Methods to aid in the detection of nonalcoholic steatohepatitis - Patent Application 20070233633

Abstract

Abstract Provided is a method that can assist in the detection of non-alcoholic steatohepatitis (NASH) through simple manipulations that are extremely non-invasive by comparison with liver biopsy and are not dependent on the skill of a technician.　The present invention is a method for assisting the detection of NASH wherein are included: a) measuring the abundance of LDL-TG included in a blood sample to be tested that has been isolated from a living body; b) measuring the abundance of one or more constituents selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST in the blood sample to be tested; and c) determining whether NASH has developed and / or the probability of NASH developing through the use of a combination of the abundance of LDL-TG and the abundance of the one or more constituents.

Description

[Technical field]

[0001] The present invention relates to a method to aid in the detection of non-alcoholic steatohepatitis. [Background technology]

[0002] Nonalcoholic fatty liver disease (NAFLD) is a pathological condition in which fatty liver is found by tissue diagnosis or imaging diagnosis, excluding liver diseases such as viral hepatitis, autoimmune hepatitis, and alcoholic liver disease, and is rapidly increasing worldwide due to the increase in the obese population. NAFLD is classified into nonalcoholic fatty liver (NAFL), which is thought to have little progression, and nonalcoholic steatohepatitis (NASH), which is progressive and can cause liver cirrhosis and liver cancer. Pathological conditions characteristic of NASH include fatty degeneration, balloon degeneration, inflammation, and fibrosis.

[0003] In order to differentiate between various pathologies that begin with fatty degeneration of the liver and to distinguish between NAFL and NASH, a definitive diagnosis by liver biopsy is necessary. However, liver biopsy is an invasive test and is expensive, which wears down the patient in many ways. In addition, liver biopsy is subject to a certain probability of sampling error and depends on the skill of the technician performing it, and the same can be said for the diagnosis of the sample taken. For this reason, patients must visit a specific institution to receive a certain level of medical care, which places a heavy burden on the patient.

[0004] On the other hand, commonly performed clinical test items for liver disease include aspartate aminotransferase (AST), alanine aminotransferase (ALT), AST / ALT ratio, etc., inflammation / cytokine markers TNF-α, high-sensitivity CRP, ferritin, etc., apoptosis marker cytokeratin 18 fragment, fibrosis marker hyaluronic acid, type 4 collagen 7S, etc. (Patent Documents 1 and 2). However, there have been no established biomarkers for suspecting NASH that are detected in multiple pathologies related to NASH. In other words, no biomarkers that comprehensively reflect pathologies related to NASH have been found so far. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2010-94131 A [Patent Document 2] JP 2018-80943 A Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a method that can assist in the detection of NASH by a simple operation, which is much less invasive than liver biopsy and does not depend on the skill of a technician. [Means for solving the problem]

[0007] As a result of intensive research, the inventors of the present application have discovered that by using the amount of LDL-TG contained in a test blood sample isolated from a living body in combination with the amount of at least one selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample, it is possible to assist in the detection of NASH by a simple procedure without performing a liver biopsy, and have completed the present invention.

