WO2014049131A1 - Test sanguin précis pour le diagnostic non invasif de la stéatohépatite non alcoolique - Google Patents

Test sanguin précis pour le diagnostic non invasif de la stéatohépatite non alcoolique Download PDF

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WO2014049131A1
WO2014049131A1 PCT/EP2013/070223 EP2013070223W WO2014049131A1 WO 2014049131 A1 WO2014049131 A1 WO 2014049131A1 EP 2013070223 W EP2013070223 W EP 2013070223W WO 2014049131 A1 WO2014049131 A1 WO 2014049131A1
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biomarker reflecting
biomarker
score
reflecting
body weight
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PCT/EP2013/070223
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Paul Calès
Jérôme BOURSIER
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Université d'Angers
Centre Hospitalier Universitaire D'angers
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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
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/576—Immunoassay; Biospecific binding assay; Materials therefor for hepatitis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G01N2333/4701—Details
    • G01N2333/4742—Keratin; Cytokeratin
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/91—Transferases (2.)
    • G01N2333/91188—Transferases (2.) transferring nitrogenous groups (2.6)
    • 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

Definitions

  • the present invention relates to the diagnosis of non-alcoholic steatohepatitis (NASH).
  • NASH non-alcoholic steatohepatitis
  • the present invention more specifically relates to a new and accurate blood test for the non-invasive diagnosis of non-alcoholic steatohepatitis, preferably in NAFLD patients.
  • BACKGROUND OF INVENTION FLD Fatty Liver Disease
  • FLD is commonly associated with alcohol or metabolic syndromes (such as, for example, diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy and lipodystrophy).
  • alcohol or metabolic syndromes such as, for example, diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy and lipodystrophy).
  • nutritional causes such as, for example, malnutrition, total parenteral nutrition, severe weight loss, refeeding syndrome, jejunoileal bypass, gastric bypass or jejunal diverticulosis with bacterial overgrowth
  • drugs and toxins such as, for example, amiodarone, methotrexate, diltiazem, highly active antiretroviral therapy, glucocorticoids, tamoxifen, environmental hepatotoxins
  • other diseases such as inflammatory bowel disease or HIV.
  • 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 has usually been accomplished by performing a liver biopsy in order to confirm FLD and determine the grading and staging of the disease, especially NASH.
  • biopsies can provide important information regarding the degree of liver damage, the procedure presents several limitations, such as sampling error, invasiveness, cost, pain for patients which in turn brings forth a certain reluctance to undergo such a procedure; and finally complications may arise from such procedure, which in some cases can even lead to mortality.
  • 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). As a result, it is not sensitive enough and often inaccurate, especially in patients with advanced fibrosis.
  • non-invasive biomarkers has gained importance in the field of hepatic diagnosis. Indeed, non-invasive methods for detecting the extent of alcoholic or non-alcoholic steatohepatitis in a patient have been described.
  • US2006/0172286 describes a non-invasive method for diagnosing alcoholic or nonalcoholic steatohepatitis in a patient comprising measuring the 3 biochemical markers ApoAl, ALT and AST in a sample from the patient.
  • the AASLD Practice Guideline (Chalasani et al, Hepatology, 55(6):2005- 2023, 2012) discloses the serum/plasma cytokeratine (CK)18 as a promising biomarker for identifying steatohepatitis.
  • CK18 serum/plasma cytokeratine
  • all the studies realized on CK18 as a biomarker utilized a study- specific cut-off value. Consequently, there is not an established cut-off value for identifying steatohepatitis using this biomarker.
  • the AASLD Practice Guideline thus concludes that although serum/plasma CK18 is a promising biomarker for identifying steatohepatitis, it may be premature to recommend in routine clinical practice, unless a solution is found.
  • This invention aims at providing the solution expected by the AASLD Practice Guideline, and provides tests that may use the serum/plasma CK18 biomarker. Also, this invention aims at overcoming the limitations and drawbacks of the prior art methods, that are resumed hereafter.
