WO2015030211A1

WO2015030211A1 - Fatigue biomarker and use thereof

Info

Publication number: WO2015030211A1
Application number: PCT/JP2014/072834
Authority: WO
Inventors: 弘彦倉恒; 洋祐片岡; 慧嗣久米; 大和　正典; 世貴田島; 恭良渡辺; 早苗福田; 朋義曽我; 恵美山野
Original assignee: 独立行政法人理化学研究所; 公立大学法人大阪市立大学
Priority date: 2013-08-30
Filing date: 2014-08-29
Publication date: 2015-03-05
Also published as: JPWO2015030211A1; JP6521321B2

Abstract

With the goal of developing a biomarker that makes possible the objective and simple diagnosis and evaluation of fatigue, and a technique for diagnosing fatigue using this biomarker, this method for evaluating fatigue comprises a step for obtaining the ratio (OC ratio) of the ornithine concentration and citrulline concentration in a biological sample obtained from a subject.

Description

Fatigue biomarkers and their use

The present invention relates to a biomarker for evaluating fatigue and evaluation and diagnosis of fatigue using the biomarker.

According to an epidemiological survey in Japan, about 60% of the general public is aware of fatigue, and more than half of them are suffering from fatigue that lasts for more than six months. About half of those who feel such chronic fatigue complain of work or academic inefficiency, and the economic loss due to such fatigue is predicted to be 1 trillion yen or more.

Some people who have chronic fatigue include patients with chronic fatigue syndrome (Chronic Fatigue Syndrome) who have experienced extreme fatigue even if they are not tired and whose cause has not yet been fully elucidated. Yes. Patients with chronic fatigue syndrome often complain of severe physical and mental fatigue, which is accompanied by significant impairment in quality of life (QOL). Clinically, chronic fatigue syndrome is treated separately from chronic fatigue due to the accumulation of fatigue, but few doctors can diagnose it, and no effective treatment has been found.

Since many of the conventional fatigue diagnoses are based on the patient's subjectivity and the doctor's diagnosis, establishment of an index for objectively diagnosing fatigue is desired. Patent Document 1 discloses a technique for evaluating viral behavior as an indicator of fatigue level, focusing on viral infections listed as one of the causes of immunity decline. Non-Patent Document 1 discloses an attempt to objectively measure / evaluate fatigue using an acceleration pulse wave.

Japanese Published Patent Publication: Japanese Patent Laid-Open No. 2007-330263 (Publication Date: December 27, 2007) Japanese published patent publication: JP-A-9-77688 (published date: March 25, 1997) Japanese published patent publication: JP-A-9-59161 (published date: March 4, 1997) Japanese Published Patent Publication: Japanese Patent Application Laid-Open No. 2011-177194 (Publication Date: September 15, 2011) Japan Published Patent Gazette: Japanese Patent Laid-Open No. 2013-111001 (Publication Date: June 17, 2013) Japanese Published Patent Publication: Japanese Patent Application Laid-Open No. 2011-182661 (Publication Date: September 22, 2011) Japanese Published Patent Gazette: Japanese Patent Application Laid-Open No. 2011-161137 (Publication Date: August 25, 2011) Japanese published patent publication: JP 2010-266238 A (publication date: November 25, 2010) Japanese Published Patent Gazette: JP 2010-85369 A (published: April 15, 2010) Japanese Published Patent Publication: Japanese Patent Application Laid-Open No. 2007-228878 (Publication Date: September 13, 2007) Japanese Patent Gazette: Special Table 2000-516818 (Publication Date: December 19, 2000) International Publication WO2006 / 123611 (International Publication Date: November 23, 2006) International Publication No. WO2005 / 012903 (International Publication Date: February 10, 2005)

The cause of fatigue and the mechanism of fatigue are complex, and the whole picture has not been elucidated. However, particularly when considering applications such as fatigue treatment, there is a demand for the development of a technique for objectively evaluating fatigue in accordance with the mechanism inherent to the living body and the cause of fatigue.

The technique described in Patent Document 1 collects the body fluid of a subject, measures the amount of human herpesvirus in the body fluid, and evaluates the relationship with the degree of fatigue. However, this technique observes the behavior of a virus infecting a human (host), and does not measure a biomarker that reflects the cause or mechanism of fatigue in humans. Therefore, the technique described in Patent Document 1 cannot evaluate the fatigue state of individual subjects or provide a treatment or prevention method. The technique described in Non-Patent Document 1 has an advantage that it can be implemented non-invasively, but requires measurement of fingertip volume pulse waves by a special device and data processing based on a special principle. Poor sex.

The present invention has been made in view of the above problems, and an object of the present invention is to provide a method that enables objective and simple evaluation and diagnosis of fatigue.

In order to solve the above-mentioned problems, the present inventors have conducted a comprehensive and comprehensive metabolomic analysis study using plasma samples from patients with chronic fatigue syndrome, chronic fatigue patients, and healthy subjects. The concentration of various metabolites present in each plasma sample involved in metabolism was examined. After statistical analysis, we found that the increase / decrease pattern of a specific metabolite reflects the fatigue state, and by quantifying or quantifying biological metabolic information based on this increase / decrease pattern, It has been clarified that a simple fatigue diagnosis can be performed, and the present invention has been completed. That is, the present invention is as follows.
(1) A step of obtaining a ratio (OC ratio) between an ornithine concentration and a citrulline concentration in a biological sample obtained from a subject, and a step of evaluating the state of fatigue in the subject based on the ratio (OC ratio). A method for evaluating fatigue.
(2) A kit for evaluating fatigue, comprising a reagent for measuring ornithine concentration and a reagent for measuring citrulline concentration.
(3) A calculation unit that receives a reference value for evaluating fatigue and a ratio of the ornithine concentration to the citrulline concentration (OC ratio) and generates evaluation information for evaluating fatigue, and the evaluation information A fatigue evaluation system including an evaluation unit that receives and evaluates fatigue.
(4) A method for evaluating fatigue, the step of obtaining a biomarker value in a biological sample obtained from a subject, and the step of evaluating the state of fatigue in the subject based on the value of the biomarker The biomarker values are 1) 2-aminobutyric acid (2AB), 2-oxoglutarate (3-oxoglutarate), 3-hydroxybutyrate (3-Hydroxybutyrate), 3-methylhistidine (3-Methylhistidine), 4-Hydroxy-3-methoxybenzoate, 4-methyl-2-oxopentanoate, 5-oxoproline, alanine ( Ala), arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelate (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline (Ch oline), citrulline, creatine, creatine, cystine, gamma-butyrobetaine, glutamine (Gln), glutamate (Glu), glycine (Gly), histidine ( His), hydroxyproline (Hydroxyproline), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), mucate (Mucate), N, N-dimethylglycine (N, N-Dimethylglycine), ornithine ( Ornithine), phenylalanine (Phe), proline (Pro), urea (Urea), pyruvate (Pyruvate), sarcosine (Sarcosine), serine (Ser), taurine (Taurine), threonine (Thr), tryptophan (Trp), tyrosine (Tyr), uric acid (Urate), and at least one biomarker biosample selected from the group consisting of valine (Val) 2) In the above group, cis-aconitic acid (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinate (Succinate) ) Is a method for evaluating fatigue, which is a ratio or difference in concentration in a biological sample of a plurality of types of biomarkers selected from the group to which) is added.

The present invention enables objective and simple evaluation and diagnosis of fatigue. The present invention may further provide techniques useful for treating fatigue.

