Method for Measuring COPD Index Values

Abstract

To use a measurement result for a diagnosis of a chronic obstructive pulmonary disease related disease by measuring an amount of a substance considered to be associated with the chronic obstructive pulmonary disease related disease.SOLUTION: A method for measuring the amount of a metabolite related to development of a chronic obstructive pulmonary disease as a COPD index value among several metabolites contained in a biological sample, based on data obtained by analyzing the biological sample collected from a subject. The COPD index value is the amount of at least one metabolite of selected from 24 metabolites consisting of glutaric acid, α-ketoisocaproic acid, phospho-glycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitric acid, erythritol, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, xylose, cystine, tyrosine, uric acid, mannose, and beta alanine.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to a method for measuring the amount of a substance related to the onset of chronic obstructive pulmonary disease among substances contained in biological samples such as blood and urine.

Background Art

[0002] Chronic obstructive pulmonary disease (COPD) is a disease characterized by irreversible progressive airflow obstruction and is mainly said to be caused by long-term inhalation exposure to harmful particles such as tobacco smoke (Non-Patent Document 1). COPD is one of the diseases with high chronic morbidity and mortality rates worldwide, and COPD patients have a high risk of developing lung cancer (Non-Patent Document 2). For example, in a WHO survey in 2004, COPD ranked fourth in the causes of death (5.1% of total deaths) (Non-Patent Document 3), and also ranked ninth in the causes of death in Japan in 2013 (Non-Patent Document 4). Since the number of COPD patients is expected to increase in the future, the prevention and countermeasures for this disease are urgent and important issues.

[0003] COPD is generally diagnosed based on the fact that the forced expiratory volume in 1 second / forced vital capacity (FEV1 / FVC) is less than 70% in a spirometry (respiratory function test using a spirometer) after administration of a bronchodilator. Here, the "forced expiratory volume in 1 second ratio" refers to the ratio (%) of the expiratory volume in the first 1 second to the amount of a deep breath exhaled in one go (forced vital capacity). The forced expiratory volume in 1 second ratio reflects the elasticity of the lungs and the degree of airway obstruction, and the fact that the forced expiratory volume in 1 second ratio is less than 70% indicates that the subject has obstructive ventilation disorder (respiratory function disorder caused by airflow obstruction).

[0004] However, since obstructive ventilation disorders are also observed in patients with pulmonary diseases other than COPD, such as asthma and pulmonary fibrosis, even if the forced expiratory volume in 1 second (FEV1) / forced vital capacity (FVC) ratio is less than 70% in spirometry, it cannot be immediately diagnosed as COPD (Non-Patent Document 5). Conventionally, the typical pathological condition of COPD has been considered to be airflow obstruction. However, in recent years, it has been considered to be a collection of various complex pathological conditions, and the disease concept of COPD is changing from a local inflammatory disease of the lung to a systemic inflammatory disease (a disease complicated by ischemic heart disease, diabetes, dyslipidemia, osteoporosis, etc.). Therefore, instead of diagnosis based on the FEV1 / FVC ratio, a method of diagnosing COPD using specific components in biological samples such as blood, serum, and plasma collected from a subject as biomarkers has been considered (Patent Documents 1 to 4).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Non-Patent Documents

[0006]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Non-Patent Document 4

Non-Patent Document 5

Non-Patent Document 6

Non-Patent Document 7

Non-Patent Document 8

Non-Patent Document 9

Non-Patent Document 10

Non-Patent Document 11

Non-Patent Document 12

Non-Patent Document 13

[0007] In the methods described in Patent Documents 1 to 4, mainly, proteins, peptides, and genes considered to be involved in the onset pathway and mechanism of COPD are used as biomarkers, and COPD is diagnosed by measuring the amounts of these biomarkers contained in the blood. However, as described above, COPD is a collection of various pathological conditions, and there are many unclear points in its onset pathway and mechanism.

[0008] For example, the greatest risk factor for COPD is smoking (Non-Patent Documents 6 to 8). Approximately 90% of COPD patients have a smoking history, and the incidence increases with age and the amount of smoking exposure. COPD is observed in approximately 50% of elderly smokers and approximately 70% of heavy smokers with a smoking index of 60 pack-years or more (Non-Patent Documents 9 and 10). The smoking index pack-years represents (average number of cigarettes smoked per day / 20) × total number of years of smoking. On the other hand, the incidence of COPD among all smokers is only about 15 to 20% (Non-Patent Document 11). There is also a report that the incidence of COPD in smokers with 20 pack-years is about 19%, and about 30% of heavy smokers with 60 pack-years or more have normal respiratory function (Non-Patent Documents 11 and 12).

