JP6270257B2 - Acquisition method, cardiovascular event evaluation device, cardiovascular event evaluation program, cardiovascular event evaluation system, and terminal device - Google Patents

Description

  The present invention relates to a cardiovascular event evaluation method, a cardiovascular event evaluation device, a cardiovascular event evaluation method, a cardiovascular event evaluation program, a cardiovascular event evaluation system, and an information communication terminal device.

  The purpose of diabetes treatment is to prevent the onset of cardiovascular events such as myocardial infarction and stroke in addition to the three major complications such as nephropathy, retinopathy, and neuropathy by performing strict blood glucose control.

  In each analysis in the Multiple Risk Factor Intervention Trial (MRFIT), Japan Diabetes Complication Study (JDCS), and Hisayama-cho study, diabetic patients are about three to four times more likely to develop cardiovascular events than non-diabetic patients. It is clear that the cardiovascular mortality rate is about three times (Non-patent Documents 1, 2, and 3). According to the report of JDCS, the annual incidence of ischemic heart disease per 1,000 diabetic patients is 6.7, and 12.8 people develop cardiovascular events per year including cerebrovascular disorders. (Non-Patent Document 2). In the Funagata Diabetes Study, the oral glucose tolerance test shows that the mortality rate in the diabetic group is about three times that in the normal glucose tolerance group, and that the cardiovascular death is about twice in the glucose tolerance group as well. (Non-Patent Document 4).

  These results are not only from the time of diabetes but also from the pre-diabetes stage, with the aim of reducing mortality due to cardiovascular events. This suggests that intervention is necessary.

  Against this background, a large-scale clinical study was conducted to examine the effectiveness of multidisciplinary treatment for cardiovascular events in diabetic patients.

  However, the results show a higher mortality rate in the multidisciplinary treatment group, and the risk of blind multidisciplinary treatment for all diabetic patients has been clarified (Non-patent Document 5).

  In addition, antiplatelet drugs are widely used for the purpose of preventing cardiovascular events in diabetic patients. Although the effectiveness of secondary prevention is clear from the results of clinical trials so far, it is effective for primary prevention. The nature is not clear (Non-Patent Documents 6 and 7). In other words, antiplatelet drugs are effective in cases where cardiovascular events occur even once, but the effect is not seen in all diabetic patients, and administration of antiplatelet drugs to unnecessary patients is It only increases the risk of side effects.

  These clinical backgrounds are as follows: (1) Risks for cardiovascular events and the need for treatment (especially the need for primary prevention) are not uniform among diabetic patients, and (2) Effective for cardiovascular events In order to carry out safe treatment, it is important to establish a new predictive index for cardiovascular events and to conduct multidisciplinary treatment only in necessary cases.

  As prior patents, Patent Literature 1, Patent Literature 2, and Patent Literature 3 relating to a method of associating an amino acid concentration with a biological state are disclosed. In addition, Patent Document 4 regarding a method for evaluating the state of metabolic syndrome using amino acid concentration, Patent Document 5 regarding a method for evaluating the state of visceral fat accumulation using amino acid concentration, and the state of impaired glucose tolerance using amino acid concentration Patent Document 6 relating to an evaluation method and Patent Document 7 relating to a method for evaluating an obesity state using amino acid concentrations are disclosed.

International Publication No. 2004/052191 International Publication No. 2006/098192 International Publication No. 2009/054351 International Publication No. 2008/015929 International Publication No. 2009/001862 International Publication No. 2009/0535050 International Publication No. 2010/095682

Stammer J. et al. et al. Diabetes Care, 1993, 16, 434-444. Sone H. et al. Diabetologia, 2010, 53, 419-428. Ohmura T et al. Diabetologia, 1993, 36, 1198-1203. Tominaga M et al. Diabetes Care, 1999, 22, 920-924. ACCORD study group. , N.M. Engl. J. et al. Med. , 2008, 358, 2545-2559. Yasue H et al. , Am. J. et al. Cardiol. 1999, 83, 1308-1313. Ogawa H. et al. et al. JAMA, 2008, 300, 2134-2141.

  However, there are no reports that show sufficient diagnostic performance for the future state of cardiovascular events in blood tests including blood amino acid concentrations. Moreover, even if the index formula group currently disclosed by patent documents 1-7 is used for the diagnosis of the future state of a cardiovascular event, since the diagnostic object differs, sufficient accuracy is sufficient about evaluation of the future state of a cardiovascular event. I can't get it. In other words, there has been a problem that a method for evaluating (predicting) the future state of a cardiovascular event using blood amino acid concentration has not been developed and has not been put into practical use.

  The present invention has been made in view of the above problems, and a cardiovascular event evaluation method capable of accurately evaluating (predicting) the future state of a cardiovascular event using the concentration of amino acids in blood, It is an object of the present invention to provide a cardiovascular event evaluation device, a cardiovascular event evaluation method, a cardiovascular event evaluation program, a cardiovascular event evaluation system, and an information communication terminal device.

  In order to solve the above-described problems and achieve the object, the cardiovascular event evaluation method according to the present invention includes an acquisition step of acquiring amino acid concentration data relating to a concentration value of amino acids in blood collected from an evaluation target; EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, included in the amino acid concentration data of the evaluation object acquired in the acquisition step A concentration value reference evaluation step for evaluating a future state of a cardiovascular event for the evaluation object based on at least one of the concentration values of 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys; It is characterized by including.

  Here, in the present specification, cardiovascular events include coronary artery disease (myocardial infarction, angina pectoris, heart failure, etc.), cerebrovascular disorder (cerebral ischemia, cerebral infarction, cerebral hemorrhage, etc.) and other vascular disorders (internal carotid artery). Stenosis, peripheral occlusive arterial disease, etc.).

In this specification, various amino acids are mainly expressed by abbreviations, and their formal names are as follows.
(Abbreviation) (official name)
EtOHNH2 Ethanolamine
Gly Glycine
Sar Sarcosine
Ala Alanine
β-AIBA β-Amino-iso-butylic acid
α-ABA α-Aminobutyric acid
Ser Serine
Pro Proline
Val Valine
Thr Threoneine
Tau Taurine
Hypro Hydroproline
Ile Isolucine
Leu Leucine
Asn Asparagine
Orn Origine
Asp Aspartic acid
Gln Glutamine
Lys Lysine
Glu Glutamic acid
Met Methionine
His Histide
α-AAA α-Aminoadidic acid
Phe Phenylalanine
1-MeHis 1-Methylhistidine
3-MeHis 3-Methylhistidine
Arg Arginine
Cit Circleline
Tyr Tyrosine
Trp Tryptophan
Cys Cystine

  The cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the concentration value reference evaluation step includes EtOHNH2, Sar, Ala, β-AIBA, Ser included in the amino acid concentration data. , Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys based on the concentration value. And evaluating whether the evaluation object will develop the cardiovascular event in the future.

  The cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the concentration value reference evaluation step includes EtOHNH2, Sar, Ala, β-AIBA, Ser included in the amino acid concentration data. , Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and Containing the amino acid concentration as a variable, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Less than Arg, Cit, Tyr, Trp, Cys A discriminant that calculates a discriminant value that is a value of the multivariate discriminant based on a preset multivariate discriminant for evaluating the future state of the cardiovascular event, including one as the variable The method further includes a value calculating step.

  The cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant, a multiple regression equation, or a support vector machine. It is one of a created formula, a formula created by Mahalanobis distance method, a formula created by canonical discriminant analysis, a formula created by a decision tree, and a Cox proportional hazard model formula .

  Further, the cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the concentration value reference evaluation step is based on the discriminant value calculated in the discriminant value calculation step. And further comprising a discriminant value criterion evaluation step for evaluating the future state of the cardiovascular event.

  The cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the discriminant value calculating step includes EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2 , Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys The multivariate comprising at least one of the variables as the variable The discriminant value is calculated based on a discriminant, and the discriminant value criterion-evaluating step evaluates whether the evaluation target will develop the cardiovascular event in the future based on the discriminant value. To do.

  Further, the cardiovascular event evaluation method according to the present invention is the cardiovascular event evaluation method, wherein the multivariate discriminant includes EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as the variable. Logistic regression equation, the logistic regression equation including Ser, Thr, Lys, Arg, Trp as the variables, the linear discriminant equation including EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp as the variables, Pro, Thr, The linear discriminant including Orn, Arg, Trp as the variable, the Cox proportional hazard model equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as the variable, or Pro, Thr, Lys, Cit, Trp Including as a variable It is ox proportional hazards model equation, and wherein.

  The cardiovascular event evaluation apparatus according to the present invention is a cardiovascular event evaluation apparatus that includes a control unit and a storage unit, and evaluates the future state of the cardiovascular event with respect to an evaluation target. EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1- 1 At least one of the concentration values of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and the concentration of the amino acid as variables, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro , Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, A number for evaluating the future state of the cardiovascular event stored in the storage unit including at least one of 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as the variable. And a discriminant value calculating means for calculating a discriminant value which is a value of the multivariate discriminant based on the variable discriminant.

  The cardiovascular event evaluation apparatus according to the present invention is the cardiovascular event evaluation apparatus according to the present invention, wherein the control unit is configured to determine the cardiovascular event for the evaluation object based on the discrimination value calculated by the discrimination value calculation unit. And a discriminant value criterion evaluating means for evaluating the future state.

  The cardiovascular event evaluation apparatus according to the present invention is the cardiovascular event evaluation apparatus, wherein (i) the control unit relates to the amino acid concentration data and a cardiovascular related to an index representing the future state of the cardiovascular event. Multivariate discriminant creating means for creating the multivariate discriminant stored in the storage unit based on cardiovascular event state information stored in the storage unit including event state index data; and (ii) The multivariate discriminant creation means is a 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 from the cardiovascular event state information. Candidate multivariate discriminant verification means for verifying the candidate multivariate discriminant created by the candidate multivariate discriminant creation means based on a predetermined verification method, and a predetermined variable selector Variable selection means for selecting a combination of the amino acid concentration data included in the cardiovascular event state information used when creating the candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on And a plurality of the candidate multivariate discriminants based on the verification results accumulated by repeatedly executing the candidate multivariate discriminant creating unit, the candidate multivariate discriminant verifying unit and the variable selecting unit. The multivariate discriminant may be created by selecting the candidate multivariate discriminant adopted as the multivariate discriminant from among the above.

  The cardiovascular event evaluation method according to the present invention is a cardiovascular event evaluation method for evaluating a future state of a cardiovascular event for each evaluation object, which is executed in an information processing apparatus including a control unit and a storage unit. The EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp included in the amino acid concentration data to be evaluated regarding the amino acid concentration value executed in the control unit. , Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, The cardiovascular stored in the storage unit including at least one of Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys as the variable And a discriminant value calculating step of calculating a discriminant value which is a value of the multivariate discriminant based on the multivariate discriminant for evaluating the future state of the event.

  The cardiovascular event evaluation program according to the present invention is a cardiovascular event evaluation program for evaluating a future state of a cardiovascular event for each evaluation target to be executed by an information processing apparatus including a control unit and a storage unit. The EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn included in the evaluation target amino acid concentration data related to the amino acid concentration value to be executed by the control unit. , Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and the concentration of the amino acid as a variable, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hy Store in the storage unit including at least one of pro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as the variables. And a discriminant value calculating step of calculating a discriminant value that is a value of the multivariate discriminant based on the multivariate discriminant for evaluating the future state of the cardiovascular event. To do.

  A recording medium according to the present invention is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the cardiovascular event evaluation method. To do.

