JP6521321B2 - Biomarkers of fatigue and their use - Google Patents

Description

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

  According to epidemiological survey in Japan, about 60% of the general public are aware of fatigue, and more than half of them are suffering from fatigue lasting more than 6 months. About half of people who feel such chronic fatigue complain of work or academic inefficiency, and the economic loss from such fatigue is predicted to be 1 trillion yen or more.

  Some people who feel chronic fatigue include patients with chronic fatigue syndrome whose cause is not yet well understood, who feel extreme fatigue even if not tired. There is. Patients with chronic fatigue syndrome often complain of intense fatigue, both physical and thinking, and their quality of life (QOL) is also severely impaired. 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 most of the fatigue diagnoses so far are based on the patient's subjectivity and the physician's diagnosis, establishment of an index for objectively diagnosing fatigue is desired. Patent Document 1 discloses a technology for evaluating the behavior of a virus as an indicator of the degree of fatigue, focusing on the virus infection mentioned as one of the causes of the reduction of the immune power. Non-Patent Document 1 discloses an attempt to objectively measure / evaluate fatigue with an acceleration pulse wave.

Japanese Patent Publication: Japanese Patent Application Publication No. 2007-330263 (Publication date: December 27, 2007) Japanese Patent Application Publication: Japanese Patent Application Laid-Open No. 9-77688 (release date: March 25, 1997) Japanese Patent Application Publication: Japanese Patent Application Laid-Open No. 9-59161 (release date: March 4, 1997) Japanese Patent Publication: JP-A-2011-177194 (release date: September 15, 2011) Japanese Patent Publication: JP-A-2013-119001 (release date: June 17, 2013) Japanese Patent Publication: JP-A-2011-182661 (release date: September 22, 2011) Japanese Patent Publication: JP-A-2011-161137 (release date: August 25, 2011) Japanese Patent Publication: JP-A-2010-266238 (publication date: November 25, 2010) Japanese Patent Publication: JP-A-2010-85369 (publication date: April 15, 2010) Japanese Patent Application Publication: Japanese Patent Application Publication No. 2007-228878 (Publication date: September 13, 2007) Japanese Patent Publication: Japanese Patent Application Publication No. 2000-516818 (Publication date: December 19, 2000) International Publication WO2006 / 123611 (International Publication Date: November 23, 2006) International Publication WO2005 / 012903 (International Publication Date: February 10, 2005)

“Physical measurement of fatigue: Acceleration pulse wave” Yamaguchi Koji et al., History of medicine Special issue “The latest science of fatigue-From Japan: Proposal for anti-fatigue and anti-overwork” Vol. 228, No. 6, 646-653 ( 2009).

  The causes of fatigue and the mechanism of fatigue are complex, and the whole picture has not been elucidated. However, for example, in consideration of application such as treatment of fatigue, development of a technique for objectively evaluating fatigue is desired in accordance with the mechanism originally possessed by the living body and the cause of fatigue.

  The technique described in Patent Document 1 collects a subject's body fluid, 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 infected to human (host) and does not measure a biomarker reflecting the cause or mechanism of fatigue in human. Therefore, the technology described in Patent Document 1 can not evaluate the fatigue condition of an individual subject, or provide a therapeutic method or a preventive method. In addition, the technology described in Non-Patent Document 1 has an advantage that it can be implemented non-invasively, but it requires measurement of finger plethysmogram 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 thereof is to provide a method which makes it possible to objectively and simply evaluate and diagnose fatigue.

The present inventors conducted comprehensive and comprehensive metabolomic analysis research using plasma samples from chronic fatigue syndrome patients, chronic fatigue patients, and healthy individuals to solve the above-mentioned problems, and complicated in vivo The concentrations of various metabolites present in each plasma sample involved in metabolism were examined. Then, when statistical analysis is added, it is found that the increase and decrease pattern of a specific metabolite reflects the state of fatigue, and it is objectively obtained by quantifying or quantifying biological metabolism information based on the increase and decrease pattern. It has been clarified that it is possible to carry out a simple fatigue diagnosis, and the present invention has been completed. That is, the present invention is as follows.
(1) obtaining a ratio (OC ratio) of ornithine concentration to citrulline concentration in a biological sample obtained from a subject, and evaluating a state of fatigue in the subject based on the ratio (OC ratio) Methods to assess fatigue, including.
(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 (OC ratio) of ornithine concentration to citrulline concentration to generate evaluation information for evaluating fatigue, and the evaluation information Fatigue evaluation system comprising an evaluation unit that receives and evaluates fatigue.
(4) A method of evaluating fatigue, comprising the steps of: obtaining a value of a biomarker in a biological sample obtained from a subject; and evaluating the state of fatigue in the subject based on the value of the biomarker. The values of the biomarkers include: 1) 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4-hydroxy-3-methoxybenzoate (4-hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (4-methyl-2-oxopentanoate), 5-oxoproline (5-Oxoproline), alanine (4-hydroxy-2-methoxybenzoate) Ala), arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelaic acid (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline (Ch) oline, Citrulline, Creatine, Creatinine, Crysine, Cystine, Gamma-Butyrobetaine, Glutamine (Gln), Glutamic Acid (Glu), Glycine (Gly), Histidine (Gly) His), Hydroxyproline (Hydroxyproline), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), 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 Biological sump of at least one biomarker selected from the group consisting of (Tyr), uric acid (Urate), and valine (Val) Concentration in the group, or 2) in the group above: cis-aconitate (Cis-aconitate), citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinic acid (Succinate) A method of evaluating fatigue which is a ratio or difference of concentrations in a biological sample of a plurality of types of biomarkers selected from the group added with).

