver Fibrosis Progression Download PDF

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US20120010824A1
US20120010824A1 US13/257,456 US201013257456A US2012010824A1 US 20120010824 A1 US20120010824 A1 US 20120010824A1 US 201013257456 A US201013257456 A US 201013257456A US 2012010824 A1 US2012010824 A1 US 2012010824A1
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fibrosis
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age
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Paul Cales
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Universite dAngers
Centre Hospitalier Universitaire dAngers
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    • A61K31/41—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with two or more ring hetero atoms, at least one of which being nitrogen, e.g. tetrazole
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    • A61K38/16—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
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    • G01N2400/38—Heteroglycans, i.e. polysaccharides having more than one sugar residue in the main chain in either alternating or less regular sequence, e.g. gluco- or galactomannans, e.g. Konjac gum, Locust bean gum, Guar gum
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    • G01N2800/08—Hepato-biliairy disorders other than hepatitis
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    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7052—Fibrosis

Definitions

  • the present invention relates to the field of hepatology and in particular to a non-invasive method for assessing the liver fibrosis progression, especially in alcohol or viral or metabolic chronic liver disease.
  • Liver fibrosis refers to the accumulation of fibrous scar tissue in the liver.
  • various techniques can be used.
  • One of these techniques is the liver needle biopsy (LNB), leading to a classification based on observation of lesions in the liver, particularly in the hepatic lobe.
  • LNB liver needle biopsy
  • Metavir classification which classifies liver fibrosis into five stages from F0 to F4. According to the Metavir classification, an F ⁇ 2 stage means that fibrosis is clinically significant, whereas a F4 stage corresponds to the ultimate stage, namely cirrhosis.
  • Fibrosis score such as for example FibrometerTM
  • OAF area of fibrosis
  • quantitative image analysis can also be used alone or in combination with LNB or Metavir classification, in order to determine with more accuracy the extent of liver fibrosis in an individual.
  • liver fibrosis progression rate of the fibrosis differs from an individual to another.
  • assessment of liver fibrosis progression would be a very important and useful tool in clinical practice for both prognostic and therapeutic reasons.
  • liver fibrosis progression depends on various genetic and host factors, it may indeed be useful to determine ahead of time whether it is reasonable to expect that liver fibrosis will progress towards cirrhosis during the patient's lifetime and if it does, at what rate will this progression occur.
  • assessing the progression rate of liver fibrosis can also be useful in order to help physicians decide whether or not to treat a patient or in order to help them monitor patients who are already following a treatment regimen.
  • physicians relied mostly on fibrosis staging (ex. Metavir stage ⁇ F2) in order to justify an antiviral treatment for chronic viral hepatitis.
  • fibrosis staging ex. Metavir stage ⁇ F2
  • it would be very useful to know early on, such as for example but not limited to patients showing a stage F0 or F1 whether his liver fibrosis will evolve rapidly or not into clinically significant fibrosis or cirrhosis, in order for the physicians to anticipate the treatment.
  • WO 03/064687 discloses a method for assessing a patient's risk of development and progression of liver cirrhosis, said method comprising the step of determining the patient's genotype or phenotype for a coagulation factor.
  • WO 2006/003654 discloses methods and kits for determining the predisposition of an individual affected by chronic hepatitis C infection to develop a fast progression rate of liver fibrosis. This method essentially consists in determining the presence or absence, in the CYP2D6 locus of the individual, of at least one fast progression liver fibrosis associated genotype.
  • EP 1887362A1 discloses a hepatic disease-evaluating method comprising a step of calculating an index indicating the degree of hepatic fibrosis from amino acid concentration data.
  • “Score” is a combination of markers (or variables) aimed at predicting a clinical event or a lesion such as fibrosis degree.
  • the score ranges from 0 (0% risk) to 1 (100% risk), i.e. the probability of the diagnostic target.
  • the score relies on multiple linear regression, the score produces a result in the same units as the diagnostic target.
  • the main scores are derived from multiple linear regression and measure a progression rate of fibrosis, i.e. expressed as a fibrosis unit per time unit.
  • “Progression” means the evolution of the fibrosis level over time.
  • Regularly means at regular intervals, such as for example, every 10-day, every month, or every year, etc.
  • Sample means a biological fluid of an individual, such as for example blood, serum, plasma, urine or saliva of an individual.
  • Non-invasive means that no tissue is taken from the body of an individual (blood is not considered as a tissue).
