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WO2013005790A1 - Procédé d'évaluation de la bio-oxydation, dispositif à cet effet, procédure à cet effet, programme à cet effet, système à cet effet, dispositif terminal de communication de données à cet effet, et procédé de recherche d'une substance pour empêcher ou améliorer la bio-oxydation - Google Patents

Procédé d'évaluation de la bio-oxydation, dispositif à cet effet, procédure à cet effet, programme à cet effet, système à cet effet, dispositif terminal de communication de données à cet effet, et procédé de recherche d'une substance pour empêcher ou améliorer la bio-oxydation Download PDF

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Publication number
WO2013005790A1
WO2013005790A1 PCT/JP2012/067166 JP2012067166W WO2013005790A1 WO 2013005790 A1 WO2013005790 A1 WO 2013005790A1 JP 2012067166 W JP2012067166 W JP 2012067166W WO 2013005790 A1 WO2013005790 A1 WO 2013005790A1
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Prior art keywords
discriminant
evaluation
amino acid
value
acid concentration
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English (en)
Japanese (ja)
Inventor
田中　孝幸
山本　浩史
實 山門
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Ajinomoto Co Inc
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Ajinomoto Co Inc
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806—Determination of free amino acids
    • G01N33/6812—Assays for specific amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7004—Stress
    • G01N2800/7009—Oxidative stress

Definitions

  • the present invention relates to a biooxidation evaluation method and biooxidation evaluation apparatus for evaluating a state of biooxidation including the degree of oxidative stress and / or antioxidative power using the amino acid concentration in blood (including plasma, serum, etc.).
  • Biooxidation evaluation method, biooxidation evaluation program, biooxidation evaluation system, information communication terminal device, and biooxidation preventive / improving substance search method for searching for substances that prevent biooxidation or improve the state of biooxidation It is about.
  • Oxidative stress means that the balance between the oxidative reaction and the antioxidant reaction of the living body is lost, the state is inclined to the oxidized state, and the living body causes oxidative damage.
  • oxidative stress is defined as the difference between the oxidative damage potential of active oxygen groups generated in vivo and the antioxidant potential of the antioxidant system in vivo. The active oxygen group is useful for energy production, invading foreign body attack, unnecessary cell processing, or cell information transmission (Non-Patent Document 1).
  • this excessive active oxygen group is a lipid, protein, enzyme, or genetic information responsible for the structure or function of the biological body. Oxidizing the genetic DNA responsible for the above, damages them, resulting in disorder of the structure or function of the living body, causing disease, premature aging, and being susceptible to cancer or lifestyle-related diseases ( Non-patent document 1). In addition, by virtue of lifestyle-related diseases, a vicious cycle in which oxidative stress is amplified occurs, and further disease or aging progresses.
  • Non- Patent Document 2 when the degree of oxidative stress is high and the antioxidant capacity is normal, the degree of oxidative stress is offset by the antioxidant capacity, but if it is not improved, problems may occur in the future (non- Patent Document 2 and Non-Patent Document 3).
  • the degree of oxidative stress when the degree of oxidative stress is normal and the antioxidant capacity is low, the immune activity is generally decreased, which may be a chronic disease. Further, when the degree of oxidative stress is high and the antioxidant power is low, the self-defense function may be lowered, and follow-up is necessary.
  • hydroperoxides in blood are markers for the degree of oxidative stress.
  • D-ROM Derivatives of Reactive Oxidative Metabolites
  • BAP Bio Antioxidant Potential
  • Non-Patent Document 4 has reported that homocystein increases with oxidative stress.
  • Tyr has a significant positive correlation with antioxidants superoxide dismutase and glutathione (a tripeptide in which Glu, Cys, and Gly are sequentially peptide-bonded) (Non-Patent Literature). 5).
  • Blanco et al. Have reported that Cys varies with glutathione (Non-patent Document 6).
  • Patent Literature 1 Patent Literature 2, and Patent Literature 3 relating to a method for associating an amino acid concentration with a biological state are disclosed as prior patents.
  • Patent Literature 4 relating to a method for evaluating a stress state including at least depression or major depression using amino acid concentration is disclosed.
  • patent document 5 regarding the method of evaluating the state of fibromyalgia and depression using amino acid concentration is disclosed.
  • Cornelli U, Terranova R, Luca S, Cornelli M, Alberti A. Bioavailability and antioxidant activity of some food supplements in men and women using the D-Romtestas a marker of oxidative. , The Journal of Nutrition, 2001, 131, 12, p3208-3211.
  • the present invention has been made in view of the above problems, and a living body capable of accurately evaluating the state of biological oxidation including the degree of oxidative stress and / or antioxidant power by utilizing the concentration of amino acids in blood.
  • Oxidation evaluation method, biological oxidation evaluation apparatus, biological oxidation evaluation method, biological oxidation evaluation program, biological oxidation evaluation system, information communication terminal device, and biological oxidation evaluation method It is an object of the present invention to provide a method for searching for a substance for preventing / ameliorating biooxidation capable of accurately searching for a substance that improves the state of oxidation.
  • the concentration of blood amino acids varies depending on the state of oxidative stress and antioxidant power. Therefore, if specific blood amino acids are identified for changes in the state of oxidative stress and antioxidant power, and if an index formula with the identified blood amino acid concentration as a variable is found, oxidative stress and antioxidant power It can be widely applied as a simple and effective discrimination method of the state. Therefore, as a result of intensive studies to solve the above-mentioned problems, the present inventors have identified amino acid variables useful for discrimination regarding oxidative stress and antioxidant power due to amino acid concentration in blood, and the concentration of the identified amino acid. As a variable, the present inventors have found a multivariate discriminant (function formula, index formula) for optimizing discriminability between two groups, and have completed the present invention.
  • the biological oxidation evaluation method includes an acquisition step of acquiring amino acid concentration data related to the concentration value of amino acids in blood collected from an evaluation target; A concentration value reference evaluation step for evaluating a state of biological oxidation including the degree of oxidative stress and / or antioxidant power for the evaluation object based on the amino acid concentration data of the evaluation object acquired in the acquisition step. It is characterized by.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, ABA, Lys.
  • the biooxidation state is evaluated.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, based on the concentration value, the oxidative stress level and the antioxidant power are both normal for the evaluation object.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, based on the concentration value, the oxidative stress level and the antioxidative power for the evaluation object And a concentration value reference determining step for determining whether the state is normal or the state in which the degree of oxidative stress is high and the antioxidant power is normal.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln. And a concentration value reference determining step for determining whether the degree is normal and the antioxidant power is normal or the oxidative stress is normal and the antioxidant power is low. To do.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His, based on the concentration value,
  • the method further includes a concentration value criterion determining step for determining whether the antioxidant power is normal or the oxidative stress level is high and the antioxidant power is low.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg. It further includes a concentration value reference determining step for determining whether the state is high and the antioxidant power is normal or the oxidative stress level is normal and the antioxidant power is low. .
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg.
  • the method further comprises a concentration value reference determining step of determining whether the state is normal in oxidizing power or the state in which the degree of oxidative stress is high and the antioxidant power is low.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln, Trp And a concentration value criterion determining step for determining whether the oxidative stress level is normal and the antioxidant power is low or the oxidative stress level is high and the antioxidant power is low. It is characterized by.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Pro, Asn included in the amino acid concentration data acquired in the acquisition step. , His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA, based on the concentration value,
  • the method further includes a concentration value reference determining step of determining whether the oxidative stress level is normal or the oxidative stress level is high.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes Glu, Ser, Asn, Thr included in the amino acid concentration data acquired in the acquisition step. , Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit.
  • the method further includes a concentration value criterion determining step for determining which of the state and the state where the antioxidant power is low.
  • the biological oxidation evaluation method is the biological oxidation evaluation method, wherein the concentration value reference evaluation step includes the amino acid concentration data acquired in the acquisition step and the amino acid concentration as variables. Based on a preset multivariate discriminant, a discriminant value calculating step for calculating a discriminant value which is a value of the multivariate discriminant, and based on the discriminant value calculated in the discriminant value calculating step, And a discriminant value reference evaluation step for evaluating the state of biooxidation.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method described above, wherein the multivariate discriminant is created by a logistic regression equation, a fractional equation, a linear discriminant, a multiple regression equation, and a support vector machine. Or an expression created by a canonical discriminant analysis, or an expression created by a decision tree.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, ABA, Lys, and Glu, Ser, Pro, Asn , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, ABA, Lys in the multivariate discriminant including at least one of the variables. Based on this, the discriminant value is calculated.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step.
  • the concentration value of at least one of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu , Phe, the discriminant value is calculated based on the multivariate discriminant including at least one of the variables as the variable, and the discriminant value criterion evaluating step is based on the discriminant value calculated in the discriminant value calculating step.
  • both the degree of oxidative stress and the antioxidant power are normal, the degree of oxidative stress At least three of a state of high and normal antioxidant power, a state of normal oxidative stress and low antioxidant power, and a state of high oxidative stress and low antioxidant power It further includes a discriminant value criterion discriminating step for discriminating one of them.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step.
  • the discriminant value criterion evaluation step includes: Based on the discriminant value calculated in the value calculating step, for the evaluation object, the oxidative stress level and Serial to antioxidant potential further comprises a discriminant value criterion discriminating step of discriminating which one of both normal and the oxidative stress level is high and the resistance to oxidation is normal state, characterized by.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method described above, wherein the multivariate discriminant is the logistic regression equation including Gln, Cit, Tyr, Met, Orn, Leu as the variables. It is characterized by being.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln, and Glu, Ser, Pro, Asn, Ala, Thr , Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln based on the multivariate discriminant including at least one of the variables.
  • the discriminant value criterion evaluation step is based on the discriminant value calculated in the discriminant value calculation step. And a criterion value for determining whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is normal and the antioxidant power is low for the evaluation object.
  • the method further includes a determination step.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method described above, wherein the multivariate discriminant is the logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as the variables. It is characterized by being.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His, and Glu, Ser, Pro, Asn, Ala, Thr, Cit , Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His, based on the multivariate discriminant including at least one of the variables, the discriminant value is calculated and the discriminant is calculated.
  • the value reference evaluation step is based on the discriminant value calculated in the discriminant value calculation step.
  • a discriminant value criterion discriminating step for discriminating between the state in which both the oxidative stress level and the antioxidant power are normal and the state in which the oxidative stress level is high and the antioxidant power is low for each elephant It is characterized by this.
  • the biooxidation evaluation method according to the present invention is characterized in that, in the biooxidation evaluation method, the multivariate discriminant is the logistic regression equation including Ala, Cit, Tyr as the variables. To do.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg, and Glu, Ser, Pro, Asn, Ala, Thr, Cit , Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg based on the multivariate discriminant including at least one of the variables, the discriminant value is calculated, and the discrimination is performed.
  • the value reference evaluation step is based on the discriminant value calculated in the discriminant value calculation step.
  • the biooxidation evaluation method according to the present invention is characterized in that, in the biooxidation evaluation method, the multivariate discriminant is the logistic regression equation including Thr, Arg, Orn as the variables. To do.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step.
  • the discriminant value criterion evaluation step includes Based on the discriminant value calculated in the value calculating step, the degree of oxidative stress is determined for each evaluation target.
  • Ku and the possible antioxidant potential further comprises a discriminant value criterion discriminating step of discriminating which one of the normal and the oxidative stress level is high and the resistance to oxidation is low, characterized by.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method described above, wherein the multivariate discriminant is the logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as the variables. It is characterized by being.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln, Trp, and Glu, Ser, Pro, Asn , Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln, and Trp as the variables in the multivariate discriminant.
  • the discriminant value is calculated based on the discriminant value reference evaluation step. Based on the discriminant value calculated in step, for the evaluation object, any of a state in which the degree of oxidative stress is normal and the antioxidant power is low and a state in which the degree of oxidative stress is high and the antioxidant power is low.
  • the method further includes a discrimination value criterion discrimination step for discriminating whether or not.
  • the multivariate discriminant is the logistic regression equation including His, Ala, ABA, Orn, and Phe as the variables. It is characterized by.
  • the biooxidation evaluation method is the biooxidation evaluation method described above, wherein the discriminant value calculation step includes Glu, Ser, Pro, Asn, included in the amino acid concentration data acquired in the acquisition step.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method, wherein the multivariate discriminant is the logistic regression equation including Gln, Ala, Cit, Tyr, Met, Ile as the variable. It is characterized by being.
  • the biooxidation evaluation method is the biooxidation evaluation method, wherein the discriminant value calculation step includes Glu, Ser, Asn, Thr, included in the amino acid concentration data acquired in the acquisition step. At least one of the concentration values of Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit, and Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu , Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit based on the multivariate discriminant including at least one of the variables, and the discriminant value criterion evaluation step Is based on the discriminant value calculated in the discriminant value calculating step, Further comprising a discriminant value criterion discriminating step of discriminating which one of the oxidizing power is normal and the antioxidant capacity is low, characterized by.
  • the biooxidation evaluation method according to the present invention is the biooxidation evaluation method described above, wherein the multivariate discriminant is the logistic regression equation including Glu, Thr, Ala, Arg, Ile, Trp as the variables. It is characterized by being.
  • the biological oxidation evaluation apparatus is a biological oxidation evaluation apparatus that includes a control unit and a storage unit, and evaluates a state of biological oxidation including an oxidative stress level and / or an antioxidant power for an evaluation target.
  • the control means is based on the previously obtained amino acid concentration data of the evaluation object relating to the amino acid concentration value and the multivariate discriminant stored in the storage means including the amino acid concentration as a variable.
  • a discriminant value calculating unit that calculates a discriminant value that is a value of the discriminant; and a discriminant value reference evaluating unit that evaluates the state of biooxidation for the evaluation object based on the discriminant value calculated by the discriminant value calculating unit.
  • the control means includes the storage means including the amino acid concentration data and biological oxidation state index data relating to an index representing the state of biological oxidation.
  • the multivariate discriminant creation means for creating the multivariate discriminant stored in the storage means based on the biooxidation state information stored in the storage means, the multivariate discriminant creation means from the biooxidation state information
  • Candidate multivariate discriminant creation means for creating a candidate multivariate discriminant that is a candidate for the multivariate discriminant based on a predetermined formula creation method, and the candidate multivariate created by the candidate multivariate discriminant creation means
  • Candidate multivariate discriminant verification means for verifying a discriminant based on a predetermined verification technique, and based on a predetermined variable selection technique based on a verification result in the candidate multivariate discriminant verification means By selecting a variable of the candidate multivariate discriminant (however, the variable of the candidate multivariate discriminant may be selected
  • Variable selection means for selecting a combination of the amino acid concentration data included in the biooxidation state information used when creating the candidate multivariate discriminant, the candidate multivariate discriminant creating means, and the candidate multivariate
  • the candidate multivariate discriminant employed as the multivariate discriminant from among the plurality of candidate multivariate discriminants based on the verification results accumulated by repeatedly executing the variable discriminant verification unit and the variable selection unit.
  • the multivariate discriminant may be created by selecting.
  • the biological oxidation evaluation method evaluates the state of biological oxidation including the degree of oxidative stress and / or the antioxidant power for an evaluation object, which is executed in an information processing apparatus including a control unit and a storage unit.
  • a biooxidation evaluation method that is executed in the control means, the amino acid concentration data of the evaluation object acquired in advance relating to the amino acid concentration value, and the multivariate stored in the storage means including the amino acid concentration as a variable
  • a discriminant value calculating step that calculates a discriminant value that is the value of the multivariate discriminant, and based on the discriminant value calculated in the discriminant value calculating step, And a discriminant value criterion evaluation step for evaluating the state.