[0009] Therefore, the existence of smoking susceptibility is suspected in the ease of onset of COPD in smokers. Until now, searches for COPD-related candidate genes (genomics) have been conducted, but due to insufficient sample size and uniformity, and the need to consider ethnic differences, etc., except for α1-antitrypsin deficiency as a single gene abnormality, the elucidation of COPD-related genes has not been completely achieved yet (Non-Patent Document 13).

[0010] Furthermore, the genomics that has been conventionally conducted to elucidate the ease of onset of COPD is limited to the examination of individual-specific genes. As described above, the onset of COPD is affected not only by genetic factors but also by environmental factors such as smoking and various state changes that can occur in the body such as aging, infection, and nutritional status. Therefore, COPD is considered to be a systemic inflammatory disease as described above, and in order to accurately diagnose COPD, it is necessary to examine factors that affect COPD from various angles such as the onset pathway and pathological condition of COPD and the genetic and environmental background of the individual.

[0011] The problem to be solved by the present invention is to measure the amount of a substance considered to be related to a chronic obstructive pulmonary disease-related disease and use the measurement result for the diagnosis of a chronic obstructive pulmonary disease-related disease.

Means for Solving the Problem

[0012] The present invention made to solve the above problems is a method for measuring, as a COPD index value, the amount of metabolites related to chronic obstructive pulmonary disease-related diseases among a plurality of types of metabolites contained in a biological sample, based on data obtained by analyzing the biological sample collected from a subject characterized in that the COPD index value is the amount of at least one metabolite selected from 24 metabolites: glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitric acid, erythritol, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, xylose, cystine, tyrosine, uric acid, mannose, beta-alanine

[0013] In the present invention, the chronic obstructive pulmonary disease-related disease (COPD-related disease) refers to, in addition to COPD, a disease at the stage prior to COPD, a disease complicated by COPD (for example, ischemic heart disease, diabetes, dyslipidemia, osteoporosis, etc.) The inventor comprehensively analyzed the results of comprehensively measuring the amounts of a large number of metabolites contained in blood samples collected from a large number of subjects using a chromatograph mass spectrometer, and found that the above-mentioned 24 metabolites are greatly related to the onset of COPD. Such an analysis method is called metabolomics, and by using this method, it is possible to comprehensively grasp the complex pathological changes related to COPD.

[0014] The above 24 metabolites were metabolites that showed a high correlation in the COPD-related group as a result of performing statistical analysis on the measured values of 135 robust metabolites among the numerous metabolites contained in the biological sample for two groups, namely the COPD-related group and the normal group. Table 1 shows a list of the 135 metabolites. The “-nTMS”, “-nTMS(m)”, “-methyloxime-nTMS”, or “-methyloxime-nTMS(m)” (n and m are natural numbers) contained in the metabolite names shown in Table 1 are due to the reagents added during analysis by a gas chromatograph mass spectrometer. Therefore, for example, “asparagine-2TMS” and “asparagine-3TMS” in Table 1 are derived from the same metabolite (asparagine). The measured values of the above 24 metabolites can all be used alone as COPD index values for the diagnosis of COPD. Note that the COPD-related group in the two-group comparison can be determined based on the value of the one-second rate (FEV1 / FVC) by a respiratory function test using a spirometer.

[0015]

Table 1

[0016] Previously, reports have been made on using metabolomics to search for biomarkers for COPD, with 23 metabolites (such as myo-inositol and fumaric acid) being proposed as biomarker candidates (Non-Patent Document 14). However, at present, there are no reports that metabolites contained in biological samples collected from COPD patients can be used alone as biomarkers useful for the diagnosis of COPD and the understanding of the disease state (symptoms, respiratory function, etc.) (Non-Patent Document 15).

[0017] Here, the "biological sample collected from the subject" can be any sample such as blood, biological tissue, feces, urine, etc., as long as the amount of metabolites contained in the biological sample can be measured. However, considering the ease of sample collection and the high metabolite content, blood (whole blood, serum, or plasma) is preferred. As serum, the liquid component obtained by coagulating the blood cell components without adding an anticoagulant to whole blood can be used. Also, as plasma, the liquid component obtained by adding an anticoagulant to whole blood without coagulating the blood cell components can be used.