  • “Individual” means a woman, a man or an animal, young or old, healthy or susceptible of being affected or clearly affected by a hepatic pathology, such as a liver fibrosis of viral origin, of alcohol origin, a chronic liver steatosis or by any other pathology.
  • “Cause” means the risk factor that induces the lesions and the ensuing pathology.
  • “Cause duration” is the time between the age when the cause started (“start age”) and the age at inclusion when fibrosis level was measured (“inclusion age”).
  • Fibrosis level is reflected by a Fibrosis Score, AOF or fractal dimension, preferably fibrosis level is a fibrosis score or an AOF score or a fractal dimension score.
  • Fibrometer may refer to a fibrosis score or to a AOF score.
  • the present invention proposes a solution to the technical issue of assessing the progression rate of fibrosis in all and any condition or disease involving fibrosis.
  • This invention results in a very accurate diagnosis of fibrosis progression and in the ability of distinguishing slow, medium and fast fibrosers.
  • condition or disease is alcohol or viral chronic liver disease (CLD).
  • CLD chronic liver disease
  • OEF area of fibrosis
  • the liver fibrosis progression is assessed by calculating the ratio fibrosis level/cause duration.
  • fibrosis level is measured by a non-invasive method.
  • the fibrosis level is a fibrosis score, preferably FibrometerTM, AOF score or fractal dimension score.
  • the liver fibrosis progression is assessed by measuring, at two different intervals t 1 and t 2 , the fibrosis levels FL(t 1 ) and FL(t 2 ) and calculating the ratio FL(t 2 ) ⁇ FL(t 1 ) to (t 2 ⁇ t 1 ).
  • t 1 is the time at which a first measure is performed in an individual and a first fibrosis level FL(t 1 ) is determined;
  • t 2 is the time at which a second measure is performed in the same individual and a second fibrosis level FL(t 2 ) is determined;
  • t 2 ⁇ t 1 is a period of time of at least 10 days; in an embodiment, t 2 ⁇ t 1 is a period of 1 to 6 months; in another embodiment, t 2 ⁇ t 1 is a period of 1 year.
  • the fibrosis level is a fibrosis score, AOF score or fractal dimension score.
  • Fibrosis Score is a score obtained by measuring in a sample of an individual and combining in a logistic or linear regression function at least three, preferably 6 to 8, markers selected in the group consisting of ⁇ -2 macroglobulin (A2M), hyaluronic acid (HA or hyaluronate), apoliprotein A1 (ApoA1), N-terminal propeptide of type III procollagen (P3P), gamma-glutamyltranspeptidase (GGT), bilirubin, gamma-globulins (GLB), platelet count (PLT), prothrombin index (PI), aspartate amino-transferase (AST), alanine amino-transferase (ALT), urea, sodium (NA), glycemia (GLY), triglycerides (TG), albumin (ALB), alkaline phosphatases (ALP), human cartilage glycoprotein 39 (YKL-40), tissue inhibitor of matrix metall
  • the Fibrosis Score is measured by combining the levels of at least three markers selected from the group consisting of glycemia (GLY), aspartate aminotransferase (AST), alanine amino-transferase (ALT), ferritin, hyaluronic acid (HA), triglycerides (TG), prothrombin index (PI) gamma-globulins (GLB), platelet count (PLT), weight, age and sex.
  • GLY glycemia
  • AST aspartate aminotransferase
  • ALT alanine amino-transferase
  • ferritin ferritin
  • HA hyaluronic acid
  • TG triglycerides
  • PI prothrombin index
  • GLB platelet count
  • weight age and sex.
  • the Fibrosis score is established by combining in a binary linear regression function, the levels of four to eight markers, preferably selected from the group consisting of Alpha2 macroglobulin (A2M), hyaluronic acid or hyaluronate (AH), Prothrombin index (PI) Platelets (PLQ), ASAT, Urea, GGT, Age and Sex.
  • A2M Alpha2 macroglobulin
  • AH hyaluronic acid or hyaluronate
  • PI Prothrombin index
  • PQ Prothrombin index
  • ASAT FibrometerTM or FibrotestTM or FibrospectTM or Hepascore.
  • the markers of the score may be selected depending on the fact that the liver condition is of viral or alcoholic origin.
