WO2020054721A1

WO2020054721A1 - Method for measuring atrial fibrillation index value

Info

Publication number: WO2020054721A1
Application number: PCT/JP2019/035556
Authority: WO
Inventors: 宏隆藤本; 青木　豊; 佐藤　孝明; 木村　武志; 勝紀増田; 勇司平家; まり子浅見; 静香宇山; 鈴木　一彦; ケビン浦山; 邦好林
Original assignee: 株式会社島津製作所; 学校法人聖路加国際大学
Priority date: 2018-09-12
Filing date: 2019-09-10
Publication date: 2020-03-19
Also published as: JP2023100797A; JP7280582B2; JPWO2020054721A1; TW202012928A

Abstract

The present invention is a method for measuring an atrial fibrillation index value, which is an index value related to the onset of atrial fibrillation. This method involves: analyzing an organism specimen harvested from a subject; and measuring, as the atrial fibrillation index value, the amount of at least one metabolite selected from among 16 types of metabolites, including beta-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutaric acid, serine, arabinose, heptadecanoic acid, ribose, isocitric acid, phenylalanine, succinic acid, fucose, citric acid, and erithritol. The result of measuring the atrial fibrillation index value described above can be useful in diagnosing atrial fibrillation.

Description

How to measure atrial fibrillation index

The present invention relates to a method for measuring a substance related to the onset of atrial fibrillation among substances contained in a biological sample such as blood or urine.

Atrial fibrillation is a relatively common cardiac arrhythmia. The prevalence of atrial fibrillation increases with age, and in each age group, men are generally more prevalent than women. In atrial fibrillation, rapid, irregular, and disordered atrial excitation occurs, causing loss of effective atrial contraction, resulting in stagnant blood flow in the atrium and thrombus formation in the atrium. Cerebral embolism (cardiogenic cerebral infarction) caused by thrombus formation in the atria is one of the serious complications of atrial fibrillation, and permanent treatment with anticoagulants is needed to prevent it In many cases.

心拍 In addition, atrial fibrillation does not provide effective atrial contraction, resulting in a decrease in cardiac output. Therefore, when atrial fibrillation occurs in a patient having a heart disease, hemodynamics cannot be maintained and heart failure worsens. Furthermore, if tachycardia due to atrial fibrillation persists, myocardial damage occurs, resulting in a long-term decline in cardiac function. Therefore, when atrial fibrillation occurs, early sinus rhythm, recurrence prevention, and heart rate regulation treatment using anti-arrhythmic drug therapy, non-drug therapy such as cardioversion and catheter ablation are required. This is important (Non-Patent Document 1).

１２As a diagnostic technique for atrial fibrillation, a 12-lead electrocardiogram is standard. However, since atrial fibrillation that occurs only occasionally, such as paroxysm, cannot be easily detected by a short examination, a Holter electrocardiograph that can continuously take an electrocardiogram for 24 hours is used. In addition, echocardiography is performed to rule out non-abnormal heart valves. Risk factors for the transition to persistent atrial fibrillation after the first occurrence of atrial fibrillation include heart failure, elderly (75 years and older), a history of transient ischemic attack and cerebral infarction, chronic obstructive pulmonary disease, Hypertension and the like (Non-Patent Document 2).

If atrial fibrillation can be diagnosed relatively early in the onset of atrial fibrillation, not only will it be possible to prevent serious complications such as cerebral infarction, but also take prompt measures to maintain sinus rhythm Can be. Furthermore, it can be used to manage background factors that promote the transition to persistent atrial fibrillation (Non-Patent Document 3). Therefore, a disease marker that specifically captures a pre-onset state or an early state of atrial fibrillation is very useful for treating or preventing atrial fibrillation.

炎症 So far, inflammatory markers such as osteoprotegerin have been reported as disease markers associated with the onset of atrial fibrillation (Non-patent Document 4). For the detection of this disease marker, an immunological detection method called ELISA (Enzyme-Linked {ImmunoSorbent} Assay)} having high versatility is used. The immunological detection method is a method of individually measuring a target object.Since it is not possible to measure a plurality of types of objects at one time, a new disease marker having a high specificity for the onset of atrial fibrillation is developed. There was a limit to exhaustive search.

