Improved diagnosis of metabolic dysfunction-associated steatohepatitis

Abstract

The present invention provides improved methods for the diagnosis of at-risk MASH.

Description

BACKGROUND OF THE INVENTION Metabolic dysfunction-associated steatohepatitis (MASH) is a chronic liver disease characterized histologically by the accumulation of fat, hepatocyte damage and inflammation resembling alcoholic hepatitis. MASH can lead to liver fibrosis, cirrhosis, liver failure and / or hepatocellular carcinoma (HCC). Until recently, MASH was largely underdiagnosed, as it carries no obvious symptoms in its early stages, and also because of the lack of widely available non-invasive tests specifically developed to diagnose the disease. At-risk MASH status, which is defined as having MASH, MAS score >4 and fibrosis stage F>2, represents an important MASH sub-population to identify. Indeed, these patients are associated to elevated risks of disease worsening, notably cirrhosis, and higher risk of liver-related and all-cause mortality. Due to the technical limitations and also risks of biopsies, which are the clinical reference standard for the diagnosis of MASH and fibrosis, the development of blood-based non-invasive tests (NITs) is of major importance. Different NITs have been developed, mainly to fit with fibrosis stages. However, there is still unmet medical need to provide more efficient and robust blood-based NIT for addressing a composite Fibrosis x NAS endpoint when detecting at-risk MASH patients. In this context, we evaluated whether it would be possible to provide a NIT improved over the NITs of the prior art. SUMMARY OF THE INVENTION The present invention is based on the detailed analysis of a significant dataset of clinical trial. It is herein provided a non-invasive method for the identification of at-risk MASH subjects. Accordingly, the present invention relates to a method for the diagnosis of at-risk metabolic dysfunction-associated steatohepatitis (MASH) in a subject, wherein said method comprises quantifying the levels of cytokeratin 18 (CK18), in particular the fragment M65 or M30 of CK18, and Thrombospondin 2 (TSP2 or TSP-2) in a biological fluid sample of said subject. Particularly, the invention relates to a method for the diagnosis, screening, monitoring or prognosis of at-risk MASH in a subject, said method comprising the step of quantifying the levels of cytokeratin 18 (CK18), in particular the fragments M65 (CK18 M65) or M30 (CK18 M30) of CK18, and Thrombospondin 2 (TSP2) in a biological fluid sample of said subject; combining the quantified levels in a mathematical function to assign a score; and comparing said score with a cutoff value to determine whether said subject is of risk of having at-risk MASH. In a particular embodiment, the score is compared with several cutoff values to determine whether said subject is of high, low or indeterminate risk of having at-risk MASH. More specifically, the invention relates to a method for the diagnosis, screening, monitoring or prognosing of at-risk MASH in a subject, said method comprising: - quantifying the levels of the fragment M30 of CK18 and TSP-2 in a biological fluid sample of said subject; - combining the quantified levels in a mathematical function to assign a score; and - comparing said score with cutoff values to determine whether said subject is of risk of having at-risk MASH. More specifically, the invention relates to a method for the diagnosis, screening, monitoring or prognosing of at-risk MASH in a subject, said method comprising: - quantifying the levels of the fragment M65 of CK18 and TSP-2 in a biological fluid sample of said subject; - combining the quantified levels in a mathematical function to assign a score; and - comparing said score with cutoff values to determine whether said subject is of risk of having at-risk MASH. In a particular embodiment, the method of the invention is for the diagnosis of at-risk MASH in a subject. In a particular embodiment, the mathematical function includes a logistic regression equation. In another embodiment, the biological fluid sample is a blood, serum or plasma sample, preferably a serum sample. In yet another embodiment, the subject suffers from obesity, insulin resistance, glucose intolerance, type 2 diabetes mellitus (T2DM), prediabetes, dyslipidaemia, hypertriglyceridaemia, or high blood pressure. According to another aspect, the invention relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to: - receive quantified levels of CK18, in particular the fragment M30 of CK18, and TSP-2; - calculate a score from these quantified levels of the subject, from a mathematical function as described herein; and - assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values. According to another aspect, the invention relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to: - receive quantified levels the fragment M65 of CK18, and TSP-2; - calculate a score from these quantified levels of the subject, from a mathematical function as described herein; and - assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values. In yet another aspect, the invention relates to a computer-readable medium comprising the computer program disclosed herein. In a particular embodiment, the computer-readable medium is a non-transitory medium or a storage medium. In addition, the present invention relates to specific anti-MASH or anti-fibrotic agents for use in the treatment of at-risk MASH in a subject in need thereof, wherein the subject has been classified as having at-risk MASH thanks to the method disclosed herein. In still another aspect, the invention relates to a kit for diagnosing, screening, monitoring or prognosis of at-risk MASH in a subject, said kit comprising means for determining the levels of TSP-2 and CK18. In a particular embodiment, the kit comprises means for determining the levels of TSP-2 and CK18 M30. In another particular embodiment, the kit comprises means for determining the levels of TSP-2 and CK18 M65. In a particular embodiment, the kit comprises an antibody or an aptamer or a peptide directed 5 against TSP-2 and an antibody or an aptamer or a peptide directed against CK18 M30 or CK18 M65. FIGURES 10 Figure 1 represents boxplots of a modelization combining TSP-2 and CK18 M30 (TCK30) and the single biomarkers (TSP-2 and CK18 M30) in matched subpopulations by gender. Y axis represents the quantity of TSP-2 expressed as logw(TSP-2 (ng / mL)) (Fig. 1A), of CK18 M30 expressed as logio(CK-18 M30 (IU / L)) (Fig. 1B) and a score calculated by the modelization TCK30 (for Fig. 10). Boxplots are presented with associated Student t-tests for mean 15 comparisons, *** p < 0.001 and NS p > 0.05. Figure 2 represents boxplots of the modelization TCK30 and the single biomarkers (TSP-2 and CK18 M30) in matched subpopulations by age. ** p < 0.01, and NS: p > 0.05. Y