JPWO2020066162A1 - Non-alcoholic fatty liver disease detection method, non-alcoholic fatty liver disease detection kit and non-alcoholic fatty liver disease detection biomarker - Google Patents

Abstract

The method for detecting non-alcoholic fatty liver disease is to obtain a sample derived from the body fluid of a mammal, and to obtain 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, etc. in the sample. It comprises detecting at least one molecule selected from the group consisting of lactic acid, uric acid, valine, pyruvate, phenylalanine, and proline.

Description

The present invention relates to a method for detecting non-alcoholic fatty liver disease, a kit for detecting non-alcoholic fatty liver disease, and a biomarker for detecting non-alcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) includes simple fatty liver (SS), which is a liver disease with only fat deposition and a good prognosis, and severe fibrosis and liver carcinogenesis. It is a disease concept that includes progressive nonalcoholic steatohepatitis (NASH) that causes disease (see Non-Patent Document 1). Since SS causes serious illness as it progresses, such as it may cause NASH, it is important to predict the onset of NAFLD including SS at an earlier stage, or if possible, before the onset of illness. Is.

However, at present, a diagnostic method for predicting the onset of NAFLD has not been established. As a method for screening and grasping the degree of SS, abdominal ultrasonography and imaging tests such as CT and MRI are used. Biopsy is the standard method for diagnosing NASH. This liver biopsy is invasive and unsuitable for screening in terms of safety and economy.

The development of biomarkers capable of diagnosing NAFLD and NASH by a simple and non-invasive method is one of the areas that are currently attracting the most attention, but biomarkers that can be effectively utilized in clinical practice have not been established. In recent years, advances in proteomics and metabolomics technologies that can comprehensively analyze a large number of proteins have led to the search for biomarkers in the sera of NAFLD patients using these technologies (non-). See Patent Document 2).

Non-Patent Document 3 and Patent Document 1 report the results of quantification of lipid metabolites in plasma in patients such as NASH and healthy subjects. Non-Patent Document 4 reports the results of quantifying dipeptides and the like in patients with various liver diseases and healthy subjects. Patent Document 2 reports the results of quantification of acetaminophen-glucuronide in plasma in patients such as NASH and healthy subjects to whom acetaminophen was administered. Patent Document 3 reports the results of quantification of kininogen-derived peptides in serum in NAFLD patients and healthy subjects. Patent Document 4 reports the results of quantifying soluble LR11 in serum in patients with various liver diseases and healthy subjects.

Japan Special Table 2010-507566 Japan Special Table 2012-504629 Gazette International Publication No. 2009/051259 Japanese Patent Application Laid-Open No. 2014-167446

Naga Chalasani, Zobair Younossi, Joel E. Lavine, Anna Mae Diehl, Elizabeth M. Brunt, Kenneth Cusi, Michael Charlton, Arun J. Sanyal. "The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Gastroenterological Association, American Association for the Study of Liver Diseases, and American College of Gastroenterology "Gastroenterology, (USA), American Gastroenterological Association, June 2012, Volume 142, Issue 7, p.1592-1609 Marc-Emmanuel Dumas, James Kinross, Jeremy K. Nicholson. "Metabolic Phenotyping and Systems Biology Approaches to Understanding Metabolic Syndrome and Fatty Liver Disease." Gastroenterology, (USA), American Gastroenterological Association, January 2014, Volume 146, Issue 1 , p.46-62 Puneet Puri, Michelle M. Wiest, Onpan Cheung, Faridoddin Mirshahi, Carol Sargeant, Hae-Ki Min, Melissa J. Contos, Richard K. Sterling, Michael Fuchs, Huiping Zhou, Steven M. Watkins, and Arun J. Sanyal1. " The plasma lipidomic signature of nonalcoholic steatohepatitis. "Hepatology, (USA), American Association for the Study of Liver Diseases, December 2009, Volume 50, Issue 6, p.1827-1838 Tomoyoshi Soga, Masahiro Sugimoto, Masashi Honma, Masayo Mori, Kaori Igarashi, Kasumi Kashikura, Satsuki Ikeda, Akiyoshi Hirayama, Takehito Yamamoto, Haruhiko Yoshida, Motoyuki Otsuka, Shoji Tsuji, Yutaka Yatomi, Tadayuki Sakuragawa, Hisayoshi Watanabe, Kouei , Sumio Kawata, Hiroshi Suzuki, Masaru Tomita, Makoto Suematsu. "Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease." Journal of Hepatology, (Olanda Kingdom), European Association of Study of the Liver, October 2011, Volume 55, Issue 4, p.896-905