It is a figure which shows the result of having compared the density | concentration of the various metabolite contained in the plasma of the healthy subject (HC) and the patient of a chronic fatigue syndrome (CFS). Random forest (Random) with all parameters including metabolite measurements in plasma samples of healthy subjects (HC) and patients with chronic fatigue syndrome (CFS) for the population plus study 1 and study 2 It is a figure which shows the result of having executed the Forest) program. FIG. 6 shows the results of running a random forest program for all populations of study 2 using all parameters including metabolite measurements in plasma samples of healthy (HC) and chronic fatigue syndrome (CFS) patients. FIG. It is a figure which shows an example of the metabolite by which the significant difference is seen in the density | concentration contained in plasma between a healthy subject (HC) and a patient of chronic fatigue syndrome (CFS). It is a figure which shows the density | concentration of the various metabolite contained in the plasma of a healthy subject (HC) and a chronic fatigue (CF) patient. It is a figure which shows an example of the metabolite by which the significant difference is seen in the density | concentration contained in plasma between a healthy subject (HC) and a patient of chronic fatigue (CF). For the purpose of analyzing healthy subjects (HC) and patients with chronic fatigue syndrome (CFS), the results of the analysis using the decision tree model for the measured values of metabolites in all plasma samples of Test 1 and Test 2. It is a figure which shows an example. For the purpose of analyzing healthy subjects (HC) and patients with chronic fatigue syndrome (CFS), the results of the analysis using the decision tree model for the measured values of metabolites in all plasma samples of Test 1 and Test 2. It is a figure which shows another example. For the purpose of analyzing patients with healthy subjects (HC) and chronic fatigue syndrome (CFS), a decision tree model analysis was performed on the ratio of measured metabolite values in all plasma samples of Study 1 and Study 2. It is a figure which shows the further another example of a result. For the purpose of analyzing patients with healthy subjects (HC) and chronic fatigue syndrome (CFS), a decision tree model analysis was performed on the ratio of measured metabolite values in all plasma samples of Study 1 and Study 2. It is a figure which shows the further another example of a result. It is a figure which shows the result of having compared the density | concentration of the various metabolite contained in the plasma of the patient of chronic fatigue (CF) and the patient of chronic fatigue syndrome (CFS). Through fatigue test of fatigue model animals (rats), metabolic abnormalities in urea circuit and TCA circuit, and fatigue from urea circuit to TCA circuit, confirmed from comprehensive metabolic analysis of blood and liver of model animal It is a figure which shows the flow of metabolism peculiar to a disease state (graph data shows the thing of a liver sample). It is a figure which shows a response | compatibility with the figure and the abbreviation and full name of an amino acid used in this specification. It is a graph which shows a mode that the density | concentration of ornithine and the density | concentration of arginine change with progress of time in the extract | collected whole human blood. In an experiment using rat whole blood, it is a graph which shows that the density | concentration change with time passage of arginine and ornithine can be suppressed by adding the inhibitor of various enzymes. A random forest (Random Forest) program was run on the study 2 population with all parameters including metabolite measurements in plasma samples of healthy (HC) and chronic fatigue (CF) patients It is a figure which shows a result. Run the random forest (Random プログラム Forest) program for the study 2 population with all parameters including metabolite measurements in plasma samples of patients with chronic fatigue (CF) and chronic fatigue syndrome (CFS) It is a figure which shows the result. It is a figure which shows the result of having performed 2 group comparison of HC group and CFS group using PLS-DA method. It is a figure which shows the result of having performed 2 group comparison of HC group and CF group using PLS-DA method. It is a figure which shows the result of having performed 2 group comparison of CF group and CFS group using PLS-DA method.

[A] Biological Sample As used herein, the term “biological sample” is intended to include any tissue (including body fluids such as blood) or cells collected from a subject, and prepared therefrom. Organized tissue sections or cell lysates can also be included in the biological sample. Preferred biological samples for use in the present invention include, but are not limited to, blood, saliva, urine, interstitial fluid, sweat, and preparations thereof (eg, serum, plasma, etc.). The biological sample is preferably blood or a blood preparation (serum, plasma, etc.).

[1] Fatigue Currently, the number of patients (including acute fatigue, chronic fatigue, and chronic fatigue syndrome) who visit a medical institution with fatigue / malaise as the main symptom is the second after the number of patients whose main symptom is pain Many. However, no method for objectively evaluating fatigue has been developed so far. Fatigue and fatigue are sensations that humans experience on a daily basis, and are important biological signals that inform the disturbance of homeostasis in the living body. However, the feeling of fatigue is subjective only and does not objectively indicate the degree of fatigue. Although there were times when lactic acid was considered to be a causative agent of fatigue, it has also been found that fluctuations in lactic acid level can be an indicator of exercise, but not an indicator of fatigue. Furthermore, no effective treatment for fatigue has been found.

The US Center for Disease Control and Prevention (CDC) reported a chronic fatigue syndrome called “chronic fatigue syndrome” in 1988, elucidating the mechanism, searching for biomarkers, and developing treatment prevention methods. Various researches have been made with a focus on. However, the onset mechanism of chronic fatigue syndrome has not yet been elucidated, and no objective biomarker has been developed. Even now, the diagnosis of chronic fatigue syndrome is made using the criteria of symptom and physical findings published by CDC in 1994.

On the other hand, fatigue biomarkers using viral activation or autonomic nerve abnormalities as indicators in chronic fatigue syndrome have been proposed. However, these are not in accordance with the causes and mechanisms of human fatigue, so they are good biomarkers specific to fatigue (including chronic fatigue and chronic fatigue syndrome, but especially chronic fatigue and chronic fatigue syndrome). I want. Furthermore, since these are indirect indicators of the maintenance / recovery mechanism of homeostasis, they are not direct indicators, and it is necessary to trial and error to develop them to provide specific treatments.

The present inventors have already found that some of the metabolites that form the glycolytic system and the TCA cycle are lower in the group of patients with chronic fatigue syndrome than in healthy individuals ( Reference: International Publication WO2011 / 161544 A2 (US Patent Serial Number 13 / 805,449)). This time, by adding data obtained from a larger, different population, we found a characteristic change between the fatigue patient group and the healthy subject in the metabolites related to the urea cycle. In a more specific example, a characteristic metabolic pathology in which a metabolic pathway flowing from the urea circuit to the TCA circuit is promoted in the fatigue patient group and as a result, a reaction system that metabolizes ammonia is suppressed is identified. Such metabolic abnormalities that indicate fatigue pathology were supported by a model experimental system in which rats were fatigued.

In this specification, unless otherwise specified, a chronic fatigue patient does not include a patient with chronic fatigue syndrome. The term “chronic fatigue” refers to a case where chronic fatigue syndrome has not been diagnosed by a doctor's diagnosis, although the main complaint is fatigue or feeling of fatigue intermittently for 6 months or longer. Chronic fatigue syndrome is a revised version of the diagnostic criteria for chronic fatigue syndrome (Ann Intern Med. 1994; 121: 953-959) created by the American Center for Disease Control and Prevention (CDC). As described in 671-677), the latest symptom of fatigue and science from Japan: Proposals for anti-fatigue / anti-overwork (1) Strong fatigue that significantly impairs life as the main symptom, at least 6 months or more Repeat or continue recurrence for a period of 50% or more. In addition, the disease is excluded based on medical history, physical findings, and laboratory findings. Furthermore, (2) refers to a case that meets 8 or more symptom criteria, or 6 symptom criteria and 2 or more physical criteria.

[2] Biomarker of the Present Invention The biomarker for fatigue according to one embodiment of the present invention is as follows.

1) The biomarker specifically described in any of FIGS. 2, 3, 4, 6, 7, 8, 8, 11, 16, and 17. More specifically, for example, 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4-methylhistidine, 4-Hydroxy-3-methoxybenzoate, 4-methyl-2-oxopentanoate, 5-oxoxoline, alanine (Ala) , Arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelate acid (Azelate), beta alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline (Choline), cis-aconitic acid (Cis-aconitate), citric acid (Citrate), citrulline (Citrulline), creatine (Creatine), creatinine (Creatinine), cystine (Cystine), gamma-butyrobetaine (Gamma-Butyrobetaine) , Glutamine (Gln), glutamic acid (Glu), glycine (Gly), histidine (His), hydroxyproline (Hydroxyproline), isoleucine (Ile), isocitrate (Isocitrate), lactic acid (Lactate), leucine (Leu), lysine ( Lys), malic acid (Malate), methionine (Met), mucate (Mucate), N, N-dimethylglycine (N, N-Dimethylglycine), ornithine (Ornithine), phenylalanine (Phe), proline (Pro), urea ( Urea), pyruvate (Pyruvate), sarcosine (Sarcosine), serine (Ser), succinate (Succinate), taurine (Taurine), threonine (Thr), tryptophan (Trp), tyrosine (Tyr), uric acid (Urate), And valine (Val). Among these, in particular, 2-oxoglutarate, 4-hydroxy-3-methoxybenzoate, 4-methyl-2-oxopentanoate (4-methyl- 2-oxopentanoate), arginine (Arg), asparagine (Asn), choline (Choline), cis-aconitic acid (Cisrate), citric acid (Citrate), citrulline (Citrulline), creatine (Creatine), gamma-butyrobetaine (Gamma-Butyrobetaine), glutamine (Gln), glycine (Gly), histidine (His), hydroxyproline (Hydroxyproline), isocitrate (Isocitrate), lysine (Lys), ornithine (Ornithine), urea (Urea), pyruvic acid (Pyruvate), succinate (Succinate), taurine (Taurine), tryptophan (Trp), and uric acid (Urate), as shown in the Examples, between different fatigue states It is a particularly useful biomarker with statistically significant differences.
Among these, metabolites related to the urea cycle and metabolites involved in the flow of metabolism from the urea cycle to the TCA cycle are more preferable as biomarkers. Metabolites related to the urea cycle and biomarkers of metabolites related to the flow of metabolism from the urea cycle to the TCA cycle include the metabolites shown in FIG. 1 or 4, ornithine, citrulline, urea (Urea), Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, and Gamma-butyrobetaine. Among them, as the biomarker, ornithine, citrulline, urea, and the like are more preferable, and ornithine and citrulline are particularly preferable because they exhibit different behavior depending on the fatigue state. Any number of the above biomarkers may be used for analysis simultaneously.
Among the biomarkers described in 1) above, cis-aconitic acid (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinic acid ( Succinate) is not used alone, but may be used in combination with the biomarkers described above in 1).

