Improving the diagnosis of nonalcoholic steatohepatitis

Abstract

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

Description

[Technical field]

[0001] Nonalcoholic steatohepatitis (NASH) is a chronic liver disease characterized histologically by fat accumulation, hepatocellular injury, and inflammation resembling alcoholic hepatitis. NASH can lead to liver fibrosis, cirrhosis, liver failure, and / or hepatocellular carcinoma (HCC). [Background technology]

[0002] NASH has no obvious symptoms in early stages and has been largely underdiagnosed until recently, because there is no widely available non-invasive test developed specifically to diagnose this disease. At-risk NASH status, defined as having NASH, NAS score 4 and fibrosis stage F2, is an important NASH subpopulation to identify. In fact, these patients are associated with increased risk of disease progression, especially cirrhosis, and increased risk of liver-related and all-cause mortality. Due to the technical limitations and risks of biopsy, which is the clinical reference standard for the diagnosis of NASH and fibrosis, the development of blood-based non-invasive tests (NITs) is also of great importance. Various NITs have been developed, mainly adapted to fibrosis stages. NIS4® is a groundbreaking blood-based NIT specifically designed to address the fibrosis-NAS composite endpoint by detecting at-risk NASH patients, containing four independent biomarkers, miR-34a-5p, alpha-2 macroglobulin (A2M), YKL-40 (or chitinase 3-like protein 1) and glycated hemoglobin (HbA1c) (Harrison et al., The Lancet Gastroenterology & Hepatology, 5(11):970-985, 2020). NIS4® makes it possible to perform a robust analysis. NIS4® is based on the evaluation of four different biomarkers, which require different biological fluids (whole blood and serum) and different testing processes. In this regard, it was evaluated whether it might be possible to provide an improved NIT over prior art NITs. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Harrison et al., The Lancet Gastroenterology & Hepatology, 5(11):970~985, 2020 [Non-Patent Document 2] Kleiner, DE et al., Hepatology, 2005 June;41(6):1313-21 Summary of the Invention [Means for solving the problem]

[0004] The present invention is based on detailed analysis of significant datasets from clinical trials. An optimized non-invasive method for the identification of at-risk NASH subjects is provided herein.

[0005] Thus, the present invention relates to a method for the diagnosis of at-risk NASH in a subject, comprising the steps of quantifying the levels of miR-34a-5p and YKL-40 in a biological fluid sample of said subject and combining the quantified levels with the subject's gender.

[0006] More specifically, the present invention provides a method for diagnosing, screening, monitoring or prognosing at risk for NASH in a subject, comprising: - quantifying the levels of miR-34a-5p and YKL-40 in a biological fluid sample from said subject; - obtaining the gender of the subject; - combining the quantified level and gender in a mathematical function to assign a score; - comparing said score to a cutoff value to determine whether said subject is at risk for having NASH; The present invention relates to a method comprising the steps of:

[0007] In certain embodiments, the methods of the invention are for diagnosing at risk for NASH in a subject.

[0008] In a further particular embodiment, the score is compared to a cutoff value to determine whether the subject is at high, low, or uncertain risk of having NASH.

[0009] In certain embodiments, the mathematical function includes a logistic regression function.

[0010] In another embodiment, the biological fluid sample is a blood, serum or plasma sample, preferably a serum sample.

[0011] In yet another embodiment, the subject suffers from obesity, insulin resistance, glucose intolerance, type 2 diabetes mellitus (T2DM), pre-diabetes, dyslipidemia or hypertriglyceridemia.

[0012] According to another aspect, the present invention relates to a method and apparatus for implementing the present invention, comprising: - receiving the quantified levels of miR34a-5p and YKL-40; - Acceptance of the target gender and - calculating a score from these quantified levels and the sex of the subject by a mathematical function as described herein; - assigning the subject to a group of at-risk or not-at-risk subjects based on the calculated score compared to a predefined cut-off value; The present invention relates to a computer program comprising instructions for causing the

[0013] In yet another aspect, the invention relates to a computer readable medium comprising the computer program disclosed herein. In certain embodiments, the computer readable medium is a non-transitory medium or storage medium.