[0018] In addition, as an analysis method for biological samples, not limited to chromatograph mass spectrometry (chromatograph MS analysis), nuclear magnetic resonance (NMR) spectroscopy, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), and other immunological analysis methods can be used. However, chromatograph MS analysis is excellent in that it can quantitatively measure the amount of metabolites contained in biological samples. Chromatograph MS analysis refers to an analysis using a gas chromatograph or a liquid chromatograph and a mass spectrometer as a detection device for the samples separated by these chromatographs. Examples of the mass spectrometer include a triple quadrupole mass spectrometer, a Q-TOF mass spectrometer, a TOF-TOF mass spectrometer, an ion trap mass spectrometer, or an ion trap time-of-flight mass spectrometer. Using these mass spectrometers, highly sensitive analysis is possible even for samples containing a large amount of contaminants other than the analyte (metabolites), and the analysis stability is improved, so that the amount of metabolites contained in biological samples can be quantitatively measured with high reproducibility.

[0019] As the above COPD index value, the measured value of any one of arabinose, ornithine, kynurenine, 2-aminoadipic acid, cystine, tyrosine, myo-inositol, uric acid, beta-alanine, and mannose can be used. When a t-test was performed on the measured values of 135 metabolites for the above 10 metabolites between the COPD-related group and the normal group, a significant difference was observed between the COPD-related group and the normal group. Therefore, by measuring the amounts of the above 10 metabolites as COPD index values and comparing the COPD index values with a predetermined threshold value, the presence or absence of COPD-related diseases can be diagnosed. The predetermined threshold value can be an average value, a median value, etc. when measuring the amounts contained in biological samples of the above 10 metabolites for a large number of subjects.

[0020] Also, as the above COPD index value, the measured value of any one of glutaric acid, hydroxyproline, xylose, phosphoglycerol, and ornithine may be used. When logistic regression analysis was performed on the measured values of 135 metabolites for the above 5 metabolites in a model in which age, gender, and smoking history were input as adjustment factors in addition to the presence or absence of COPD-related diseases as a dependent variable, the P-value was less than 0.05, and these are metabolites that showed a high correlation with the presence or absence of COPD onset. Therefore, by measuring the measured values of these 5 metabolites as COPD index values and comparing them with a predetermined threshold value, the presence or absence of COPD onset can be diagnosed.

[0021] Also, as the above COPD index value, it is preferable to use the measured value of any one of erythritol, ornithine, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, and arabinose. When ROC analysis was performed on the measured values of 135 metabolites for the above 10 metabolites with the presence or absence of COPD-related diseases as a dependent variable and the AUC value was obtained, these are metabolites with a large value. Therefore, by measuring the measured values of these 10 metabolites as COPD index values respectively and comparing them with a predetermined threshold value, the presence or absence of COPD onset can be diagnosed, and moreover, misdiagnosis can be reduced.

[0022] In addition, as the COPD index value, it is preferable to use the measured value of any one of glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, and isocitric acid. For the above-mentioned 10 metabolites, when the C statistic was calculated for the measured values of 135 metabolites in a model in which the presence or absence of a COPD-related disease was used as a dependent variable and age, gender, and smoking history were used as adjustment factors, these were the metabolites with large values. Therefore, by measuring the measured values of these 10 metabolites as COPD index values respectively and comparing them with a predetermined threshold value, it is possible to diagnose the presence or absence of COPD onset, and moreover, it is possible to reduce misdiagnosis.

Advantages of the Invention

[0023] In the present invention, by searching for metabolites with a strong correlation with COPD-related diseases using a metabolomics analysis method and measuring the amount of the metabolites found in a biological sample as a COPD index value, the COPD index value can be used for diagnosing COPD-related diseases.

Embodiments for Carrying Out the Invention

[0024] The inventor analyzed a biological sample (serum) collected from a subject using a chromatograph mass spectrometer (chromatograph MS), and comprehensively measured the amounts of a large number of metabolites contained in the biological sample. Then, by analyzing those measured values using various methods, metabolites that are considered to be greatly related to the presence or absence of COPD-related diseases were searched. Table 2 shows the baseline characteristics of the subjects from whom biological samples were collected for searching metabolites related to the onset of COPD. The extraction method of the subjects shown in Table 2, the preparation and analysis method of the biological sample, the measurement method of metabolites, the analysis of the measurement results, etc. will be described below.