  In addition, the cardiovascular event evaluation system according to the present invention includes a control unit and a storage unit, includes a cardiovascular event evaluation apparatus that evaluates the future state of the cardiovascular event for each evaluation target, and a control unit. A cardiovascular event evaluation system configured to be communicably connected via a network to an information communication terminal device that provides the evaluation target amino acid concentration data relating to a value, the control unit of the information communication terminal device Receives amino acid concentration data transmitting means for transmitting the amino acid concentration data to be evaluated to the cardiovascular event evaluation apparatus, and a discriminant value that is a value of a multivariate discriminant transmitted from the cardiovascular event evaluation apparatus A result receiving means, wherein the control unit of the cardiovascular event evaluation device transmits the amino acid transmitted from the information communication terminal device. Amino acid concentration data receiving means for receiving degree data, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hyper, Ile, Leu, included in the amino acid concentration data received by the amino acid concentration data receiving means, EtOHNH2, Sar containing at least one of the concentration values of Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys and the concentration of the amino acid as variables. , Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys Including at least one of the variables Based on the multivariate discriminant for evaluating the future state of the cardiovascular event stored in the storage unit, the discriminant value calculating unit calculates the discriminant value, and the discriminant value calculating unit calculates the discriminant value. And a result transmitting means for transmitting the discriminated value to the information communication terminal device.

  Further, the cardiovascular event evaluation system according to the present invention is the cardiovascular event evaluation system, wherein the control unit of the cardiovascular event evaluation apparatus is based on the discriminant value calculated by the discriminant value calculating means. And further comprising a discriminant value criterion-evaluating unit that evaluates the future state of the cardiovascular event per evaluation object, and the result transmitting unit transmits the evaluation result of the evaluation object obtained by the discriminant value criterion-evaluating unit to the information communication It transmits to a terminal device, The said result receiving means receives the said evaluation result of the said evaluation object transmitted from the said information communication terminal device, It is characterized by the above-mentioned.

  The information communication terminal device according to the present invention is an information communication terminal device that includes a control unit and provides amino acid concentration data to be evaluated regarding the amino acid concentration value, wherein the control unit is a multivariate discriminant value. The discriminant value is obtained as EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hyper, Ile, Leu included in the amino acid concentration data to be evaluated. , Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and a concentration of the amino acid as a variable and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys for evaluating the future state of a cardiovascular event including at least one of the variables It is calculated based on the multivariate discriminant.

  The information communication terminal device according to the present invention is connected to the cardiovascular event evaluation device that evaluates the future state of the cardiovascular event for the evaluation object via the network in the information communication terminal device. The control unit further comprises amino acid concentration data transmitting means for transmitting the amino acid concentration data to be evaluated to the cardiovascular event evaluation apparatus, and the result acquisition means is transmitted from the cardiovascular event evaluation apparatus. Receiving the discriminated discriminant value.

  The information communication terminal device according to the present invention is the information communication terminal device, wherein the result acquisition means transmits the future state of the cardiovascular event to be evaluated, transmitted from the cardiovascular event evaluation device. An evaluation result is received, and the evaluation result is a result of evaluating the future state of the cardiovascular event for the evaluation object based on the discriminant value.

  The cardiovascular event evaluation apparatus according to the present invention includes a control unit and a storage unit that are communicably connected to an information communication terminal device that provides amino acid concentration data to be evaluated regarding amino acid concentration values via a network. A cardiovascular event evaluation apparatus for evaluating a future state of a cardiovascular event for the evaluation object, wherein the control unit receives the amino acid concentration data transmitted from the information communication terminal device And EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His included in the amino acid concentration data received by the amino acid concentration data receiving unit. , Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Tr , Cys containing at least one of the concentration values and the amino acid concentration as variables, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, Evaluation of the future state of the cardiovascular event stored in the storage unit including at least one of His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as the variable. A discriminant value calculating means for calculating a discriminant value which is a value of the multivariate discriminant based on the multivariate discriminant to transmit the discriminant value calculated by the discriminant value calculating means to the information communication terminal device And a result transmitting means.

  The cardiovascular event evaluation apparatus according to the present invention is the cardiovascular event evaluation apparatus according to the present invention, wherein the control unit is configured to determine the cardiovascular event for the evaluation object based on the discrimination value calculated by the discrimination value calculation unit. Further comprising discriminant value criterion evaluating means for evaluating the future state, and the result transmitting means transmits the evaluation result of the evaluation object obtained by the discriminant value criterion evaluating means to the information communication terminal device. It is characterized by.

  The method for searching for a substance for preventing / ameliorating a cardiovascular event according to the present invention is an amino acid concentration related to a concentration value of amino acids in blood collected from an evaluation subject to which a desired substance group consisting of one or more substances is administered. An acquisition step for acquiring data, a concentration value reference evaluation step for evaluating a future state of a cardiovascular event for the evaluation object based on the amino acid concentration data of the evaluation object acquired in the acquisition step, and the concentration value Based on the evaluation result in the reference evaluation step, it is determined whether the desired substance group is to prevent the future cardiovascular event or to improve the future state of the cardiovascular event. And a determination step.

  According to the present invention, amino acid concentration data related to the concentration value of amino acids in blood collected from an evaluation object is acquired, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro included in the acquired amino acid concentration data of the evaluation object. , Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys. Assess (predict) the future state of the cardiovascular event per subject. As a result, it is possible to accurately evaluate (predict) the future state of the cardiovascular event using the amino acid concentration useful for evaluating the future state of the cardiovascular event among the amino acid concentrations in the blood. Play.

  Further, according to the present invention, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-Phe, 1-H, contained in the amino acid concentration data. Based on at least one concentration value among MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys, it is evaluated whether the evaluation object will develop a cardiovascular event in the future. Thereby, the effect that the said evaluation can be accurately performed using the density | concentration of an amino acid useful for evaluation regarding the future onset of a cardiovascular event among the density | concentrations of the amino acid in blood is produced.

  Further, according to the present invention, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-Phe, 1-H, contained in the amino acid concentration data. At least one concentration value among MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and the concentration of amino acid as variables, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile , Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys as a variable of a preset cardiovascular event Based on the multivariate discriminant for evaluating future conditions, A discriminant value which is a value of the multivariate discriminant is calculated. Thus, it is possible to obtain a discriminant value that reflects the future state of the cardiovascular event to be evaluated using a multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  Further, according to the present invention, 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. One of an expression created by analysis, an expression created by a decision tree, and a Cox proportional hazard model expression. Accordingly, it is possible to obtain a discriminant value that reflects the future state of the cardiovascular event to be evaluated by using a multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  Further, according to the present invention, the future state of the cardiovascular event is evaluated for each evaluation object based on the calculated discriminant value. This produces an effect that the future state of the cardiovascular event can be accurately evaluated using the discriminant value obtained by the multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  Further, according to the present invention, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-Phe, 1-H, contained in the amino acid concentration data. At least one concentration value among MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, A discriminant value is calculated based on a multivariate discriminant including at least one of Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. Based on the above, it is evaluated whether the evaluation object will develop a cardiovascular event in the future. Thereby, the discriminant value obtained by the multivariate discriminant useful for the evaluation regarding the future onset of the cardiovascular event is used, and the effect that the evaluation can be performed with high accuracy is achieved.

  Further, according to the present invention, the multivariate discriminant includes a logistic regression equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as a variable, and a logistic regression including Ser, Thr, Lys, Arg, Trp as a variable. A linear discriminant including EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp as a variable, a linear discriminant including Pro, Thr, Orn, Arg, Trp as a variable, EtOHNH2, Hypro, Glu, 3-MeHis, It is a Cox proportional hazard model formula including Tyr and Trp as variables, or a Cox proportional hazard model formula including Pro, Thr, Lys, Cit, and Trp as variables. Thereby, the discriminant value obtained by the multivariate discriminant that is particularly useful for the evaluation regarding the future onset of the cardiovascular event is used, and the effect that the evaluation can be performed with higher accuracy is achieved.

  According to the present invention, the storage means based on the cardiovascular event state information stored in the storage means including the amino acid concentration data and the cardiovascular event state index data relating to the index representing the future state of the cardiovascular event. A multivariate discriminant to be stored may be created. Specifically, (i) a candidate multivariate discriminant is created based on a predetermined formula creation method from cardiovascular event state information, and (ii) the created candidate multivariate discriminant is verified based on a predetermined verification method. (Iii) A combination of amino acid concentration data included in cardiovascular event state information used when creating a candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method Is selected from among a plurality of candidate multivariate discriminants based on the verification results accumulated by repeatedly executing (iv) (i), (ii) and (iii). A multivariate discriminant may be created by selecting a variable discriminant. Thereby, there is an effect that a multivariate discriminant optimum for evaluation of the future state of the cardiovascular event can be created.

  Further, according to the present invention, 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. Based on the evaluation results, the future state of the cardiovascular event is evaluated, and based on the evaluation result, the desired substance group prevents the future cardiovascular event or improves the future state of the cardiovascular event. Since it is determined whether or not there is a cardiovascular event evaluation method that can accurately evaluate the future state of the cardiovascular event using the concentration of amino acids in the blood, There is an effect that a substance that can prevent or improve the future state of the cardiovascular event can be searched with high accuracy. In addition, according to the present invention, by using information on typical amino acid concentration fluctuation patterns in future cardiovascular events and multivariate discriminants corresponding to future cardiovascular events, the future state of cardiovascular events It is possible to select an existing animal model that partially reflects the above and a clinically effective drug at an early stage.

  In the present invention, when evaluating the future state of the cardiovascular event, in addition to the amino acid concentration, other biological information (for example, age, AER (urinary albumin excretion rate), BNP (brain natriuretic peptide), etc. ) May be further used. In the present invention, when evaluating the future state of the cardiovascular event, as a variable in the multivariate discriminant, in addition to the amino acid concentration, other biological information (for example, age, AER (urinary albumin excretion rate), BNP (brain natriuretic peptide) and the like may be further used.

FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment. FIG. 2 is a flowchart illustrating an example of a cardiovascular event evaluation method according to the first embodiment. FIG. 3 is a principle configuration diagram showing the basic principle of the second embodiment. 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 cardiovascular event 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 cardiovascular event state information file 106c. FIG. 10 is a diagram illustrating an example of information stored in the designated cardiovascular event 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 cardiovascular event 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 cardiovascular event evaluation service processing performed in the present system. FIG. 22 is a flowchart showing an example of multivariate discriminant creation processing performed by the cardiovascular event evaluation apparatus 100 of the present system. FIG. 23 is a diagram showing a list of logistic regression equations having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 24 is a diagram showing a list of logistic regression equations having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 amino acids. is there. FIG. 25 is a diagram showing a list of logistic regression equations having good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 26 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 27 is a diagram showing a list of logistic regression equations having a good discriminating ability for discriminating whether it is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 28 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 29 is a diagram showing a list of logistic regression equations having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 30 is a diagram showing a list of logistic regression equations having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 31 is a diagram showing a list of linear discriminants having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 32 is a diagram showing a list of linear discriminants having good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 33 is a diagram showing a list of linear discriminants having good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 34 is a diagram showing a list of linear discriminants having good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 31 kinds of amino acids. is there. FIG. 35 is a diagram showing a list of linear discriminants having a good discriminating ability for discriminating whether it is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 36 is a diagram showing a list of linear discriminants having a good discriminating ability for discriminating whether it is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 37 is a diagram showing a list of linear discriminants having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 38 is a diagram showing a list of linear discriminants having a good discriminating ability for discriminating whether the group is a normal group or a cardiovascular event group in the future, obtained as a result of searching for combinations of variables from 19 kinds of amino acids. is there. FIG. 39 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 31 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 40 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 31 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 41 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 31 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 42 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 31 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 43 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 19 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 44 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 19 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG. FIG. 45 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 19 kinds of amino acids and having good discrimination ability for discrimination between the normal group and the cardiovascular event group in the future. FIG. FIG. 46 shows a list of Cox proportional hazard model formulas obtained as a result of searching for combinations of variables from 19 kinds of amino acids and having good discrimination ability for discrimination as to whether the group is a normal group or a cardiovascular event group in the future. FIG.

  Embodiments of Cardiovascular Event Evaluation Method (First Embodiment), Cardiovascular Event Evaluation Device, Cardiovascular Event Evaluation Method, Cardiovascular Event Evaluation Program, Recording Medium, Cardiovascular Event Evaluation System, and Information Communication Terminal An apparatus embodiment (second embodiment) will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[First Embodiment]
[1-1. Overview of First Embodiment]
Here, an overview of the first embodiment will be described with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.

