Biomarker-based metabolic dysfunction liver disease risk analysis system and use thereof

Abstract

The present invention relates to a method and device for predicting or diagnosing the risk of metabolic dysfunction-associated steatotic liver disease. The risk of the onset of liver disease or the progression of liver cirrhosis can be predicted and / or diagnosed through a prediction model based on gene information and clinical indicators of single nucleotide polymorphism markers.

Description

Biomarker-based metabolic liver disease risk analysis system and its uses

[0001] The present invention relates to a method and apparatus for predicting or diagnosing the risk of metabolic-related fatty liver disease.

[0002] Metabolic dysfunction-associated steatotic liver disease (MASLD) is a group of diseases characterized by the accumulation of fat in the liver associated with metabolic abnormalities. It progresses from simple fatty liver to a form accompanied by inflammation, and the accumulation of liver fibrosis can ultimately lead to severe complications such as cirrhosis and hepatocellular carcinoma. In particular, the stage of liver fibrosis is used as an indicator reflecting the severity of liver disease and the risk of future complications; clinically, an approach is utilized in MASLD patients to assess the presence and extent of fibrosis to identify high-risk groups and implement differentiated management strategies.

[0003] Traditionally, liver biopsy has been used as the reference standard for evaluating liver fibrosis. However, as an invasive procedure, liver biopsy carries risks such as bleeding, pain, and infection, and has limitations in that it is difficult to repeat. It has also been pointed out that the limited scope of tissue sampling can lead to sampling error, and inter-reader variability may exist. For these reasons, it is difficult to apply liver biopsy to all subjects in actual clinical practice, and the need for non-invasive evaluation methods has been continuously raised.

[0004] In response to these clinical needs, non-invasive blood test-based indicators (e.g., indices using liver enzymes, platelets, age, etc.) and imaging-based elastography (e.g., ultrasound or magnetic resonance-based elastography) are widely used to evaluate liver fibrosis, but there are limitations in reliably distinguishing risk levels in all subjects using only a single test or a single indicator.

[0005] Meanwhile, although there are reports suggesting that individual genetic predisposition, as well as lifestyle and environmental factors, may be involved in the onset and progression of MASLD, genetic information often does not directly reflect clinical conditions (such as current liver damage, inflammation, and fibrosis accumulation). Therefore, there are limitations in accurately assessing an individual subject's current risk level or potential for progression based solely on information regarding simple gene expression or mutations.

[0006] Accordingly, for the risk assessment and screening of high-risk groups in MASLD, there is a need to develop new evaluation strategies and technologies that are applicable in clinical settings and can improve predictive accuracy and classification reliability.

[0007] The object of the present invention is to provide a method for providing information necessary for predicting or diagnosing the risk of metabolic dysfunction-associated steatotic liver disease (MASLD).

[0008] Another objective of the present invention is to provide a method for diagnosing the risk of metabolic-related fatty liver disease.

[0009] Another objective of the present invention is to provide a device for predicting or diagnosing the risk of metabolic-related fatty liver disease.

[0010] One aspect of the present invention for achieving the above objectives relates to a method for providing information necessary for predicting or diagnosing the risk of metabolic dysfunction-associated steatotic liver disease (MASLD), comprising: (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313 from a sample; and (b) assigning weights according to the conjugation status of the single nucleotide polymorphisms.

[0011] In the present invention, "Metabolic dysfunction-associated steatotic liver disease (MASLD)" is a disease in which fat accumulates in the liver and includes all liver diseases that occur or worsen in association with metabolic abnormalities (e.g., obesity, insulin resistance, type 2 diabetes, dyslipidemia, hypertension, etc.). Furthermore, it may include not only subjects classified as having MASLD based on clinical diagnostic criteria or a diagnosis from a medical institution, but also subjects suspected of having the said disease or for whom the risk of onset or progression of the said disease is assessed.

[0012] In addition, in the present invention, MASLD includes cases where there are stages of liver fibrosis (F0 to F4) according to pathological or clinical criteria, and specifically, may include cases where stage F4 corresponding to liver cirrhosis or cases where the risk or probability of progression to stage F4 is evaluated.

[0013] In the present invention, "risk prediction" refers to providing for a subject the likelihood of occurrence, presence, progression, or worsening of a specific disease or condition, or the likelihood of future clinical outcomes (e.g., severity, complications, prognosis). The said risk may be calculated in the form of a score, grade (risk group classification), or probability value based on genetic information obtained from the subject (e.g., single nucleotide polymorphism (SNP) genotype and / or its zygosity status) and / or clinical information, but is not limited thereto. Furthermore, "risk prediction" includes all cases where the calculated result can be utilized for the diagnosis, screening, follow-up, determination of the need for additional testing, or establishment of management strategies for the subject.

[0014] In the present invention, "diagnosis" refers to determining or deciding whether a specific disease or condition already exists in a subject, or whether the subject can be classified as corresponding to a specific disease or condition. The diagnosis may be determined or decided based on genetic information obtained from the subject (e.g., single nucleotide polymorphism (SNP) genotype and / or its zygosity) and / or clinical information, but is not limited thereto. The diagnosis is not limited to a definitive diagnosis and includes screening, aided diagnosis, risk group classification, or determination of the need for additional testing.

[0015] The above "subject" refers to an individual from whom biological samples may be collected or from whom genetic and / or clinical information may be gathered, and may be included regardless of the presence or absence of disease, symptoms, or medical diagnosis. Specifically, it may include not only patients diagnosed with metabolic fatty liver disease (MASLD), but also individuals suspected of having MASLD or possessing risk factors, individuals undergoing health checkups or screening tests, and individuals classified as clinically normal (normal individuals).

[0016] Additionally, the above "subject" may include all individuals that may be subject to analysis of genetic and / or clinical information for the purpose of evaluation, classification, or management of a specific disease or condition.

[0017] In the present invention, "single nucleotide polymorphism (SNP)" refers to a variation in which a single nucleotide difference is observed between individuals at a specific locus on the genome. "rsID" is a dbSNP registration identifier for identifying the above SNP, and in the present invention, it is described in the form of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, rs17782313, etc.