  • prior art methods have usually been developed in cohorts of patients undergoing bariatric surgery. These morbidly obese patients represent a very particular subgroup of NAFLD patients, and thus the external validation of blood tests calibrated in such cohorts is debatable.
  • pathological definition of NASH i.e. the main study endpoint, was heterogeneous among studies and not in accordance with the latest admitted definition (Sanyal et al, Hepatology, 54(l):344-353, 2011).
  • these tests have limited accuracy for the diagnosis of NASH. Finally, none of these tests has been externally and independently validated.
  • the Inventors thus aimed at developing a new blood test for the non-invasive diagnosis of NASH, as defined by the latest admitted pathological definition, in a well- representative cohort of NAFLD patients, wherein said blood test is more accurate than the tests of the prior art, such as, for example, more accurate than the test of US2006/0172286.
  • the present invention thus relates to an in-vitro non-invasive method for assessing the presence and/or severity of NASH lesions in the liver of a subject, wherein said method comprises the steps of: a. measuring in a sample of said subject at least one biomarker, preferably 1, 2, 3 or 4 biomarkers, selected from at least one of:
  • iii. at least one biomarker reflecting metabolic activity, and/or iv. at least one biomarker reflecting liver status; and b. combining said at least one biomarker measure in a mathematical function.
  • step (a) three of the following biomarkers are measured:
  • 11 at least one biomarker reflecting anthropometry, and/or
  • IV at least one biomarker reflecting liver status.
  • At step (a) at least one biomarker reflecting apoptosis and at least one of the following biomarkers are measured: i. at least one biomarker reflecting anthropometry, and/or
  • iii at least one biomarker reflecting liver status.
  • At step (a) at least one biomarker reflecting apoptosis and at least one biomarker reflecting anthropometry, and at least one biomarker reflecting metabolic activity are measured.
  • the method of the invention comprises the steps of: a. measuring in a sample of said subject:
  • biomarker reflecting apoptosis i. at least one biomarker reflecting apoptosis, wherein said biomarker reflecting apoptosis is selected from the group comprising cytokeratin 18
  • biomarker reflecting anthropometry wherein said biomarker reflecting anthropometry is selected from the group comprising body mass index (BMI), body weight, age, sex, waist circumference, abdominal height, hip circumference, waist/hip ratio and any combination thereof and iii. at least one biomarker reflecting metabolic activity, wherein said biomarker reflecting metabolic activity is a biomarker reflecting glucose metabolism, a biomarker reflecting lipid metabolism, or any combination thereof, preferably a biomarker reflecting glucose metabolism and iv. optionally at least one biomarker reflecting liver status; and b. combining said biomarkers in a mathematical function.
  • said biomarker reflecting apoptosis is CK18.
  • said biomarker reflecting anthropometry is body mass index (BMI) or body weight, preferably body mass index (BMI).
  • said biomarker reflecting glucose activity is selected from the group comprising glycaemia, hyperglycemia, HbAlc, insulin, C peptide, HOMA, QUICKI antidiabetic treatment, diabetes, type 2 diabetes and impaired fasting glucose tolerance, preferably said biomarker reflecting glucose activity is glycaemia or hyperglycemia, and more preferably said biomarker reflecting glucose activity is hyperglycemia.
  • step (a) four of the following biomarkers are measured: i. at least one biomarker reflecting apoptosis, and
  • iv at least one biomarker reflecting liver status.
  • the biomarker reflecting apoptosis is selected from the group comprising cytokeratin 18 (CK18), CK18 M30, CK18 M65, AST, ALT and any combination thereof.
  • the biomarker reflecting anthropometry is selected from the group comprising body mass index (BMI), body weight, age, sex, waist circumference, abdominal height, hip circumference, waist/hip ratio and any combination thereof.
  • BMI body mass index
  • the biomarker reflecting metabolic activity is a biomarker reflecting glucose metabolism, a biomarker reflecting lipid metabolism, or any combination thereof.