  • “Area Of Fibrosis” may be determined by image analysis, or by a non invasive method wherein a score is obtained by measuring in a sample of said patient and then combining in a logistic or linear regression function, preferably in a multiple linear regression function, at least two, preferably 3, more preferably 6 to 8 variables selected from the group consisting of ⁇ -2 macroglobulin (A2M), hyaluronic acid (HA or hyaluronate), apoliprotein A1 (ApoA1), procollagen Type III-N-terminal propeptide (P3P), gamma-glutamyltranspeptidase (GGT), bilirubin, gamma-globulins (GLB), platelet count (PLT), prothrombin index (PI), aspartate amino-transférase (AST), alanine amino-transferase (ALT), urea, sodium (NA), glycemia, triglycerides, albumin (ALB), alkaline
  • “Fractal dimension” reflects the liver architecture and may be obtained by image analysis or by a non invasive method wherein a score is obtained by measuring in a sample of an individual and combining in a logistic or linear regression function (preferably a multiple linear regression function) at least three, preferably 4 markers selected in the group comprising or consisting of ⁇ -2 macroglobulin (A2M), albumine (ALB), Prothrombin index (PI), hyaluronic acide (HA ou hyaluronate), alanine amino-transferase (ALAT), aspartate amino-transferase (ASAT) and age.
  • A2M macroglobulin
  • ALB albumine
  • PI Prothrombin index
  • HA ou hyaluronate alanine amino-transferase
  • ASAT aspartate amino-transferase
  • the fibrosis level is selected from the scores set forth in the table below:
  • This invention also relates to a non-invasive method for assessing whether or not an individual is a fast fibroser, including measuring the liver progression of said individual by using a non-invasive method here above described, preferably by calculating FL/cause duration and/or FL(t2) ⁇ FL (t 1 )/t 2 ⁇ t 1 , wherein FL preferably is a fibrosis score, an AOF score or a fractal dimension score.
  • the fast fibroser is identified with reference to statistical data as having an increased AOF, younger inclusion age and older start age (or cause duration replacing the two previous variables) by stepwise binary logistic regression. According the Applicant experiments, the diagnostic accuracy seems to be of 100.0% by stepwise binary logistic regression.
  • This invention also relates to a non-invasive method for assessing if an individual is a slow, medium or fast fibroser using discriminant analyses with reference to a population of fibrosers, ranked from their fibrosis progression rate in three categories, i.e. slow, medium and fast fibrosers: first, a method of assessing the fibrosis progression, preferably by AOF progression, as described above, is implemented, and the individual is ranked in slow, medium, fast fibrosers categories determined by statistical analysis.
  • the non-invasive method here above described is preferably FL/cause duration or FL(t2) ⁇ FL(t1)/t2 ⁇ t1, wherein FL preferably is AOF score.
  • liver fibrosis progression is assessed by a score.
  • the invention relates to a non-invasive method for assessing liver fibrosis progression in an individual, said method comprising the steps of:
  • the method includes combining at least two biological variables or at least two scores, and at least one clinical variable selected from cause duration, especially Chronic Liver Disease duration and age at first contact with cause (also named “start age”).
  • the at least one clinical variable is cause duration.
  • the at least one clinical variable is age at first contact with cause (“start age”).
  • the method includes two clinical variables.
  • the two clinical variables are cause duration and start age.
  • the at least one score is selected from the group consisting of Area of Fibrosis (AOF) and/or the Fibrosis Score and/or Fractal dimension.
  • AOF Area of Fibrosis
  • the liver fibrosis progression is assessed by measuring Metavir F progression, said Metavir F progression being established by measuring the following:
  • variable sex is not selected.
  • the variables are:
  • the variables are:
  • the variables are:
  • the liver fibrosis progression is assessed by measuring the area of fibrosis (AOF) progression, said AOF progression being established by measuring the following:
  • the variables are:
  • the variables are:
  • the variables are:
  • the variables are:
  • the variables are:
  • the non-invasive method of the invention includes at least two fibrosis scores, measured at regular intervals, such as for example, every 10-day, every month, or every year.
  • the individual may be at risk of suffering or is suffering from a condition selected from the group consisting of a chronic liver disease, a hepatitis viral infection, an hepatoxicity, a liver cancer, a non alcoholic fatty liver disease (NAFLD), an autoimmune disease, a metabolic liver disease and a disease with secondary involvement of the liver.
  • a condition selected from the group consisting of a chronic liver disease, a hepatitis viral infection, an hepatoxicity, a liver cancer, a non alcoholic fatty liver disease (NAFLD), an autoimmune disease, a metabolic liver disease and a disease with secondary involvement of the liver.
  • a condition selected from the group consisting of a chronic liver disease, a hepatitis viral infection, an hepatoxicity, a liver cancer, a non alcoholic fatty liver disease (NAFLD), an autoimmune disease, a metabolic liver disease and a disease with secondary involvement of the liver.