In these reports, the number of subjects for NAFLD was not as high as tens or less. It is desirable to obtain information about NAFLD from samples derived from subjects using more reliable biomarkers obtained based on the quantification of various molecules in more cases.

According to the first aspect of the present invention, the method for detecting non-alcoholic fatty liver disease is to obtain a sample derived from the body fluid of a mammal and to obtain 2-aminoadiponic acid, 2-oxocaproic acid, in the sample. It comprises detecting at least one molecule selected from the group consisting of 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvate, phenylalanine, and proline.
According to the second aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to the first aspect, the mammal obtained based on the magnitude of the detection signal corresponding to the molecule in the detection. However, it is preferable to output information as to whether or not the patient has non-alcoholic fatty liver disease.
According to the third aspect of the present invention, in the method for detecting a non-alcoholic fatty sensation disease according to the second aspect, the concentration of the molecule in the sample was calculated and calculated based on the magnitude of the detection signal. It is preferable to output information as to whether or not the mammal suffers from non-alcoholic fatty liver disease, which is obtained based on whether or not the concentration satisfies a condition based on a predetermined threshold.
According to a fourth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to third aspects, the detection is gas chromatography, liquid chromatography, mass spectrometry, gas chromatography / mass. It is preferably performed by at least one of analysis, liquid chromatography / mass spectrometry, nuclear magnetic resonance spectroscopy, and immunological detection.
According to the fifth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to fourth aspects, the mammal is preferably a human.
According to the sixth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to fifth aspects, the body fluid is preferably blood.
According to a seventh aspect of the present invention, the kit for detecting non-alcoholic fatty liver disease includes 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, Includes a reagent for measuring at least one molecule selected from the group consisting of glutamic acid, phenylalanine, and proline.
According to the eighth aspect of the present invention, the biomarkers for detecting non-alcoholic fatty liver disease are 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid and valine. Includes at least one molecule selected from the group consisting of, glutamic acid, phenylalanine, and proline.

According to the present invention, it is possible to more accurately obtain information on NAFLD from a sample derived from a subject based on a more reliable biomarker obtained based on a large-scale analysis of thousands of people.

FIG. 1 is a flowchart showing the flow of the detection method of one embodiment.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. The method for detecting non-alcoholic fatty liver disease (NAFLD) in the following embodiment detects a predetermined molecule in a sample derived from a target individual, and based on the detection, whether or not this individual suffers from NAFLD. To get information about.

The predetermined molecule is a molecule having a different concentration between a sample derived from an individual suffering from NAFLD and a sample derived from a healthy individual. By detecting the molecule in the sample, information on whether or not the individual suffers from NAFLD can be obtained. Therefore, the predetermined molecule is a biomarker of NAFLD, and is hereinafter referred to as a marker.

The NAFLD marker in this embodiment is selected from the group consisting of 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvic acid, phenylalanine, and proline. Will be done. In the method for detecting non-alcoholic fatty liver disease of the present embodiment, at least one marker selected from this group is detected, and the individual is NAFLD based on the magnitude of the detection signal corresponding to the detected marker. It is possible to obtain information on whether or not the patient is suffering from.
In the following embodiment, "detecting a marker" means performing detection for quantifying a marker contained in a sample, and is an ionized marker, a dissociated marker, or a derivative of the marker. It also includes the case where the marker-derived substance is directly detected, but the marker itself is not directly detected.