[0014] In addition, the present invention relates to certain anti-NASH or anti-fibrotic agents for use in treating at risk for NASH in a subject in need thereof, where the subject has been classified as having at risk for NASH by the methods disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The present invention relates to non-invasive methods that can be used to aid in distinguishing between at-risk and not-at-risk NASH in a subject from a sample of the subject's biological fluid.

[0016] Histological scoring / staging systems have been developed to assess NAFLD activity levels and fibrosis stage, as well as to estimate the risk of progression to clinical liver outcomes. To assess the severity of NAFLD, the NAFLD Activity Score (NAS) has been developed. The NAS is a set of three histological scores determined from liver biopsy slices: - S: Fatty liver score: 0: less than 5%; 1: 5-33%; 2: 34-66% and 3: more than 66%; - LI: Lobular inflammation score (foci per 20x field): 0: none; 1: less than 2 foci; 2: 2-4 foci and 3: more than 4 foci; and - HB: Ballooning score: 0: none; 1: few; 2: many cells / significant swelling is the sum of.

[0017] Using this scoring system, a "patient with NASH" has at least 1 score for fatty liver, at least 1 score for lobular inflammation, and at least 1 score for hepatocyte swelling, with an NAS of 3 or more. A "non-NASH" patient is one who has either (i) an NAS of 3 or more, with fatty liver, lobular inflammation, and hepatocyte swelling scores equal to 0; or (ii) an NAS of less than 3. In addition, for the purposes of the present invention, a patient is excluded from being a NASH patient if he or she 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.

[0018] The location and extent of fibrosis (F) on histological examination indicates the severity (progression) of NASH. The NASH-CRN (Nonalcoholic Steatohepatitis Clinical Research Network) has developed a dedicated fibrosis staging system (Kleiner, DE et al., Hepatology, June 2005;41(6):1313-21). NASH Clinical Research Network Scoring System Definition F-Score Perisinusoidal or periportal fibrosis 1 Mild perisinusoidal fibrosis (area 3) 1a Moderate perisinusoidal fibrosis (area 3) 1b Portal / periportal fibrosis 1c Perisinusoidal and portal / periportal fibrosis 2 Pontine fibrosis 3 Cirrhosis of the Liver 4

[0019] Using this fibrosis staging system, patients with no or minimal fibrosis (F=0-1) are generally not considered to be at risk for cirrhosis, liver failure, HCC (hepatocellular carcinoma) or liver-related death. Patients with significant (F=2) and advanced fibrosis (F=3) are at high risk for developing cirrhosis, liver failure, HCC and liver-related death. Patients with compensated cirrhosis have severe fibrosis (F=4) and are at high risk for liver failure (decompensated cirrhosis), HCC and liver-related death. Identifying patients at risk for developing HCC, cirrhosis complications and liver-related death is the underlying reason for liver evaluation. As defined by the FDA and EMA, patients at risk for liver outcomes that should receive pharmacological treatment are those with NAS 4 or higher (scores of 1 or higher for each of fatty liver, lobular inflammation and swelling) and NASH-CRN fibrosis score (F) 2 or higher.

[0020] Thus, in the context of the present invention, "NASH at risk" patients, also referred to as "patients at risk" or "patients at risk for liver outcomes", are patients with an NAS of 4 or greater, an S score of 1 or greater, an LI score of 1 or greater, an HB score of 1 or greater, and an F score of 2 or greater, which defines a subgroup of NASH patients with a high risk of developing at least one life-threatening liver outcome, such as cirrhosis, liver failure, HCC, and liver-related death.

[0021] The terms "subject" and "patient" may be used interchangeably herein and refer to human subjects. As mentioned above, NASH occurs more commonly in patients suffering from metabolic disorders. In addition, NASH is known to be associated with comorbidities such as metabolic disorders. Therefore, the method of the present invention may more particularly benefit patients who exhibit such comorbidities. Common comorbidities of NASH include obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidemia, hypertriglyceridemia, hypertension and cardiovascular disease. In addition, the older the NASH patient, the more likely they are to suffer from HCC. Therefore, in certain embodiments, the patient suffers from metabolic disorders, such as obesity, insulin resistance, glucose intolerance, T2DM, prediabetes, dyslipidemia and hypertriglyceridemia.

[0022] As used herein, the expression "biological fluid sample" refers to a blood, serum or plasma sample, preferably a serum sample.