  • NAFLD non alcoholic fatty liver disease
  • Hepatitis viral infection may be caused by a virus selected from the group consisting of hepatitis C virus, hepatitis B virus and hepatitis D virus.
  • Hepatoxicity may be alcohol induced hepatoxicity and/or drug-induced hepatoxicity (i.e. any xenobiotic like alcohol or drug).
  • autoimmune disease is selected from the group consisting of autoimmune hepatitis (AIH), primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC).
  • Metabolic liver disease may be selected from the group consisting of Hemochromatosis, Wilson's disease and alpha 1 anti trypsin. Secondary involvement of the liver may be celiac disease or amyloidosis.
  • FIGS. 1-7 are to be read with regard to Example 1.
  • r s is the coefficient of correlation of Spearman
  • r p is the coefficient of correlation of Pearson.
  • FIG. 2 is a graph showing the progression rate of fibrosis as a function of Metavir F stage.
  • FIG. 3 is a graph showing the fibrosis progression rates for Metavir F (3A) and AOF (3B) in alcoholic and viral chronic liver disease (CLD). Transition lines are drawn only to show the differences between patient groups.
  • FIG. 4 is a graph showing the AOF as a function of cause duration according to CLD cause (alcoholic in black and viral in grey) and to Metavir F stage.
  • FIG. 5 is the AOF progression rate as a function of cause duration according to Metavir fibrosis (F) stage.
  • the curve has an inverse shape (1/x) by definition.
  • FIG. 6 is the relationship between fibrosis progression rate, Metavir fibrosis stage (6A) and AOF (6B) and age at 1st contact. Lines are provided by polynomial regression. The axis of AOF progression was truncated at 3.
  • FIG. 7 is the effects of antifibrotic treatment on area of fibrosis and Metavir F stage. Box plots indicate median, quartiles and extremes.
  • FIGS. 8-16 are to be read with regard to Example 2.
  • FIG. 8 is a graph showing the correlation between Metavir fibrosis (F) stage and area of fibrosis (AOF) progression in populations 1 (panel a) and 2 (panel b) of Example2 Lines depict linear regression.
  • FIG. 9 is a graph showing relationship between Metavir fibrosis (F) stage (left panels) or area of fibrosis (AOF) progression (right panels), during cause duration, as a function of Metavir fibrosis (F) stage at inclusion age in populations 1 (top panels) and 2 (bottom panels).
  • FIG. 10 is a graph showing the correlation between Metavir fibrosis (F) stage (left panels) or area of fibrosis (AOF) progression (right panels) and respective predicted progression in populations 1 (top panels, alcoholic CLD only) and 2 (bottom panels, viral CLD).
  • FIG. 11 shows the relationship between Metavir fibrosis (F) stage (left panels) or area of fibrosis (AOF) (right panels) and cause duration in populations 1 (top panels) and 2 (bottom panels). Curves depict Lowess regression.
  • FIG. 12 shows the Relationship between Metavir fibrosis (F) stage (left panels) or area of fibrosis (AOF) (right panels) progression and start age in populations 1 (top panels) and 2 (bottom panels). Curves depict Lowess regression.
  • FIG. 13 shows the relationship between Metavir fibrosis (F) stage (left panels) or area of fibrosis (AOF) (right panels) and start age in populations 1 (top panels) and 2 (bottom panels). Curves depict Lowess regression.
  • FIG. 14 shows the relationship between Metavir fibrosis (F) stage progression (left panels) or area of fibrosis progression (medium panels) or area of fibrosis (right panels) and inclusion age in populations 1 (top panels) and 2 (bottom panels). Curves depict Lowess regression.
  • FIG. 15 shows the relationship between fibrosis characteristics and cause duration showing different fibrosers as a function of fibrosis progression in population 2. Curves depict Lowess regression.
  • FIG. 16 shows the impact of special patient subgroups on curves of Metavir fibrosis (F) stage as a function of different times in population 2. The impact was determined according to the method shown in FIG. 11 a.
  • a 1 st population of 185 patients (all of which had been subjected to one liver biopsy) was selected according to the availability of an estimation of the contact date (or exposure) to the risk factor (or cause) of CLD. The difference between inclusion date and contact date is herein called “duration of cause”.
  • a 2 nd population of 16 patients (all of which had been subjected to two liver biopsies) was selected.