First, amino acid concentration data relating to the concentration value of amino acids in blood (eg, including plasma, serum, etc.) collected from an evaluation target (eg, an individual such as an animal or a human (eg, a diabetic patient)) is acquired (step S11). In step S11, for example, amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired. Further, for example, 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. Here, 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.
(A) Serum was separated from blood by centrifuging the collected blood sample. All serum samples were stored frozen at −80 ° C. until measurement of amino acid concentration. At the time of amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatograph mass spectrometry The amino acid concentration was analyzed by a total (LC / MS / MS) (see International Publication No. 2003/069328, International Publication No. 2005/116629).
(B) Serum was separated from blood by centrifuging the collected blood sample. All serum samples were stored frozen at −80 ° C. until measurement of 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.

  Next, based on the amino acid concentration data acquired in step S11, the future state of the cardiovascular event is evaluated (predicted) for each evaluation target (step S12).

  As described above, according to the first embodiment, the amino acid concentration data related to the amino acid concentration value in the blood collected from the evaluation object is acquired, and the future of the cardiovascular event for the evaluation object based on the acquired amino acid concentration data of the evaluation object. (In short, it provides information for assessing the future state of the cardiovascular event per subject of assessment). This makes it possible to accurately evaluate the future state of the cardiovascular event using the concentration of amino acids in the blood (in short, provide accurate information for evaluating the future state of the cardiovascular event. be able to).

  Here, before executing step S12, data such as missing values and outliers may be removed from the amino acid concentration data acquired in step S11. Thereby, the future state of the cardiovascular event can be evaluated more accurately.

  In step S12, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe contained in the amino acid concentration data acquired in step S11. , 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, the future state of the cardiovascular event may be evaluated for each evaluation target. This makes it possible to accurately evaluate the future state of the cardiovascular event by using the amino acid concentration useful for evaluating the future state of the cardiovascular event among the amino acid concentrations in the blood.

  Specifically, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3 included in amino acid concentration data. -Based on the concentration value of at least one of MeHis, Arg, Cit, Tyr, Trp, and Cys, it may be evaluated whether the evaluation target will develop a cardiovascular event in the future. For example, it is determined whether or not the evaluation target will develop a cardiovascular event in the future, and among a plurality of categories (ranks) defined in consideration of the possibility of developing a cardiovascular event in the future The evaluation target may be classified into any one of the above. Thereby, the evaluation can be performed with high accuracy by using the amino acid concentration useful for evaluating the future development of the cardiovascular event among the amino acid concentrations in the blood.

Further, the range that the density value can take is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or −10. For example, an arbitrary value is added / subtracted / multiplied / divided with respect to the density value so as to fall within a range from 0 to 10.0, or the density value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, The density value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, or the like, or by combining these calculations with respect to the density value. For example, a value of an exponential function having a concentration value as an index and a Napier number as a base (specifically, a natural logarithm ln (p / (1-p)) when a probability p of developing a cardiovascular event in the future is defined. (P / (1-p) value in the case where is equal to the density value), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, The value of probability p) may be further calculated.
Further, the density value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.

  In 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. Further, the future state of the cardiovascular event may be evaluated for each evaluation target based on the calculated discriminant value. This makes it possible to obtain a discriminant value reflecting the future state of the cardiovascular event to be evaluated using a multivariate discriminant including the amino acid concentration as a variable, and further, using the amino acid concentration as a variable. The future state of the cardiovascular event can be accurately evaluated by using the discriminant value obtained by the multivariate discriminant including it.

  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 an expression created by a decision tree and a Cox proportional hazard model expression may be used. Thereby, the future state of the cardiovascular event can be accurately evaluated using the discriminant value obtained by the multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  In step S12, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe contained in the amino acid concentration data acquired in step S11. , 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, based on a multivariate discriminant including at least one as a variable Based on the calculated discriminant value, cardiovascular per evaluation object The future state of the event may be evaluated. Thereby, the future state of the cardiovascular event can be accurately evaluated using the discriminant value obtained by the multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  Specifically, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3 included in amino acid concentration data. A concentration value of at least one of MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hyper, Ile, Leu, Orn, Asp, Lys, Glu , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, based on a multivariate discriminant including as a variable, a discriminant value is calculated, and the calculated discriminant value Based on this, it may be evaluated whether the evaluation target will develop a cardiovascular event in the future . For example, it is determined whether or not the evaluation target will develop a cardiovascular event in the future, and among a plurality of categories (ranks) defined in consideration of the possibility of developing a cardiovascular event in the future The evaluation target may be classified into any one of the above. Thereby, the said evaluation can be accurately performed using the discriminant value obtained by the multivariate discriminant useful for evaluation regarding the future onset of a cardiovascular event.

  Note that the multivariate discriminant used in the evaluation of the future onset of the cardiovascular event is a logistic regression equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as variables, Ser, Thr, Lys, Arg, Trp. Logistic regression equation including EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp as variables, linear discriminant including Pro, Thr, Orn, Arg, Trp as variables, EtOHNH2, Hypro, A Cox proportional hazard model equation including Glu, 3-MeHis, Tyr, Trp as a variable or a Cox proportional hazard model equation including Pro, Thr, Lys, Cit, Trp as a variable may be used. Thereby, the evaluation can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant that is particularly useful for the evaluation regarding the future onset of the cardiovascular event.

Further, the range that the discriminant value can take is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10. For example, an arbitrary value is added / subtracted / multiplied / divided with respect to the discriminant value so as to fall within a range from 0 to 10.0, or the discriminant value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, The discriminant value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, or reciprocal conversion, or by combining these calculations with respect to the discriminant value. For example, the value of an exponential function with the discriminant value as the exponent and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p)) when the probability p of developing a cardiovascular event in the future is defined) (P / (1−p) value in the case where is equal to the discriminant value), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, The value of probability p) may be further calculated.
Also, the discriminant value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the discriminant value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Note that the discriminant value in this specification may be the value of the multivariate discriminant itself, or may be a value after converting the value of the multivariate discriminant.

  Here, each multivariate discriminant described above is described in, for example, 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 create by the method of description. If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for evaluating the future state of the cardiovascular event regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.

  The multivariate discriminant means a formula format generally used in multivariate analysis. For example, a fractional equation, multiple regression equation, multiple logistic regression equation, linear discriminant function, Mahalanobis distance, canonical discriminant function, support vector Includes machines, decision trees, and Cox proportional hazard models. Also included are expressions as indicated by the sum of different forms of multivariate discriminants. In the multiple regression equation, multiple logistic regression equation, and canonical discriminant function, 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. Values belonging to the range of 99% confidence intervals of the coefficients and constant terms obtained from the data, more preferably belonging to the range of 95% confidence intervals of the coefficients and constant terms obtained from the data Any value can be used. In addition, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto. When using display formulas such as logistic regression, linear discriminant, multiple regression analysis as indicators, linear transformation (addition of constants, multiple of constants) or monotonically increasing (decreasing) transformations of display formulas (such as logit transformation) have discriminative performance. The display formulas include them because they are equivalent, not changed.

  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 In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. 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. In addition, amino acids used in the numerator and denominator may overlap. Moreover, an appropriate coefficient may be attached to each fractional expression. Moreover, the value of the coefficient of each variable and the value of the constant term may be real numbers. In the fractional expression, the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the objective variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.

  In the first embodiment, when evaluating the future state of the cardiovascular event, in addition to the amino acid concentration, other biological information (eg, age, AER (urinary albumin excretion rate), BNP (brain natriuretic peptide) ) Etc.) may be further used. In the first embodiment, when evaluating the future state of the cardiovascular event, as a variable in the multivariate discriminant, in addition to the amino acid concentration, other biological information (for example, age, AER (urinary albumin excretion rate) ), BNP (brain natriuretic peptide, etc.) may be further used.

  Before executing step S11, a desired substance group consisting of one or more substances is administered to the evaluation object, blood is collected from the evaluation object, and in step S11, amino acids are collected from the collected blood. When acquiring the concentration data, based on the evaluation result in step S12 (for example, the discrimination result / classification result relating to the future onset of the cardiovascular event), the administered substance group is changed to the future cardiovascular event (cardiac event). It may be determined whether the future occurrence of a vascular event is prevented or the future state of the cardiovascular event is improved. Thus, by using the cardiovascular event evaluation method that can accurately evaluate the future state of the cardiovascular event using the concentration of amino acids in the blood, the cardiovascular event can be prevented or prevented. It is possible to accurately search for substances that improve the future state of. For example, before executing step S11, an appropriate combination of existing drugs, amino acids, foods, and supplements that can be administered to humans (eg, known to be effective in preventing or improving future cardiovascular events) A drug (for example, an ACE (angiotensin converting enzyme) inhibitor, ARB (angiotensin II receptor antagonist), etc.), a supplement (for example, a low protein diet, polyphenol, etc.) or the like combined for a predetermined period (for example, It may be administered by a predetermined administration method (for example, oral administration) at a predetermined frequency / timing (for example, 3 times a day / after meal) over a range of 1 day to 12 months). Here, 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.

  If the determination result is “prevent or improve”, the substance group administered is searched for to prevent future cardiovascular events or improve the future state of cardiovascular events. May be. In addition, as a substance searched by this searching method, for example, “EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1 -A group of amino acids including at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys.

  In addition, “EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr , Trp, Cys ”and the substance that normalizes the discriminant value of each multivariate discriminant according to the cardiovascular event evaluation method of the first embodiment and the substance of the second embodiment. Selection can be made using a cardiovascular event evaluation device.

  “Searching for a substance to prevent or ameliorate” not only finds a new substance effective in preventing or improving a future cardiovascular event, but also uses a known substance for preventing or improving a future cardiovascular event. New compositions combining existing drugs and supplements that can be expected to be effective in preventing and improving future cardiovascular events, and finding the appropriate usage, dosage, and combination described above , Make it a kit, present a prevention / treatment menu including meals / exercise, etc., monitor the effect of the prevention / treatment menu, and present menu changes for each individual as needed Is included.

[1-2. Specific Example of First Embodiment]
Here, a specific example of the first embodiment will be described with reference to FIG. FIG. 2 is a flowchart illustrating an example of a cardiovascular event evaluation method according to the first embodiment.

  First, amino acid concentration data relating to the concentration value of amino acids in blood collected from an individual such as an animal or a human (eg, a diabetic patient) is acquired (step SA11). In step SA11, for example, amino acid concentration data measured by a company or the like that measures amino acid concentration may be acquired, and a measuring method such as (A) or (B) described above from blood collected from an individual. The amino acid concentration data may be obtained by measuring the amino acid concentration data.

  Next, data such as missing values and outliers are removed from the individual amino acid concentration data acquired in step SA11 (step SA12).

Next, it is determined whether or not the individual will develop a cardiovascular event in the future based on the amino acid concentration data of the individual from which data such as missing values and outliers have been removed in step SA12 (step SA13). Specifically, the following two-group discrimination (11A) or (11B) is executed.
(11A) EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis included in amino acid concentration data , Arg, Cit, Tyr, Trp, and Cys to determine whether the individual will develop a cardiovascular event in the future by comparing the concentration value with a preset threshold value (cutoff value) To do.
(11B) EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis included in amino acid concentration data , Arg, Cit, Tyr, Trp, Cys at least one concentration value, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His , Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys is calculated based on a multivariate discriminant including at least one of the variables, and the calculated discriminant value and a preset value are set. By comparing the measured threshold value (cutoff value), Determine whether or not to develop a tube event.

[Second Embodiment]
[2-1. Outline of Second Embodiment]
Here, an outline of the second embodiment will be described with reference to FIG. FIG. 3 is a principle configuration diagram showing the basic principle of the second embodiment.

  First, the control unit stores the amino acid concentration data of an evaluation object (for example, an individual such as an animal or a human (for example, a diabetic patient)) acquired in advance regarding the amino acid concentration value, and the storage unit including the amino acid concentration as a variable. Based on the multivariate discriminant, a discriminant value that is the value of the multivariate discriminant is calculated (step S21).

  Next, the control unit evaluates (predicts) the future state of the cardiovascular event for each evaluation object based on the discriminant value calculated in step S21 (step S22).