(About the sample)
If the concentration of the marker is different between the sample derived from an individual suffering from NAFLD and the sample derived from a healthy individual, the biological species of the target individual is not particularly limited, but mammals are preferable, and humans are more preferable.

If the concentration of the marker is different between the sample derived from an individual suffering from NAFLD and the sample derived from a healthy individual, a sample can be prepared using any body fluid of the target individual. Blood is preferred. In the following, an example of detecting a marker in a sample derived from human blood will be described. A human being who is the target of the NAFLD detection method of the present embodiment is called a subject.

The subject is not particularly limited, but is, for example, a human who has not been diagnosed with NAFLD. It is preferable to target people who undergo regular health examinations such as human docks, but it may also be people who are planning to take out life insurance who undergo health examinations. Humans who consume more than a predetermined amount of alcohol and those who have a predetermined disease now or in the past may be excluded from the target as appropriate. The predetermined amount is, for example, 30 g / day or more for men and 20 g / day or more for women in terms of ethanol. The predetermined diseases are, for example, liver diseases, diabetes, dyslipidemia, hyperuricemia or malignant diseases.

(About marker detection)
The method for detecting a marker in a sample is not particularly limited as long as it can be determined with desired accuracy whether or not the concentration of the detected marker is a concentration corresponding to NAFLD.

From the viewpoint of quantification and the like, the markers contained in the sample are detected by gas chromatography, liquid chromatography, mass spectrometry, nuclear magnetic resonance spectroscopy, a combination thereof, or an immunological detection method represented by ELISA. Is preferable. From the viewpoint of suitably separating and detecting a target marker from a sample containing various kinds of substances, the marker contained in the sample is gas chromatography / mass spectrometry (hereinafter referred to as GC / MS) or liquid chromatography / mass spectrometry. More preferably, it is detected by analysis (referred to as LC / MS). Here, GC / MS and LC / MS include the case of performing a plurality of mass separations such as ^{tandem mass spectrometry and MS n.}

Therefore, as a detection device or an inspection device for detecting a marker contained in a sample, a gas chromatograph, a liquid chromatograph, a mass spectrometer, an NMR spectrometer, or a device combining these is used. As described above, performing GC / MS with a gas chromatograph-mass spectrometer (hereinafter referred to as GC-MS) or performing LC / MS with a liquid chromatograph-mass spectrometer (hereinafter referred to as LC-MS). Is preferable. The mass spectrometer may be a single mass spectrometer or a mass spectrometer capable of separating masses in two or more stages. The type of the mass spectrometer that separates these masses inside the mass spectrometer is not particularly limited, and a quadrupole mass filter, an ion trap, a time-of-flight mass spectrometer, and the like can be appropriately included.

On the other hand, from the viewpoint of ease and speed of detection, it is preferable to add a detection reagent to the sample and detect the concentration of the marker by utilizing the chemical reaction between the detection reagent and the marker. For example, the detection reagent can contain an indicator that develops a color by a chemical reaction with the marker, and the concentration of the marker can be detected from the color tone of the sample solution to which the indicator is added.

Consumables used for the above-mentioned gas chromatography, liquid chromatography, mass spectrometry or nuclear magnetic resonance spectroscopy, or a kit for detecting non-alcoholic fatty liver disease including the above-mentioned detection reagent is provided. By performing the method for detecting non-alcoholic fatty liver disease of the present embodiment using such a kit, information on whether or not the subject has NAFLD can be obtained more quickly and / or more easily. can do.

(About sample pretreatment)
The sample obtained from the subject by blood sampling or the like is appropriately stored and then subjected to pretreatment. The sample may be stored as blood, or may be stored in the form of plasma or serum obtained by centrifuging the blood. The storage method of the sample is not particularly limited, and the sample can be stored frozen, refrigerated, or stored at room temperature.