  • the biomarker reflecting liver status is a biomarker reflecting liver fibrosis, preferably selected from the list comprising score of FIBROMETERTM, BARD score, NFSA score, APRI score, AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, ferritin, platelets (PLT), AST/PLT, prothrombin time (PT) or prothrombin index (PI), INR (international normalized ratio), hyaluronic acid (HA or hyaluronate), haemoglobin, triglycerides, alpha-2 macroglobulin (A2M), gamma-glutamyl transpeptidase (GGT), urea, bilirubin, apolipoprotein Al (ApoAl),
  • the biomarker reflecting liver status is a biomarker reflecting liver function, preferably selected from the list comprising AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, gamma- glutamyltranspeptidase (GGT), bilirubine, gamma-globulins (GLB), urea, albumine (ALB), alcaline phosphatases (ALP), alpha GST, 5 'nucleotidase, ratios and mathematical combinations thereof.
  • the biomarker reflecting liver status is a data resulting from a physical method for assessing liver status, preferably issued from elastometry such as, for example, a FibroscanTM.
  • the biomarker reflecting liver status is a score resulting from a test selected from the group comprising FIBROMETERTM, CIRRHOMETERTM, BARD, NFSA, APRI, FIB-4, Forns, Hepascore, FibrotestTM, Fibrosure, QuantiMeter, CombiMeter, E-FibroMeter, NAFLD fibrosis score, ELF, FibrospectTM.
  • biomarkers are measured in step (a):
  • the mathematical function is a binary logistic regression, a multiple linear regression or any multivariate analysis.
  • the NASH lesion is borderline NASH or definite NASH, preferably definite NASH or borderline and definite NASH.
  • the subject is an animal, preferably a human.
  • the NASH lesions are associated to or caused by a liver disease, preferably selected from the list comprising metabolic syndrome, non-alcoholic fatty liver disease and alcoholic chronic liver disease.
  • the sample is a blood sample.
  • NAFLD Nonalcoholic Fatty Liver Disease
  • NASH Nonalcoholic Steatohepatitis
  • An object of the present invention is thus a method for measuring by non-invasive means (such as for example biomarkers) NASH lesions in the liver, and thus for diagnosing NASH in a subject.
  • the method of the invention is more accurate than the methods of the prior art.
  • the present invention thus relates to an in-vitro non-invasive method for assessing the presence and/or severity of NASH lesions in the liver of a subject, wherein said method comprises the steps of: a. measuring in a sample of said subject at least one biomarker, preferably 1, 2, 3 or 4 biomarkers, selected from at least one of:
  • At least one biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and/or
  • step (a) of the method of the invention at least one biomarker reflecting apoptosis is measured, and optionally at least one biomarker selected from at least one of: i. at least one biomarker reflecting anthropometry, and/or
  • At least one biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and/or
  • iii at least one biomarker reflecting liver status.
  • the method of the invention is for assessing the presence or the absence of NASH lesions in the liver of a subject. In another embodiment, the method of the invention is for diagnosing NASH in a subject.
  • the method of the invention is for assessing the severity of NASH lesions in the liver of a subject.
  • the mathematical combination of step (b) leads to a diagnostic score.
  • the diagnostic score may correspond to a probability for the subject to present NASH lesions.
  • the method of the invention leads to the classification of the patient in a class of a classification according to the value obtained at step (b), preferably according to the latest recommendations (Sanyal et al, 2011), i.e. the subject is diagnosed as having steatohepatitis corresponding to either definite steatohepatitis and/or borderline steatohepatitis; or no steatohepatitis.
  • the minimal criteria for the diagnosis of steatohepatits include the presence of more than 5% macrovesicular steatosis, inflammation and liver cell ballooning, typically with a predominantly centrilobular (acinar zone 3) distribution.
  • "definite steatohepatitis” such as, for example, “definite NASH” is defined by zone 3 accentuation of macrovesicular steatosis of any grade, hepatocellular ballooning of any degree, and lobular inflammatory infiltrates of any amount.