  • the 185 patients included in this population were admitted for alcoholic liver disease, or for chronic viral hepatitis B or C. Patients were included who had drunk at least 50 g of alcohol per day for the past five years or were positive for serum hepatitis B surface antigen or C antibodies. None of the patient had clinical, biological, echographic or histological evidence of other causes of chronic liver disease (Wilson's disease, hemochromatosis, ⁇ 1-antitrypsin deficiency, biliary disease, auto-immune hepatitis, hepatocellular carcinoma). Blood samples were taken at entry and a transcostal (suction needle) or transjugular (cutting needle) liver biopsy was performed within one week.
  • Analyses of blood samples provided the following measurements: hemoglobin, mean corpuscular volume, lymphocyte count, platelet count, cholesterol, urea, creatinine, sodium (NA), bilirubin, ⁇ -glutamyltranspeptidase (GGT), alkaline phosphatases (ALP), aspartate aminotransferase (AST) and alanine aminotransferase (ALT), albumin (ALB), ⁇ 1 and ⁇ 2-globulins, ⁇ -globulins, ⁇ -globulins, ⁇ -block, prothrombin index (PI), apolipoprotein A1 (ApoA1). Some of them are indirect blood markers of fibrosis (1).
  • the direct blood markers of fibrosis used in this study were the following: ⁇ -2-macroglobulin (A 2 M), the N-terminal peptide of type III procollagen (P3P), hyaluronic acid (HA), TGF ⁇ 1, and laminin.
  • the following blood tests were calculated: AST/ALT ratio, PGA score (2), PGAA score (3), APRI (4), different FibroMeters (5), and Hepascore (6).
  • Sera were kept at ⁇ 80° C. for a maximum of 48 months for assay.
  • Biopsy specimens were fixed in a formalin-alcohol-acetic solution and embedded in paraffin; 5 ⁇ m thick sections were stained with haematoxylin-eosin-saffron and 0.1% picrosirius red solution.
  • Fibrosis was staged by two independent pathologists according to the Metavir staging (7).
  • the Metavir staging is also well adapted to the semi-quantitative evaluation of fibrosis in alcoholic CLD since porto-septal fibrosis is more frequent and developed than centrolobular fibrosis (8). Observers were blinded for patient characteristics. When the pathologists did not agree, the specimens were re-examined under a double-headed microscope to analyse discrepancies and reach a consensus. All specimens were also evaluated according to the following grades: Metavir activity (7), steatosis and centrolobular fibrosis (CLF) as previously described (9).
  • AOF was measured on the same sections as the microscopic analysis using a Leica Quantimet Q570 image processor as previously described (9). Fractal dimension of fibrosis was also measured in population 2 (10).
  • Quantitative variables were expressed as mean ⁇ SD, unless otherwise specified.
  • the Pearson's rank correlation coefficient (r p ) was used for correlations between continuous variables or Spearman correlation coefficient (r s ) when necessary.
  • r p Pearson's rank correlation coefficient
  • r s binary logistic regression coefficient
  • the predictive performance of each model is expressed by the adjusted R 2 coefficient ( a R 2 ) and by the diagnostic accuracy, i.e. true positives and negatives, respectively.
  • a ⁇ risk ⁇ 5% for a two-sided test was considered statistically significant.
  • the statistical software used was SPSS version 11.5.1 (SPSS Inc., Chicago, Ill., USA).
  • PR progression rate
  • AST/ALT 0.5412244415007 with limits of confidence interval at 95%: 2.07804027617.e-006 & 0.3283727153579,
  • ⁇ -globulins 0.1594071294621 with limits of confidence interval at 95%: 0.001915414369681 & 0.06022006972876,
  • FibroMeterTM 1.15299980586 with limits of confidence interval at 95%: 0.002487655344947 & 0.4161078148282.
  • Metavir unit (MU) per year, ranged from 0 to 2.0 MU/yr for Metavir F (mean: 0.22 ⁇ 0.29, median: 0.13) and from 0.1 to 17.2%/yr for the area of fibrosis (mean: 1.8 ⁇ 2.6, median: 1.0).
  • FIG. 3 shows a progressive but irregular increase in fibrosis rate as a function of Metavir F stage.
  • FIG. 3 shows a rather stable progression rate of area of fibrosis from F stage 0 to 3 and a dramatic increase in patients with cirrhosis whereas the increase was progressive through all F stages for progression rate of Metavir F stage.