[0018] In the present invention, "detection" refers to confirming the presence and / or genotype of the SNP from a sample, and may include single nucleotide polymorphism (SNP) genotyping. Detection may be performed by one or more of known methods, such as real-time PCR, allele-specific PCR, microarray, and nucleotide sequencing.

[0019] In the present invention, "zygotic state" refers to a state classified based on the genotype of the SNP, wherein homozygosity refers to the case where two alleles are identical (e.g., AA or GG), and heterozygosity refers to the case where two alleles are different (e.g., AG). Furthermore, homozygosity is classified into normal homozygosity that does not contain a risk allele and risk homozygosity that contains two risk alleles.

[0020] In the present invention, "weight" refers to a numerical value set in correspondence with the genotype or conjugation state of an SNP, and is a single nucleotide polymorphism (SNP) genotype information (SNP) corresponding to the i index used in the present invention. i It is a coefficient that quantifies the extent to which ) contributes to the risk (or probability of progression) of liver cirrhosis (F4) in MASLD. The above weight (Weighti ) is applied as a multiplication coefficient multiplied to the SNP in the mathematical formula for calculating the linear exponent (Z), and can be set to the same or different values ​​for each SNP. In the present invention, the weight i ) can be pre-set considering the junction status (heterozygous or homozygous state) of the relevant SNP, and for multiple SNPs (SNP i Х Weight i The sum of the terms can be used to calculate the linear exponent (Z).

[0021] Specifically, in step (b) above, weights are assigned as follows: 0.388 if the junction state of rs738409 is risky homojunction and 0.188 if it is heterojunction; 0.153 if the junction state of rs72613567 is risky homojunction and 0.281 if it is heterojunction; -0.057 if the junction state of rs58542926 is risky homojunction and -0.114 if it is heterojunction; 0.218 if the junction state of rs1260326 is risky homojunction and 0.202 if it is heterojunction; and 0.685 if the junction state of rs626283 is risky homojunction and 0.44 if it is heterojunction; A weight of 0.829 is assigned if the binding status of rs2896019 is risk homozygous and 1.131 if it is heterozygous; a weight of 0.103 is assigned if the binding status of rs12483959 is risk homozygous and -0.741 if it is heterozygous; a weight of 0.383 is assigned if the binding status of rs1229984 is risk homozygous and 0.036 if it is heterozygous; a weight of 1.029 is assigned if the binding status of rs6265 is risk homozygous and 0.417 if it is heterozygous; and a weight of 0.499 is assigned if the binding status of rs17782313 is risk homozygous and 0.243 if it is heterozygous, but is not limited thereto. The above weights may be appropriately changed or adjusted when other evaluation factors, such as clinical indicators, are additionally combined.

[0022] Specifically, the SNP detected in step (a) and the weight derived in step (b) can be applied to the following Equation 1 to be calculated as a linear exponent (Z).

[0023] [Equation 1]

[0024] Z = -3.536 + Σ (SNP i × Weight(Weight i ))

[0025] i: Index to identify applied SNPs and weights

[0026] The above SNP i represents the numerical value of the genotype analysis result (including zygosity status) for the SNP corresponding to i according to pre-established rules, and the weight i ) is the above SNP i As a coefficient multiplied by to reflect the contribution of the SNP corresponding to the above i, it may be a value pre-set according to the junction state. Specifically, when the junction state is normal homozygous, the SNP i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value can be 1.

[0027] The term calculated for the SNP corresponding to i (SNP i Х Weight i ) is a numerical value of the genotype analysis result of the SNP corresponding to the above i (SNP i The weight of the corresponding SNP in ) i It is a value obtained by multiplying by ), and corresponds to the partial score to which the corresponding SNP contributes to the calculation of the overall risk.

[0028] Also, Σ(SNP i Х Weight i ) is for each of one or more SNPs selected in the present invention (SNP i Х Weight i It represents the sum of the terms, and the number of selected SNPs (n) may be 1 or more.

[0029] More specifically, the linear index calculated by Equation 1 above can be applied to Equation 2 below to calculate the probability of progression to a high-risk lesion. A logistic function can be applied for this.

[0030] [Equation 2]

[0031] P(F4) = 1 / (1 + e -Z )

[0032] P(F4) calculated in this way is a probability value indicating whether the subject corresponds to the stage of liver cirrhosis (F4) or the likelihood of progressing to the F4 stage, and may have a range of 0 to 1. In addition, by applying a pre-set cut-off based on P(F4), it can be used to non-invasively screen for a risk group for liver cirrhosis or to determine the need for additional examinations.

[0033] The method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease may be to predict the risk of developing liver disease or progression of liver cirrhosis due to genetic factors.

[0034] In one embodiment of the present invention, it was confirmed that the genetic baseline risk (genetic vulnerability) of a subject can be quantitatively evaluated by calculating the linear index (Z) of Equation 1 and the probability value P (F4) of Equation 2 using only one or more SNP genotype information detected in step (a) and the weight assigned in step (b).

[0035] As described above, since the subject's baseline genetic risk (genetic vulnerability) can be quantitatively assessed based on genetic information (SNP genotypes), this method can be utilized to identify potential high-risk groups early, even when the current degree of liver damage is unclear or clinical abnormalities are limited. Furthermore, it reduces reliance on invasive tests (e.g., liver biopsy) or repetitive blood tests, allowing for risk assessment to be performed in a non-invasive and convenient manner, thereby alleviating the burden on the subject. Moreover, based on the calculated risk or probability values, subjects can be classified into risk groups and utilized for preemptive measures such as setting follow-up cycles, determining the need for additional precision tests, and establishing lifestyle improvements and management strategies. Through risk assessment based on genetic predisposition, this method offers precision medicine utility by enabling personalized management and early intervention tailored to individual risk levels.

[0036] Another aspect of the present invention relates to a method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, comprising: (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from a group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313 from a sample; (b) deriving a FIB-4 (Fibrosis-4) index which is a clinical indicator; and (c) assigning weights according to the conjugation status of the single nucleotide polymorphisms.

[0037] "Single nucleotide polymorphism (SNP)", "detection", "junction status", "weights", "metabolism-related fatty liver disease", "risk prediction", and "diagnosis" are as described above.