  • the NASH lesion is borderline NASH or definite NASH, preferably definite NASH.
  • the method of the invention is for classifying the subject in a class of another known or new classification of NASH.
  • At least one biomarker reflecting apoptosis, and at least one biomarker reflecting anthropometry are measured.
  • At least one biomarker reflecting apoptosis, and at least one biomarker reflecting metabolic activity, preferably at least one biomarker reflecting glucose metabolism, are measured.
  • At least one biomarker reflecting apoptosis, and at least one biomarker reflecting liver status are measured.
  • At least one biomarker reflecting anthropometry and at least one biomarker reflecting metabolic activity, preferably at least one biomarker reflecting glucose metabolism, are measured.
  • At least one biomarker reflecting anthropometry and at least one biomarker reflecting liver status are measured.
  • At least one biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and at least one biomarker reflecting liver status are measured.
  • three of the following biomarkers are measured in step (a): iv. at least one biomarker reflecting apoptosis, and/or
  • v. at least one biomarker reflecting anthropometry, and/or
  • biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and/or
  • At least one biomarker reflecting apoptosis and at least one biomarker reflecting anthropometry and at least one biomarker reflecting metabolic activity, preferably at least one biomarker reflecting glucose metabolism, are measured.
  • At least one biomarker reflecting apoptosis, and at least one biomarker reflecting anthropometry and at least one biomarker reflecting liver status are measured.
  • At least one biomarker reflecting apoptosis and at least one biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and at least one biomarker reflecting liver status are measured.
  • At least one biomarker reflecting anthropometry and at least one biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism, and at least one biomarker reflecting liver status are measured.
  • four of the following biomarkers are measured in step (a): i. at least one biomarker reflecting apoptosis, and
  • biomarker reflecting metabolic activity preferably at least one biomarker reflecting glucose metabolism
  • iv. at least one biomarker reflecting liver status.
  • biomarkers reflecting apoptosis include, but are not limited to, cytokeratin 18 (CK18), CK18 M30, CK18 M65, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and any combination thereof.
  • said biomarker reflecting apoptosis is CK18 or AST, more preferably CK18.
  • biomarkers reflecting anthropometry include, but are not limited to, body mass index (BMI), body weight, age, sex, waist circumference, abdominal height, hip circumference, waist/hip ratio and any combination thereof.
  • BMI body mass index
  • said biomarker reflecting anthropometry is selected from the group comprising BMI and body weight.
  • the biomarker reflecting metabolic activity is a biomarker reflecting glucose metabolism, or lipid metabolism, or any combination thereof.
  • the biomarker reflecting metabolic activity is a biomarker reflecting glucose metabolism.
  • biomarkers reflecting glucose metabolism include, but are not limited to glycaemia or hyperglycemia, HbAlc, insulin, C peptide, HOMA, QUICKI antidiabetic treatment, diabetes, type 2 diabetes and impaired fasting glucose tolerance.
  • said biomarker reflecting glucose metabolism is hyperglycemia, glycemia or insulin. More preferably, said biomarker reflecting glucose metabolism is hyperglycemia or glycemia, and even more preferably hyperglycemia.
  • Hyperglycemia corresponds to a binary marker: the value of said marker is 0 if the subject does not present hyperglycemia, and 1 if the subject presents hyperglycemia. The skilled artisan knows how to determine if a subject presents or not hyperglycemia.
  • hyperglycemia corresponds to any abnormal blood glucose level, such as, for example, higher than about 7 mmol.L (i.e. about 126 mg/dL) or antidiabetic treatment required.
  • biomarkers reflecting lipid metabolism include, but are not limited to triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol, adiponectin, leptin, resistin, lipid-lowering drugs and dyslipidemia.
  • biomarkers reflecting lipid metabolism is HDL cholesterol.
  • said biomarker reflecting metabolic activity is selected from the group comprising hyperglycemia, glycemia, and insulin.
  • HOMA or Homeostatic Model Assessment is a test based on a ratio of glycemia and insulin.