In recent years, attempts have been made to search for various disease markers using metabolome analysis (metabolomics) for analyzing metabolites. There have been two reports on metabolome analysis in atrial fibrillation so far. One was a report by Mayr et al. In 2008 on a prospective study (a follow-up study) that combined metabolomic and proteomic analysis using nuclear magnetic resonance spectroscopy (NMR). A group that subsequently developed atrial fibrillation was compared with a control group that maintained sinus rhythm, and changes in metabolites of atrial muscle tissue in the group that developed atrial fibrillation were observed (Non-Patent Document 5). In the group that progressed to atrial fibrillation after cardiac surgery, uncontrolled metabolite changes were observed in atrial muscle tissue, suggesting the presence of ketone bodies as metabolites associated with the development of atrial fibrillation.

The other is the report of the ALIC (Atherosclerosis Risk in Communities) study by Alonso et al. In 2015, in which 1919 African Americans were followed up for 22 years and 183 newly developed atrial fibrillation. The serum of several subjects is analyzed (Non-Patent Document 6). Then, metabolites related to the onset of atrial fibrillation were analyzed from the results of the analysis, and as a result, Glycolithocholate sulfate and Glycocholenate sulfate related to bile acid metabolism were reported as metabolites related to the onset of atrial fibrillation. Was.
As described above, although there are reports of marker candidates related to the onset of atrial fibrillation, none of the marker candidates has actually been clinically applied.

Atrial fibrillation is considered to be caused by two factors, a genetic factor (genetic predisposition) and an environmental factor (lifestyle) (Non-Patent Document 7). Genetic predisposition is important in terms of the likelihood of developing a disease, and genetic information can be used as a disease marker to estimate the risk of developing a disease in the future. It is difficult to diagnose. In addition, lifestyle is an important environmental factor as well as genetic factors in the case of habits that affect genetic predisposition, but lifestyle habits often change with age, and a variety of factors It is difficult to diagnose overlapping diseases at an early stage.

The problem to be solved by the present invention is to measure the amount of metabolites considered to be related to the onset of atrial fibrillation, and to use the measurement result for diagnosis of atrial fibrillation.

The present invention has been made to solve the above problems, a method of measuring an atrial fibrillation index value is an index related to the onset of atrial fibrillation,
A biological sample collected from the subject was analyzed and 16 metabolites, β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutarate, serine, arabinose, margaric acid, ribose , Isocitrate, phenylalanine, succinic acid, fucose, citric acid, and erythritol, wherein the amount of at least one metabolite is measured as the atrial fibrillation index value.

(4) The present inventor searched for metabolites that serve as indices related to atrial fibrillation using a technique called metabolomics in order to comprehensively capture complex changes in pathological conditions associated with atrial fibrillation. Specifically, a blood sample (serum) collected from a large number of subjects is analyzed using a chromatograph mass spectrometer, and the result of comprehensively measuring the amounts of a large number of metabolites contained in the blood sample is obtained. By comprehensive analysis, it was found that the above 16 metabolites were significantly associated with atrial fibrillation. Therefore, at least one of these 16 metabolite amounts can be used as an atrial fibrillation index value for diagnosis of atrial fibrillation.

As for the above-mentioned 16 metabolites, as a result of performing statistical analysis on two groups with and without atrial fibrillation, a measurement value of 135 robust metabolites among many metabolites contained in serum was obtained. Is a metabolite with a high correlation with the presence or absence of Table 1 shows a list of 135 metabolites. “-NTMS”, “-nTMS (m)”, “-methyloxime-nTMS”, or “-methyloxime-nTMS (m)” (n and m are natural numbers) included in the metabolite names shown in Table 1 are , Due to the reagent added during the analysis by the gas chromatograph mass spectrometer. Therefore, for example, “asparagine-2TMS” and “asparagine-3TMS” in Table 1 are derived from the same metabolite (asparagine). The presence or absence of atrial fibrillation in the comparison between the two groups was determined based on an electrocardiogram.

