JP7687635B2 - Method for measuring COPD index values - Google Patents

Description

The present invention relates to a method for measuring the amount of a substance contained in a biological sample such as blood or urine that is associated with the onset of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a disease characterized by irreversible and progressive airflow obstruction, and is said to be caused mainly 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 in the world, and COPD patients are at high risk of developing lung cancer (Non-Patent Document 2). For example, in a 2004 WHO survey, COPD was the fourth leading cause of death (5.1% of all deaths) (Non-Patent Document 3), and in Japan it was the ninth leading cause of death in 2013 (Non-Patent Document 4). Since the number of COPD patients is expected to continue to increase, prevention and countermeasures for this disease are urgent and important issues.

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

However, because obstructive ventilation disorders are also seen in patients with lung diseases other than COPD, such as asthma and pulmonary fibrosis, even if the FEV1 is less than 70% in spirometry, COPD cannot be immediately diagnosed (Non-Patent Document 5). In addition, while the typical pathology of COPD was traditionally considered to be airflow obstruction, it has recently come to be considered to be a collection of various complex pathologies, and the concept of COPD is changing from a local pulmonary inflammatory disease to a systemic inflammatory disease (a disease accompanied by ischemic heart disease, diabetes, dyslipidemia, osteoporosis, etc.). Therefore, instead of a diagnosis based on the FEV1, a method of diagnosing COPD using specific components in biological samples such as blood, serum, and plasma collected from the subject as biomarkers has been considered (Patent Documents 1 to 4).

JP 2011-526684 A JP 2013-152236 A JP 2015-507196 A JP 2015-509596 A

GOLD: Global Initiative for chronic Obstructive Pulmonary Disease: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease update 2006. National Institute of Health, National Lung and Blood Institute. 2006. Tockman MS, Anthonisen NR, Wright E, et al.,: Airway obstruction and the risk of lung cancer. Ann Int Med 1987; 106: 512-8. World Health Organization: The global burden of disease: 2004 update. Geneva. WHO Press 2008. Illustrated Trends in National Health 2015/2016. 53 pages, Health, Labor and Welfare Statistics Association. Guidelines for the Diagnosis and Treatment of COPD, 4th Edition: Chapter II, Diagnosis A, Diagnostic Criteria, p.28 Svanes C, Sunyer J, Plana E, et al: Early life origins of chronic obstructive pulmonary disease. Thorax 2010; 65: 14-20. Postma DS, Kerkhof M, Boezen HM, et al: Asthma and chronic obstructive pulmonary disease: common gene, common environments? Am J Respir Crit Care Med 2011; 83: 1588-94. De Marco R, Accordini S, Marcon A, et al. Risk factors for chronic obstructive pulmonary disease in a European cohort of young adults. Am J Respir Crit Care Med 2011; 183: 891-7. Snider GL; Chronic obstructive pulmonary disease: risk factors, pathophysiology and pathogenesis. Annu Rev Med 1989; 40: 411-29. Lundback B, Lindberg A, Lindstrom M, et al: Not 15 but 50% of smokers develop COPD?-Report from the Obstructive Lung Disease in Northern Sweden Studies. Respir Med 2003; 97: 115-22. American Thoracic Society: Cigarette smoking and health. Am J Respir Crit Care Med 1996; 153: 861-5. Fletcher C, Peto R: The natural history of chronic airflow obstruction. Br Med J. 1977; 1: 1645-8. Laurell CB, Eriksson S: The electrophoretic alpha-1-globulin pattern of serum in alpha-anti-trypsin deficiency. Scand J Clin Lab Invest 1963; 15: 197-205. Serum Metabolite Biomarkers Discriminate Healthy Smokers from COPD Smokers. PLoS ONE 2015 10(12): e0143937. Biomarker Development for Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2015; 192:1162-70.

In the methods described in Patent Documents 1 to 4, proteins, peptides, and genes that are believed to be involved in the pathogenesis and mechanism of COPD are used as biomarkers, and COPD is diagnosed by measuring the amounts of these biomarkers in the blood. However, as mentioned above, COPD is a collection of various pathological conditions, and there are many unknowns regarding its pathogenesis and mechanism of disease.

For example, the biggest risk factor for COPD is smoking (Non-Patent Documents 6-8). Approximately 90% of COPD patients have a smoking history, and the incidence rate increases with age and smoking exposure, with COPD being present 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, 10). The smoking index pack-years represents (average number of cigarettes smoked per day/20 cigarettes) x total number of years of smoking. On the other hand, the incidence rate of COPD among all smokers is only about 15-20% (Non-Patent Document 11). It has also been reported that the incidence rate of COPD among smokers with 20 pack-years is approximately 19%, and that approximately 30% of heavy smokers with 60 pack-years or more have normal respiratory function (Non-Patent Documents 11, 12).