  As described above, according to the second embodiment, based on the amino acid concentration data to be evaluated and the multivariate discriminant including the amino acid concentration as a variable, the discriminant value (in short, the value of the evaluation target). A discriminant value reflecting the future state of the cardiovascular event), and based on the calculated discriminant value, the future state of the cardiovascular event is evaluated for each evaluation target (in short, the future of the cardiovascular event for each evaluation target) Provide information to assess the condition). This makes it possible to obtain a discriminant value reflecting the future state of the cardiovascular event to be evaluated using a multivariate discriminant including the amino acid concentration as a variable, and further, using the amino acid concentration as a variable. It is possible to accurately evaluate the future state of the cardiovascular event by using the discriminant value obtained by the multivariate discriminant including (in short, high accuracy for evaluating the future state of the cardiovascular event. Information can be provided).

  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 an expression created by a decision tree and a Cox proportional hazard model expression may be used. Thereby, the future state of the cardiovascular event can be accurately evaluated using the discriminant value obtained by the multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  In step S21, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, included in the amino acid concentration data. At least one concentration value among 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hyper, Ile, Leu, Orn, Asp, Lys, A discriminant value is calculated based on a multivariate discriminant including at least one of Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. In step S22, Based on the discriminant value calculated in step S21, the evaluation target The future state of the cardiovascular event may be evaluated. Thereby, the future state of the cardiovascular event can be accurately evaluated using the discriminant value obtained by the multivariate discriminant useful for evaluating the future state of the cardiovascular event.

  Specifically, in step S21, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1 included in the amino acid concentration data. -At least one concentration value among MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp , Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, based on a multivariate discriminant including at least one variable, In S22, the evaluation is performed based on the discriminant value calculated in Step S21. It may be evaluated whether the subject to be evaluated will develop a cardiovascular event in the future. For example, it is determined whether or not the evaluation target will develop a cardiovascular event in the future, and among a plurality of categories (ranks) defined in consideration of the possibility of developing a cardiovascular event in the future The evaluation target may be classified into any one of the above. Thereby, the said evaluation can be accurately performed using the discriminant value obtained by the multivariate discriminant useful for evaluation regarding the future onset of a cardiovascular event.

  Note that the multivariate discriminant used in the evaluation of the future onset of the cardiovascular event is a logistic regression equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as variables, Ser, Thr, Lys, Arg, Trp. Logistic regression equation including EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp as variables, linear discriminant including Pro, Thr, Orn, Arg, Trp as variables, EtOHNH2, Hypro, A Cox proportional hazard model equation including Glu, 3-MeHis, Tyr, Trp as a variable or a Cox proportional hazard model equation including Pro, Thr, Lys, Cit, Trp as a variable may be used. Thereby, the evaluation can be performed with higher accuracy by using the discriminant value obtained by the multivariate discriminant that is particularly useful for the evaluation regarding the future onset of the cardiovascular event.

Further, the range that the discriminant value can take is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10. For example, an arbitrary value is added / subtracted / multiplied / divided with respect to the discriminant value so as to fall within a range from 0 to 10.0, or the discriminant value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, The discriminant value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, or reciprocal conversion, or by combining these calculations with respect to the discriminant value. For example, the value of an exponential function with the discriminant value as the exponent and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p)) when the probability p of developing a cardiovascular event in the future is defined) (P / (1−p) value in the case where is equal to the discriminant value), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, The value of probability p) may be further calculated.
Also, the discriminant value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the discriminant value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Note that the discriminant value in this specification may be the value of the multivariate discriminant itself, or may be a value after converting the value of the multivariate discriminant.

  Here, each multivariate discriminant described above is described in, for example, 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 create by the method of description. If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for evaluating the future state of the cardiovascular event regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.

  The multivariate discriminant means a formula format generally used in multivariate analysis. For example, a fractional equation, multiple regression equation, multiple logistic regression equation, linear discriminant function, Mahalanobis distance, canonical discriminant function, support vector Includes machines, decision trees, and Cox proportional hazard models. Also included are expressions as indicated by the sum of different forms of multivariate discriminants. In the multiple regression equation, multiple logistic regression equation, and canonical discriminant function, 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. Values belonging to the range of 99% confidence intervals of the coefficients and constant terms obtained from the data, more preferably belonging to the range of 95% confidence intervals of the coefficients and constant terms obtained from the data Any value can be used. In addition, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto. When using display formulas such as logistic regression, linear discriminant, multiple regression analysis as indicators, linear transformation (addition of constants, multiple of constants) or monotonically increasing (decreasing) transformations of display formulas (such as logit transformation) have discriminative performance. The display formulas include them because they are equivalent, not changed.

  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 In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. 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. In addition, amino acids used in the numerator and denominator may overlap. Moreover, an appropriate coefficient may be attached to each fractional expression. Moreover, the value of the coefficient of each variable and the value of the constant term may be real numbers. In the fractional expression, the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the objective variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.

  In the second embodiment, when evaluating the future state of the cardiovascular event, in addition to the amino acid concentration, other biological information (for example, age, AER (urinary albumin excretion rate), BNP (brain natriuretic peptide) ) Etc.) may be further used. In the second embodiment, when evaluating the future state of the cardiovascular event, as a variable in the multivariate discriminant, in addition to the amino acid concentration, other biological information (for example, age, AER (urinary albumin excretion rate) ), BNP (brain natriuretic peptide, etc.) may be further used.

  Here, the outline | summary of a multivariate discriminant preparation process (process 1-process 4) is demonstrated 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.

First, the control unit is based on a predetermined formula creation method from cardiovascular event state information stored in the storage unit including amino acid concentration data and cardiovascular event state index data relating to an index representing the future state of the cardiovascular event. , A candidate multivariate discriminant that is a candidate for the multivariate discriminant (for example, y = a ₁ x ₁ + a ₂ x ₂ +... + A _n x _n , y: cardiovascular event state index data, x _i : amino acid concentration Data, a _i : constant, i = 1, 2,..., N) are created (step 1). Note that data having missing values, outliers, and the like may be removed from the cardiovascular event state information in advance.

  In step 1, a plurality of different formula creation methods (principal component analysis and discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree, A plurality of candidate multivariate discriminants may be created by using a combination of the Cox proportional hazard model and the like related to multivariate analysis. Specifically, for cardiovascular event state information that is multivariate data composed of amino acid concentration data and cardiovascular event state index data obtained by analyzing blood obtained from a number of healthy groups and cardiovascular event groups Thus, a plurality of groups of candidate multivariate discriminants may be created concurrently using a plurality of different algorithms. For example, two different candidate multivariate discriminants may be created by performing discriminant analysis and logistic regression analysis simultaneously using different algorithms. In addition, the candidate multivariate discriminant created by performing principal component analysis is used to convert the cardiovascular event state information, and discriminant analysis is performed on the converted cardiovascular event state information to obtain the candidate multivariate discriminant. You may create it. Thereby, finally, an appropriate multivariate discriminant suitable for the diagnosis condition can be created.

  Here, the candidate multivariate discriminant created using principal component analysis is a linear expression including each amino acid variable that maximizes the dispersion of all amino acid concentration data. In addition, the candidate multivariate discriminant created using discriminant analysis is a high-order formula (exponential or exponential) including each amino acid variable that minimizes the ratio of the sum of variances within each group to the variance of all amino acid concentration data. Including logarithm). In addition, the candidate multivariate discriminant created using the support vector machine is a higher-order formula (including a kernel function) including each amino acid variable that maximizes the boundary between groups. In addition, the candidate multivariate discriminant created using multiple regression analysis is a high-order expression including each amino acid variable that minimizes the sum of distances from all amino acid concentration data. A candidate multivariate discriminant created using logistic regression analysis is a linear model representing the log odds of probability, and is a linear expression including each amino acid variable that maximizes the likelihood of the probability. The k-means method searches k neighborhoods of each amino acid concentration data, defines the largest number of groups to which 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. Further, the decision tree is a technique for predicting a group of amino acid concentration data from patterns that can be taken by amino acid variables having higher ranks by adding ranks to amino acid variables. The Cox proportional hazard model is a technique for predicting the influence of amino acid variables on survival or events while taking into consideration survival time, event time, and censoring.

  Returning to the description of the multivariate discriminant creation process, the control unit verifies (mutually verifies) the candidate multivariate discriminant created in step 1 based on a predetermined verification method (step 2). The candidate multivariate discriminant is verified for each candidate multivariate discriminant created in step 1.

  In 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. Verification may be made with respect to at least one of ROC_AUC (area under the curve of the receiver characteristic curve) and the like. Thereby, a candidate multivariate discriminant having high predictability or robustness in consideration of cardiovascular event state information and diagnosis conditions can be created.

  Here, the discrimination rate is a ratio in which the future state of the cardiovascular event evaluated in the present embodiment is correct among all input data. Sensitivity is the correct proportion of the future state of the cardiovascular event evaluated in the present embodiment among the future states of the cardiovascular event described in the input data. Further, the specificity is a ratio in which the future state of the cardiovascular event evaluated in the present embodiment is correct among the cases where the future state of the cardiovascular event described in the input data is normal. The information criterion refers to the number of amino acid variables of the candidate multivariate discriminant created in Step 1, the future state of the cardiovascular event evaluated in this embodiment, and the future of the cardiovascular event described in the input data. This is the sum of the difference in the state of. ROC_AUC (area under the curve of the receiver characteristic curve) is a receiver characteristic curve (ROC) that is a curve created by plotting (x, y) = (1−specificity, sensitivity) on a two-dimensional coordinate. ), 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.

  Returning to the description of the multivariate discriminant creation process, the control unit selects a variable of the candidate multivariate discriminant based on a predetermined variable selection method, so that the cardiovascular event used when creating the candidate multivariate discriminant A combination of amino acid concentration data included in the state information is selected (step 3). The selection of amino acid variables may be performed 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 cardiovascular event state information including the amino acid concentration data selected in Step 3.

  In 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 the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm. .

  Here, 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.

  Returning to the description of the multivariate discriminant creation process, the control unit repeatedly executes the above-described step 1, step 2 and step 3, and based on the verification results accumulated thereby, the control unit can select from a plurality of candidate multivariate discriminants. A multivariate discriminant is created by selecting candidate multivariate discriminants to be adopted as the multivariate discriminant (step 4). In selecting candidate multivariate discriminants, for example, selecting the optimal one from among candidate multivariate discriminants created by the same formula creation method, and selecting the most suitable from all candidate multivariate discriminants There is a case to choose one.

  As described above, the multivariate discriminant creation process relates to the creation of a candidate multivariate discriminant, the verification of the candidate multivariate discriminant, and the selection of the variable of the candidate multivariate discriminant based on the cardiovascular event state information. By executing the systematized (systematized) process in a series of flows, it is possible to create a multivariate discriminant that is optimal for evaluating the future state of the cardiovascular event. In other words, in the multivariate discriminant creation process, amino acid concentrations are used for multivariate statistical analysis, and variable selection methods and cross-validation are combined in order to select optimal and robust variable sets. Extract the variable discriminant. As the multivariate discriminant, logistic regression, linear discrimination, support vector machine, Mahalanobis distance method, multiple regression analysis, cluster analysis, Cox proportional hazard model, and the like can be used.

[2-2. Configuration of Second Embodiment]
Here, the configuration of the cardiovascular event evaluation system according to the second embodiment (hereinafter sometimes referred to as the present system) will be described with reference to FIGS. 4 to 20. This system is merely an example, and the present invention is not limited to this.

  First, the overall configuration of this system will be described with reference to FIGS. 4 and 5. FIG. 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. As shown in FIG. 4, the present system includes a cardiovascular event evaluation apparatus 100 that evaluates the future state of a cardiovascular event for each evaluation object, and a client apparatus 200 that provides amino acid concentration data of the evaluation object related to the amino acid concentration value. (Corresponding to the information communication terminal device of the present invention) via a network 300 so as to be communicable.