  • FIG. 4 shows that the area of fibrosis as a function of cause duration markedly varied among patients, so patients might develop cirrhosis within a short period and others after a prolonged period. However, all patients with the fastest rate, as expected, and those with the longest follow-up, as less expected, had cirrhosis. A short cause duration was surprising in cirrhosis, however this was mainly observed in alcoholic CLD.
  • FIG. 5 The graph of AOF progression plotted against cause duration ( FIG. 5 ) clearly shows that individual patients had different patterns of progression rate of area of fibrosis within each F stage.
  • previous multivariate analyses indicated that “cause duration” or “age at 1 st contact” was the main clinical independent predictor of Metavir F or area of fibrosis progression.
  • FIG. 6 shows that the F progression dramatically increased by 40 years in viral and alcoholic CLD.
  • the AOF progression displayed a linear increase over age in alcoholic CLD whereas there was a plateau followed by a linear increase by 40 years in viral CLD.
  • Fibrosis progression was calculated as the ratio fibrosis level/cause duration, with fibrosis level indicating stage or amount AOF. So, this is a mean value as a function of time.
  • LB reference for fibrosis level determination
  • the progression rate is a mean as a function of cause duration, cause duration being the time between the age when the cause started (“start age”) and the age at inclusion when fibrosis level was measured (“inclusion age”). Progression course is the trend as a function of time (increase, stability, decrease).
  • start age the age when the cause started
  • inclusion age the age at inclusion when fibrosis level was measured
  • Progression course is the trend as a function of time (increase, stability, decrease).
  • fibrosis determination LB or non-invasive test
  • duration recording retrospective/transversal or prospective/longitudinal
  • Populations 1 and 2 were selected according to the availability of estimation of the age when the cause started (“start age”). The period between start age and age at inclusion when fibrosis level was measured (“inclusion age”), was called “cause duration”.
  • Start age The period between start age and age at inclusion when fibrosis level was measured (“inclusion age”), was called “cause duration”.
  • Population 1 provided comparison between alcoholic and viral CLD.
  • Population 2 with viral CLD had a sufficient high number of patients to validate the previous viral subpopulation and to allow subgroup analysis.
  • Population 3 was a large population with viral CLD providing a validation of inclusion age effect.
  • Population 4 allowed validating in patients with 2 LB the previous progression estimated with 1 LB.
  • population 5 was used to validate the progression calculated with two blood tests.
  • Image analysis was measured on the same sections as the microscopic analysis using either a Leica Quantimet Q570 image processor as previously described from 1996 to 2006 (10) or an Aperio digital slide scanner (Scanscope® CS System, Aperio Technologies, Vista Calif. 92081, USA) image processor providing high quality images of 30,000 ⁇ 30,000 pixels and a resolution of 0.5 ⁇ m/pixel (magnification ⁇ 20) since 2007.
  • a binary image (white and black) was obtained via an automatic thresholding technique using an algorithm developed in our laboratory.
  • Quantitative variables were expressed as mean ⁇ SD, unless otherwise specified.
  • the Pearson's rank correlation coefficient (r p ) was used for correlations between continuous variables or the Spearman correlation coefficient (r s ) when necessary.
  • the Lowess regression by weighted least squares was used to determine the average trend of relationships between variables, mainly the progression course (18). The line rupture observed in these curves were checked by cut-offs determined according to maximum Youden index and diagnostic accuracy (data not shown). The curve shape was evaluated by corresponding test, e.g. quadratic trend test. To assess independent predictors, multiple linear regression for quantitative dependent variables, binary logistic regression for qualitative dependent variables and discriminant analysis for ordered variables were used with forward stepwise addition of variables.
  • each model is expressed by the adjusted R 2 coefficient ( a R 2 ) and/or by the diagnostic accuracy, i.e. true positives and negatives, respectively.
  • An ⁇ risk ⁇ 5% for a two-sided test was considered statistically significant.
  • the statistical software used was SPSS version 11.5.1 (SPSS Inc., Chicago, Ill., USA).
  • FIG. 12 a shows that the F progression dramatically increased by 30-40 years of start age in alcoholic ( ⁇ 40 years) and viral ( ⁇ 30 years) CLD (population 1).
  • the latter figure was confirmed in population 2 especially in men ( FIG. 12 c ).
  • the AOF progression displayed an almost linear increase over start age in alcoholic CLD whereas there was a plateau followed by a linear increase by ⁇ 40 years of start age in viral CLD (population 1) ( FIG. 12 b ). This was confirmed in population 2 especially in men ( FIG.
