WO2010058295A2 - Procédé in vitro non invasif pour quantifier des lésions hépatiques - Google Patents

Procédé in vitro non invasif pour quantifier des lésions hépatiques Download PDF

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WO2010058295A2
WO2010058295A2 PCT/IB2009/007719 IB2009007719W WO2010058295A2 WO 2010058295 A2 WO2010058295 A2 WO 2010058295A2 IB 2009007719 W IB2009007719 W IB 2009007719W WO 2010058295 A2 WO2010058295 A2 WO 2010058295A2
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Prior art keywords
score
ast
fibrosis
steatosis
group
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PCT/IB2009/007719
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English (en)
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WO2010058295A3 (fr
Inventor
Paul Cales
Christophe Aube
Vincent Roullier
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Universite D'angers
Centre Hospitalier Universitaire D'angers
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Priority to US13/129,999 priority Critical patent/US20110313276A1/en
Priority to EP09796060.3A priority patent/EP2359285B1/fr
Priority to ES09796060.3T priority patent/ES2621838T3/es
Publication of WO2010058295A2 publication Critical patent/WO2010058295A2/fr
Publication of WO2010058295A3 publication Critical patent/WO2010058295A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/08—Hepato-biliairy disorders other than hepatitis
    • G01N2800/085—Liver diseases, e.g. portal hypertension, fibrosis, cirrhosis, bilirubin
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10T—TECHNICAL SUBJECTS COVERED BY FORMER US CLASSIFICATION
    • Y10T436/00—Chemistry: analytical and immunological testing
    • Y10T436/14—Heterocyclic carbon compound [i.e., O, S, N, Se, Te, as only ring hetero atom]
    • Y10T436/142222—Hetero-O [e.g., ascorbic acid, etc.]
    • Y10T436/143333—Saccharide [e.g., DNA, etc.]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10T—TECHNICAL SUBJECTS COVERED BY FORMER US CLASSIFICATION
    • Y10T436/00—Chemistry: analytical and immunological testing
    • Y10T436/14—Heterocyclic carbon compound [i.e., O, S, N, Se, Te, as only ring hetero atom]
    • Y10T436/142222—Hetero-O [e.g., ascorbic acid, etc.]
    • Y10T436/143333—Saccharide [e.g., DNA, etc.]
    • Y10T436/144444—Glucose

Definitions

  • This invention relates to the field of hepatic diagnosis and more precisely to an in-vitro non-invasive method for quantifying liver lesions, especially due or related to liver impairment, liver steatosis, non-alcoholic fatty liver disease (NAFLD), or nonalcoholic steatohepatitis (NASH) .
  • NASH nonalcoholic steatohepatitis
  • quantifying in the meaning of this invention is meant determining amount and/or architecture of hepatic lesions.
  • FLD Fatty Liver Disease
  • FLD describes a wide range of potentially reversible conditions involving the liver, wherein large vacuoles of triglyceride fat accumulate in hepatocytes via the process of steatosis (i.e. the abnormal retention of lipids within a cell) .
  • FLD is commonly associated with alcohol or metabolic syndromes (diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy, lipodystrophy) .
  • FLD encompasses a morphological spectrum consisting from the mildest type “liver steatosis” (fatty liver) , called NAFL, to the potentially more serious type “steatohepatitis", called NASH, which is associated with liver- damaging inflammation and, sometimes, the formation of fibrous tissue.
  • steatohepatitis has the inherent propensity to progress towards the development of fibrosis then cirrhosis which can produce progressive, irreversible liver scarring or towards hepatocellular carcinoma (liver cancer) .
  • liver steatosis For a long time, the diagnosis of liver steatosis has usually been accomplished by measuring markers such as g-glutamyl- transpeptidase (GGT) and alanine aminotransferase (ALT) while at the same time performing a liver biopsy in order to confirm FLD and determine the grading and staging of the disease.
  • markers such as g-glutamyl- transpeptidase (GGT) and alanine aminotransferase (ALT)
  • GTT g-glutamyl- transpeptidase
  • ALT alanine aminotransferase
  • Ultrasonography is also used to diagnose liver steatosis.
  • this method is subjective as it is based on echo intensity (echogenicity) and special patterns of echoes (texture) .