  • the biological oxidation evaluation program indicates the state of biological oxidation including the degree of oxidative stress and / or antioxidant power for an evaluation target to be executed by an information processing apparatus including a control unit and a storage unit.
  • a bio-oxidation evaluation program to be evaluated which is stored in the storage means including the amino acid concentration data of the evaluation target obtained in advance relating to the amino acid concentration value to be executed by the control means, and the amino acid concentration as a variable
  • a discriminant value calculating step for calculating a discriminant value that is a value of the multivariate discriminant, and based on the discriminant value calculated in the discriminant value calculating step
  • a discriminant value reference evaluation step for evaluating the state of oxidation.
  • a recording medium according to the present invention is a computer-readable recording medium, and is characterized in that the biooxidation evaluation program is recorded.
  • the biological oxidation evaluation system includes a control means and a storage means, and a biological oxidation evaluation apparatus that evaluates the state of biological oxidation including the degree of oxidative stress and / or antioxidant power for an evaluation object, and a control
  • a bio-oxidation evaluation system comprising: an information communication terminal device that provides the evaluation target amino acid concentration data relating to the amino acid concentration value, and is configured to be communicable via a network.
  • the control means of the terminal device includes an amino acid concentration data transmitting means for transmitting the amino acid concentration data to be evaluated to the biological oxidation evaluation apparatus, and the biological oxidation state evaluation transmitted from the biological oxidation evaluation apparatus.
  • Evaluation result receiving means for receiving the evaluation result of the evaluation target, and the control means of the biological oxidation evaluation apparatus is configured to receive the information communication.
  • Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the terminal device; the amino acid concentration data received by the amino acid concentration data receiving means; and the storage means stored in the storage means including the amino acid concentration as variables.
  • a discriminant value calculating unit that calculates a discriminant value that is a value of the multivariate discriminant, and based on the discriminant value calculated by the discriminant value calculating unit, A discriminant value criterion-evaluating unit that evaluates the state, and an evaluation result transmitting unit that transmits the evaluation result of the evaluation target in the discriminant value criterion-evaluating unit to the information communication terminal device.
  • the information communication terminal device is connected to a biological oxidation evaluation device that evaluates the state of biological oxidation including the degree of oxidative stress and / or antioxidant power for an evaluation target via a network
  • An information communication terminal device comprising a control means and providing the evaluation target amino acid concentration data relating to an amino acid concentration value, wherein the control means transmits the evaluation target amino acid concentration data to the biological oxidation evaluation device.
  • a device receives the amino acid concentration data transmitted from the information communication terminal device, and the received amino acid concentration data And based on the multivariate discriminant stored in the biooxidation evaluation apparatus including the concentration of the amino acid as a variable, a discriminant value that is a value of the multivariate discriminant is calculated, and based on the calculated discriminant value,
  • the evaluation target is a result of evaluating the state of biooxidation.
  • the biooxidation evaluation apparatus includes a control unit and a storage unit that are communicably connected via an information communication terminal device that provides amino acid concentration data to be evaluated regarding the amino acid concentration value.
  • a biooxidation evaluation apparatus that evaluates the state of biooxidation including the degree of oxidative stress and / or antioxidant power for the evaluation object, wherein the control means transmits the amino acid concentration data transmitted from the information communication terminal apparatus Based on the multivariate discriminant stored in the storage means including the amino acid concentration data receiving means, the amino acid concentration data received by the amino acid concentration data receiving means, and the amino acid concentration as a variable.
  • a discriminant value calculating means for calculating a discriminant value which is a discriminant value; and the discriminant value calculated by the discriminant value calculating means Therefore, for the evaluation object, a discriminant value criterion-evaluating unit that evaluates the state of biooxidation, and an evaluation result transmitting unit that transmits the evaluation result of the evaluation object in the discriminant value criterion-evaluating unit to the information communication terminal device It is characterized by having provided.
  • the method for searching for a substance for preventing / ameliorating biooxidation is amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation subject to which a desired substance group consisting of one or a plurality of substances is administered.
  • a concentration value reference evaluation step for evaluating the state of biological oxidation including the degree of oxidative stress and / or antioxidant power based on the amino acid concentration data acquired in the acquiring step.
  • amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation object is obtained, and based on the obtained amino acid concentration data of the evaluation object, the degree of oxidative stress and / or antioxidant is evaluated for the evaluation object. Evaluate the state of biooxidation including force. Thereby, there exists an effect that the state of biological oxidation can be accurately evaluated using the concentration of amino acids in blood.
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp Based on the concentration value of at least one of Arg, Gly, ABA, and Lys, the state of biological oxidation is evaluated for the evaluation target. This produces an effect that the state of biooxidation can be accurately evaluated using the concentration of amino acids related to the state of biooxidation in the concentration of amino acids in blood.
  • the present invention based on at least one concentration value among Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, and Phe included in the acquired amino acid concentration data.
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal and the antioxidant power is low, and oxidative stress. It is determined which of the at least three states among the states having high degrees and low antioxidant power.
  • the amino acid concentration useful for the 3-group discrimination or the 4-group discrimination between these states among the amino acid concentrations in the blood can be used, and this discrimination can be performed with high accuracy.
  • Based on one concentration value it is determined for each evaluation target whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is high and the antioxidant power is normal.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the evaluation target is either a state in which both the oxidative stress level and the antioxidant power are normal, or a state in which the oxidative stress level is normal and the antioxidant power is low. Is determined.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln included in the acquired amino acid concentration data.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp Based on the concentration value of at least one of Arg, whether the evaluation target is in a state where the oxidative stress level is high and the antioxidant power is normal or the oxidative stress level is normal and the antioxidant power is low Is determined.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • At least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg contained in the acquired amino acid concentration data. Based on one concentration value, it is determined whether the evaluation target is in a state where the degree of oxidative stress is high and the antioxidant power is normal or in a state where the degree of oxidative stress is high and the antioxidant power is low.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the evaluation target is a state in which the degree of oxidative stress is normal and low in antioxidant power and a state in which the degree of oxidative stress is high and low in antioxidant power It is determined whether it is.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the concentration value of at least one of Gly, Tyr, and ABA it is determined whether the oxidative stress level is normal or the oxidative stress level is high for the evaluation target.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit included in the acquired amino acid concentration data.
  • the evaluation target is determined as to whether the antioxidant power is normal or the antioxidant power is low.
  • the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood is utilized, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the discriminant value that is the value of the multivariate discriminant is calculated, and based on the calculated discriminant value.
  • the state of biological oxidation is evaluated for each evaluation target.
  • the biooxidation state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, a canonical discriminant.
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe included in the amino acid concentration data, and Glu is calculated based on a multivariate discriminant including at least one of Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, and Phe as a variable.
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal and the antioxidant power is low, and It is determined which of at least three states out of the states where the degree of oxidative stress is high and the antioxidant power is low.
  • a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable.
  • a discriminant value is calculated, and based on the calculated discriminant value, for each evaluation object, either the state of normal oxidative stress and antioxidative power or the state of high oxidative stress and normal antioxidative power are selected. Is determined.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Gln, Cit, Tyr, Met, Orn, Leu as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • a discriminant value is calculated, and based on the calculated discriminant value, both the oxidative stress level and the antioxidant power are normal and the oxidative stress level is normal for each evaluation target And in which the antioxidant power is low.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, and His are included in the amino acid concentration data.
  • At least one of the concentration values and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, and His are variables. Based on the multivariate discriminant included, the discriminant value is calculated, and on the basis of the calculated discriminant value, the oxidative stress level and the antioxidant power are both normal, the oxidative stress level is high, and the antioxidant power Is in a low state.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Ala, Cit, Tyr as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • the Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg included in the amino acid concentration data are also included.
  • At least one of the concentration values and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg are variables.
  • the discriminant value is calculated, and based on the calculated discriminant value, the state of high oxidative stress and normal antioxidant power and normal oxidative stress level and It is determined whether the state is low in oxidizing power.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Thr, Arg, Orn as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • At least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg included in the amino acid concentration data.
  • concentration value and multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg as a variable.
  • the discriminant value is calculated, and based on the calculated discriminant value, the evaluation object has either a high oxidative stress level and normal antioxidant power, or a high oxidative stress level and low antioxidative power. Is determined.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • At least one concentration value of Lys, Gln, Trp, and Glu Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Based on a multivariate discriminant including at least one of Lys, Gln, and Trp as a variable, a discriminant value is calculated, and based on the calculated discriminant value, the degree of oxidative stress is normal and the antioxidant power It is determined whether the state is low or the state of high oxidative stress and low antioxidant power.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including His, Ala, ABA, Orn, Phe as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • a discriminant value is calculated based on a multivariate discriminant including at least one of ABAs as a variable, and based on the calculated discriminant value, a state in which the oxidative stress level is normal and the oxidative stress level is high for each evaluation target Is determined.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • At least one of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit included in the amino acid concentration data.
  • Multivariate including one concentration value and at least one of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit as variables.
  • a discriminant value is calculated, and based on the calculated discriminant value, it is determined whether the evaluation target is in a normal state of antioxidant power or a state of low antioxidant power.
  • the discriminant value obtained by the multivariate discriminant useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed with high accuracy.
  • the multivariate discriminant is a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables.
  • the discriminant value obtained by the multivariate discriminant that is particularly useful for discriminating between the two groups between these states is used, and this has the effect that the two-group discrimination can be performed more accurately.
  • the multivariate discriminant stored in the storage means based on the biological oxidation state information stored in the storage means including the amino acid concentration data and the biological oxidation state index data relating to the index representing the state of biological oxidation. May be created. Specifically, (1) a candidate multivariate discriminant is created from biological oxidation state information based on a predetermined formula creation method, and (2) the created candidate multivariate discriminant is verified based on a predetermined verification method.
  • a discriminant variable may be selected.
  • a combination of amino acid concentration data included in the bio-oxidation state information used when creating a candidate multivariate discriminant is selected, and (4) (1), (2) Based on the verification results accumulated by repeatedly executing (3) and (3), by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from among a plurality of candidate multivariate discriminants, You may create it. Thereby, there exists an effect that the multivariate discriminant most suitable for the state evaluation of biooxidation can be created.
  • the computer since the biooxidation evaluation program recorded on the recording medium is read and executed by the computer, the computer executes the biooxidation evaluation program, so that the same effect as described above can be obtained. There is an effect that can be done.
  • amino acid concentration data relating to amino acid concentration values collected from an evaluation subject to which a desired substance group consisting of one or a plurality of substances is administered is obtained, and the obtained amino acid concentration data is obtained.
  • the state of biooxidation including the degree of oxidative stress and / or antioxidative power is evaluated for the evaluation target, and the desired substance group prevents biooxidation or improves the state of biooxidation based on the evaluation result Therefore, it is possible to prevent bio-oxidation using a bio-oxidation evaluation method that can accurately evaluate the state of bio-oxidation using the concentration of amino acids in blood. There is an effect that a substance that improves the oxidation state can be searched with high accuracy.
  • an existing animal model partially reflecting the state of biooxidation by using information on typical amino acid concentration fluctuation patterns in biooxidation and multivariate discriminants corresponding to biooxidation
  • the present invention when evaluating the state of biological oxidation, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as sugars, lipids, proteins, peptides, minerals, hormones, (Value, sex, age, liver disease index, eating habits, drinking habits, exercise habits, obesity, disease history, etc.) may be further used.
  • the present invention also provides other biological information (for example, biological metabolism such as sugars, lipids, proteins, peptides, minerals, hormones, etc.) in addition to the concentration of amino acids as variables in the multivariate discriminant when evaluating the state of biological oxidation. For example, blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc.).
  • FIG. 1 is a principle configuration diagram showing the basic principle of the present invention.
  • FIG. 2 is a flowchart illustrating an example of a biooxidation evaluation method according to the first embodiment.
  • FIG. 3 is a principle configuration diagram showing the basic principle of the present invention.
  • FIG. 4 is a diagram illustrating an example of the overall configuration of the present system.
  • FIG. 5 is a diagram showing another example of the overall configuration of the present system.
  • FIG. 6 is a block diagram showing an example of the configuration of the biological oxidation evaluation apparatus 100 of the present system.
  • FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a.
  • FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b.
  • FIG. 9 is a diagram illustrating an example of information stored in the biological oxidation state information file 106c.
  • FIG. 10 is a diagram illustrating an example of information stored in the designated biological oxidation state information file 106d.
  • FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1.
  • FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2.
  • FIG. 13 is a diagram illustrating an example of information stored in the selected biological oxidation state information file 106e3.
  • FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4.
  • FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f.
  • FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g.
  • FIG. 17 is a block diagram showing a configuration of the multivariate discriminant-preparing part 102h.
  • FIG. 18 is a block diagram illustrating a configuration of the discriminant value criterion-evaluating unit 102j.
  • FIG. 19 is a block diagram illustrating an example of the configuration of the client apparatus 200 of the present system.
  • FIG. 20 is a block diagram showing an example of the configuration of the database apparatus 400 of this system.
  • FIG. 21 is a flowchart showing an example of the biooxidation evaluation service process performed in the present system.
  • FIG. 22 is a flowchart showing an example of multivariate discriminant creation processing performed by the biological oxidation evaluation apparatus 100 of the present system.
  • FIG. 23 is a principle configuration diagram showing the basic principle of the present invention.
  • FIG. 24 is a flowchart showing an example of a method for searching for a biooxidation prevention / ameliorating substance according to the third embodiment.
  • FIG. 25 is a diagram showing the distribution of amino acid variables among the four groups.
  • FIG. 26 is a diagram showing a list of logistic regression equations with equally good discrimination between the healthy group and the second group.
  • FIG. 27 is a diagram showing a list of logistic regression equations with equally good discrimination between the healthy group and the second group.
  • FIG. 28 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the second group.
  • FIG. 29 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the second group.
  • FIG. 30 is a diagram showing a list of logistic regression equations with equally good discrimination between the healthy group and the third group.
  • FIG. 31 is a diagram showing a list of logistic regression equations with equally good discrimination between the healthy group and the third group.
  • FIG. 32 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the third group.
  • FIG. 33 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the third group.
  • FIG. 34 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the healthy group and the fourth group.
  • FIG. 35 is a diagram showing a list of logistic regression equations with equally good discrimination between the healthy group and the fourth group.
  • FIG. 36 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the fourth group.
  • FIG. 37 is a diagram showing a list of fractional expressions with equally good discrimination ability between the healthy group and the fourth group.
  • FIG. 38 is a diagram showing a list of logistic regression equations with equally good discrimination performance between the second group and the third group.
  • FIG. 39 is a diagram showing a list of logistic regression equations with equally good discrimination performance between the second group and the third group.
  • FIG. 40 is a diagram showing a list of fractional expressions with equally good discrimination performance between the second group and the third group.
  • FIG. 41 is a diagram showing a list of fractional expressions with equally good discrimination ability between the second group and the third group.
  • FIG. 42 is a diagram showing a list of logistic regression equations with equally good discrimination performance between the second group and the fourth group.
  • FIG. 43 is a diagram showing a list of logistic regression equations with equally good discrimination performance between the second group and the fourth group.
  • FIG. 44 is a diagram showing a list of fractional expressions with equally good discrimination ability between the second group and the fourth group.