[0023] In certain embodiments, the term "screening" as used herein refers to the selection of patients to be treated or not treated in a cohort or in a clinical study. Screening of patients may indicate a diagnostic step and then a distribution of patients within the group to be treated or not treated.

[0024] In the present invention, the selection of at-risk NASH patients among NASH patients depends on the score calculated according to the levels of hsa-miR34a, YKL40 and the gender of the patient. Further comparison of the score with a cut-off value can be used to assign the patient to a group of patients to be treated or not to be treated.

[0025] In the context of the present invention, the patients who are treated are those at risk for NASH and the patients who are not treated are those not at risk for NASH.

[0026] As used herein, the term "monitoring" refers to the comparison of the scores calculated at two points in time. Monitoring is the ongoing systematic collection and analysis of data as a project or condition progresses, such as a clinical study or treatment protocol. According to the present invention, the score increases over time, the pathology progresses, whereas the score decreases, the pathology reverses. Monitoring is possible due to the non-invasive nature of the method according to the present invention. In fact, the ease of use of this assay allows repeated measurements and follow-up of patients in their pathological course. In conclusion, monitoring is simply the application of a diagnostic method over time on a patient.

[0027] Analysis of biological fluid samples can be performed using several analytical methods, depending on the type of biomarker to be quantified. Such analytical methods include quantitative RT-PCR, mass spectrometry, immuno-PCR and immunodetection. Also included is the use of biochips to perform simultaneous analysis of multiple biomarkers.

[0028] In certain embodiments, the level of miR-34a-5p is measured as Cq (amplification cycle) or fold change, preferably as fold. Suitable methods are readily available to the skilled artisan. The specific RNA can be quantified after extraction of total RNA from the biological fluid sample, for example using a commercially available kit. Extraction efficiency may be monitored to minimize sample-to-sample variation. Such monitoring can be performed by adding an internal process control (IPC) to the sample prior to total RNA extraction. Such an IPC may correspond to a miRNA molecule of known sequence heterologous to the sample. For example, a miRNA molecule found in a species different from humans may be used, for example a miRNA molecule from the nematode Caenorhabditis elegans, in particular Cel-miR-40-3p (SEQ ID NO: 1: UCACCGGGUGUACAUCAGCUAA). Such an IPC may be contained in a synthetic vesicle. To quantify the level of miR-34a-5p, a positive control with a known miR-34a-5p Cq value may be used, for example, three positive controls with known miR-34a-5p Cq values. These positive controls may cover the range of miR-34a-5p levels in NASH populations. When three positive controls are used, one corresponds to low miR-34a-5p levels, one to medium levels, and one to high levels. The medium level is also referred to as a calibrator and serves to calculate the fold change value in the assay. The positive control is processed simultaneously with the sample to be tested of interest. Reverse transcription is performed simultaneously on i) the total RNA extracted from the sample spiked with IPC and ii) the total RNA from the positive control, also spiked with IPC, for example, using a commercially available reverse transcription kit. Quantification may then be performed on cDNA, for example, using a commercially available quantitative PCR kit. The Cq determination mode may be regression. Furthermore, for each patient sample, the Cq of both the IPC and calibrator can be used to normalize the calibrator to the IPC and the sample to the IPC, and transcript abundance can be expressed as a fold change according to the formula used to quantify miR-34a-5p levels as fold change.