Specifically, for example, the presence or absence of atrial fibrillation can be determined by measuring an atrial fibrillation index value in blood collected from a subject and comparing the measured value with a predetermined threshold value. In this case, the range of the atrial fibrillation index value in the blood of a person diagnosed with atrial fibrillation (atrial fibrillation onset) and a person diagnosed as not having atrial fibrillation onset (normal person) is determined in advance. It is determined whether the subject has atrial fibrillation depending on whether the atrial fibrillation index value of the subject falls within the range of a normal person or within the range of atrial fibrillation onset. It is better to determine whether or not. Further, the range of the atrial fibrillation index value in the normal state of the subject is measured in advance, and then the atrial fibrillation index value of the subject is compared with the range, and the atrial fibrillation index value is in the range. If it is out of the range, it may be determined whether or not the subject has atrial fibrillation.

The atrial fibrillation index value, which is a measured value of the above 16 metabolites, can be used alone for diagnosis of atrial fibrillation. However, by using a combination of a plurality of atrial fibrillation index values, atrial fibrillation Diagnosis accuracy can be improved. For example, both of the two types of atrial fibrillation index values cause atrial fibrillation, compared to a case where only one of the two types of atrial fibrillation index values is in the range indicating that atrial fibrillation has developed. It can be determined that there is a higher possibility of atrial fibrillation when it is within the range indicating that the patient is performing atrial fibrillation.

In the present invention, "a biological sample collected from a subject" refers to any blood, biological tissue, stool, urine, etc., as long as the amount of a metabolite contained in the biological sample can be measured. It may be something. However, blood (whole blood, serum, or plasma) is preferable in consideration of the ease of sample collection and the large content of metabolites. As the serum, a liquid component obtained by coagulating blood cell components without adding an anticoagulant to whole blood can be used. In addition, as the plasma, a liquid component obtained by adding an anticoagulant to whole blood without coagulating blood cell components can be used.

In addition, although chromatographic mass spectrometry (chromatographic MS analysis) was used to search for metabolites related to the onset of atrial fibrillation, the biological sample analysis method of the present invention is not limited to chromatographic MS analysis. Immunological analysis methods such as nuclear magnetic resonance (NMR) spectroscopy, enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA) can be used. However, chromatographic MS analysis is excellent in that the amount of metabolites contained in a biological sample can be quantitatively measured. The chromatographic MS analysis refers to an analysis using a gas chromatograph or a liquid chromatograph, and a mass spectrometer as a device for detecting a sample 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, and an ion trap time-of-flight mass spectrometer. With these mass spectrometers, highly sensitive analysis is possible even for samples containing a large amount of contaminants other than the analyte (metabolite), and the analysis stability is improved. The amount of metabolite contained in the sample can be quantitatively measured.

In the method of measuring the atrial fibrillation index value,
The atrial fibrillation index value is, particularly of the 16 metabolites, isocitrate, arabinose, ribose, 2-oxoglutaric acid, malic acid, phenylalanine, fumaric acid, succinic acid, fucose, citric acid It is preferably the amount of the substance.

As for the above-mentioned 10 metabolites, the t-test was carried out between the two groups of the group with AF (atrial fibrillation) and the normal group (without AF) for the measurement values of 135 metabolites. Significant differences were observed. Further, these had a P value of less than 0.001 in a separate Mann-Whitney U test. Therefore, the presence or absence of atrial fibrillation can be diagnosed by measuring the amounts of the ten metabolites as an atrial fibrillation index value and comparing the index value with a predetermined threshold value. The predetermined threshold value may be an average value, a median value, or the like when the amount of each of the ten metabolites contained in the biological sample is measured for a large number of subjects.