[0038] Specifically, in step (b) above, a weight of 0.505 may be assigned to the FIB-4 index, but is not limited thereto.

[0039] Additionally, specifically, in step (c) above, weights are assigned as follows: 0.419 if the junction state of rs738409 is risky homojunction and 0.152 if it is heterojunction; 0.326 if the junction state of rs72613567 is risky homojunction and 0.22 if it is heterojunction; -0.037 if the junction state of rs58542926 is risky homojunction and -0.09 if it is heterojunction; 0.144 if the junction state of rs1260326 is risky homojunction and 0.072 if it is heterojunction; and 0.211 if the junction state of rs626283 is risky homojunction and 0.431 if it is heterojunction; A weight of 0.542 is assigned if the junction state of rs2896019 is risky homojunction and 1.125 if it is heterojunction; a weight of 0.329 is assigned if the junction state of rs12483959 is risky homojunction and -0.619 if it is heterojunction; a weight of 0.429 is assigned if the junction state of rs1229984 is risky homojunction and 0.023 if it is heterojunction; a weight of 1.022 is assigned if the junction state of rs6265 is risky homojunction and 0.431 if it is heterojunction; and a weight of 0.418 is assigned if the junction state of rs17782313 is risky homojunction and 0.247 if it is heterojunction, may be assigned, but is not limited thereto. The above weights may be appropriately changed or adjusted when other evaluation factors are additionally combined.

[0040] In addition, specifically, the SNP detected in step (a), the FIB-4 index derived in step (b), and the weight assigned in step (c) can be applied to Equation 3 below to be calculated as a linear index (Z).

[0041] [Equation 3]

[0042] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4)

[0043] i: Index to identify applied SNPs and weights

[0044] The above SNP refers to a value obtained by quantifying the genotype analysis result (including zygosity status) for the SNP corresponding to i according to a pre-established rule, and the weight (Weight i ) is the above SNP i As a coefficient multiplied by to reflect the contribution of the SNP corresponding to the above i, it may be a value pre-set according to the junction state. Specifically, when the junction state is normal homozygous, the SNP i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value can be 1.

[0045] More specifically, the linear index calculated by Equation 3 above can be applied to Equation 2 below to calculate the probability of progression to a high-risk lesion. A logistic function can be applied for this.

[0046] [Equation 2]

[0047] P(F4) = 1 / (1 + e -Z )

[0048] P(F4) calculated in this way is a probability value indicating whether the subject corresponds to the stage of liver cirrhosis (F4) or the likelihood of progressing to the F4 stage, and may have a range of 0 to 1. In addition, by applying a pre-set cut-off based on P(F4), it can be used to non-invasively screen for a risk group for liver cirrhosis or to determine the need for additional examinations.

[0049] In the present invention, the "FIB-4 (Fibrosis-4) index" is a serum-based clinical indicator for non-invasively estimating the risk of liver fibrosis, and can be calculated using the patient's age and AST (aspartate aminotransferase), ALT (alanine aminotransferase), and platelet count obtained from standard blood tests. For example, it can be calculated by the following Equation 4.

[0050] [Equation 4]

[0051] FIB-4 = (Age [years] χAST [U / L]) / (Platelets [10 9 / L] × √(ALT[U / L]))

[0052] This FIB-4 can be calculated using variables obtained from routine clinical practice without the need for additional expensive equipment or specialized specimen analysis, and can be used as a primary risk assessment (non-invasive test, NIT) tool for patients with metabolic fatty liver disease or suspected patients.

[0053] The method for providing information necessary for predicting or diagnosing the risk of the above-mentioned metabolic fatty liver disease may be to predict the risk of developing liver disease or progression of liver cirrhosis based on genetic factors and clinical indicators.

[0054] In one embodiment of the present invention, it was confirmed that the risk level of a subject can be quantitatively evaluated by calculating a risk score or probability value using one or more single nucleotide polymorphism (SNP) genotype information detected in step (a), a weight assigned according to the conjugation status in step (b), and a clinical indicator (e.g., FIB-4) in step (c). In particular, it was confirmed that when the genotype information and the clinical indicator are combined while reflecting the weights, sensitivity and specificity are improved, resulting in excellent risk classification performance for the subject.

[0055] Genetic information (SNP genotypes) reflects the subject's innate vulnerability (basic risk), while clinical indicators (e.g., FIB-4) can reflect the current liver status and pathophysiological changes at the time of testing. When risk is calculated by considering both genetic information and clinical indicators as described above, the accuracy and discrimination of risk assessment can be improved, even for subjects who are difficult to distinguish based on a single source of information (genetic information alone or clinical indicators alone). In particular, for subjects whose clinical indicators fall within the intermediate range or whose judgment is uncertain, genetic information provides supplementary information, thereby enhancing the precision of risk group classification. Conversely, even if a subject is genetically high-risk, if their current clinical indicators are low, the intensity of current management and follow-up strategies can be adjusted more rationally.

[0056] Furthermore, since genetic information does not change over time while clinical indicators can fluctuate over time, calculating risk by combining the two types of information enables an integrated assessment that simultaneously reflects congenital risk and acquired / current conditions; thus, the method of the present invention for providing information necessary for predicting or diagnosing the risk of metabolism-related fatty liver disease can enhance the reliability of establishing personalized risk group classification and management strategies.

[0057] Another aspect of the present invention relates to a method for diagnosing the risk of metabolic-related fatty liver disease, comprising: (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313 from a sample; (b) assigning weights according to the conjugation state of the single nucleotide polymorphisms; and (c) applying the SNPs detected in step (a) and the weights derived in step (b) to the following Equation 1 to calculate a linear index (Z).

[0058] [Equation 1]

[0059] Z = -3.536 + Σ (SNP i × Weight(Weight i ))

[0060] i: Index to identify applicable SNPs and weights

[0061] The above SNP refers to a value obtained by quantifying the genotype analysis result (including zygosity status) for the SNP corresponding to i according to a pre-established rule, and the weight (Weight i ) is the above SNP i As a coefficient multiplied by to reflect the contribution of the SNP corresponding to the above i, it may be a value pre-set according to the junction state. Specifically, when the junction state is normal homozygous, the SNP i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value can be 1.