axis represents the quantity of TSP-2 expressed as logw(TSP-2 (ng / mL)) (Fig. 2A), of CK18 M30 expressed as logw(CK18 M30 (IU / L)) (Fig. 2B) and a score calculated by the modelization TCK30 (for 20 Fig. 2C). Figure 3 represents boxplots of the modelization TCK30 and the single biomarkers (TSP-2 and CK18 M30) in matched subpopulations by T2D status. NS: p > 0.05. Y axis represents the quantity of TSP-2 expressed as logw(TSP-2 (ng / mL)) (Fig. 3A), of CK18 M30 expressed as logw(CK-18 M30 (IU / L)) (Fig. 3B) and a score calculated by the modelization TCK30 (for Fig. 25    3C). Figure 4 represents Roc curves for at-risk MASH endpoint obtained in validation cohort with different NITs. DETAILED DESCRIPTION OF THE INVENTION 30 The present invention relates to a non-invasive method that can be used to aid discrimination between at-risk MASH and not at-risk MASH in a subject, from a biological fluid sample of the subject. Histological scoring / staging systems have been developed to assess Metabolic dysfunction-associated steatotic liver disease (MASLD) activity level and fibrosis stage and to estimate the risk of evolution to clinical liver outcomes. The MASLD-Activity-Score (MAS) has been developed to assess the severity of MASLD. MAS is the sum of three histological scores determined from liver biopsy slices: - S: Steatosis score: 0: <5%; 1: 5-33%; 2: 34-66% and 3: >66%; - LI: Lobular Inflammation score (foci per 20x field): 0: none; 1: <2 foci; 2: 2-4 foci and 3: >4 foci; and - HB: Ballooning degeneration score: 0: none; 1: few; 2: many cells / prominent ballooning. Using this scoring system, a "patient with MASH" has MAS>3, with at least 1 point in steatosis, at least 1 point in lobular inflammation and at least 1 point in hepatocyte ballooning. A "nonMASH" patient is a patient having either (i) a MAS>3 with at least one of steatosis, lobular inflammation and hepatocyte ballooning scores equal to 0; or (ii) a MAS<3. In addition, in the context of the present invention, a patient is excluded as being a MASH patient if said patient has viral hepatitis, autoimmune liver disease, alcohol-related liver disease, drug-induced liver disease or congenital causes of chronic liver disease such as hereditary hemochromatosis, Wilson's disease, alpha-1-antitrypsin deficiency and polycystic ovary syndrome. Localization and extent of fibrosis (F) at histological exam signs the severity (advancement) of MASH. The NASH-CRN (Nonalcoholic SteatoHepatitis Clinical Research Network) has developed a dedicated fibrosis staging system (Kleiner, D.E et al, Hepatology, 2005 Jun; 41(6):1313-21). MASH Clinical Research Network Scoring System Definitions F Score Perisinusoidal or periportal fibrosis                                         1 Mild perisinusoidal fibrosis (zone 3)                                      1a Moderate perisinusoidal fibrosis (zone 3)                                1b Portal / periportal fibrosis                                                    1c Perisinusoidal and portal / periportal fibrosis                                2 Bridging fibrosis                                                            3 Cirrhosis                                                                4 Using this fibrosis staging system, patients with no or minimal fibrosis (F=0-1) are generally not considered at risk of cirrhosis, liver failure, HCC (hepatocellular carcinoma) or liver-related death. Patients with significant (F=2) and advanced fibrosis (F=3) are at increasing risk of developing cirrhosis, liver failure, HCC and liver-related death. Patients with compensated cirrhosis have severe fibrosis (F=4) and are at high risk of liver failure (decompensated cirrhosis), HCC and liver-related deaths. Identifying patients who are at risk of developing HCC, cirrhotic complications and liver-related deaths is the ultimate reason for liver assessment. As defined by the FDA and EMA, patients at risk of liver outcomes who should be pharmacologically treated are those with MAS>4 (with score > 1 for each of steatosis, lobular inflammation and ballooning) and NASH-CRN fibrosis score (F) > 2. Accordingly, in the context of the present invention, a patient with "at-risk MASH", otherwise referred to as an "at-risk patient" or as a "patient at risk of hepatic outcome", is a patient with a MAS higher or equal to 4, a S score higher or equal to 1, a LI score higher or equal to 1, a HB score higher or equal to 1 and a F score of higher or equal to 2. It defines a subgroup of MASH patients having a high risk of developing at least one life-threatening liver outcome such as cirrhosis, liver failure, HCC and liver-related death. The terms "subject" and "patient" may be used interchangeably herein and refer to a human subject. As mentioned above, MASH occurs more commonly in patients suffering from metabolic disorders. In addition, MASH is known to be associated to comorbidities such as metabolic disorders. Therefore, the method of the present invention can more particularly benefit to those patients presenting such comorbidities. Common comorbidities of MASH include obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidaemia, hypertriglyceridaemia, hypertension (high blood pressure) and cardiovascular disease. Older age may also predispose to HCC in MASH patients. Therefore, in a particular embodiment, the patient suffers from a metabolic disorder, such as obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidaemia, hypertriglyceridaemia, and high blood pressure. In a particular embodiment, as used herein the expression “screening” refers to the selection of a patient to be treated or not to be treated in a cohort or in a clinical study. The screening of patients may imply a diagnostic step and then the distribution of the patient in a group: to be treated and not to be treated. As used herein, the term "monitoring" refers to a comparison of the score calculated at two or more time points. Monitoring is the ongoing, systematic collection and analysis of data as a project or condition progresses, like a clinical study or a treatment protocol. According to the invention, if the score increases during time, the pathology progresses whereas if the score decreases during time, the pathology regresses. The monitoring is possible due to the noninvasiveness of the method according to the present invention. Indeed, the ease of use of this assay allows repetitive measures and the follow-up of a patient in the time course of a pathology. In conclusion, the monitoring is the application of the method of diagnosis along time for a patient. As used herein, the expression "biological fluid sample" refers to a blood, serum or plasma sample, preferably a serum sample. Analysis of a biological fluid sample can be performed using several analytical methods, depending on the type of biomarkers to be quantified. Such analytical