  • FIG. 45 is a diagram showing a list of fractional expressions with equally good discrimination ability between the second group and the fourth group.
  • FIG. 46 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the third group and the fourth group.
  • FIG. 47 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the third group and the fourth group.
  • FIG. 48 is a diagram showing a list of fractional expressions with equally good discrimination ability between the third group and the fourth group.
  • FIG. 49 is a diagram showing a list of fractional expressions with equally good discrimination ability between the third group and the fourth group.
  • FIG. 50 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the thirteenth group and the twenty-fourth group.
  • FIG. 51 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the thirteenth group and the twenty-fourth group.
  • FIG. 52 is a diagram showing a list of fractional expressions with equally good discrimination ability between the thirteenth group and the twenty-fourth group.
  • FIG. 53 is a diagram showing a list of fractional expressions with equally good discrimination ability between the thirteenth group and the twenty-fourth group.
  • FIG. 54 is a diagram showing a list of logistic regression equations with equally good discrimination performance between the 12th group and the 34th group.
  • FIG. 55 is a diagram showing a list of logistic regression equations with equally good discrimination ability between the 12th group and the 34th group.
  • FIG. 56 is a diagram showing a list of fractional expressions with equally good discrimination ability between the 12th group and the 34th group.
  • FIG. 57 is a diagram showing a list of fractional expressions with equally good discrimination ability between the 12th group and the 34th group.
  • an embodiment of a biooxidation evaluation method according to the present invention (first embodiment), a biooxidation evaluation apparatus, a biooxidation evaluation method, a biooxidation evaluation program, a recording medium, a biooxidation evaluation system according to the present invention, Embodiment of the information communication terminal device (second embodiment) and the embodiment of the biooxidation prevention / amelioration substance search method (third embodiment) according to the present invention will be described in detail with reference to the drawings. To do. In addition, this invention is not limited by this Embodiment.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the present invention.
  • amino acid concentration data relating to the concentration value of amino acids in blood (eg, including plasma, serum, etc.) collected from an evaluation target is acquired (step S11).
  • amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired.
  • the following (A) or (B) may be obtained from blood collected from an evaluation target.
  • Amino acid concentration data may be obtained by measuring amino acid concentration data by a measurement method.
  • the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
  • LC-MS liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC-MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
  • amino acid concentration When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • step S12 based on the amino acid concentration data acquired in step S11, the state of biological oxidation including the degree of oxidative stress and / or antioxidant power is evaluated for the evaluation target (step S12).
  • amino acid concentration data relating to the concentration value of amino acids in blood collected from an evaluation object is obtained, and based on the obtained amino acid concentration data of the evaluation object, the degree of oxidative stress and / or Evaluate the state of bio-oxidation including antioxidant power. Thereby, the state of biological oxidation can be accurately evaluated using the concentration of amino acids in blood.
  • step S12 data such as missing values and outliers may be removed from the amino acid concentration data acquired in step S11. Thereby, the state of biological oxidation can be evaluated more accurately.
  • step S12 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp included in the amino acid concentration data acquired in step S11. , Arg, Gly, ABA, Lys, based on at least one concentration value, the state of biological oxidation may be evaluated for the evaluation target. Thereby, the state of biological oxidation can be accurately evaluated using the concentration of amino acids related to the state of biological oxidation among the concentrations of amino acids in blood.
  • Oxidative stress level and antioxidant power are both normal, oxidative stress level is high and normal antioxidant power level is normal, oxidative stress level is normal and low antioxidant power level is high, and oxidative stress level is high and anti-oxidative power level is high. It may be determined which of at least three of the states having low oxidizing power is.
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln included in the amino acid concentration data is used. Based on the evaluation target, it may be determined whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is high and the antioxidant power is normal. This makes it possible to accurately perform the two-group discrimination by using the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood.
  • concentration value of at least one of Trp whether the oxidative stress level is normal and the antioxidant power level is low or the oxidative stress level is high and the antioxidant power level is low May be determined. This makes it possible to accurately perform the two-group discrimination by using the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood.
  • Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA are included in the amino acid concentration data.
  • step S12 based on the amino acid concentration data acquired in step S11 and the preset multivariate discriminant including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated. Based on the discriminated value, the state of biological oxidation may be evaluated for the evaluation target. Thereby, the state of biooxidation can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. Thereby, the biooxidation state can be evaluated with higher accuracy by using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • step S12 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp included in the amino acid concentration data acquired in step S11.
  • Arg, Gly, ABA, Lys at least one concentration value
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp Arg, Gly, ABA, Lys
  • a discriminant value is calculated based on a multivariate discriminant including at least one of them as a variable, and the state of biooxidation is evaluated for the evaluation object based on the calculated discriminant value. May be. Thereby, the biooxidation state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the biooxidation state.
  • the discriminant value is calculated, and based on the calculated discriminant value
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal and the antioxidant power is low, and the oxidative stress level. It may be determined which of the at least three states among the states having high and low antioxidant power. Thereby, these discrimination
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln included in the amino acid concentration data Discrimination based on a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable
  • the value is calculated, and based on the calculated discriminant value, whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is normal for the evaluation target May be determined.
  • the multivariate discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • the discriminant value is calculated, and on the basis of the calculated discriminant value, the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is normal and It may be determined which state is low in oxidizing power.
  • the multivariate discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • At least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg included in the amino acid concentration data.
  • a plurality of concentration values and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg as variables.
  • the discriminant value is calculated, and on the basis of the calculated discriminant value, the evaluation target has a high oxidative stress level and a normal antioxidant power, and a normal oxidative stress level and an antioxidant power.
  • the multivariate discriminant may be a logistic regression equation including Thr, Arg, and Orn as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg included in the amino acid concentration data Discrimination based on a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg as a variable
  • the value is calculated, and based on the calculated discriminant value, whether the evaluation target is in a state of high oxidative stress and normal antioxidant power, or a state of high oxidative stress and low antioxidant power May be determined.
  • the multivariate discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Trp at least one concentration value, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln , Trp calculates a discriminant value based on a multivariate discriminant including at least one of the variables as a variable, and based on the calculated discriminant value, a state in which the degree of oxidative stress is normal and the antioxidant power is low It may also be determined whether the state is high in oxidative stress and low in antioxidant power. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including His, Ala, ABA, Orn, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA are included in the amino acid concentration data.
  • a discriminant value is calculated, and based on the calculated discriminant value, a state in which the oxidative stress level is normal and the oxidative stress level is high for each evaluation target You may determine which is. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Multivariate discriminant including a value and at least one of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit as a variable
  • the discriminant value may be determined based on the calculated discriminant whether the antioxidant power is normal or the antioxidant power is low.
  • the multivariate discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may produce by the method (The multivariate discriminant creation process as described in 2nd Embodiment mentioned later). If the multivariate discriminant obtained by these methods is used, it is preferable to use the multivariate discriminant for biooxidation state evaluation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. it can.
  • the multivariate discriminant generally means the format of formulas used in multivariate analysis. For example, fractional formulas, multiple regression formulas, multiple logistic regression formulas, linear discriminant functions, Mahalanobis distances, canonical discriminant functions, support vectors Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants.
  • a coefficient and a constant term are added to each variable. In this case, the coefficient and the constant term are preferably real numbers, more preferably data.
  • each coefficient and its confidence interval may be obtained by multiplying it by a real number
  • the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / dividing an arbitrary real constant thereto.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression.
  • An appropriate coefficient may be added to each amino acid used in the numerator and denominator.
  • amino acids used in the numerator and denominator may overlap.
  • an appropriate coefficient may be attached to each fractional expression.
  • the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.
  • this invention evaluates the state of biological oxidation, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as saccharides, lipids, proteins, peptides, minerals, hormones, (Value, sex, age, liver disease index, eating habits, drinking habits, exercise habits, obesity, disease history, etc.) may be further used.
  • the present invention also provides other biological information (for example, biological metabolism such as sugars, lipids, proteins, peptides, minerals, hormones, etc.) in addition to the concentration of amino acids as variables in the multivariate discriminant when evaluating the state of biological oxidation. For example, blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc.).
  • FIG. 2 is a flowchart illustrating an example of a biooxidation evaluation method according to the first embodiment.
  • amino acid concentration data relating to the concentration value of amino acids in blood collected from individuals such as animals and humans is acquired (step SA11).
  • step SA11 amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired, and measurement such as (A) or (B) described above is performed from blood collected from an evaluation target.
  • Amino acid concentration data may be obtained by measuring amino acid concentration data by a method.
  • step SA12 data such as missing values and outliers are removed from the amino acid concentration data of the individual obtained in step SA11 (step SA12).
  • step SA13 based on the amino acid concentration data of individuals from which data such as missing values and outliers have been removed in step SA12, the following is shown for each individual: To 19. Any one of these determinations is executed (step SA13).
  • Any of at least three states of a state having a high degree of normality and an antioxidant power, a state having a normal degree of oxidative stress and a low level of antioxidant power, and a state having a high degree of oxidative stress and a low level of antioxidant power Is determined.
  • Second Group Discrimination Regarding Oxidative Stress Level and Antioxidant Power (i) Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr included in amino acid concentration data , Trp, Arg, Gly, His, Gln, a state where both the oxidative stress level and the antioxidant power are normal for each individual by comparing at least one concentration value with a preset threshold value (cut-off value) And (ii) Glu, Ser, Pro, Asn, Ala, Thr, Cit, which are included in the amino acid concentration data.
  • At least one of Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln Concentration value and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln as variables
  • the discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cutoff value). Whether the state and the degree of oxidative stress are normal and the state of low antioxidant power is determined.
  • the discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cut-off value), so that the individual has a high degree of oxidative stress and normal antioxidant power and oxidative stress. It is determined whether the state is normal and the antioxidant power is low.
  • oxidative stress level and antioxidant power (i) Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His included in amino acid concentration data , Gln, Arg, and a threshold value (cut-off value) set in advance, the individual has a high oxidative stress level and a normal antioxidative power level and an oxidative stress level.
  • a discriminant value is calculated based on a multivariate discriminant including at least one of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg as a variable, and the calculated discriminant value Is compared with a preset threshold value (cut-off value), so that an individual has a high degree of oxidative stress and a normal level of antioxidant power and a high level of oxidative stress and a low level of antioxidant power. Determine which is.
  • a discriminant value is calculated based on a multivariate discriminant including at least one as a variable, and the calculated discriminant value and a preset threshold value (cutoff value) To determine whether the individual has a normal antioxidant power or a low antioxidant power.
  • the multivariate discriminant used in step SA13 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, and a canonical discriminant. Any one of an expression created by analysis and an expression created by a decision tree may be used. Thereby, these discrimination
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables. Thereby, the above-mentioned 12. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables. Thereby, the above-mentioned 13. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables. Thereby, the above-mentioned 14. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in discriminating the above may be a logistic regression equation including Thr, Arg, Orn as variables. Thereby, the above-mentioned 15. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables. As a result, the above-described 16. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in discriminating the above may be a logistic regression equation including His, Ala, ABA, Orn, Phe as variables. As a result, the above-described 17. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables. As a result, the above-described 18. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables. Thereby, the above-described 19. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described in the second embodiment to be described later). If the multivariate discriminant obtained by these methods is used, it is preferable to use the multivariate discriminant for biooxidation state evaluation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. it can.
  • FIG. 3 is a principle configuration diagram showing the basic principle of the present invention.
  • control unit obtains the amino acid concentration data of the evaluation target (for example, an individual such as an animal or a human) previously obtained with respect to the amino acid concentration value, and the multivariate discriminant stored in the storage unit for varying the amino acid concentration. Based on, a discriminant value that is the value of the multivariate discriminant is calculated (step S21).
  • the evaluation target for example, an individual such as an animal or a human
  • control unit evaluates the state of biological oxidation including the degree of oxidative stress and / or antioxidant power for the evaluation object based on the discriminant value calculated in step S21 (step S22).
  • the discriminant value that is the value of the multivariate discriminant is calculated, and the calculated discriminant value Based on the above, the state of biological oxidation including the degree of oxidative stress and / or antioxidant power is evaluated for each evaluation object. Thereby, the state of biooxidation can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. Thereby, the biooxidation state can be evaluated with higher accuracy by using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • step S21 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, Concentration value of at least one of ABA and Lys, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, Based on a multivariate discriminant including at least one of ABA and Lys as a variable, a discriminant value is calculated.
  • step S22 the state of biological oxidation is evaluated for the evaluation object based on the discriminant value calculated in step S21. You may evaluate. Thereby, the biooxidation state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the biooxidation state.
  • step S21 at least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe included in the amino acid concentration data, and Glu , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, and a discriminant value is calculated based on a multivariate discriminant including at least one as a variable.
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal, and At least three states among a state with low antioxidant power and a state with high degree of oxidative stress and low antioxidant power Which of out may be determined.
  • determination can be accurately performed using the discriminant value obtained by the multivariate discriminant useful for 3 group discrimination or 4 group discrimination between these states.
  • step S21 at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, and Gln included in the amino acid concentration data.
  • a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable.
  • the judgment value is calculated based on the discriminant value calculated in step S21.
  • the oxidative stress level and the antioxidant power are both normal, the oxidative stress level is high, and the antioxidant power is evaluated. It may be determined which is in a normal state.
  • the multivariate discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His included in the amino acid concentration data. , Gln, and at least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln
  • a discriminant value is calculated based on a multivariate discriminant including at least one as a variable.
  • step S22 both the degree of oxidative stress and the antioxidant power are normal for the evaluation object based on the discriminant value calculated in step S21.
  • the multivariate discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His included in the amino acid concentration data. At least one concentration value, and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His included in the amino acid concentration data. At least one concentration value, and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His.
  • a discriminant value is calculated, and in step S22, based on the discriminant value calculated in step S21, a state in which both the oxidative stress level and the antioxidant power are normal and oxidized It may be determined whether the stress level is high and the antioxidant power is low.
  • the multivariate discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables.
  • step S21 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg included in the amino acid concentration data. And at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg. Based on the multivariate discriminant included as a variable, a discriminant value is calculated.
  • step S22 based on the discriminant value calculated in step S21, a state in which the degree of oxidative stress is high and the antioxidant power is normal, You can determine whether the level of oxidative stress is normal and the antioxidant capacity is low .
  • the multivariate discriminant may be a logistic regression equation including Thr, Arg, and Orn as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg included in the amino acid concentration data.
  • a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg as a variable. Based on the discriminant value calculated in step S21, the discriminant value is calculated based on the discriminant value calculated in step S21. It may be determined whether the force is low.
  • the multivariate discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA included in the amino acid concentration data.
  • Lys, Gln, Trp at least one concentration value
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA Lys, Gln, Trp
  • a discriminant value is calculated based on a multivariate discriminant including at least one of them as a variable.
  • step S22 oxidative stress is evaluated for each evaluation object based on the discriminant value calculated in step S21.
  • Normal level and low antioxidant capacity, high oxidative stress level and antioxidant capacity It may determine which one of the low state. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including His, Ala, ABA, Orn, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly included in the amino acid concentration data.
  • Tyr, ABA at least one concentration value
  • Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr , ABA, and a discriminant value is calculated based on a multivariate discriminant including at least one of ABA as a variable.
  • step S22 the degree of oxidative stress is normal for the evaluation object based on the discriminant value calculated in step S21. It may be determined which of the state and the state where the degree of oxidative stress is high. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • step S21 among Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit included in the amino acid concentration data.