[0062] “Single nucleotide polymorphism (SNP)”, “detection”, “conjugation status”, “weights”, “metabolism-related fatty liver disease”, “risk prediction”, and “diagnosis” are as described above.

[0063] In step (b) above, weights are assigned as follows: 0.388 if the junction state of rs738409 is risky homojunction and 0.188 if it is heterojunction; 0.153 if the junction state of rs72613567 is risky homojunction and 0.281 if it is heterojunction; -0.057 if the junction state of rs58542926 is risky homojunction and -0.114 if it is heterojunction; 0.218 if the junction state of rs1260326 is risky homojunction and 0.202 if it is heterojunction; and 0.685 if the junction state of rs626283 is risky homojunction and 0.44 if it is heterojunction; A weight of 0.829 is assigned if the binding status of rs2896019 is risk homozygous and 1.131 if it is heterozygous; a weight of 0.103 is assigned if the binding status of rs12483959 is risk homozygous and -0.741 if it is heterozygous; a weight of 0.383 is assigned if the binding status of rs1229984 is risk homozygous and 0.036 if it is heterozygous; a weight of 1.029 is assigned if the binding status of rs6265 is risk homozygous and 0.417 if it is heterozygous; and a weight of 0.499 is assigned if the binding status of rs17782313 is risk homozygous and 0.243 if it is heterozygous, but is not limited thereto. The above weights may be appropriately changed or adjusted when other evaluation factors, such as clinical indicators, are additionally combined.

[0064] Another aspect of the present invention relates to a method for diagnosing the risk of metabolic-related fatty liver disease, comprising: (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from a group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313 from a sample; (b) deriving a clinical indicator FIB-4 (Fibrosis-4) index; (c) assigning weights according to the conjugation status of the single nucleotide polymorphisms; and (d) applying the SNPs detected in step (a), the FIB-4 index derived in step (b), and the weights assigned in step (c) to the following Equation 3 to calculate a linear index (Z).

[0065] [Equation 3]

[0066] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4)

[0067] i: Index to identify applicable SNPs and weights

[0068] The above SNP refers to a value obtained by quantifying the genotype analysis result (including zygosity status) for the SNP corresponding to i according to a pre-established rule, and the weight (Weight i ) is the above SNP i As a coefficient multiplied by to reflect the contribution of the SNP corresponding to the above i, it may be a value pre-set according to the junction state. Specifically, when the junction state is normal homozygous, the SNP i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value can be 1.

[0069] “Single nucleotide polymorphism (SNP)”, “detection”, “conjugation status”, “FIB-4 (Fibrosis-4) index”, “weights”, “metabolism-related fatty liver disease”, “risk prediction”, and “diagnosis” are as described above.

[0070] Specifically, in step (c) above, weights are assigned as follows: 0.419 if the junction state of rs738409 is risky homojunction and 0.152 if it is heterojunction; 0.326 if the junction state of rs72613567 is risky homojunction and 0.22 if it is heterojunction; -0.037 if the junction state of rs58542926 is risky homojunction and -0.09 if it is heterojunction; 0.144 if the junction state of rs1260326 is risky homojunction and 0.072 if it is heterojunction; and 0.211 if the junction state of rs626283 is risky homojunction and 0.431 if it is heterojunction; A weight of 0.542 is assigned if the junction state of rs2896019 is risky homojunction and 1.125 if it is heterojunction; a weight of 0.329 is assigned if the junction state of rs12483959 is risky homojunction and -0.619 if it is heterojunction; a weight of 0.429 is assigned if the junction state of rs1229984 is risky homojunction and 0.023 if it is heterojunction; a weight of 1.022 is assigned if the junction state of rs6265 is risky homojunction and 0.431 if it is heterojunction; and a weight of 0.418 is assigned if the junction state of rs17782313 is risky homojunction and 0.247 if it is heterojunction, may be assigned, but is not limited thereto. The above weights may be appropriately changed or adjusted when other evaluation factors are additionally combined.

[0071] Another aspect of the present invention relates to an apparatus for predicting or diagnosing the risk of metabolic-related fatty liver disease, comprising: a first receiver receiving one or more single nucleotide polymorphism (SNP) gene information selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313, and a weight according to the conjugation state of said single nucleotide polymorphism; a second receiver receiving a clinical indicator FIB-4 (Fibrosis-4) index; and a calculation unit inputting said gene information and clinical indicator into the following Equation 3 to calculate the risk.

[0072] [Equation 3]

[0073] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4)

[0074] i: Index to identify applicable SNPs and weights

[0075] "Single nucleotide polymorphism (SNP)", "detection", "junction status", "weights", "metabolism-related fatty liver disease", "risk prediction", and "diagnosis" are as described above.

[0076] The above SNP refers to a value obtained by quantifying the genotype analysis result (including zygosity status) for the SNP corresponding to i according to a pre-established rule, and the weight (Weight i ) is the above SNP i As a coefficient multiplied by to reflect the contribution of the SNP corresponding to the above i, it may be a value pre-set according to the junction state. Specifically, when the junction state is normal homozygous, the SNP i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value can be 1.

[0077] In the present invention, the "device" may be implemented as an electronic device or system comprising at least one processor and memory to provide information necessary for predicting or diagnosing the risk of metabolically related fatty liver disease (MASLD). The device may be implemented, for example, as a server, a cloud-based system, a desktop computer, a laptop computer, a tablet, a smartphone, or an analysis device linked to an electronic medical record (EMR) system of a medical institution, but is not limited thereto.

[0078] The first receiver above may receive SNP gene information from an external genotype analysis device or test results, and the genetic information may be input in the form, for example, an SNP genotype, a fusion status (homogeneous / heterogeneous), or a numerical value thereof. In addition, the first receiver may [receive] a weight (Weight] applied according to the fusion status i It can receive ) together, or call weight information stored in memory and provide it to the operation unit.

[0079] The second receiver may receive an FIB-4 index from blood test results or clinical data, and the FIB-4 index may be input as a value calculated from an external system, or as a value calculated internally within the device based on age, AST, ALT, and platelet counts.