The method of pretreatment before submitting the sample to the detection device for detecting the marker is not particularly limited. When performing GC / MS, an internal standard can be added to the sample, solvent extraction can be performed, and then centrifugal concentration, lyophilization, and derivatization can be appropriately performed. The internal standard can be appropriately selected according to the m / z of the marker to be detected, the type of sample (serum, plasma, etc.) and the like. For example, when measuring a region of m / z 500 or less in a serum sample, 2-isopropylmalic acid or the like, which is a synthetic compound not contained in serum, can be appropriately used. The derivatization method is not particularly limited, and for example, a methoxyamine solution is added to a sample after freeze-drying for oxime formation, and then N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) is added for silylation. The pretreated sample is introduced into the detector.

(About analysis of data obtained by detection)
The method for analyzing the data obtained by the detection is not particularly limited, and it is sufficient that the marker concentration in the sample (hereinafter referred to as the marker concentration) can be calculated. The analysis of the data is preferably performed by an information processing device such as a personal computer or an analysis device such as a processing device included in the detection device, but may be performed by a human. When a marker is detected by mass spectrometry, data corresponding to a mass spectrum or a mass chromatogram is created. After that, the detection amount of the marker is calculated based on the intensity and area of the peaks in these, and the marker concentration is calculated by the product of the detection amount of the marker divided by the detection amount of the internal standard and the known concentration of the internal standard. It is calculated.

Once the marker concentration is calculated, there is a high risk that the subject will have NAFLD or will have NAFLD, depending on whether the marker concentration meets a condition based on a predetermined threshold (hereinafter referred to as the marker threshold). Whether or not it is determined (hereinafter referred to as morbidity determination). The marker threshold is based on the concentration of the marker measured in the past in patients with NAFLD and in healthy subjects, and is preset so that, for example, the sensitivity or specificity becomes a desired value. For example, if the concentration of the marker in a patient with NAFLD is statistically higher than the concentration of the marker in a healthy subject, the marker is determined so that the subject is determined to be a patient with NAFLD when the marker concentration in the subject is higher than the marker threshold. Conditions based on the threshold are set.

The morbidity determination can be made by the marker concentration for one marker. When the morbidity determination is performed using the marker concentrations of a plurality of markers, it can be performed by multivariate analysis or the like based on the values of the plurality of marker concentrations and the corresponding plurality of marker thresholds. Alternatively, the morbidity may be determined by the number of a plurality of markers whose marker concentration satisfies the condition based on the marker threshold value. In this case, the risk may be classified into three stages such as "high", "medium", and "low" according to the number.

Information indicating the mass spectrum or mass chromatogram, marker concentration, or morbidity determination result obtained by analyzing the data obtained by the detection is displayed to doctors, medical professionals, and the like. With this information, the physician can diagnose whether the subject has NAFLD and, if the subject has NAFLD, treat it. In addition, medical staff and the like can grasp the state of the subject.

FIG. 1 is a flowchart showing a flow of a method for detecting non-alcoholic fatty liver disease according to the present embodiment. The detection method of the present embodiment is a method of detecting a marker in vitro from a sample derived from the blood of a subject in order to diagnose NAFLD. The flowchart of FIG. 1 shows an example of detecting a marker by GC / MS, but the present invention is not limited thereto.

In the diagnosis step S1001 of NAFLD, blood is collected by a medical worker or the like, and blood is obtained from the subject. When step S1001 is completed, step S1003 is started. In step S1003, the examiner, the user of the detection device, and the like perform pretreatment on blood to prepare a sample derived from blood. When step S1003 is completed, step S1005 is started.

In step S1005, the sample is separated by gas chromatography. When step S1005 is completed, step S1007 is started. In step S1007, the marker contained in the separated sample is detected by mass spectrometry. When step S1007 is completed, step S1009 is started.