These biomarkers are obtained, for example, as concentrations in a biological sample, and compared with a predetermined threshold value (reference value) to be used for fatigue evaluation. The predetermined threshold is appropriately set for each biomarker according to the analysis method and the like.

The analysis method is not particularly limited as long as it is generally used for measuring the concentration of a metabolite in a living body. For example, CE-MS / MS (capillary electrophoresis mass spectrometry) and LC-MS / MS (liquid chromatography mass spectrometry) are widely used.

An example of the predetermined threshold is a mode value obtained from an average value of biomarker concentrations in a biological sample in a plurality of healthy subjects, a binomial distribution, or the like. Also, for example, as shown in FIGS. 2, 3, 4, 6, 7, 8, and 11, and the examples, the amount of the biomarker is in what state (for example, If it is above or below a threshold value, it is possible to evaluate whether it is fatigue (or whether it is chronic fatigue, chronic fatigue syndrome, etc.).

Although not particularly limited, the biomarkers described in FIGS. 2, 3, 4, 7, and 8 are particularly useful as markers for evaluating whether a subject is a healthy person or a patient with chronic fatigue syndrome. It is. More specifically, for example, Ornithine, Citrulline, Urea, Hydroxyproline, Urate, 4-Hydroxy-3-methoxybenzoate, Isocitrate, Citrate, Pyruvate, Cis-aconitate, Gamma-Butyrobetaine, 4-methyl-2-oxopentanoate, and 2-oxoglutarate is a particularly useful biomarker with statistically significant differences between healthy individuals and patients with chronic fatigue syndrome. In addition, Succinate, Pro, Leu, 3-Methylhistidine, Creatinine, His, Ala, N, N-Dimethylglycine, Ser, Lys, Betaine, Glu, Taurine, 2AB, Arg, 3-Hydroxybutyrate, Creatine, Trp , Mucate, Cystine, Asp, Sarcosine, Thr, and Val can be markers for evaluating whether a subject is a healthy person or a patient with chronic fatigue syndrome as a result of random forest analysis. In addition, the markers from Succinate to Val are listed in order from the top (ie, promising as a marker) as a result of random forest analysis.
Although not particularly limited, the biomarker described in FIGS. 6 and 16 evaluates whether the subject is a healthy person or a patient with chronic fatigue (especially a patient with chronic fatigue who is not chronic fatigue syndrome). It is particularly useful as a marker. More specifically, for example, Choline, Gly, Tarine, Asn, Gamma-Butyrobetaine, Gln, Lys, Trp, 4-methyl-2-oxopentanoate, and Succinate are used for healthy subjects and chronic fatigue patients (especially chronic fatigue). It is a particularly useful biocar car with statistically significant differences from patients with chronic fatigue who are not syndromes). Other than these, Cis-Aconitate, Lactate, Arg, Mucate, His, Azelate, N, N-Dimethylglycine, Creatinine, beta-Ala, Tyr, Ornithine, 2AB, Leu, Isocitrate, Hydroxyproline, Phe, Met, 3 -Methylhistidine, Sarcosine, Ile, Betaine, Pyruvate, Urate, 4-Hydroxy-3-methoxybenzoate, Ser, Asp, Carnitine, Urea, 5-oxoproline, and Citrate, as a result of random forest analysis, the subject is healthy It can be a marker for evaluating whether the patient is chronic fatigue (particularly chronic fatigue who is not chronic fatigue syndrome). In addition, the markers from Cis-Aconitate to Citrate are listed in order from the top (that is, promising as a marker) as a result of random forest analysis.
In addition, although not particularly limited, the biomarker described in FIG. 11 and FIG. 17 is a marker for evaluating whether a subject is a patient with chronic fatigue syndrome or a patient with chronic fatigue syndrome that is not chronic fatigue syndrome. It is particularly useful. More specifically, for example, Choline, Succinate, Gln, Taurine, Asn, Lys, Arg, His, Urate, and 4-Hydroxy-3-methoxybenzoate are used in patients with chronic fatigue syndrome and chronic but not chronic fatigue syndrome. It is a particularly useful biomarker with statistically significant differences from fatigue patients. Other than these, N, N-Dimethylglycine, 3-Methylhistidine, Azelate, Ile, Trp, 4-methyl-2-oxopentanoate, Ser, Cis-Aconitate, Met, Phe, Gly, Lactate, Ala, Leu, Citrulline , Malate, gamma-Butyrobetaine, Sarcosine, beta-Ala, Glu, 2-Oxoglutarate, Pyruvate, Asp, Urea, Creatinine, Carnitine, 2AB, Betaine, Citrate, and Tyr. It can be a marker for evaluating whether the patient is a syndrome patient or a patient with chronic fatigue (not chronic fatigue syndrome). In addition, the markers from N, N-dimethylglycine to Tyr are listed in order from the top (that is, promising as a marker) as a result of random forest analysis.

2) A biomarker configured as a correlation between a plurality of biomarkers of 1) above. For example, the ratio of the concentration of the plurality of biomarkers in 1) above in the biological sample, the difference in the concentration in the biological sample, and the like are applicable. As an example, the ratio of ornithine concentration and citrulline concentration in a biological sample (OC ratio, for example, O / C or C / O), which is the biomarker specifically shown in FIGS. 9 and 10; A ratio of pyruvic acid concentration and isocitrate concentration in a sample (PI ratio); a ratio of alanine concentration and hydroxyproline concentration in a biological sample (AP ratio); Any number of biomarkers may be used for analysis simultaneously. In addition, metabolites located at the top in random forest analysis (see Fig. 3: for example, Ornithine, Citrulline, Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, Gamma-butyrobetaine, etc.) The concentration ratio of two metabolites selected from the group consisting of can also be used as a biomarker in combination with the OC ratio or in combination with the OC ratio, or alternatively with the OC ratio or in combination with the OC ratio Preferably, as a biomarker used, ornithine concentration in a biological sample and any concentration selected from Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, Gamma-butyrobetaine Can be mentioned.

These biomarkers are used, for example, for fatigue evaluation by comparison with a predetermined threshold (reference value). The predetermined threshold is appropriately set for each biomarker according to the analysis method and the like.

An example of the predetermined threshold is a mode value obtained from an average value of biomarker values in a plurality of healthy subjects, a binomial distribution, or the like. Further, for example, as shown in FIG. 9 and FIG. 10 and the description of the examples, if the value of the biomarker is in any state (for example, whether it is greater than or less than the threshold), fatigue (Or, for example, whether it is chronic fatigue, whether it is chronic fatigue syndrome, etc.).

Although not particularly limited, the biomarkers described in FIGS. 9 and 10 are particularly useful as markers for evaluating whether a subject is a healthy person or a patient with chronic fatigue syndrome.

As also referred to in the Examples, one aspect of the biomarker of the present invention is observed by comprehensively analyzing the changes in metabolites in the plasma of patients with chronic fatigue syndrome and patients with chronic fatigue and the plasma of healthy subjects. This is an objective fatigue biomarker based on the mechanism of fatigue disease. Therefore, as one aspect of the biomarker of the present invention, it is possible to objectively and easily evaluate and diagnose fatigue. Furthermore, by using the biomarker of the present invention, diagnostic methods and treatment methods for chronic fatigue and chronic fatigue syndrome can be developed.

As another aspect of the present invention, it is possible to create an inexpensive and simple fatigue diagnosis system by using the biomarker of the present invention, and further, an effective treatment method can be found based on the diagnosis result. There is a possibility. For example, for patients who complain of strong fatigue or long-term fatigue, blood is collected and the biomarker of the present invention is examined to determine whether the patient is chronic fatigue or chronic fatigue syndrome. Can be objectively evaluated. This evaluation can be examined in a general medical facility without a high level of knowledge about fatigue.