  The present invention makes it possible to objectively and simply evaluate and diagnose fatigue. The present invention may further provide techniques useful for the treatment of 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 chronic fatigue syndrome (CFS). Random Forest (Random Random (Random) using all parameters, including measured values of metabolites in plasma samples of healthy (HC) and chronic fatigue syndrome (CFS) patients, for the population with trials 1 and 2 added Forest) shows the result of running the program. Show the results of running the Random Forest program with all parameters, including measured values of metabolites in plasma samples of healthy (HC) and chronic fatigue syndrome (CFS) patients, for the Study 2 population FIG. It is a figure which shows an example of the metabolite which the significant difference is seen in the density | concentration contained in plasma between the healthy subject (HC) and the patient of chronic fatigue syndrome (CFS). FIG. 2 shows the concentrations of various metabolites contained in the plasma of healthy (HC) and chronic fatigue (CF) patients. It is a figure which shows an example of the metabolite which the significant difference is seen in the density | concentration contained in plasma between the healthy subject (HC) and the patient of chronic fatigue (CF). Analysis of a decision tree model on the measured values of metabolites in all plasma samples in trials 1 and 2 for the purpose of analyzing patients with normal subjects (HC) and chronic fatigue syndrome (CFS) It is a figure which shows an example. Analysis of a decision tree model on the measured values of metabolites in all plasma samples in trials 1 and 2 for the purpose of analyzing patients with normal subjects (HC) and chronic fatigue syndrome (CFS) It is a figure which shows another example. Analysis of the decision tree model was performed on the ratio of measured values of metabolites in all plasma samples in trials 1 and 2 for the purpose of analyzing patients with normal subjects (HC) and chronic fatigue syndrome (CFS) It is a figure which shows the further another example of a result. Analysis of the decision tree model was performed on the ratio of measured values of metabolites in all plasma samples in trials 1 and 2 for the purpose of analyzing patients with normal subjects (HC) and chronic fatigue syndrome (CFS) 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 concentration of various metabolites contained in the plasma of the patient of chronic fatigue (CF), and the patient of chronic fatigue syndrome (CFS). Fatigue abnormalities in the urea cycle and TCA cycle, and fatigue from the urea cycle to the TCA cycle, which were confirmed from exhaustive metabolic analysis of blood and liver of the model animal through fatigue load experiments of fatigue model animals (rats) It is a figure which shows the inflow of the metabolism peculiar to a pathological condition (the graph data show that of a liver sample). It is a figure which shows the correspondence with the abbreviation of an amino acid and the full name which were used in the figure and this specification. FIG. 6 is a graph showing how the ornithine concentration and the arginine concentration change over time in the collected human whole blood. In the experiment using rat whole blood, it is a graph which shows that the concentration change by the time progress of arginine and ornithine is suppressed by adding the inhibitor of various enzymes. The Random Forest program was run on the population of Study 2 using all parameters including measurements of metabolites in plasma samples of healthy (HC) and chronic fatigue (CF) patients It is a figure which shows a result. Run Random Forest program with all parameters in the trial 2 population, including measurements of metabolites 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 two-group comparison of HC group and CFS group using PLS-DA method. It is a figure which shows the result of having performed two-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" refers to any tissue (including body fluids such as blood) or cells taken from a subject and prepared therefrom Tissue sections or cell lysates may 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, preparations therefrom (eg, serum, plasma etc.) and the like. 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 and fatigue as the main symptoms is second only to the number of patients with pain as the main symptom. There are many. However, no method of objectively evaluating fatigue has been developed so far. Feelings of fatigue and fatigue are the senses that humans experience on a daily basis, and are important biological signals that indicate disturbances in homeostasis of the living body. However, the sense of fatigue is only subjective and does not objectively indicate the degree of fatigue. Although lactic acid is considered to be a causative agent of fatigue at some times, it has also been found that fluctuations in lactic acid levels can be indicators of exercise but not indicators of fatigue. Furthermore, no effective treatment for fatigue has been found.

  The American Center for Disease Prevention and Control (CDC) reported a chronic fatigue syndrome disease with strong fatigue of unknown cause in 1988, and then elucidated the mechanism, searched for biomarkers, and developed treatment and prevention methods. A variety of research has been done, focusing on However, the onset mechanism of chronic fatigue syndrome has not been elucidated yet, and no objective biomarker has been developed. Even now, chronic fatigue syndrome is diagnosed using the criteria of symptoms and physical findings published by CDC in 1994.

  On the other hand, so far, in chronic fatigue syndrome, fatigue biomarkers have been proposed which use viral activation or autonomic nervous abnormality as an index. However, since these are not based on the cause of human fatigue or the mechanism thereof, it is good as a biomarker specific to fatigue (including chronic fatigue and chronic fatigue syndrome, but particularly chronic fatigue and chronic fatigue syndrome) I want to. Furthermore, since these are indirect indicators of the mechanism of maintenance / restoration of homeostasis, they are not direct indicators, so it is necessary to make trial and error to develop specific treatments.

  The inventors of the present invention have already found that some of the metabolites forming the glycolytic system and the TCA cycle are reduced in the group of patients with chronic fatigue syndrome as compared to healthy subjects ( Reference: International Publication WO 2011/161544 A2 (US Patent Serial No. 13/805, 449)). Now, by adding data from a larger, different population, we have found a characteristic change between the fatigue patient group and the healthy person in the metabolite newly involved in the urea cycle. In a more specific example, in a group of fatigue patients, a characteristic metabolic condition has been identified in which the metabolic pathway flowing from the urea cycle to the TCA cycle is promoted, and as a result, the reaction system that metabolizes ammonia is suppressed. The metabolic abnormalities that represent such fatigue conditions were also supported by a model experiment system in which rats were subjected to fatigue loading.

  In the present specification, unless otherwise specified, chronic fatigue patients do not encompass patients with chronic fatigue syndrome. In addition, chronic fatigue refers to a case in which a chief complaint of fatigue or fatigue is intermittent for six months or more but not diagnosed as chronic fatigue syndrome by a doctor's diagnosis. Chronic fatigue syndrome is a revision of the diagnostic criteria for chronic fatigue syndrome (Ann Intern Med. 1994; 121: 953-959) prepared by the American Center for Disease Prevention and Control (CDC) for domestic use in Japan. The latest science of fatigue-From Japan: Recommendations for anti-fatigue and anti-exhaustion (671-677) (1) The main symptom is strong fatigue that severely impairs life, and at least six months or more Repeat or recur for a period of 50% or more. And exclude disease by medical history, physical findings, laboratory findings. Furthermore, (2) refers to the case where the symptom criteria is 8 items or more, or the symptom criteria is 6 items and the body criteria is 2 items or more.

[2] Biomarker of the Present Invention The biomarker of 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, 11, 16 and 17. More specifically, for example, 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4- Hydroxy-3-methoxybenzoate (4-hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (4-methyl-2-oxopentanoate), 5-oxoproline (5-Oxoproline), alanine (Ala) , Arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelaic acid (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline (Choline), cis-aconitic acid (Cis-aconitate), Citric acid (Citrate), Citrulline, Creatine, Creatinine, Creatinine, Cystine, Gammabutyrobetaine (Gamma-Butyrobetaine) , Glutamine (Gln), glutamic acid (Glu), glycine (Gly), histidine (His), hydroxyproline (Hydroxyproline), isoleucine (Ile), isocitrate (Isocitrate), lactate (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 (Pro) 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, 2-oxoglutarate (2-oxoglutarate), 4-hydroxy-3-methoxybenzoate (4-hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (4-methyl-), among others. 2-oxopentanoate), arginine (Arg), asparagine (Asn), choline (Choline), cis-aconitate (Cis-aconitate), citric acid (Citrate), citrulline (Citruline), creatine (Creatine), gammabutyrobetaine (Gamma-Butyrobetaine), glutamine (Gln), glycine (Gly), histidine (His), hydroxyproline (Hydroxyproline), isocitrate (Isocitrate), lysine (Lys), ornithine (Ornithine), urea (Urea), pyruvate (Pyruvate), succinic acid (Succinate), taurine (Taurine), tryptophan (Trp), and uric acid (Urate), as also shown in the examples, between different fatigue states These are particularly useful biomarkers that have statistically significant differences.
Among these, metabolites associated with the urea cycle and metabolites involved in the inflow of metabolism from the urea cycle to the TCA cycle are more preferable as biomarkers. As biomarkers of metabolites related to the urea cycle and metabolites involved in the flow of metabolism from the urea cycle to the TCA cycle, the metabolites shown in FIG. 1 or FIG. 4, Ornithine, Orithine, Citrulline, Urea (Urea), Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, Gamma-butyrobetaine and the like. Among them, as the above-mentioned biomarker, ornithine (Citrulline), urea (Urea) or the like is more preferable since it exhibits different behavior depending on the state of fatigue, and Ornithine (Citrulline) is particularly preferable. The biomarkers may be used for analysis simultaneously in arbitrary plural numbers.
Among the biomarkers described above in 1), 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 a concentration in a biological sample, compared with a predetermined threshold (reference value), and used for evaluation of fatigue. The predetermined threshold is appropriately set for each biomarker according to the analysis method or the like.