  • a multiplicity including at least one concentration value and at least one of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit as variables.
  • a discriminant value is calculated, and in step S22, based on the discriminant value calculated in step S21, for each evaluation target, either the state of normal antioxidant power or the state of low antioxidant power is selected. It may be determined whether or not.
  • the multivariate discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by a method (multivariate discriminant creation process described later). If the multivariate discriminant obtained by these methods is used, it is preferable to use the multivariate discriminant for biooxidation state evaluation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. it can.
  • the multivariate discriminant generally means the format of formulas used in multivariate analysis. For example, fractional formulas, multiple regression formulas, multiple logistic regression formulas, linear discriminant functions, Mahalanobis distances, canonical discriminant functions, support vectors Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants.
  • a coefficient and a constant term are added to each variable. In this case, the coefficient and the constant term are preferably real numbers, more preferably data.
  • each coefficient and its confidence interval may be obtained by multiplying it by a real number
  • the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / dividing an arbitrary real constant thereto.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression.
  • An appropriate coefficient may be added to each amino acid used in the numerator and denominator.
  • amino acids used in the numerator and denominator may overlap.
  • an appropriate coefficient may be attached to each fractional expression.
  • the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.
  • this invention evaluates the state of biological oxidation, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as saccharides, lipids, proteins, peptides, minerals, hormones, (Value, sex, age, liver disease index, eating habits, drinking habits, exercise habits, obesity, disease history, etc.) may be further used.
  • the present invention also provides other biological information (for example, biological metabolism such as sugars, lipids, proteins, peptides, minerals, hormones, etc.) in addition to the concentration of amino acids as variables in the multivariate discriminant when evaluating the state of biological oxidation. For example, blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc.).
  • step 1 to step 4 the outline of the multivariate discriminant creation process (step 1 to step 4) will be described in detail. Note that the processing described here is merely an example, and the method of creating the multivariate discriminant is not limited to this.
  • the present invention is based on a predetermined formula creation method based on a predetermined formula creation method from biological oxidation state information stored in a storage unit including amino acid concentration data and biological oxidation state index data relating to an index representing the state of biological oxidation.
  • Step 1 a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree, etc.) are obtained from biological oxidation state information.
  • a plurality of candidate multivariate discriminants may be created using a combination of those related to multivariate analysis. Specifically, many healthy groups (specifically, groups in which both oxidative stress level and antioxidant power are normal) and biological oxidative groups (specifically, at least one of the oxidative stress level and antioxidant power is normal)
  • the biooxidation state information which is multivariate data composed of amino acid concentration data and biooxidation state index data obtained by analyzing blood obtained from non-groups), is obtained by using a plurality of different algorithms.
  • Candidate multivariate discriminants may be created concurrently. For example, two different candidate multivariate discriminants may be created by performing discriminant analysis and logistic regression analysis simultaneously using different algorithms.
  • the candidate multivariate discriminant created by performing principal component analysis is converted to bio-oxidation state information, and discriminant analysis is performed on the converted bio-oxidation state information to create a candidate multivariate discriminant. May be. Thereby, finally, an appropriate multivariate discriminant suitable for the diagnostic condition can be created.
  • the candidate multivariate discriminant created using principal component analysis is a linear expression composed of amino acid variables that maximizes the variance of all amino acid concentration data.
  • the candidate multivariate discriminant created using discriminant analysis is a higher-order formula (index or Including logarithm).
  • the candidate multivariate discriminant created using the support vector machine is a higher-order formula (including a kernel function) made up of amino acid variables that maximizes the boundary between groups.
  • the candidate multivariate discriminant created using multiple regression analysis is a higher-order expression composed of amino acid variables that minimizes the sum of distances from all amino acid concentration data.
  • a candidate multivariate discriminant created using logistic regression analysis is a fractional expression having a natural logarithm as a term, which is a linear expression composed of amino acid variables that maximize the likelihood.
  • the k-means method searches k neighborhoods of each amino acid concentration data, defines the largest group among the groups to which the neighboring points belong as the group to which the data belongs, This is a method of selecting an amino acid variable that best matches the group to which the group belongs.
  • Cluster analysis is a method of clustering (grouping) points that are closest to each other in all amino acid concentration data.
  • the decision tree is a technique for predicting a group of amino acid concentration data based on patterns that can be taken by amino acid variables having higher ranks by adding ranks to amino acid variables.
  • the present invention verifies (mutually verifies) the candidate multivariate discriminant created in step 1 based on a predetermined verification method in the control unit (step 2).
  • the candidate multivariate discriminant is verified for each candidate multivariate discriminant created in step 1.
  • step 2 the discrimination rate, sensitivity, specificity, information criterion of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, leave one out method, etc.
  • the verification may be performed on at least one of ROC_AUC (area under the curve of the receiver characteristic curve) and the like.
  • the discrimination rate is the ratio of the state of biological oxidation evaluated by the present invention in the total input data.
  • Sensitivity is the correct ratio of the state of biooxidation evaluated in the present invention in the state of biooxidation described in the input data.
  • the specificity is a ratio in which the state of biological oxidation evaluated in the present invention is correct among those in which the state of biological oxidation described in the input data is normal.
  • the information criterion is the number of amino acid variables in the candidate multivariate discriminant created in step 1 and the difference in the state of biooxidation evaluated in the present invention and the state of biooxidation described in the input data. It is an addition.
  • ROC_AUC area under the curve of the receiver characteristic curve
  • ROC receiver characteristic curve
  • the value of ROC_AUC is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability.
  • the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of the candidate multivariate discriminant.
  • Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate multivariate discriminants.
  • the present invention allows the control unit to select a candidate multivariate discriminant variable from the verification result in step 2 based on a predetermined variable selection method (however, the process 2), the variable of the candidate multivariate discriminant may be selected based on a predetermined variable selection method without considering the verification result in 2), and the biological oxidation state information used when creating the candidate multivariate discriminant A combination of amino acid concentration data included is selected (step 3). Amino acid variables are selected for each candidate multivariate discriminant created in step 1. Thereby, the amino acid variable of a candidate multivariate discriminant can be selected appropriately. Then, Step 1 is executed again using the biological oxidation state information including the amino acid concentration data selected in Step 3.
  • step 3 the amino acid variable of the candidate multivariate discriminant may be selected from the verification result in step 2 based on at least one of stepwise method, best path method, neighborhood search method, and genetic algorithm. .
  • the best path method is a method of selecting amino acid variables by sequentially reducing amino acid variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant. is there.
  • the present invention repeatedly executes the above-described step 1, step 2 and step 3 in the control unit, and a plurality of candidate multivariate discriminants based on the verification results accumulated thereby.
  • a multivariate discriminant is created by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from the equations (step 4).
  • candidate multivariate discriminants for example, selecting the optimal one from among candidate multivariate discriminants created by the same formula creation method, and selecting the optimum from all candidate multivariate discriminants Sometimes there is a choice.
  • the multivariate discriminant creation process processing related to creation of a candidate multivariate discriminant, verification of the candidate multivariate discriminant, and selection of a variable of the candidate multivariate discriminant based on the biological oxidation state information Is systematized (systematized) in a series of flows, and a multivariate discriminant optimum for biooxidation state evaluation can be created.
  • the amino acid concentration is used for multivariate statistical analysis, and the variable selection method and cross-validation are combined to select the optimal and robust variable set. Extract the variable discriminant.
  • logistic regression, linear discrimination, support vector machine, Mahalanobis distance method, multiple regression analysis, cluster analysis, and the like can be used.
  • FIG. 4 is a diagram showing an example of the overall configuration of the present system.
  • FIG. 5 is a diagram showing another example of the overall configuration of the present system.
  • the system includes a biological oxidation evaluation apparatus 100 that performs a biological oxidation state evaluation for an evaluation target, and a client apparatus 200 that provides amino acid concentration data of an evaluation target related to an amino acid concentration value (of the present invention). And an information communication terminal device) are communicably connected via the network 300.
  • the present system uses biological oxidation state information and biological oxidation used when creating a multivariate discriminant in the biological oxidation evaluation apparatus 100.
  • the database apparatus 400 storing the multivariate discriminant used for performing the state evaluation may be configured to be communicably connected via the network 300.
  • information on the state of biological oxidation is provided from the biological oxidation evaluation apparatus 100 to the client apparatus 200 and the database apparatus 400, or from the client apparatus 200 and database apparatus 400 to the biological oxidation evaluation apparatus 100 via the network 300.
  • the information on the state of biooxidation is information on values measured for specific items (specifically, the degree of oxidative stress, antioxidative power, etc.) on the state of biooxidation of organisms including humans.
  • information on the state of biooxidation is generated by the biooxidation evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measurement apparatuses) and is mainly stored in the database apparatus 400.
  • FIG. 6 is a block diagram showing an example of the configuration of the biological oxidation evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the bio-oxidation evaluation apparatus 100 includes a control unit 102 such as a CPU that comprehensively controls the bio-oxidation evaluation apparatus, a communication apparatus such as a router, and a wired or wireless communication line such as a dedicated line.
  • a communication interface unit 104 that connects to the network 300 in a communicable manner, a storage unit 106 that stores various databases, tables, files, and the like, and an input / output interface unit 108 that connects to the input device 112 and the output device 114
  • these units are communicably connected via an arbitrary communication path.
  • the biological oxidation evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analyzer or the like).
  • the specific form of dispersion / integration of the biooxidation evaluation apparatus 100 is not limited to the illustrated one, and all or a part thereof is functional in arbitrary units according to various additions or according to functional load. Or it can be physically distributed and integrated.
  • the embodiments of this specification may be implemented in any combination, and the embodiments may be selectively implemented.
  • a part of the processing may be realized using CGI (Common Gateway Interface).
  • the storage unit 106 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System).
  • the storage unit 106 includes a user information file 106a, an amino acid concentration data file 106b, a biooxidation state information file 106c, a designated biooxidation state information file 106d, a multivariate discriminant-related information database 106e, The discriminant value file 106f and the evaluation result file 106g are stored.
  • the user information file 106a stores user information related to users.
  • FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a.
  • the information stored in the user information file 106a includes a user ID for uniquely identifying a user and authentication for whether or not the user is a valid person.
  • the amino acid concentration data file 106b stores amino acid concentration data relating to amino acid concentration values.
  • FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b.
  • the information stored in the amino acid concentration data file 106b is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with amino acid concentration data. Yes.
  • the amino acid concentration data is treated as a numerical value, that is, a continuous scale, but the amino acid concentration data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • amino acid concentration data includes other biological information (for example, biological metabolites such as sugars, lipids, proteins, peptides, minerals, hormones, etc. You may combine biomarkers such as habits, exercise habits, obesity levels, and disease histories.
  • the bio-oxidation state information file 106c stores bio-oxidation state information used when creating a multivariate discriminant.
  • FIG. 9 is a diagram illustrating an example of information stored in the biological oxidation state information file 106c.
  • the information stored in the bio-oxidation state information file 106c includes bio-oxidation related to individual numbers and indices (index T 1 , index T 2 , index T 3 ...) Representing the state of bio-oxidation.
  • State index data (T) and amino acid concentration data are associated with each other.
  • the biological oxidation state index data and the amino acid concentration data are treated as numerical values (that is, a continuous scale), but the biological oxidation state index data and the amino acid concentration data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • the biooxidation state index data is a known single state index serving as a biooxidation state marker, and numerical data may be used.
  • the designated biological oxidation state information file 106d stores the biological oxidation state information designated by the biological oxidation state information designation unit 102g described later.
  • FIG. 10 is a diagram illustrating an example of information stored in the designated biological oxidation state information file 106d. As shown in FIG. 10, the information stored in the designated biological oxidation state information file 106d is configured by associating an individual number, designated biological oxidation state index data, and designated amino acid concentration data with each other. .
  • the multivariate discriminant-related information database 106e includes a candidate multivariate discriminant file 106e1 for storing the candidate multivariate discriminant created by the candidate multivariate discriminant-preparing part 102h1, which will be described later, and a candidate multivariate discriminant described later.
  • a verification result file 106e2 for storing a verification result in the discriminant verification unit 102h2
  • a selected bio-oxidation state information file 106e3 for storing bio-oxidation state information including a combination of amino acid concentration data selected by a variable selection unit 102h3 described later
  • a multivariate discriminant file 106e4 for storing the multivariate discriminant created by the multivariate discriminant-preparing part 102h described later.
  • the candidate multivariate discriminant file 106e1 stores the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 described later.
  • FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1.
  • information stored in the candidate multivariate discriminant file 106e1 includes a rank, a candidate multivariate discriminant (in FIG. 11, F 1 (Gly, Leu, Phe,%)) And F 2. (Gly, Leu, Phe,%), F 3 (Gly, Leu, Phe,...)) Are associated with each other.
  • FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2.
  • the information stored in the verification result file 106e2 includes rank, candidate multivariate discriminant (in FIG. 12, F k (Gly, Leu, Phe,%) And F m (Gly, Le, Phe,%), Fl (Gly, Leu, Phe, etc) And the verification results of each candidate multivariate discriminant (for example, the evaluation value of each candidate multivariate discriminant). They are related to each other.
  • the selected biological oxidation state information file 106e3 stores biological oxidation state information including a combination of amino acid concentration data corresponding to variables selected by the variable selection unit 102h3 described later.
  • FIG. 13 is a diagram illustrating an example of information stored in the selected biological oxidation state information file 106e3. As shown in FIG. 13, information stored in the selected bio-oxidation state information file 106e3 includes an individual number, bio-oxidation state index data designated by a bio-oxidation state information designating unit 102g described later, and a variable selection unit 102h3 described later. And the amino acid concentration data selected in (1) are associated with each other.
  • the multivariate discriminant file 106e4 stores the multivariate discriminant created by the multivariate discriminant-preparing part 102h described later.
  • FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4.
  • the information stored in the multivariate discriminant file 106e4 includes the rank, the multivariate discriminant (in FIG. 14, F p (Phe,%) And F p (Gly, Leu, Phe). ), F k (Gly, Leu, Phe,...)), A threshold corresponding to each formula creation method, a verification result of each multivariate discriminant (for example, an evaluation value of each multivariate discriminant), Are related to each other.
  • the discriminant value file 106f stores the discriminant value calculated by the discriminant value calculator 102i described later.
  • FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f. As shown in FIG. 15, information stored in the discriminant value file 106f includes an individual number for uniquely identifying an individual (sample) to be evaluated and a rank (for uniquely identifying a multivariate discriminant). Number) and the discriminant value are associated with each other.
  • the evaluation result file 106g stores an evaluation result in a discriminant value criterion-evaluating unit 102j described later (specifically, a discrimination result in a discriminant value criterion-discriminating unit 102j1 described later).
  • FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g.
  • Information stored in the evaluation result file 106g includes an individual number for uniquely identifying an individual (sample) to be evaluated, amino acid concentration data of the evaluation target acquired in advance, and a discriminant value calculated by a multivariate discriminant. And the evaluation result relating to the evaluation of the state of biooxidation are associated with each other.
  • the storage unit 106 stores various types of Web data for providing the Web site to the client device 200, CGI programs, and the like as other information in addition to the information described above.
  • the Web data includes data for displaying various Web pages to be described later, and these data are formed as text files described in HTML or XML, for example.
  • a part file, a work file, and other temporary files for creating Web data are also stored in the storage unit 106.