Also, as one aspect of the present invention, it can be determined whether or not the subject belongs to chronic fatigue syndrome by measuring the blood component of the subject. According to the conventional diagnostic criteria, (1) the main symptom is severe fatigue that significantly impairs life, and it continues or repeats for a period of at least 6 months (recognized for a period of 50% or more). In addition, the disease is excluded based on medical history, physical findings, and laboratory findings. In addition, it is necessary to satisfy (2) symptom criteria 8 items or more, or symptom criteria 6 items, body criteria 2 items or more (Ayumi's Special Issue “Latest Science of Fatigue-From Japan: Anti-Fatigue / Over-Working” 671-677), the number of measurement items until diagnosis, long-term observation (measurement) time, economic burden, and individual differences in doctor's judgment criteria. As one aspect of the present invention, it is useful not only to make an objective diagnosis by measuring a limited amount of metabolites in blood, but also to make a treatment policy because the diagnosis result represents the disease state itself. In addition, many of the metabolites revealed in the present invention are deeply related to the urea metabolism system (urea circuit) that metabolizes ammonia, which is extremely toxic in the liver, to urea, and not only chronic fatigue syndrome but also diagnosis of chronic fatigue. Can also be used. In particular, by producing a measurement kit that can be used in actual medical settings, it is considered that chronic fatigue syndrome and chronic fatigue can be objectively diagnosed faster, cheaper.

The biomarker of the present invention can be used in combination with a known biomarker related to fatigue. Known biomarkers are not particularly limited, and examples thereof include those based on energy (ATP) -producing metabolites reported in International Publication WO2011 / 161544 A2 (US Patent Serial No. 13 / 805,449). By analyzing in combination with the biomarker described in International Publication WO2011 / 161544 A2, it is possible to more accurately estimate which part of the living body is abnormal, and for life guidance for patients with chronic fatigue syndrome and patients with chronic fatigue. In addition to being able to make use of it, it is also possible to provide a remedy that corrects the metabolic system considered to be the cause. In addition, as one aspect of the present invention, in combination with a method for determining a fatigue patient group using only an energy-producing metabolite such as a glycolytic system or a TCA circuit, the change of the urea circuit and its surrounding metabolism is used as an index. By using a biomarker, it becomes possible to particularly improve the ability to evaluate fatigue with respect to individual individual variations and provide a more suitable treatment policy.

[3] Utilization of Biomarker of the Present Invention The present invention provides a method, kit and system for evaluating fatigue based on the biomarker described above.

[3-1] Fatigue Evaluation Method A method for evaluating fatigue according to the present invention includes a step of obtaining the above-described biomarker value of the present invention in a biological sample obtained from a subject (biomarker value acquisition step), And a step of evaluating using the value of the marker (evaluation step).

The biomarker value acquisition step can be appropriately performed according to the type of biomarker. When performing the biomarker value acquisition step, it is necessary to measure the concentration of the metabolite in the biological sample. The concentration of the metabolite in the biological sample can be measured by an appropriate method according to the type of metabolite, regardless of the method of the embodiment. As an example, when an enzyme reaction using a metabolite as a substrate is well known, a commercially available kit for measuring the substrate concentration using the enzyme reaction may be used. In other words, those skilled in the art can successfully measure the concentration of a metabolite in a biological sample by arbitrarily using such various techniques. In addition, it may be preferable that the biological sample to be measured for the concentration of the metabolite is subjected to a treatment for suppressing the activity of the enzyme involved in the degradation reaction of arginine contained in the biological sample. When a biomarker related to arginine, ornithine, citrulline, or the like is used, it may be preferable for the purpose of further improving measurement accuracy. Here, the suppression of the activity of the enzyme involved in the degradation reaction of arginine by the treatment means that the biological sample after the treatment is inhibited more than the biological sample before the treatment. An example of such treatment includes adding an inhibitor of arginase and / or nitric oxide synthase, which is an enzyme involved in the degradation reaction of arginine, to a biological sample (for example, blood or blood preparation). . In addition, the kind of inhibitor is not specifically limited, A specific illustration is demonstrated in the [3-2] column regarding a fatigue evaluation kit.

The evaluation step can be appropriately performed according to the method of performing the evaluation. For example, the evaluation is performed by comparing the value of the biomarker with a predetermined threshold (reference value) or the value of the biomarker is set to the threshold (reference value). You can also evaluate without comparing with). For example, by performing analysis such as discriminant analysis, PartialPartLeast Square, or Support Vector Machine, depending on the type of biomarker, fatigue can be evaluated (diagnosis or determination) without using a threshold value (reference value). it can. In the case of using a threshold value, even if the threshold value is a predetermined value, the healthy person is sampled simultaneously with the sampling from the subject, and the concentration of each metabolite in the biological sample obtained in the healthy person is calculated. It may be a value calculated based on this. An example of the predetermined threshold is a mode value obtained from an average value of biomarker values in a plurality of healthy persons, a binomial distribution, or the like.

The evaluation process is not particularly limited. For example, 1) evaluation of whether or not the subject is fatigued, 2) evaluation of whether or not the subject is chronic fatigue other than chronic fatigue syndrome, and 3) the subject is Evaluation of whether or not the patient has chronic fatigue syndrome, 4) A combination of at least two evaluations selected from 1) to 3) (for example, whether the subject has chronic fatigue syndrome or chronic fatigue syndrome that is not chronic fatigue syndrome) 5) Evaluation of the degree of fatigue, etc. are performed.

A specific example of the method for evaluating fatigue according to the present invention includes a step (1) of obtaining a ratio (OC ratio) between an ornithine concentration and a citrulline concentration in a biological sample obtained from a subject. Furthermore, the process (2) which compares the threshold value corresponding to the said OC ratio with the said OC ratio is included after the said process (1) as needed. Furthermore, if necessary, the ratio (PI ratio) between the pyruvic acid concentration and the isocitrate concentration in the biological sample obtained from the same subject, assuming that the step (1) or the step (2) is performed. Obtaining step (3) is included. Further, if necessary, a step (4) of comparing the PI ratio with a threshold corresponding to the PI ratio is included after the step (3). The step (3) of obtaining the ratio (PI ratio) between the pyruvate concentration and the isocitrate concentration in the biological sample obtained from the subject, the threshold corresponding to the ratio (PI ratio), and the ratio (PI ratio) It is highly positive to perform the step (2) of comparing the threshold corresponding to the OC ratio and the OC ratio on the subject who has been evaluated for fatigue by performing the step (4) of comparing the OC ratio) In some cases, it is preferable to obtain a discrimination rate and a low misclassification rate. For example, the evaluation by the decision tree shown in FIGS. 9 and 10 is also referred to. In FIGS. 9 and 10, for the subjects divided into two groups by performing a decision tree analysis using pyruvic acid concentration / isocitrate concentration as the PI ratio, it was further determined using ornithine concentration / citrulline concentration as the OC ratio. It has been shown to do tree analysis. Further, from the viewpoint of obtaining the same effect, fatigue was evaluated by performing the step (1) of obtaining the OC ratio and the step (2) of comparing the threshold corresponding to the OC ratio with the OC ratio. It may be preferable to perform the step (4) of comparing the PI ratio with the threshold corresponding to the PI ratio for the subject.

In addition, prior to the biomarker value acquisition step, a step of directly taking out tissue or cells from a human subject as a first step of sample acquisition is performed by a doctor. In addition, a step in which a doctor makes a diagnosis as to whether or not the patient is fatigue (including chronic fatigue and chronic fatigue syndrome) using the result obtained by the evaluation step is also performed as necessary. These steps are distinguished as separate steps not included in the biomarker value acquisition step and the evaluation step.

[3-2] Fatigue evaluation kit A kit having a reagent used for carrying out the fatigue evaluation method as described above is also within the scope of the present invention. That is, the kit according to the present invention includes a reagent for measuring the concentration of a metabolite in a biological sample necessary for obtaining the value of the biomarker of the present invention in order to evaluate fatigue. As an example, when ornithine is used as a biomarker, the kit according to the present invention includes a reagent for measuring the ornithine concentration in a biological sample. As another example, when the ratio between the ornithine concentration and the citrulline concentration is used as a biomarker, the kit according to the present invention includes a reagent for measuring the ornithine concentration and the citrulline concentration in the biological sample. Examples of the reagent for measuring the ornithine concentration include ornithine cyclase that reacts with ornithine to generate ammonia. Examples of the reagent for measuring the citrulline concentration include argininosuccinate synthase that reacts with citrulline and consumes ATP. It is done. The kit may also preferably contain an inhibitor of the activity of the enzyme involved in the degradation reaction of arginine contained in the biological sample, particularly when measuring the concentration of arginine, ornithine or citrulline, etc. In some cases, it is preferable for the purpose of further improving the accuracy of measurement. Here, the enzyme involved in the degradation reaction of arginine includes arginase and nitric oxide synthase (NOS) involved in the reaction of producing nitric oxide and citrulline from arginine. Included in biological samples such as blood preparations. Examples of the arginase inhibitor include N ^ω -Hydroxy-nor-L-arginine (nor-NOHA) or a salt thereof (for example, diacetate). As an inhibitor of nitric oxide synthase, Is, for example, N ^G -Nitro-L-Arginine (L-NNA) or a salt thereof. These inhibitors may be provided separately from the other components of the kit, but for example, a necessary amount may be charged in advance in a container for storing a biological sample.