  • the biomarker reflecting liver status is a biomarker reflecting liver fibrosis.
  • biomarkers reflecting liver fibrosis include, but are not limited to, score of FIBROMETERTM, BARD score, NFSA score, APRI score, AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, ferritin, platelets (PLT), AST/PLT, prothrombin time (PT) or prothrombin index (PI), INR (international normalized ratio), hyaluronic acid (HA or hyaluronate), haemoglobin, triglycerides, alpha-2 macroglobulin (A2M), gamma-glutamyl transpeptidase (GGT), urea, bilirubin, apolipoprotein Al (A
  • the biomarker reflecting liver status is a biomarker reflecting liver function.
  • biomarkers reflecting liver function include, but are not limited to, AST (aspartate aminotransferase), ALT (alanine aminotransferase), AST/ALT, AST.ALT, gamma-glutamyltranspeptidase (GGT), bilirubine, gammaglobulins (GLB), urea, albumine (ALB), alcaline phosphatases (ALP), alpha GST, 5 'nucleotidase, ratios and mathematical combinations thereof.
  • the biomarker reflecting liver status is a data resulting from a physical method for assessing liver status.
  • physical methods for assessing liver status include, but are not limited to, medical imaging data and clinical measurements, such as, for example, measurement of spleen, especially spleen length.
  • the physical method is selected from the group comprising ultrasonography, especially Doppler-ultrasonography and elastometry ultrasonography and velocimetry ultrasonography (preferred tests using said data are FibroscanTM, ARFI, VTE, supersonic imaging), MRI (Magnetic Resonance Imaging), and MNR (Magnetic Nuclear Resonance) as used in spectroscopy, especially MRI elastometry or velocimetry.
  • the data are Liver Stiffness Evaluation (LSE) data or spleen stiffness evaluation.
  • the data resulting from a physical method are issued from a FibroscanTM.
  • the biomarker reflecting liver status is a score resulting from a test selected from the list comprising FIBROMETERTM, CIRRHOMETERTM, BARD, NFSA, APRI, FIB-4, Forns, Hepascore, FibrotestTM, Fibrosure, QuantiMeter (Cales, Hepatology 2005;42: 1373-1381), CombiMeter, NAFLD fibrosis score, ELF, FibrospectTM.
  • BARD is a blood test based on three variables combined in a weighted sum of BMI, AST/ALT ratio and Diabetes Melitus.
  • NFSA NAFLD fibrosis score (NFS) of Angulo is a blood test based on age, hyperglycemia, body mass index, platelet count, albumin, and AST/ALT ratio. Forns is a blood test based on age, GGT, cholesterol, and platelet count.
  • APRI is a blood test based on platelet and AST.
  • ELF or European liver fibrosis is a blood test based on hyaluronic acid, P3P, TIMP-1 and age.
  • FibroSpectTM is a blood test based on hyaluronic acid, TEVIP-l and A2M.
  • FIB-4 is a blood test based on platelet, ASAT, ALT and age.
  • HEPASCORE is a blood test based on hyaluronic acid, bilirubin, alpha2-macroglobulin, GGT, age and sex.
  • FIBROTESTTM is a blood test based on alpha2-macroglobulin, haptoglobin, apolipoprotein Al, total bilirubin, GGT, age and sex.
  • FIBROSURETM is the American name of FIBROTESTTM.
  • FIBROMETERTM and CIRRHOMETERTM together form to a family of blood tests, the content of which depends on the cause of chronic liver disease and the diagnostic target, and this blood test family is called FM family and detailed in the Table below:
  • A2M alpha-2 macroglobulin
  • HA hyaluronic acid
  • PI prothrombin index
  • PLT platelets
  • Bili bilirubin
  • Fer ferritin
  • Glu glucose
  • FS Fibroscan
  • b HA may be replaced by GGT
  • COMBIMETERTM or E-FibroMeterTM is a family of tests based on the mathematical combination of variables of the FM family (as detailed in the Table above) or of the result of a test of the FM family with elastometry, such as, for example, FIBROSCANTM result.