  • the storage unit 106 stores audio for transmission to the client device 200 as an audio file such as WAVE format or AIFF format, and stores still images or moving images as image files such as JPEG format or MPEG2 format as necessary. Can be stored.
  • the communication interface unit 104 mediates communication between the biological oxidation evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 108 is connected to the input device 112 and the output device 114.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114).
  • the input device 112 a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.
  • the control unit 102 has an internal memory for storing a control program such as an OS (Operating System), a program defining various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a request interpretation unit 102a, a browsing processing unit 102b, an authentication processing unit 102c, an email generation unit 102d, a Web page generation unit 102e, a reception unit 102f, and a biological oxidation state information designation unit 102g.
  • the control unit 102 removes data with missing values, removes data with many outliers, removes missing values from the bio-oxidation state information sent from the database device 400 and the amino acid concentration data sent from the client device 200. Data processing such as removal of variables with a lot of data is also performed.
  • the request interpretation unit 102a interprets the request content from the client device 200 or the database device 400, and passes the processing to each unit of the control unit 102 according to the interpretation result.
  • the browsing processing unit 102b Upon receiving browsing requests for various screens from the client device 200, the browsing processing unit 102b generates and transmits Web data for these screens.
  • the authentication processing unit 102c makes an authentication determination.
  • the e-mail generation unit 102d generates an e-mail including various types of information.
  • the web page generation unit 102e generates a web page that the user browses on the client device 200.
  • the receiving unit 102 f receives information (specifically, amino acid concentration data, bio-oxidation state information, multivariate discriminant, etc.) transmitted from the client device 200 or the database device 400 via the network 300.
  • the biooxidation state information designating unit 102g designates target biooxidation state index data and amino acid concentration data when creating a multivariate discriminant.
  • the multivariate discriminant creation unit 102h creates a multivariate discriminant based on the biooxidation state information received by the receiving unit 102f and the biooxidation state information specified by the biooxidation state information designating unit 102g. Specifically, the multivariate discriminant-preparing part 102h is accumulated by repeatedly executing the candidate multivariate discriminant-preparing part 102h1, the candidate multivariate discriminant-verifying part 102h2, and the variable selecting part 102h3 from the biological oxidation state information. Based on the verification results, a multivariate discriminant is created by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from among a plurality of candidate multivariate discriminants.
  • the multivariate discriminant-preparing unit 102h selects a desired multivariate discriminant from the storage unit 106, A multivariate discriminant may be created.
  • the multivariate discriminant creation unit 102h creates a multivariate discriminant by selecting and downloading a desired multivariate discriminant from another computer device (for example, the database device 400) that stores the multivariate discriminant in advance. May be.
  • FIG. 17 is a block diagram showing the configuration of the multivariate discriminant-preparing part 102h, and conceptually shows only the part related to the present invention.
  • the multivariate discriminant creation unit 102h further includes a candidate multivariate discriminant creation unit 102h1, a candidate multivariate discriminant verification unit 102h2, and a variable selection unit 102h3.
  • the candidate multivariate discriminant-preparing part 102h1 creates a candidate multivariate discriminant that is a candidate for the multivariate discriminant from the biological oxidation state information based on a predetermined formula creation method.
  • the candidate multivariate discriminant creation unit 102h1 may create a plurality of candidate multivariate discriminants from the biological oxidation state information by using a plurality of different formula creation methods.
  • the candidate multivariate discriminant verification unit 102h2 verifies the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 based on a predetermined verification method.
  • the candidate multivariate discriminant verification unit 102h2 determines the discriminant rate, sensitivity, and specificity of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, and leave one out method.
  • Information criterion, ROC_AUC area under the receiver characteristic curve
  • variable selection unit 102h3 creates a candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method from the verification result in the candidate multivariate discriminant verification unit 102h2.
  • a combination of amino acid concentration data included in the biological oxidation state information to be used is selected.
  • the variable selection unit 102h3 may select a variable of the candidate multivariate discriminant from the verification result based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm.
  • the discriminant value calculation unit 102 i determines the multivariate discriminant based on the multivariate discriminant created by the multivariate discriminant creation unit 102 h and the evaluation target amino acid concentration data received by the receiver 102 f.
  • the discriminant value which is a value is calculated.
  • Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used.
  • the discriminant value calculating unit 102i includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, included in the amino acid concentration data.
  • the discriminant value may be calculated based on a multivariate discriminant including at least one of Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp, Arg, Gly, ABA, and Lys as a variable. .
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal, and the anti-oxidant level is normal.
  • the discriminant value calculating unit 102i is included in the amino acid concentration data Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn , Met, Val, Ile, Leu, Phe, based on a multivariate discriminant including at least one as a variable, It may be.
  • the discriminant value calculation unit 102i includes at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, and Gln included in the amino acid concentration data. Based on the concentration value and a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable. Thus, the discrimination value may be calculated.
  • the multivariate discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables.
  • the discriminant value calculation unit 102i includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, At least one concentration value of Gln, and at least of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln
  • the discriminant value may be calculated based on a multivariate discriminant including one as a variable.
  • the multivariate discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • the discriminant value calculation unit 102i includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, and His included in the amino acid concentration data.
  • the discriminant value may be calculated on the basis of the multivariate discriminant that is included.
  • the multivariate discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables.
  • the discriminant value criterion discriminating unit 102j1 discriminates between a state in which the degree of oxidative stress is high and the antioxidant power is normal and a state in which the degree of oxidative stress is normal and the anti-oxidant power is low.
  • the discriminant value calculation unit 102i includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg included in the amino acid concentration data. And at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg.
  • the discriminant value may be calculated based on a multivariate discriminant included as a variable. Note that the multivariate discriminant may be a logistic regression equation including Thr, Arg, and Orn as variables.
  • the discriminant value calculation unit 102i includes at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg included in the amino acid concentration data. Based on concentration value and multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg as a variable. Thus, the discrimination value may be calculated.
  • the multivariate discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables.
  • the discrimination value calculation unit 102i includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, included in the amino acid concentration data.
  • the multivariate discriminant may be a logistic regression equation including His, Ala, ABA, Orn, and Phe as variables.
  • the discriminant value calculating unit 102i adds the amino acid concentration data to the amino acid concentration data.
  • the discriminant value may be calculated based on the discriminant.
  • the multivariate discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables.
  • the discriminant value calculating unit 102i adds the amino acid concentration data to the amino acid concentration data. At least one concentration value of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit, and Glu, Ser, Asn, A discriminant value is calculated based on a multivariate discriminant including at least one of Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit as a variable. Also good.
  • the multivariate discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables.
  • the discriminant value criterion-evaluating unit 102j evaluates the state of biological oxidation including the degree of oxidative stress and / or antioxidant power for each evaluation object based on the discriminant value calculated by the discriminant value calculator 102i.
  • the discrimination value criterion evaluation unit 102j further includes a discrimination value criterion discrimination unit 102j1.
  • FIG. 18 is a block diagram showing the configuration of the discriminant value criterion-evaluating unit 102j, and conceptually shows only the portion related to the present invention.
  • the discriminant value criterion discriminating unit 102j1 determines, for each evaluation object, “a state in which both the oxidative stress level and the antioxidant power are normal, a state in which the oxidative stress level is high and the antioxidant power is normal, Discrimination as to which one of at least two states of a normal state with low antioxidant power and a high level of oxidative stress and low antioxidant power (specifically, two groups of six patterns) Any one of the three patterns, one of the three groups of three patterns, or one group of the four groups)), “Determination of whether the Oxidative Stress Level is Normal or the Oxidative Stress Level is High” Or “determination of whether the antioxidant power is normal or the antioxidant power is low”. Specifically, the discriminant value criterion discriminating unit 102j1 executes any one of these discriminators for each evaluation target by comparing the discriminant value with a preset threshold value (cut-off value). .
  • the result output unit 102k displays the processing results in the respective processing units of the control unit 102 (evaluation results in the discrimination value criterion evaluation unit 102j (specifically, discrimination results in the discrimination value criterion discrimination unit 102j1)). Output) to the output device 114.
  • the transmission unit 102m transmits the evaluation result to the client device 200 that is the transmission source of the amino acid concentration data to be evaluated, or the multivariate discriminant or the evaluation result created by the biological oxidation evaluation device 100 to the database device 400. Or send.
  • FIG. 19 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the client device 200 includes a control unit 210, a ROM 220, an HD 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are communicably connected via an arbitrary communication path. Has been.
  • the control unit 210 includes a web browser 211, an electronic mailer 212, a reception unit 213, and a transmission unit 214.
  • the web browser 211 performs browse processing for interpreting the web data and displaying the interpreted web data on a monitor 261 described later.
  • the Web browser 211 may be plugged in with various software such as a stream player having a function of receiving, displaying, and feeding back a stream video.
  • the electronic mailer 212 transmits and receives electronic mail according to a predetermined communication protocol (for example, SMTP (Simple Mail Transfer Protocol), POP3 (Post Office Protocol version 3), etc.).
  • the receiving unit 213 receives various types of information such as evaluation results transmitted from the biological oxidation evaluation apparatus 100 via the communication IF 280.
  • the transmission unit 214 transmits various types of information such as evaluation target amino acid concentration data to the biological oxidation evaluation apparatus 100 via the communication IF 280.
  • the input device 250 is a keyboard, a mouse, a microphone, or the like.
  • a monitor 261 which will be described later, also realizes a pointing device function in cooperation with the mouse.
  • the output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like.
  • the input / output IF 270 is connected to the input device 250 and the output device 260.
  • the communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other.
  • the client device 200 is connected to the network 300 via a communication device such as a modem, TA, or router and a telephone line, or via a dedicated line.
  • the client apparatus 200 can access the biooxidation evaluation apparatus 100 according to a predetermined communication protocol.
  • an information processing device for example, a known personal computer, workstation, home game device, Internet TV, PHS terminal, portable terminal, mobile object
  • peripheral devices such as a printer, a monitor, and an image scanner as necessary.
  • the client device 200 may be realized by installing software (including programs, data, and the like) that realizes a Web data browsing function and an e-mail function in a communication terminal / information processing terminal such as a PDA).
  • control unit 210 of the client device 200 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit 210.
  • the ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System).
  • the computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU.
  • the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary. .
  • all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.
  • the network 300 has a function of connecting the biological oxidation evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, a LAN (including both wired and wireless), and the like.
  • the network 300 includes a VAN, a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), a CATV network, and a mobile line switching network.
  • mobile packet switching network including IMT2000 system, GSM (registered trademark) system or PDC / PDC-P system
  • wireless paging network including local wireless network such as Bluetooth (registered trademark)
  • PHS network including CS, BS or ISDB
  • satellite A communication network including CS, BS or ISDB
  • FIG. 20 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.
  • the database device 400 is the biooxidation evaluation apparatus 100 or the biooxidation state information used when creating the multivariate discriminant in the database apparatus, the multivariate discriminant created by the biooxidation evaluation apparatus 100, and the biooxidation evaluation apparatus 100. It has a function to store the evaluation results.
  • the database device 400 includes a control unit 402 such as a CPU that comprehensively controls the database device, a communication device such as a router, and a wired or wireless communication circuit such as a dedicated line.
  • a communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414.
  • an output interface unit 408. These units are communicably connected via an arbitrary communication path.
  • the storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 406 stores various programs used for various processes.
  • the communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 408 is connected to the input device 412 and the output device 414.
  • the output device 414 in addition to a monitor (including a home TV), a speaker or a printer can be used as the output device 414 (hereinafter, the output device 414 may be described as the monitor 414).
  • the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.
  • the control unit 402 has an internal memory for storing a control program such as an OS (Operating System), a program that defines various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 402 is roughly divided into a request interpreting unit 402a, a browsing processing unit 402b, an authentication processing unit 402c, an e-mail generating unit 402d, a Web page generating unit 402e, and a transmitting unit 402f.
  • a control program such as an OS (Operating System)
  • OS Operating System
  • the request interpretation unit 402a interprets the request content from the biological oxidation evaluation apparatus 100, and passes the processing to each unit of the control unit 402 according to the interpretation result.
  • the browsing processing unit 402b Upon receiving browsing requests for various screens from the biological oxidation evaluation apparatus 100, the browsing processing unit 402b generates and transmits Web data for these screens.
  • the authentication processing unit 402c receives the authentication request from the biological oxidation evaluation apparatus 100 and makes an authentication determination.
  • the e-mail generation unit 402d generates an e-mail including various types of information.
  • the web page generation unit 402e generates a web page that the user browses on the client device 200.
  • the transmission unit 402f transmits various kinds of information such as biological oxidation state information and multivariate discriminants to the biological oxidation evaluation apparatus 100.
  • FIG. 21 is a flowchart illustrating an example of the biological oxidation evaluation service process.
  • the amino acid concentration data used in the present processing is analyzed by a specialist in the blood (including plasma, serum, etc.) collected in advance from an individual by a measuring method such as the following (A) or (B) or independently. It is related with the concentration value of the amino acid obtained as described above.
  • the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
  • Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at ⁇ 80 ° C. until the measurement of amino acid concentration.
  • acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC-MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
  • LC-MS liquid chromatography mass spectrometry
  • amino acid concentration When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • the client apparatus 200 transfers to the biological oxidation evaluation apparatus 100. to access.
  • the Web browser 211 uses a predetermined communication protocol to set the address of the Web site provided by the biological oxidation evaluation device 100 to the biological oxidation evaluation device 100. To the biooxidation evaluation apparatus 100 through a routing based on the address.
  • the biological oxidation evaluation apparatus 100 receives the transmission from the client apparatus 200 by the request interpretation unit 102a, analyzes the content of the transmission, and moves the processing to each unit of the control unit 102 according to the analysis result. Specifically, when the content of the transmission is a transmission request for a Web page corresponding to the amino acid concentration data transmission screen, the biological oxidation evaluation apparatus 100 is mainly stored in a predetermined storage area of the storage unit 106 by the browsing processing unit 102b. Web data for displaying the stored Web page is acquired, and the acquired Web data is transmitted to the client device 200.
  • the biological oxidation evaluation apparatus 100 first inputs a user ID and a user password at the control unit 102. To the user. When the user ID and password are input, the biooxidation evaluation apparatus 100 causes the authentication processing unit 102c to input the user ID and password stored in the user information file 106a. Authentication with the user password. The biooxidation evaluation apparatus 100 transmits Web data for displaying a Web page corresponding to the amino acid concentration data transmission screen to the client apparatus 200 by the browsing processing unit 102b only when authentication is possible. The client device 200 is identified by the IP address transmitted from the client device 200 together with the transmission request.
  • the client apparatus 200 receives the Web data transmitted from the biological oxidation evaluation apparatus 100 (for displaying a Web page corresponding to the amino acid concentration data transmission screen) by the receiving unit 213, and receives the received Web data.
  • the data is interpreted by the Web browser 211 and an amino acid concentration data transmission screen is displayed on the monitor 261.
  • step SA21 when the user inputs / selects individual amino acid concentration data or the like via the input device 250 on the amino acid concentration data transmission screen displayed on the monitor 261, the client device 200 uses the transmission unit 214 to input information and By transmitting an identifier for specifying the selection item to the biological oxidation evaluation apparatus 100, the amino acid concentration data of the individual to be evaluated is transmitted to the biological oxidation evaluation apparatus 100 (step SA21).
  • the transmission of amino acid concentration data in step SA21 may be realized by an existing file transfer technique such as FTP.