  • echo intensity echogenicity
  • special patterns of echoes texture
  • it is not sensitive enough and often inaccurate in patients with advanced fibrosis.
  • ultrasonography it is generally admitted that around half of all FLD cases are detected by usual blood tests and around half by ultrasonography, resulting in around one quarter of missed diagnosis when both are used.
  • the test relates to a method of diagnosing the presence and/or severity of a hepatic pathology and/or of monitoring the effectiveness of a curative treatment against a hepatic pathology in an individual, by establishing at least one noninvasive diagnostic score, in particular a diagnostic score for portal and septal fibrosis and/or an estimate for amount of fibrosis (the area of fibrosis) and/or an estimate for the architecture of fibrosis (fractal dimension) .
  • WO 2006/082522 has demonstrated that a single or a panel of biomarkers can be used as an alternative to liver biopsy for the diagnosis of steatosis, whether induced by alcohol, viral hepatitis or NAFLD, the most common causes of steatosis.
  • this document provides for a new panel of biomarkers known as a SteatoTest (ST) with predictive values for the diagnosis of steatosis due to alcohol, NAFLD and hepatitis C and B. Serum GGT and ALT were considered as the standard biochemical markers.
  • the performance of this kind of test may be insufficient, especially due to the reference based on a subjective grading of liver steatosis with a poor inter-observer reproducibility.
  • this grading is a semiquantitative variable which implies an imprecise and limited reflect of the original pattern.
  • Watkins et al. has provided methods for assessing the level of triglycerides in the liver of a subject. Such methods comprise determining the amount of lipid metabolites in a sample collected from a body fluid of the subject and comparing it with a reference value representing the normal level of the lipid metabolites, or correlating the amount with the presence of a liver disorder.
  • the methods are said to be used, for example, for the diagnosis and monitoring liver disorders such as steatosis, NAFLD and NASH (Non-Alcoholic Steato-Hepatitis) .
  • steatosis steatosis
  • NAFLD Non-Alcoholic Steato-Hepatitis
  • Watkins only uses one type of biomarkers (i.e. metabolic lipids) in a random body fluid.
  • the aim of the present invention is to provide new non-invasive methods using mixed biomarkers from different sources, which circumvent the above-mentioned limitations and are more precise and reliable than the methods cited in the prior art, as shown by their high diagnostic performance.
  • the methods of the invention considerably reduce the need of biopsies, as they catch more information than in the prior art about the lesions evaluated, they ensure reproducibility and performance, while attenuating causes of false results (generally, sources of false results are antagonized in a score including several markers provided that some precautions are included) .
  • the Applicant has now observed that, in the liver, the three lesions, fibrosis, steatosis and inflammation (NASH) are deeply interconnected.
  • NASH fibrosis, steatosis and inflammation
  • the grade of liver steatosis is a predictive factor of NASH in patients with NAFLD; also, significant fibrosis is associated with the development of steatosis and NASH in NAFLD .
  • the Applicant also observed that, as a hepatic disease is due to the cause, leading to lesions, said lesions inducing symptoms, and ending up into complications, the most reliable information for the patient was the degree of his/her hepatic lesions.
  • the Applicant hereby shows that the lesions are related: there is a relationship between lesions.
  • Fibrosis may be quantified by determination of score (reflecting stage), area of fibrosis (reflecting amount) or fractal dimension (reflecting architecture) .
  • Steatosis may be quantified by determination of score, area of fibrosis or fractal dimension.
  • NASH may be evaluated by determination of score (s).
  • This invention thus relates to an in vitro method for quantifying the lesions of a patient, preferably with NAFLD, comprising addressing a diagnostic target, i.e. quantifying fibrosis, steatosis and/or Nash by - carrying out a score, an area or a fractal dimension of fibrosis in said patient and/or carrying out a score, an area or a fractal dimension of steatosis in said patient and/or - carrying out a score of steato-hepatitis (NASH) in said patient through measuring at least one, preferably 2,3,4,5,6,7,8,9 marker (s) selected from the group consisting of biomarkers and possibly clinical markers and possibly scores, the biomarkers being selected from the group consisting of glycemia, AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, ferritin, platelets, prothrombin index, hyalur
  • combining is obtained by implementing the statistical method involving a mathematical function called binary (or ordinal) logistic regression or multiple linear regression with the following procedure:
  • the independent variables are tested by univariate analysis; - second, the independent variables that were significant in univariate analysis are tested in multivariable analysis by binary logistic (or ordinal regression or multiple linear regression with ascending or descending step by stepwise selection; the selection of variables can be performed in two ways o
  • the conventional one where the full sample of patient population is used. This selection procedure could also include score (s) of other lesions than the diagnostic target.