[0080] The above-mentioned operation unit can calculate a linear index (Z) according to Equation 3 using the received genetic information and clinical indicators, and can output the Z value or the probability value / score value based thereon as a “risk level.”

[0081] In addition, the calculation unit can classify and display the calculated risk level as a risk group according to a pre-set standard (e.g., cutoff), and the result can be provided to a user terminal or medical information system through an output unit (display unit) or a communication unit.

[0082] Specifically, the weights for the single nucleotide polymorphism (SNP) gene information are assigned as follows: 0.419 if the junction status of rs738409 is risk homozygous and 0.152 if it is heterozygous; 0.326 if the junction status of rs72613567 is risk homozygous and 0.22 if it is heterozygous; -0.037 if the junction status of rs58542926 is risk homozygous and -0.09 if it is heterozygous; 0.144 if the junction status of rs1260326 is risk homozygous and 0.072 if it is heterozygous; and 0.211 if the junction status of rs626283 is risk homozygous and 0.431 if it is heterozygous. A weight of 0.542 is assigned if the junction state of rs2896019 is risky homojunction and 1.125 if it is heterojunction; a weight of 0.329 is assigned if the junction state of rs12483959 is risky homojunction and -0.619 if it is heterojunction; a weight of 0.429 is assigned if the junction state of rs1229984 is risky homojunction and 0.023 if it is heterojunction; a weight of 1.022 is assigned if the junction state of rs6265 is risky homojunction and 0.431 if it is heterojunction; and a weight of 0.418 is assigned if the junction state of rs17782313 is risky homojunction and 0.247 if it is heterojunction, may be assigned, but is not limited thereto. The above weights may be appropriately changed or adjusted when other evaluation factors are additionally combined.

[0083] According to the present invention, the genetic baseline risk (genetic vulnerability) of a subject can be quantitatively evaluated through a risk or probability value calculated by applying weights considering the conjugation status to multiple single nucleotide polymorphism (SNP) genotype information. Accordingly, this can be utilized to identify potential high-risk groups early, even when clinical abnormalities are limited or the current liver condition is not distinct, and can contribute to performing risk assessment in a non-invasive and simple manner by reducing reliance on invasive or repeated tests.

[0084] Furthermore, by combining genetic information with clinical indicators (FIB-4), the present invention enables an integrated risk assessment that simultaneously reflects genetic predisposition and the state at the time of testing. As a result, the precision of risk group classification can be improved even for subjects that are difficult to classify using a single source of information alone. In particular, predictive performance can be enhanced by having genetic information act complementarily when clinical indicators are located in an intermediate range, making judgment uncertain. Moreover, subject classification based on the calculated risk can provide utility for precision medicine by supporting clinical decision-making, such as setting follow-up observation cycles, determining the need for additional tests, and establishing individual management strategies.

[0085] The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

[0086] Figure 1 shows the process of analyzing genotypes from F0 and F4 subjects to select candidate SNPs and deriving target SNPs with high association with F4.

[0087] Figure 2 shows the distribution of the probability of progression of liver cirrhosis (F4) P (F4) calculated based on SNP genotype information by pathology stage (F0~F4).

[0088] Figure 3 shows the distribution of the probability of progression of liver cirrhosis (F4) P (F4) by pathology stage (F0~F4) calculated by combining SNP genotype information and clinical indicators (FIB-4).

[0089] Figure 4 shows the results of comparing the predictive performance (AUC) of the SNP-only model, the FIB-4-only model, and the SNP+FIB-4 combined model.

[0090] The present invention will be explained in detail below by way of examples. However, the following examples are merely illustrative of the present invention, and the present invention is not limited by the following examples.

[0091] Example 1. Genotype Analysis

[0092] DNA was extracted from oral epithelial cells of 149 patients with liver fibrosis stage 0 and 91 patients with liver fibrosis stage 4, and 22 genotypes were identified using real-time qPCR. When comparing the number of patients in stages F0 and F4 who possessed the risk genotypes as isomorphs, SNPs of the genes PNPLA3, PNPLA3 #4, PNPLA3 #5, PNPLA3 #7, PNPLA3 #8, HSD17B13, TM6SF2, GCKR, MBOAT7, TMC4, MC4R, ADH1B, and BDNF showed a proportion associated with the stage of liver fibrosis. Among these, PNPLA3 #4 and #8, PNPLA3 #5 and #7, and MBOAT7 and TMC4 showed genotypes that matched relative genotypes, so PNPLA3 #7, PNPLA3 #8, and TMC4 were selected. In particular, PNPLA3 markers #7 and #8 were used together in additional analysis as "additional markers in a linkage disequilibrium relationship" with PNPLA3 (Fig. 1).

[0093] Subsequently, genotyping of the samples was performed on 10 target SNPs.

[0094] Example 2. Construction of an SNP-based Prediction Model

[0095] The following method was performed to effectively select risk groups with a high probability of advancing to the F4 stage by utilizing genetic baseline risk. Weights were derived using a multimodal algorithm as shown in Table 1 below, considering the normal, homozygous, and heterozygous zygosity states of the 10 selected genotypes. Normal homozygosity refers to homozygosity that does not contain the risk allele; risk homozygosity contains two risk alleles; and heterozygosity contains one risk allele. SNPs when the zygosity state is normal homozygosity i Value (SNP i The value is 0, and the SNP when at-risk homozygous or heterozygous i The value corresponds to 1.

[0096] Gene i genotype SNP i Value-based genotype SNP i Value weight (Weight i )Genetic SNP i Value weight (Weight i )PNPLA31CC0GC10.188GG10.388HSD17B132TATA0AATA10.281AAAA10.153TM6SF23CC0TC1-0.114TT1-0.057GCKR4CC0CT10.202TT10.218TMC45GG0CG10.44CC10.685PNPLA3 #76TT0GT11.131GG10.829PNPLA3 #87GG0AG1-0.741AA10.103ADH1B8CC0CT10.036TT10.383BDNF9TT0TC10.417CC11.029MC4R10TT0CT10.243CC10.499

[0097] In addition, the single nucleotide polymorphism (SNP) markers in the genes derived above are as summarized in Table 2 below.