Also, a kit having a configuration for visually detecting the biomarker of the present invention is also within the scope of the present invention. That is, the second kit according to the present invention includes a presentation unit that indicates the value of the biomarker of the present invention in order to evaluate fatigue. As the presenting part, for example, at least one of a first presenting part indicating the ratio of the ornithine concentration to the citrulline concentration (OC ratio) and a second presenting part indicating the ratio of the pyruvic acid concentration to the isocitrate concentration (PI ratio) The provided kit is also a category of the kit according to the present invention.

As used herein, the term “kit” is intended as a package with a container (eg, bottle, plate, tube, dish, etc.) containing a particular material, but as a composition. Forms containing the material in the substance are also encompassed by the term “kit”. The kit preferably includes instructions for using each material. The instruction sheet may further display a predetermined threshold value (reference value) for evaluation by comparison with the value of the biomarker, if necessary. As used herein, in the aspect of a kit, “comprising” is intended to mean being contained in any of the individual containers that make up the kit. Moreover, the kit which concerns on this invention may be the packaging which packed several different compositions in one, and in the case of a solution form, you may enclose in the container. The kit according to the present invention may be provided with a plurality of components mixed in the same container or in separate containers. The “instructions” may be written or printed on paper or other media, or may be affixed to electronic media such as magnetic tape, computer readable disk or tape, CD-ROM, etc. . The kit according to the present invention may also include a container containing a diluent, a solvent, a washing solution or other reagent. Furthermore, the kit according to the present invention may include instruments and reagents necessary for collecting a biological sample. In addition, the kit according to the present invention may be provided with instruments and reagents necessary for preparing a target preparation from a biological sample.

By having the above-described configuration, the kit according to the present invention can be used easily in an actual medical field, so that it is possible to provide an objective diagnosis with faster and cheaper fatigue.

The kit according to the present invention not only can evaluate fatigue and propose a treatment method, but can also provide a criterion for determining fatigue (including chronic fatigue and chronic fatigue syndrome). It becomes easy to diagnose whether or not That is, the kit according to the present invention diagnoses a kit (for example, fatigue (including chronic fatigue and chronic fatigue syndrome)) for providing a diagnostic standard or a criterion for fatigue (including chronic fatigue and chronic fatigue syndrome). Or a kit for acquiring data for determination).

[3-3] Fatigue evaluation system A system used for executing the fatigue evaluation method as described above is also within the scope of the present invention. In the following embodiment, each member constituting the fatigue evaluation system according to the present invention is a functional block realized by executing a program code stored in a recording medium such as a ROM or a RAM by a calculation means such as a CPU. A case of “some” will be described as an example, but it may be realized by hardware that performs the same processing. Moreover, it is also possible to realize a combination of hardware that performs a part of the processing and the above arithmetic unit that executes program code for controlling the hardware and the remaining processing. Further, even among the members described above as hardware, the hardware for performing a part of the processing and the arithmetic means for executing the program code for performing the control of the hardware and the remaining processing It can also be realized in combination. The arithmetic means may be a single unit, or a plurality of arithmetic means connected via a bus inside the apparatus or various communication paths may execute the program code jointly.

The fatigue evaluation system according to the present invention includes at least an input receiving unit 11A, a storage unit 12, a CPU 13, and a display unit 14 as functional blocks. The input receiving unit 11A is an interface that receives an input of a measurement result from the measuring unit 11, and is configured as an interface that connects the measuring unit 11 and the fatigue evaluation system, or as an input device such as a keyboard or a mouse in some cases. Composed. The measurement unit 11 has a function of a measurement device that measures the concentration of a metabolite (for example, ornithine or citrulline) related to the biomarker of the present invention. The storage unit 12 functions as a measurement value receiving unit 12a that receives a measurement value of the concentration of the metabolite and a reference value storage unit 12b that stores a reference value (threshold value) for evaluating fatigue. (For example, configured as a memory). The CPU 13 has a function as an operation unit 13a that generates information for evaluating fatigue and an evaluation unit 13b that receives evaluation information from the operation unit 13a and evaluates fatigue. The display unit 14 has a function as the evaluation result display unit 14 that displays the evaluation result by the evaluation unit 13b (for example, configured as a liquid crystal display device). This functional block is realized by the CPU 13 executing a program stored in the storage unit 12 and controlling peripheral circuits such as an input / output circuit.

Hereinafter, as an aspect of the fatigue evaluation system, a system for evaluating fatigue using the ratio of the ornithine concentration to the citrulline concentration (OC ratio) as one of the biomarkers will be specifically described. In this system, as step 1, the measuring unit 11 measures the ornithine concentration and the citrulline concentration in the biological sample. Next, as step 2, the measured value receiving unit 12a receives (stores) the ornithine concentration and citrulline concentration measured in step 1 through the input receiving unit 11A. Next, as step 3, the calculation unit 13a reads the ornithine concentration and citrulline concentration stored in the measurement value receiving unit 12a, and calculates the ratio (OC ratio) between the ornithine concentration and the citrulline concentration. The OC ratio is stored in the storage unit 12 as necessary. Next, as Step 4, the calculation unit 13a generates evaluation information for evaluating fatigue from the calculated OC ratio and the reference value stored in the reference value storage unit 12b. Next, as step 5, the evaluation unit 13 b evaluates fatigue based on the evaluation information generated at step 4. Next, as step 6, the fatigue evaluation result generated by the evaluation unit 13 b is displayed on the display unit 14. As Step 2, the ratio (or OC ratio) between the ornithine concentration and the citrulline concentration may be received by the measurement value receiving unit 12a via the input receiving unit 11A. In this case, instead of the above steps 3 to 4, the calculation unit 13a reads the OC ratio stored in the measurement value receiving unit 12a, and the OC ratio and the reference value stored in the reference value storage unit 12b. From this, evaluation information for evaluating fatigue is generated. Step 5 is as described above. Note that the steps from generation of evaluation information to fatigue evaluation can be executed by operating an evaluation program (for example, a decision tree analysis program) stored in the storage unit 12, for example. For example, when the results shown in FIGS. 9 and 10 are obtained, the ratio (PI ratio) between the pyruvate concentration and the isocitrate concentration in the biological sample of the subject, and the biological sample of the subject in the same manner as the OC ratio What is necessary is just to operate the program for evaluation, after storing the ratio of the alanine density | concentration and hydroxyproline density | concentration in a fatigue evaluation system (for example, the storage part 12).

In addition, if the system according to the present invention is used, not only can fatigue be evaluated and a treatment method can be proposed, but also a criterion for determining fatigue (including chronic fatigue and chronic fatigue syndrome) can be provided. It becomes easy to diagnose whether or not That is, the system according to the present invention diagnoses a system (for example, fatigue (including chronic fatigue and chronic fatigue syndrome)) for providing a diagnosis standard or determination standard for fatigue (including chronic fatigue and chronic fatigue syndrome). Or a system for obtaining data for determination).