For this reason, it is suspected that smokers are susceptible to developing COPD, and searches for COPD-related candidate genes (genomics) have been conducted to date. However, due to factors such as insufficient sample size and uniformity, and the need to take racial differences into account, COPD-related genes have not yet been fully elucidated, with the exception of α1-antitrypsin deficiency as a single genetic abnormality (Non-Patent Document 13).

Furthermore, genomics, which has been used to elucidate susceptibility to the development of COPD, has been limited to examining genes specific to each individual. As mentioned above, the onset of COPD is influenced not only by genetic factors, but also by environmental factors such as smoking, and various changes in conditions that can occur in the body, such as aging, infection, and nutritional state. Therefore, as mentioned above, COPD is considered a systemic inflammatory disease, 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 pathology of COPD, and the genetic and environmental background of each individual.

The problem that the present invention aims to solve is to measure the amount of a substance that is thought to be related to chronic obstructive pulmonary disease-related diseases, and to use the measurement results to help diagnose chronic obstructive pulmonary disease-related diseases.

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

In the present invention, chronic obstructive pulmonary disease-related diseases (COPD-related diseases) refer to COPD, as well as diseases that are pre-stages of COPD and diseases that are complicated by COPD (such as ischemic heart disease, diabetes, dyslipidemia, osteoporosis, etc.).
The present inventors analyzed blood samples collected from many subjects using a chromatography mass spectrometer, and comprehensively measured the amounts of many metabolites contained in the blood samples, and found that the above-mentioned 24 metabolites are closely related to the onset of COPD by comprehensively analyzing the results. This type of analysis method is called metabolomics, and by using this method, it is possible to comprehensively grasp the complex pathological changes related to COPD.

The 24 metabolites mentioned above are metabolites that showed a high correlation with the COPD-related group as a result of statistical analysis of the measured values of 135 robust metabolites among the many metabolites contained in biological samples, for the COPD-related group and the normal group. Table 1 shows a list of the 135 metabolites. The metabolite names "-nTMS", "-nTMS(m)", "-methyloxime-nTMS", or "-methyloxime-nTMS(m)" (n and m are natural numbers) 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 24 metabolites can be used alone as COPD index values to diagnose COPD. The COPD-related group when comparing two groups can be determined based on the value of the forced expiratory volume in one second (FEV1/FVC) obtained by a respiratory function test using a spirometer.

Previously, metabolomics-based biomarker searches for COPD have reported that 23 types of metabolites (myo-inositol, fumaric acid, etc.) have been identified as potential biomarkers (Non-Patent Document 14). However, at present, there have been no reports that metabolites contained in biological samples collected from COPD patients can serve as useful biomarkers for diagnosing COPD or understanding the disease state (symptoms, respiratory function, etc.) on their own (Non-Patent Document 15).

Here, the "biological sample collected from the subject" may 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 collecting the sample and the large amount of metabolites contained, blood (whole blood, serum, or plasma) is preferred. As serum, a liquid component obtained by clotting blood cell components without adding an anticoagulant to whole blood can be used. As plasma, a liquid component obtained by adding an anticoagulant to whole blood without clotting blood cell components can be used.

In addition, analytical methods for biological samples are not limited to chromatographic mass spectrometry (chromatographic MS analysis), and may include immunological analysis methods such as nuclear magnetic resonance (NMR) spectroscopy, enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA), but chromatographic MS analysis is superior in that it can quantitatively measure the amount of metabolites contained in a biological sample. Chromatographic MS analysis refers to analysis using a gas chromatograph or liquid chromatograph and a mass spectrometer as a detector for samples separated by these chromatographs. Examples of mass spectrometers include triple quadrupole mass spectrometers, Q-TOF mass spectrometers, TOF-TOF mass spectrometers, ion trap mass spectrometers, and ion trap time-of-flight mass spectrometers. Using these mass spectrometers, highly sensitive analysis is possible even for samples containing many impurities other than the analyte (metabolite), and the analysis stability is improved, so the amount of metabolites contained in a biological sample can be quantitatively measured with high reproducibility.