[0008] That is, the present invention is as follows. [1] A method for assisting in the detection of nonalcoholic steatohepatitis, a) measuring the amount of LDL-TG present in a test blood sample isolated from a living body; b) measuring the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample; and c) the abundance of said LDL-TG and the abundance of said at least one component and compare the cutoff value, Including, The method indicates that a patient has developed non-alcoholic steatohepatitis and / or that there is a high possibility that a patient has developed non-alcoholic steatohepatitis, when the amount of LDL-TG present is equal to or greater than a cutoff value and the amount of at least one component selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, total cholesterol, ALT, and AST is equal to or less than the cutoff value, or when the amount of LDL-TG present is equal to or greater than the cutoff value and the amount of ApoE present is equal to or greater than the cutoff value. [2] 1. A method for aiding in the detection of non-alcoholic steatohepatitis, comprising: a) measuring the amount of LDL-TG present in a test blood sample isolated from a living body; b) measuring the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample; and c) creating a model equation with the amount of LDL-TG and the amount of at least one component as variables, and comparing a score obtained by substituting the amount of LDL-TG and the amount of at least one component contained in the test blood sample into the model equation with a score obtained for a blood sample of a patient suffering from nonalcoholic fatty liver; Including, The method indicates the onset of non-alcoholic steatohepatitis and / or a high likelihood of onset of non-alcoholic steatohepatitis if the score obtained for the test blood sample is higher than the score obtained for a blood sample from a patient suffering from non-alcoholic fatty liver. [3] The LDL subfraction-C is small, dense (sd) LDL-C.[1] or [2] The method described above. [4] The HDL subfraction-C is ApoE-containing HDL-C and / or HDL2-C and / or HDL3-C.[1] or [2] The method described above. [5] A method for creating a model equation for determining an index to assist in the detection of non-alcoholic steatohepatitis, comprising measuring the amount of LDL-TG present in blood samples from patients suffering from non-alcoholic steatohepatitis and patients suffering from non-alcoholic fatty liver, and the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST, and creating a model equation using the measured amounts present as variables. [6] A measurement kit used in the method for assisting in the detection of non-alcoholic steatohepatitis described in any one of [1] to [4], comprising a measurement reagent for LDL-TG, and a measurement reagent for at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST. [7] The kit described in [6], wherein the LDL subfraction-C is small, dense (sd) LDL-C. [8] The kit described in [6], wherein the HDL subfraction-C is ApoE-containing HDL-C and / or HDL2-C and / or HDL3-C. [9] Use of the kit according to any one of [1] to [4] as a kit for assisting in the detection of non-alcoholic steatohepatitis. Effect of the Invention

[0009] According to the present invention, detection of NASH can be assisted by a safe and simple operation without using liver biopsy, which is burdensome and risky for patients, and without depending on the skill of a technician performing the liver biopsy. As a result, treatment of NASH can be started early, and progression to severe disease or death can be prevented or delayed. [Brief description of the drawings]

[0010] [Figure 1] FIG. 13 is a diagram showing an ROC curve for distinguishing NASH from NAFL by substituting the amounts of LDL-TG, sdLDL-C, HDL2-C, ApoE, and ALT in blood collected from a subject in Example 20 into a model formula. [Diagram 2] FIG. 13 shows ROC curves for distinguishing between NASH and NAFL by substituting binary variables binarized at the cutoffs for the amounts of LDL-TG, HDL2-C, ApoE, and total cholesterol in blood collected from a subject in Example 36 into a model equation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] The present invention is a method for assisting in the detection of NASH by combining the amount of at least LDL-TG present in a test blood sample separated from a living body with the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST present in the test blood sample. The method of the present invention can assist in the detection of NASH more accurately than a method using only ALT, which is a conventional liver disease marker, or a method using only LDL-TG.

[0012] At least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT and AST contained in the test blood sample may be any one component alone or two or more components may be selected.

[0013] In the present invention, LDL-TG means triglyceride (TG) in low density lipoprotein (hereinafter referred to as LDL). LDL-C means cholesterol (C) in low density lipoprotein (LDL). LDL subfractions are fractions obtained by subclassifying LDL based on particle size, density, or constituents, and include sdLDL, large, buoyant (lb) LDL, and LDL subfraction-C means cholesterol (C) in LDL subfractions. sdLDL-C means cholesterol (C) in small particle, high density (d=1.044-1.063g / mL) LDL. HDL-C means cholesterol (C) in high density lipoprotein (HDL). HDL subfractions are fractions obtained by subclassifying HDL based on particle size, density, or constituents, and include HDL2, HDL3, ApoE-containing HDL, and HDL subfraction-C means cholesterol (C) in HDL subfractions. HDL2-C refers to cholesterol (C) in large, low-density (d = 1.063-1.125 g / mL) HDL, and HDL3-C refers to cholesterol (C) in high-density (d = 1.125-1.210 g / mL) HDL. ApoE-containing HDL-C refers to cholesterol (C) in high-density lipoproteins rich in apolipoprotein E (hereafter referred to as ApoE-containing HDL). ApoB refers to apolipoprotein B. ApoE refers to apolipoprotein E.