  • the biooxidation evaluation apparatus 100 interprets the request content of the client apparatus 200 by interpreting the identifier transmitted from the client apparatus 200 by the request interpretation unit 102a, and multivariate discriminant for evaluating the state of biooxidation.
  • a multivariate discriminant for 3-group discrimination or 4-group discrimination regarding oxidative stress level and antioxidant power a multivariate discriminant for 2-group discrimination regarding oxidative stress level and anti-oxidative power
  • oxidative stress level A request for transmission of a multivariate discriminant for 2-group discrimination or a multivariate discriminant for 2-group discrimination regarding antioxidant power is made to the database apparatus 400.
  • the database apparatus 400 interprets a transmission request from the biological oxidation evaluation apparatus 100 by the request interpretation unit 402a and stores a multivariate discriminant (for example, the updated latest one) stored in a predetermined storage area of the storage unit 406. ) Is transmitted to the biological oxidation evaluation apparatus 100 (step SA22).
  • a multivariate discriminant for example, the updated latest one
  • step SA26 the state in which both the degree of oxidative stress and antioxidant power are normal, the state in which the degree of oxidative stress is high and normal in antioxidant power, the state in which the degree of oxidative stress is normal and low in antioxidant power, and oxidation
  • step SA22 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, A multivariate discriminant including at least one of Met, Val, Ile, Leu, and Phe as a variable is transmitted to the biological oxidation evaluation apparatus 100.
  • step SA26 when it is determined whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is high and the antioxidant power is normal, in step SA22, the Glu , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, a multivariate discriminant including at least one as a variable to the biooxidation evaluation apparatus 100 Send.
  • step SA26 If it is determined in step SA26 whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is normal and the antioxidant power is low, in step SA22, the Glu , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln including a multivariate discriminant including at least one as a variable It transmits to the biological oxidation evaluation apparatus 100.
  • step SA26 when it is determined whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is low, in step SA22, Glu, Biooxidation evaluation of multivariate discriminant including at least one of Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, His as a variable. Transmit to device 100.
  • step SA26 when it is determined whether the oxidative stress level is high and the antioxidant power is normal or the oxidative stress level is normal and the antioxidant power is low, in step SA22, A multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg as a living body It transmits to the oxidation evaluation apparatus 100.
  • step SA26 when it is determined whether the state is a state in which the degree of oxidative stress is high and the antioxidant power is normal or the state in which the degree of oxidative stress is high and the antioxidant power is low, in step SA22, Glu , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg to the biooxidation evaluation apparatus 100 including a multivariate discriminant including at least one as a variable. Send.
  • step SA26 If it is determined in step SA26 whether the oxidative stress level is normal and the antioxidant power is low or the oxidative stress level is high and the antioxidant power is low, in step SA22 Glu , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln, Trp
  • the variable discriminant is transmitted to the biological oxidation evaluation apparatus 100.
  • step SA26 When it is determined in step SA26 whether the oxidative stress level is normal or the oxidative stress level is high, in step SA22, Glu, Ser, Pro, Asn, His, Thr, Orn. , Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, and ABA, a multivariate discriminant including at least one as a variable is transmitted to the biological oxidation evaluation apparatus 100.
  • step SA26 when it is determined whether the antioxidant power is normal or the antioxidant power is low, in step SA22, Glu, Ser, Asn, Thr, Met, Orn, Ile. , Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit, a multivariate discriminant including at least one as a variable is transmitted to the biological oxidation evaluation apparatus 100.
  • the biological oxidation evaluation apparatus 100 receives the individual amino acid concentration data transmitted from the client apparatus 200 and the multivariate discriminant transmitted from the database apparatus 400 by the receiving unit 102f, and the received amino acid concentration data is converted into the amino acid concentration data.
  • the stored multivariate discriminant is stored in a predetermined storage area of the density data file 106b, and the received multivariate discriminant is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SA23).
  • control unit 102 removes data such as missing values and outliers from the individual amino acid concentration data received in step SA23 (step SA24).
  • the discriminant value calculation unit 102i is based on the individual amino acid concentration data from which data such as missing values and outliers have been removed in step SA24, and the multivariate discriminant received in step SA23.
  • the discrimination value is calculated (step SA25).
  • step SA26 both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, and the oxidative stress level is normal and the antioxidant power is low.
  • the biooxidation evaluation apparatus 100 uses the discriminant value calculation unit 102i to determine amino acid concentration data when determining which of the at least three states among the states having a high degree of oxidative stress and a low antioxidant power. , Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, and Glu, Ser, Pro, Asn, Ala, Thr, Cit , Orn, Met, Val, Ile, Leu, Phe based on multivariate discriminant including at least one variable It is calculated.
  • step SA26 when it is determined whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is normal, the biological oxidation evaluation apparatus 100 is determined. Is at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, and Gln included in the amino acid concentration data.
  • a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable. Based on this, a discrimination value is calculated.
  • Step SA26 when it is determined whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is normal and the antioxidant power is low, the biological oxidation evaluation apparatus 100 Is the discriminant value calculation unit 102i and includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His included in the amino acid concentration data.
  • a discriminant value is calculated based on a multivariate discriminant including at least one as a variable.
  • Step SA26 when it is determined whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is low, the biological oxidation evaluation apparatus 100
  • the discriminant value calculation unit 102i Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, and His are included in the amino acid concentration data.
  • At least one of the concentration values and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Trp, ABA, Tyr, Gln, and His are variables.
  • the discriminant value is calculated based on the multivariate discriminant included as
  • step SA26 when it is determined whether the state is high in the degree of oxidative stress and normal in antioxidant power or in the state in which the degree of oxidative stress is normal and low in antioxidant power, 100 is a discriminant value calculation unit 102i, which includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, At least one concentration value of Arg and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, Arg
  • the discriminant value is calculated based on a multivariate discriminant that includes as a variable.
  • step SA26 when it is determined whether the state is high in the degree of oxidative stress and normal in antioxidant power, or in the state in which the degree of oxidative stress is high and low in anti-oxidant power, the biological oxidation evaluation apparatus 100 Is at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg included in the amino acid concentration data. Multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg as a variable. Based on this, a discrimination value is calculated.
  • step SA26 when it is determined whether the state is normal in the degree of oxidative stress and low in antioxidant power or in the state in which the degree of oxidative stress is high and low in antioxidative power, the biological oxidation evaluation apparatus 100 Is the discriminant value calculation unit 102i and includes Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, and ABA included in the amino acid concentration data.
  • Lys, Gln, Trp at least one concentration value, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA , Lys, Gln, Trp based on a multivariate discriminant including at least one as a variable, To calculate the different values.
  • step SA26 when it is determined whether the oxidative stress level is normal or the oxidative stress level is high, the biological oxidation evaluation apparatus 100 uses the determination value calculation unit 102i to determine amino acid concentration data. At least one concentration value of Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA, And many including at least one of Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, and ABA as a variable. A discriminant value is calculated based on the variable discriminant.
  • step SA26 when it is determined whether the antioxidant power is normal or the antioxidant power is low, the biological oxidation evaluation apparatus 100 uses the determination value calculation unit 102i to determine amino acid concentration data. , Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit, and Glu, Ser, Asn , Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, and Cit are used to calculate a discriminant value based on a multivariate discriminant including at least one as a variable. .
  • the biooxidation evaluation apparatus 100 compares the discriminant value calculated in step SA25 with a preset threshold value (cut-off value) by the discriminant value criterion discriminator 102j1, and shows the following 21 for each individual. . 29.
  • One of the determinations is executed, and the determination result is stored in a predetermined storage area of the evaluation result file 106g (step SA26).
  • a state in which both the degree of oxidative stress and antioxidant power are normal a state in which the degree of oxidative stress is high and normal in antioxidant capacity, a state in which the degree of oxidative stress is normal and low in antioxidant capacity, and a degree of oxidative stress that is high and antioxidant 21.
  • the biological oxidation evaluation apparatus 100 transmits the determination result obtained in step SA26 to the client apparatus 200 and the database apparatus 400 that are the transmission source of amino acid concentration data by the transmission unit 102m (step SA27). Specifically, first, the biooxidation evaluation apparatus 100 creates a web page for displaying the discrimination result in the web page generation unit 102e, and stores web data corresponding to the created web page in a predetermined unit of the storage unit 106. Store in the storage area. Next, after the user inputs a predetermined URL to the Web browser 211 of the client device 200 via the input device 250 and performs the above-described authentication, the client device 200 sends a browsing request for the Web page to the biological oxidation evaluation device 100. Send.
  • the browsing processing unit 102b interprets the browsing request transmitted from the client device 200, and stores Web data corresponding to the Web page for displaying the determination result in a predetermined storage of the storage unit 106. Read from area. Then, the biological oxidation evaluation apparatus 100 transmits the read Web data to the client apparatus 200 and transmits the Web data or the determination result to the database apparatus 400 by the transmission unit 102m.
  • the biological oxidation evaluation apparatus 100 may notify the user client apparatus 200 of the determination result by e-mail at the control unit 102. Specifically, the biological oxidation evaluation apparatus 100 first refers to the user information stored in the user information file 106a based on the user ID and the like in accordance with the transmission timing in the e-mail generation unit 102d. The e-mail address of the user. Next, the bio-oxidation evaluation apparatus 100 uses the e-mail generation unit 102d to generate data related to the e-mail including the name and determination result of the user with the acquired e-mail address as the destination. Next, the biological oxidation evaluation apparatus 100 transmits the generated data to the user client apparatus 200 by the transmission unit 102m.
  • step SA27 the biological oxidation evaluation apparatus 100 may transmit the determination result to the user client apparatus 200 using an existing file transfer technology such as FTP.
  • control unit 402 receives the determination result or Web data transmitted from the biological oxidation evaluation device 100, and stores the received determination result or Web data in the storage unit 406. Save (accumulate) in the area (step SA28).
  • the client device 200 receives the Web data transmitted from the biological oxidation evaluation device 100 by the receiving unit 213, interprets the received Web data by the Web browser 211, and displays the Web page on which the individual discrimination result is written.
  • the screen is displayed on the monitor 261 (step SA29).
  • the client apparatus 200 uses the known function of the electronic mailer 212 to send the electronic mail transmitted from the biological oxidation evaluation apparatus 100 at an arbitrary timing.
  • the received e-mail is displayed on the monitor 261.
  • the user browses the Web page displayed on the monitor 261, and the 21. 29. It is possible to confirm an individual discrimination result related to any one of the discriminations. Note that the user may print the display content of the Web page displayed on the monitor 261 with the printer 262.
  • the user browses the e-mail displayed on the monitor 261 so that the above 21. 29. It is possible to confirm an individual discrimination result related to any one of the discriminations.
  • the user may print the content of the e-mail displayed on the monitor 261 with the printer 262.
  • the client device 200 transmits the amino acid concentration data of the individual to the biological oxidation evaluation device 100, and the database device 400 receives a request from the biological oxidation evaluation device 100. 21. 29.
  • the multivariate discriminant for discrimination in any one of the discriminations is transmitted to the biological oxidation evaluation apparatus 100.
  • the biooxidation evaluation apparatus 100 (1) receives amino acid concentration data from the client apparatus 200 and receives a multivariate discriminant from the database apparatus 400, and (2) based on the received amino acid concentration data and the multivariate discriminant.
  • the discriminant value is calculated, and (3) the individual discriminating value is compared with the calculated discriminant value and a preset threshold value. 29. (4)
  • This determination result is transmitted to the client device 200 and the database device 400.
  • the client device 200 receives and displays the determination result transmitted from the biological oxidation evaluation device 100
  • the database device 400 receives and stores the determination result transmitted from the biological oxidation evaluation device 100. This makes it possible to accurately perform the discrimination using the discriminant value obtained by the multivariate discriminant useful for the above-described 2-group discrimination, 3-group discrimination, or 4-group discrimination.
  • the multivariate discriminant used in step SA25 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, or a Mahalanobis distance method. Any one of an expression created, an expression created by canonical discriminant analysis, and an expression created by a decision tree may be used. Thereby, these discrimination
  • step SA25 when it is determined in step SA26 whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is normal, step SA25 is performed.
  • the multivariate discriminant used in 1 may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables. As a result, the above-described 22. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 If it is determined in step SA26 whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is normal and the antioxidant power is low, it is used in step SA25.
  • the multivariate discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables. Thereby, the 23. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 when it is determined in step SA26 whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is high and the antioxidant power is low, the multiple used in step SA25 is used.
  • the variable discriminant may be a logistic regression equation including Ala, Cit, and Tyr as variables. Thereby, the 24. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 If it is determined in step SA26 whether the state is high in oxidative stress and normal in antioxidant power or in the state in which normal oxidative stress is low and low in antioxidative power, it is used in step SA25.
  • the obtained multivariate discriminant may be a logistic regression equation including Thr, Arg, and Orn as variables. Thereby, the 25. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 when it is determined whether the state is high in oxidative stress and normal in antioxidant power or in the state in high oxidative stress and low in antioxidative power, it is used in step SA25.
  • the multivariate discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables. Thereby, the 26. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 when it is determined in step SA26 whether the oxidative stress level is normal and the antioxidant power is low or the oxidative stress level is high and the antioxidant power is low, it is used in step SA25.
  • the multivariate discriminant may be a logistic regression equation including His, Ala, ABA, Orn, and Phe as variables. Thereby, the 27. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 when determining whether the oxidative stress level is normal or the oxidative stress level is high, the multivariate discriminant used in step SA25 is Gln, Ala, Cit, A logistic regression equation including Tyr, Met, and Ile as variables may be used. As a result, the above-described 28. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • step SA26 when it is determined whether the antioxidant power is normal or the antioxidant power is low, the multivariate discriminant used in step SA25 is Glu, Thr, Ala, A logistic regression equation including Arg, Ile, and Trp as variables may be used. Thereby, the 29. mentioned above. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described later). If the multivariate discriminant obtained by these methods is used, it is preferable to use the multivariate discriminant for biooxidation state evaluation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. it can.
  • the biological oxidation evaluation apparatus the biological oxidation evaluation method, the biological oxidation evaluation program, the recording medium, the biological oxidation evaluation system, and the information communication terminal device according to the present invention are not limited to the second embodiment described above.
  • the present invention can be implemented in various different embodiments within the scope of the technical idea described in the above.
  • all or part of the processes described as being automatically performed can be manually performed, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method.
  • the processing procedures, control procedures, specific names, information including parameters such as various registration data and search conditions, screen examples, and database configurations shown in the above documents and drawings, unless otherwise specified. It can be changed arbitrarily.
  • each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • the processing functions (particularly the processing functions performed by the control unit 102) of each unit or each device of the biooxidation evaluation apparatus 100 are determined by a CPU (Central Processing Unit) and a program interpreted and executed by the CPU. All or any part thereof may be realized, or may be realized as hardware by wired logic.
  • the biooxidation evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured by connecting an arbitrary peripheral device to the information processing apparatus.
  • the biooxidation evaluation apparatus 100 may be realized by installing software (including programs, data, and the like) that realizes the method of the present invention in the information processing apparatus.
  • program is a data processing method described in an arbitrary language or description method, and may be in any form such as source code or binary code.
  • the “program” is not necessarily limited to a single configuration, but is distributed in the form of a plurality of modules and libraries, or in cooperation with a separate program typified by an OS (Operating System). Includes those that achieve that function.