The COPD index value can be any of the measured values of arabinose, ornithine, kynurenine, 2-aminoadipic acid, cystine, tyrosine, myoinositol, uric acid, beta-alanine, and mannose.
The above 10 kinds of metabolites were those that showed a significant difference between the COPD-related group and the normal group when a t-test was performed between the two groups, the COPD-related group and the normal group, for the measured values of 135 kinds of metabolites. Therefore, the amount of the above 10 kinds of metabolites is measured as a COPD index value, and the presence or absence of COPD-related disease can be diagnosed by comparing the COPD index value with a predetermined threshold value. The predetermined threshold value can be the average value, median value, etc., when the amounts of the above 10 kinds of metabolites contained in biological samples of a large number of subjects are measured.

Furthermore, the COPD index value may be any one of the measured values of glutaric acid, hydroxyproline, xylose, phosphoglycerol, and ornithine.
The above five metabolites are metabolites that showed a high correlation with the onset of COPD, with a P value of less than 0.05, when a logistic regression analysis was performed on the measured values of 135 metabolites in a model in which the presence or absence of COPD-related disease was used as a dependent variable, as well as age, sex, and smoking history as adjustment factors. Therefore, the measured values of these five metabolites are measured as COPD index values, and the presence or absence of COPD can be diagnosed by comparing the measured values with a predetermined threshold value.

As the COPD index value, it is preferable to use a measured value of any one of erythritol, ornithine, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, and arabinose.
The above 10 types of metabolites were the ones that had the largest AUC values when the measured values of 135 types of metabolites were subjected to ROC analysis with the presence or absence of COPD-related disease as the dependent variable. Therefore, by measuring the measured values of these 10 types of metabolites as COPD index values and comparing them with a predetermined threshold value, the presence or absence of COPD onset can be diagnosed and misdiagnosis can be reduced.

As the COPD index value, it is preferable to use a measured value of any one of glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, and isocitrate.
The above 10 types of metabolites were the metabolites with the largest C-statistics when the measured values of 135 types of metabolites were calculated in a model in which the presence or absence of COPD-related disease was input as the dependent variable and age, sex, and smoking history were input as adjustment factors. Therefore, by measuring the measured values of these 10 types of metabolites as COPD index values and comparing them with a predetermined threshold, it is possible to diagnose the presence or absence of COPD onset and to reduce erroneous diagnosis.

In the present invention, metabolomics analysis techniques are used to search for metabolites that are strongly correlated with COPD-related diseases, and the amounts of the metabolites found in biological samples are measured as COPD index values, which can be used to diagnose COPD-related diseases.

The inventor analyzed biological samples (serum) collected from subjects using a chromatography mass spectrometer (chromatograph MS) and comprehensively measured the amounts of numerous metabolites contained in the biological samples. These measured values were then analyzed using various techniques to search for metabolites that are thought to be closely related to the presence or absence of COPD-related diseases. Table 2 shows the baseline characteristics of subjects from whom biological samples were collected to search for metabolites related to the onset of COPD. The method of extracting subjects whose baseline characteristics are shown in Table 2, the method of preparing and analyzing biological samples, the method of measuring metabolites, and the analysis of the measurement results are described below.

<Selection of subjects>
With the approval of the research ethics committee, approximately 10,000 people were randomly sampled from among those who visited the St. Luke's International Hospital Attached Clinic Preventive Medicine Center during the year from October 2015 to September 2016 for health checkups (approximately 45,000 people), and the subjects were the remaining 6,610 people after excluding those who declared that they would not participate in this experiment, those with missing measurement data, and those who met the exclusion criteria. The exclusion criteria were that the subjects were under 40 years old at the time of the health checkup, based on the findings of previous research that the onset of the disease begins about 20 years after starting smoking.

The St. Luke's International Hospital Attached Clinic Preventive Medicine Center is located in Chuo Ward, Tokyo, and many of the people undergoing health checkups live in the Tokyo area. Therefore, the external factors (environmental factors such as air pollution) related to COPD of the subjects in this study were roughly uniform. In addition, by selecting all subjects as Japanese, ethnic factors were also uniform.

The median age of the group of 6,610 subjects was 54 years (interquartile range 47-63 years), of which 3,467 (52.5%) were women. The smoking rate of the group was 37.1%, and 692 subjects (10.5%) had a FVFR of less than 70%. Hereinafter, the group of subjects with a FVFR of less than 70% will be referred to as the COPD-related group, and the group of subjects with a FVFR of 70% or more (5,918 subjects) will be referred to as the normal group. As can be seen from Table 2, there were statistically significantly more men than women in the COPD-related group. In addition, both age and smoking rate were higher in the COPD-related group than in the normal group.
In clinical tests, patients with a 1-second forced expiratory volume of less than 70% in a respiratory function test using a spirometer after administration of a bronchodilator are judged to have COPD, but no bronchodilators were administered in this experiment. Therefore, the COPD-related group includes not only subjects who actually have COPD, but also subjects who have a COPD-related disease.