methods include quantitative RT-PCR, mass spectrometry, immunoPCR and immunodetection. One can also cite the use of a biochip to implement the simultaneous analysis of multiple biomarkers. Cytokeratin 18 (CK18) is a 45 kD protein belonging to keratins which are the superfamily of filamentous structural proteins that form the intermediate filaments within epithelial cells. CK18 represents the main intermediate filament family member expressed in the liver and other epithelial tissues. The CK18 full-length form is released from necrotic cells, whereas a caspase-cleaved fragment is released during apoptosis. Soluble total fragments of CK18 can be detected in human serum with enzyme-linked immunoassay (ELISA). The CK18 full-length (amino-acids 1 to 429) and CK18 caspase-cleaved fragments (amino-acids 239 to 396) are generated during cell necrosis and apoptosis and are detected in human serum using M65 assay. The CK18 neoepitope of 30kDa fragment (amino-acids 397 to 429) is generated by caspases following cell apoptosis and is detected in human serum using the M30 immunoassay. In a particular embodiment, a fragment of CK18 is quantified. In a particular embodiment, the CK18 fragment is the fragment M30 of CK18. In another particular embodiment, the CK18 fragment is the fragment M65 of CK18. The circulating levels of TSP-2, CK18 M65 and CK18 M30 may be measured by any conventional methodology well known in the art, such as immunoassays (e.g. ELISA (enzyme-linked immunosorbent assay), immunoturbidimetry, immuno-nephelometry, immune cytometry, protein array). For example, the levels of TSP-2 can be determined by antibodies, aptamers or peptides directed against said marker. In a particular embodiment, the level of TSP-2 is measured as ng / mL. Specific TSP-2 can be quantified by the Quantikine® ELISA for Human Thrombospondin-2 Immunoassay from R&D Systems (Minneapolis, USA). The assay employs the quantitative sandwich enzyme immunoassay technique. A monoclonal antibody specific for human TSP-2 has been precoated onto a microplate. Standards (from 0.313 ng / mL to 20 ng / mL) and serum (diluted 15fold) are pipetted into the wells and any TSP-2 present is bound by the immobilized antibody. After washing away any unbound substances, an enzyme-linked polyclonal antibody (human TSP-2 conjugated to horseradish peroxidase with preservatives) is added to the wells. Following a wash to remove any unbound antibody-enzyme reagent, a substrate solution is added to the wells and color develops in proportion to the amount of TSP-2 bound in the initial step. The color development is stopped, and the intensity of the color is measured. The standard curve is created by reducing the data using computer software capable of generating a four-parameter logistic (4-PL) curve-fit. The circulating levels of CK18 M65 and CK18 M30 fragment concentrations may be measured by any conventional methodology well known in the art. For example, the levels of CK18 fragment concentrations may be determined using antibodies, aptamers or peptides directed against said marker. In a particular embodiment, the levels of CK18 M30 are measured as U / L and could be performed using the M30® ELISA kit (TECO medical AG, Sissach, Switzerland). The assay uses a monoclonal antibody that recognizes the M30 neoepitope (K18Asp396-NE). For M30, the range is 40 - 1000 U / L (units are defined against a synthetic peptide standard containing the M30 epitope). 25 pl sample (serum or plasma) is used and the incubation time is 4h30. Absorbance could be measured with the FilterMax F3 Multi-Mode Microplate Reader, using the SoftMax Pro software, and the concentration of the fragment could be calculated from the corresponding standard as U / L. In another particular embodiment, the levels of CK18 M65 are measured as U / L and could be performed using the PEVIVA M65® ELISA kit (TECO medical AG, Sissach, Switzerland). The assay uses two monoclonal antibodies directed to epitopes in the 284 - 396 region of the CK18 protein. Soluble full length CK18 as well as CK18 fragments and protein complexes that expose these epitopes will be detected by the assay. For M65, the range is 125 - 2000 U / L (the units measured by the M65® ELISA are defined against a synthetic standard). 25 pl sample (serum or plasma) is used and the incubation time is 2h20 min. Absorbance could be measured with the FilterMax F3 Multi-Mode Microplate Reader, using the SoftMax Pro software, and the concentration of each fragment could be calculated from the corresponding standard as U / L. In a particular embodiment, the circulating levels of CK18 M65, CK18 M30 and TSP-2 are measured from one or more blood-derived sample(s) from the subject. In that case, the same kind of sample is used each time a measure has to be done. For the sake of clarity, this means that if a previous measure was done from a serum sample, the subsequent measures are done from serum samples of the same subject. Likewise, if the previous measure was done from a blood or plasma sample, the subsequent measures are done from blood or plasma samples, respectively, of the same subject. In a particular embodiment, the circulating levels of the markers are measured from one or more serum sample(s) from the subject. According to another embodiment, the selection of at-risk MASH patients among MASLD patients depends on a score calculated with the levels of CK18 M30 and TSP-2 of the patient. The further comparison of the score with cutoff values can be used to assign the patient in a group of patients who will receive a treatment or in a group of patients who will not receive a treatment. According to the invention, each said cutoff value may be a specific value or a range of values. Thus, preferably, each of the biomarker level of the subject can be introduced into a mathematical function to produce an output value that correlates with at-risk MASH status. The method thus can be used to discriminate subjects as having at-risk MASH or not having at-risk MASH. One skilled in the art is aware of numerous suitable methods for developing mathematical function, and all of these are within the scope of the present invention. In a particular embodiment, the mathematical function includes a logistic regression equation. In a further embodiment, the method of the present invention implements the following formula: 1 S = ------—- 1 + exp (—y) wherein y = / 30 +   * logw(TSP-2 (ng / mL)) + / 32 * logi0(CK18 M30 (IU / L)). In a particular embodiment, Po is comprised between -16,0 and -6,0 in particular between -15.2 and -7.7, more particularly equal to -10.0742. In a particular embodiment, Pi is comprised between 1.2 and 7.8, in particular between 2.2 and 6.8, more particularly equal to 3.8284. In a particular embodiment, p2 is comprised between 0.3 and 3.5, in particular between 0.61 and 2.96, more particularly equal to 