[0029] The following section describes a specific, but non-limiting, method for achieving miR34a-5p quantification. Briefly, Promega magnetic bead-based extraction Maxwell® plasma serum kit (AS1680, Promega) and RCS48 instrument (AS8500, Promega) are used to extract total RNA from patient serum samples according to the manufacturer's instructions. To monitor extraction efficiency and minimize sample-to-sample variation, synthetic vesicles containing C. elegans Cel-miR-40-3p (mature miRNA sequence UCACCGGGUGUACAUCAGCUAA-3', Integrated DNA Technologies, RNase-free HPLC purification) are used as IPC and added to each sample before RNA extraction. Three positive controls with known miR-34a Cq values ​​(low [C1=32 Cq], medium [C2=30.7 Cq], also called calibrators, and high [C3=28 Cq] hsa-miR34a-5p levels) covering the range of miR-34a expression in NASH populations are processed simultaneously with the test samples, including the medium standard, which also serves as the calibrator of the assay, to calculate fold-change values. Total RNA from serum samples containing IPC and total RNA from positive controls, also containing IPC, are reverse transcribed simultaneously using TaqMan MicroRNA Reverse Transcription Kit (4366597, Applied Biosystems, Thermo Fisher Scientific). Reverse transcription reactions are performed in 24 μL final mixture containing 3 μL TaqMan MicroRNA Assay 5X and incubated in a Thermal Cycler T100 (Biorad). cDNA is stored in low-binding tubes at -20°C until further use. Mature miRNA expression is quantified using TaqMan miRNA RT-qPCR Assay 20X and TaqMan Universal Master Mix II, Uracil-N-Glycosylase (UNG)-free (4440040, Applied Biosystems, ThermoFisher Scientific) according to the manufacturer's instructions.A fixed volume of 5 μL of total cDNA is used as template for qPCR using the CFX96 Real-time PCR Detection System. The miR-34a TaqMan assay (mature hsa-miR-34a-5p sequence=UGGCAGUGUCUUAGCUGGUUGU (SEQ ID NO:2), miR-base count=MIMAT0000255; and mature Cel-miR-40-3p sequence=UCACCGGGUGUACAUCAGCUAA (SEQ ID NO:1), miR-base count=MIMAT0000011) is used. The Cq determination mode is regression. For each patient sample, miRNA levels are expressed as fold change using the Cq of both IPC and calibrator according to the following formula: - Step 1: Normalization of calibrator C2 to internal process control ΔCq C2 miR-34 =Cq C2 miR-34a -Cq C2のC2 miR-40 - Step 2: Normalization of samples to internal process controls ΔCq 試料miR-34a =Cq 試料miR-34a -Cq 試料miR-40 - Step 3: Sample miR-34a delta expression calculation (Cq) ΔΔCq 試料miR-34 =ΔCq 試料 -ΔCq C2 - Step 4: Sample delta expression of miR-34a expressed as fold hsa-miR-34a fold change (FC) = 2 -ΔΔCq

[0030] In a particular embodiment, the level of YKL-40 is measured in ng / ml. Methods for determining the level of a protein such as YKL-40 in a biological fluid sample are readily available to those skilled in the art, for example immunodetection-based methods such as ELISA.

[0031] The method further comprises determining the gender of the subject.It is shown herein that gender has a large effect on the score output that performs both miR-34a-5p and YKL-40 levels.Surprisingly, the levels of these biomarkers and gender of the subject can be used to develop a mathematical function (i.e., a statistical algorithm) that can accurately predict the probability of NASH at risk.

[0032] Thus, preferably, each of the subject's biomarker levels and gender can be introduced into a mathematical function to generate an output value that correlates with the at-risk NASH status. Thus, the method can be used to distinguish subjects as at-risk for NASH or not at-risk for NASH. Those skilled in the art will know many suitable methods for developing a mathematical function, all of which are within the scope of the present invention. In certain embodiments, the mathematical function comprises a logistic regression equation. In further embodiments, the method of the present invention comprises the following equation:

[0033]

number

[0034] Execute the formula: y = β0 + β1 * log10(miR-34a-5p (fold)) + β2 * log10(YKL-40 (ng / ml)) + β3 * gender + β4 * log10(miR-34a-5p(fold))*gender, and Here, gender is 0 if the subject is female, or gender is 1 if the subject is male.

[0035] In a particular embodiment, β0 is between -3 and 3, in particular between -2 and 2. In a particular embodiment, β1 is between 1 and 5, in particular between 2 and 4. In a particular embodiment, β2 is between 0 and 4.5, in particular between 0.5 and 3. In a particular embodiment, β3 is between -2 and 2, in particular between -1 and 1. In a particular embodiment, β4 is between -1 and 2, in particular between 0 and 2. In a further particular embodiment, β0 is between -3 and 3, β1 is between 1 and 5, β2 is between 0 and 4.5, β3 is between -2 and 2, and β4 is between -1 and 2. In yet another particular embodiment, β0 is between -2 and 2, β1 is between 2 and 4, β2 is between 0.5 and 3, β3 is between -1 and 1, and β4 is between 0 and 2.