  • the bootstrap method where the mathematical function is applied to different samples with less patients sorted by chance, usually 1000 samples.
  • the selected independent variables are those with an absolute majority occurrence, i.e. at least 500 times among 1000 samples. This method also included only variables without significant colinearity (this means variables that are strongly linked) .
  • This procedure did not include score (s) of other lesions than the diagnostic target.
  • this step provided a minimum number of predictive variables compared to the previous step.
  • the target is fibrosis
  • a score, area and/or fractal dimension of fibrosis is performed, and at least 5, preferably 5,6,7,8 markers, are measured, said markers being selected from the group consisting of glycemia, AST, ALT, ferritine, platelets, prothrombin index, hyaluronic acid, haemoglobin, triglycerides, weight, body mass index, sex and age, preferably glycemia, AST, ALT, ferritin, platelets, weight and age or glycemia, AST, ALT, prothrombin index, weight.
  • the target is steatosis and a score, area and/or fractal dimension of steatosis is performed, and at least 5 markers are measured, said markers selected from the group consisting of glycemia, AST, ALT, AST/ALT, AST.ALT ferritine, platelets, prothrombin index, hyaluronic acid, haemoglobin, triglycerides, weight, body mass index, sex and age, and preferably but not mandatory, a score, area or fractal dimension of fibrosis.
  • the target is steato-hepatitis, and a NASH score is performed.
  • the diagnostic target is fibrosis
  • biomarkers are selected from the group consisting of weight, age, glycemia, AST, ALT, ferritin and platelets or from the group consisting of weight, age, glycemia, AST, ALT, ferritin and prothrombin index; or
  • biomarkers are selected from the group consisting of hyaluronic acid, glycemia, AST, ALT, platelets, and prothrombin index; or
  • fractal dimension is carried out wherein the biomarkers are selected from the group consisting of weight, Hyaluronic acid, glycemia, AST, age, and prothrombin index.
  • the diagnostic target is steatosis and - a score is carried out wherein the biomarkers are selected from the group consisting of glycemia, AST/ALT, triglycerides, haemoglobin, age, and sex and optionally at least one fibrosis score; or from the group consisting of glycemia, AST, triglycerides, AST/ALT, haemoglobin, BMI, and weight and optionally fibrosis scores and/or fractal dimension of fibrosis score; or from the group consisting of BMI, glycemia, and triglycerides, or from the group consisting of BMI, glycemia, triglycerides and ferritin; or
  • biomarkers are selected from the group consisting of glycemia, AST, triglycerides, BMI, weight and optionally at least one fibrosis score; or from the group consisting of BMI, glycemia, triglycerides and ferritin; or - a fractal dimension is carried out wherein the biomarkers are selected from the group consisting of glycemia, AST, ALT, triglycerides, BMI, weight and optionally at least one fibrosis score; or from the group consisting of BMI, glycemia, triglycerides and ferritin.
  • the diagnostic target is NASH
  • the biomarkers are selected from the group consisting of AST, ferritin, and AST.ALAT and optionally fibrosis score and/or steatosis score and/or NASH score; or from the group consisting of BMI, AST, and ferritin; or ferritin alone.
  • the diagnostic target is significant fibrosis determined by Metavir scoring system implemented by Bedossa et al.
  • the method of the invention is performed by measuring the level of at least one, preferably 2,3,4,5,6,7, more preferably all markers selected from the group consisting of glycemia, AST, ALT, AST/ALT, ferritin, platelets and measuring the clinical markers weight and age, combining said measures through a mathematical function as described above with the conventional method.
  • the coefficients of the mathematical function are as described in the examples.
  • the variables are unchanged for this score.
  • the coefficients of the mathematical function are as described in the examples .
  • the diagnostic target is significant fibrosis determined by the NASH-CRN system implemented by Kleiner et al .