In addition, the above-mentioned atrial fibrillation index value is preferably the amount of any of metabolites of β-alanine, fumaric acid, threonine, succinic acid, and 2-oxoglutaric acid among the 16 metabolites.
For the above five metabolites, in addition to the presence or absence of atrial fibrillation as dependent variables, age, gender, smoking history, current history of hypertension / diabetes, history of heart disease, and eGFR value are input as adjustment factors. When a logistic regression analysis of the measured values of 135 metabolites was performed for the model obtained, the P value was smaller than 0.05, indicating that the metabolite showed a high correlation with the presence or absence of atrial fibrillation. . Therefore, atrial fibrillation can be diagnosed at an early stage by measuring the measured values of these five metabolites as atrial fibrillation index values and comparing the measured values with a predetermined threshold value.

In addition, as the above-mentioned atrial fibrillation index value, any one of the 16 metabolites, especially 2-oxoglutaric acid, isocitrate, fucose, arabinose, fumaric acid, erythritol, succinic acid, phenylalanine, malic acid, and citric acid It is good to be the amount of that metabolite.
The above 10 metabolites were subjected to ROC analysis on the measured values of 135 metabolites in the presence or absence of atrial fibrillation as dependent variables, and their AUC values were determined. The AUC values were large. Therefore, the measured values of these 10 metabolites are each measured as an atrial fibrillation index value, and by comparing this with a predetermined threshold value, the presence or absence of atrial fibrillation can be diagnosed, and erroneous diagnosis is reduced. can do.

Furthermore, the above-mentioned atrial fibrillation index value is particularly β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutaric acid, serine, arabinose, margaric acid among the 16 metabolites. It may be the amount of any metabolite of ribose.
The above 10 metabolites are modeled with the presence or absence of atrial fibrillation as dependent variables, age, sex, smoking history, current history of hypertension / diabetes, history of heart disease, and eGFR value as adjustment factors When the C statistic was determined for the measured values of 135 metabolites, the metabolite was found to have a large value. Therefore, by measuring the measured values of these 10 metabolites as atrial fibrillation index values and comparing them with a predetermined threshold value, it is possible to diagnose the presence or absence of atrial fibrillation with high accuracy.

In the present invention, the amount of a metabolite found in a biological sample by searching for a metabolite having a high relationship with atrial fibrillation using a metabolomics analysis method is measured as an atrial fibrillation index value. The fibrillation index value can be used to diagnose atrial fibrillation.

(4) The present inventor analyzed a biological sample (serum) collected from a subject using a gas chromatograph mass spectrometer (GCMS), and comprehensively measured the amounts of a large number of metabolites contained in the biological sample. Then, by analyzing the measured values using various methods, metabolites that seemed to be significantly related to the presence or absence of atrial fibrillation were searched. Table 2 shows the baseline characteristics of subjects who collected biological samples to search for metabolites associated with the development of atrial fibrillation. A method for extracting a subject exhibiting the baseline characteristics shown in Table 2, a method for preparing and analyzing a biological sample, a method for measuring a metabolite, and an analysis of a measurement result will be described below.

<Extraction of subject>
With the approval of the Research Ethics Committee, a total of 45,000 people who visited the St. Luke's International Hospital Clinic Preventive and Medical Center during the year from October 2015 to September 2016 7601 examinees were sampled randomly, and the remaining 7592 subjects were excluded as subjects, excluding those who indicated that they did not participate in this experiment. It should be noted that all subjects are accompanied by self-completed questionnaires regarding medical histories and lifestyle (such as smoking). In addition, the height, weight, body mass index (BMI), blood pressure, and results of blood tests (hemoglobin A1c, fasting blood glucose, creatinine (GFR estimated value), etc.) are also attached to the subject at the medical checkup.

予防 The St. Luke's International Hospital Clinic Preventive Medical Center is located in Chuo-ku, Tokyo, and many residents of health check-ups are located near Tokyo. The subjects were all Japanese. Therefore, it can be said that the subjects this time are a group with uniform environmental and ethnic factors.