[0036] As an example, the following formula can be used for the diagnosis of at risk for NASH:

[0037] Formula 1: y=-1.4539+2.3003*log10(miR-34a-5p(fold))+1.0598*log10(YKL-40(ng / ml))-0.0533*gender+0.4514*log10(miR-34a-5p(fold))*gender

[0038] Formula 2: y=-0.8756+3.3957*log10(miR-34a-5p(fold))+2.5248*log10(YKL-40(ng / ml))-0.6496*gender+0.2873*log10(miR-34a-5p(fold))*gender

[0039] Formula 3: y = 1.1543 + 2.5678 * log10(miR-34a-5p(fold)) + 1.7859 * log10(YKL-40(ng / ml)) + 0.3514 * gender + 0.7264 * log10(miR-34a(5p(fold)) * gender

[0040] The score calculated from the mathematical function can then be compared to predefined cutoff values, such as low and high cutoff values, in which a calculated S value below the low cutoff is indicative of a subject not at risk for NASH, and a calculated S value equal to or greater than the high cutoff is indicative of a subject at risk for NASH.

[0041] In a particular embodiment, the low cutoff is comprised between 0.24 and 0.5, in particular between 0.41 and 0.49. In another particular embodiment, the high cutoff is comprised between 0.6 and 0.95, in particular between 0.62 and 0.74. In a further particular embodiment, the low cutoff is equal to 0.4564. In a further particular embodiment, the high cutoff is equal to 0.6815. In a further particular embodiment, the low cutoff is equal to 0.4564 and the high cutoff is equal to 0.6815.

[0042] The present invention also relates to a processor / processing means, when implemented by the processor / processing means: - determining the quantified levels of miR-34a-5p and YKL-40; - Acceptance of the target gender and - calculating a score from these quantified levels and the sex of the subject by the mathematical function described above; - assigning the subject to a group of at-risk or not-at-risk subjects based on the calculated score compared to a predefined cut-off value; The present invention relates to a computer program comprising instructions for causing the

[0043] The invention further provides a computer readable medium comprising a computer program as described herein. According to certain embodiments, the computer readable medium is a non-transitory medium or a storage medium.

[0044] In some embodiments, the method of the present invention can be used to determine whether to provide the subject with lifestyle recommendations (e.g., provide a dietary regimen or physical activity recommendations), to provide medical care to the subject (e.g., by setting up regular clinic visits for consultations or regular check-ups, such as to monitor liver damage markers periodically), or to administer at least one NASH or liver fibrosis treatment to the patient to treat or prevent NASH risk. In certain embodiments, the method of the present invention can be used to determine whether to provide the subject with lifestyle recommendations or to administer at least one NASH or liver fibrosis treatment. Thus, the present invention further relates to an anti-NASH or anti-fibrosis compound for use in a method for treating NASH in a subject in need thereof, wherein the subject is identified by the method according to the present invention.

[0045] The term "treatment" as used herein relates to both therapeutic measures and preventive or preventive measures, the purpose being to prevent or slow down (reduce) unwanted physiological changes or disorders. Beneficial or desired clinical outcomes include, but are not limited to, alleviation of symptoms, stabilization of pathological conditions (specifically not worsening), slowing or stopping disease progression, improving or alleviating pathological conditions. In particular, for the purposes of the present invention, treatment is directed to slowing down the progression of NASH and / or fibrosis and reducing the risk of further complications. It may also include prolonged survival compared to expected survival in the absence of treatment.

[0046] The anti-NASH or anti-fibrotic agent is administered in a therapeutically effective amount. As used herein, the phrase "therapeutically effective amount" refers to an amount of drug effective to achieve a desired therapeutic result. The therapeutically effective amount of a drug may vary depending on factors such as the disease state, age, sex, and weight of the individual, and the ability of the drug to induce a desired response in the individual. A therapeutically effective amount is also an amount in which any toxic or adverse effects of the drug are outweighed by the therapeutically beneficial effects. The effective dosage and administration regimen for a drug depends on the disease or condition being treated and can be determined by a person skilled in the art. A physician having ordinary skill in the art can easily determine and prescribe the effective amount of the pharmaceutical composition required. For example, a physician may start the dose of the drug used in the pharmaceutical composition at a level lower than required to achieve the desired therapeutic effect, and gradually increase the dosage until the desired effect is achieved. In general, the suitable dose of the composition of the present invention is the amount of the compound that is the lowest dose effective to produce a therapeutic effect according to a particular administration regimen. Such an effective dose generally depends on the factors described above.