  • the method of the invention is performed by measuring the level of at least one, preferably 2,3,4, more preferably all markers selected from the group consisting of glycemia, AST, ALT, ferritin, prothrombin index and measuring the clinical markers age, weight and combining said measures through a mathematical function as described above with the conventional method.
  • the coefficients of the mathematical function are as described in the examples.
  • the coefficients of the mathematical function are as described in the examples. Score#3
  • the diagnostic target is area of fibrosis
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5, 6, more preferably all markers selected from the group consisting of hyaluronic acid, glycemia, AST, ALT, platelets, prothrombin index and combining said measures through a mathematical function as described above.
  • the coefficients of the mathematical function are as described in the examples.
  • the coefficient's of the mathematical function are as described in the examples.
  • the diagnostic target is fractal dimension of fibrosis
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5, more preferably all markers selected from the group consisting of hyaluronic acid, glycemia, AST, prothrombin index and the clinical marker weight, age, combining said levels in a mathematical function, obtained as described above.
  • the coefficients of the mathematical function are as described in the examples .
  • the variables are the following :
  • the coefficients of the mathematical function are as described in the examples.
  • the diagnostic target is significant steatosis determined according to a threshold of area of steatosis ( ⁇ 3%), and the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5,6, more preferably all markers selected from the group consisting of glycemia, AST/ALT, triglycerides, haemoglobin, age and sex, and optionally a score as obtained from the first embodiment (score #1A) or the second embodiment (score#2A) of the present invention, as described above, and combining said levels in a mathematical function, obtained as described above.
  • a threshold of area of steatosis ⁇ 3%
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5,6, more preferably all markers selected from the group consisting of glycemia, AST/ALT, triglycerides, haemoglobin, age and sex, and optionally a score as obtained from the first embodiment (score #1A)
  • the coefficients of the mathematical function are as described in the examples .
  • variables are the following:
  • the coefficients of the mathematical function are as described in the examples.
  • the diagnostic target is significant steatosis determined by NASH-CRN GRADE ⁇ l
  • the method of the invention is performed by measuring the level of the biomarkers selected from glycemia, AST, triglycerides, AST/ALT, haemoglobin, the clinical markers are selected from the group consisting of BMI and weight, and optionally, one or more of the score 1, 2 and/or 4 and combining said levels in a mathematical function, obtained as described above.
  • the coefficients of the mathematical function are as described in the examples.
  • variables are the following:
  • the coefficients of the mathematical function are as described in the examples.
  • the diagnostic target is area of steatosis
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5, more preferably all markers selected from the group consisting of glycemia, AST, triglycerides, ferritin, BMI and weight, and optionally, one or more of the scores 1 and/or 2, combining said levels in a mathematical function, obtained as described above.
  • the coefficients of the mathematical function are as described in the examples.
  • the variables are the following:
  • the coefficients of the mathematical function are as described in the examples.
  • the diagnostic target is fractal dimension of steatosis
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, 5, more preferably all markers selected from the group consisting of glycemia, AST, ALT/ALAT, AST.ALT, ferritin, triglycerides, BMI, weight, and optionally scores IA and 2A and combining said levels in a mathematical function, obtained as defined in embodiment 1 and 2 here above.
  • GLYCEMIA GLYCEMIA, AST, ALT/ALAT, AST. ALT,
  • the coefficients of the mathematical function are as described in the examples .
  • the variables are the following :
  • the coefficients of the mathematical function are as described in the examples.
  • the diagnostic target is NASH according to three definitions based on NASH activity score (NAS) by Kleiner et al.
  • the method of the invention is performed by measuring the level of at least one, preferably 2, 3, 4, more preferably all markers selected from the group consisting of AST, ferritin, AST.ALT, BMI, and optionally scores 2A and/or 5A and/or 8A and combining said levels in a mathematical function, obtained as described above.
  • the coefficients of the mathematical function are as described in the examples.
  • the variables are the following :
  • the coefficients of the mathematical function are as described in the examples.
  • Fibrosis is defined as a pathological lesion made of scaring tissue including fibrillar proteins or glycoproteins (collagens, proteoglycans%) .
  • Fibrosis Score is defined as a combination of markers (usually linked by a mathematical function) for which the diagnostic target is fibrosis.
  • Steatosis is defined as accumulation of lipids, usually triglycerides, within vacuoles in cells. It mainly concerns liver and muscle in metabolic syndrome.