[0014] In the present invention, blood samples are used as test samples, which is much less invasive than liver biopsy and allows for a comprehensive understanding of the pathology of the entire liver without relying on a specific sample cut out from a part of the liver. In addition, blood samples are easily collected, and if an automatic analyzer-compatible measurement kit is used to measure the amounts of LDL-TG, LDL-C, sdLDL-C, HDL-C, ApoE-containing HDL-C, HDL2-C, HDL3-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the blood sample, it is possible to perform measurements with simple operations similar to those used in daily medical care and health checkups. Examples of blood samples in the present invention include whole blood, plasma, and serum.

[0015] A specific method for assisting in the detection of NASH in the present invention will be described below. In the present invention, the amount of LDL-TG present in a test blood sample separated from a living body is measured. Also, the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST present in the test blood sample is measured. The amount of LDL-TG present and the amount of the at least one component present are combined to provide an index for assisting in the detection of NASH.

[0016] In order to obtain a result that supports the detection of NASH according to the present invention, a score is obtained by applying a combination of statistical methods known to those skilled in the art to the obtained data (abundance of each component in blood) and evaluating it.As a method for obtaining a score, a method is available in which a combination of variables that is effective for supporting the detection of NASH is selected from a large number of variables (abundance of each component in blood), a model formula is created using the selected combination of variables, and the amount of each component in the test blood sample is applied to the model formula.

[0017] The value of the abundance of each component in the test blood sample used in creating the model formula may be a continuous variable of the measured value as it is, a continuous variable of the converted value obtained by performing logarithmic transformation or the like on the measured value, a binary variable divided into two groups by an arbitrary value, or a nominal variable or rank variable divided into three or more groups by a plurality of arbitrary values, but any of these may be used and are not limited to any of them. Specifically, when a binary variable is used, the measured value of each component may be compared with an arbitrary value, and if it is smaller, the abundance may be set to 0, and if it is larger, the abundance may be set to 1. Here, as the arbitrary value, for example, a cutoff value obtained by obtaining an ROC curve for distinguishing NASH from NAFL using each component alone and using the Youden Index may be used. The Youden Index is a method in which the point farthest from the dotted line where the AUC in the ROC curve is 0.500, that is, the point where (sensitivity + specificity - 1) is the maximum value, is set as the cutoff value.

[0018] Methods for selecting a combination of variables that is effective in assisting in the detection of NASH include, but are not limited to, the stepwise variable addition / deletion method, the Losso method, and the Elastic net method.

[0019] After selecting a combination of effective variables, a model formula optimal for assisting in the detection of NASH is created by an appropriate method such as regression analysis. Methods for creating the model formula include, but are not limited to, the least squares method, linear discriminant analysis, logistic regression, maximum likelihood method, Lasso regression, Ridge regression, and elastic net regression.

[0020] In the present invention, a model formula for determining a score serving as an index for assisting in the detection of NASH is represented, for example, by the following general formula (1).

[0021] Score = a + b × [abundance of LDL-TG] + c × [abundance of LDL-C] + d × [abundance of sdLDL-C] + e × [abundance of HDL-C] + f × [abundance of ApoE-containing HDL-C] + g × [abundance of HDL2-C] + h × [abundance of HDL3-C] + i × [abundance of ApoB] + j × [abundance of ApoE] + k × [abundance of total cholesterol] + l × [abundance of ALT] + m × [abundance of AST] ··· (1) (In formula (1), a represents an intercept, b to m represent coefficients for each measurement value, and a to m may be numbers obtained by multiplying each of a to m by the same positive number.)