In step S1009, the concentration of the marker in the sample is calculated from the magnitude of the detection signal corresponding to the marker. When step S1009 is completed, step S1011 is started. In step S1011 it is determined whether the calculated marker concentration satisfies the condition based on a predetermined threshold value (marker threshold value), and based on the determination result, data on whether or not the subject suffers from NAFLD is created. Will be done. When step S1011 is completed, step S1013 is started.

In step S1013, based on the data created in step S1011, information about whether or not the subject suffers from NAFLD is output to the display device or the like. When step S1013 is completed, the process is completed.

According to the above-described embodiment, the following effects can be obtained.
(1) The method for detecting non-alcoholic fatty liver disease of the present embodiment is to obtain a sample derived from human blood and to obtain 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutamic acid in the sample. It comprises detecting at least one molecule selected from the group consisting of alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvate, phenylalanine, and proline. This makes it possible to quickly screen for non-alcoholic fatty liver disease. Moreover, since these molecules are markers obtained by analysis based on a large-scale case as described in Examples described later, accurate screening can be performed.

(2) In the method for detecting non-alcoholic fatty liver disease of the present embodiment, whether or not the subject suffers from NAFLD obtained based on the magnitude of the detection signal corresponding to the marker in the detection of the marker. Output information. This allows more precise screening based on the quantified markers.

(3) In the method for detecting non-alcoholic fatty liver disease of the present embodiment, the concentration of the marker in the sample is calculated based on the magnitude of the detection signal corresponding to the marker, and the calculated concentration is a condition based on the marker threshold. Information about whether or not the subject has NAFLD is output based on whether or not the above conditions are satisfied. This allows more precise screening based on the quantified marker concentration.

(4) In the method for detecting non-alcoholic fatty liver disease of the present embodiment, the marker can be detected by gas chromatography, liquid chromatography, mass spectrometry, gas chromatography / mass spectrometry, liquid chromatography / mass spectrometry, nuclear magnetic resonance spectroscopy or It can be performed by immunological detection. As a result, each component contained in the sample can be separated and screened more precisely.

(5) The biomarkers for detecting non-alcoholic fatty liver disease according to the present embodiment are 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, and valine in the sample. Includes at least one molecule selected from the group consisting of, glutamic acid, phenylalanine, and proline. This makes it possible to quickly screen for non-alcoholic fatty liver disease. Moreover, since these molecules are markers obtained by analysis based on a large-scale case as described in Examples described later, accurate screening can be performed.

The present invention is not limited to the contents of the above embodiment. Other aspects conceivable within the scope of the technical idea of the present invention are also included within the scope of the present invention.

In the following examples, sera obtained from patients diagnosed with NAFLD by abdominal ultrasonography (NAFLD group) and healthy subjects (normal group) were subjected to GC / MS, and the concentrations were concentrated in the NAFLD group and the normal group. The results of the analysis that searched for components in different sera of the same are explained.
The present invention is not limited to the numerical values and conditions shown in the following examples.

(Target person)
Among those randomly selected from Japanese human dock examinees who visited the hospital from October 2015 to September 2016, we analyzed the sera of 3733 people who met the recruitment criteria. The recruitment criteria are not applicable to any of the following (1) to (5).
(1) Those who expressed disagreement with participation in the study (2) Those who had alcohol intake (male: ethanol equivalent 30 g / day or more, female: ethanol equivalent 20 g / day or more) (3) Under treatment for liver disease Those who have a current medical history (4) Those who have a current medical history during treatment for at least one of diabetes, dyslipidemia, and hyperuricemia (5) Those who have a current medical history during treatment for malignant diseases Meet the above recruitment criteria All of them had abdominal ultrasound findings in the human dock, and the clinical information and the results of serum analysis were combined with the consent of the in-hospital research ethics review committee.

Of the 3733 patients in the NAFLD group and the normal group, 2252 (60.3%) were female, and the median age was 51 years (interquartile range: 43 to 61 years). The baseline characteristics of the study populations analyzed are shown in Table 1 below. Arithmetic mean was used as the average of BMI.