  As shown in FIG. 5, in addition to the cardiovascular event evaluation apparatus 100 and the client apparatus 200, this system uses cardiovascular event state information used when creating a multivariate discriminant in the cardiovascular event evaluation apparatus 100. The database apparatus 400 storing a multivariate discriminant used for evaluating the future state of the cardiovascular event may be configured to be communicably connected via the network 300. Accordingly, the cardiovascular event evaluation apparatus 100 to the client apparatus 200 or the database apparatus 400 or the client apparatus 200 or the database apparatus 400 to the cardiovascular event evaluation apparatus 100 via the network 300 is related to the future state of the cardiovascular event. Information is provided. Here, the information regarding the future state of the cardiovascular event is information regarding a value measured for a specific item regarding the future state of the cardiovascular event of an organism including a human. Information regarding the future state of the cardiovascular event is generated by the cardiovascular event evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measuring apparatuses) and is mainly stored in the database apparatus 400.

  Next, the configuration of the cardiovascular event evaluation apparatus 100 of this system will be described with reference to FIGS. FIG. 6 is a block diagram showing an example of the configuration of the cardiovascular event evaluation apparatus 100 of this system, and conceptually shows only the portion related to the present invention in the configuration.

  The cardiovascular event evaluation apparatus 100 includes a control unit 102 such as a CPU that comprehensively controls the cardiovascular event evaluation apparatus, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. A communication interface unit 104 that connects the event evaluation apparatus to the network 300 to be communicable, 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 parts are connected to be communicable via an arbitrary communication path. Here, the cardiovascular event evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analyzer or the like).

  The storage unit 106 is a storage unit, 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). As illustrated, the storage unit 106 includes a user information file 106a, an amino acid concentration data file 106b, a cardiovascular event state information file 106c, a designated cardiovascular event state information file 106d, and 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. As shown in FIG. 7, 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. A user password, a user name, an affiliation ID for uniquely identifying the affiliation to which the user belongs, a department ID for uniquely identifying the department to which the user belongs, The department name and the user's e-mail address are associated with each other.

  Returning to FIG. 6, 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. As shown in FIG. 8, 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. Here, in FIG. 8, 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. Further, other biological information (for example, age, AER (urinary albumin excretion rate), BNP (brain natriuretic peptide), etc.) may be combined with the amino acid concentration data.

Returning to FIG. 6, the cardiovascular event state information file 106c stores cardiovascular event state information used when creating a multivariate discriminant. FIG. 9 is a diagram illustrating an example of information stored in the cardiovascular event state information file 106c. As shown in FIG. 9, information stored in the cardiovascular event state information file 106c includes an individual number and an index (index T ₁ , index T ₂ , index T ₃ ... ) Cardiovascular event state index data (T) and amino acid concentration data are associated with each other. Here, in FIG. 9, the cardiovascular event state index data and the amino acid concentration data are treated as numerical values (that is, a continuous scale), but the cardiovascular event 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 cardiovascular event state index data is a known single state index serving as a marker of the future state of the cardiovascular event, and numerical data may be used.

  Returning to FIG. 6, the designated cardiovascular event state information file 106d stores the cardiovascular event state information specified by the cardiovascular event state information specifying unit 102g described later. FIG. 10 is a diagram illustrating an example of information stored in the designated cardiovascular event state information file 106d. As shown in FIG. 10, the information stored in the designated cardiovascular event state information file 106d is configured by associating an individual number, designated cardiovascular event state index data, and designated amino acid concentration data with each other. ing.

  Returning to FIG. 6, 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 selected cardiovascular event state information file 106e3 for storing a verification result file 106e2 for storing the verification result in the discriminant verification unit 102h2 and cardiovascular event state information including a combination of amino acid concentration data selected by the variable selection unit 102h3 to be described later And a multivariate discriminant file 106e4 for storing the multivariate discriminant created by the multivariate discriminant creation unit 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. As shown in FIG. 11, 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.

Returning to FIG. 6, the verification result file 106e2 stores the verification result in the candidate multivariate discriminant verification unit 102h2 described later. FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2. As shown in FIG. 12, 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,...)) 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.

  Returning to FIG. 6, the selected cardiovascular event state information file 106e3 stores cardiovascular event 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 cardiovascular event state information file 106e3. As shown in FIG. 13, the information stored in the selected cardiovascular event state information file 106e3 includes an individual number, cardiovascular event state index data specified by a cardiovascular event state information specifying unit 102g described later, and variables described later. The amino acid concentration data selected by the selection unit 102h3 is associated with each other.

Returning to FIG. 6, 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. As shown in FIG. 14, 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.

  Returning to FIG. 6, 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 discrimination value are associated with each other.

  Returning to FIG. 6, the evaluation result file 106g stores an evaluation result in a discriminant value criterion-evaluating unit 102j described later (specifically, a discrimination result / classification 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 an evaluation result relating to an evaluation of a future state of the cardiovascular event are associated with each other.

  Returning to FIG. 6, in the storage unit 106, various Web data for providing the Web site to the client device 200, a CGI program, and the like are recorded as other information in addition to the information described above. The Web data includes data for displaying various Web pages, which will be described later, and the data is formed as a text file described in, for example, HTML or XML. In addition, 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 and moving images as image files such as JPEG format or MPEG2 format as necessary. Or can be stored.

  The communication interface unit 104 mediates communication between the cardiovascular event 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. Here, in addition to 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). As 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 that defines various processing procedures, and necessary data, and performs various information processing based on these programs. Run. 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 cardiovascular event state information designation unit. 102g, a multivariate discriminant creation unit 102h, a discriminant value calculation unit 102i, a discriminant value criterion evaluation unit 102j, a result output unit 102k, and a transmission unit 102m. The control unit 102 removes data with missing values, removes data with many outliers, and missing values with respect to cardiovascular event state information transmitted from the database device 400 and amino acid concentration data transmitted 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. 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. Upon receiving an authentication request from the client device 200 or the database device 400, 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, cardiovascular event state information, multivariate discriminant, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The cardiovascular event state information designation unit 102g designates target cardiovascular event state index data and amino acid concentration data when creating a multivariate discriminant.

  The multivariate discriminant creating unit 102h creates a multivariate discriminant based on the cardiovascular event state information received by the receiving unit 102f and the cardiovascular event state information specified by the cardiovascular event state information specifying unit 102g. Specifically, the multivariate discriminant creation unit 102h accumulates the cardiovascular event state information by repeatedly executing the candidate multivariate discriminant creation unit 102h1, the candidate multivariate discriminant verification unit 102h2, and the variable selection unit 102h3. 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 based on the verified results.

  When the multivariate discriminant is stored in advance in a predetermined storage area of the storage unit 106, the multivariate discriminant-preparing unit 102h selects a desired multivariate discriminant from the storage unit 106, A multivariate discriminant may be created. In addition, 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.

  Here, the configuration of the multivariate discriminant-preparing part 102h will be described with reference to FIG. 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 creation unit 102h1 creates a candidate multivariate discriminant that is a candidate for the multivariate discriminant based on a predetermined formula creation method from the cardiovascular event state information. The candidate multivariate discriminant creation unit 102h1 may create a plurality of candidate multivariate discriminants from the cardiovascular event 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. Note that the candidate multivariate discriminant verification unit 102h2 determines the discrimination 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) may be verified. The variable selection unit 102h3 selects the variable of the candidate multivariate discriminant based on a predetermined variable selection method, thereby the amino acid concentration data included in the cardiovascular event state information used when creating the candidate multivariate discriminant. Select a combination. Note that 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.

  Returning to FIG. 6, the discriminant value calculation unit 102 i uses the multivariate discriminant created by the multivariate discriminant creation unit 102 h and the evaluation target amino acid concentration data received by the receiving unit 102 f to calculate the multivariate discriminant. A discriminant value that 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 an expression created by a decision tree and a Cox proportional hazard model expression may be used.

  The discriminant value calculation unit 102i includes EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis included in the amino acid concentration data. , 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys may be used to calculate the discriminant value based on a multivariate discriminant including at least one as a variable.

  Specifically, the discriminant value criterion-evaluating unit 102j evaluates whether the evaluation target will develop a cardiovascular event in the future (for example, the discriminant value criterion discriminating unit 102j1 described later will develop the cardiovascular event in the future). Or whether the evaluation target is classified into one of a plurality of categories (ranks) that are defined in consideration of the possibility of developing a cardiovascular event in the future. The discriminant value calculation unit 102i includes EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His included in the amino acid concentration data. , Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and a concentration value of EtOHNH2, Sa , Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys The discriminant value may be calculated based on a multivariate discriminant including at least one as a variable. Note that the multivariate discriminant used in the evaluation of the future onset of the cardiovascular event is a logistic regression equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as variables, Ser, Thr, Lys, Arg, Trp. Logistic regression equation including EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp as variables, linear discriminant including Pro, Thr, Orn, Arg, Trp as variables, EtOHNH2, Hypro, A Cox proportional hazard model equation including Glu, 3-MeHis, Tyr, Trp as a variable or a Cox proportional hazard model equation including Pro, Thr, Lys, Cit, Trp as a variable may be used.

  The discriminant value criterion-evaluating unit 102j evaluates (predicts) the future state of the cardiovascular event for each evaluation object based on the discriminant value calculated by the discriminant value calculating unit 102i. The discriminant value criterion-evaluating unit 102j evaluates, for example, whether the evaluation target will develop a cardiovascular event in the future. The discrimination value criterion evaluation unit 102j further includes a discrimination value criterion discrimination unit 102j1. Here, the configuration of the discriminant value criterion-evaluating unit 102j will be described with reference to FIG. 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.

  Based on the discriminant value, the discriminant value criterion discriminating unit 102j1 discriminates whether or not the evaluation target will develop a cardiovascular event in the future, and considers the degree of possibility of developing a cardiovascular event in the future. The evaluation target is classified into one of a plurality of categories (ranks) defined in the above. Specifically, the discriminant value criterion discriminating unit 102j1 discriminates whether or not the evaluation target will develop a cardiovascular event in the future by comparing the discriminant value with a preset threshold value (cut-off value). In addition, the evaluation target is classified into one of a plurality of categories (ranks) defined in consideration of the degree of possibility of developing a cardiovascular event in the future.

  Returning to FIG. 6, the result output unit 102k outputs the processing results in each processing unit of the control unit 102 (evaluation results in the discrimination value criterion evaluation unit 102j (specifically, discrimination results or classification in the discrimination value criterion discrimination unit 102j1). Including the result) is output to the output device 114.

  The transmission unit 102m transmits, for example, a discrimination value, an evaluation result (eg, a discrimination result, a classification result, etc.) to the client device 200 that is a transmission source of the amino acid concentration data to be evaluated, or a database device 400. For example, a multivariate discriminant created by the cardiovascular event evaluation apparatus 100, an evaluation result (for example, a discrimination result, a classification result, etc.), etc. are transmitted.

  Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 19 is a block diagram showing an example of the configuration of the client device 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 interprets the web data and performs a browsing process for displaying the interpreted web data on a monitor 261 described later. Note that the web browser 211 may be plugged in with various software such as a stream player having a function of receiving, displaying, and feedbacking the stream video. The electronic mailer 212 performs transmission / reception of electronic mail in accordance with a predetermined communication protocol (for example, Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), etc.). The reception unit 213 (corresponding to an example of the result acquisition unit of the present invention), such as a discrimination value and an evaluation result (for example, a discrimination result, a classification result, etc.) transmitted from the cardiovascular event evaluation apparatus 100 via the communication IF 280, etc. Receive various information. In short, the client device has a function of acquiring various information such as a discrimination value and an evaluation result. The transmission unit 214 transmits various information such as amino acid concentration data to be evaluated to the cardiovascular event 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. In other words, 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. Thereby, the client apparatus 200 can access the cardiovascular event evaluation apparatus 100 according to a predetermined communication protocol.

  Here, an information processing device (for example, a known personal computer, workstation, home game device, Internet TV, PHS terminal, portable terminal, mobile body) connected with peripheral devices such as a printer, a monitor, and an image scanner as necessary. The client apparatus 200 may be realized by mounting software (including programs, data, and the like) that realizes a Web data browsing function and an electronic mail function in a communication terminal / information processing terminal such as a PDA.