[0025]

Table 2

[0026] <Extraction of Subjects> Under the approval of the Research Ethics Committee, about 10,000 examinees were randomly sampled from about 45,000 physical examination examinees who visited the Preventive Medicine Center of the Clinic affiliated with St. Luke's International Hospital from October 2015 to September 2016. Among them, those who indicated non-participation in this experiment, those with missing measurement data, and those who met the exclusion criteria were excluded, and the remaining 6,610 subjects were used as the test subjects. The exclusion criterion was set to be under 40 years old at the time of the physical examination based on the findings of previous studies that the onset begins around 20 years after starting smoking.

[0027] The Preventive Medicine Center of the Clinic affiliated with St. Luke's International Hospital is located in Chuo-ku, Tokyo, and many of the residential areas of the physical examination examinees are in the vicinity of Tokyo. Therefore, the external factors related to COPD (environmental factors such as air pollution) of the test subjects in this study are substantially uniform. Also, by setting all the test subjects in this study to be Japanese, the ethnic factors were made uniform.

[0028] The median age of the group of 6,610 test subjects was 54 years old (interquartile range 47 - 63 years old), and among them, 3,467 (52.5%) were women. Also, the smoking rate of the test subject group was 37.1%, and the number of test subjects with a 1-second ratio of less than 70% was 692 (10.5%). Hereinafter, the group of test subjects with a 1-second ratio of less than 70% is referred to as the COPD-related group, and the group of test subjects with a 1-second ratio of 70% or more (5,918 people) is referred to as the normal group. As can be seen from Table 2, the COPD-related group was statistically significantly more male than female. Also, both the age and the smoking rate were higher in the COPD-related group than in the normal group. In clinical examinations, a patient with a 1-second ratio value of less than 70% by a respiratory function test using a spirometer after administration of a bronchodilator is determined to have COPD, but in this experiment, no bronchodilator was administered. Therefore, the above COPD-related group includes not only test subjects who actually have COPD but also test subjects with COPD-related diseases.

[0029] <Sample Preparation> (1) Pretreatment Blood samples were collected from 6,610 subjects, allowed to stand for a predetermined period of time, and then centrifuged to collect the supernatant (serum). The collected serum was stored at -80°C until analysis. Note that the time from blood collection from the subject to freezing of the serum affects the measured values of metabolites in the serum. Therefore, in this study, subjects were selected such that those with a time of 5 to 8 hours from blood collection to freezing accounted for approximately half of the total.

[0030] (2) This treatment The serum stored at -80°C was thawed at 25°C for 5 minutes. After thawing, the serum was centrifuged at 10,000×g, 4°C, for 1 min, then allowed to stand on ice. To this, 6 μL of the internal standard solution (2-isopropylmalic acid: 0.1 mg / mL) was added to 250 μL of the first mixture (methanol / water / chloroform, 2.5:1:1), and the mixture was mixed using a vortex mixer to obtain a second mixture.

[0031] Thereafter, the serum and the second mixture were shaken at 37°C for 30 minutes at 1,200 rpm, then centrifuged at 25°C for 5 minutes at 16,000×g. 150 μL of the supernatant was added to a tube pre-added with 140 μL of water, and the mixture was mixed using a vortex mixer to obtain a third mixture. Next, the third mixture was centrifuged at 25°C for 5 minutes at 16,000×g, and 180 μL of the supernatant was collected into a new tube. This was concentrated using a centrifugal evaporator at room temperature for 60 minutes with the speed memory set to "7". The supernatant (concentrate) of the concentrated third mixture was frozen at -80°C for 30 minutes, and then the dried product overnight was used as a sample. The obtained sample was stored in a desiccator until analysis.

[0032] <GC-MS Analysis of Samples> To the sample stored in the desiccator, 80 μL of methoxyamine solution (20 mg / mL, dissolved in pyridine) was added, and then it was shaken at 37 °C for 30 minutes at 1,200 rpm. Subsequently, 40 μL of N-methyl-N-trimethylsilyltrifluoroacetamide was added, and after shaking at 37 °C for 30 minutes at 1,200 rpm, it was centrifuged at 25 °C for 5 minutes at 16,000×g. 50 μL of the supernatant was placed in a glass vial, and this vial was set in the autosampler of the GCMS.