  • AOF progression did not depend on the inclusion age in alcoholic CLD ( FIG. 14 b ) whereas there was a late increase in viral CLD ( FIGS. 14 b and 14 e ). Consequently, the AOF level linearly increased with age in alcoholic CLD ( FIG. 14 c ) whereas this occurred by age 50 yr in viral CLD (FIG. 14 f ).
  • Sex We state here the particular relationship between sexes and CLD cause since sex effect has been already mentioned in viral CLD. Whereas there was a global parallelism between males and females in viral CLD, females in alcoholic CLD had two particularities: a slowdown between 30-50 yr and a late increase in fibrosis progression and level by 50 yr of start age (data not shown). The same differences were observed for inclusion age at the difference that the slowdown was observed later between 45-50 yr, as expected.
  • the start age increased with fibroser degree: 25.2 ⁇ 10.5, 28.7 ⁇ 10.8 and 33.0 ⁇ 13.6 yr, respectively (p ⁇ 10 ⁇ 3 ).
  • fibrosers were predicted by Metavir F, AOF, F progression and cause duration (diagnostic accuracy: 91.4%).
  • the fast fibrosers were predicted by increased AOF, younger inclusion age and older start age with diagnostic accuracy: 100.0% by stepwise binary logistic regression.

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120177260A1 (en) * 2011-01-10 2012-07-12 The Regents Of The University Of Michigan System and methods for detecting liver disease
WO2014011925A3 (en) * 2012-07-11 2015-07-16 University Of Mississippi Medical Center Detection and staging of liver fibrosis
US20160012583A1 (en) * 2013-03-01 2016-01-14 Universite D' Angers Automatic measurement of lesions on medical images
WO2018024748A1 (en) * 2016-08-01 2018-02-08 Centre Hospitalier Universitaire D'angers Multi-targeted fibrosis tests
CN109111451A (zh) * 2017-06-26 2019-01-01 广东东阳光药业有限公司 二氢嘧啶类化合物及其在药物中的应用
US10165987B2 (en) 2014-08-01 2019-01-01 Centre Hospitalier Universitaire D'angers Method for displaying medical images
CN111279195A (zh) * 2017-10-16 2020-06-12 生物预测公司 原发性肝癌的预后和随访方法
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Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2815416A1 (en) * 2010-10-21 2012-04-26 Vertex Pharmaceuticals Incorporated Biomarkers for hcv infected patients
US20140170741A1 (en) * 2011-06-29 2014-06-19 Inner Mongolia Furui Medical Science Co., Ltd Hepatic fibrosis detection apparatus and system
US9002656B2 (en) 2011-10-17 2015-04-07 Daxor Corporation Automated total body albumin analyzer
US9710908B2 (en) 2013-01-08 2017-07-18 Agency For Science, Technology And Research Method and system for assessing fibrosis in a tissue
EP2930515A1 (en) * 2014-04-08 2015-10-14 Universite D'angers Prognostic tests for hepatic disorders
CN105092855B (zh) * 2014-05-23 2017-10-03 杭州普望生物技术有限公司 一种用于肝纤维化和肝硬化检测的试剂盒
AU2015336935A1 (en) * 2014-10-21 2017-04-13 Yi Huang Liver test
CN104887243B (zh) * 2015-07-02 2017-08-01 郭璐 一种血小板指数测评快查计算滑动尺
CN107368689B (zh) * 2017-07-31 2019-11-19 董云鹏 人体数据采集和分析系统
CN110237263B (zh) * 2018-03-07 2022-06-17 昆山新蕴达生物科技有限公司 HFn包载阿霉素的方法及其产物
CN108931645A (zh) * 2018-07-26 2018-12-04 北京大学第医院 一种hcv清除后肝纤维化评估系统及评估方法
CN109473175A (zh) * 2018-11-07 2019-03-15 中山大学附属第三医院（中山大学肝脏病医院） 一种针对肝纤维化的非侵入性血清学评分模型及其设计方法
US11166976B2 (en) 2018-11-08 2021-11-09 Aligos Therapeutics, Inc. S-antigen transport inhibiting oligonucleotide polymers and methods
TWI685854B (zh) * 2019-02-01 2020-02-21 中國醫藥大學附設醫院 肝纖維化評估模型、肝纖維化評估系統及肝纖維化評估方法
US20220257717A1 (en) * 2019-07-12 2022-08-18 Primegen Biotech, Llc Methods of Treatment Using Encapsulated Cells