1.9149. By way of example, the following equation can be used for the diagnosis of at-risk MASH. y= -10.0742+ 3.8284* log10(TSP2 (ng / mL)) + 1.9149* log10(CK18 M30 (IU / L)). The score calculated from the mathematical function can then be compared to a predetermined cutoff value. According to an embodiment, the cutoff is comprised between 0.210 and 0.852. In a particular embodiment, the cutoff is comprised between 0.419 and 0.643. In a more particular embodiment, the cutoff is equal to 0.4261. According to another embodiment, the score calculated from the mathematical function can be compared to predetermined cutoff values, such as low and high cutoff values. In this context, a calculated S value lower than the low cutoff is indicative of a subject not having at-risk MASH, and a calculated S value greater or equal to the high cutoff value is indicative of a subject having at-risk MASH. In a particular embodiment, the low cutoff is comprised between 0.25 and 0.49, in particular between 0.318 and 0.462. In a further particular embodiment, the low cutoff is equal to 0.419. In another particular embodiment, the high cutoff is comprised between 0.58 and 0.85, in particular between 0.614 and 0.765. In a further particular embodiment, the high cutoff is equal to 0.643. The present invention also relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to: - receive quantified levels of CK18 and TSP-2; - calculate a score from these quantified levels of the subject, from a mathematical function as described above; and - assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values. In a particular embodiment, the invention relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to: - receive quantified levels of CK18 M30 and TSP-2; - calculate a score from these quantified levels of the subject, from a mathematical function as described above; and - assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values. In another particular embodiment, the invention relates to a computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to: - receive quantified levels of CK18 M65 and TSP-2; - calculate a score from these quantified levels of the subject, from a mathematical function as described above; and - assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values. The present invention further provides a computer readable medium comprising the computer program described therein. According to a particular embodiment, the computer-readable medium is non-transitory medium or a storage medium. In some embodiments, thanks to the method of the invention, a decision may be taken to give life-style recommendations to a subject (such as a food regimen or providing physical activity recommendations), to medically take care of a subject (e.g. by setting regular visits to a physician or regular examinations, for example for regularly monitoring markers of liver damage), or to administer at least one MASH or liver fibrosis therapy to the patient, to treat or prevent at-risk MASH. In a particular embodiment, a decision may be taken to give life-style recommendations to a subject or to administer at least one MASH or liver fibrosis therapy. The invention thus further relates to an anti-MASH or anti-fibrotic compound for use in a method for treating MASH in a subject in need thereof, wherein the subject has been identified thanks to a method according to the invention. The term "treatment", as used herein, relates to both therapeutic measures and prophylactic or preventive measures, wherein the goal is to prevent or slow down (lessen) an undesired physiological change or disorder. Beneficial or desired clinical results include, but are not limited to, alleviation of symptoms, stabilizing pathological state (specifically not worsening), slowing down or stopping the progression of the disease, improving or mitigating the pathological condition. Particularly, for the purpose of the present invention, treatment is directed to slow the progression of MASH and / or fibrosis and reduce the risk of further complications. It can also involve prolonging survival in comparison with the expected survival if the treatment is not received. In the context of the present invention, a patient to be treated is a patient with at-risk MASH and a patient not to be treated is a patient not at-risk MASH. The anti-MASH or anti-fibrotic agent is administered in a therapeutically effective amount. As used herein, the expression "therapeutically effective amount" refers to an amount of the drug effective to achieve a desired therapeutic result. A therapeutically effective amount of a drug may vary according to factors such as the disease state, age, sex, and weight of the individual, and the ability of drug to elicit a desired response in the individual. A therapeutically effective amount is also one in which any toxic or detrimental effects of agent are outweighed by the therapeutically beneficial effects. The effective dosages and dosage regimens for drug depend on the disease or condition to be treated and may be determined by the persons skilled in the art. A physician having ordinary skill in the art may readily determine and prescribe the effective amount of the pharmaceutical composition required. For example, the physician could start doses of drug employed in the pharmaceutical composition at levels lower than that required in order to achieve the desired therapeutic effect and gradually increase the dosage until the desired effect is achieved. In general, a suitable dose of a composition of the present invention will be that amount of the compound which is the lowest dose effective to produce a therapeutic effect according to a particular dosage regimen. Such an effective dose will generally depend upon the factors described above. In a particular embodiment, the invention relates to an anti-MASH compound for use in a method for treating MASH in a subject suffering from at-risk MASH, wherein the subject has been classified as having at-risk MASH thanks to the method according to the invention. Illustrative anti-MASH and anti-fibrotic compounds are listed below: - a compound of formula (I) or a pharmaceutically acceptable salt thereof: R5 (I) wherein: X1 represents a halogen atom, a R1 group or G1-R1 group; A represents a CH=CH or CH2-CH2 group; X2 represents a G2-R2 group; G1 represents an atom of oxygen; G2 represents an atom of oxygen or sulfur; R1 represents a hydrogen atom, an unsubstituted alkyl group, an aryl group or an alkyl group that is substituted by one or more substituents selected from halogen atoms, alkoxy groups, alkylthio groups, cycloalkyl groups, cycloalkylthio groups and heterocyclic groups; R2 represents an alkyl group substituted by a -COOR3 group, wherein R3 represents a hydrogen atom or an alkyl group that is substituted or not by one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups; and