  • said mathematical combination is a binary logistic regression.
  • QUANTIMETERTM is a family of tests based on the mathematical combination of variables of the FM family targeted for the area of fibrosis.
  • the biomarker reflecting liver status is selected from the group comprising or consisting of BARD score, haptoglobin, NFSA score, APRI score, albumin, FibroMeterTM score, hyaluronate, alpha-2-macro globulin and ApoAl.
  • 1, 2, 3 4 or more of the following biomarkers are measured in step (a): - CK18, hyperglycemia and BMI,
  • the method of the invention does not comprise measuring in step (a) a biomarker reflecting lipid metabolism.
  • the method of the invention does not comprise measuring in step (a) CK-18 M30, CK18 M65, resistin and adiponectin. In one embodiment, the method of the invention does not comprise measuring in step (a) at least four biomarkers selected from the group consisting of adiponectin, CK-18, CK-18 M30, C-reactive protein, insulin-like growth factor binding protein 1 and alanine aminotransferase. In one embodiment, the method of the invention does not comprise measuring in step (a) adiponectin, CK-18, CK-18 M30, insulin-like growth factor binding protein 1 and body mass index (BMI).
  • BMI body mass index
  • the method of the invention does not comprise measuring in step (a) diabetes, sex, body mass index, triglycerides, CK-18 and CK-18 M30. In one embodiment, the method of the invention does not comprise measuring in step (a) type 2 diabetes, triglycerides, TEVIP-l and aspartate aminotransferase.
  • the mathematical function is a binary logistic regression, a multiple linear regression or any multivariate analysis.
  • the subject is an animal, preferably a human.
  • the subject is a male.
  • the subject is a female.
  • the subject presents a BMI superior to 25 (i.e. the subject is overweight), preferably superior to 30 (i.e. the subject is obese).
  • the subject is diagnosed with NAFLD.
  • the subject is at risk of developing NASH lesions. Said risk may for example correspond to a genetic predisposition to NASH lesions, a familial history of NASH lesions or to overweight or obesity.
  • the NASH lesions are associated to or caused by a liver disease, preferably selected from the list comprising alcohol syndromes, metabolic syndromes (such as, for example, diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy and lipodystrophy), non-alcoholic fatty liver disease and alcoholic chronic liver disease.
  • a liver disease preferably selected from the list comprising alcohol syndromes, metabolic syndromes (such as, for example, diabetes, hypertension, dyslipidemia, abetalipoproteinemia, glycogen storage diseases, Weber-Christian disease, Wolman disease, acute fatty liver of pregnancy and lipodystrophy), non-alcoholic fatty liver disease and alcoholic chronic liver disease.
  • the NASH lesions are associated to or caused by nutritional causes (such as, for example, malnutrition, total parenteral nutrition, severe weight loss, refeeding syndrome, jejuno-ileal bypass, gastric bypass or jejunal diverticulosis with bacterial overgrowth).
  • nutritional causes such as, for example, malnutrition, total parenteral nutrition, severe weight loss, refeeding syndrome, jejuno-ileal bypass, gastric bypass or jejunal diverticulosis with bacterial overgrowth).
  • the NASH lesions are associated to or caused by various drugs and toxins (such as, for example, amiodarone, methotrexate, diltiazem, highly active antiretroviral therapy, glucocorticoids, tamoxifen, environmental hepato toxins).
  • drugs and toxins such as, for example, amiodarone, methotrexate, diltiazem, highly active antiretroviral therapy, glucocorticoids, tamoxifen, environmental hepato toxins.
  • the NASH lesions are associated to or caused by inflammatory bowel disease or HIV.
  • biomarkers are measured in a sample obtained, preferably previously obtained, from the subject.
  • the sample is a bodily fluid sample, such as, for example, a blood or urine sample.