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

  An example of the predetermined threshold is an average value of the concentrations of biomarkers in biological samples of a plurality of healthy persons, or a mode value obtained from a binomial distribution or the like. In addition, as shown in, for example, FIG. 2, FIG. 3, FIG. 4, FIG. 6, FIG. 7, FIG. 8, and FIG. If it is above the threshold or below the threshold, it is possible to evaluate whether it is fatigue (or, for example, whether it is chronic fatigue, chronic fatigue syndrome, etc.).

Although not particularly limited, the biomarkers described in FIG. 2, FIG. 3, FIG. 4, FIG. 7, and FIG. 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 people and patients with chronic fatigue syndrome. Besides these, 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 are markers that can be used to evaluate whether a subject is a healthy person or a patient with chronic fatigue syndrome as a result of random forest analysis. The markers from Succinate to Val are listed in order from those that come in the top (that is, promising as markers) as a result of random forest analysis.
Also, although not particularly limited, the biomarkers described in FIG. 6 and FIG. 16 evaluate whether the subject is a healthy person or a patient with chronic fatigue (especially a patient with chronic fatigue not having chronic fatigue syndrome). It is particularly useful as a marker. More specifically, for example, Choline, Gly, Taurine, Asn, Gamma-Butyrobetaine, Gln, Lys, Trp, 4-methyl-2-oxopentanoate, and Succinate are patients with normal fatigue and chronic fatigue (especially chronic fatigue) It is a particularly useful biocar that has statistically significant differences with chronic fatigue patients who do not have a syndrome. In addition to these, Cis-Aconitate, Lactate, Arg, Mucate, His, Azolate, 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 indicate that the subjects are healthy as a result of random forest analysis It can be a marker to evaluate whether it is a patient with chronic fatigue (especially a patient with chronic fatigue not having chronic fatigue syndrome). In addition, the markers from Cis-Aconitate to Citrate are listed in order from those that come to the top (that is, promising as a marker) as a result of random forest analysis.
Also, although not particularly limited, the biomarkers described in FIG. 11 and FIG. 17 are used as markers for evaluating whether a subject is a patient with chronic fatigue syndrome or a patient with chronic fatigue not having 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 patients with chronic fatigue syndrome and chronic fatigue syndrome and not chronic fatigue syndrome. It is a particularly useful biomarker that has statistically significant differences with patients with fatigue. Also, 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 are subjects whose subjects are chronic fatigue as a result of random forest analysis. It can be a marker to assess whether you are a patient with syndrome or chronic fatigue (not chronic fatigue syndrome). The markers from N, N-Dimethylglycine to Tyr are listed in order from those that come in the top (that is, promising as markers) as a result of random forest analysis.

  2) A biomarker configured as a correlation of the plurality of biomarkers of 1) above. For example, the ratio of the concentration of the plurality of biomarkers in the biological sample of the above 1), the difference in the concentration in the biological sample, and the like correspond thereto. As an example, the ratio of the ornithine concentration to the citrulline concentration (OC ratio, for example, O / C or C / O) in the biological sample, which is the biomarker specifically shown in FIG. 9 and FIG. 10; The ratio of the concentration of pyruvate to the concentration of isocitrate (PI ratio) in the sample; the ratio of the concentration of alanine to the concentration of hydroxyproline in the biological sample (AP ratio); A plurality of biomarkers may be used simultaneously for analysis. In addition, metabolites located at the top of the 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: the OC ratio alternatively or in combination with the OC ratio can also be used as a biomarker; alternatively the OC ratio or the OC ratio in combination Preferably, the ornithine concentration in the biological sample and any concentration selected from Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, and Gamma-butyrobetaine as a biomarker to be used. The ratio of

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

  An example of the predetermined threshold is an average value of biomarker values in a plurality of healthy persons, or a mode value obtained from a binomial distribution or the like. Also, for example, as shown in FIG. 9 and FIG. 10, and in the description of the examples, if the value of the biomarker is in any state (for example, above threshold or below threshold) with reference to the threshold, fatigue is It is possible to evaluate whether or not (or, for example, whether it is chronic fatigue, 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 can be seen by comprehensively analyzing the variation of metabolites in the plasma of patients with chronic fatigue syndrome and patients with chronic fatigue and in the plasma of healthy people. It is an objective fatigue biomarker based on the mechanism of fatigue disease. Therefore, as one aspect of the biomarker of the present invention, it enables objective and simple evaluation and diagnosis of fatigue. Furthermore, the biomarkers of the present invention can be used to develop diagnostic methods and treatments for chronic fatigue and chronic fatigue syndrome.

  Moreover, as another aspect of the present invention, using the biomarker of the present invention makes it possible to create an inexpensive and simple fatigue diagnostic system, and further, it is possible to find an effective therapeutic method based on the diagnostic result. There is also the possibility. For example, blood is collected for a patient who complains of a strong feeling of fatigue or feeling of fatigue for a long time, and the biomarker of the present invention is examined to determine whether or not chronic fatigue syndrome or chronic fatigue syndrome, fatigue level Can objectively evaluate This assessment can be tested in a general medical facility without advanced knowledge of fatigue.

  In one aspect of the present invention, by measuring the blood component of the subject, it can be determined whether the subject belongs to chronic fatigue syndrome. According to the conventional diagnostic criteria, (1) The main symptom is strong fatigue that life is seriously impaired, and the continuation or relapse is repeated for at least six months or more (a period of 50% or more is recognized). And exclude disease by medical history, physical findings, laboratory findings. Furthermore, it is necessary to satisfy (2) Symptom criteria 8 items or more, symptom criteria 6 items, body criteria 2 items or more. Recommendations “671-677), a large number of measurement items until diagnosis, long-term observation (measurement) time, financial burden, individual differences among judgment criteria of doctors, and the like. According to one aspect of the present invention, measurement of a limited number of metabolites in blood can not only make an objective diagnosis, but also helps to make a treatment policy because the diagnosis result represents the pathological condition itself. In addition, many of the metabolites revealed in the present invention are deeply related to the urea metabolism system (urea cycle) that metabolizes ammonia, which is a heavy poison, into urea in the liver (urea cycle), and it is not only chronic fatigue syndrome but also diagnosis of chronic fatigue. It can also be used. In particular, it is considered that chronic fatigue syndrome and chronic fatigue can be diagnosed more quickly, cheaply and objectively by producing a measurement kit that can be used in actual medical practice.

  The biomarkers of the present invention can also be used in combination with known biomarkers for fatigue. The known biomarkers are not particularly limited, and, for example, those based on energy (ATP) -producing metabolites reported in International Publication WO 2011/161544 A2 (US Patent Serial No. 13/805, 449). By analyzing in combination with the biomarkers described in International Publication WO 2011/161544 A2, it is possible to more accurately estimate which part in the living body has an abnormality, and for life guidance of patients with chronic fatigue syndrome and patients with chronic fatigue. As well as being able to use it, it will be possible to provide dietary agents that correct the metabolic system that is considered to be the cause. In addition, according to one aspect of the present invention, in combination with a method of determining a fatigue patient group only by energy producing metabolites such as glycolysis and TCA cycle, the present invention using a change in metabolism of urea cycle and its periphery as an indicator The use of biomarkers makes it possible to significantly improve the ability to assess fatigue with respect to individual's metabolic variability and to provide a more appropriate treatment policy.