JP2023514837A (ja) * 2020-02-19 2023-04-11 ヘピオン ファーマシューティカルズ インコーポレイテッド 線維症を治療するためのシクロスポリン類似体の使用
CN111783792B (zh) * 2020-05-31 2023-11-28 浙江大学 一种提取b超图像显著纹理特征的方法及其应用

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE60129314T2 (de) 2000-04-28 2008-07-17 Bayer Ag Diagnose von leberfibrose mit hilfe von serummarkeralgorithmen
US7668661B2 (en) * 2000-04-28 2010-02-23 Siemens Healthcare Diagnostics Inc. Liver disease-related methods and systems
GB0202213D0 (en) 2002-01-31 2002-03-20 Imp College Innovations Ltd Methods
US6986995B2 (en) 2002-02-28 2006-01-17 Prometheus Laboratories, Inc. Methods of diagnosing liver fibrosis
JP4575354B2 (ja) 2003-01-14 2010-11-04 フラームス・インテルウニフェルシタイル・インステイチュート・フォール・ビオテヒノロヒー・ヴェーゼットウェー（ヴェーイーベー・ヴェーゼットウェー） 肝線維症測定用血清マーカー
US20070197424A1 (en) * 2003-09-16 2007-08-23 Friedman Scott L Glatiramer acetate for use as an immuno-modulatory agent
FR2870348B1 (fr) 2004-05-14 2010-08-27 Univ Angers Methode pour diagnostiquer la presence et/ou la severite d'une pathologie hepathique chez un sujet
EP1778861A4 (en) 2004-07-01 2008-11-19 Medical Res Fund At The Tel Av METHODS AND KITS FOR PREDICTING THE SPEED OF PROGRESSION OF HEPATIC FIBROSIS IN PATIENTS WITH CHRONIC HEPATITIS C
DE602004004775T2 (de) * 2004-08-12 2007-11-22 Roche Diagnostics Gmbh Verfahren zur Diagnose von Leberfibrose.
JPWO2006129513A1 (ja) 2005-05-30 2008-12-25 味の素株式会社 肝疾患評価装置、肝疾患評価方法、肝疾患評価システム、肝疾患評価プログラムおよび記録媒体
DE602006019262D1 (de) * 2005-08-11 2011-02-10 Siemens Healthcare Diagnostics QUANTITATIVE ASSAYS FÜR PDGFR-beta- IN KÖRPERFLÜSSIGKEITEN
CN101779128A (zh) * 2007-06-14 2010-07-14 弗拉芒区生物技术研究所 早期肝癌检测的诊断测试
US20100049029A1 (en) 2008-08-20 2010-02-25 Hong Kong Applied Science And Technology Research Institute Co., Ltd. Piezoelectric magnetic resonance elastograph (mre) driver system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Baker et al. "Metalloproteinase Inhibitors: Biological Actions and Therapeutic Opportunities," Journal of Cell Science (2002) volume 115, number 19, pages 3719-3727. *

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120177260A1 (en) * 2011-01-10 2012-07-12 The Regents Of The University Of Michigan System and methods for detecting liver disease
US9036883B2 (en) * 2011-01-10 2015-05-19 The Regents Of The University Of Michigan System and methods for detecting liver disease
WO2014011925A3 (en) * 2012-07-11 2015-07-16 University Of Mississippi Medical Center Detection and staging of liver fibrosis
US10130295B2 (en) 2012-07-11 2018-11-20 University Of Mississippi Medical Center Method for the detection and staging of liver fibrosis from image acquired data
US20180075600A1 (en) * 2013-03-01 2018-03-15 Universite D'angers Automatic measurement of lesions on medical images
US9830699B2 (en) * 2013-03-01 2017-11-28 Universite D'angers Automatic measurement of lesions on medical images
US20160012583A1 (en) * 2013-03-01 2016-01-14 Universite D' Angers Automatic measurement of lesions on medical images
US10269118B2 (en) * 2013-03-01 2019-04-23 Universite D'angers Automatic measurement of lesions on medical images
US10165987B2 (en) 2014-08-01 2019-01-01 Centre Hospitalier Universitaire D'angers Method for displaying medical images
US11605460B2 (en) 2016-01-08 2023-03-14 Centre Hospitalier Universitaire D'angers Multi-targeted fibrosis tests
WO2018024748A1 (en) * 2016-08-01 2018-02-08 Centre Hospitalier Universitaire D'angers Multi-targeted fibrosis tests
CN109111451A (zh) * 2017-06-26 2019-01-01 广东东阳光药业有限公司 二氢嘧啶类化合物及其在药物中的应用
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