  • AOS Area of steatosis
  • SAOS Significant area of steatosis
  • Kleiner grading/staging this is a pathological classification devoted to NAFLD based on a morphological description in different classes either for steatosis (conventionally referred as grading) or fibrosis (conventionally referred as staging) .
  • This semi-quantitative (ordinal in statistics) system is the most recent and conventional histological classification.
  • This system is also known as the NASH Clinical Research Network (NASH-CRN) system.
  • NASH-CRN NASH Clinical Research Network
  • Metavir grading/staging this is a pathological classification devoted to chronic viral hepatitis based on a morphological description in different classes either for necro-inflammatory activity (conventionally referred as grading) or fibrosis (conventionally referred as staging) .
  • This semi-quantitative (ordinal in statistics) system is the most used histological classification in viral hepatitis. It is also used in other causes of chronic liver diseases such as alcohol and NAFLD.
  • Level the quantity of a particular biomarker measured in the blood or plasma of a patient, usually expressed as a concentration.
  • Target is steatosis
  • the Applicant especially focused on the target "steatosis” and found that the inclusion of fibrosis score in an in vitro method for diagnosing a liver condition involving steatosis, might be of high interest because of the interaction between fibrosis and steatosis
  • a method of the invention comprises measuring the level of at least one, preferably 2, 3, 4, 5, 6, 7, 8 biological marker (s) and at least one, preferably two, clinical marker (s) and optionally, but preferably, a Fibrosis Score, and combining said levels, measures and score in a suitable mathematical function.
  • the at least one biological marker is selected from the group consisting of glycemia (GLY) , aspartate aminotransferase (AST) , alanine aminotransferase (ALT) ,
  • AST/ALT hyaluronic acid
  • Hb Haemoglobin
  • triglycerides preferably glycemia (GLY) , aspartate aminotransferase (AST) , alanine aminotransferase (ALT) , AST/ALT, hyaluronic acid (HA) , Haemoglobin (Hb) and triglycerides; the level of these blood markers may be easily dosed with methods already known in the art; preferably two clinical markers are selected from the group consisting of age, sex, BMI, weight; preferably, a score is performed.
  • the Fibrosis Score of the present invention is determined through the method described in WO2005/116901, herein incorporated by reference.
  • the Fibrosis Score is determined by measuring in a sample of said patient and combining in a logistic regression function at least three markers selected in the group consisting of ⁇ -2 macroglobulin (A2M) , hyaluronic acid (HA or hyaluronate) , apoliprotein Al (ApoAl), N-terminal propeptide of type III procollagen (P3P) , gamma-glutamyltranspeptidase (GGT) , bilirubin, gamma-globulins (GLB) , platelets (PLQ) , prothrombin level (TP) , aspartate amino-transferase (AST) , alanine amino-transferase (ALT) , urea, sodium (NA) , glycemia (GLY) , triglycerides (TG) , albumin (ALB) , alkaline phosphatases (PAL) , YK
  • A2M
  • the Fibrosis Score is measured by combining the levels of at least three biological markers selected from the group consisting of glycemia (GLY) , aspartate aminotransferase (AST) , alanine amino-transferase (ALT) , ferritine, hyaluronic acid (HA), triglycerides (TG), prothrombin index (PI) gamma-globulins (GLB), platelets (PLQ), weight, age and sex.
  • GLY glycemia
  • AST aspartate aminotransferase
  • ALT alanine amino-transferase
  • ferritine ferritine
  • HA hyaluronic acid
  • TG triglycerides
  • PI prothrombin index
  • GLB platelets
  • weight age and sex.
  • At least 2, preferably at least 3, more preferably at least 4, even more preferably at least 5 biological markers, and even most preferably at least 6 biological markers and optionally Fibrosis Score are measured and combined in the method of the present invention.
  • the method according to the invention further comprises measuring at least one clinical marker.
  • the clinical marker is selected from the group consisting of the age (AGE), the body mass index (BMI), the body weight, the hip perimeter, the abdominal perimeter and a ratio thereof, such as for example hip perimeter/abdominal perimeter; more preferably two clinical markers are measured.