[0098] Gene SNPSNP iPNPLA3rs738409SNP1HSD17B13rs72613567SNP2TM6SF2rs58542926SNP3GCKRrs1260326SNP4TMC4rs626283SNP5PNPLA3 #7rs2896019SNP6PNPLA3 #8rs12483959SNP7ADH1Brs1229984SNP8BDNFrs6265SNP9MC4Rrs17782313SNP 10

[0099] The linear exponent (Z) with the weights derived from Table 1 above was calculated using Equation 1 below. SNP when the fitting state is normal homozygous. i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value corresponds to 1.

[0100] [Equation 1]

[0101] Z = -3.536 + Σ (SNP i × Weight(Weight i ))

[0102] (i: Index to identify applicable SNPs and weights)

[0103] The probability of progressing to step F4 can be calculated by applying a linear exponent to Equation 2.

[0104] [Equation 2]

[0105] P(F4) = 1 / (1 + e -Z )

[0106] The genotype results confirmed from F0 to F4 stage specimens collected from the multi-center of Catholic St. Mary's Hospital and Seoul National University Hospital were applied to the formula derived above to calculate the probability of progression to the F4 stage.

[0107] As a result, as shown in Figure 2, the probability distributions from the pathology stage F0 to F3 overlap significantly and rise gradually. This is because the genetic information reflects the patient's innate genetic vulnerability to progression to liver cirrhosis rather than directly representing 'current rapid liver damage.'

[0108] The F4 pathology group shows a box plot distribution (median and interquartile range) that is shifted relatively upward compared to other stages, representing an average risk of approximately 28.6%. This suggests that the higher the genetic risk group, the higher the probability of actual progression to severe fibrosis and / or liver cirrhosis.

[0109] In addition, patients with outliers—those with P(F4) SNP values ​​higher than 40% despite having a low pathological stage (F0–F1)—should be classified as a 'genetic high-risk group' with a very high likelihood of progressing to liver cirrhosis in the future, even if they currently have mild symptoms, suggesting the need for follow-up observation and preemptive management. These results demonstrate that SNP-based indicators can serve as useful biomarker candidates for predicting the risk of progression to liver cirrhosis and / or high-risk groups.

[0110] Example 3. Construction of a Prediction Model for SNP and FIB-4 Binding

[0111] This study was conducted to effectively select risk groups by utilizing FIB-4 values ​​in addition to genetic baseline risk and simultaneously reflecting the current liver status. To this end, weights were derived as shown in Table 2 using a multimodal algorithm, considering FIB-4 and the homozygous and heterozygous states of 10 genotypes.

[0112] Item i Genotype SNP i Value-based genotype SNP i Value weight (Weight i )Genetic SNP i Value weight (Weight i)FIB-4-----0.505---PNPLA31CC0GC10.152GG10.419HSD17B132TATA0AATA10.22AAAA10.326T M6SF23CC0TC1-0.09TT1-0.037GCKR4CC0CT10.072TT10.144TMC45GG0CG10.431CC10.211PNPLA3 #76TT0GT11.125GG10.542PNPLA3 #87GG0AG1-0.619AA10.329ADH1B8CC0CT10.023TT10.429BDNF9TT0TC10.431CC11.022MC4R10TT0CT10.247CC10.418

[0113] The linear exponent (Z) with the weights derived from Table 3 above was calculated using Equation 3 below. SNP when the fitting state is normal homozygous. i Value (SNP i The value is 0, and the SNP when at-risk homozygous and / or heterozygous i The value corresponds to 1.

[0114] [Equation 3]

[0115] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4)

[0116] (i: Index to identify applied SNPs and weights)

[0117] The probability of progressing to step F4 can be calculated by applying a linear exponent to Equation 2 above.

[0118] The genotype results confirmed from F0 to F4 stage specimens collected from the multi-center of Catholic St. Mary's Hospital and Seoul National University Hospital were applied to the formula derived above to calculate the probability of progression to the F4 stage.

[0119] As a result, as shown in Figure 3, the probability values ​​significantly increase in stages F2–F3 compared to pathology stages F0–F1, and in particular, in F4, the distribution of the box plot (median and interquartile range) is formed at the top compared to other stages. This demonstrates that this combined model is highly effective in screening patients with liver cirrhosis. In other words, it shows that when genetic mutations and clinical indicators are combined, the correlation between the actual pathology stage and the predicted probability (P(F4)) is strengthened the most. Furthermore, it demonstrates that setting a cut-off based on the probability value P(F4) of this combined model enables screening and diagnosis of liver cirrhosis and / or high-risk groups with high reliability using a non-invasive method.

[0120] Example 4. Evaluation of Prediction Model Performance

[0121] The accuracy, sensitivity, and specificity as biomarkers were compared when comparing the probability of progression to F4 (P(F4)) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) under conditions of SNP alone, FIB-4 alone, and SNP+FIB-4 combination.

[0122] The predictive model of the present invention was performed using clinical data from 352 subjects collected from the multi-center of Catholic St. Mary's Hospital and Seoul National University Hospital. For each subject, stages F0 to F4 were classified according to pathological or clinical criteria, and these were used as labels to evaluate the predictive performance of the model. Specifically, DNA was extracted from biological samples isolated from each subject, and genotype information was obtained by analyzing the genotypes of the 10 SNPs listed in Table 2. Genotype analysis can be performed using SNP genotyping methods including real-time qPCR, and heterozygous and homozygous status were determined for each SNP. In addition, FIB-4 values ​​were calculated or collected from clinical test results for each subject and used as clinical indicators.

[0123] The genotype information (zygotic state) of the 10 types of SNPs and the FIB-4 values ​​were input into the prediction formula of the present invention to calculate the linear exponent (Z) and the F4 progression probability P(F4). Here, the SNP-only model calculates P(F4) using only the genotype information of the 10 types of SNPs as input, the FIB-4-only model calculates P(F4) using only the FIB-4 values ​​as input, and the combined model calculates P(F4) using both the genotype information of the 10 types of SNPs and the FIB-4 values ​​as input.

[0124] The discrimination performance of the F4 stage was evaluated using P(F4) calculated from each model, and performance indicators including AUC were compared.