R4 and R5, identical or different, represent an alkyl group that is substituted or not by one or more substituent selected from halogen atoms, cycloalkyl groups and heterocyclic groups; - AMP activated protein kinase stimulators such as PXL-770, MB-11055, Debio-0930B, metformin, CNX-012, 0-304, mangiferin calcium salt, eltrombopag, carotuximab, and imeglimin; - Bile acids such as obeticholic acid (OCA), ursodeoxycholic acid (LIDCA), norursodeoxycholic acid, and ursodiol; - OCR antagonists such as cenicriviroc (CCR2 / 5 antagonist), PG-092, RAP-310, INCB-10820, RAP-103, PF-04634817, and CCX-872; - Dipeptidyl peptidase IV (DPP4) inhibitors such as evogliptin, vidagliptin, fotagliptin, alogliptin, saxagliptin, tilogliptin, anagliptin, sitagliptin, retagliptin, melogliptin, gosogliptin, trelagliptin, teneligliptin, dutogliptin, linagliptin, gemigliptin, yogliptin, betagliptin, imigliptin, omarigliptin, vidagliptin, and denagliptin; - Farnesoid X receptor (FXR) agonists such as obeticholic acid (OCA), tropifexor (LJN452), cilofexor (GS9674), Nidufexor (LMB763), EDP-305, AKN-083, INT-767, GNF-5120, LY2562175, INV-33, NTX-023-1, EP-024297, Px-103, SR-45023, TERN-101 (6-{4-[5-Cyclopropyl-3-(2,6-dichloro-phenyl)-isoxazol-4-ylmethoxy]-piperidin-1-yl}-1-methyl-1H-indole-3 carboxylic acid), TERN-201, TERN-501 and TERN-301; - Fibroblast Growth Factor 19 (FGF-19) receptor ligand or functional engineered variant of FGF-19; - Fibroblast Growth Factor 21 (FGF-21) agonists such as PEG-FGF21 (pegbelfermin, formely BMS-986036), YH-25348, BMS-986171, YH-25723, LY-3025876, and NNC-0194-0499; - engineered Fibroblast Growth Factor 19 (FGF-19) analogues such as NGM-282 (aldafermin); - Glucagon-like peptide-1 (GLP-1) analogs such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, efpeglenatide, taspoglutide, MKC-253, DLP-205, and ORMD-0901; - Nicotinic acid such as Niacin and Vitamin B3; - nitazoxanide (NTZ), its active metabolite tizoxanide (TZ) or other prodrugs of TZ such as RM-5061; - PPAR alpha agonists such as fenofibrate, ciprofibrate, pemafibrate, gemfibrozil, clofibrate, binifibrate, clinofibrate, clofibric acid, nicofibrate, pirifibrate, plafibride, ronifibrate, theofibrate, tocofibrate, and SR10171; - PPAR gamma agonists such as pioglitazone, deuterated pioglitazone, rosiglitazone, efatutazone, ATx08-001, OMS-405, CHS-131, THR-0921, SER-150-DN, KDT-501, GED-0507-34-Levo, CLC-3001, and ALL-4; - PPAR delta agonists such as GW501516 (Endurabol or ({4-[({4-methyl-2-[4-(trifluoromethyl)phenyl]-1,3-thiazol-5-yl}methyl)sulfanyl]-2-methylphenoxy}acetic acid)), MBX8025 (Seladelpar or {2-methyl-4-[5-methyl-2-(4-trifluoromethyl- phenyl)-2H-[l,2,3]triazol-4-ylmethylsylfanyl]-phenoxy}-aceticacid), GW0742 ([4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]thio]-2-methyl phenoxy]aceticacid), L165041, HPP-593, and NCP-1046; - PPAR alpha / gamma dual agonists (also named glitazars) such as saroglitazar, aleglitazar, muraglitazar, tesaglitazar, and DSP-8658; - PPAR gamma / delta dual agonists such as conjugated linoleic acid (CLA), and T3D-959; - PPAR alpha / gamma / delta pan agonists or PPARpan agonists such as IVA337, TTA (tetradecylthioacetic acid), bavachinin, GW4148, GW9135, bezafibrate, lanifibranor, lobeglitazone, and CS038; - Sodium-glucose transport (SGLT) 2 inhibitors such as licoglifozin, remogliflozin, dapagliflozin, empagliflozin, ertugliflozin, sotagliflozin, ipragliflozin, tianagliflozin, canagliflozin, tofogliflozin, janagliflozin, bexagliflozin, luseogliflozin, sergliflozin, HEC-44616, AST-1935, and PLD-101. - stearoyl CoA desaturase-1 inhibitors / fatty acid bile acid conjugates such as aramchol, GRC-9332, steamchol, TSN-2998, GSK-1940029, and XEN-801; - thyroid receptor p (THR P) agonists such as VK-2809, resmetirom (MGL-3196), MGL-3745, SKL-14763, sobetirome, BCT-304, ZYT-1, MB-07811 and eprotirome; - Vitamin E and isoforms; vitamin E combined with vitamin C and atorvastatin. In a particular embodiment, the anti-MASH agent is selected from pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201 and tropifexor. The invention also relates to a kit for diagnosing, screening, monitoring or prognosing of at-risk MASH in a subject. Said kit comprises means for determining the levels of TSP-2 and CK18 M30. In a particular embodiment, said kit comprises specific antibodies, aptamers or peptide to measure TSP-2 and an antibody or an aptamer or a peptide directed against CK18 M30. In another embodiment, the invention also relates to a kit for diagnosing, screening, monitoring or prognosing of at-risk MASH in a subject. Said kit comprises means for determining the levels of TSP-2 and CK18 M65. In a particular embodiment, said kit comprises specific antibodies, aptamers or peptide to measure TSP-2 and an antibody or an aptamer or a peptide directed against CK18 M65. Antibodies directed against TSP-2 may be any monoclonal, polyclonal and / or conjugated antibodies directed against TSP-2 known in the art. For instance, said antibody directed against TSP-2 is Monoclonal antibody Thrombospondin 2 (4) (Ref: sc-136238, Santa Cruz Biotechnology, Texas, USA). Antibodies directed against CK18 M30 and CK18 M65 may be any monoclonal, polyclonal and / or conjugated antibodies directed respectively against CK18 M30 and CK18 M65 known in the art. The kit of the invention may further comprise immunoassay standards and reagents. In a particular embodiment, the kit comprises at least one specific positive control for TSP-2. Particularly, said positive control for TSP-2 comprises from 11 to 300 ng / mL of TSP-2. More particularly, the kit of the invention further comprises 3 positive controls for TSP-2, wherein said positive controls for TSP-2 respectively comprise 30, 50 and 100 ng / mL of TSP-2. The invention is further described with reference to the following, non-limiting, examples. EXAMPLES 1. Datasets The study population consists of patients that were screened for potential inclusion in the Resolve-lt phase 3 clinical trial. All patients with biopsy results available, and blood samples used for biomarkers measurements with less than 90 days between biopsies and blood collections dates were selected as of potential utility for this study. We then selected all patients with full data on a selected set of biomarkers, as well as on the usual demographical and clinical parameters (age, sex, Type-2 Diab, Dyslipidemia, Arterial Hypertension [HT], BMI). This selection led to a total of 1950 patients. Among those patients, we randomly selected 500 (-25%) patients, keeping the rest of the population for being potentially included in the validation dataset. To control for potential confounding factors when processing the modelization and the selection of relevant biomarkers, we applied a propensity score matching 5 algorithm to homogenize the following parameters among patients with and without at-risk MASH : Age, sex, Type-2 Diab, Dyslipidemia, Arterial Hypertension, BMI. This process led to a selection of 350 patients, 175 with and 175 without at-risk MASH, well-balanced for the parameters indicated in Table 1. In this training cohort, the mean age of patients was 55 years of age, 63% of them were male, 42% had T2D and 69% were obese. 