[0022] In the case where a continuous variable of the measured value is used as the abundance, in the above general formula (1), the intercept a is preferably -10 to 10, more preferably -5 to 5. The coefficient b is preferably 0.1 to 1.5, more preferably 0.2 to 1.0. The coefficient c is preferably -1.0 to 0, more preferably -0.1 to 0. The coefficient d is preferably -1 to 0, more preferably -0.5 to 0. The coefficient e is preferably -1.0 to 0, more preferably -0.2 to 0, more preferably -0.1 to 0. The coefficient f is preferably -2 to 0, more preferably -1.5 to 0. The coefficient g is preferably -1.0 to 0, more preferably -0.3 to 0. The coefficient h is preferably -1.0 to 0, more preferably -0.2 to 0. The coefficient i is preferably -1.0 to 0, more preferably -0.2 to 0. The coefficient j is preferably 0 to 10, and more preferably 0 to 5. The coefficient k is preferably -1.0 to 0, and more preferably -0.1 to 0. The coefficient l is preferably -1.0 to 0, and more preferably -0.1 to 0. The coefficient m is preferably -1.0 to 0, and more preferably -0.2 to 0.

[0023] When using a binary variable in which the abundance is divided into two groups at an arbitrary value, for example, when the arbitrary values ​​are the values ​​used in Comparative Examples 3 and 4, Examples 21 to 36, and Examples 53 to 57 described below, namely, LDL-TG 17.6 mg / dL, LDL-C 116 mg / dL, sdLDL-C 34.3 mg / dL, HDL-C 47 mg / dL, HDL2-C 43.8 mg / dL, HDL3-C 22.7 mg / dL, ApoE-containing HDL-C (AE-HDL) 4.2 mg / dL, ApoB 88.9 mg / dL, ApoE 3.4 mg / dL, total cholesterol (TC) 178 mg / dL, ALT 32 units, and AST 20 units, the intercept a in the above general formula (1) is preferably -100 to 100, and more preferably 0 to 40. The coefficient b is preferably 1 to 10, and more preferably 2 to 5. The coefficient c is preferably -5.0 to 0, and more preferably -3 to 0. The coefficient d is preferably -5 to 0, and more preferably -4 to 0, and more preferably -3 to 0. The coefficient e is preferably -30 to 0, and more preferably -20 to 0. The coefficient f is preferably -30 to 0, and more preferably -20 to 0. The coefficient g is preferably -5 to 0, and more preferably -3 to 0. The coefficient h is preferably -30 to 0, and more preferably -20 to 0. The coefficient i is preferably -5 to 0, and more preferably -3 to 0. The coefficient j is preferably 0 to 10, and more preferably 0 to 3. The coefficient k is preferably -30 to 0, and more preferably -20 to 0. The coefficient l is preferably -5 to 0, and more preferably -2 to 0. The coefficient m is preferably -5 to 0, and more preferably -2 to 0.

[0024] The above general formula (1) is an example of a model formula in the present invention, and the intercept a and the coefficients b to m of the above general formula (1) may be multiplied by the same positive number to be used as a model formula, or an arbitrary number may be added to or subtracted from the intercept a to be used as a model formula. Furthermore, the boundary value when dividing into two-value variables can also be set arbitrarily. Therefore, the intercept and coefficients of the model formula used in the present invention are not limited to the above ranges.

[0025] As in the above general formula (1), a score can be obtained by substituting the abundance of various items into the obtained model formula. By comparing the obtained score with the standard value, if the score is greater than the standard value, it is highly likely that NASH has developed, and if the score is smaller than the standard value, it is low that NASH has developed. The standard value can be set arbitrarily according to the purpose, taking into consideration sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). For example, if it is desired to reduce false negatives, the standard value can be set low, and if it is desired to reduce false positives, the standard value can be set high. In the present invention, the standard value was set using the Youden Index, which is considered to have a good balance between these. Note that the standard values ​​described in the present invention are merely examples, and other values ​​can be adopted as standard values ​​according to the purpose as described above. In addition, if the patient population from which data is collected is different, the best standard value will also be different. Furthermore, as described above, if the coefficient and intercept of the model formula are multiplied by the same positive number, or if an arbitrary number is added or subtracted from the intercept of the model formula, the best standard value will also change in conjunction with it.