[0047] In certain embodiments, the present invention relates to an anti-NASH compound for use in a method for treating NASH in a subject at risk of NASH, wherein the subject has been classified as at risk of NASH by a method according to the present invention.

[0048] Exemplary anti-NASH and anti-fibrotic compounds are listed below. - a compound of formula (I) or a pharma- ceutically acceptable salt thereof

[0049] [ka]

[0050] {In the formula, X1 represents a halogen atom, an R1 group or a G1-R1 group; A represents a CH=CH or CH2-CH2 group, X2 represents a G2-R2 group; G1 represents an oxygen atom; G2 represents an oxygen or sulfur atom; R1 represents a hydrogen atom; an unsubstituted alkyl group; an aryl group; or an alkyl group substituted by one or more substituents selected from a halogen atom, an alkoxy group, an alkylthio group, a cycloalkyl group, a cycloalkylthio group, and a heterocyclic group; R2 represents an alkyl group substituted by a -COOR3 group, where R3 represents a hydrogen atom; or an alkyl group unsubstituted or substituted by one or more substituents selected from halogen atoms, cycloalkyl groups and heterocyclic groups, R4 and R5, which may be the same or different, represent an alkyl group which may or may not be substituted by one or more substituents 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, O-304, mangiferin calcium salt, eltrombopag, carotuximab and imeglimin; Bile acids, such as obeticholic acid (OCA), ursodeoxycholic acid (UDCA), norursodeoxycholic acid and ursodiol; - CCR 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, vildagliptin, fotagliptin, alogliptin, saxagliptin, tilogliptin, anagliptin, sitagliptin, retagliptin, melogliptin, gosogliptin, trelagliptin, teneligliptin, dutogliptin, linagliptin, gemigliptin, yogliptin, betagliptin, imigliptin, omarigliptin, vildagliptin 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, - a fibroblast growth factor 19 (FGF-19) receptor ligand or a functional engineered variant of FGF-19, - fibroblast growth factor 21 (FGF-21) agonists, such as PEG-FGF21 (pegbelfermin, formerly 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) analogues, such as semaglutide, liraglutide, exenatide, albiglutide, dulaglutide, lixisenatide, loxenatide, efpeglenatide, taspoglutide, MKC-253, DLP-205 and ORMD-0901; - nicotinic acids, 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, lonifibrate, 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-[1,2,3]triazol-4-ylmethylsulfanyl]-phenoxy}-acetic acid), GW0742 ([4-[[[2-[3-fluoro-4-(trifluoromethyl)phenyl]-4-methyl-5-thiazolyl]methyl]thio]-2-methylphenoxy]acetic acid), 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 transporter (SGLT) 2 inhibitors, such as licogliflozin, remogliflozin, dapagliflozin, empagliflozin, ertugliflozin, sotagliflozin, ipragliflozin, titanagliflozin, 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 beta (THRβ) 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 in combination with Vitamin C and Atorvastatin.

[0051] In certain embodiments, the anti-NASH agent is selected from pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmethylem, saroglitazar magnesium, seradelpal, semaglutide, sitagliptin, TERN-101, TERN-201, and tropifexol. EXAMPLES

[0052] NIS4® is a groundbreaking blood-based NIT specifically designed to address the fibrosis x NAS composite endpoint by detecting at-risk NASH patients, and contains four independent biomarkers, miR-34a-5p, alpha-2 macroglobulin (A2M), YKL-40 (or chitinase 3-like protein 1) and glycated hemoglobin (HbA1c) (Harrison et al., The Lancet Gastroenterology & Hepatology, 5(11):970-985, 2020). However, NIS4® is based on the evaluation of four different biomarkers, which require different biological fluids (whole blood and serum) and different laboratory processes.

[0053] Therefore, the objective was to improve the NIT, if possible, while achieving performance as high as NIS4® for detecting at-risk NASH subjects, and to improve the robustness of the model by limiting external factors that may affect NIT performance. The objective was also to provide the test at a low cost.