[0125] As a result, as shown in Figure 4, the AUC of the SNP-only model was 0.729 and the AUC of the FIB-4-only model was 0.838, but the combined model of the present invention (SNP+FIB-4) showed significantly superior prediction and / or diagnostic performance with an AUC of 0.860.

[0126] Furthermore, regarding sensitivity and specificity, using the combined model improved overall classification performance compared to standalone models, and demonstrated enhanced performance in both sensitivity and specificity for identifying actual F4 patients.

[0127] In other words, combining SNP gene markers and FIB-4 levels improves the accuracy of predicting the stage of liver cirrhosis (F4), because genetic predisposition (SNP) and indicators of current liver status (FIB-4) work complementarily.

[0128] The prediction model of the present invention can be utilized for the prediction and early diagnosis of liver disease progressing to a high-risk group, and is expected to serve as a key platform for the development of commercial models capable of predicting the progression of liver disease.

[0129] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0130] The scope of the present invention is defined by the claims set forth below, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention.

Claims

1. (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265 and rs17782313 from a sample; and (b) a method for providing information necessary for predicting or diagnosing the risk of metabolic dysfunction-associated steatotic liver disease (MASLD), comprising the step of assigning weights according to the conjugation status of the single nucleotide polymorphisms.

2. In paragraph 1, the weight in step (b) above is, If the junction status of rs738409 is critical homojunction, 0.388 is assigned, and if it is heterojunction, 0.188 is assigned; If the junction status of rs72613567 is critical homojunction, 0.153 is assigned, and if it is heterojunction, 0.281 is assigned; -0.057 is assigned if the junction status of rs58542926 is dangerous homojunction, and -0.114 if it is heterojunction; If the junction status of rs1260326 is critical homojunction, 0.218 is assigned, and if it is heterojunction, 0.202 is assigned; If the junction status of rs626283 is critical homojunction, 0.685 is assigned, and if it is heterojunction, 0.44 is assigned; If the junction status of rs2896019 is critical homojunction, 0.829 is assigned, and if it is heterojunction, 1.131 is assigned; If the junction status of rs12483959 is critical homojunction, 0.103 is assigned, and if it is heterojunction, -0.741 is assigned; If the junction status of rs1229984 is critical homojunction, 0.383 is assigned, and if it is heterojunction, 0.036 is assigned; A value of 1.029 is assigned if the rs6265 junction status is dangerous homojunction, and 0.417 if it is heterojunction; and A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein a value of 0.499 is assigned when the fusion status of rs17782313 is risk homozygous and 0.243 is assigned when it is heterozygous.

3. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein the SNP detected in step (a) and the weight derived in step (b) are applied to the following Equation 1 and calculated as a linear index (Z). [Equation 1] Z = -3.536 + Σ (SNP i × Weight(Weight i )) i: Index to identify applicable SNPs and weights In Equation 1 above, when the junction state is normal homozygous, the SNP i The value is 0, and it is the SNP when at risk homozygous or heterozygous. i The value corresponds to 1.

4. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein, in paragraph 3, the linear index calculated by the above Equation 1 is applied to the following Equation 2 to calculate the probability of progression to a high-risk lesion. [Equation 2] P(F4) = 1 / (1 + e -Z ) 5. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein the method predicts the risk of developing liver disease or progression of liver cirrhosis due to genetic factors.

6. (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265 and rs17782313 from a sample; (b) a step of deriving the clinical indicator FIB-4 (Fibrosis-4) index; and (c) a step of assigning weights according to the conjugation state of the single nucleotide polymorphisms; comprising a method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease.

7. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein, in step (b) of claim 6, a weight of 0.505 is assigned to the FIB-4 index.

8. In paragraph 6, the weight in step (c) above is, If the junction status of rs738409 is critical homojunction, 0.419 is assigned, and if it is heterojunction, 0.152 is assigned; If the junction status of rs72613567 is critical homojunction, 0.326 is assigned, and if it is heterojunction, 0.22 is assigned; -0.037 is assigned if the junction status of rs58542926 is critical homojunction, and -0.09 if it is heterojunction; If the junction status of rs1260326 is critical homojunction, 0.144 is assigned, and if it is heterojunction, 0.072 is assigned; If the junction status of rs626283 is critical homojunction, 0.211 is assigned, and if it is heterojunction, 0.431 is assigned; If the junction status of rs2896019 is critical homojunction, 0.542 is assigned, and if it is heterojunction, 1.125 is assigned; If the junction status of rs12483959 is dangerous homojunction, 0.329 is assigned, and if it is heterojunction, -0.619 is assigned; If the junction status of rs1229984 is critical homojunction, 0.429 is assigned, and if it is heterojunction, 0.023 is assigned; If the junction status of the rs6265 is critical homojunction, 1.022 is assigned, and if it is heterojunction, 0.431 is assigned; and A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein a value of 0.418 is assigned when the junction status of rs17782313 is risk homozygous and 0.247 is assigned when it is heterozygous.

9. In Paragraph 6, A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein the SNP detected in step (a), the FIB-4 index derived in step (b), and the weight assigned in step (c) are applied to the following Equation 3 to calculate a linear index (Z). [Equation 3] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4) i: Index to identify applicable SNPs and weights In Equation 3 above, when the junction state is normal homozygous, the SNP i The value is 0, and it is the SNP when at risk homozygous or heterozygous. i The value corresponds to 1.

10. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein, in claim 9, the linear index calculated by the above Equation 3 is applied to the following Equation 2 to calculate the probability of progression to a high-risk lesion. [Equation 2] P(F4) = 1 / (1 + e -Z ) 11. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein the method predicts the risk of developing liver disease or progression of liver cirrhosis based on genetic factors and clinical indicators.

12. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein, in any one of claims 1 to 11, the sample is blood, tissue, cell, whole blood, plasma, serum, saliva, sputum, lymph, cerebrospinal fluid, interstitial fluid, or urine.

13. A method for providing information necessary for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein, in any one of claims 1 to 11, the sample is isolated from a human.