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

[3-1] Fatigue evaluation method A method of evaluating fatigue according to the present invention comprises the steps of: obtaining a value of the above-mentioned biomarker of the present invention in a biological sample obtained from a subject (biomarker value acquiring step); And a step of performing evaluation using the value of the marker (evaluation step).

  The biomarker value acquisition step can be appropriately performed depending on the type of biomarker. In carrying out the biomarker value acquisition step, it is necessary to measure the concentration of a metabolite in a biological sample. The measurement of the concentration of a metabolite in a biological sample can be performed by an appropriate method according to the type of metabolite, regardless of the method of the example. As one example, when an enzyme reaction using a metabolite as a substrate is well known, a commercially available kit may be used which measures the substrate concentration using an enzyme reaction. That is, one of ordinary skill in the art can use such various techniques optionally to successfully measure the concentration of a metabolite in a biological sample. In some cases, it is preferable that the biological sample to be subjected to the measurement of the concentration of the metabolite is treated to suppress the activity of the enzyme involved in the degradation reaction of arginine contained in the biological sample, in particular, When arginine, ornithine, citrulline, etc. use a related biomarker, it may be preferable in order to improve the measurement accuracy more. Here, to suppress the activity of the enzyme involved in the degradation reaction of arginine by the treatment means that the activity of the enzyme after the treatment is suppressed more than the biological sample before the treatment. One example of such treatment is adding an inhibitor of arginase, which is an enzyme involved in the degradation reaction of arginine, and / or nitric oxide synthase to a biological sample (eg, blood or a 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.

  An evaluation process can be suitably implemented according to the method to evaluate, for example, compares the value of a biomarker with a predetermined threshold value (reference value), or evaluates the value of a biomarker threshold value (reference value It is also possible to make an evaluation without comparing with. For example, by performing analysis such as discriminant analysis, Partial Least Square, or Support Vector Machine, it is possible to evaluate (diagnose or judge) fatigue without using a threshold (reference value) depending on the type of biomarker. it can. In the case of using a threshold value, even if the threshold value is a predetermined value, sampling of a healthy subject is performed simultaneously with sampling from a subject, and the concentration of each metabolite in a biological sample obtained in a healthy subject is It may be a value calculated based on this. An example of the predetermined threshold is an average value of biomarker values in a plurality of healthy persons, or a mode value obtained from a binomial distribution or the like.

  In the evaluation process, although not particularly limited, for example, 1) evaluation of whether or not the subject is fatigue, 2) evaluation of whether or not the subject is chronic fatigue not having chronic fatigue syndrome, 3) the subject is 4) a combination of at least two evaluations selected from the above 1) to 3) (for example, whether the subject is not chronic fatigue syndrome or chronic fatigue syndrome) Evaluation, etc., 5) evaluation of the degree of fatigue, etc. are performed.

  One specific example of the method for evaluating fatigue according to the present invention includes the step (1) of obtaining a ratio (OC ratio) of ornithine concentration to citrulline concentration in a biological sample obtained from a subject. Furthermore, as necessary, after the step (1), the step (2) of comparing the OC ratio with a threshold value corresponding to the OC ratio is included. Furthermore, if necessary, the ratio (PI ratio) of the pyruvate concentration to the isocitrate concentration in the biological sample obtained from the same subject on the premise that the above step (1) or step (2) is carried out The step of obtaining (3) is included. Furthermore, if necessary, after the step (3), the step (4) of comparing the PI ratio with a threshold value corresponding to the PI ratio is included. The step (3) of obtaining the ratio (PI ratio) of the pyruvate concentration to the isocitrate concentration in the biological sample obtained from the subject, the threshold corresponding to the ratio (PI ratio), and the ratio (PI ratio) And performing a step (2) of comparing the OC ratio with a threshold value corresponding to the OC ratio for a subject who has been evaluated for fatigue by performing the step (4) to compare with It may be preferable to obtain the discrimination rate and the low misclassification rate. For example, reference is also made to the evaluation by the decision tree shown in FIG. 9 and FIG. In FIG. 9 and FIG. 10, the determination tree analysis is performed using the pyruvate concentration / isocitric acid concentration as the PI ratio, and the subjects divided into two groups are further determined using the ornithine concentration / citrulline concentration as the OC ratio. It has been shown to perform tree analysis. In addition, 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 OC ratio with a threshold value corresponding to the OC ratio. It may be preferable to perform the step (4) of comparing the PI ratio with the threshold value corresponding to the PI ratio for the subject.

  In addition, prior to the biomarker value acquiring step, the step of directly removing the tissue or cells from the human subject as the first step of sample acquisition is performed by a doctor. In addition, using the results obtained by the above evaluation process, a process in which a doctor makes a diagnosis of fatigue (including chronic fatigue and chronic fatigue syndrome) is also performed as necessary. These processes are distinguished as another process which is not included in the biomarker value acquisition process and the evaluation process.

[3-2] Fatigue Evaluation Kit A kit having a combination of reagents used to carry out the above-described fatigue evaluation method is also within the scope of the present invention. That is, the kit according to the present invention is provided with a reagent for measuring the concentration of a metabolite in a biological sample, which is necessary to obtain 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 is provided with a reagent for measuring the ornithine concentration in a biological sample. As another example, when the ratio of ornithine concentration to citrulline concentration is used as a biomarker, the kit according to the present invention is provided with a reagent for measuring ornithine concentration and citrulline concentration in a biological sample. The reagent for measuring the ornithine concentration includes, for example, ornithine cyclase which reacts with ornithine to generate ammonia, and the reagent for measuring the concentration of citrulline, for example, argininosuccinic acid synthetase which reacts with citrulline and consumes ATP. Be The kit may also preferably include an inhibitor of the activity of an enzyme involved in the degradation reaction of arginine contained in a biological sample, in particular when measuring the concentration of arginine, ornithine or citrulline etc. In order to further improve the accuracy of measurement, it may be preferable. Here, enzymes involved in the degradation reaction of arginine include arginase and nitric oxide synthase (NOS) involved in the reaction of producing arginine to nitric oxide and citrulline, and these are especially blood or Included in biological samples such as blood preparations. In addition, as an inhibitor of arginase, for example, N ^ω -Hydroxy-nor-L-arginine (nor-NOHA) or a salt thereof (eg, diacetate etc.) can be mentioned, and as an inhibitor of nitric oxide synthase Examples include 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, the necessary amount may be provided in advance in a container for storing a biological sample.

  Also within the scope of the present invention is a kit having a configuration for visually detecting the biomarker of the present invention. That is, the second kit according to the present invention is provided with a display unit showing the value of the biomarker of the present invention to evaluate fatigue. As a presentation part, for example, at least one of a first presentation part showing a ratio (OC ratio) of an ornithine concentration to a citrulline concentration, and a second presentation part showing a ratio of a pyruvate concentration to an isocitrate concentration (PI ratio) The provided kit is also in the category of the kit according to the present invention.