10 Regarding histology, 49% of patients without at-risk MASH had F=1, while at-risk MASH patients were equally distributed between F2 and F3 (45 and 46%, resp). WO 2025 / 181063                                   PCT / EP2025 / 055008 Training cohort Validation cohort Non at-risk MASH At-risk MASH Non at-risk MASH At-risk MASH n         value n        value n        value p value n           value n         value p value Gender, Male 2206    61.24% (1351) 175    62.29% (109) 175    63.43% (111) 0.9119 864       66.32% (573) 754     55.97% (422) <0.0001 Age (years) 2206     53.91±11.73 175    55.57±11.13 175    55.11±11.11 0.7009 864       52.83±11.88 754     54.53±11.66 0.0038 Age by class,<45 / 46-55 / 56- 23.6% / 27.2% / 20.6% / 28.6% / 20% / 29.7% / 26.3% / 28.1% / 27.1% / 22.1% / 24.4% / 64 / >65 2206    29.3% / 19.9% 175    26.9% / 24% 175   27.4% / 22.9% 0.9732 864         18.5% 754    33.3% / 20.2% <0.0001 Type 2 Diabetes 2206    42.16% (930) 175    41.71% (73) 175    42.29% (74) 1 864       30.9% (267) 754     53.85% (406) <0.0001 Dylsipidemia 2206    48.19% (1063) 175    45.14% (79) 175    45.71% (80) 1 864       44.44% (384) 754     51.86% (391) 0.0034 Arterial hypertension 2206    56.39% (1244) 175    60.57% (106) 175     60% (105) 1 864       48.15% (416) 754     63.26% (477) <0.0001 Metabolic Syndrom 1944    83.95% (1632) 135    90.37% (122) 173    80.35% (139) 0.0234 692       79.62% (551) 747     85.27% (637) 0.0059 BMI (kg / m2) 2203     33.54±6.07 175     33.27±5.63 175     33.32±6.11 0.9457 862        33.06±6.11 754      34.1±6.01 0,0006 Obese 2203    70.4% (1551) 175    67.43% (118) 175    71.43% (125) 0.4863 862       68.1% (587) 754     73.47% (554) 0.0208 Ast (IU / L) 2205    41.81±30.06 175    31.11±18.61 175     47.82±25.1 <0.0001 864        32.78±23.9 753     51.54±32.47 <0.0001 Alt (IU / L) 2205     57.37±43.51 175    44.46±38.11 175    65.47±40.22 <0.0001 864       45.36±32.96 753     69.77±48.59 <0.0001 Ast / Alt ratio 2205      0.81±0.32 175     0.81±0.26 175     0.82±0.34 0.7358 864        0.82±0.35 753       0.81±0.3 0.8455 CK18-m30 (IU / L) 1968   578.48±534.09 175    398.3±306.93 175   702.49±589.25 <0.0001 864     404.21±310.78 754     791.21±664.2 <0.0001 GGT(IU / L) 2205     74.47±94.18 175    68.11±91.55 175     81.76±99.5 0.1825 864       62.08±80.56 753     89.03±106.74 <0.0001 ALP (IU / L) 2204    84.88±31.12 175    85.81±33.52 175     88.56±32.2 0.4336 864       83.46±30.88 752      86.46±30.7 0.051 FPG (mmol / L) 2197      6.02±1.82 175     5.57±1.35 175     6.12±1.68 0,0008 862        5.65±1.55 751      6.46±2.06 <0.0001 HbAlc(%) 2206      6.18±1.01 175     6.08±0.95 175      6.24±0.9 0.1089 864        5.97±0.95 754      6.41±1.07 <0.0001 Triglycerides (mmol / L) 2205      1.96±1.19 175     1.84±0.97 175     2.03±1.12 0.0871 864        1.87±1.08 753      2.09±1.42 0,0004 Platelet (10e9 / L) 2197    237.53±67.92 175    238.98±65.8 175    227.08±66.06 0.0923 861       243.47±68.95 749     232.92±66.24 0.0018 MASLD 2206    92.38% (2038) 175    87.43% (153) 175     100% (175) <0.0001 864       84.72% (732) 754      100% (754) <0.0001 MASH 2206    69.31% (1529) 175    45.71% (80) 175     100% (175) <0.0001 864       37.73% (326) 754      100% (754) <0.0001 At-risk MASH 2206    48.64% (1073) 175       0% (0) 175     100% (175) <0.0001 864          0% (0) 754      100% (754) <0.0001 Fibrosis 2206      1.9±1.15 175      1.3±1.09 175     2.63±0.64 <0.0001 864         1.19±1.1 754      2.67±0.64 <0.0001 13.2% / 25.6% / 22.3% / 48.6% / 0% / 0% / 25.3% / 29.7% / 10.3% / 14.9% / 45.1% / 46.3% / 27.3% / 47.8% / 7.5% / 0% / 0% / 42.4% / Fibrosis by stage, 0 / 1 / 2 / 3 / 4 2206       6.1% 175       4% 175       8.6% NA 864       13.5% / 3.8% 754     48.3% / 9.3% NA MAS 2206      4.24±1.98 175      2.99±1.6 175       5.64±1 <0.0001 864        2.82±1.64 754      5.65±1.09 <0.0001 MAS by category, 0-1, 2-3, 12.2% / 20.3% / 18.9% / 42.3% / 0% / 0% / 24.5% / 40.7% / 29.4% / 0% / 0% / 45.1% / 4-5, 6-8 2206    37.5% / 30% 175     32% / 6.9% 175   45.1% / 54.9% NA 864          5.3% 754        54.9% NA The training process led to the selection of a biomarker panel comprising two biomarkers: TSP-2 and CK18 M30. Based on this result, we selected all patients with available measures of these two biomarkers, fulfilling the condition regarding biopsies results availability and < 90 days between blood and biopsy dates, and who were not part of the training dataset. This led to an independent validation dataset comprising 1618 patients. Principal characteristics are reported in Table 1 above. In this validation dataset, and as usually observed in MASH databases, population with at-risk MASH had significantly higher percentages of T2D, dyslipidemia, HT and obesity. Concerning histology, 85% of patients without at-risk MASH had MASLD, and 38% were affected with MASH. 2. Statistical analyses Descriptive statistics were generated for baseline characteristics and reported as means±sd or %(n), and patients without and with at-risk MASH were compared using appropriate 2-sample tests (Student t test for comparison of means and x2 test for comparison of percentages). The modelization process was performed using a Stepwise algorithm, comparing sequentially binomial logistic regression models for the detection of at-risk MASH based on their associated BIC (Bayesian Information Criterion), starting with an empty model and sequentially inserting the variables that minimize this criterion. The process stopped when none of the remaining variables led to a decrease of the BIC value. For this, the stat R package (step function) was used. The regression coefficients of the selected model were extracted and associated 95% Cl were reported. Specific cutoffs values were derived using the training dataset. A low cutoff to achieve 85% sensitivity (Sen) and a high cutoff to achieve 90% specificity (Spe) were extracted, and diagnostic performances (Sen, Spe, PPV, NPV, Acc) associated to these cutoffs were computed in both the training and validation datasets. Also, a Youden cutoff that maximize Sen + Spe is reported. The overall diagnostic performance of non-invasive tests and biomarkers are estimated using ALIROC values, and these statistics are tested for significant differences using Delong tests. AU ROC values are reported with 95%CI estimated using 1000 bootstrap samples. Associated ROC curves are graphically reported. Boxplots are presented with associated Student t-tests for mean comparisons, and p values are graphically