  • Methods for measuring biomarkers in a sample are well-known from the skilled artisan. Examples of such methods include, but are not limited to, biochemistry, immunology, molecular biology or omic technologies, and physical techniques, such as, for example, infrared spectroscopies.
  • the method of the present invention is computerized.
  • Another object of the present invention is thus a computer software for implementing the method of the invention.
  • the present invention also relates to an in-vitro non-invasive diagnostic test for implementing the in-vitro non-invasive method as hereinabove described.
  • the present invention also relates to a kit for implementing the in-vitro non-invasive method as hereinabove described.
  • the kit comprises means for measuring biomarkers in a sample obtained, preferably previously obtained, from a subject.
  • said means are PCR primers, buffers and reagents for measuring the expression of biomarkers in the sample from the subject.
  • said means are antibodies, such as, for example, monoclonal antibodies, for detecting proteins in the sample from the subject.
  • antibodies may be used for measuring CK-18, and are commercially available, such as for example, from Santa Cruz Biotech or Abeam.
  • said means are standard reagents, such as, for example, reagents for measuring glucose concentration in the sample from the subject, preferably in a blood sample from the subject.
  • Figure 1 is a graph showing CK18 concentration as a function of NASH subgroups (no, borderline, definite).
  • Figure 2 is a graph showing the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), diagnostic accuracy (DA) and Youden index (sensitivity + specificity -1) as a function of NASH-score results.
  • the present invention is further illustrated by the following example.
  • NAFLD liver steatosis on liver biopsy after exclusion of concomitant steatosis-inducing drugs (such as corticosteroids, tamoxifene, amiodarone, or methotrexate), excessive alcohol consumption (>30 g/day in men or >20 g/day in women), chronic hepatitis B or C infection, and histological evidence of other concomitant chronic liver disease. Patients were excluded if they had cirrhosis complication (ascites, variceal bleeding, systemic infection, or hepatocellular carcinoma). The study protocol was conformed to the ethical guidelines of the current Declaration of Helsinki and all patients gave informed written consent.
  • concomitant steatosis-inducing drugs such as corticosteroids, tamoxifene, amiodarone, or methotrexate
  • excessive alcohol consumption >30 g/day in men or >20 g/day in women
  • chronic hepatitis B or C infection chronic
  • Arterial hypertension corresponded to systolic arterial pressure >130 mm Hg and/or diastolic arterial pressure >85 mm Hg or antihypertensive drug; hyperglycaemia to fasting serum glucose >5.55 mmol/L or anti-diabetic drug; hypertriglyceridemia to serum triglycerides > ⁇ .l mmol/L or lipid-lowering drug; and low HDL cholesterol to serum HDL-cholesterol ⁇ 1.1 mmol/L in male or ⁇ 1.3 mmol/L in female, or lipid-lowering drug.
  • Serum level of apoptotic caspase-3 generated CK-18 fragments was measured on frozen samples stored at -80°C, using the M30- Apoptosense enzyme-linked immunosorbent assay kit (PEVIVA, Bromma, Sweden). 5 blood tests for the diagnosis of NASH were calculated according to published formula: HAIR (Dixon Gy 2001), Palekar score (Palekar LI 2006), Gholam score (Gholam AJG 2007), Hossain score (Hosain CGH 2009), and Nice Model (Anty APT 2010).
  • NASH Newcastle disease virus
  • Pathological diagnosis of NASH - Pathological examination of liver biopsies was performed by a senior expert specialized in hepatology, blinded for patient data. NASH was diagnosed as "no", “borderline”, or "definite” according to the latest recommended definition (Sanyal et al, 2011). Histological grading and staging of NAFLD were scored according to the NASH Clinical Research Network system (Kleiner 2005). NAFLD activity score (NAS), ranging from 0 to 8, corresponded to the sum of scores for steatosis, lobular inflammation and hepatocellular ballooning.