The median age of the population of $ 7592 subjects was 52 years (interquartile range 42-62 years), of which 3967 (52.3%) were women. In addition, 58 patients (0.8%) diagnosed as having atrial fibrillation (AF) by electrocardiography, and others (diagnosed without AF (“normal”) Group)) were 7534 (99.2%). As shown in Table 2 above, in the AF group, the males were statistically significantly more than the females, and the AF group was statistically significantly older than the normal group.

<Sample preparation>
(1) Pretreatment Blood collected from a subject was allowed to stand for a predetermined time, and then centrifuged to collect a supernatant (serum). The collected serum was stored at -80 ° C until analysis. The time from when blood is collected from the subject until the serum is frozen affects the measured value of metabolites in the serum. Approximately half of the subject population had five to eight hours from blood collection to freezing.

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

Thereafter, the serum and the second mixed solution were shaken at 1,200 rpm at 37 ° C. for 30 minutes, centrifuged at 16,000 × g at 25 ° C. for 5 minutes, and 150 μL of the supernatant was added to 140 μL of water in advance. And mixed using a vortex mixer to obtain a third mixed solution.
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. This was concentrated with a centrifugal evaporator at room temperature for 60 minutes with the speed memory set to "7".
After the concentrated supernatant (concentrated liquid) of the third liquid mixture was frozen at −80 ° C. for 30 minutes, it was dried overnight to obtain a sample. The obtained sample was stored in a desiccator until analysis.

<GCMS analysis of sample>
After 80 μL of a methoxyamine solution (20 mg / mL, dissolved in pyridine) was added to the sample stored in the desiccator, the mixture was shaken at 37 ° C. for 30 minutes at 1,200 rpm. Subsequently, 40 μL of N-methyl-N-trimethylsilyltrifluoroacetamide was added, and the mixture was shaken at 1,200 rpm at 37 ° C. for 30 minutes, then centrifuged at 16,000 × g at 25 ° C. for 5 minutes, and 50 μL of the supernatant was collected. The vial was placed in a glass vial, and the vial was set in an auto sampler of GCMS.

A triple quadrupole gas chromatograph mass spectrometer (GCMS-TQ8040) manufactured by Shimadzu Corporation was used for GCMS. The analysis conditions of GCMS are shown below.
(1) Column: DB-5 ((5% -phenyl) -methylpolysiloxane, nonpolar), 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; rise 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>
<Measurement of metabolites>
Comprehensively detect peaks from the mass spectrum of a sample and compare their peak information (mass-to-charge ratio and signal intensity) with the mass-to-charge ratios of many metabolite-specific substances stored in the MS library Then, metabolites were identified, and measured values (hereinafter, referred to as MS data) of 135 types of metabolites were obtained.

<Statistical analysis>
Obtain the clinical information and test data required for statistical analysis from the subject who obtained the MS data from the St. Luke's International University Information System Center with the consent of the Research Ethics Committee of St. Luke's International Hospital, Clinical information, test data and MS data were compared. The clinical information and test data of the subject are all managed by a management number indicating the subject, and the subject is not specified when the data is matched.

In statistical analysis, unpaired t-test and Mann-Whitney U test were used as univariate analysis. Logistic regression analysis was used as multivariate analysis. In the evaluation of the 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 the adjustment factor.

Table 3 shows the results of the t-test of both groups as to the results of measuring the amounts of 135 types of metabolites in the AF group and the normal group. Table 3 shows 10 metabolites (isocitrate, arabinose, ribose, 2-oxoglutarate, malate, phenylalanine, fumaric acid, succinate, etc.) that showed a statistically significant difference between the AF group and the normal group. The P value and t statistic of (acid, fucose, citric acid) are listed in ascending order of P value. In Table 3, “mEn (m is a real number, n is an integer)” means “m × 10 ⁿ ”. For example, “3.74E-06” becomes “3.74 × 10 ⁻⁶ ”. All 10 metabolites shown in Table 3 had a P value of less than 0.001 in a separate Man-Whitney U test.