reported as follow : **** if p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05 and NS if p > 0.05. All propensity score matching algorithms were executed using the matchit function from the Matchit R package, and all statistical analyses were performed using R programming language version 4.3.0. When executing the stepwise algorithm, the combination of TSP-2 and CK18 M30 was found as the best combination, as it returned the lowest BIC before any further biomarker added led to an increase in BIC. Based on this selection of biomarkers, the following equation was obtained using a binomial logistic regression model: 1 S = ------—- 1 + exp (—y) wherein y = / 30 + / 3i * logw(TSP2 (ng / mL)) + / 32 * logw(CK18 M30 (IU / L)). Coefficients of this model are reported in Table 2 with associated 95%CI, all of them reaching high significance with associated p values <0.001. Table 2: Po Ptsp2 PcK-18 M30 Coefficient -10.0742 3.8284 1.9149 Cl (95%) (-13.2809, -7.7302) (2.2273, 4.8504) (0.8905, 2.7636) Based on this modelization and the training dataset, cutoff values to achieve 80% sensitivity (Low cutoff) and 85% specificity (high cutoff) are fixed at 0.3806 et 0.6337, respectively. The Youden cutoff, which maximizes the sum of sensitivity and specificity of the present diagnostic method, is fixed at 0.4261. 3. Results 3.1 Performance of the combination TSP-2 and CK18 M30 vs single biomarkers The performance of the modelization described above (named TCK30) is then tested in the independent validation dataset, in comparison with that of TSP-2 alone or CK18 M30 alone. To measure this, we computed the ALIROC values for TSP2 alone and CK18 M30 alone and compare those ALIROCs with the one achieved by modelization TCK30. Results obtained in the validation dataset, comprising 1618 patients, are reported in Table 3. Table 3: ALIROC values for at-risk MASH detection N n at-risk AUC (95% Cl) p value TCK30 1618 754 0.846 (0.828, 0.866) - TSP2 1618 754 0.823 (0.801, 0.844) <0.0001 CK-18 M30 1618 754 0.755 (0.731, 0.779) <0.0001 The modelization TCK30 achieved a significantly higher ALIROC than both biomarkers when used alone, with p values<0.0001, confirming the better performance the combination of TSP-2 and CK18 M30 in one model. 3.2 Robustness of the combination TSP-2 and CK18 M30 We further analyzed the potential robustness of the combination of TSP-2 and CK18 M30 against three different parameters of interest in the diagnostic and MASH fields. These parameters were the age, sex and T2D status of patients. Indeed, it’s important to guarantee that any diagnostic test could be interpreted irrespective of these patients’ parameters. To do this, we used the validation dataset and applied a propensity score matching algorithm to extract two subpopulations in each case, while balancing for the following list of potential confounding factors: age, sex, prevalence of T2D, dyslipidemia and HT, BMI, prevalence of at-risk MASH, fibrosis, steatosis, ballooning and lobular inflammation by class. This process led to the creation of 2 subpopulations for each parameter of interest (Age, sex, T2D), each time these subpopulations being well-balanced for the other parameters listed above. For the 2 subpopulations of age, we create one subpopulation comprising patients ages <50 and the second with patients ages >60 to ensure a clear separation of age between both subpopulations. For gender, age and T2D, we extracted a total of 1112 (556 in each category), 548 (274 in each category) and 914 (457 in each category) patients respectively. The distributions of the modelization TCK30 and its associated biomarkers in each associated categories (gender, age and T2D status) are reported as boxplots in Fig. 1, 2 and 3. As we can observed in Fig. 1, TSP-2 expression was reduced in male vs female, while the expression of CK18 M30 was higher in Male vs Female, as previously observed in past studies. The combination of both biomarkers in a modelization, led this test to be associated with distributions that were not significantly impacted by the gender of patients. Regarding age, while TSP-2 was not impacted by this parameter, CK18 M30 was significantly reduced in older patients. However, the magnitude of this decrease did not have a sufficient impact once combined with TSP-2 to lead to a significant age impact on the modelization TCK30 as reported in Fig. 2. Finally, when focusing and comparing patients with and without T2D, we observed that none of the biomarkers were significantly impacted by this parameter, both returning similar distributions. This led the modelization TCK30 to be associated with similar distribution across these two subpopulations as reported in Fig. 3. These results show that the diagnostic method of the invention is not significantly impacted by the gender, the age and T2D status of a patient. Overall, the combination of TSP-2 with CK18 M30 shows high robustness and could be used with fixed cutoff irrespective of the patients’ parameters, e.g. the gender, the age and T2D status. 3.3 Performance of the combination TSP-2 and CK18 M30 vs other NITs Finally, the overall performances of the combination of TSP-2 and CK18 M30 in the modelization TCK30 for the detection of at-risk MASH were compared with other usual non-invasive tests: MACK-3, FIB-4, ELF, NFS and ALT. To do so, among all patients included in the validation cohort, we selected those with full data on these NITs, leading to a dataset of 1577 patients. We derived the AU ROC values and associated Delong tests for AUROC difference between the combination and other NITs. Results are summarized in Table 4. The associated ROC curves are also graphically reported in Fig. 4. Table 4: AUROC values for at-risk MASH detection N n at-risk AUC (95% Cl) p value TCK30 1577 734 0.847 (0.828, 0.867) - MACK-3 1577 734 0.8 (0.778, 0.823) <0.0001 FIB4 1577 734 0.66 (0.634, 0.687) <0.0001 ELF 1577 734 0.713 (0.689, 0.74) <0.0001 NFS 1577 734 0.618 (0.589, 0.646) <0.0001 ALT 1577 734 0.694(0.667, 0.72) <0.0001 In this population, the modelization TCK30 achieved an AUROC of 0.85, significantly higher than those achieved by the other NITs, all p values <0.0001. In particular, the comparison with the blood test MACK-3 (AST, HOMA and CK18 markers) shows and confirms the high performance of the combination of biomarkers of the present invention and notably the gain of using this new diagnostic test over the usual ones. Therefore, we have identified an efficient and robust NIT. This new diagnostic method needs a limited number of biomarkers (TSP-2 and CK18 M30), the quantification of both of them being performed in serum. Most interestingly, this new model provides a high diagnostic performance. Moreover, we have 5 demonstrated that this new NIT is not impacted by gender, age and T2DM status. This provides a new valuable tool for diagnosing at-risk MASH subjects.