  • the program is recorded on a recording medium and mechanically read by the biological oxidation evaluation apparatus 100 as necessary. That is, in the storage unit 106 such as a ROM or an HDD (Hard Disk Drive), a computer program for giving instructions to the CPU in cooperation with an OS (Operating System) and performing various processes is recorded.
  • This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU. Further, this computer program may be stored in an application program server connected to the biological oxidation evaluation apparatus 100 via a dedicated network 300, and may be downloaded in whole or in part as necessary. Is also possible. As a specific configuration for reading the program recorded on the recording medium by each device, a reading procedure, an installation procedure after reading, and the like, a well-known configuration and procedure can be used.
  • “recording medium” includes any “portable physical medium”.
  • the “portable physical medium” is a memory card, USB memory, SD card, flexible disk, magneto-optical disk, ROM, EPROM, EEPROM, CD-ROM, MO, DVD, Blu-ray Disc, or the like.
  • the program according to the present invention may be stored in a computer-readable recording medium, or may be configured as a program product.
  • FIG. 22 is a flowchart illustrating an example of multivariate discriminant creation processing. Note that the multivariate discriminant-preparing process may be performed by the database device 400 that manages biological oxidation state information.
  • the biological oxidation evaluation apparatus 100 stores biological oxidation state information acquired in advance from the database device 400 in a predetermined storage area of the biological oxidation state information file 106c.
  • the biooxidation evaluation apparatus 100 also stores biooxidation state information including biooxidation state index data and amino acid concentration data designated in advance by the biooxidation state information designating unit 102g in a predetermined storage area of the designated biooxidation state information file 106d. Is stored.
  • the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, based on a predetermined formula creation method from biological oxidation state information stored in a predetermined storage area of the designated biological oxidation state information file 106d.
  • the candidate multivariate discriminant is created, and the created candidate multivariate discriminant is stored in a predetermined storage area of the candidate multivariate discriminant file 106e1 (step SB21).
  • the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression) Analysis, k-means method, cluster analysis, decision tree, etc. related to multivariate analysis.) Select a desired one from among them, and create candidate multivariate discrimination based on the selected formula creation method Determine the form of the expression (form of the expression).
  • the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and executes various calculations (for example, average and variance) corresponding to the selected formula selection method based on the biological oxidation state information. To do.
  • the multivariate discriminant-preparing part 102h determines the calculation result and parameters of the determined candidate multivariate discriminant-expression in the candidate multivariate discriminant-preparing part 102h1.
  • a candidate multivariate discriminant is created based on the selected formula creation method.
  • the above-described processing may be executed in parallel for each selected formula creation method.
  • biooxidation state information is obtained using a candidate multivariate discriminant created by performing a principal component analysis.
  • a candidate multivariate discriminant may be created by converting and performing discriminant analysis on the converted biological oxidation state information.
  • the multivariate discriminant-preparing part 102h verifies (mutually verifies) the candidate multivariate discriminant created in step SB21 with the candidate multivariate discriminant-verifying part 102h2, and verifies the verification result.
  • the result is stored in a predetermined storage area of the verification result file 106e2 (step SB22).
  • the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-verifying part 102h2, based on the biological oxidation state information stored in a predetermined storage area of the designated biological oxidation state information file 106d.
  • the verification data used when verifying the variable discriminant is created, and the candidate multivariate discriminant is verified based on the created verification data.
  • the multivariate discriminant creation unit 102h creates each formula in the candidate multivariate discriminant verification unit 102h2.
  • Each candidate multivariate discriminant corresponding to the method is verified based on a predetermined verification method.
  • the discrimination rate, sensitivity, specificity, information criterion of the candidate multivariate discriminant based on at least one of the bootstrap method, holdout method, N-fold method, leave one out method, etc. , ROC_AUC (area under the curve of the receiver characteristic curve) or the like.
  • the multivariate discriminant-preparing part 102h selects the variable of the candidate multivariate discriminant based on a predetermined variable selection method from the verification result in step SB22 by the variable selection part 102h3 (however, the step The variable of the candidate multivariate discriminant may be selected based on a predetermined variable selection method without considering the verification result in SB22.), Biooxidation state information used when creating the candidate multivariate discriminant Is selected, and biooxidation state information including the selected combination of amino acid concentration data is stored in a predetermined storage area of the selected biooxidation state information file 106e3 (step SB23).
  • step SB21 a plurality of candidate multivariate discriminants are created in combination with a plurality of different formula creation methods, and in step SB22, each candidate multivariate discriminant corresponding to each formula creation method is verified based on a predetermined verification method
  • the multivariate discriminant-preparing part 102h is predetermined for each candidate multivariate discriminant (for example, the candidate multivariate discriminant corresponding to the verification result in step SB22) by the variable selector 102h3.
  • the variable of the candidate multivariate discriminant may be selected based on the variable selection method.
  • the variable of the candidate multivariate discriminant may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result.
  • the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant.
  • the multivariate discriminant-preparing part 102h uses the variable selection part 102h3 to combine amino acid concentration data based on the biological oxidation state information stored in the predetermined storage area of the designated biological oxidation state information file 106d. May be selected.
  • the multivariate discriminant-preparing part 102h determines whether or not all combinations of amino acid concentration data included in the biological oxidation state information stored in the predetermined storage area of the designated biological oxidation state information file 106d have been completed. When the determination result is “end” (step SB24: Yes), the process proceeds to the next step (step SB25), and when the determination result is not “end” (step SB24: No), the step is performed. Return to SB21.
  • the multivariate discriminant-preparing part 102h determines whether or not the preset number of times has ended, and if the determination result is “end” (step SB24: Yes), the next step (step SB25). If the determination result is not “end” (step SB24: No), the process may return to step SB21.
  • the multivariate discriminant-preparing part 102h includes the amino acid concentration data in which the combination of the amino acid concentration data selected in step SB23 is included in the biological oxidation state information stored in the predetermined storage area of the designated biological oxidation state information file 106d. Or the combination of the amino acid concentration data selected in the previous step SB23 is determined. If the determination result is “same” (step SB24: Yes), the next step (step SB25) is determined. If the determination result is not “same” (step SB24: No), the process may return to step SB21. Further, when the verification result is specifically an evaluation value related to each candidate multivariate discriminant, the multivariate discriminant creation unit 102h compares the evaluation value with a predetermined threshold corresponding to each formula creation method. Based on the result, it may be determined whether to proceed to step SB25 or to return to step SB21.
  • the multivariate discriminant-preparing part 102h selects a multivariate discriminant by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the verification result.
  • the determined multivariate discriminant (selected candidate multivariate discriminant) is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SB25).
  • step SB25 for example, when the optimum one is selected from candidate multivariate discriminants created by the same formula creation method, and when the optimum one is selected from all candidate multivariate discriminants There is.
  • FIG. 23 is a principle configuration diagram showing the basic principle of the present invention.
  • a desired substance group composed of one or a plurality of substances is administered to an evaluation target (for example, an individual such as an animal or a human) (step S31).
  • an evaluation target for example, an individual such as an animal or a human
  • drugs for example, insulin sensitizers, biguanites, ursodeoxycholic acid, antihyperlipidemic drugs, or antioxidants
  • supplements for example, anthocyanins, coenzyme Q10 (CoQ10), ⁇ lipo Acid (alpha lipoic acid), super lutein, lutein, astaxanthin, beta carotene (vitamin A), vitamin C (vitamin P), vitamin E, carnitine, goggle (blood lipid-suppressing herb), lycopene, MSM supplement, horsetail ( Silica), potassium, EPA, DHA (omega 3
  • the administration method, dose, and dosage form may be appropriately combined depending on the disease state.
  • the dosage form may be determined based on a known technique.
  • the dose is not particularly defined, but may be given, for example, in a form containing 1 ug to 100 g as an active ingredient.
  • step S32 blood is collected from the evaluation target to which the substance group has been administered in step S31 (step S32).
  • amino acid concentration data relating to the concentration value of amino acids in blood collected in step S32 is acquired (step S33).
  • step S11 amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired.
  • the following Amino acid concentration data may be obtained by measuring amino acid concentration data by a measurement method such as (A) or (B).
  • the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
  • Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at ⁇ 80 ° C.
  • amino acid concentration When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • step S34 based on the amino acid concentration data of the evaluation target acquired in step S33, the state of biological oxidation including the degree of oxidative stress and / or antioxidant power is evaluated for the evaluation target (step S34).
  • the substance group administered in step S31 prevents biological oxidation (specifically, prevents an increase in the degree of oxidative stress or prevents a decrease in antioxidant power) or a living body. It is determined whether or not the state of oxidation is improved (specifically, the degree of oxidative stress is normalized or the antioxidant power is normalized) (step S35).
  • step S35 If the determination result in step S35 is “prevent or improve”, the substance group administered in step S31 is searched for as preventing biooxidation or improving the state of biooxidation. .
  • a desired substance group is administered to an evaluation object, blood is collected from the evaluation object to which the substance group is administered, and amino acid concentration data relating to the concentration value of amino acids in the collected blood is obtained. Based on the acquired amino acid concentration data, the state of biological oxidation is evaluated for the evaluation target, and based on the evaluation result, the desired substance group prevents biological oxidation or improves the state of biological oxidation. It is determined whether or not there is.
  • the biooxidation evaluation method that can accurately evaluate the state of biooxidation using the concentration of amino acids in blood, the substance that prevents biooxidation or improves the state of biooxidation is accurate. You can explore well.
  • step S34 data such as missing values and outliers may be removed from the amino acid concentration data. Thereby, the state of biological oxidation can be evaluated more accurately.
  • step S34 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp included in the amino acid concentration data acquired in step S33.
  • Arg, Gly, ABA, and Lys the state of biooxidation may be evaluated for the evaluation target based on at least one concentration value. Thereby, the state of biological oxidation can be accurately evaluated using the concentration of amino acids related to the state of biological oxidation among the concentrations of amino acids in blood.
  • Oxidative stress level and antioxidant power are both normal, oxidative stress level is high and normal antioxidant power level is normal, oxidative stress level is normal and low antioxidant power level is high, and oxidative stress level is high and anti-oxidative power level is high. It may be determined which of at least three of the states having low oxidizing power is.
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln included in the amino acid concentration data is used. Based on the evaluation target, it may be determined whether the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is high and the antioxidant power is normal. This makes it possible to accurately perform the two-group discrimination by using the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood.
  • concentration value of at least one of Trp whether the oxidative stress level is normal and the antioxidant power level is low or the oxidative stress level is high and the antioxidant power level is low May be determined. This makes it possible to accurately perform the two-group discrimination by using the amino acid concentration useful for the two-group discrimination between these states among the amino acid concentrations in the blood.
  • Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA are included in the amino acid concentration data.
  • step S34 based on the amino acid concentration data acquired in step S33 and the preset multivariate discriminant including the amino acid concentration as a variable, a discriminant value that is the value of the multivariate discriminant is calculated and calculated. Based on the discriminated value, the state of biological oxidation may be evaluated for the evaluation target. Thereby, the state of biooxidation can be accurately evaluated using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. Thereby, the biooxidation state can be evaluated with higher accuracy by using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
  • step S34 Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp included in the amino acid concentration data acquired in step S33.
  • Arg, Gly, ABA, Lys at least one concentration value
  • Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln, Trp Arg, Gly, ABA, Lys
  • a discriminant value is calculated based on a multivariate discriminant including at least one of them as a variable, and the state of biooxidation is evaluated for the evaluation object based on the calculated discriminant value. May be. Thereby, the biooxidation state can be accurately evaluated using the discriminant value obtained by the multivariate discriminant having a significant correlation with the biooxidation state.
  • the discriminant value is calculated, and based on the calculated discriminant value
  • both the oxidative stress level and the antioxidant power are normal, the oxidative stress level is high and the antioxidant power is normal, the oxidative stress level is normal and the antioxidant power is low, and the oxidative stress level. It may be determined which of the at least three states among the states having high and low antioxidant power. Thereby, these discrimination
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln included in the amino acid concentration data Discrimination based on a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln as a variable
  • the value is calculated, and based on the calculated discriminant value, whether the oxidative stress level and the antioxidant power are both normal or the oxidative stress level is high and the antioxidant power is normal for the evaluation target May be determined.
  • the multivariate discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • the discriminant value is calculated, and on the basis of the calculated discriminant value, the oxidative stress level and the antioxidant power are both normal and the oxidative stress level is normal and It may be determined which state is low in oxidizing power.
  • the multivariate discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • At least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg included in the amino acid concentration data.
  • a plurality of concentration values and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys, Gln, Trp, and Arg as variables.
  • the discriminant value is calculated, and on the basis of the calculated discriminant value, the evaluation target has a high oxidative stress level and a normal antioxidant power, and a normal oxidative stress level and an antioxidant power.
  • the multivariate discriminant may be a logistic regression equation including Thr, Arg, and Orn as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • At least one concentration value of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg included in the amino acid concentration data Discrimination based on a multivariate discriminant including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, Arg as a variable
  • the value is calculated, and based on the calculated discriminant value, whether the evaluation target is in a state of high oxidative stress and normal antioxidant power, or a state of high oxidative stress and low antioxidant power May be determined.
  • the multivariate discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Trp at least one concentration value, and Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Gly, Tyr, His, Arg, ABA, Lys, Gln , Trp calculates a discriminant value based on a multivariate discriminant including at least one of the variables as a variable, and based on the calculated discriminant value, a state in which the degree of oxidative stress is normal and the antioxidant power is low It may also be determined whether the state is high in oxidative stress and low in antioxidant power. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including His, Ala, ABA, Orn, and Phe as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Glu, Ser, Pro, Asn, His, Thr, Orn, Ala, Cit, Val, Met, Ile, Leu, Phe, Trp, Gln, Arg, Gly, Tyr, ABA are included in the amino acid concentration data.
  • a discriminant value is calculated, and based on the calculated discriminant value, a state in which the oxidative stress level is normal and the oxidative stress level is high for each evaluation target You may determine which is. This makes it possible to accurately perform the two-group discrimination using the discriminant value obtained by the multivariate discriminant useful for the two-group discrimination between these states.
  • the multivariate discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • Multivariate discriminant including a value and at least one of Glu, Ser, Asn, Thr, Met, Orn, Ile, Leu, Arg, Ala, Trp, Phe, His, Val, Gln, Pro, Cit as a variable
  • the discriminant value may be determined based on the calculated discriminant whether the antioxidant power is normal or the antioxidant power is low.
  • the multivariate discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables.
  • the two-group discrimination can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant particularly useful for the two-group discrimination between these states.
  • each multivariate discriminant described above is described in the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by a method (multivariate discriminant creation process described in the second embodiment described above). In addition, if the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for evaluation of the state of biological oxidation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can do.
  • the multivariate discriminant generally means the format of formulas used in multivariate analysis. For example, fractional formulas, multiple regression formulas, multiple logistic regression formulas, linear discriminant functions, Mahalanobis distances, canonical discriminant functions, support vectors Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants.
  • a coefficient and a constant term are added to each variable. In this case, the coefficient and the constant term are preferably real numbers, more preferably data.
  • each coefficient and its confidence interval may be obtained by multiplying it by a real number
  • the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / dividing an arbitrary real constant thereto.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression.
  • An appropriate coefficient may be added to each amino acid used in the numerator and denominator.
  • amino acids used in the numerator and denominator may overlap.
  • an appropriate coefficient may be attached to each fractional expression.
  • the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.