[4] One aspect of the present invention One aspect of the present invention includes, for example, the following inventions.
(1) A step of obtaining a ratio (OC ratio) between an ornithine concentration and a citrulline concentration in a biological sample obtained from a subject, and a step of evaluating the state of fatigue in the subject based on the ratio (OC ratio). A method for evaluating fatigue.
(2) The method according to (1), comprising a step of comparing a threshold corresponding to the ratio (OC ratio) and the ratio (OC ratio).
(3) Furthermore, obtaining a ratio (PI ratio) of pyruvic acid concentration and isocitrate concentration in the biological sample, and evaluating a fatigue state in the subject based on the ratio (PI ratio); The method according to (1) or (2), comprising:
(4) The method according to (3), comprising a step of comparing a threshold corresponding to the ratio (PI ratio) with the ratio (PI ratio).
(5) obtaining a ratio (PI ratio) between pyruvic acid concentration and isocitrate concentration in the biological sample, and comparing a threshold corresponding to the ratio (PI ratio) with the ratio (PI ratio); The method according to (2), wherein the step of comparing the ratio (OC ratio) with a threshold corresponding to the ratio (OC ratio) is performed on a subject who has been subjected to the evaluation of the fatigue state.
(6) A kit for evaluating fatigue, comprising a reagent for measuring ornithine concentration and a reagent for measuring citrulline concentration.
(7) The kit according to (6), further including a first presenting unit indicating a ratio (OC ratio) between the ornithine concentration and the citrulline concentration.
(8) The kit according to (6) or (7), further comprising a reagent for measuring pyruvic acid concentration and a reagent for measuring isocitrate concentration.
(9) The kit according to (8), further including a second presentation unit indicating a ratio (PI ratio) between the pyruvic acid concentration and the isocitrate concentration.
(10) A calculation unit that receives a reference value for evaluating fatigue and a ratio of the ornithine concentration to the citrulline concentration (OC ratio) and generates evaluation information for evaluating fatigue, and the evaluation information A fatigue evaluation system with an evaluation unit that receives and evaluates fatigue.
(11) 1) An input receiving unit that receives an input of a ratio of ornithine concentration to citrulline concentration (OC ratio) or 2) an input receiving unit that receives input of measured values of ornithine concentration and citrulline concentration, and the calculation The fatigue evaluation system according to (10), wherein the unit calculates a ratio (OC ratio) between the ornithine concentration and the citrulline concentration from the measured value in which the input is accepted.
(12) Instead of the ratio of the ornithine concentration and citrulline concentration (OC ratio), the ornithine concentration in the biological sample, Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, The method according to any one of (1) to (5), wherein a ratio with any concentration selected from Gamma-butyrobetaine is used.
(13) A method for evaluating fatigue, the step of obtaining a biomarker value in a biological sample obtained from a subject, and the step of evaluating the state of fatigue in the subject based on the value of the biomarker The biomarker values are 1) 2-aminobutyric acid (2AB), 2-oxoglutarate (3-oxoglutarate), 3-hydroxybutyrate (3-Hydroxybutyrate), 3-methylhistidine (3-Methylhistidine), 4-Hydroxy-3-methoxybenzoate, 4-methyl-2-oxopentanoate, 5-oxoproline, alanine ( Ala), arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelate (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline Choline), Citrulline, Creatine, Creatinine, Cystine, Gamma-Butyrobetaine, Glutamine (Gln), Glutamate (Glu), Glycine (Gly), Histidine ( His), hydroxyproline (Hydroxyproline), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), mucate (Mucate), N, N-dimethylglycine (N, N-Dimethylglycine), ornithine ( Ornithine), phenylalanine (Phe), proline (Pro), urea (Urea), pyruvate (Pyruvate), sarcosine (Sarcosine), serine (Ser), taurine (Taurine), threonine (Thr), tryptophan (Trp), tyrosine (Tyr), uric acid (Urate), and at least one biomarker biological sun selected from the group consisting of valine (Val) 2) In the above group, cis-aconitic acid (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinic acid ( A method for assessing fatigue, which is a ratio or difference of concentrations in a biological sample of a plurality of types of biomarkers selected from the group to which Succinate is added.
(14) The method for evaluating fatigue according to (13), further comprising cis-aconitic acid (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid A method for evaluating fatigue, wherein a concentration of at least one biomarker selected from the group consisting of (Malate) and succinate (Succinate) in a biological sample is used as the value of the biomarker.
(15) The method for evaluating fatigue according to (13) or (14), wherein the value of the biomarker is 1) citrulline, isocitrate, pyruvate, and A concentration of at least one biomarker selected from the group consisting of ornithine in a biological sample, or 2) a plurality of types selected from the group consisting of citrulline, isocitrate, pyruvate, and ornithine A method for evaluating fatigue, which is a ratio or difference in the concentration of biomarkers in a biological sample.
(16) The biological sample has been subjected to treatment for suppressing the activity of arginase and / or nitric oxide synthase involved in the degradation reaction of arginine, (1) to (5), (12) to (15 ).
(17) The kit according to (6), which contains an inhibitor of arginase or nitric oxide synthase involved in the degradation reaction of arginine.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention.

[1. (Evaluation in humans)
In

Study

1, 20 healthy subjects (10 men, 10 women; average age = 36.10 years; provided by Osaka City University Hospital), 20 chronic fatigue syndrome (10 men, 10 women; average age) = 36.15 years old; provided by Osaka City University Hospital). In Study 2, 46 healthy subjects (5 men, 41 women; average age = 38.78 years; provided by Osaka City University Hospital), 47 chronic fatigue syndrome (6 men, 41 women; average) Age = 37.96; provided by Osaka City University Hospital), 13 chronic fatigue (5 men, 8 women; average age = 33.62 years; provided by Osaka City University Hospital).

Chronic fatigue syndrome in

Trials

1 and 2 is a revised version of the diagnostic criteria for chronic fatigue syndrome (Ann Intern Med. 1994; 121: 953-959) prepared by the American Center for Disease Control and Prevention (CDC) ( As described in the special issue of Ayumi, “The latest science of fatigue—from Japan: Proposals for anti-fatigue / anti-overwork” (671-677) Repeat for at least 6 months or repeat (receive at least 50%), and exclude the disease by medical history, physical findings, and laboratory findings. It is a person who has been diagnosed with a disease according to a diagnostic standard such as “satisfying 6 criteria for symptom criteria and 2 items for physical criteria” Chronic fatigue in Test 2 is a person who has not been satisfied with the diagnosis of chronic fatigue syndrome described above by a doctor, but who has seen fatigue or a feeling of fatigue intermittently for 6 months or longer and has visited the hospital. In addition, healthy subjects in

Tests

1 and 2 were recruited in consideration of age, fatigue level, presence or absence of mental illness, presence or absence of sleep disorders, presence or absence of medications for mental illness or sleep disorders, etc. Recruiter).

In both

tests

1 and 2, plasma samples were prepared from blood collected from healthy subjects (HC) and patients with chronic fatigue syndrome (CFS: Chronic FatigueomeSyndrome) and chronic fatigue (CF: Chronic Fatigue). We asked Keio University Institute for Advanced Life Sciences to measure metabolites in pretreated plasma samples. A capillary electrophoresis-mass spectrometer was used to measure plasma metabolites. On the other hand, Glucose analysis II (Biovision) was used for blood glucose level measurement. For statistical analysis, significant difference test, correlation analysis, and path analysis using SPSS 17.0 and Amos 18.0 were adopted.

As a result of evaluation in a group including Test 1 and Test 2, as reported by the present inventors in International Publication WO2011 / 161544A2 (US Patent Serial No. 13 / 805,449), healthy subjects and patients with chronic fatigue syndrome The reproducibility of changes in energy production metabolites was confirmed. Furthermore, when detailed analysis including other metabolic pathways was conducted, a new biomarker was found from the metabolic system that changed very reproducibly through Test 1 and Test 2 mainly on urea cycle related metabolites. We found the possibility of more accurate fatigue diagnosis by combining with the found biomarkers.

That is, in the group of patients with chronic fatigue syndrome, the blood level of ornithine increased, while the levels of citrulline and urea (Urea) decreased (FIG. 1).

The random forest (Random Forest) program was executed using the analysis software R (free software, version 3.0.1) on the measured values of metabolites in the plasma samples for the population to which test 1 and test 2 were added. Also, the same random forest program was run on the test 2 population. Random Forest is an algorithm used for identification, regression, and clustering.

As shown in FIG. 2, when classifying healthy subjects and patients with chronic fatigue syndrome, these urea circuits are combined with TCA circuit-related metabolites such as Isocitrate, Succinate, and Cis-Aconitate among a plurality of measurement items. Related metabolites appeared at the top. The urea cycle-related metabolite also appeared in the upper rank in the same analysis only for test 2 (FIG. 3). In addition to Ornithine, Citrulline, and Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, and Gamma-butyrobetaine also appeared at the top. In addition, these metabolites were significantly increased or decreased in the comparison between the normal group and the chronic fatigue syndrome (FIG. 4).

From this result, by using a urea cycle-related metabolite that is relatively stable with respect to individual variation of the subject alone or in combination with a biomarker of the energy metabolic system (glycolytic system to TCA circuit), It was expected that the stability of discrimination due to individual differences between chronic fatigue patients and healthy individuals would be improved.

On the other hand, a similar change was observed in the urea cycle-related metabolites in the group of patients with chronic fatigue (Fig. 5), but when evaluated using the ratio of Citrulline / Ornithine or Urea / Ornithine, the ratio of these was found to be chronic. The degree of decline was less in patients with chronic fatigue than in patients with fatigue syndrome. For urea and Aspartate, detection was possible only in Test 2. Furthermore, some metabolites that were not found in the chronic fatigue syndrome patient group were found to be significantly reduced or increased compared to the healthy subject group (FIG. 6). FIG. 16 shows a random forest (Random) using all parameters including the measured values of metabolites in plasma samples of healthy subjects (HC) and chronic fatigue (CF) patients for the population of Study 2. It is a figure which shows the result of having executed the Forest) program. FIG. 17 shows a random forest (Random Forest) for all populations, including metabolite measurements in plasma samples of patients with chronic fatigue (CF) and chronic fatigue syndrome (CFS), for the study 2 population. It is a figure which shows the result of having executed the program.

Next, for all the data in Test 1 and Test 2, the correlation between the metabolite whose amount in plasma significantly changed corresponding to the presence or absence of fatigue and the performance status (PS) was examined. PS is a measure of the subjective severity of fatigue, with higher numbers indicating more severe. As a result, it was found that some of the metabolites showed significant correlation with PS (Proline; R = 0.323, P = 0.027: Urate or 4-hydroxy-3-methoxybenzoate; R = -0.334, P = 0.022).