  As used herein, the term "kit" is intended for packaging comprising a container (eg, a bottle, a plate, a tube, a dish, etc.) containing a particular material, but as a composition Forms containing the material in the substance are also included in the term "kit". Preferably, the kit comprises instructions for using each material. The instruction sheet may further display a predetermined threshold (reference value) to be evaluated in comparison with the value of the biomarker, as necessary. As used herein in the kit aspect, "equipped" is intended to be contained within any of the individual containers that make up the kit. In addition, the kit according to the present invention may be a package in which a plurality of different compositions are packaged into one, and in the case of a solution form, may be enclosed in a container. The kit according to the present invention may be prepared by mixing the plurality of components in the same container or in separate containers. The "instructions" may be written or printed on paper or other media, or may be attached to electronic media such as magnetic tape, computer readable disks or tapes, CD-ROMs, etc. . The kit according to the present invention may also comprise a container containing a diluent, solvent, washing solution or other reagent. Furthermore, the kit according to the present invention may be equipped with the necessary tools and reagents for collecting a biological sample. The kit according to the present invention may also be equipped with the necessary equipment and reagents to prepare the preparation of interest from a biological sample.

  By having the above configuration, the kit according to the present invention can be conveniently used in an actual medical field, and can provide fatigue faster, cheaper and objective diagnosis.

  The kit according to the present invention can be used not only to evaluate fatigue and to propose a treatment but also to provide a criterion for determining fatigue (including chronic fatigue and chronic fatigue syndrome). This makes it easy to diagnose whether the condition is or not. That is, the kit according to the present invention diagnoses a kit (for example, fatigue (including chronic fatigue, chronic fatigue syndrome)) for providing a diagnostic standard or criterion of fatigue (including chronic fatigue, chronic fatigue syndrome). Or a kit for obtaining data for determination.

[3-3] Fatigue Evaluation System A system used to perform the above-described fatigue evaluation method 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 “the calculation means such as the CPU executes the program code stored in the recording medium such as the ROM and the RAM. Although the case will be described as an example, it may be realized by hardware that performs the same process. The present invention can also be realized by combining hardware that performs part of processing and the above-described arithmetic unit that executes program code that performs control of the hardware and the remaining processing. Furthermore, among the above-described members, even if the members are described as hardware, hardware that performs a part of processing, and the arithmetic unit that executes program code that performs control of the hardware and the remaining processing are included. It can also be realized in combination. The above computing means may be a single unit, or a plurality of computing means connected via a bus inside the apparatus or various communication paths may jointly execute the program code.

  The fatigue evaluation system according to the present invention includes at least an input reception unit 11A, a storage unit 12, a CPU 13, and a display unit 14 as its functional blocks. The input reception unit 11A is an interface for receiving an input of the measurement result in the measurement unit 11, and is configured as an interface for connecting the measurement unit 11 and the fatigue evaluation system, or in some cases, as an input device such as a keyboard or a mouse. Configured The measurement unit 11 has a function of a measurement device that measures the concentration of a metabolite (for example, ornithine or citrulline) associated with the biomarker of the present invention. The storage unit 12 has a function as a measurement value receiving unit 12a that receives the 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. (Eg configured as a memory). The CPU 13 has a function as an arithmetic unit 13a that generates information for evaluating fatigue, and an evaluation unit 13b that receives evaluation information from the arithmetic unit 13a and evaluates fatigue. The display unit 14 has a function as an evaluation result display unit 14 that displays the evaluation result by the evaluation unit 13 b (for example, configured as a liquid crystal display device). Note that 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 one aspect of the fatigue evaluation system, a system for evaluating fatigue as a ratio of ornithine concentration to citrulline concentration (OC ratio) as one of the biomarkers will be specifically described. In this system, as step 1, the measurement unit 11 measures the ornithine concentration and the citrulline concentration in the biological sample. Next, in step 2, the measured value receiving unit 12a receives (stores) the ornithine concentration and citrulline concentration measured in step 1 via the input receiving unit 11A. Next, in step 3, the calculation unit 13a reads the ornithine concentration and the citrulline concentration stored in the measured value receiving unit 12a, and calculates the ratio (OC ratio) of the ornithine concentration to the citrulline concentration. The OC ratio is stored in the storage unit 12 as needed. Next, in step 4, the calculation unit 13 a generates evaluation information for evaluating fatigue from the calculated OC ratio and the reference value stored in the reference value storage unit 12 b. Next, in step 5, the evaluation unit 13 b evaluates fatigue based on the evaluation information generated in step 4. Next, at step 6, the evaluation result of fatigue generated by the evaluation unit 13 b is displayed on the display unit 14. In step 2, the measured value receiving unit 12a may receive the ratio (OC ratio) of the ornithine concentration and the citrulline concentration via the input receiving unit 11A. In this case, the arithmetic unit 13a reads out the OC ratio stored in the measurement value receiving unit 12a instead of the steps 3 to 4, and the OC ratio and the reference value stored in the reference value storage unit 12b. To generate evaluation information for evaluating fatigue. Step 5 is as described above. The steps from generation of evaluation information to evaluation of fatigue can be executed, for example, by operating a program for evaluation (for example, a decision tree analysis program or the like) stored in the storage unit 12. For example, when obtaining the results shown in FIG. 9 and FIG. 10, the ratio of the pyruvate concentration to the isocitrate concentration (PI ratio) in the subject's biological sample and the subject's biological sample The program for evaluation may be operated after storing the ratio of the alanine concentration to the hydroxyproline concentration in the fatigue evaluation system (for example, the storage unit 12).

  The system according to the present invention can be used not only to evaluate fatigue and to propose a treatment but also to provide a criterion for determining fatigue (including chronic fatigue and chronic fatigue syndrome). This makes it easy to diagnose whether the condition is or not. That is, the system according to the present invention diagnoses a system (for example, fatigue (including chronic fatigue, chronic fatigue syndrome)) for providing a diagnostic standard or criterion of fatigue (including chronic fatigue, chronic fatigue syndrome). Or a system for acquiring data for determination.