<Sample preparation>
(1) Pretreatment: Blood collected from 6,610 subjects was left to stand for a specified period of time, then centrifuged to collect the supernatant (serum). The collected serum was stored at -80°C until analysis. Note that the time between collecting blood from the subjects and freezing the serum affects the measured values of metabolites in the serum. Therefore, this time, subjects were selected so that about half of the subjects had blood collected and frozen 5 to 8 hours later.

(2) Main Treatment Serum stored at -80°C was thawed at 25°C for 5 minutes. The thawed serum was centrifuged at 10,000×g, 4°C, for 1 minute, and then allowed to stand on ice. 6 μL of an internal standard solution (2-isopropylmalic acid: 0.1 mg/mL) was added to 250 μL of the first mixed solution (methanol/water/chloroform, 2.5:1:1), and mixed using a vortex mixer to obtain a second mixed solution.

The serum and the second mixture were then shaken at 1,200 rpm at 37°C for 30 minutes, and then centrifuged at 16,000 x g at 25°C for 5 minutes. 150 μL of the supernatant was added to a tube to which 140 μL of water had previously been added, and mixed using a vortex mixer to obtain a third mixture.
Next, the third mixture was centrifuged at 16,000×g for 5 minutes at 25° C., and 180 μL of the supernatant was collected in a new tube, which was then concentrated in a centrifugal evaporator at room temperature for 60 minutes with the speed setting set to “7”.
The supernatant of the concentrated third mixed solution (concentrated solution) was frozen at -80°C for 30 minutes, and then dried overnight to prepare a sample. The obtained sample was stored in a desiccator until analysis.

GCMS analysis of samples
To the sample stored in a desiccator, 80 μL of methoxyamine solution (20 mg/mL dissolved in pyridine) was added, and the mixture was shaken at 1,200 rpm for 30 minutes at 37° C. Then, 40 μL of N-methyl-N-trimethylsilyltrifluoroacetamide was added, and the mixture was shaken at 1,200 rpm for 30 minutes at 37° C., and centrifuged at 16,000×g for 5 minutes at 25° C. 50 μL of the supernatant was placed in a glass vial, and the vial was set in the autosampler of the GCMS.

The GCMS used was a triple quadrupole gas chromatograph mass spectrometer (GCMS-TQ8040) manufactured by Shimadzu Corporation. The analysis conditions for the GCMS are as follows:
(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; increased 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

Data Analysis
<Metabolite measurement>
Peaks were comprehensively detected from the mass spectrum of the sample, and the peak information (mass-to-charge ratio and signal intensity) was compared with the mass-to-charge ratios of numerous metabolite-specific substances stored in an MS library to identify the metabolites, and measurement values (hereinafter referred to as MS data) for 135 types of metabolites were obtained.

<Statistical analysis>
For subjects from whom MS data was obtained, the clinical information and test data necessary for statistical analysis were obtained from the Information Systems Center of St. Luke's International University with the consent of the Research Ethics Committee of St. Luke's International Hospital, and the clinical information and test data were matched with the MS data. All clinical information and test data of subjects were managed by a management number that identifies the subject, so that the subjects could not be identified when matching the data.

For statistical analysis, unpaired t-test was used as univariate analysis, and logistic regression analysis was used as multivariate analysis. To evaluate diagnostic ability, AUC values calculated from ROC curves were used for models of each metabolite alone, and C statistics were used for multivariate analysis models using adjustment factors.

Table 3 shows the results of a t-test performed 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, and beta-alanine) that showed a statistically significant difference between the COPD-related group and the normal group, in order of decreasing P value. In Table 3, "mEn (m is a real number, n is an integer)" means "m×10 ⁿ ." For example, "4.14E-14" becomes "4.14×10 ^-14 ."

As shown in Table 3, for all ten metabolites, the smaller the P value, the larger the absolute value of the t statistic, and all values were negative, indicating that the amount of metabolites was smaller in the normal group than 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 any of the measured values is greater than a predetermined threshold, a diagnosis of COPD-related disease can be made.