  Further, the 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 with all or any part of the processing performed by the control unit 210. The ROM 220 or the HD 230 stores a computer program for giving instructions to the CPU in cooperation with an OS (Operating System) and performing various processes. The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. Further, 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. . In addition, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.

  Next, the network 300 of this system will be described with reference to FIGS. The network 300 has a function of connecting the cardiovascular event evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other, such as the Internet, an intranet, or a LAN (including both wired and wireless). . 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. Or mobile packet switching network (including IMT2000 system, GSM (registered trademark) system or PDC / PDC-P system), wireless paging network, local wireless network such as Bluetooth (registered trademark), PHS network, satellite A communication network (including CS, BS or ISDB) may be used.

  Next, the configuration of the database apparatus 400 of this system will be described with reference to FIG. 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 a cardiovascular event evaluation device 100 or cardiovascular event state information used when creating a multivariate discriminant in the database device, a multivariate discriminant created by the cardiovascular event evaluation device 100, or a cardiovascular event. It has a function of storing evaluation results and the like in the evaluation apparatus 100. As shown in FIG. 20, the database device 400 includes a control unit 402 such as a CPU that controls the database device in an integrated manner, 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. The output interface unit 408 is configured to be communicable via an arbitrary communication path.

  The storage unit 406 is a storage unit, 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. Here, in addition to a monitor (including a home television), 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). In addition to the keyboard, mouse, and microphone, 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. Run. As shown in the figure, the control unit 402 is roughly divided into a request interpretation unit 402a, a browsing processing unit 402b, an authentication processing unit 402c, an email generation unit 402d, a Web page generation unit 402e, and a transmission unit 402f.

  The request interpretation unit 402a interprets the request content from the cardiovascular event evaluation apparatus 100, and passes the processing to each unit of the control unit 402 according to the interpretation result. Upon receiving browsing requests for various screens from the cardiovascular event evaluation apparatus 100, the browsing processing unit 402b generates and transmits Web data for these screens. Upon receiving an authentication request from the cardiovascular event evaluation apparatus 100, the authentication processing unit 402c 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 transmitting unit 402f transmits various information such as cardiovascular event state information and multivariate discriminant to the cardiovascular event evaluation apparatus 100.

[2-3. Specific Example of Second Embodiment]
Here, a specific example of the second embodiment will be described with reference to FIG. FIG. 21 is a flowchart illustrating an example of a cardiovascular event evaluation service process according to the second embodiment.

The amino acid concentration data used in this process is, for example, blood (including plasma, serum, etc.) collected in advance from an individual such as an animal or a human (eg, a diabetic patient), and the following (A) or (B): It is related to the concentration value of the amino acid obtained by analysis or independent analysis by a specialist in this measurement method. Here, 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.
(A) Serum was separated from blood by centrifuging the collected blood sample. All serum samples were stored frozen at −80 ° C. until measurement of amino acid concentration. At the time of amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatograph mass spectrometry The amino acid concentration was analyzed by a total (LC / MS / MS) (see International Publication No. 2003/069328, International Publication No. 2005/116629).
(B) Serum was separated from blood by centrifuging the collected blood sample. All serum samples were stored frozen at −80 ° C. until measurement of 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.

  First, when a user designates an address (URL or the like) of a Web site provided by the cardiovascular event evaluation apparatus 100 via the input device 250 on the screen displaying the Web browser 211, the client apparatus 200 causes the cardiovascular event evaluation apparatus. 100 is accessed. Specifically, when the user instructs to update the screen of the Web browser 211 of the client device 200, the Web browser 211 uses the predetermined communication protocol to evaluate the cardiovascular event evaluation by using the address of the Web site provided by the cardiovascular event evaluation device 100. By transmitting to the apparatus 100, a transmission request for a Web page corresponding to the amino acid concentration data transmission screen is made to the cardiovascular event evaluation apparatus 100 by routing based on the address.

  Next, in the cardiovascular event evaluation apparatus 100, the request interpretation unit 102a receives transmission from the client device 200, 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 cardiovascular event evaluation apparatus 100 is a predetermined storage area of the storage unit 106 mainly in the browsing processing unit 102b. Web data for displaying the Web page stored in is acquired, and the acquired Web data is transmitted to the client device 200. More specifically, when there is a web page transmission request corresponding to the amino acid concentration data transmission screen from the user, the cardiovascular event evaluation apparatus 100 first uses the control unit 102 to set the user ID and the user password. Ask the user for input. When the user ID and password are input, the cardiovascular event evaluation apparatus 100 causes the authentication processing unit 102c to input the input user ID and password and the user ID stored in the user information file 106a. Make authentication with user password. The cardiovascular event 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.

  Next, the client apparatus 200 receives the Web data (for displaying a Web page corresponding to the amino acid concentration data transmission screen) transmitted from the cardiovascular event evaluation apparatus 100 by the receiving unit 213, and receives the received Web data. Is interpreted by the Web browser 211, and the amino acid concentration data transmission screen is displayed on the monitor 261.

  Next, 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 cardiovascular event evaluation apparatus 100, the amino acid concentration data of the individual is transmitted to the cardiovascular event 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.

  Next, the cardiovascular event 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 evaluates the future state of the cardiovascular event. To the database apparatus 400 is transmitted to the multi-variate discriminant (specifically, a multi-variate discriminant for 2-group discrimination regarding the future occurrence of a cardiovascular event).

  Next, the database apparatus 400 interprets the transmission request from the cardiovascular event evaluation apparatus 100 by the request interpretation unit 402a and stores the multivariate discriminant (for example, the updated latest data) stored in a predetermined storage area of the storage unit 406. Is transmitted to the cardiovascular event evaluation apparatus 100 (step SA22).

  Specifically, when it is determined in step SA26 whether the individual will develop a cardiovascular event in the future, in step SA22, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, A multivariate discriminant that includes at least one of Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. It transmits to the blood vessel event evaluation apparatus 100.

  Next, the cardiovascular event evaluation apparatus 100 receives the individual amino acid concentration data transmitted from the client device 200 and the multivariate discriminant transmitted from the database device 400 by the receiving unit 102f, and receives the received amino acid concentration data. The stored multivariate discriminant is stored in a predetermined storage area of the amino acid concentration data file 106b, and the received multivariate discriminant is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SA23).

  Next, in the cardiovascular event evaluation apparatus 100, the controller 102 removes data such as missing values and outliers from the individual amino acid concentration data received in step SA23 (step SA24).

  Next, the cardiovascular event evaluation apparatus 100 uses the discriminant value calculation unit 102i to convert the amino acid concentration data of the individual from which data such as missing values and outliers have been removed in step SA24 and the multivariate discriminant received in step SA23. Based on this, a discriminant value is calculated (step SA25).

  Specifically, in step SA23, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3- When a multivariate discriminant including at least one of MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable is received, the cardiovascular event evaluation apparatus 100 uses the discriminant value calculation unit 102i to perform amino acid concentration data EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr , Trp, Cys at least one concentration value, and EtOHNH 2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hyper, Ile, Leu, Orn, Asp, Lys, Glu, His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, A discriminant value is calculated based on a multivariate discriminant including at least one of Cys as a variable.

  Next, the cardiovascular event evaluation apparatus 100 discriminates whether or not the individual will develop a cardiovascular event in the future based on the discriminant value calculated in step SA25 by the discriminant value criterion discriminator 102j1, and the discrimination result Is stored in a predetermined storage area of the evaluation result file 106g (step SA26). Specifically, it is determined whether or not the individual will develop a cardiovascular event in the future by comparing the determination value calculated in step SA25 with a preset threshold value (cutoff value).

  Next, the cardiovascular event evaluation apparatus 100 uses the transmission unit 102m to transmit the determination result obtained in step SA26 (which may include the determination value calculated in step SA25) to the client apparatus 200 that is the transmission source of amino acid concentration data. The data is transmitted to the database device 400 (step SA27). Specifically, first, in the cardiovascular event evaluation apparatus 100, the Web page generation unit 102e generates a Web page for displaying the discrimination result, and stores Web data corresponding to the generated Web page in the storage unit 106. Stored 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 request for browsing the Web page to the cardiovascular event evaluation device 100. Send to. Next, in the cardiovascular event evaluation apparatus 100, 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 part of the storage unit 106. Read from storage area. Then, the cardiovascular event 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.

  Here, in step SA27, the cardiovascular event evaluation apparatus 100 may notify the user client apparatus 200 of the determination result by e-mail at the control unit 102. Specifically, first, the cardiovascular event evaluation apparatus 100 refers to the user information stored in the user information file 106a based on the user ID or the like in the e-mail generation unit 102d according to the transmission timing, Get the user's email address. Next, the cardiovascular event 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 cardiovascular event evaluation apparatus 100 transmits the generated data to the user client apparatus 200 by the transmission unit 102m.

  In step SA27, the cardiovascular event evaluation apparatus 100 may transmit the determination result to the user client apparatus 200 using an existing file transfer technology such as FTP.

  Returning to the description of FIG. 21, in the database apparatus 400, the control unit 402 receives the determination result or Web data transmitted from the cardiovascular event evaluation apparatus 100, and stores the received determination result or Web data in a predetermined unit of the storage unit 406. Save (accumulate) in the storage area (step SA28).

  Further, the client device 200 receives the Web data transmitted from the cardiovascular event evaluation device 100 by the receiving unit 213, interprets the received Web data by the Web browser 211, and a Web page on which the individual determination result is written. Is displayed on the monitor 261 (step SA29). When the determination result is transmitted by e-mail from the cardiovascular event evaluation apparatus 100, the client apparatus 200 arbitrarily selects the e-mail transmitted from the cardiovascular event evaluation apparatus 100 by a known function of the electronic mailer 212. The received e-mail is displayed on the monitor 261.

  As described above, the user can check the determination result regarding the above-described two-group determination by browsing the Web page displayed on the monitor 261. Note that the user may print the display content of the Web page displayed on the monitor 261 with the printer 262.

  When the determination result is transmitted from the cardiovascular event evaluation apparatus 100 by e-mail, the user confirms the determination result regarding the above-described two-group determination by viewing the e-mail displayed on the monitor 261. be able to. The user may print the display content of the e-mail displayed on the monitor 261 with the printer 262.

  This concludes the description of the cardiovascular event evaluation service process.

[2-4. Other Embodiments]
A cardiovascular event evaluation apparatus, a cardiovascular event evaluation method, a cardiovascular event evaluation program, a recording medium, a cardiovascular event evaluation system, and an information communication terminal device according to the present invention are claimed in addition to the second embodiment described above. The present invention may be implemented in various different embodiments within the scope of the technical idea described in the above.

  In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method.

  In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including registration data for each processing, parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings Can be changed arbitrarily.

  In addition, regarding the cardiovascular event evaluation apparatus 100, each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.

  For example, the processing functions provided in the cardiovascular event evaluation apparatus 100, in particular, the processing functions performed by the control unit 102, are interpreted and executed by a CPU (Central Processing Unit) and the CPU. It may be realized by a program or hardware based on wired logic. The program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the cardiovascular event evaluation method according to the present invention. It is mechanically read by the vascular event evaluation apparatus 100. That is, in the storage unit 106 such as a ROM or HDD, 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.

  The computer program may be stored in an application program server connected to the cardiovascular event evaluation apparatus 100 via an arbitrary network, and may be downloaded in whole or in part as necessary. Is possible.

  In addition, the cardiovascular event evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, and may be configured as a program product. Here, the “recording medium” means a memory card, USB memory, SD card, flexible disk, magneto-optical disk, ROM, EPROM, EEPROM (registered trademark), CD-ROM, MO, DVD, and Blu-ray. It includes any “portable physical medium” such as Disc.

  The “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 represented by an OS (Operating System). Including those that achieve the function. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.

  Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.

  The cardiovascular event evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus connected to an arbitrary peripheral device. The cardiovascular event evaluation apparatus 100 may be realized by installing software (including a program or data) for realizing the cardiovascular event evaluation method of the present invention in the information processing apparatus.