[0026] In the present invention, the area under the ROC curve (AUC) and the correct diagnosis rate (the proportion of test patients who were correctly determined to have NASH or NAFL) were used as indicators of the accuracy of detection of NASH. Regarding AUC, an AUC of 0.80 or more indicates high accuracy, an AUC of 0.85 or more indicates very high accuracy, and an AUC of 0.90 or more indicates extremely high accuracy. Therefore, in the present invention, an AUC of 0.80 or more is preferable, an AUC of 0.85 or more is more preferable, and an AUC of 0.90 or more is particularly preferable. Regarding the correct diagnosis rate, an AUC of 75% or more indicates high accuracy, an AUC of 85% or more indicates very high accuracy, and an AUC of 95% or more indicates extremely high accuracy. Therefore, in the present invention, an AUC of 75% or more is preferable, an AUC of 85% or more is more preferable, and an AUC of 95% or more is particularly preferable.

[0027] Among the components in the test blood sample, when the measured amounts were used as they were, a combination of LDL-TG, sdLDL-C, HDL2-C, ApoE, and ALT (Example 20 described below) was used, and when binary variables obtained by dividing the measured values ​​into two groups were used as the amounts, a combination of LDL-TG, HDL2-C, ApoE, and total cholesterol (Example 36 described below) was used, and therefore an AUC of 0.90 or more and a diagnostic accuracy of 95% were achieved, enabling NASH to be detected with extremely high accuracy.

[0028] In the present invention, even without using the above-mentioned model formula, the presence of LDL-TG contained in the test blood sample can be combined with the presence of at least one component selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample, and the possibility of developing NASH can be determined based on whether the presence of each of the combined components is above or below the cutoff value. The cutoff value can be determined, for example, by using the Youden Index from the ROC curve when each component is used alone to distinguish between NASH and NAFL.

[0029] Specifically, the cutoff values ​​were the values ​​used in Examples 37 to 47, namely, 17.6 mg / dL for LDL-TG, 116 mg / dL for LDL-C, 34.3 mg / dL for sdLDL-C, 47 mg / dL for HDL-C, 43.8 mg / dL for HDL2-C, 22.7 mg / dL for HDL3-C, and 22.7 mg / dL for ApoE-containing In the case of HDL-C (AE-HDL) 4.2 mg / dL, ApoB 88.9 mg / dL, ApoE 3.4 mg / dL, total cholesterol (TC) 178 mg / dL, ALT 32 units, and AST 20 units, if the amount of LDL-TG is equal to or greater than the cutoff value and the amount of any of the test items selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, total cholesterol, ALT, and AST is equal to or less than the cutoff value, or if the amount of LDL-TG contained in the test blood sample is equal to or greater than the cutoff value and the amount of ApoE is equal to or greater than the cutoff value, it can be determined that there is a high possibility of developing NASH. However, the cutoff value can be set arbitrarily depending on the purpose, such as whether to prioritize sensitivity or specificity, and the combination is not limited to this. However, the cutoff value can usually be set within the range of ±50%, more preferably ±30%, of the above values ​​used in Examples 37 to 47 (the values ​​listed in Table 5).

[0030] In this way, by using the amount of LDL-TG contained in the test blood sample in combination with the amount of at least one selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample, it is possible to easily and accurately assist in the differentiation of NASH from NAFL, which previously required a liver biopsy, with a simple operation.

[0031] The present invention also provides a method for detecting LDL-TG and at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST in a test blood sample from a human suspected of having or suffering from non-alcoholic steatohepatitis. That is, a method for detecting LDL-TG and at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST in a test blood sample from a human suspected of having non-alcoholic steatohepatitis or suffering from non-alcoholic steatohepatitis, comprising: Obtaining a blood sample from a human; and measuring the amount of LDL-TG and the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST in the blood sample; Here, there is also provided a method for creating a model equation with the amount of LDL-TG and the amount of at least one component as variables, and substituting the amount of LDL-TG and the amount of at least one component contained in the blood sample into the model equation to obtain a score that is higher than a score obtained for a blood sample from a patient suffering from non-alcoholic fatty liver.