As shown in Table 3, the absolute value of the t statistic was larger as the P value was smaller, and all of the 10 metabolites were negative except ribose. From this, the amounts of the nine metabolites excluding ribose were statistically significantly lower in subjects in the normal group than in the AF group, and the amount of ribose was lower in the test group in the normal group than in the AF group. It can be seen that the number was statistically significantly higher for the participants.
From the above results, the measured values of the above 10 metabolites are useful as atrial fibrillation index values, and all of the measured values are compared with a predetermined threshold value. Can be diagnosed as motion.

Table 4 shows that AF group / normal group as dependent variables, age, gender, smoking history, current history of hypertension / diabetes, history of heart disease, eGFR (Estimate glomerular filtration rate, estimated glomerular filtration) 2 shows the results of investigating the relationship between the AF group and the measured values of 135 metabolites in the living body by logistic regression analysis. Table 4 shows the five metabolites (β-alanine, fumaric acid, threonine, succinic acid, and 2-oxoglutaric acid) having a small P value as a result of logistic regression analysis, together with regression coefficients, standard errors, and P values. They are listed. As shown in Table 4, among the five metabolites, β-alanine had the smallest P value, and a strong correlation was observed between β-alanine and atrial fibrillation. Table 4 also shows a t-statistic, a P-value, and a t-test P-value rank, which are the results of the t-test when the adjustment was not performed with the adjustment factor. Among the above five metabolites, the P value rank was higher when adjusted with the adjusting factor than when not adjusted with the adjusting factor. By adjusting the adjustment factor in this way, the ranking is raised, and the usefulness as an index is increased.

Next, in order to examine the diagnostic ability (that is, high diagnostic accuracy of atrial fibrillation) when the measured value of each metabolite is used as an atrial fibrillation index value, the dependent variable is set as AF group / normal group. ROC analysis (Receiver \ Operating \ Characteristic \ analysis) was performed on 135 kinds of metabolites. As a result, measured values of 10 metabolites (2-oxoglutarate, isocitrate, fucose, arabinose, fumarate, erythritol, succinate, phenylalanine, malate, and citric acid) are useful for diagnosis of atrial fibrillation. I understood that. Table 5 shows the AUC values of ROC curves (ROC curve) of ten metabolites useful for diagnosing atrial fibrillation.

The ROC curve is obtained by plotting the ordinate as a true positive rate, that is, sensitivity, and the abscissa as a false positive rate, that is, “1-specificity”. That is, the sensitivity is calculated from the ratio of subjects determined to be positive to all subjects with atrial fibrillation, and the number of subjects (non-AF) determined to be positive to all subjects in the normal group is calculated. Calculate the false positive rate from the ratio. Similarly, the ROC curve can be obtained by calculating the sensitivity and the false positive rate at other cutoff values and plotting the values thus obtained on a graph. The AUC value is the value of the area of the portion surrounded by the ROC curve and the horizontal axis. It can be determined that the larger the AUC value, the higher the accuracy of diagnosing the presence or absence of AF. As a result of calculating the AUC value from the ROC curve, metabolites in the living body having a large AUC value were 2-oxoglutaric acid (AUC = 0.733), isocitrate (AUC = 0.723), and fucose (AUC = 0.722).

On the other hand, Table 6 shows that the dependent variables same as those in the ROC analysis from which the results shown in Table 5 were obtained include age, sex, smoking history, current history of hypertension / diabetes, history of heart disease, and eGFR as adjusting factors. ROC analysis of 135 metabolites for the model into which the values were entered shows 10 metabolites with high C statistics. The C statistic corresponds to the AUC value, and the closer the C statistic is to 1, the higher the diagnostic ability is. Table 6 shows that 10 metabolites (β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutarate, serine, arabinose, Margaric acid, ribose) are listed with C statistics. All of the measured values of these 10 metabolites are statistically correlated with atrial fibrillation and are useful as indices for diagnosing the presence or absence of atrial fibrillation. In particular, the measured value of β-alanine (C statistic: 0.917) having the largest C statistic has a strong correlation with atrial fibrillation, and is an excellent index for diagnosing the presence or absence of atrial fibrillation. It can be said that.