  Furthermore, the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.

  Finally, an example of the multivariate discriminant creation process performed by the cardiovascular event evaluation apparatus 100 will be described in detail with reference to FIG. Note that the processing described here is merely an example, and the method of creating the multivariate discriminant is not limited to this. FIG. 22 is a flowchart illustrating an example of multivariate discriminant creation processing. The multivariate discriminant creation process may be performed by the database apparatus 400 that manages cardiovascular event state information.

  In this description, it is assumed that the cardiovascular event evaluation apparatus 100 stores cardiovascular event state information acquired in advance from the database apparatus 400 in a predetermined storage area of the cardiovascular event state information file 106c. Further, the cardiovascular event evaluation apparatus 100 converts the cardiovascular event state information including the cardiovascular event state index data and amino acid concentration data specified in advance by the cardiovascular event state information specifying unit 102g into the specified cardiovascular event state information file 106d. Are stored in a predetermined storage area.

  First, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and creates a predetermined formula from cardiovascular event state information stored in a predetermined storage area of the designated cardiovascular event state information file 106d. The candidate multivariate discriminant is created based on the above, and the created candidate multivariate discriminant is stored in a predetermined storage area of the candidate multivariate discriminant file 106e1 (step SB21). Specifically, first, 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, multivariate analysis such as Cox proportional hazard model, etc.), select one desired, and create based on the selected formula creation method Determine the shape of the candidate multivariate discriminant (form of the formula). Next, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1 that performs various calculations (for example, average and variance) corresponding to the selected formula selection method based on the cardiovascular event state information. Run. Next, 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. Thereby, a candidate multivariate discriminant is created based on the selected formula creation method. In addition, when a candidate multivariate discriminant is created simultaneously and in parallel (in parallel) by using a plurality of different formula creation techniques, the above-described processing may be executed in parallel for each selected formula creation technique. In addition, when creating a candidate multivariate discriminant serially using a combination of different formula creation methods, for example, cardiovascular event state information using a candidate multivariate discriminant created by performing principal component analysis And a candidate multivariate discriminant may be created by performing discriminant analysis on the converted cardiovascular event state information.

  Next, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-verifying part 102h2 that verifies (mutually verifies) the candidate multivariate discriminant created in step SB21 based on a predetermined verification method. The result is stored in a predetermined storage area of the verification result file 106e2 (step SB22). Specifically, the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-verifying part 102h2 based on the cardiovascular event state information stored in a predetermined storage area of the designated cardiovascular event state information file 106d. The verification data used when verifying the candidate multivariate discriminant is created, and the candidate multivariate discriminant is verified based on the created verification data. In addition, when a plurality of candidate multivariate discriminants are created in combination with a plurality of different formula creation methods in step SB21, 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. Here, in step SB22, 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. Thereby, a candidate index formula having high predictability or robustness in consideration of cardiovascular event state information and diagnosis conditions can be selected.

  Next, when the multivariate discriminant-preparing part 102h creates a candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method in the variable selector 102h3. A combination of amino acid concentration data included in the cardiovascular event state information to be used is selected, and cardiovascular event state information including the selected combination of amino acid concentration data is stored in a predetermined storage area of the selected cardiovascular event state information file 106e3 ( Step SB23). In 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. In this case, in step SB23, the multivariate discriminant-preparing part 102h selects a variable of the candidate multivariate discriminant based on a predetermined variable selection method for each candidate multivariate discriminant in the variable selector 102h3. Also good. Here, in step SB23, 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. In step SB23, the multivariate discriminant-preparing part 102h uses the variable selection part 102h3 to determine amino acid concentration data based on the cardiovascular event state information stored in a predetermined storage area of the designated cardiovascular event state information file 106d. You may select the combination.

  Next, the multivariate discriminant-preparing part 102h determines whether or not all combinations of amino acid concentration data included in the cardiovascular event state information stored in the predetermined storage area of the designated cardiovascular event 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). Returns to step 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. In addition, the multivariate discriminant-preparing part 102h uses the amino acid concentration data selected in step SB23 as the amino acid contained in the cardiovascular event state information stored in the predetermined storage area of the designated cardiovascular event state information file 106d. It is determined whether the combination of the concentration data or the combination of the amino acid concentration data selected in the previous step SB23 is the same. If the determination result is “same” (step SB24: Yes), the next step ( The process proceeds to step SB25), and 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.

  Next, 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). Here, in 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.

  This concludes the description of the multivariate discriminant creation process.

  The amino acid concentration in serum collected from 392 diabetic patients was measured by the measurement method (A) described in the above-described embodiment. A diabetic patient who developed a cardiovascular event within 10 years after measuring the amino acid concentration was regarded as a cardiovascular event developing person. 392 diabetic patients were divided into two groups: those with cardiovascular events and those without cardiovascular events. There were 64 and 328 patients with cardiovascular events and no cardiovascular events, respectively. The breakdown of 64 cardiovascular event patients is myocardial infarction 11 people, angina 29 people, heart failure 5 people, cerebral ischemia 1 person, cerebral infarction 12 people, cerebral hemorrhage 5 people, internal carotid artery stenosis 1 person . For each amino acid, the discriminating ability between the future cardiovascular event-onset group and the non-cardiovascular event-onset group was evaluated by ROC_AUC (area under the curve of the receiver characteristic curve).

  The amino acids for which ROC_AUC was significant (p <0.05) in the test when the null hypothesis was “ROC_AUC = 0.5” under non-parametric assumptions were β-AIBA, 3-MeHis, Cit , Trp, Cys. Among these amino acids, β-AIBA, 3-MeHis, Cit, and Cys increased significantly in the future cardiovascular event onset group, while Trp significantly decreased in the future cardiovascular event onset group.

  Next, using the same amino acid concentration data, the presence or absence of the occurrence of cardiovascular events, and the number of years from the time of amino acid concentration measurement until the onset of cardiovascular events The ability to discriminate between the onset of future cardiovascular events and the absence of cardiovascular events for amino acids was evaluated using a univariate Cox proportional hazard model. As a result, amino acids that were significant (p <0.05) as future cardiovascular event onset predictors were β-AIBA, 3-MeHis, Cit, Trp, Cys.

  Using the same amino acid concentration data as measured in Example 1, effective for the diagnosis of the future cardiovascular event onset described in Example 1 and the future cardiovascular event onset with serum amino acid concentration as a variable A multivariate discriminant (multivariate function) for discrimination was obtained.

  First, a logistic regression equation was used as a multivariate discriminant. Search for combinations of variables to be included in the logistic regression equation from 31 types of amino acids, and use the Leave-One-Out method as a cross-validation method to search for logistic regression equations with good discrimination ability for future cardiovascular events. We carried out diligently.

  Here, 31 kinds of amino acids are EtOHNH2, Gly, Sar, Ala, β-AIBA, α-ABA, Ser, Pro, Val, Thr, Tau, Hypro, Ile, Leu, Asn, Orn, Asp, Gln, Lys. , Glu, Met, His, α-AAA, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys (the same applies hereinafter).

  A list of logistic regression equations with equally good discrimination ability evaluated by ROC_AUC is shown in FIGS. Here, FIGS. 23 to 26 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross validation, and ROC_AUC values without cross validation. When enumerated in descending order of the appearance frequency of variables in the expressions included in FIGS. 23 to 26, they are EtOHNH2,3-MeHis, Glu, Hypro, Trp, Leu, Tyr, Sar, Ile, and Phe.

  Among logistic regression equations having equally good discrimination ability, for example, an index formula “(−0.1452) + (− 0) having a variable set“ EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp ”is used. .2230) EtOHNH2 + (− 0.04637) Hypro + (0.01303) Glu + (0.3524) 3-MeHis + (0.01250) Tyr + (− 0.03093) Trp ”has a discriminating ability of ROC_AUC = 0.725, sensitivity = 0.703 and specificity = 0.710. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  Search for combinations of variables to be included in the logistic regression equation from 19 types of amino acids, and adopt the Leave-One-Out method as cross-validation to search for logistic regression equations with good discrimination ability for future cardiovascular events. We carried out diligently.

  Here, the 19 kinds of amino acids are Gly, Ala, Ser, Pro, Val, Thr, Ile, Leu, Asn, Orn, Gln, Lys, Met, His, Phe, Arg, Cit, Tyr, Trp (hereinafter referred to as “Ly”). The same.)

  A list of logistic regression equations with equally good discriminability evaluated by ROC_AUC is shown in FIGS. Here, FIGS. 27 to 30 show combinations of variables included in the logistic regression equation, ROC_AUC values with cross-validation, and ROC_AUC values without cross-validation. When enumerating to the 10th place in the descending order of the appearance frequency of the variables in the expressions included in FIGS. 27 to 30, they are Trp, Thr, Lys, Cit, Arg, Ser, Orn, Pro, Ala, and His.

  Among logistic regression equations with equally good discriminating ability, for example, an index formula “(0.3452) + (− 0.009171) Ser + () having a set of variables“ Ser, Thr, Lys, Arg, Trp ”. -0.01119) Thr + (0.006555) Lys + (0.01067) Arg + (− 0.03345) Trp ”has good discrimination with ROC_AUC = 0.622, sensitivity = 0.656, specificity = 0.659. It was something. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  Next, a linear discriminant was used as the multivariate discriminant. Search for combinations of variables to be included in the linear discriminant from 31 types of amino acids, and adopt the bootstrap method as a cross-validation to search for a linear discriminant that has a good discriminating ability for future cardiovascular events. did.

  A list of linear discriminants with equally good discrimination ability evaluated by ROC_AUC is shown in FIG. 31 to FIG. Here, FIGS. 31 to 34 show the linear discriminant, the average value of ROC_AUC values with cross validation, and the ROC_AUC value without cross validation. When enumerating to the 10th place in the descending order of the appearance frequency of the variables in the expressions included in FIGS. 31 to 34, they are 3-MeHis, EtOHNH2, Glu, Trp, Hypro, Leu, Tyr, 1-MeHis, Ile, Asp.

  Among linear discriminants with equally good discriminability, for example, an index formula “(−0.1491) + (− 0) having a variable set“ EtOHNH2, Hypro, Leu, Glu, 3-MeHis, Trp ”is used. .0434) EtOHNH2 + (− 0.0099) Hypro + (− 0.0016) Leu + (0.0046) Glu + (0.1117) 3-MeHis + (− 0.0052) Trp ”has a discriminating ability of ROC_AUC = 0.713 , Sensitivity = 0.594, specificity = 0.817. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  Search for combinations of variables to be included in the linear discriminant from 19 types of amino acids, and use the bootstrap method as a cross-validation to search for linear discriminants with good discriminating ability for future cardiovascular events. did.

  A list of linear discriminants with equally good discrimination ability evaluated by ROC_AUC is shown in FIGS. Here, FIGS. 35 to 38 show a linear discriminant, an average value of ROC_AUC values with cross validation, and an ROC_AUC value without cross validation. When enumerating to the 10th place in the descending order of appearance frequency of variables in the expressions included in FIGS. 35 to 38, they are Trp, Thr, Arg, Pro, Lys, Orn, Cit, Ser, His, and Ala.

  Of the linear discriminants with equally good discriminating ability, for example, the index formula “(−0.5164) + (0.0018) Pro + () having the variable set“ Pro, Thr, Orn, Arg, Trp ”is used. −0.0040) Thr + (0.0038) Orn + (0.0026) Arg + (− 0.0074) Trp ”are ROC_AUC = 0.666, sensitivity = 0.661, specificity = 0.537. It was good. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  Next, the Cox proportional hazard model was used as a multivariate discriminant. We searched for combinations of variables to be included in the Cox proportional hazard model from 31 types of amino acids, and diligently searched for Cox proportional hazard model formulas with good discrimination ability for future cardiovascular events.

  A list of Cox proportional hazard model equations with equally good discrimination performance evaluated by ROC_AUC is shown in FIGS. Here, FIGS. 39 to 42 show the Cox proportional hazard model formula, the average value of the ROC_AUC values with cross validation, and the ROC_AUC values without cross validation. When enumerating to the 10th place in the descending order of the appearance frequency of variables in the formulas included in FIGS. 39 to 42, they are 3-MeHis, EtOHNH2, Glu, Trp, Hypro, Leu, Tyr, Sar, Phe, and β-AIBA.