In addition, analysis using a decision tree model was performed using the analysis software R for the measured values of metabolites in all the plasma samples of Test 1 and Test 2. This program is used in the field of decision theory to plan and reach goals. For example, when it is classified into 4 trees as shown in FIG. 7, it is classified according to whether Isocitrate is 8.15 μM or more, Ornithine is 62 μM or more, or Hydroxyproline is 9.3 μM or less. Thus, patients with chronic fatigue syndrome can be discriminated with a positive discrimination rate of 79.0% (49 out of 62 CFS) from the groups of patients and healthy subjects. In this case, the misclassification rate was 18.5% (12 out of 65 HC). When the number of classification trees is further increased to 9 trees, whether Succinate is 12.5 μM or more, Glutamine (Gln) is 1069 μM or less, or Glutamate (Glu) is 50 μM or more as shown in FIG. Patients with chronic fatigue syndrome from the group of patients with chronic fatigue syndrome and healthy subjects by classifying them with less than or less than 95 μM Leucine (Leu) or less than 181.5 μM Lysine (Lys) Can be discriminated with a positive discrimination rate of 90.3% (56 out of 62 CFS). In this case, the misclassification rate was 7.7% (5 out of 65 HC). Furthermore, by using the metabolite ratio, the correct discrimination rate can be increased and the misclassification rate can be reduced even with a smaller number of trees. For example, as shown in FIG. 9, the classification into 3 trees with Pyruvate / Isocitrate (PI ratio) of 20.61 or less or Ornithine / Citrulline (OC ratio) of 3.215 or less is only 80.6. It is possible to discriminate patients with chronic fatigue syndrome with a positive discrimination rate of 50% (50 out of 62 CFS) and an incorrect discrimination rate of 20.0% (13 out of 65 HC). Furthermore, by adding whether Ornithine / Citrulline is 1.321 or less or Alanine / Hydroxyproline is 37.71 or less, the correct discrimination rate of 80.6% is maintained, while the misclassification rate is 10.7. % ((7 out of 65 HC)) (Figure 10) However, the numerical value that determines the branch on the above decision tree varies depending on the measurement conditions and purpose, and is not a fixed numerical value. Tree analysis can also be used as a diagnostic algorithm.

On the other hand, we examined whether there was a difference in metabolism between chronic fatigue syndrome and chronic fatigue. As a result, as shown in FIG. 11, there was a significant difference between the urea cycle-related metabolites and metabolites related to the metabolic flow from the urea cycle to the TCA cycle, such as Arginine, Succinate, and Glutamine. Such metabolites are useful in performing decision tree analysis for diagnosis as described above.

[2. (Evaluation in rats that have been evaluated in humans)
If one embodiment of the present invention is used, fatigue diagnosis can be performed with a simple kit. Furthermore, there is a possibility of finding an effective treatment based on the diagnosis result using one embodiment of the present invention. For example, by applying one form of the present invention to a patient who complains of intense fatigue or long-term fatigue, it is possible to objectively evaluate the degree of fatigue and to determine whether or not it is chronic fatigue syndrome. In addition, by grasping the metabolic pathology, it becomes possible to select a pharmacological component that realizes treatment / prevention according to the individual's fatigue pathology. The evaluation of the degree of fatigue according to the present invention does not require a high level of knowledge about fatigue, and therefore can be performed in general medical facilities. Moreover, if one form of the present invention is used, it is possible to infer which part of the energy (ATP) production metabolic system and urea circuit system in the living body is abnormal, so that it can be used only for patient life guidance. However, it is possible to provide a remedy that corrects the metabolic pathology by promoting or suppressing the metabolic system considered to be the cause. In particular, not only from the analysis results of human blood components, but also from comprehensive metabolic analysis of the blood and liver of fatigue model animals (FIG. 12: graph data shows measured values of liver samples), urea circuit and TCA The abnormality of the circuit and the flow of metabolism peculiar to the fatigue condition from the urea circuit to the TCA circuit can be confirmed. That is, in fatigue pathology, Citrate, cis-Aconitate, and Isocitrate in the TCA circuit are significantly decreased, while Succinate and Malate are significantly increased. In addition, in a fatigue condition, a system in which ornithine in the urea circuit is metabolized in order of Putrescine, GABA, and Succinate is dominant, and flows into the TCA circuit. At the same time, promotion of this metabolic pathway is associated with a decrease in the metabolism of ornithine to citrulline in the urea cycle and an increase in the metabolism of arginine to ornithine in the cycle.

In the experiment of fatigue model animals, male 7-week-old SD rats (Japan SLC) were used, and each animal was bred under conditions of 12 hours light-dark cycle, room temperature 23 ° C. and humidity 50%. The purchased rats were randomly divided into an untreated group (control group), a diet restriction group, and a fatigue group. The rats in the untreated group were bred for 5 days in an environment where food and water could be sufficiently consumed. The rats in the fatigue group were raised for 5 days in cages filled with water at a depth of 2.2 cm. Rats in the dietary restriction group were fed 10 g of food per day and were raised for 5 days. Thereafter, the rat was sacrificed, blood perfusion was performed using a phosphate buffer, and a rat liver was obtained. Each metabolite of the obtained liver sample was measured by both the cation mode and anion mode methods of the capillary electrophoresis-mass spectrometer, and about 85 types of metabolites were detected.

[3. Analysis by PLS-DA method]
The PLS-DA method (Partial least squares discriminant analysis) is a method used for constructing a prediction model using regression analysis. Using this method, discrimination between HC (healthy person) and CFS (chronic fatigue syndrome), HC and CF (chronic fatigue), and CF and CFS was performed. R (free software, version 3.0.1) was used as software for performing analysis by the PLS-DA method.

= HC and CFS discrimination result =
As biological information, statistically significant differences were observed in the above-mentioned test 2 between the HC group and the CFS group, Ornithine, Citrullline, Urea, Hydroxyproline, Urate, 4-Hydroxy-3-methoxybenzoate, Isocitrate, Citrate Pyruvate, Cis-aconitate, Gamma-Butyrobetaine, 4-methyl-2-oxopentanoate, 2-oxoglutarate, and random forest analysis results, Succinate, Pro, Leu, 3-Methylhistidine, Creatinine, Using all concentrations of His, Ala, N, N-Dimethylglycine, Ser, Lys, Betaine, Glu, Taurine, 2AB, Arg, 3-Hydroxybutyrate, Creatine, Trp, Mucate, Cystine, Asp, Sarcosine, Thr, Val PLS-DA analysis was performed. As a result, as shown in FIG. 18, HC and CFS showed different distributions in the PLS-DA score plot. Moreover, the metabolite in which the statistical significance difference was recognized showed the high load amount with respect to discrimination of 2 groups. Note that one plot in FIG. 18 corresponds to one person, HC indicates a healthy person, and CFS indicates a patient with chronic fatigue syndrome.

= HC and CF discrimination results =
As biological information, statistically significant differences were observed in the above-mentioned test 2 in comparison between the HC group and the CF group, Choline, Gly, Tarine, Asn, Gamma-Butyrobetaine, Gln, Lys, Trp, 4- Concentrations of methyl-2-oxopentanoate and Succinate, and promising markers from random forest results, Cis-Aconitate, Lactate, Arg, Mucate, His, Azelate, N, N-Dimethylglycine, Creatinine, beta-Ala, Tyr, Ornithine , 2AB, Leu, Isocitrate, Hydroproproline, Phe, Met, 3-Methylhistidine, Sarcosine, Ile, Betaine, Pyruvate, Urate, 4-Hydroxy-3-methoxybenzoate, Ser, Asp, Carnitine, Urea, 5-oxoproline, Citrate PLS-DA analysis was performed using all concentrations. As a result, as shown in FIG. 19, in the PLS-DA score plot, HC and CF showed different distributions. In addition, metabolites that were found to have a statistically significant difference showed a high load for discrimination between the two groups. One plot in FIG. 19 corresponds to one person, HC indicates a healthy person, and CF indicates a patient with chronic fatigue.