[0032] In addition, when the detection of non-alcoholic steatohepatitis is assisted by the above-mentioned method of the present invention, non-alcoholic steatohepatitis can be treated by administering an effective amount of a drug for treating non-alcoholic steatohepatitis to a patient in whom non-alcoholic steatohepatitis has been detected. Examples of the drug include pioglitazone in patients with diabetes mellitus and vitamin E in patients without diabetes mellitus.

[0033] In the present invention, the amount of LDL-C, sdLDL-C, HDL2-C, HDL3-C, HDL-C, ApoE-containing HDL-C, ApoB, ApoE, total cholesterol, ALT, and AST present in a blood sample can be measured by a conventional method. For example, the method for measuring various lipid items includes a method in which the target lipoprotein is separated by a fractionation separation operation such as ultracentrifugation, electrophoresis, or high performance liquid chromatography, and then triglycerides and cholesterol are quantified by a quantitative operation, and a method in which triglycerides and cholesterol are removed from all lipoproteins other than the target lipoprotein in the first step without fractionation, and then triglycerides and cholesterol in the target lipoprotein are measured in the second step. The method for measuring ApoB and ApoE includes ELISA and immunoturbidity methods using antibodies specific to them. The method for measuring ALT is the LDH-UV method (JSCC commonly used standard method), and the method for measuring AST is the MDH-UV method (JSCC commonly used standard method).

[0034] Specifically, the method for measuring the amount of LDL-TG can be the method described in WO2013 / 157642, the method for measuring the amount of LDL-C can be the method described in WO98 / 47005, the method for measuring the amount of sdLDL-C can be the method described in WO08 / 105486, the method for measuring the amount of HDL-C can be the method described in WO98 / 26090, the method for measuring the amounts of HDL2-C and HDL3-C can be the method described in Ito Y et al. (2014) “Development of a homogeneous assay for measurement of high-density lipoprotein-subclass cholesterol,” Clinica Chimica Acta, 427:86-93., and the method for measuring the amount of ApoE-containing HDL-C can be the method described in JP-A-2014-030393, but is not limited to the above methods as long as they measure the amounts of various items.

[0035] The present invention will be described in detail below with reference to examples, but the present invention is not limited to the following examples. EXAMPLES

[0036] The amounts of LDL-TG, LDL-C, sdLDL-C, HDL-C, HDL2-C, HDL3-C, ApoE-containing HDL-C, ApoB, ApoE, total cholesterol, ALT, and AST in blood samples taken from a total of 42 patients, consisting of 8 NAFL patients and 34 NASH patients, were measured using an automatic analyzer. The amounts of LDL-C, HDL-C, ApoB, ApoE, total cholesterol, ALT, and AST were measured using the respective measurement reagents for automatic analyzers commonly used in clinical testing. LDL-TG levels were measured using LDLTG-EX "SEIKEN" (manufactured by Denka Seiken Co., Ltd.), sdLDL-C levels were measured using sdLDL-EX "SEIKEN" (manufactured by Denka Seiken Co., Ltd.), HDL2-C and HDL3-C levels were measured using the method described in Ito Y et al. (2014) "Development of a homogeneous assay for measurement of high-density lipoprotein-subclass cholesterol," Clinica Chimica Acta, 427:86-93., and ApoE-containing HDL-C levels were measured using the method described in JP 2014-030393 A.

[0037] (Comparative Examples 1 and 2, Examples 1 to 20) In Comparative Examples 1 and 2 and Examples 1 to 20, the items shown in Table 1 were used, and the amounts thereof were expressed as continuous variables that were the measured values ​​themselves.

[0038] [Table 1]

[0039] The model formula obtained by logistic regression for each combination is shown in Table 2. The score value can be obtained by substituting the measured value of each item for the item name shown in this model formula.