(Preprocessing)
After blood collection, serum was extracted by centrifugation and stored frozen at −80 ° C. within 5 to 8 hours after blood collection. The cryopreserved serum was allowed to thaw at 25 ° C. for 5 minutes. The thawed serum was centrifuged at 10,000 × g at 4 ° C. for 1 minute to transfer the supernatant, and the serum was allowed to stand on ice. 6 μL of an internal standard solution (0.1 mg / mL 2-isopropylmalic acid) was added to 250 μL of a mixed solution of methanol / water / chloroform in a ratio of 2.5: 1: 1 and the mixed solution was transferred. In addition to the serum sample, the mixture was stirred using a vortex mixer. The serum sample was shaken at 37 ° C. for 30 minutes at 1,200 rpm. The sample after shaking was centrifuged at 16,000 × g at 25 ° C. for 5 minutes. 150 μL of the supernatant after centrifugation was added to a tube to which 140 μL of water had been added in advance, and the mixture was stirred using a vortex mixer. The stirred solution was centrifuged at 16,000 xg at 25 ° C. for 5 minutes. 180 μL of the supernatant after centrifugation was collected in a new tube. Concentration was carried out by a centrifugal evaporator at room temperature for 60 minutes. The concentrated sample was allowed to freeze at −80 ° C. for 30 minutes. The frozen sample was dried overnight. The dried sample was stored in a desiccator. 80 μL of a methoxyamine solution (20 mg / mL, dissolved in pyridine) was added to the dried sample, and the sample was shaken at 37 ° C. for 30 minutes at 200 rpm. Then, 40 μL of N-methyl-N-trimethylsilyltrifluoroacetamide was added, and the mixture was shaken at 37 ° C. for 30 minutes at 1200 rpm. The sample after shaking was centrifuged at 16000 × g at 25 ° C. for 5 minutes. 50 μL of the supernatant after centrifugation was transferred to a glass vial, and the vial was placed on a GC-MS autosampler.

(GC / MS)
Condition system for gas chromatography: GCMS-TQ8040 (Shimadzu Corporation)
Column oven temperature: 80 ° C, injection temperature: 280 ° C, injection mode: split introduction method, carrier gas: helium, carrier gas flow rate: 39 cm / sec
Separation column:
DB-5 (Agilent Technologies): stationary phase; (5% -phenyl) -methylpolysiloxane (non-polar), length; 30.0 m, inner diameter: 0.25 mm, film thickness: 1.00 μm
Column temperature program:
Hours (minutes) Temperature (℃)
0 80
2.5 80
18.5 280
23.0 280

Conditional system for mass spectrometry: GCMS-TQ8040 (Shimadzu Corporation)
Ion source temperature: 200 ° C, interface temperature: 250 ° C, detector voltage: 0.2 kV, measurement mode: scan mode, scanning range: m / z 85-500

(Data analysis)
A mass spectrum at each holding time was created from the detection intensities obtained by scanning m / z at each holding time. Based on the mass spectrum data of the serum samples obtained in the past, metabolites (136 species) corresponding to each peak in the prepared mass spectrum were identified. The detection intensity corresponding to each metabolite was standardized using the detection intensity corresponding to the internal standard isopropylmalic acid, and the concentration in the sample was calculated. The concentrations of each metabolite in the NAFLD group and the normal group were compared by statistical analysis. In the statistical analysis, unpaired t-test and Mann-Whitney U-test were used as univariate analysis, and logistic regression analysis was used as multivariate analysis for multiple metabolites. The diagnostic ability of NAFLD was evaluated by creating a ROC (Receiver Operating Characteristic) curve for each metabolite and using the area (AUC) of the portion below the ROC curve. The Lasso method was used for the examination of multiple metabolites. Models with metabolites and regulators were evaluated using the c-statistic.