  • NAS NAFLD activity score
  • Quantitative variables were expressed as mean + standard deviation. Correlations between quantitative variables were determined by using the Spearman correlation coefficient (Rs). To identify the best combination of variables for the diagnosis of NASH, we performed a stepwise forward binary logistic regression including clinical data and blood parameters as independent variables. The regression score of this multivariate analysis was used to construct a new blood test ranging from 0 to 1. Diagnostic accuracy of this blood test was evaluated by the Area Under the Receiver Operating Characteristics (AUROC) and the rate of patients included in the intervals of >90% negative or positive predictive values. Statistical analyses were performed using SPSS version 18.0 software (IBM, Armonk, NY, USA).
  • Characteristics of the 159 patients included are detailed in Table 1. 59.7% were male and mean age was 56.4+11.6 years. Mean biopsy length was 30+14 mm. 64 patients (40%) had no NASH, 19 (12%) had borderline NASH, and 76 (48%) had definite NASH. Mean NAS and rate of patients with F>2 stages between no, borderline, and definite NASH was, respectively: 2.0+1.1, 3.4+0.8, 4.3+1.1 (p ⁇ 0.001) and 37.5%, 73.7%, 84.2 (p ⁇ 0.001).
  • Haptoglobin (g/L) 1.24 + 0.60 1.07 + 0.55 1.34 + 0.52 1.34 + 0.64 0.040
  • Apolipoproteine Al (g/L) 1.48 + 0.35 1.54 + 0.37 1.39 + 0.36 1.45 + 0.33 0.339
  • CK18 level was significantly different among the 3 NASH subgroups (p ⁇ 0.001) but post hoc analysis showed the difference was significant only between no NASH and borderline or definite NASH ( Figure 1).
  • AUROC of CK18 for the diagnosis of borderline/definite NASH was 0.743+0.048, and 0.716+0.048 for the diagnosis of definite NASH (Table 2).
  • AUROC of HAIR, Palekar score, Gholam score, Hossain score, and Nice Model for the diagnosis of borderline/definite or definite NASH are depicted in Table 2.
  • Table 3 Main independent predictors of borderline/definite NASH by stepwise forward binary logistic regression.
  • Table 4 Summary of combinations of independent predictors of borderline/definite NASH by stepwise forward binary logistic regression.
  • b Bard, NFS A, APRI and FibroMeter are fibrosis tests: they can be replaced by other known non-invasive fibrosis tests.
  • c Haptoglobin , hyaluronate, A2M, ApoaAl are single fibrosis markers: they can be replaced by other known non-invasive single fibrosis markers.
  • Negative and positive predictive values as a function of the NASH-Score results are depicted in Figure 2.
  • the NASH-score included 16.5% of the patients in the interval of >90% negative predictive value for the diagnosis of borderline/definite NASH, 53.0% in the interval of >90% positive predictive value, and 30.4% in the remained grey zone.

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Abstract

La présente invention concerne un procédé in vitro non invasif pour évaluer la présence et/ou la sévérité de lésions NASH dans le foie d'un sujet, dans lequel ledit procédé comprend les étapes de : a. mesure dans un échantillon dudit sujet d'au moins un biomarqueur, de preference 1, 2, 3 ou 4 biomarqueurs, sélectionnés parmi au moins un des : i. au moins un biomarqueur traduisant l'apoptose, et/ou ii. au moins un biomarqueur traduisant l'anthropométrie, et/ou iii. au moins un biomarqueur traduisant l'activité métabolique, et/ou iv. au moins un biomarqueur traduisant le statut du foie; et b. combinaison de ladite au moins une mesure de biomarqueur dans une fonction mathématique.
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EP3599615A1 (fr) * 2018-07-27 2020-01-29 Biopredictive Procédé de diagnostic de la stéatose du foie
JP2021156648A (ja) * 2020-03-25 2021-10-07 富士レビオ株式会社 被験者のスクリ−ニング方法及び鑑別方法
WO2022258788A1 (fr) * 2021-06-09 2022-12-15 Université Catholique de Louvain Système et procédé pour déterminer un risque d'avoir ou de développer une stéato-hépatite et/ou une complication de celle-ci

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