Claims

1. A method for the diagnosis, screening, monitoring or prognosis of at-risk metabolic dysfunction-associated steatohepatitis (MASH) in a subject, said method comprising the steps of:- quantifying the levels of cytokeratin 18 (CK18) and Thrombospondin 2 (TSP-2) in a biological fluid sample of said subject;- combining the quantified levels in a mathematical function to assign a score; and- comparing said score with a cutoff value to determine whether said subject is an at-risk MASH subject.

2. The method according to claim 1, wherein the levels of fragment M30 of CK18 (CK18 M30) and TSP-2 are quantified.

3. The method according to claim 1, wherein the levels of fragment M65 of CK18 (CK18 M65) and TSP-2 are quantified.

4. The method according to claim 1 or 2, wherein the comparison of said score is made with cutoff values.

5. The method according to any one of claims 1, 2 or 4, wherein the mathematical function includes a logistic regression function.

6. The method according to any one of claims 1 to 5, wherein the biological fluid sample is a blood, serum or plasma sample.

7. The method according to any one of claims 1 to 6, wherein the biological fluid sample is a serum sample.

8. The method according to any one of claims 1 to 7, wherein the subject suffers from obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidaemia, hypertriglyceridaemia, or high blood pressure.

9. A computer program comprising instructions that, when executed by a processor / processing means, cause the processor / processing means to:- receive quantified levels of CK18 and TSP-2;- calculate a score from these quantified levels of the subject, from a mathematical function; and- assign the subject into the group of at-risk subjects or not at-risk subjects based upon the calculated score compared to predetermined cutoff values.

10. The computer program according to claim 9, wherein the levels of CK18 and TSP-2 are quantified.

11. The computer program according to claim 10, wherein the levels of CK18 M30 and TSP-2 are quantified.

12. The computer program according to claim 10, wherein the levels of CK18 M65 and TSP-2 are quantified.

13. A computer readable medium comprising the computer program according to claim 9 to 12.

14. The computer readable medium according to claim 13, which is a non-transitory medium or a storage medium.

15. An anti-MASH or anti-fibrotic agent for use in the treatment of at-risk MASH in a subject in need thereof,wherein the anti-MASH agent is selected from pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmetirom, saroglitazar magnesium, seladelpar, semaglutide, sitagliptin, TERN-101, TERN-201 and tropifexor, andwherein the subject has been classified as having at-risk MASH thanks to the method according to any one of claims 1 to 8.

16. A kit for diagnosing, screening, monitoring or prognosis of at-risk MASH in a subject, said kit comprising means for determining the levels of TSP-2 and CK18.

17. The kit according to claim 16, wherein the levels of CK18 are the levels of CK18 M30.

18. The kit according to claim 16, wherein the levels of CK18 are the levels of CK18 M65.

19. The kit according to claim 16 or 17, comprising an antibody or an aptamer or a peptide directed against TSP2 and an antibody or an aptamer or a peptide directed against CK18 M30.

20. The kit according to claim 18, comprising an antibody or an aptamer or a peptide directed 5 against TSP2 and an antibody or an aptamer or a peptide directed against CK18 M65.

Images