[4] One Aspect of the Present Invention One aspect of the present invention includes, for example, the following inventions.
(1) obtaining a ratio (OC ratio) of ornithine concentration to citrulline concentration in a biological sample obtained from a subject, and evaluating a state of fatigue in the subject based on the ratio (OC ratio) Methods to assess fatigue, including.
(2) The method according to (1), including the step of comparing the threshold (OC ratio) with a threshold value corresponding to the ratio (OC ratio).
(3) Further, a step of obtaining a ratio (PI ratio) of the pyruvate concentration to the isocitrate concentration in the biological sample, and a step of evaluating the state of fatigue in the subject based on the ratio (PI ratio) The method according to (1) or (2), including
(4) The method according to (3), including the step of comparing the ratio (PI ratio) with a threshold value corresponding to the ratio (PI ratio).
(5) obtaining a ratio (PI ratio) of pyruvate concentration to isocitrate acid concentration in the biological sample, and comparing the ratio (PI ratio) with a threshold value corresponding to the ratio (PI ratio) The method according to (2), wherein the step of comparing the threshold (OC ratio) with the threshold corresponding to the ratio (OC ratio) is performed on a subject who is evaluated for fatigue status.
(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 that indicates a ratio (OC ratio) of ornithine concentration to citrulline concentration.
(8) The kit according to (6) or (7), further comprising a reagent for measuring pyruvate concentration and a reagent for measuring isocitrate acid concentration.
(9) The kit according to (8), including a second presentation unit that indicates a ratio (PI ratio) of pyruvate concentration to isocitrate concentration.
(10) A computing unit that receives a reference value for evaluating fatigue and a ratio (OC ratio) of ornithine concentration to citrulline concentration to generate evaluation information for evaluating fatigue, and the evaluation information Fatigue evaluation system with evaluation unit to receive and evaluate fatigue.
(11) 1) An input receiving unit for receiving an input of a ratio of an ornithine concentration to a citrulline concentration (OC ratio) or 2) an input receiving unit for receiving an input of measured values of an ornithine concentration and a citrulline concentration; The fatigue evaluation system according to (10), wherein the part calculates the ratio (OC ratio) of the ornithine concentration to the citrulline concentration from the measurement value of which the input is received.
(12) The ornithine concentration in the biological sample and Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, instead of the ratio (OC ratio) of the ornithine concentration to the citrulline concentration The method according to any of (1) to (5), which uses a ratio to any concentration selected from Gamma-butyrobetaine.
(13) A method of evaluating fatigue, comprising the steps of: obtaining a value of a biomarker in a biological sample obtained from a subject; and evaluating the state of fatigue in the subject based on the value of the biomarker. The values of the biomarkers include: 1) 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4-hydroxy-3-methoxybenzoate (4-hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (4-methyl-2-oxopentanoate), 5-oxoproline (5-Oxoproline), alanine (4-hydroxy-2-methoxybenzoate) Ala), arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelaic acid (Azelate), beta-alanine (beta-Ala), betaine (Betaine), carnitine (Carnitine), choline Choline), Citrulline, Creatine, Creatinine, Crysine, Cystine, Gamma-Butyrobetaine, Glutamine (Gln), Glutamic Acid (Glu), Glycine (Gly), Histidine (Gly) His), Hydroxyproline (Hydroxyproline), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), 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 The biological sun of at least one biomarker selected from the group consisting of (Tyr), uric acid (Urate), and valine (Val) (2) Cis-aconitate (Cis-aconitate), Citric acid (Citrate), isocitrate (Isocitrate), lactic acid (Lactate), malic acid (Malate), and succinic acid A method for evaluating fatigue, which is the ratio or difference in concentration in a biological sample of a plurality of biomarkers selected from the group to which Succinate is added.
(14) The method for evaluating fatigue as described in (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 in a biological sample of at least one biomarker selected from the group consisting of (Malate) and succinic acid (Succinate) 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 species selected from the group consisting of citrulline, isocitrate, pyruvate and ornithine A method of evaluating fatigue, which is the ratio or difference in concentration of a biomarker in a biological sample.
(16) The biological sample is treated to suppress the activity of arginase involved in the degradation reaction of arginine and / or the activity of nitric oxide synthase, (1) to (5), (12) to (15) The method according to any one of the above.
(17) The kit according to (6), which comprises 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 can be made within the scope of the claims, and embodiments obtained by appropriately combining the technical means disclosed in the different embodiments. Is also included in the technical scope of the present invention.

[1. Evaluation in humans]
In trial 1, 20 healthy subjects (10 males and 10 females; average age = 36.10 years; provided from Osaka City University Hospital), chronic fatigue syndrome 20 people (10 males and 10 females; average age) = 36.15 years old; provided by Osaka City University Hospital). In Test 2, 46 healthy subjects (5 males and 41 females; average age = 38.78 years; provided from Osaka City University Hospital) and 47 chronic fatigue syndromes (6 males and 41 females; average) Age: 27.96 years old; provided from Osaka City University Hospital, chronic fatigue: 13 people (5 male, 8 women; average age = 33.62 years; provided from Osaka City University Hospital).

  Chronic fatigue syndrome in trials 1 and 2 is a revision of the chronic fatigue syndrome diagnostic criteria (Ann Intern Med. 1994; 121: 953-959) prepared by the American Center for Disease Prevention and Control (CDC) for domestic use ( History of medicine As described in the special feature “The latest science of fatigue-From Japan: Recommendations for anti-fatigue and anti-overwork” 671-677) (1) The main symptom is the strong fatigue that life is seriously impaired And continue or recur for at least 6 months or more (50% or more period is recognized) and exclude the disease by medical history, physical findings and laboratory findings. A person who has been diagnosed with a disease according to the diagnostic criteria such as: 6 symptom criteria, 2 physical criteria or more. Chronic fatigue in Test 2 is a patient who did not meet the above-mentioned diagnosis of chronic fatigue syndrome by a doctor, but was observed fatigue or fatigue intermittently for 6 months or more and visited the hospital. In addition, healthy subjects in trials 1 and 2 were recruited in consideration of age, fatigue level, presence or absence of mental illness, presence or absence of sleep disorder, presence or absence of medication for treatment of mental disorder or sleep disorder, etc. Recruit) is a person.

  Plasma samples were prepared from blood collected from healthy subjects (HC) and patients with chronic fatigue syndrome (CFS) and chronic fatigue (CF) for both tests 1 and 2. The measurement of metabolites in the pretreated plasma sample was requested to the Institute of Advanced Life Science, Keio University. A capillary electrophoresis-mass spectrometer was used to measure metabolites in plasma. On the other hand, Glucose analysis kit II (Biovision) was used for the measurement of a blood glucose level. For statistical analysis, significant difference test using SPSS 17.0 and Amos 18.0, correlation analysis, and path analysis were adopted.

  As a result of evaluation in the test 1 and test 2 added groups, as reported by the present inventors in International Publication WO 2011/161544 A2 (US Patent Serial No. 13/805, 449), normal subjects and chronic fatigue syndrome patients Reproducibility of changes in energy-producing metabolites among them was confirmed. Furthermore, when analysis including other metabolic pathways was conducted in detail, new biomarkers were found from the metabolic system which has been extremely reproducibly changed through Tests 1 and 2 focusing on urea cycle related metabolites, The possibility of higher accuracy fatigue diagnosis was found by the combination with the biomarker found.

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

  For the test 1 and test 2 added populations, the Random Forest program was run using analysis software R (free software, version 3.0.1) with measured values of metabolites in plasma samples. The same random forest program run was also performed on the trial 2 population. Random Forest is an algorithm used for identification, regression, and clustering.

  As shown in FIG. 2, when classifying a healthy subject and a chronic fatigue syndrome patient, TCA-related metabolites such as Isocitrate, Succinate and Cis-Aconitate among these measurement items are combined with these urea circuits. Related metabolites appeared at the top. Urea cycle related metabolites appeared at the top also in the same analysis for only Test 2 (FIG. 3). Besides 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 healthy group and chronic fatigue syndrome in the two-group comparison (FIG. 4).

  From this result, it is possible to use a urea cycle related metabolite that is relatively stable against individual variability of the subject alone or by combining it with a biomarker of energy metabolism (from glycolysis to TCA cycle). It is expected that the stability of discrimination due to individual differences among chronic fatigue patients and healthy people is improved.