[0033] For the GCMS, a triple quadrupole gas chromatograph mass spectrometer (GCMS-TQ8040) manufactured by Shimadzu Corporation was used. The analysis conditions of the GCMS are shown below. (1) Column: DB-5 ((5%-phenyl)-methylpolysiloxane, non-polar), length 30 m, inner diameter 0.25 mm, film thickness 1.00 μm (2) Column oven temperature: 80 °C (3) Injection temperature: 280 °C (4) Injection mode: Split (5) Helium gas flow rate: 39 cm / sec (6) Column temperature: 0 - 2.5 min; 80 °C, 2.5 - 18.5 min; rising to 80 - 280 °C, 18.5 - 23.0 min; 280 °C (7) MS ion source temperature: 200 °C (8) Interface temperature: 250 °C (9) Detector voltage: 0.2 kV (10) Mass range: m / z 85 - 500

[0034] <Data analysis> <Measurement of metabolites> Peaks were comprehensively detected from the mass spectrum of the sample, and by comparing their peak information (mass-to-charge ratio and signal intensity) with the mass-to-charge ratios of substances specific to a large number of metabolites stored in the MS library, metabolites were identified, and measurement values of 135 types of metabolites (hereinafter referred to as MS data) were obtained.

[0035] <Statistical analysis> For the subjects from whom MS data were obtained, clinical information and test data necessary for statistical analysis were obtained from the Information System Center of St. Luke's International University under the approval of the Research Ethics Committee of St. Luke's International Hospital, and the clinical information, test data, and MS data were matched. All of the clinical information and test data of the subjects were managed by a management number representing the subject, and the subject was not identified during the data matching process.

[0036] For statistical analysis, unpaired t-tests were used as univariate analysis, and logistic regression analysis was used as multivariate analysis. For the evaluation of diagnostic ability, the AUC value calculated from the ROC curve was used in the model of each metabolite alone, and the C statistic was used in the multivariate analysis model using adjustment factors.

[0037] Table 3 shows the results of performing t-tests on the measured values of 135 metabolites between the COPD-related group and the normal group. Table 3 lists the P-values and t-statistics of 10 metabolites (ornithine, kynurenine, 2-aminoadipic acid, cystine, tyrosine, myo-inositol, uric acid, arabinose, mannose, beta-alanine) for which statistically significant differences were observed between the COPD-related group and the normal group, in ascending order of the P-values. In Table 3, "mEn (m is a real number, n is an integer)" means "m × 10 n ". For example, "4.14E-14" means "4.14 × 10 -14 ".

[0038]

Table 3

[0039] As shown in Table 3, for all 10 metabolites, the smaller the P-value, the larger the absolute value of the t-statistic, and all were negative values. It can be seen that the amount of metabolites in the subjects of the normal group was less than that in the COPD-related group. From the above results, the measured values of the above 10 metabolites are useful as COPD index values, and when the measured value is compared with a predetermined threshold and is greater than the threshold, it can be diagnosed as a COPD-related disease.

[0040] Table 4 shows the results of examining the relationship between the COPD-related group / normal group as the dependent variable, age, gender, and smoking history as adjustment factors, and the measured values of 135 metabolites using logistic regression analysis. Table 4 lists the five metabolites (glutaric acid, hydroxyproline, xylose, phosphoglycerol, ornithine) with small P-values as regression coefficients, standard errors, and P-values as a result of the logistic regression analysis. As shown in Table 4, the P-value of glutaric acid among the five metabolites was the smallest, and a strong correlation was observed between glutaric acid and COPD-related diseases. Table 4 also shows the ranking, t-statistic, and P-value when no adjustment was made with the adjustment factors. Thus, the usefulness as an indicator increases by adjusting with the adjustment factors.

[0041]

Table 4

[0042] Next, in order to examine the diagnostic ability (that is, the high diagnostic accuracy of COPD-related diseases) when the measured values of each metabolite were used as COPD index values, ROC analysis (Receiver Operating Characteristic analysis) was performed on 135 metabolites with the COPD-related group / normal group as the dependent variable. As a result, it was found that 10 metabolites (erythritol, ornithine, myo-inositol, treitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, arabinose) are useful for the diagnosis of COPD. Table 5 shows the AUC values of the ROC curves (ROC curves) of the 10 metabolites useful for the determination of COPD.