  Among the Cox proportional hazard model formulas having equally good discrimination ability, for example, an index formula “(−0.1263) EtOHNH2 + () having a set of variables“ EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp ”. −0.0296) Hypro + (0.0080) Glu + (0.2112) 3-MeHis + (0.0095) Tyr + (− 0.0280) Trp ”has a discriminating ability of ROC_AUC = 0.718, sensitivity = 0.661. Specificity = 0.726 was good. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  The combination of variables included in the Cox proportional hazard model formula was searched from 19 kinds of amino acids, and the Cox proportional hazard model formula having a good discriminating ability for the onset of future cardiovascular events was eagerly conducted.

  43 to 46 show a list of Cox proportional hazard model equations with equally good discrimination performance evaluated by ROC_AUC. Here, FIGS. 43 to 46 show the Cox proportional hazard model formula, the average value of ROC_AUC values with cross validation, and the ROC_AUC values without cross validation. When the appearance frequency of the variables in the formulas included in FIGS. 43 to 46 is listed in descending order, they are Trp, Thr, Lys, Cit, Ser, Arg, Pro, Orn, Ala, and His.

  Of the Cox proportional hazard model formulas with equally good discrimination ability, for example, an index formula “(0.0042) Pro + (− 0.0133) having a set of variables“ Pro, Thr, Lys, Cit, Trp ”. The discriminating ability of “Thr + (0.0075) Lys + (0.0137) Cit + (− 0.0279) Trp” was as good as ROC_AUC = 0.622, sensitivity = 0.997, and specificity = 0.500. It was. The sensitivity and specificity are values when the highest discriminant point at which the average of sensitivity and specificity is the highest is taken as a cutoff value.

  As described above, the cardiovascular event evaluation method and the like according to the present invention can be widely implemented in many industrial fields, particularly pharmaceuticals, foods, and medical fields. It is extremely useful in the field of bioinformatics that performs state progression prediction, disease risk prediction, proteome and metabolomic analysis.

DESCRIPTION OF SYMBOLS 100 Cardiovascular event evaluation apparatus 102 Control part 102a Request interpretation part 102b Browse process part 102c Authentication process part 102d E-mail production | generation part 102e Web page production | generation part 102f Reception part 102g Cardiovascular event state information designation | designated part 102h Multivariate discriminant creation part 102h1 Candidate multivariate discriminant creation unit 102h2 Candidate multivariate discriminant verification unit 102h3 Variable selection unit 102i Discriminant value calculation unit 102j Discriminant value criterion evaluation unit 102j1 Discriminant value criterion discriminator 102k Result output unit 102m Transmitter 104 Communication interface unit 106 Storage unit 106a User information file 106b Amino acid concentration data file 106c Cardiovascular event state information file 106d Designated cardiovascular event state information file 106e Multivariate discriminant-related information database 106 1 candidate multivariate discriminant file 106e2 verification result file 106e3 selected cardiovascular event state information file 106e4 multivariate discriminant file 106f discriminant value file 106g evaluation result file 108 input / output interface unit 112 input device 114 output device 200 client device (information communication) Terminal device)
300 network 400 database device

Claims (23)

EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, including a concentration value reference acquisition step for acquiring information for evaluating the future state of the cardiovascular event for the evaluation object based on at least one concentration value of Tyr, Trp, Cys Acquisition method. The concentration value standard acquisition step includes EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1- Obtaining information for evaluating whether the evaluation target will develop a cardiovascular event in the future based on the concentration value of at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys. ,
The acquisition method according to claim 1, wherein: The density value reference acquisition step includes:
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, in the blood to be evaluated The concentration value of at least one of Cit, Tyr, Trp, Cys, EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe , 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys based on an expression for obtaining information for evaluating the future state of the cardiovascular event including at least one as a variable , Further including a calculation step of calculating a value of the formula,
The acquisition method according to claim 1, wherein: The above formulas are logistic regression, fractional formula, linear discriminant, multiple regression, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis, decision tree One of the created formula and the Cox proportional hazard model formula,
The acquisition method according to claim 3. The density value reference acquisition step includes:
An acquisition step of acquiring information for evaluating a future state of the cardiovascular event for the evaluation object based on the value calculated in the calculation step;
The acquisition method according to claim 3 or 4, wherein: The calculation step includes EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3 in the blood to be evaluated. The concentration value of at least one of MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, based on the formula including the variable, the value of the formula is calculated,
The obtaining step obtains information for evaluating whether the evaluation target will develop a cardiovascular event in the future based on the value of the expression;
The acquisition method according to claim 5. The equation includes the logistic regression equation including EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, Trp as the variables, the logistic regression equation including Ser, Thr, Lys, Arg, Trp as the variables, EtOHNH2, Hypro, The linear discriminant including Leu, Glu, 3-MeHis, Trp as the variable, the linear discriminant including Pro, Thr, Orn, Arg, Trp as the variable, EtOHNH2, Hypro, Glu, 3-MeHis, Tyr, The Cox proportional hazard model formula including Trp as the variable, or the Cox proportional hazard model formula including Pro, Thr, Lys, Cit, Trp as the variable,
The acquisition method according to claim 6. EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, at least one concentration value, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 Based on an expression for obtaining information for evaluating a future state of a cardiovascular event including at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. The acquisition method characterized by including the calculation step which calculates the value of. The acquisition step of acquiring information for evaluating the future state of the cardiovascular event for the evaluation object based on the value calculated in the calculation step. Acquisition method. Obtaining an information for evaluating a future state of the cardiovascular event for the evaluation object based on the value of the expression;
The value of the formula is EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, At least one concentration value among 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys for obtaining information for evaluating the future state of the cardiovascular event including at least one variable Calculated based on the above formula,
An acquisition method characterized by A cardiovascular event evaluation apparatus including a control unit,
The controller is
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, at least one concentration value, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 A calculation for calculating the value of the expression based on an expression for evaluating the future state of the cardiovascular event including at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. Having means,
Cardiovascular event evaluation device characterized by this. The control unit further includes an evaluation unit that evaluates a future state of the cardiovascular event for the evaluation object based on the value calculated by the calculation unit;
The cardiovascular event evaluation apparatus according to claim 11. A cardiovascular event evaluation apparatus including a control unit,
The controller is
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, based on at least one concentration value, the evaluation object comprises an evaluation means for evaluating the future state of the cardiovascular event,
Cardiovascular event evaluation device characterized by this. A cardiovascular event evaluation apparatus including a control unit,
The controller is
An evaluation means for evaluating the future state of the cardiovascular event for each evaluation object based on the value of the expression;
The value of the formula is EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, At least one concentration value among 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys based on the above formula for evaluating the future state of the cardiovascular event including as a variable Be calculated,
Cardiovascular event evaluation device characterized by this. An acquisition method executed in an information processing apparatus including a control unit,
Executed in the control unit,
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, at least one concentration value, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 Based on an expression for obtaining information for evaluating a future state of a cardiovascular event including at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. Including a calculating step for calculating a value of
An acquisition method characterized by An acquisition method executed in an information processing apparatus including a control unit,
Executed in the control unit,
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, including an acquisition step of acquiring information for evaluating a future state of a cardiovascular event for the evaluation object based on at least one concentration value
An acquisition method characterized by An acquisition method executed in an information processing apparatus including a control unit,
Executed in the control unit,
Obtaining an information for evaluating a future state of the cardiovascular event for the evaluation object based on the value of the expression;
The value of the formula is EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, At least one concentration value among 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys for obtaining information for evaluating the future state of the cardiovascular event including at least one variable Calculated based on the above formula,
An acquisition method characterized by A cardiovascular event evaluation program for execution in an information processing apparatus including a control unit,
For executing in the control unit,
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Tyr, Trp, Cys, at least one concentration value, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 A calculation for calculating the value of the expression based on an expression for evaluating the future state of the cardiovascular event including at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable. Including steps,
A cardiovascular event evaluation program. A cardiovascular event evaluation program for execution in an information processing apparatus including a control unit,
For executing in the control unit,
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit in the blood to be evaluated , Including an evaluation step of evaluating a future state of a cardiovascular event for the evaluation object based on a concentration value of at least one of Tyr, Tyr, Trp, and Cys.
A cardiovascular event evaluation program. A cardiovascular event evaluation program for execution in an information processing apparatus including a control unit,
For executing in the control unit,
An evaluation step for evaluating a future state of the cardiovascular event for each evaluation object based on the value of the expression;
The value of the formula is EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, At least one concentration value among 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys based on the above formula for evaluating the future state of the cardiovascular event including as a variable Be calculated,
A cardiovascular event evaluation program. A cardiovascular event evaluation device including a control unit and a terminal device that includes a control unit and provides amino acid concentration data to be evaluated regarding the amino acid concentration value in blood are communicably connected via a network. A cardiovascular event evaluation system,
The control unit of the terminal device is
Amino acid concentration data transmitting means for transmitting the amino acid concentration data to be evaluated to the cardiovascular event evaluation apparatus;
A result receiving means for receiving an evaluation value transmitted from the cardiovascular event evaluation apparatus and an evaluation result relating to a future state of the value of the cardiovascular event; and
The control unit of the cardiovascular event evaluation apparatus includes:
Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the terminal device;
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 included in the amino acid concentration data received by the amino acid concentration data receiving means The concentration value of at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys Based on the calculation means for calculating the value of the formula, or
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 included in the amino acid concentration data received by the amino acid concentration data receiving means The concentration value of at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, an expression for evaluating the future state of the cardiovascular event including at least one of the variables A calculating means for calculating the value of the formula based on Based on the value calculated by the serial calculation means, per the evaluation target, and evaluating means for evaluating the future state of the cardiovascular events,
A result transmission unit that transmits the value calculated by the calculation unit or the evaluation result of the evaluation target by the evaluation unit to the terminal device;
Having
A cardiovascular event evaluation system. A terminal device comprising a control unit,
The controller is
A result acquisition means for acquiring an evaluation result relating to a value of an expression or a future state of a cardiovascular event;
The value of the above formula is EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3 in the blood to be evaluated. A concentration value of at least one of MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , H is, Phe, 1-MeHis , 3-MeHis, Arg, Cit, Tyr, Trp, was calculated based on the equation for evaluating the future state of the cardiovascular events comprising at least one as a variable of the Cys Or
The evaluation results are EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1-MeHis, 3- At least one concentration value among MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , H calculated based on an expression for evaluating the future state of the cardiovascular event including at least one of is, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, and Cys as a variable Based on the value of the formula, the future state of the cardiovascular event for the evaluation object It is the result of the evaluation,
A terminal device characterized by the above. A cardiovascular event evaluation device having a control unit, connected to a terminal device that provides amino acid concentration data to be evaluated regarding the concentration value of an amino acid in blood through a network,
The controller is
Amino acid concentration data receiving means for receiving the amino acid concentration data transmitted from the terminal device;
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 included in the amino acid concentration data received by the amino acid concentration data receiving means The concentration value of at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys as an expression for evaluating the future state of a cardiovascular event as a variable. Based on the calculation means for calculating the value of the formula, or
EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn, Asp, Lys , His, Phe, 1 included in the amino acid concentration data received by the amino acid concentration data receiving means The concentration value of at least one of MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys, and EtOHNH2, Sar, Ala, β-AIBA, Ser, Pro, Thr, Hypro, Ile, Leu, Orn , Asp, Lys , His, Phe, 1-MeHis, 3-MeHis, Arg, Cit, Tyr, Trp, Cys as an expression for evaluating the future state of a cardiovascular event as a variable. Based on the calculation means for calculating the value of the formula, and Based on the value calculated by means output, per the evaluation target, and evaluating means for evaluating the future state of the cardiovascular events,
A result transmission unit that transmits the value calculated by the calculation unit or the evaluation result of the evaluation target by the evaluation unit to the terminal device;
Having
Cardiovascular event evaluation device characterized by this.

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