  • the method according to the invention comprises measuring at least three biomarkers selected from the group consisting of glycemia (GLY) , aspartate aminotransferase (AST) , alanine aminotransferase (ALT) , hyaluronic acid (HA) , Hemoglobin (Hb) and triglycerides, and at least one, preferably two clinical markers selected from the group consisting of the age (AGE), the body mass index (BMI), the body weight, the hip perimeter, the abdominal perimeter and a ratio thereof, such as for example hip perimeter/abdominal perimeter and a Fibrosis score and combining said levels of biological markers, measured Fibrosis Score and measured clinical markers, through a suitable mathematical function, preferably a logistic function or a multiple regression function.
  • GLY glycemia
  • AST aspartate aminotransferase
  • ALT alanine aminotransferase
  • HA hyaluronic acid
  • Hb Hemoglobin
  • Another object of the present invention is a method for quantifying the area of liver steatosis (AOS) in a patient, comprising performing a multi-echo gradient-echo MRI called MFGRE on whole or part of the liver of the patient, measuring the liver fat content on the resulting MRI signal and comparing said liver fat content on the resulting MRI signal to the area of lipid vacuoles of the reference image.
  • AOS liver steatosis
  • MFGRE is a new method developed by the applicant and based on a multi-echo prototype MRI sequence.
  • the principle of the sequence is an echo gradient in-phase and out-of-phase but with the acquisition of 16 echos, allowing the precise calculation of signal decay parameters: water signal (W), fat (F) signal, local field in homogeneity (F) and noise (N) .
  • Fat fraction is calculated using the following formula:
  • MFGRE is used as a non invasive marker of steatosis either per se or with equivalence in AOS by using a linear regression score.
  • This marker can be used as part of a test in the same way as a blood or clinical marker.
  • a third object of the present invention is a method for quantifying an area of liver steatosis (AOS) in a patient, comprising : a) measuring the fat content on a MRI signal of the liver of a patient; b) measuring the level of at least one biological marker and/or Fibrosis Score, and/or at least one, preferably two, clinical marker (s) ; c) combining the measured fat content index obtained in step (a) and the measured levels, score and/or clinical markers obtained in step
  • the method for quantifying an area of liver steatosis measures at least 2, preferably at least 3, more preferably at least 4 and even more preferably at least 5 biological markers and most preferably at least 6 biological markers and optionally Fibrosis Score in step (b) .
  • a fourth object of the present invention is a method for diagnosing the presence and/or the severity of a liver steatosis or fatty liver disease in a patient, comprising implementing a method of measuring the area of steatosis as described in the present invention.
  • the method of the invention is of high interest to diagnose and quantify main liver lesions, especially NAFLD lesions.
  • Figure 1 Correlation between morphometric characteristics of liver fibrosis or steatosis and corresponding blood tests as a function of fibrosis stages or steatosis grades.
  • Panel a area of fibrosis
  • panel b fractal dimension of fibrosis.
  • Panel c area of steatosis,
  • panel d fractal dimension of steatosis.
  • the lines depict the linear regression .
  • Figure 2 Relationship between the area and fractal dimension of steatosis as a function of steatosis grades.
  • the lines depict the LOWESS regression curves.
  • the Y axis was truncated to show more details .
  • Figure 3 Relationship between blood tests for NAS (panel a) or NASH (panel b) (Y axis) and NAS (X axis) . Box plots (median, interquartile range and extremes) .
  • Figure 4 Relationship between area and fractal dimension as a function of fibrosis stages or steatosis grades.
  • Left panels (a and c) morphometric results.
  • Right panels (b and d) blood tests.
  • Upper panels (a and b) fibrosis; bottom panels (c and d) : steatosis.
  • the lines depict the LOWESS regression curves.
  • Figure 5 Relationship between area or fractal dimension of fibrosis or steatosis (Y axis) as a function of fibrosis stages (X axis) .
  • Left panels (a and c) morphometric results.
  • Right panels (b and d) blood tests.
  • Upper panels (a and b) area; bottom panels (c and d) : fractal dimension. Box plots (median, interquartile range and extremes) .
  • Clinical data and blood tests Fasting blood samples were taken at inclusion (date of liver biopsy ⁇ 7 days) .
  • the usual clinical and blood variables were included, especially fasting glucose at inclusion, as were the following fibrosis markers: hyaluronic acid, ⁇ 2-macroglobulin, apolipoprotein- Al, prothrombin index, platelets, aspartate and alanine aminotransferases (AST, ALT) , ⁇ -glutamyltranspeptidase, total bilirubin and urea.