(result)
An unpaired t-test was performed between the NAFLD group and the normal group, and the metabolites that showed a significant difference were glutamate, 2-oxoglutaric acid, valine, tyrosine, 2-aminoadiponic acid, phenylalanine, pyruvic acid, It was uric acid, 2-oxocaproic acid and alanine (the metabolites with the smallest p value were ranked first, and the metabolites up to the 10th position were listed in ascending order of p value). Table 2 below shows the p-values and t-statistics of these metabolites. Since the t-statistic was a negative value for all metabolites, it can be seen that the amount of metabolites was smaller in the normal group than in the NAFLD group. The notation of "xE-y" in the table indicates a value obtained by multiplying x by 10 −y, and is the same in each of the following tables.

NAFLD group / normal group was used as the dependent variable, and age, sex and BMI were used as regulators, and the relationship between the NAFLD group and each metabolite was examined by logistic regression analysis. As a result, the strongest association was found with 2-oxoglutaric acid (regression). Coefficient 0.5658, standard error 0.0547, odds ratio to change per SD 1.761, 95% confidence interval 1.582-1.960, p-value = 4.10E-25). Table 3 below shows the regression coefficients, standard errors and p-values for logistic regression analysis of the five metabolites containing 2-oxoglutaric acid. In addition, the rank when the adjustment by the adjusting factor was not performed (that is, in Table 2), and the t-statistic and the P value are described.

The diagnostic ability was examined. The dependent variable was the NAFLD group / normal group, and ROC analysis was performed on each metabolite, and as a result of examining AUC, the metabolites with large values were glutamic acid (AUC = 0.759) and 2-oxoglutaric acid (AUC = 0.729). ), Valin (AUC = 0.699). The table below shows the AUC of each metabolite.

In the above, the diagnostic ability with a single metabolite was examined, but the same examination was carried out using a plurality of metabolites. As a result of examination by the Lasso method, 70 metabolites were selected, and the AUC in that case was 0.870. The 70 metabolites are: Boric acid, phenol, 2-hydroxyisobutyric acid, glycolic acid, alanine, glycine, 2-hydroxybutyric acid, p-cresol, 3-hydroxybutyric acid, 3-hydroxyisobutyric acid, 2-hydroxyisovaleric acid, 2-aminobutyric acid, 3-Hydroxyisovaleric acid, valine, urea, capric acid, glycerol, acetulic acid, phosphoric acid, isoleucine, proline, succinic acid, fumaric acid, serine, glutaric acid, 2-deoxytetronic acid, decanoic acid, aspartic acid, methionine , Pyroglutamic acid, hydroxyproline, glutamic acid, phenylalanine, lauric acid, asparagine, 2-aminoadipic acid, aconitic acid, glutamine, histidine, tyrosine, palmitreic acid, uric acid, stearic acid, tryptophan, 2-pyridone, pyruvate, 3- Aminopropanoic acid, erythritol, toreonic acid, 2-oxoglutaric acid, creatinine, xylitol, arabitol, fucose, ethanolamine phosphate, isocitrate, hypoxanthin, horse uric acid, 1,5-anhydro-D-glucitol, 3-methylhistidine , Mannose, lysine, paraxanthin, silinositol, 3-indole propionic acid, quinurenin, cystine, acetoacetic acid, gluconic acid, ribose.

In the model in which age, sex and BMI were input as adjusting factors for the same dependent variable, the c-statistic was 0.873 for glutamic acid, which was the largest. Table 5 below shows the c-statistic of each metabolite. In addition, the ranking when the adjustment by the adjusting factor was not performed (that is, in Table 4) and the AUC are described.

In recent years, NAFLD in the group where BMI is not high, that is, not obese, has been clinically regarded as a problem. Therefore, when the above analysis was performed in a non-obese group with a BMI of less than 25, similar results were obtained.

Furthermore, when the above analysis was performed in a group having a BMI of less than 23 and not less than the standard weight, similar results were obtained.