[0040] [Table 2]

[0041] The area under the ROC curve (AUC) when NASH was judged to be positive, the standard value set by the Youden Index, and the sensitivity, specificity, PPV (positive predictive value), NPV (negative predictive value), and accuracy when judged by comparing the standard value and the score value are shown in Tables 3-1 and 3-2. As a representative example, the ROC curve of Example 20 is shown in Figure 1.

[0042] [Table 3-1]

[0043] [Table 3-2]

[0044] As shown in Tables 3-1 and 3-2, compared with Comparative Example 1 using only ALT, a conventional liver disease marker, and Comparative Example 2 using only LDL-TG, Examples 1 to 20 all had larger AUCs and higher accuracy rates, demonstrating that it is possible to predict with a higher probability whether the patient has NASH or NAFL.

[0045] (Comparative Examples 3 and 4, Examples 21 to 36) In Comparative Examples 3 and 4 and Examples 21 to 36, the items shown in Table 4 were used, and the abundances were represented by binary variables divided into two groups based on the cutoff values ​​obtained using the Youden Index when the ROC curves were obtained for each item alone.

[0046] [Table 4]

[0047] The cutoff values ​​for each item used in Comparative Examples 3 and 4 and Examples 21 to 36 are shown in Table 5.

[0048] [Table 5]

[0049] The model formula obtained by logistic regression for each combination is shown in Table 6. The score value can be obtained by substituting 1 for the item name shown in this model formula if the measured value of each item is larger than the cutoff value, and 0 if it is smaller than the cutoff value.

[0050] [Table 6]

[0051] The area under the ROC curve (AUC) when NASH was positive, the standard value set by Youden Index, the sensitivity when the standard value and the score value were compared, the specificity, PPV (positive predictive value), NPV (negative predictive value), and the correct diagnosis rate are shown in Tables 7-1 and 7-2. As a representative example, the ROC curve of Example 36 is shown in Figure 2.

[0052] [Table 7-1]

[0053] [Table 7-2]

[0054] As shown in Tables 7-1 and 7-2, compared with Comparative Example 3 using only ALT, a conventional liver disease marker, and Comparative Example 4 using only LDL-TG, Examples 21 to 36 all had larger AUCs and higher accuracy rates, indicating that it is possible to predict with a higher probability whether the patient has NASH or NAFL.

[0055] (Examples 37 to 47) In Examples 37 to 47, the items shown in Table 8 were used, and the amounts of each component contained in the test blood sample were evaluated based on whether the amount was greater than or equal to the cutoff value, without using a model formula. The values ​​shown in Table 5 were used as the cutoff values ​​for each item.

[0056] [Table 8]

[0057] Table 9 shows the combinations of the measurement values ​​for each item evaluated in Examples 37 to 47, the number of affected individuals, and the proportion of NASH patients among those affected.

[0058] [Table 9]

[0059] As shown in Table 9, the proportion of individuals with the combinations of measurement values ​​in Examples 37 to 47 who were NASH patients was high, indicating that the possibility of developing NASH can be determined by the combination of measurement values ​​for these items.

[0060] (Examples 48 to 52) In Examples 48 to 52, the items shown in Table 10 were used, and the amounts thereof were expressed as continuous variables, i.e., the measured values ​​themselves.

[0061] [Table 10]

[0062] The model formula obtained by logistic regression for each combination is shown in Table 11. The score value can be obtained by substituting the measured value of each item for the item name shown in this model formula.

[0063] [Table 11]

[0064] Table 12 shows the area under the ROC curve (AUC) when NASH is determined to be positive, the set reference value, and the sensitivity, specificity, PPV (positive predictive value), NPV (negative predictive value), and accuracy when determining by comparing the score value with the set reference value.

[0065] [Table 12]

[0066] As shown in Table 12, compared with Comparative Example 1 using only ALT, which is a conventional liver disease marker shown in Table 3-1, and Comparative Example 2 using only LDL-TG, Examples 48 to 52 all had larger AUCs and higher accuracy rates, indicating that it is possible to predict with a higher probability whether the patient is suffering from NASH or NAFL.