14. (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265 and rs17782313 from a sample; (b) a step of assigning weights according to the conjugation state of the single base polymorphisms above; and (c) A method for diagnosing the risk of metabolic-related fatty liver disease, comprising the step of applying the SNP detected in step (a) and the weight derived in step (b) to the following Equation 1 to calculate a linear index (Z). [Equation 1] Z = -3.536 + Σ (SNP i × Weight(Weight i )) i: Index to identify applicable SNPs and weights In Equation 1 above, when the junction state is normal homozygous, the SNP i The value is 0, and it is the SNP when at risk homozygous or heterozygous. i The value corresponds to 1.

15. In paragraph 14, the weight in step (b) above is, If the junction status of rs738409 is critical homojunction, 0.388 is assigned, and if it is heterojunction, 0.188 is assigned; If the junction status of rs72613567 is critical homojunction, 0.153 is assigned, and if it is heterojunction, 0.281 is assigned; -0.057 is assigned if the junction status of rs58542926 is dangerous homojunction, and -0.114 if it is heterojunction; If the junction status of rs1260326 is critical homojunction, 0.218 is assigned, and if it is heterojunction, 0.202 is assigned; If the junction status of rs626283 is critical homojunction, 0.685 is assigned, and if it is heterojunction, 0.44 is assigned; If the junction status of rs2896019 is critical homojunction, 0.829 is assigned, and if it is heterojunction, 1.131 is assigned; If the junction status of rs12483959 is critical homojunction, 0.103 is assigned, and if it is heterojunction, -0.741 is assigned; If the junction status of rs1229984 is critical homojunction, 0.383 is assigned, and if it is heterojunction, 0.036 is assigned; A value of 1.029 is assigned if the rs6265 junction status is dangerous homojunction, and 0.417 if it is heterojunction; and A method for diagnosing the risk of metabolic-related fatty liver disease, wherein a value of 0.499 is assigned when the fusion status of rs17782313 is risk homozygous and 0.243 is assigned when it is heterozygous.

16. (a) detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265 and rs17782313 from a sample; (b) A step of deriving the clinical indicator FIB-4 (Fibrosis-4) index; (c) a step of assigning weights according to the conjugation state of the single base polymorphism; and (d) A method for diagnosing the risk of metabolic-related fatty liver disease, wherein the SNP detected in step (a), the FIB-4 index derived in step (b), and the weight assigned in step (c) are applied to the following Equation 3 to calculate a linear index (Z). [Equation 3] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4) i: Index to identify applied SNPs and weights In Equation 3 above, when the junction state is normal homozygous, the SNP i The value is 0, and it is the SNP when at risk homozygous or heterozygous. i The value corresponds to 1.

17. In Clause 16, the weight in step (c) above is, If the junction status of rs738409 is critical homojunction, 0.419 is assigned, and if it is heterojunction, 0.152 is assigned; If the junction status of rs72613567 is critical homojunction, 0.326 is assigned, and if it is heterojunction, 0.22 is assigned; -0.037 is assigned if the junction status of rs58542926 is critical homojunction, and -0.09 if it is heterojunction; If the junction status of rs1260326 is critical homojunction, 0.144 is assigned, and if it is heterojunction, 0.072 is assigned; If the junction status of rs626283 is critical homojunction, 0.211 is assigned, and if it is heterojunction, 0.431 is assigned; If the junction status of rs2896019 is critical homojunction, 0.542 is assigned, and if it is heterojunction, 1.125 is assigned; If the junction status of rs12483959 is dangerous homojunction, 0.329 is assigned, and if it is heterojunction, -0.619 is assigned; If the junction status of rs1229984 is critical homojunction, 0.429 is assigned, and if it is heterojunction, 0.023 is assigned; If the junction status of the rs6265 is critical homojunction, 1.022 is assigned, and if it is heterojunction, 0.431 is assigned; and A method for diagnosing the risk of metabolic-related fatty liver disease, wherein a value of 0.418 is assigned when the fusion status of rs17782313 is risk homozygous and 0.247 is assigned when it is heterozygous.

18. A first receiver receiving one or more single nucleotide polymorphism (SNP) gene information selected from the group consisting of rs738409, rs72613567, rs58542926, rs1260326, rs626283, rs2896019, rs12483959, rs1229984, rs6265, and rs17782313, and a weight according to the conjugation state of the single nucleotide polymorphism; A second receiver that receives the clinical indicator FIB-4 (Fibrosis-4) index; and An apparatus for predicting or diagnosing the risk of metabolic-related fatty liver disease, comprising: a calculation unit that calculates the risk by inputting the above genetic information and clinical indicators into the following Equation 3. [Equation 3] Z = -4.755 + Σ (SNP i × Weight(Weight i )) + (0.505 × FIB-4) i: Index to identify applicable SNPs and weights In Equation 3 above, when the junction state is normal homozygous, the SNP i The value is 0, and it is the SNP when at risk homozygous or heterozygous. i The value corresponds to 1.

19. In paragraph 18, the weight for the single nucleotide polymorphism (SNP) gene information is, If the junction status of rs738409 is critical homojunction, 0.419 is assigned, and if it is heterojunction, 0.152 is assigned; If the junction status of rs72613567 is critical homojunction, 0.326 is assigned, and if it is heterojunction, 0.22 is assigned; -0.037 is assigned if the junction status of rs58542926 is critical homojunction, and -0.09 if it is heterojunction; If the junction status of rs1260326 is critical homojunction, 0.144 is assigned, and if it is heterojunction, 0.072 is assigned; If the junction status of rs626283 is critical homojunction, 0.211 is assigned, and if it is heterojunction, 0.431 is assigned; If the junction status of rs2896019 is critical homojunction, 0.542 is assigned, and if it is heterojunction, 1.125 is assigned; If the junction status of rs12483959 is dangerous homojunction, 0.329 is assigned, and if it is heterojunction, -0.619 is assigned; If the junction status of rs1229984 is critical homojunction, 0.429 is assigned, and if it is heterojunction, 0.023 is assigned; If the junction status of the rs6265 is critical homojunction, 1.022 is assigned, and if it is heterojunction, 0.431 is assigned; and A device for predicting or diagnosing the risk of metabolic-related fatty liver disease, wherein the binding state of rs17782313 is assigned 0.418 when it is risk homozygous and 0.247 when it is heterozygous.

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