= CF and CFS discrimination result =
As biometric information, statistically significant differences were found in the above-mentioned test 2 in comparison between the CF group and the CFS group, Choline, Succinate, Gln, Taurine, Asn, Lys, Arg, His, Urate, 4- N, N-Dimethylglycine, 3-Methylhistidine, Azelate, Ile, Trp, 4-methyl-2-oxopentanoate, Ser, Cis-Aconitate, Met are promising markers from the concentration of each of Hydroxy-3-methoxybenzoate and random forest results , Phe, Gly, Lactate, Ala, Leu, Citrulline, Malate, gamma-Butyrobetaine, Sarcosine, beta-Ala, Glu, 2-Oxoglutarate, Pyruvate, Asp, Urea, Creatinine, Carnitine, 2AB, Betaine, Citrate, Tyr PLS-DA analysis was performed using all concentrations. As a result, as shown in FIG. 20, in the PLS-DA score plot, CF and CFS showed different distributions. In addition, metabolites that were found to have a statistically significant difference showed a high load for discrimination between the two groups. Note that one plot in FIG. 20 corresponds to one human, CF indicates a patient with chronic fatigue, and CFS indicates a patient with chronic fatigue syndrome.

From the results shown in FIGS. 18 to 20, it is possible to qualitatively understand the physiology / pathology of HC, CF, and CFS using detected biological information, and it is useful for evaluating and diagnosing fatigue. Indicated.

[4. (Evaluation of the effect of arginine degradation)
Using the blood (whole blood) of 10 healthy subjects, it was examined how much time would remain at room temperature after collecting the blood and the change in the value of the biomarker would appear. As a result, it was found that the ornithine value increased and the arginine value decreased when the blood was left for a long time after blood collection (FIG. 14). On the other hand, some metabolites such as citric acid did not show a change in concentration even after standing (not shown). The blood contains an enzyme called arginase that promotes the reaction from arginine to ornithine, and nitric oxide synthase (NOS) involved in the reaction that produces nitric oxide and citrulline from arginine. It was suggested that the enzymatic reaction was progressing in the test tube.

Therefore, using rat blood (whole blood), whether or not the change in the concentration of ornithine or arginine can be suppressed by placing an inhibitor of arginase or nitric oxide synthase into a test tube containing the collected blood. investigated. Inhibition experiments with and is arginase inhibitor ^{N ω -Hydroxy-nor-L-} arginine (nor-NOHA) and nitric oxide synthase inhibitor ^{N G -Nitro-L-Arginine (} L-NNA) Attempts were made to quantitatively analyze arginine and ornithine using an LC-MS system. Nor-NOHA does not substantially inhibit the activity of nitric oxide synthase, and L-NNA does not substantially inhibit the activity of arginase.

As a result, as shown in FIG. 15, it was found that when the blood of the rat was allowed to stand at room temperature after blood collection, both the arginine value and the ornithine value increased. Next, it was found that increases in arginine and ornithine levels can be suppressed by adding nor-NOHA or both nor-NOHA and L-NNA immediately after blood collection. In FIG. 15, the left side shows the measurement result of the arginine concentration, and the right side shows the measurement result of the ornithine concentration. In FIG. 15, in order from the left side, 0h is a state immediately after blood collection, 2h is a state after blood collection and left for 2 hours at room temperature without addition of an inhibitor, and 5h is room temperature after blood collection without addition of an inhibitor. And 100 μM nor-NOHA (5 h) is 100 μM nor-NOHA added at a final concentration and left at room temperature for 5 hours. 100 μM nor-NOHA + 100 μM L-NNA (5 h) is Add 100 μM nor-NOHA and L-NNA at final concentration and let stand for 5 hours at room temperature. Add 300 μM nor-NOHA at final concentration for 5 hours at 300 μM nor-NOHA (5 h). 300 μM nor-NOHA + 100 μM L-NNA (5 h) shows the state after adding 300 μM nor-NOHA and 100 μM L-NNA at final concentrations and left at room temperature for 5 hours.

Assuming that it is not possible to cool the blood sample immediately after blood collection, adding these inhibitors in advance to a blood collection test tube for fatigue diagnosis will reduce the change in the biomarker value. This can prevent and improve the accuracy of inspection.

Also, all documents referred to in this specification are hereby incorporated by reference as part of this specification.

The use of the present invention makes it possible to objectively and easily perform fatigue evaluation and diagnosis. The present invention may further provide techniques useful for treating fatigue. Because fatigue is a very significant health problem, the realization of fatigue assessment, diagnosis and treatment contributes greatly across all industries.

Claims

Including a step of obtaining a ratio (OC ratio) of ornithine concentration and citrulline concentration in a biological sample obtained from a subject, and a step of evaluating a fatigue state in the subject based on the ratio (OC ratio). A method of assessing fatigue.
The method according to claim 1, comprising a step of comparing a threshold corresponding to the ratio (OC ratio) with the ratio (OC ratio).
Furthermore, the method includes a step of obtaining a ratio (PI ratio) between pyruvic acid concentration and isocitrate concentration in the biological sample, and a step of evaluating a fatigue state in the subject based on the ratio (PI ratio). The method according to claim 1 or 2.
The method according to claim 3, comprising comparing a threshold corresponding to the ratio (PI ratio) with the ratio (PI ratio).
Performing a step of obtaining a ratio (PI ratio) of pyruvic acid concentration and isocitrate concentration in the biological sample, and a step of comparing a threshold corresponding to the ratio (PI ratio) with the ratio (PI ratio). The method according to claim 2, wherein the step of comparing a threshold corresponding to the ratio (OC ratio) with the ratio (OC ratio) is performed on a subject who has been evaluated for fatigue status.
A kit for evaluating fatigue, comprising a reagent for measuring ornithine concentration and a reagent for measuring citrulline concentration.
The kit according to claim 6, further comprising a first presenting portion that indicates a ratio (OC ratio) between an ornithine concentration and a citrulline concentration.
The kit according to claim 6 or 7, further comprising a reagent for measuring pyruvic acid concentration and a reagent for measuring isocitrate concentration.
The kit according to claim 8, further comprising a second presentation unit indicating a ratio (PI ratio) between a pyruvic acid concentration and an isocitrate concentration.
A calculation unit that receives a reference value for evaluating fatigue and a ratio of the ornithine concentration and citrulline concentration (OC ratio) and generates evaluation information for evaluating fatigue; and receiving the evaluation information, Fatigue evaluation system with an evaluation unit that evaluates
1) whether an input receiving unit that receives an input of a ratio (OC ratio) between an ornithine concentration and a citrulline concentration is provided;
2) An input receiving unit that receives input of measured values of ornithine concentration and citrulline concentration, and the calculation unit calculates a ratio (OC ratio) between the ornithine concentration and the citrulline concentration from the measured value that is input.
The fatigue evaluation system according to claim 10.
Instead of the ratio of the ornithine concentration to the citrulline concentration (OC ratio), the ornithine concentration in the biological sample, Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, Gamma-butyrobetaine The method according to any one of claims 1 to 5, wherein a ratio with any concentration selected from the above is used.
A method for assessing fatigue,
Including the step of obtaining the value of a biomarker in a biological sample obtained from a subject, and the step of evaluating the state of fatigue in the subject based on the value of the biomarker,
The biomarker values are 1) 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4-hydroxyhistidine -4-methoxybenzoate (4-Hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (5-methyl-2-oxopentanoate), 5-oxoproline (5-Oxoproline), alanine (Ala), Arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelate (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline (Choline), citrulline (Citrulline), Creatine, Creatinine, Cystine, Gamma-Butyrobetaine, Glutamine (Gln), Glutamic acid (Glu), Glycine (Gly , Histidine (His), hydroxyproline (Hydroxyproline), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), mucate (Nucate), N, N-dimethylglycine (N, N-Dimethylglycine) , Ornithine, phenylalanine (Phe), proline (Pro), urea (Urea), pyruvate (Pyruvate), sarcosine (Sercosine), serine (Ser), taurine (Taurine), threonine (Thr), tryptophan (Trp) ), Tyrosine (Tyr), uric acid (Urate), and valine (Val), or at least one biomarker selected from the group consisting of valine (Val) in the biological sample, or 2) cis-aconitic acid ( Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinic acid (Succinate) The ratio or difference in the concentration of the multiple types of biomarkers in the biological sample,
A method of assessing fatigue.
A method for assessing fatigue according to claim 13,
Furthermore, at least selected from the group consisting of cis-aconitic acid (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinate (Succinate) The concentration of a kind of biomarker in a biological sample is used as the value of the biomarker.
A method of assessing fatigue.
A method for evaluating fatigue according to claim 13 or 14,
The concentration of the biomarker in the biological sample of at least one biomarker selected from the group consisting of 1) citrulline, isocitrate, pyruvate, and ornithine Or 2) a ratio or a difference in concentration in a biological sample of a plurality of biomarkers selected from the group consisting of citrulline, isocitrate, pyruvate, and ornithine,
A method of assessing fatigue.
The biological sample according to any one of claims 1 to 5, and 12 to 15, wherein the biological sample is subjected to a treatment that suppresses the activity of arginase and / or the activity of nitric oxide synthase involved in the degradation reaction of arginine. Method.
The kit according to claim 6, comprising an inhibitor of arginase or nitric oxide synthase involved in the decomposition reaction of arginine.