[0054] The dataset issued from the Golden-505 clinical trial (NCT01694849) was used as the training cohort. The Golden dataset contains only NASH patients with at least a NAS score equal to 3. There are no patients with cirrhosis in this dataset. The final extracted training dataset contains 198 NASH patients with a prevalence of NASH at risk of 50%. Both the non-risk and risk NASH populations were expectedly well balanced for potential confounding factors by retaining all the risk NASH patients originally present in the dataset and selecting the best control for each of them.

[0055] From the Golden dataset, we calculated the Bayesian information criterion (BIC) for different models that include different combinations of biomarkers that make up NIS4®. In addition, we evaluated different parameters that may affect the robustness of the test, such as the age, sex and T2DM status of the patient. These models were all trained using logistic regression. Then, we determined the robustness of the simplified model using a dataset derived from the Resolve-It clinical trial (NCT02704403). This dataset contains 684 patients, most of whom are NASH patients (95.32%), with a prevalence of NASH at risk of 66%. Using this dataset, we constructed different fitting subpopulations to analyze the robustness of the scores obtained with the simplified model.

[0056] The matchit function of the MatchIt package, version 4.3.2, was used together with the genetic methods. In each case, 20 runs were started in parallel, creating calipers ranging from 0.005 to 0.1 in increments of 0.005. The choice of calipers was made to achieve a standardized difference for all variables less than 0.1, and possibly 0.05, among all calipers tested, by also keeping the largest number of patients. By using this process, in each case, we obtained subpopulations that were sufficiently balanced for all factors that the only feature that differed between both populations was the feature under study. Thus, if there was an effect, it could be reasonably assumed that this particular factor was the source of the influence on the biomarker.

[0057] Interestingly, the combination of miR-34 and YKL-40 produced a model that was stable with respect to age and T2DM status. Conversely, gender was found to have an effect on the model output. Therefore, a new model was developed to take into account the effect of gender.

[0058] To attempt to correct for the effect of gender in the combination of miR-34a-5p and YKL-40, we decided to train a new model using the Golden training cohort described above that also contained the "miR-34a-5p x gender" interaction parameter.

[0059] The following formula:

[0060]

number

[0061] is derived from this study, where: y = β0 + β1 * log10(miR-34a-5p (fold)) + β2 * log10(YKL-40 (ng / ml)) + β3 * gender + β4 * log10(miR-34a-5p(fold))*gender, and Here, gender is 0 if the subject is female, or gender is 1 if the subject is male.

[0062] This model is hereinafter referred to as "GBM."

[0063] An example is shown in table 1.

[0064] [Table 1]

[0065] The cut-offs were then computed: thus, a low (Lc, 80% sensitivity) cut-off value equal to 0.4564 and a high (Hc, 90% specificity) cut-off value equal to 0.6815 were calculated.

[0066] The performance of this model was then compared to that of NIS4® using patients from the Resolve-it clinical trial. This dataset, called "RIt2", is derived from information obtained from 2035 patients. This is an independent validation dataset for the Golden dataset used as the training dataset.

[0067] As expected, at-risk NASH patients were associated with a higher prevalence of T2DM, dyslipidemia, hypertension and also obesity compared to the non-risk NASH population.With regard to the histological spectrum, we observed a significant panel of patients with F3 / F4 fibrosis scores but with a NAS score of 3.

[0068] Then, AUROC analysis was performed. To begin this validation process, we first focused on the overall performance of various tests in detecting at-risk NASH patients. We started by plotting the ROC curve of the NIS4® / GBM model, as well as the ROC curves of fibrosis-4 index (FIB-4) and alanine aminotransferase (ALT). In fact, FIB-4 is often used as a reference / surrogate marker for fibrosis, while ALT can be considered as a surrogate marker for NASH. Then, using the Delong test, we compared the various GBM, FIB-4 and ALT AUROCs with the AUROC of NIS4®, and the results are reported in table 2.

[0069] [Table 2]

[0070] GBM was overall superior to other NITs, including NIS4®, for detecting at-risk NASH patients with AUROC 0.81.