  On the other hand, similar changes were seen in urea cycle related metabolites in the group of patients with chronic fatigue (Fig. 5), but when evaluated using the values of Citrulline / Ornithine or Urea / Ornithine, which is the ratio of these, chronic The degree of decrease was smaller in chronic fatigue patients than in fatigue syndrome patients. Incidentally, Urea and Aspartate could be detected only in Test 2. Furthermore, with respect to some metabolites not found in the chronic fatigue syndrome patient group, those significantly decreased or increased as compared to the healthy group were found (FIG. 6). Note that FIG. 16 is a diagram showing the random forest (Random Random (Random) using all parameters including the measured values of metabolites in plasma samples of healthy people (HC) and chronic fatigue (CF) patients for the test 2 population. Forest) shows the result of running the program. FIG. 17 shows the Random Forest (Random Forest) using all parameters, including measured values of metabolites in plasma samples of chronic fatigue (CF) and chronic fatigue syndrome (CFS) patients, for the population of Study 2. ) Shows the result of executing the program.

  Next, the correlation between the performance status (PS) and the metabolite in which the amount in plasma significantly fluctuated according to the presence or absence of fatigue was examined for all the data of Test 1 and Test 2. 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 a 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 with the decision tree model was performed using the analysis software R on the measured values of metabolites in all 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 classified into four trees as shown in FIG. 7, chronic fatigue syndrome is classified by classifying whether Isocitrate is 8.15 μM or more, Ornithine is 62 μM or more, or Hydroxyproline is 9.3 μM or less. From the patient group and the group of healthy people, patients with chronic fatigue syndrome can be discriminated at a positive discrimination rate of 79.0% (49 out of 62 CFS). In this case, the misclassification rate was 18.5% (12 out of 65 HCs). When the number of classification trees is further increased and classified into 9 trees, whether Succinate is at least 12.5 μM or less, Glutamine (Gln) is at least 1069 μM, or Glutamate (Glu) is at least 50 μM, as shown in FIG. Patients with chronic fatigue syndrome from the group of patients with chronic fatigue syndrome and the group of healthy people by classifying as less than, less than 95 μM or less of Leucine (Leu), or less than 181.5 μM or less of Lysine (Lys) Can be determined with a positive determination rate of 90.3% (56 out of 62 CFSs). In this case, the misclassification rate was 7.7% (5 out of 65 HCs). Furthermore, by using the ratio of metabolites, the positive discrimination rate can be increased and the misclassification rate can be decreased even with a smaller number of trees. For example, as shown in FIG. 9, 80.6 with only classification into three trees, where Pyruvate / Isocitrate (PI ratio) is at least 20.61 or less, and Ornithine / Citruline (OC ratio) is at least 3.215. It is possible to identify patients with chronic fatigue syndrome with a% positive discrimination rate (50 out of 62 CFSs) and a 20.0% misclassification rate (13 out of 65 HCs). Furthermore, by adding whether Ornithine / Citruline is 1.321 or more or Alanine / Hydroxyproline is 37.71 or more, the 80.6% positive discrimination rate is maintained while the misclassification rate is 10.7. It decreases to% ((7 out of 65 HCs) (Fig. 10) However, the numbers that determine the branching on the above decision tree change depending on the measurement conditions and purpose, and are not fixed numbers. Tree analysis can also be used as a diagnostic algorithm.

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

[2. Evaluation in Rats Evaluated in Humans]
The use of one aspect of the present invention enables fatigue diagnosis using a simple kit. Furthermore, based on the results of diagnosis using one aspect of the present invention, there is the possibility of finding an effective treatment. For example, by applying one embodiment of the present invention to a patient who complains of strong fatigue and long-term fatigue, it is possible to objectively evaluate the degree of fatigue, and further to determine whether it is chronic fatigue syndrome or not. In addition, by grasping the metabolic condition, it becomes possible to select a food drug component that realizes the treatment / prevention according to the fatigue condition of the individual. The assessment of the degree of fatigue according to the present invention can also be carried out in general medical facilities because it does not require a high level of knowledge on fatigue. In addition, since one aspect of the present invention can be used to infer which part of the metabolic (urea) system and the energy producing (ATP) system in the living body are abnormal, it can be used only for life guidance of patients. It is possible to provide a food which corrects the metabolic condition by promoting or suppressing the metabolic system which is considered to be the cause. In particular, not only from the above analysis results of human blood components, but also from exhaustive metabolic analysis targeting blood and liver of fatigue model animals (FIG. 12: graph data show measured values of liver samples), urea cycle and TCA It has been confirmed that the circuit abnormality and the flow of the metabolism characteristic of fatigue condition from the urea circuit to the TCA circuit. That is, in fatigue conditions, Citrate, cis-Aconitate, and Isocitrate in the TCA cycle decrease significantly, while Succinate and Malate increase significantly. In addition, in fatigue conditions, a system in which ornithine in the urea cycle is metabolised in order of Putrescine, GABA and Succinate flows into the TCA cycle. At the same time, the promotion of this metabolic pathway is associated with decreased metabolism of ornithine to citrulline in the urea cycle and enhanced metabolism of arginine to ornithine in the same cycle.

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

[3. Analysis by PLS-DA method]
The PLS-DA method (partial least squares discriminant analysis) is a method used to construct a prediction model using regression analysis. This method was used to discriminate between HC (healthy person) and CFS (chronic fatigue syndrome), HC and CF (chronic fatigue), and CF and CFS. As software for analysis by the PLS-DA method, R (free software, version 3.0.1) was used.

= Discrimination result of HC and CFS =
As biological information, in the above-mentioned test 2, statistically significant difference was observed between HC group and CFS group in Ornithine, Citrulline, Urea, Hydroxyproline, Urate, 4-Hydroxy-3-methoxybenzoate, Isocitrate, Citrate Succinate, Pro, Leu, 3-Methylhistidine, Creatinine, Probable as a marker from the results of random forest analysis, concentrations of Pyruvate, Cis-aconitate, Gamma-Butyrobetaine, 4-methyl-2-oxopentanoate, 2-oxoglutarate, and results of random forest analysis 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, and Val PLS-DA analysis was performed. As a result, as shown in FIG. 18, in the PLS-DA score plot, HC and CFS showed different distributions. Also, metabolites with statistically significant differences showed high loading for the discrimination of the 2 groups. One plot in FIG. 18 corresponds to one human, HC indicates a healthy person, and CFS indicates a patient with chronic fatigue syndrome.

= Discrimination result of HC and CF =
As biological information, statistically significant differences were observed in the comparison of the HC group and the CF group in Test 2 described above, Choline, Gly, Taurine, Asn, Gamma-Butyrobetaine, Gln, Lys, Trp, 4- Concentrations of methyl-2-oxopentanoate, Succinate, and Cis-Aconitate, Lactate, Arg, Mucate, His, Azelate, N, N-Dimethylglycine, Creatinine, beta-Ala, Tyr, Ornithine promising as markers from random forest results , 2AB, Leu, Isocitrate, Hydroxyproline, Phe, Met, 3-Methylhistidine, Sarcosine, Ile, Betaine, Pyruvate, Urate, 4-Hydroxy-3-methoxybenzoate, Ser, Asp, Carnitine, Urea, 5-oxoproline, Citrate respectively 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 with statistically significant differences showed high loading for discrimination in the 2 groups. One plot in FIG. 19 corresponds to one human, HC indicates a healthy person, and CF indicates a patient with chronic fatigue.