The disclosure of the next priority basic application is incorporated here as a quotation.
Japanese Patent Application No. 180886, 2018 (filed on September 26, 2018)

According to the first aspect of the present invention, the method for detecting non-alcoholic fatty liver disease is to obtain a sample derived from the body fluid of a mammal, to detect 2-aminoadipic acid in the sample, and to detect 2-aminoadipic acid in the sample. To be equipped with.
According to the second aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease in the first aspect, it was obtained based on the magnitude of the detection signal corresponding to 2-aminoadiponic acid in the detection. It outputs information as to whether or not the mammal suffers from non-alcoholic fatty liver disease.
According to the third aspect of the present invention, in the method for detecting a non-alcoholic fatty sensation disease according to the second aspect, the concentration of 2-aminoadipic acid in the sample is calculated based on the magnitude of the detection signal. It outputs information about whether or not the mammal suffers from non-alcoholic fatty liver disease, which is obtained based on whether or not the calculated concentration satisfies a condition based on a predetermined threshold.
According to a fourth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to third aspects, the detection is gas chromatography, liquid chromatography, mass spectrometry, gas chromatography / mass. It is performed by at least one of analysis, liquid chromatography / mass spectrometry, nuclear magnetic resonance spectroscopy, and immunological detection.
According to a fifth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to fourth aspects, the mammal is a human being.
According to the sixth aspect of the present invention, in the method for detecting non-alcoholic fatty liver disease according to any one of the first to fifth aspects, the body fluid is blood.
According to a seventh aspect of the present invention, the non-alcoholic fatty liver disease detection kit contains a reagent for measuring 2-aminoadipic acid.
According to an eighth aspect of the present invention, the biomarker for detecting non-alcoholic fatty liver disease includes 2-aminoadipic acid.

Claims (8)

Obtaining samples derived from mammalian body fluids
At least one molecule selected from the group consisting of 2-aminoadic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvic acid, phenylalanine, and proline in the sample. And a method for detecting non-alcoholic fatty liver disease. In the method for detecting non-alcoholic fatty liver disease according to claim 1,
Non-alcoholic fatty liver that outputs information about whether or not the mammal suffers from non-alcoholic fatty liver disease, which is obtained based on the magnitude of the detection signal corresponding to the molecule in the detection. How to detect the disease. In the method for detecting non-alcoholic fatty liver disease according to claim 2.
The mammal obtained by calculating the concentration of the molecule in the sample based on the magnitude of the detection signal and obtaining whether or not the calculated concentration satisfies the condition based on a predetermined threshold value. A method for detecting non-alcoholic fatty liver disease, which outputs information as to whether or not the patient has non-alcoholic fatty liver disease. The method for detecting non-alcoholic fatty liver disease according to any one of claims 1 to 3.
The detection is performed by at least one of gas chromatography, liquid chromatography, mass spectrometry, gas chromatography / mass spectrometry, liquid chromatography / mass spectrometry, nuclear magnetic resonance spectroscopy, and immunological detection. How to detect the disease. The method for detecting non-alcoholic fatty liver disease according to any one of claims 1 to 3.
A method for detecting non-alcoholic fatty liver disease, wherein the mammal is a human. The method for detecting non-alcoholic fatty liver disease according to any one of claims 1 to 3.
A method for detecting non-alcoholic fatty liver disease, wherein the body fluid is blood. A reagent for measuring at least one molecule selected from the group consisting of 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvic acid, phenylalanine, and proline. Kit for detecting non-alcoholic fatty liver disease, including. Includes at least one molecule selected from the group consisting of 2-aminoadiponic acid, 2-oxocaproic acid, 2-oxoglutaric acid, alanine, glutamic acid, tyrosine, lactic acid, uric acid, valine, pyruvic acid, phenylalanine, and proline. Biomarker for detecting non-alcoholic fatty liver disease.

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