  • AST, ALT aspartate and alanine aminotransferases
  • a percutaneous liver biopsy was performed generally within one week (maximum 3 months) of blood sampling. Specimen lengths were measured before paraffin-embedding. Then, 5 ⁇ m-thick sections were stained with hematoxylin-eosin-saffron or 0.1 % picrosirius red solution and used for both optical and image analyses.
  • Biopsy specimens were centrally re-examined by two liver pathologists; discordant cases were reviewed by a third pathologist to reach consensus.
  • Steatosis, lobular inflammation and hepatocyte ballooning were separately graded according to the NASH Clinical Research Network (NASH-CRN) system (Kleiner DE et al, Hepatology 2005, 41:1313-1321).
  • the addition of these 3 grades provided the NAFLD activity score (NAS) .
  • the diagnosis of NASH was evaluated according to three definitions based on NAS: >3, ⁇ 5 and 0-2 / 3-4 / 5-7, respectively no, possible/borderline, definite NASH (Kleiner DE et al, Hepatology 2005, 41:1313-1321).
  • Fibrosis was graded into 5 stages, called here FO to F4, according to NASH-CRN staging specifically developed for NAFLD (Kleiner DE et al, Hepatology 2005, 41:1313-1321).
  • the Metavir staging (Hepatology 1994, 20: 15-20) developed for viral hepatitis, but also used in alcoholic chronic liver diseases and in NAFLD, was also evaluated to stage porto- septal fibrosis in acinar zone 1.
  • Image acquisition We used an Aperio digital slide scanner (Scanscope ® CS System, Aperio Technologies, Vista CA 92081, USA) image processor that provided high quality 30,000 x 30,000 pixel images at a resolution of 0.5 ⁇ m/pixel (magnification x20) .
  • a binary image (white and black) was obtained via an automatic thresholding technique using an algorithm developed in our laboratory. The entire specimen area was analyzed. Liver specimen artifacts (folds, dust) were manually removed.
  • Algorithm for steatosis Some aspects of the algorithm have been previously described (Roullier V et al .
  • AOS % was calculated as the ratio: area of steatosis vesicles / complete liver surface, and relative AOS (rAOS, %) as the ratio: area of steatosis / non-fibrous liver area (i.e.
  • Quantitative variables were expressed as mean ⁇ SD, unless otherwise specified.
  • the Lowess regression by weighted least squares was used to determine the average trend of relationships between variables (Borkowf CB et al. Stat Med 2003, 22:1477-1493).
  • Independent predictors were determined by the bootstrapping method. This method was also determined to calculate the optimism bias. Diagnostic performance was expressed by the area under the receiver operating characteristic (AUROC) or diagnostic accuracy. Diagnostic cut-offs were determined according to maximum Youden index and diagnostic accuracy. Stepwise multiple linear regression and binary logistic regression were used to determine independent variables. In the linear regression, all categorical variables were dichotomized and the performance of each model was expressed by the adjusted R 2 (aR 2 ).
  • Statistics software programs were SPSS version 11.5.1 (SPSS Inc., Chicago, IL, USA) and SAS 9.1 (SAS Institute Inc., Cary, NC, USA) .
  • Steatosis grade (% grade 0/1/2/3) 45.3 / 21.5 / 21.1 /
  • Table 2 Multivariate analyses showing variables independently associated with different diagnostic targets. Diagnostic performance was expressed by either AUROC for score obtained by binary logistic regression or a R 2 for score obtained by multiple linear regression. In these models, the prediction was the mam objective so that conditions of independent variables were less stringent than in an explanatory model.
  • Hyaluronic acid glycemia, AST, ALT,
  • Fractal dimension of glycemia, AST, age, #4A aR2 0.524 fibrosis prothrombin index
  • NASH-CRN AST/ALT haemoglobin
  • #6A AUROC O.848 grade >1 BMI
  • AST aspartate aminotransferase ALT: alanine aminotransferase BMI : body mass index
  • Table 2bis Multivariate analyses showing variables independently associated with different diagnostic targets. Diagnostic performance was expressed by either AUROC for score obtained by binary logistic regression or a R 2 for score obtained by multiple linear regression. In these models, the prediction was the main objective so that conditions of independent variables were less stringent than in an explanatory model.