  • this invention evaluates the state of biological oxidation, in addition to the concentration of amino acids, other biological information (for example, biological metabolites such as saccharides, lipids, proteins, peptides, minerals, hormones, (Value, sex, age, liver disease index, eating habits, drinking habits, exercise habits, obesity, disease history, etc.) may be further used.
  • the present invention also provides other biological information (for example, biological metabolism such as sugars, lipids, proteins, peptides, minerals, hormones, etc.) in addition to the concentration of amino acids as variables in the multivariate discriminant when evaluating the state of biological oxidation. For example, blood glucose level, blood pressure level, gender, age, liver disease index, dietary habits, drinking habits, exercise habits, obesity level, disease history, etc.).
  • FIG. 24 is a flowchart showing an example of a method for searching for a biooxidation prevention / ameliorating substance according to the third embodiment.
  • a desired substance group consisting of one or a plurality of substances is administered to an individual such as a bio-oxidized animal or human (step SA31).
  • step SA32 blood is collected from the individual to which the substance group has been administered in step S31 (step SA32).
  • step SA33 amino acid concentration data relating to the concentration value of amino acids in blood collected in step S32 is acquired (step SA33).
  • step SA33 for example, amino acid concentration data measured by a company or the like that measures amino acid concentration may be acquired, and measurement such as (A) or (B) described above is performed from blood collected from an evaluation target. Amino acid concentration data may be obtained by measuring amino acid concentration data by a method.
  • step SA34 data such as missing values and outliers are removed from the amino acid concentration data of the individual obtained in step S33 (step SA34).
  • step SA35 One of the determinations is executed (step SA35).
  • Any of at least three states of a state having a high degree of normality and an antioxidant power, a state having a normal degree of oxidative stress and a low level of antioxidant power, and a state having a high degree of oxidative stress and a low level of antioxidant power Is determined.
  • Second Group Discrimination Regarding Oxidative Stress Level and Antioxidant Power (i) Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr included in amino acid concentration data , Trp, Arg, Gly, His, Gln, a state where both the oxidative stress level and the antioxidant power are normal for each individual by comparing at least one concentration value with a preset threshold value (cut-off value) And (ii) Glu, Ser, Pro, Asn, Ala, Thr, Cit, which are included in the amino acid concentration data.
  • At least one of Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln Concentration value and at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, Gln as variables
  • the discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cutoff value). Whether the state and the degree of oxidative stress are normal and the state of low antioxidant power is determined.
  • the discriminant value is calculated, and the calculated discriminant value is compared with a preset threshold value (cut-off value), so that the individual has a high degree of oxidative stress and normal antioxidant power and oxidative stress. It is determined whether the state is normal and the antioxidant power is low.
  • a discriminant value is calculated based on a multivariate discriminant including at least one of Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Gln, and Arg as a variable, and the calculated discriminant value Is compared with a preset threshold value (cut-off value), so that an individual has a high degree of oxidative stress and a normal level of antioxidant power and a high level of oxidative stress and a low level of antioxidant power. Determine which is.
  • a discriminant value is calculated based on a multivariate discriminant including at least one as a variable, and the calculated discriminant value and a preset threshold value (cutoff value) To determine whether the individual has a normal antioxidant power or a low antioxidant power.
  • step SA35 based on the determination result in step SA35, it is determined whether or not the substance group administered in step SA31 is one that prevents biological oxidation or improves the state of biological oxidation (step SA36).
  • step SA36 When the determination result in step SA36 is “prevent or improve”, the substance group administered in step SA31 is searched for as preventing biooxidation or improving the state of biooxidation.
  • a substance searched by this search method for example, “a group of amino acids containing at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, and Phe”.
  • amino acid group including at least one of Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, His, Tyr, Gln”, “Glu, Ser, Pro , Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Trp, Arg, Gly, His, and Gln, an amino acid group containing “Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, T p, ABA, Tyr, Gln, His group containing at least one amino acid "," Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr, Lys , Gln, Trp, Arg ”,“ Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe, Tyr
  • a desired substance group is administered to an individual, and (ii) the substance group is administered.
  • the multivariate discriminant used in step SA35 is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, and a canonical discriminant. Any one of an expression created by analysis and an expression created by a decision tree may be used. Thereby, these discrimination
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Gln, Cit, Tyr, Met, Orn, and Leu as variables.
  • This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Ser, Thr, Met, Orn, Ile, and Trp as variables.
  • This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Ala, Cit, Tyr as variables. Thereby, 34. mentioned above. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in discriminating the above may be a logistic regression equation including Thr, Arg, Orn as variables. Thereby, the 35. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including His, Thr, Ala, Cit, Ile, and Phe as variables. Thereby, the 36. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in discriminating the above may be a logistic regression equation including His, Ala, ABA, Orn, Phe as variables. As a result, the aforementioned 37. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Gln, Ala, Cit, Tyr, Met, and Ile as variables. As a result, 38. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • the multivariate discriminant used in the discriminant may be a logistic regression equation including Glu, Thr, Ala, Arg, Ile, and Trp as variables. Thereby, 39. This discrimination can be performed with higher accuracy by using a discriminant value obtained by a multivariate discriminant that is particularly useful for the discrimination.
  • Each multivariate discriminant described above is a method described in International Publication No. 2004/052191 which is an international application by the present applicant or a method described in International Publication No. 2006/098192 which is an international application by the present applicant. It may be created by (multivariate discriminant creation processing described in the second embodiment described above). In addition, if the multivariate discriminant obtained by these methods is used, the multivariate discriminant is preferably used for evaluation of the state of biological oxidation regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can do.
  • the method for searching for a biooxidation preventing / ameliorating substance according to the third embodiment is at least one of “Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, and Phe.
  • searching for a substance for preventing / ameliorating not only finds a new substance effective for preventing / ameliorating biooxidation, Finding new uses for preventing and improving biooxidation of known substances, finding new compositions that combine existing drugs and supplements that can be expected to be effective in preventing and improving biooxidation, Find usage / dose / combination, make it a kit, present a prevention / treatment menu including meals / exercise, etc., monitor the effectiveness of the prevention / treatment menu, and if necessary, for each individual Includes presenting menu changes.
  • the blood amino acid concentration was measured from the blood sample of the medical checkup examinee by the measurement method (A) described in the above embodiment.
  • the subjects were healthy groups (first group, 54 subjects) in a normal state (d-ROM ⁇ 300 U. CARR, BAP> 2200 ⁇ mol / l) in which both the oxidative stress level and the antioxidant power were normal, and the oxidative stress level was high and
  • the second group (184 persons) with normal antioxidant power (d-ROM> 300 U. CARR, BAP> 2200 ⁇ mol / l), normal oxidative stress and low antioxidant power (d-ROM ⁇ 300 U.
  • CARR, BAP ⁇ 2200 ⁇ mol / l the third group (29 persons), high in oxidative stress and low in antioxidant capacity (d-ROM> 300 U.
  • CARR, BAP ⁇ 2200 ⁇ mol / l) Divided into 4 groups (38 people).
  • the distribution of amino acid variables among the 4 groups is shown in FIG. In FIG. 25, 1 represents a healthy group, 2 represents a second group, 3 represents a third group, and 4 represents a fourth group.
  • a Kruskal Wallis test was performed between the four groups for the purpose of evaluating the state of oxidative stress and antioxidant power. Among the four groups, “Glu, Ser, Pro, Asn, Ala, Thr, Cit, Orn, Met, Val, Ile, Leu, Phe” are significantly changed, and these amino acids are discriminating between the four groups. Turned out to have.
  • the logistic regression equation is used as a multivariate discriminant, the combination of variables to be included in the logistic regression equation is searched, and the random sampling method is adopted as a cross-validation to have good discrimination between the healthy group and the second group
  • the search of the logistic regression equation was carried out earnestly.
  • FIG. 26 and FIG. 27 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • FIGS. 26 and 27 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • the variables appearing in the formulas included in FIGS. 26 and 27 are listed in the descending order of the appearance frequency of variables, and are “Tyr, Gln, Met, Phe, Leu, Ile, Val, Orn, Ser, His”.
  • fractional expressions as multivariate discriminants, searching for combinations of variables to be included in fractional expressions, and adopting random sampling as cross-validation, fractional expressions having good discriminating ability between healthy group and second group We conducted an extensive search.
  • FIG. 28 and 29 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 28 and FIG. 29 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • Listed in descending order of appearance frequency of variables in the expressions included in FIG. 28 and FIG. 29 is “Tyr, Gln, Met, Ile, Leu, Cit, Orn, His, Ser, Val”.
  • Example 2 The same amino acid concentration data as measured in Example 1 was used.
  • the amino acids that were significant (p ⁇ 0.05) in the test when the ROC_AUC of the healthy group and the third group were nonparametric and the null hypothesis was ROC_AUC 0.5 were “Ser, Asn” , Thr, Tyr, Met, Orn ". These amino acids showed a significant increase in the third group.
  • FIGS. 30 and 31 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 30 and FIG. 31 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation. Enumerating the appearance frequency of the variables in the formulas included in FIGS. 30 and 31 up to the tenth, “Thr, Ser, Orn, Met, Trp, Ile, Arg, Asn, Glu, Gly”.
  • fractional expressions as multivariate discriminants, searching for combinations of variables to be included in fractional expressions, and adopting random sampling as cross-validation, fractional expressions with good discriminating ability between healthy group and third group We conducted an extensive search.
  • FIG. 32 and FIG. 33 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 32 and FIG. 33 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. Enumerating up to 10th place in the descending order of the appearance frequency of variables in the expressions included in FIGS. 32 and 33, “Orn, Trp, Thr, Ile, Cit, His, Ser, Val, Gln, Tyr” is obtained.
  • the logistic regression equation is used as a multivariate discriminant, a combination of variables included in the logistic regression equation is searched, and a random sampling method is adopted as a cross-validation to have a good discrimination ability between the healthy group and the fourth group.
  • the search of the logistic regression equation was carried out earnestly.
  • FIG. 34 and FIG. 35 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • FIG. 34 and FIG. 35 “Ala, Cit, Phe, Trp, Met, Ile, Thr, ABA, Pro, Ser” is listed in descending order of the appearance frequency of variables in the formulas included in FIGS.
  • fractional expressions as multivariate discriminants, searching for combinations of variables to be included in the fractional expressions, and adopting random sampling as cross-validation, fractional expressions having good discriminating ability between the healthy group and the fourth group We conducted an extensive search.
  • FIG. 36 and FIG. 37 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 36 and FIG. 37 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • the appearance frequency of the variables in the expressions included in FIG. 36 and FIG. 37 is listed in descending order, they are “Ala, Tyr, Thr, Cit, Gln, Trp, His, Phe, Met, Ser”.
  • FIGS. 38 and 39 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • FIGS. 38 and 39 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • the frequency of occurrence of variables in the formulas included in FIGS. 38 and 39 is listed in descending order, they are “Thr, Orn, Ile, Met, Glu, Asn, Ser, Cit, Leu, Ala”.
  • FIG. 40 and 41 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 40 and FIG. 41 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. 40 and 41 are listed in descending order of the appearance frequency of variables in the formulas, “Gln, Trp, Ile, Thr, Orn, Met, Lys, Val, Arg, Leu”.
  • FIGS. 42 and 43 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • FIGS. 42 and 43 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • 42 and FIG. 43 “Thr, Ala, His, Phe, Ile, Cit, Leu, Orn, Gln, Val” is listed in order of increasing frequency of occurrence of variables in the expressions included in FIGS.
  • FIG. 44 and 45 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 44 and FIG. 45 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • “Ala, Thr, His, Ile, Phe, Cit, Gln, Leu, Arg, Orn” is listed.
  • FIGS. 46 and 47 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • FIGS. 46 and 47 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • the frequency of occurrence of the variables in the formulas included in FIGS. 46 and 47 is listed in descending order, they are “Orn, Ala, His, Gly, Arg, ABA, Lys, Phe, Met, Ser”.
  • FIGS. 48 and 49 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 48 and FIG. 49 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. 48.
  • it is “Orn, His, Ala, ABA, Gln, Arg, Met, Phe, Val, Trp”.
  • FIG. 50 and FIG. 51 show a list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC.
  • 50 and 51 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • 50 and 51 are listed in descending order of the appearance frequency of variables in the formulas, “Tyr, Met, Gln, Cit, Ile, Phe, Glu, Arg, Gly, Leu”.
  • a fractional expression is used as a multivariate discriminant, a combination of variables included in the fractional expression is searched, and a random sampling method is adopted as a cross-validation, so that the group 13 and the group 24 have good discriminating ability.
  • the search of the mathematical formula was conducted earnestly.
  • FIG. 52 and FIG. 53 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 52 and FIG. 53 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation. 52.
  • the variables appearing in the descending order of the frequency of occurrence are listed as “Gln, Met, Tyr, Ile, Cit, Leu, Arg, ABA, Val, Gly”.
  • a fractional expression is used as a multivariate discriminant, a combination of variables included in the fractional expression is searched, and a random sampling method is adopted as a cross-validation, so that the group 12 and the group 34 have good discriminating ability.
  • the search of the mathematical formula was conducted earnestly.
  • FIG. 56 and FIG. 57 show a list of fractional expressions with equally good discrimination ability evaluated by ROC_AUC.
  • FIG. 56 and FIG. 57 show fractional expressions, average values of ROC_AUC values with cross validation, and ROC_AUC values without cross validation.
  • FIG. 56 and FIG. 57 when the appearance frequency of the variables in the formulas is listed in descending order up to tenth, “Thr, Ile, Gln, Trp, Ala, Arg, Phe, His, Met, Asn” is obtained.
  • the biooxidation evaluation method and the like according to the present invention can be widely implemented in many industrial fields, in particular, in fields such as pharmaceuticals, foods, and medicine, and in particular, the progress prediction of the state of biooxidation. It is extremely useful in the field of bioinformatics that performs disease risk prediction, proteome and metabolomic analysis.

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Abstract

La présente invention concerne des problèmes tels que la réalisation d'un procédé d'évaluation de la bio-oxydation qui peut évaluer avec précision un état bio-oxydatif, y compris le niveau de contrainte oxydative et/ou la capacité anti-oxydative en utilisant la concentration des acides aminés dans un échantillon de sang, ainsi qu'un procédé de recherche d'une substance pour empêcher ou améliorer la bio-oxydation qui permet une recherche précise d'une substance qui peut empêcher la bio-oxydation ou améliorer un état bio-oxydatif. Dans ce procédé d'évaluation de la bio-oxydation, des données de concentration d'acide aminé en rapport avec les valeurs de la concentration d'acides aminés dans un échantillon de sang collecté sur un sujet d'évaluation sont obtenues et, en se basant sur les données de concentration d'acide aminé obtenues, l'état bio-oxydatif, y compris le niveau de contrainte oxydative et/ou la capacité anti-oxydative, est évalué pour le sujet d'évaluation.
PCT/JP2012/067166 2011-07-07 2012-07-05 Procédé d'évaluation de la bio-oxydation, dispositif à cet effet, procédure à cet effet, programme à cet effet, système à cet effet, dispositif terminal de communication de données à cet effet, et procédé de recherche d'une substance pour empêcher ou améliorer la bio-oxydation Ceased WO2013005790A1 (fr)

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Publication number Priority date Publication date Assignee Title
WO2019198601A1 (fr) * 2018-04-10 2019-10-17 味の素株式会社 Procédé, dispositif et programme d'évaluation de l'état de santé et/ou de l'état de croissance de bétail

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