[0071] The overall performance of GBM was compared for endpoints different from NASH at risk after the NIMBLE process. To do so, it was decided to analyze three different endpoints mainly focused on NASH (NASH F3 risk, NASH, NAS4) and three fibrosis-only endpoints (F2, F3, F4). For the first endpoint, ALT was included in the comparator as a criterion, and for the second group, FIB-4 was included as a criterion. The results are summarized in Tables 3 and 4, respectively.

[0072] [Table 3]

[0073] [Table 4]

[0074] GBM maintained high performance across a range of NASH-prone endpoints and also improved the performance of NIS4® for the NASH endpoint.

[0075] We then focused on comparing the clinical performance of NIS4® with GBM for exclusion and inclusion of patients at risk for NASH according to their respective Lc and Hc determined above. The results are summarized in Table 5.

[0076] [Table 5]

[0077] Regarding exclusion performance, we observed a significant increase in specificity of GBM vs. NIS4® for Lc with both NITs achieving similar sensitivity (85-86%) and NPV (82-83%).

[0078] Regarding inclusion performance, GBM achieved significantly higher values ​​of specificity (85% vs. 81%) and PPV (77% vs. 73%) compared to NIS4®. GBM also provided significantly higher sensitivity (62% vs. 59%) compared to NIS4®.

[0079] Finally, focusing on the uncertainty region, the results were again in favor of GBM.

[0080] Subpopulation analyses were also performed comparing NIS4® with GBM, see Table 6 below.

[0081] [Table 6]

[0082] NIS4® was affected by age and T2DM status, but GBM scores were no longer significantly affected by age and T2DM status, thus yielding GBM modeling that was robust to these factors.

[0083] GBM also effectively reduced the effect of gender on the score. We observed that the correction to the GBM score was mainly for low values, which corresponded to the observation that the effect of gender on miR-34a-5p was mainly present for non-risk NASH patients.

[0084] Therefore, a simplified and more robust NIT was identified compared to NIS4®. This new model is based on a reduced number of biomarkers, both of which are quantified in serum.

[0085] Interestingly, this new model requires only one biofluid sample to perform marker analysis. For this reason, this NIT is also cheaper and easier to perform. Furthermore, we have demonstrated that this new NIT is less affected by gender, age and T2DM status than NIS4®. This provides a new valuable tool for diagnosing at-risk NASH subjects.

Claims

1. 1. A method for diagnosing, screening, monitoring or prognosing at risk for non-alcoholic steatohepatitis (NASH) in a subject, comprising: - quantifying the levels of miR-34a-5p and YKL-40 in a biological fluid sample from said subject; - obtaining the gender of the subject; - combining said quantified level and said gender in a mathematical function to assign a score; - comparing said score to a cutoff value to determine whether said subject is an at-risk NASH subject; The method includes:

2. The method of claim 1 , wherein the mathematical function comprises a logistic regression function.

3. The method of claim 1 or 2, wherein the biological fluid sample is a blood, serum or plasma sample.

4. The method according to any one of claims 1 to 3, wherein the biological fluid sample is a serum sample.

5. 5. The method of any one of claims 1 to 4, wherein the subject suffers from obesity, insulin resistance, glucose intolerance, T2DM, pre-diabetes, dyslipidemia or hypertriglyceridemia.

6. When executed by a processor / processing means, said processor / processing means: - receiving the quantified levels of miR-34a-5p and YKL-40; - Acceptance of the target gender and - calculating a score from these quantified levels and the sex of said subject by a mathematical function; - assigning said subject to a group of at-risk or not-at-risk subjects based on said calculated score compared to a predefined cut-off value; A computer program containing instructions that result in

7. 7. A computer readable medium comprising the computer program of claim 6.

8. The computer-readable medium of claim 7, which is a non-transitory medium or a storage medium.

9. 1. An anti-NASH or anti-fibrotic agent for use in treating at risk for NASH in a subject in need thereof, comprising: the anti-NASH agent is selected from pegbelfermin, cenicriviroc, dapagliflozin, dulaglutide, empagliflozin, fenofibrate, lanifibranor, liraglutide, obeticholic acid, pioglitazone, resmethylome, saroglitazar magnesium, seradelpal, semaglutide, sitagliptin, TERN-101, TERN-201, and tropifexol; The subject is classified as having at risk for NASH by the method of any one of claims 1 to 5. Anti-NASH or anti-fibrotic agents.

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