[0043]

Table 5

[0044] The ROC curve (Receiver Operating Characteristic curve) is plotted with the true positive rate, i.e., sensitivity, on the vertical axis and the false positive rate, i.e., "1 - specificity", on the horizontal axis. That is, the sensitivity is calculated from the proportion of subjects judged positive among all subjects in the COPD - related group, and the false positive rate is calculated from the proportion of subjects judged positive (non - COPD) among all subjects in the normal group. Similarly, the sensitivity and false positive rate at other cut - off values are calculated, and the ROC curve is obtained by plotting the values thus obtained on a graph. The AUC value is the value of the area enclosed by the ROC curve and the horizontal axis. The larger the AUC value, the higher the accuracy of diagnosing the presence or absence of COPD - related diseases can be judged. As a result of obtaining the AUC from the ROC curve, the metabolites with large AUC values were erythritol (AUC = 0.616), ornithine (AUC = 0.594), and myo - inositol (AUC = 0.590).

[0045] On the other hand, Table 6 shows the results of ROC analysis of 135 metabolites for a model in which age, gender, and smoking history were input as adjustment factors for the same dependent variable as the ROC analysis whose results are shown in Table 5, and lists the 10 metabolites with high C - statistics. The C - statistic corresponds to the size of the area (AUC) enclosed by the ROC curve and the horizontal axis, indicating that the closer the C - statistic is to 1, the higher the diagnostic ability. Table 6 lists the C - statistics of 10 metabolites (glutaric acid, α - ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitric acid) out of 135 metabolites in descending order of C - statistic. The measured values of these 10 metabolites are all statistically correlated with COPD - related diseases and are useful as indicators for determining the presence or absence of COPD - related diseases. In particular, the measured value of glutaric acid, which has the largest C - statistic (C - statistic: 0.751), shows a strong correlation with the onset of COPD and can be said to be excellent as an indicator for determining the possibility of COPD onset. Table 6 also shows the ranking and AUC values when no adjustment was made with the adjustment factors. Adjusting with adjustment factors in this way increases the usefulness as an indicator.

[0046]

Table 6

[0047] Thus, the results of measuring the amounts of 24 metabolites contained in the biological sample of the subject can be used for diagnosing the presence or absence of COPD in the subject. That is, the method for diagnosing COPD includes analyzing a biological sample collected from a subject with an analyzer, obtaining a COPD index value which is the amount of at least one metabolite selected from 24 metabolites including glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitric acid, erythritol, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, xylose, cystine, tyrosine, uric acid, mannose, and beta-alanine from the data obtained by the analyzer, and diagnosing the presence or absence of COPD in the subject based on the result of comparing the COPD index value with a predetermined threshold value.

[0048] Another method for diagnosing COPD is analyzing a biological sample collected from a subject with a chromatograph mass spectrometer in the subject, obtaining a COPD index value which is the amount of at least one metabolite selected from 24 metabolites including glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitric acid, erythritol, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, xylose, cystine, tyrosine, uric acid, mannose, and beta-alanine from the data obtained by the chromatograph mass spectrometer, and diagnosing the presence or absence of COPD in the subject based on the result of comparing the COPD index value with a predetermined threshold value.

Claims

1. Measuring the amount of metabolites related to the presence or absence of chronic obstructive pulmonary disease-related diseases contained in a biological sample collected from a subject as a COPD index value; Measuring the amount of metabolites, which are the COPD index values, contained in biological samples collected from smokers belonging to a COPD-related group, smokers belonging to a normal group, non-smokers belonging to a COPD-related group, and non-smokers belonging to a normal group, each different from the subject; Setting a threshold value based on the amount of metabolites, which are the COPD index values, contained in biological samples collected from smokers belonging to a COPD-related group, smokers belonging to a normal group, non-smokers belonging to a COPD-related group, and non-smokers belonging to a normal group, respectively; Comparing the COPD index value with the threshold value and including, wherein the biological sample is a blood sample, A method for measuring a COPD index value, wherein the COPD index value is the amount of tyrosine.

2. In the method for measuring a COPD index value according to Claim 1, A method for measuring a COPD index value, wherein the COPD index value is the amount of tyrosine measured based on data obtained by subjecting a biological sample to chromatograph MS analysis.

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