= Discrimination result of CF and CFS =
As biological information, statistically significant differences were observed in the comparison between the CF group and the CFS group in Test 2 described above, Choline, Succinate, Gln, Taurine, Asn, Lys, Arg, His, Urate, 4- Concentrations of Hydroxy-3-methoxybenzoate, and N, N-Dimethylglycine, 3-Methylhistidine, Alate, Ile, Trp, 4-Methyl-2-oxopentanoate, Ser, Cis-Aconitate, Met promising as markers from 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, respectively 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 with statistically significant differences showed high loading for discrimination in the 2 groups. 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.

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

[4. Evaluation of the effect of degradation of arginine]
The whole blood after blood collection was examined for the amount of time it took for the biomarker value to change when left at room temperature using blood (whole blood) of 10 healthy persons. As a result, it was found that the ornithine level increased and the arginine level decreased as the standing time of blood after blood collection increased (FIG. 14). On the other hand, some metabolites such as citric acid did not show any change in concentration upon standing (not shown). Blood contains an enzyme called arginase, which promotes the reaction from arginine to ornithine, and nitric oxide synthase (NOS), which is involved in the reaction from arginine to nitric oxide and citrulline. It was suggested that the enzyme reaction was in progress in the test tube.

Therefore, whether or not the change in concentration of ornithine or arginine can be suppressed by inserting an inhibitor of arginase or nitric oxide synthase into a test tube containing collected blood using rat blood (whole blood) investigated. Inhibition experiments using the arginase inhibitor N ^ω -Hydroxy-nor-L-arginine (nor-NOHA) and the nitric oxide synthase inhibitor N ^G -Nitro-L-Arginine (L-NNA) Attempts were made to analyze arginine and ornithine quantitatively using the LC-MS system. In addition, 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 left at room temperature after blood collection, both the arginine level and the ornithine level became high. Next, it was found that the increase in arginine level and ornithine level 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 arginine concentration, and the right side shows the measurement result of ornithine concentration. In FIG. 15, from the left sequentially, 0 h represents the state immediately after blood collection, 2 h represents the state left at room temperature without addition of an inhibitor after blood collection, and 5 h represents a room temperature without addition of an inhibitor after blood collection. 100 μM nor-NOHA (5 h) is added at a final concentration of 100 μM nor-NOHA, and left at room temperature for 5 hours, 100 μM nor-NOHA + 100 μM L-NNA (5 h) The final concentration of 100 μM nor-NOHA and L-NNA were added, and left at room temperature for 5 hours, and 300 μM nor-NOHA (5 h) was added at a final concentration of 300 μM nor-NOHA, and room temperature for 5 hours In the standing condition, 300 μM nor-NOHA + 100 μM L-NNA (5 h) shows the final concentration of 300 μM nor-NOHA and 100 μM L-NNA, respectively, for 5 hours at room temperature.

  Assuming that the blood sample can not be cooled immediately after blood collection, adding such inhibitors in advance to the blood collection test tube for fatigue diagnosis changes the biomarker value. This can prevent and further improve the inspection accuracy.

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

  Also, all documents mentioned in the present specification are incorporated herein by reference as forming a part of the present specification.

  According to the present invention, it is possible to objectively and simply evaluate and diagnose fatigue. The present invention may further provide techniques useful for the treatment of fatigue. Since fatigue is a very serious health problem, achieving assessment, diagnosis and treatment of fatigue contributes greatly to all industries.

Claims (16)

  Obtaining a ratio (OC ratio) of ornithine concentration to citrulline concentration in a biological sample obtained from a subject, and evaluating a state of fatigue in the subject based on the ratio (OC ratio). How to assess fatigue.   The method according to claim 1, comprising the step of comparing the ratio (OC ratio) with a threshold value corresponding to the ratio (OC ratio).   The method further includes the steps of obtaining a ratio (PI ratio) of the pyruvate concentration to the isocitrate concentration in the biological sample, and evaluating the state of fatigue in the subject based on the ratio (PI ratio). The method according to claim 1 or 2.   The method according to claim 3, comprising the step of comparing the ratio (PI ratio) with a threshold value corresponding to the ratio (PI ratio).   A step of obtaining a ratio (PI ratio) of the pyruvate concentration to the isocitrate concentration in the biological sample and a step of comparing the threshold (PI ratio) corresponding to the ratio (PI ratio) are performed. The method according to claim 2, wherein the step of comparing the ratio (OC ratio) with a threshold value corresponding to the ratio (OC ratio) is performed on a subject whose fatigue status has been evaluated.   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 unit that indicates a ratio (OC ratio) of ornithine concentration to citrulline concentration.   The kit according to claim 6 or 7, further comprising a reagent for measuring pyruvate concentration and a reagent for measuring isocitrate acid concentration.   The kit according to claim 8, further comprising a second presentation unit that indicates a ratio of pyruvate concentration to isocitrate concentration (PI ratio). A calculation unit that receives a reference value for evaluating fatigue and a ratio (OC ratio) of ornithine concentration to citrulline concentration to generate evaluation information for evaluating fatigue; and receives the evaluation information; Fatigue evaluation system with an evaluation unit that evaluates 1) Do you have an input acceptance unit that accepts input of the ratio (OC ratio) of ornithine concentration and citrulline concentration,
2) An input receiving unit for receiving input of measured values of ornithine concentration and citrulline concentration, and the calculation unit calculates a ratio (OC ratio) of ornithine concentration and citrulline concentration from the measured value received as input,
The fatigue evaluation system according to claim 10.   Urea, Urate, 4-hydroxy-3-methoxybenzoate, Hydroxyproline, 4-Methyl-2-oxopentanoate, Gamma-butyrobetaine, and the ornithine concentration in the biological sample in place of the ratio (OC ratio) of the ornithine concentration to the citrulline concentration The method according to any one of claims 1 to 5, wherein the ratio to any concentration selected from is used. A method to assess fatigue,
Obtaining a value of a biomarker in a biological sample obtained from a subject, and evaluating the state of fatigue in the subject based on the value of the biomarker,
The values of the biomarkers are 1) 2-aminobutyric acid (2AB), 2-oxoglutarate, 3-hydroxybutyrate, 3-methylhistidine, 4-hydroxy -3-Methoxybenzoate (4-Hydroxy-3-methoxybenzoate), 4-methyl-2-oxopentanoate (4-methyl-2-oxopentanoate), 5-oxoproline (5-Oxoproline), alanine (Ala), Arginine (Arg), asparagine (Asn), aspartic acid (Asp), azelaic acid (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, if the concentration of the biological sample of at least one biomarker selected from the group consisting of valine (Val), 2) b plumb acid to said group (Isocitrate ) which is a ratio or difference between the concentration in a biological sample of a plurality of types of biomarkers selected from the pressurized example was the group,
How to assess fatigue. 14. A method of evaluating fatigue according to claim 13 , wherein
The value of the biomarker is 1) concentration of at least one biomarker selected from the group consisting of citrulline , pyruvate, and ornithine in a biological sample, or 2) A ratio or difference in concentration in a biological sample of a plurality of biomarkers selected from the group consisting of citrulline, isocitrate, pyruvate and ornithine,
How to assess fatigue. The biological sample according to any one of claims 1 to 5 or 12 to 14 , wherein the biological sample is treated to suppress the activity of arginase involved in the degradation reaction of arginine and / or the activity of nitric oxide synthase. Method.   The kit according to claim 6, which comprises an inhibitor of arginase or nitric oxide synthase involved in the degradation reaction of arginine.

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