[0067] (Examples 53 to 57) In Examples 53 to 57, the items shown in Table 13 were used, and the abundances of the items were determined as binary variables divided into two groups by the cutoff values ​​obtained by the Youden Index when the ROC curves were obtained for each item alone. The values ​​shown in Table 5 were used as the cutoff values ​​for each item.

[0068] [Table 13]

[0069] The model formula obtained by logistic regression for each combination is shown in Table 14. A score can be obtained by substituting 1 for the item name shown in this model formula if the measured value of each item is larger than the cutoff value, or 0 if the measured value is smaller than the cutoff value.

[0070] [Table 14]

[0071] Table 15 shows the area under the ROC curve (AUC) when NASH is determined to be positive, the set reference value, and the sensitivity, specificity, PPV (positive predictive value), NPV (negative predictive value), and accuracy when determining by comparing the score value with the set reference value.

[0072] [Table 15]

[0073] As shown in Table 15, compared to Comparative Example 3 using ALT alone, which is a conventional liver disease marker shown in Table 7-1, and Comparative Example 4 using LDL-TG alone, Examples 53 to 57 all had larger AUCs and higher accuracy rates, indicating that it is possible to predict whether the patient has NASH or NAFL with a higher probability.

Claims

1. A method for assisting in the detection of non-alcoholic steatohepatitis, a) measuring the amount of LDL-TG present in a test blood sample isolated from a living body; b) measuring the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample; and c) comparing the abundance of LDL-TG and the abundance of at least one component with cutoff values; Including, The method indicates the onset of non-alcoholic steatohepatitis and / or a high possibility of the onset of non-alcoholic steatohepatitis when the amount of LDL-TG present is equal to or greater than a cutoff value and the amount of at least one component selected from LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, total cholesterol, ALT, and AST is equal to or less than the cutoff value, or when the amount of LDL-TG present is equal to or greater than the cutoff value and the amount of ApoE present is equal to or greater than the cutoff value.

2. A method for assisting in the detection of nonalcoholic steatohepatitis, comprising: a) measuring the amount of LDL-TG present in a test blood sample isolated from a living body; b) measuring the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST contained in the test blood sample; and c) creating a model equation with the amount of LDL-TG and the amount of at least one component as variables, and comparing a score obtained by substituting the amount of LDL-TG and the amount of at least one component contained in the test blood sample into the model equation with a score obtained for a blood sample of a patient suffering from non-alcoholic fatty liver; Including, The method indicates the onset of non-alcoholic steatohepatitis and / or a high likelihood of onset of non-alcoholic steatohepatitis if the score obtained for the test blood sample is higher than the score obtained for a blood sample from a patient suffering from non-alcoholic fatty liver.

3. The method according to claim 1 or 2, wherein the LDL subfraction-C is small, dense (sd) LDL-C.

4. The method according to claim 1 or 2, wherein the HDL subfraction-C is ApoE-containing HDL-C and / or HDL2-C and / or HDL3-C.

5. A method for creating a model formula for determining an index for assisting in the detection of non-alcoholic steatohepatitis, comprising measuring the amount of LDL-TG present in blood samples from a patient suffering from non-alcoholic steatohepatitis and a patient suffering from non-alcoholic fatty liver, and the amount of at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST, and creating a model formula using the measured amounts present as variables.

6. A measurement kit for use in the method for assisting in the detection of non-alcoholic steatohepatitis according to any one of claims 1 to 4, comprising a measurement reagent for LDL-TG, and a measurement reagent for at least one component selected from the group consisting of LDL-C, LDL subfraction-C, HDL-C, HDL subfraction-C, ApoB, ApoE, total cholesterol, ALT, and AST.

7. The kit according to claim 6, wherein the LDL subfraction-C is small, dense (sd) LDL-C.

8. The kit according to claim 6, wherein the HDL subfraction-C is ApoE-containing HDL-C and / or HDL2-C and / or HDL3-C.

9. Use of the kit according to any one of claims 6 to 8 as a kit for assisting in the detection of non-alcoholic steatohepatitis.

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