  • AST aspartate aminotransferase ALT: alanine aminotransferase BMI: body mass index
  • Table 3 Multivariate analyses showing variables independently associated with different diagnostic targets. Diagnostic performance was expressed by either AUROC for score obtained by binary/ordinal logistic regression or a R 2 for score obtained by multiple linear regression. Figures into brackets denote AUROC corrected with optimism bias. In these models, the explanation was the main objective so that conditions of independent variables were more stringent than in a predictive model. Variables were selected by the stepwise bootstrap method.
  • Metavir AUROC O.932 glycemia, AST, ALT, #1B F> 2 (0.918)
  • NASH-CRN AUROC O.866 AST, ALT, prothrombin #2B F> 2 (0.856) index
  • Hyaluronic acid, glycemia, AST, ALT, aR2 0.530
  • Fractal dimension of aR2 0.529 glycemia, AST/ALT, #4B fibrosis (0.517) weight, platelets
  • BMI body mass index a
  • the original model determined by conventional binary logistic regression b According to 3 definitions based on NAS
  • Table 3bis Multivariate analyses showing variables independently associated with different diagnostic targets. Diagnostic performance was expressed by either AUROC for score obtained by binary/ordinal logistic regression or a R 2 for score obtained by multiple linear regression. Figures into brackets denote AUROC corrected from optimism bias. In these models, the explanation was the main objective so that condition of non-collinearity between independent variables was more stringent than in a predictive model. Variables were selected by a bootstrap method (stepwise regression analysis on 1,000 bootstrap samples).
  • a R 2 0.247
  • Diagnosis of NASH NASH was independently predicted as shown in tables 2 and 3 with corresponding AUROC.
  • Liver biopsy was performed in order to diagnose alcoholic or non- alcoholic steatohepatitis.
  • Steatosis degree was evaluated by an expert using steatosis grading -SO (0% of hepatocytes) ,
  • AOS Steatosis area
  • MFGRE multi-echo gradient-echo MRI
  • AOS was significantly different between all steatosis grades
  • Correlation coefficients obtained by other methods were, respectively: 0.71 and 0.57 for Hussain, 0.80 and 0.61 for FSE; 0.71 and 0.55 for 2-point Dixon; 0.61 and 0.28 for 3-point Dixon.
  • MFGRE was the only MRI quantification method independently linked to AOS.

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Abstract

La présente invention concerne un procédé in vitro non invasif pour quantifier les lésions hépatiques d’un patient ayant une stéatose métabolique correspondant à une cible diagnostique, c’est-à-dire une fibrose, une stéatose et/ou une stéatohépatite (NASH) et mesurer au moins un marqueur choisi dans le groupe constitué de biomarqueurs et éventuellement de marqueurs cliniques et éventuellement de scores, les biomarqueurs étant choisis dans le groupe constitué de la glycémie, AST (aspartate aminotransférase), ALT (alanine aminotransférase), AST/ALT, AST. ALT, la ferritine, les plaquettes, l’indice de prothrombine, l’acide hyaluronique, l’hémoglobine, les triglycérides; le biomarqueur clinique étant choisi dans le groupe constitué du poids, de l’indice de masse corporelle, du sexe et de l’âge, du périmètre des hanches, du périmètre abdominal et du rapport de ceux-ci, tel que, par exemple, périmètre des hanches/périmètre abdominal; et les scores étant choisis dans le groupe constitué du score de fibrose, de la surface de fibrose, de la dimension fractale de fibrose, du score de stéatose, de la surface de stéatose, de la dimension fractale de stéatose et d’une combinaison desdites mesures dans une fonction mathématique combinant lesdites mesures par l’intermédiaire d’une fonction logistique binaire (ou ordinale) ou d’une régression linéaire multiple comprenant lesdits marqueurs afin d’obtenir une valeur finale; un procédé non invasif pour quantifier les lésions hépatiques d’un patient, comprenant l’exécution d’un IRM multi-échos et d’écho de gradient appelé MFGRE sur tout ou une partie du foie du patient, mesurer la teneur en lipides du foie sur le signal IRM résultant et comparer ladite teneur en lipides du foie sur le signal IRM résultant à la surface de vacuoles de lipides de l’image de référence.
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