Table 4 shows the results of a logistic regression analysis examining the relationship between the COPD-related group and the measured values of 135 metabolites, with the COPD-related group/normal group as the dependent variable and age, sex, and smoking history as adjustment factors. Table 4 lists the five metabolites with the smallest P values in the logistic regression analysis (glutaric acid, hydroxyproline, xylose, phosphoglycerol, and ornithine) along with their regression coefficients, standard errors, and P values. As shown in Table 4, glutaric acid had the smallest P value of the five metabolites, and a strong correlation was observed between glutaric acid and COPD-related disease. Table 4 also lists the rankings, t-statistics, and P values when adjustments were not made with adjustment factors. Adjusting with adjustment factors in this way increases the usefulness of the index.

Next, to examine the diagnostic ability (i.e., the accuracy of diagnosing COPD-related diseases) when the measured values of each metabolite were used as the COPD index value, a receiver operating characteristic analysis (ROC analysis) was performed on 135 metabolites, with the dependent variable being the COPD-related group/normal group. As a result, it was found that 10 metabolites (erythritol, ornithine, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabinose, and arabinose) are useful for diagnosing COPD. Table 5 shows the AUC values of the ROC curves for the 10 metabolites useful for determining COPD.

The ROC curve is a plot of the true positive rate, or sensitivity, on the vertical axis and the false positive rate, or "1-specificity," on the horizontal axis. In other words, the sensitivity is calculated from the proportion of subjects judged to be positive to all subjects in the COPD-related group, and the false positive rate is calculated from the proportion of subjects judged to be positive (non-COPD) to all subjects in the normal group. In the same way, the sensitivity and false positive rate are calculated for other cutoff values, 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. It can be determined that the higher the AUC value, the higher the accuracy of diagnosing the presence or absence of COPD-related diseases. When the AUC was calculated from the ROC curve, the metabolites with the highest AUC were erythritol (AUC = 0.616), ornithine (AUC = 0.594), and myo-inositol (AUC = 0.590).

On the other hand, Table 6 shows the 10 metabolites with the highest C statistics as a result of ROC analysis of 135 metabolites using a model in which the same dependent variables as those used in the ROC analysis that produced the results shown in Table 5 were used, with age, sex, and smoking history used as adjustment factors. The C statistics corresponds to the area enclosed by the ROC curve and the horizontal axis (AUC), and the closer the C statistics is to 1, the higher the diagnostic ability. Table 6 lists the C statistics of the 10 metabolites (glutaric acid, α-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, and isocitrate) out of the 135 metabolites in descending order of C statistics. All of the measured values of these 10 metabolites are statistically correlated with COPD-related diseases and are useful as indicators for determining whether or not COPD-related diseases are present. In particular, the measured value of glutaric acid (C statistic: 0.751), which had the largest C statistic, showed a strong correlation with the onset of COPD and can be said to be an excellent indicator for determining whether or not there is a possibility of developing COPD. Table 6 also lists the rankings and AUC values when adjustments with adjustment factors were not made. In this way, adjustments with adjustment factors increase the usefulness of the indicator.

In this way, the results of measuring the amounts of the 24 types of metabolites contained in a biological sample from a subject can be used to diagnose 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 analytical device, determining a COPD index value, which is the amount of at least one metabolite selected from 24 types of metabolites, namely, glutaric acid, alpha-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitrate, erythritol, myo-inositol, threitol, 2-aminoadipic acid, kynurenine, fucose, phenylalanine, arabitol, xylose, cystine, tyrosine, uric acid, mannose, and beta-alanine, from the data obtained with the analytical device, 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.

Another method for diagnosing COPD is
Analyzing a biological sample collected from a subject using a chromatographic mass spectrometer;
The method includes determining a COPD index value, which is the amount of at least one metabolite selected from 24 types of metabolites, namely glutaric acid, alpha-ketoisocaproic acid, phosphoglycerol, hydroxybutyric acid, arabinose, acetoacetic acid, ornithine, hydroxyproline, norvaline, isocitrate, 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 chromatography-mass spectrometry device, 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 (2)

Measuring the amount of a metabolite associated with the presence or absence of a chronic obstructive pulmonary disease-related disease contained in a biological sample collected from the 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, all of which are 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 the COPD-related group, smokers belonging to the normal group, non-smokers belonging to the COPD-related group, and non-smokers belonging to the normal group;
comparing the COPD index value with a threshold value;
Including,
the biological sample is a blood sample,
A method for measuring a COPD index value, wherein the COPD index value is an amount of tyrosine . 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 an amount of tyrosine measured based on data obtained by chromatographic MS analysis of a biological sample.