Table 6 also shows AUC values and AUC ranks obtained by ROC analysis when adjustment was not performed using adjustment factors. The above 10 metabolites had higher AUC ranks when adjusted with the adjusting factor than when not adjusted with the adjusting factor. By performing the adjustment with the adjustment factor in this way, the order of the ten metabolites is increased, and the usefulness as an index is increased.

Thus, the result of measuring the amounts of the 16 metabolites contained in the biological sample of the subject can be used for diagnosing the presence or absence of atrial fibrillation in the subject.
That is, a method for diagnosing atrial fibrillation is to analyze a biological sample collected from a subject with an analyzer, and from data obtained by the analyzer, 16 metabolites, β-alanine, threonine, Amount of at least one metabolite selected from malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutaric acid, serine, arabinose, margaric acid, ribose, isocitric acid, phenylalanine, succinic acid, fucose, citric acid, erythritol And diagnosing the presence or absence of atrial fibrillation in the subject based on a result of comparing the atrial fibrillation index value with a predetermined threshold value.

Another method of diagnosing atrial fibrillation is to analyze a biological sample collected from a subject with a chromatograph mass spectrometer, and to analyze 16 types of metabolism from data obtained by the chromatograph mass spectrometer. Selected from β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutaric acid, serine, arabinose, margaric acid, ribose, isocitric acid, phenylalanine, succinic acid, fucose, citric acid, erythritol Determining an atrial fibrillation index value that is the amount of at least one metabolite to be determined, and diagnosing the presence or absence of atrial fibrillation in the subject based on a result of comparing the atrial fibrillation index value with a predetermined threshold value. Including.

Claims

A method of measuring an atrial fibrillation index value that is an index related to the onset of atrial fibrillation,
A biological sample collected from the subject was analyzed and 16 metabolites, β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutarate, serine, arabinose, margaric acid, ribose A method for measuring an atrial fibrillation index value, wherein the amount of at least one metabolite selected from isocitrate, phenylalanine, succinic acid, fucose, citric acid, and erythritol is measured as the atrial fibrillation index value.
The method of measuring an atrial fibrillation index value according to claim 1,
The atrial fibrillation index value is isocitrate, arabinose, ribose, 2-oxoglutaric acid, malic acid, phenylalanine, fumaric acid, succinic acid, fucose, the amount of any metabolite of citric acid, atrial fibrillation index How to measure the value.
The method of measuring an atrial fibrillation index value according to claim 1,
A method for measuring an atrial fibrillation index value, wherein the atrial fibrillation index value is an amount of a metabolite of any of β-alanine, fumaric acid, threonine, succinic acid, and 2-oxoglutarate.
The method of measuring an atrial fibrillation index value according to claim 1,
The atrial fibrillation index value is an amount of any metabolite of 2-oxoglutarate, isocitrate, fucose, arabinose, fumarate, erythritol, succinate, phenylalanine, malate, or citric acid. How to measure the value.
The method of measuring an atrial fibrillation index value according to claim 1,
The atrial fibrillation index value is β-alanine, threonine, malic acid, fumaric acid, 2-hydroxypyridine, 2-oxoglutarate, serine, arabinose, margaric acid, the amount of any metabolite of ribose, atria How to measure fibrillation index values.
The method of measuring an atrial fibrillation index value according to claim 1,
A method for measuring an atrial fibrillation index value, wherein the atrial fibrillation index value is an amount of β-alanine.
The method for measuring an atrial fibrillation index value according to any one of claims 1 to 6,
A method for measuring an atrial fibrillation index value, wherein the atrial fibrillation index value is an amount of a metabolite measured based on data obtained by performing a chromatographic MS analysis on a biological sample.