Disease onset and fatality risk prediction model, disease onset and fatality risk prediction method, disease onset and fatality risk prediction device, program, and recording medium

Abstract

Provided is a disease onset and fatality risk prediction model for increasing the accuracy of predicting the onset risk of a cardiovascular disease and the risk of fatality due to onset of the cardiovascular disease.　A disease onset and fatality risk prediction model according to the present disclosure predicts a disease onset and fatality risk by using: disease onset and fatality risk prediction markers that include BNP and / or N-terminal pro-BNP, and ANTR2, MACOI, SP-B, and PLOD3, and that serve as indices for predicting at least one of the onset risk of a cardiovascular disease and the risk of fatality due to onset of the cardiovascular disease; and, as input data, values obtained by quantifying the disease onset and fatality risk prediction markers by using aptamers.

Description

Morbidity and Mortality Probability Prediction Model, Morbidity and Mortality Probability Prediction Method, Morbidity and Mortality Probability Prediction Device, Program, and Recording Medium

[0001] The present disclosure relates to a morbidity and mortality probability prediction model, a morbidity and mortality probability prediction method, a morbidity and mortality probability prediction device, a program, and a recording medium.

[0002] Cardiovascular diseases such as heart failure are among the leading causes of death in Japan. Therefore, it is extremely important to accurately predict the likelihood of developing cardiovascular disease and the likelihood of death due to cardiovascular disease. Patent Document 1 discloses a method for determining the risk of developing heart failure within a specific period of time, which includes clinical information such as health checkup information as a predictive factor.

[0003] Japanese Patent Application Laid-Open No. 2020-089746

[0004] On the other hand, methods that include clinical information as predictive factors, such as those in Patent Document 1, tend to have low prediction accuracy.

[0005] Therefore, the present disclosure aims to provide an incidence and mortality probability prediction model, an incidence and mortality probability prediction method, an incidence and mortality probability prediction device, a program, and a recording medium to improve the prediction accuracy of the incidence of cardiovascular disease and the probability of death due to the incidence of cardiovascular disease.

[0006] In order to achieve the above-mentioned object, the morbidity and mortality probability prediction model of the present disclosure includes at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3 as morbidity and mortality probability prediction markers, which are indicators for predicting at least one of the probability of developing cardiovascular disease and the probability of death due to the development of said cardiovascular disease, and which predicts morbidity and mortality probability using the quantitative values ​​of said markers determined by aptamers as input data.

[0007] The morbidity and mortality probability prediction method of the present disclosure includes a prediction step in which the prediction step predicts at least one of the subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of said cardiovascular disease based on the quantitative value of a morbidity and mortality probability prediction marker determined by an aptamer, and the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3, and the step is carried out by a computer.

[0008] The morbidity and mortality probability prediction device of the present disclosure includes a prediction unit that predicts at least one of a subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of the cardiovascular disease based on a quantitative value of a morbidity and mortality probability prediction marker determined by an aptamer, and the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3.

[0009] The program of the present disclosure includes a prediction step, which predicts at least one of a subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of said cardiovascular disease based on the quantitative value of a marker for predicting likelihood of morbidity and mortality determined by an aptamer, and the markers for predicting likelihood of morbidity and mortality include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3. The program is for causing a computer to execute the above step.

[0010] The recording medium for recording the program of the present disclosure is a computer-readable recording medium that records a program for causing a computer to execute each procedure in the program of the present disclosure.

[0011] According to the present disclosure, it is possible to provide a morbidity and mortality probability prediction model, a morbidity and mortality probability prediction method, a morbidity and mortality probability prediction device, a program, and a recording medium for improving the prediction accuracy of the probability of developing cardiovascular disease and the probability of death due to developing cardiovascular disease.

[0012] Fig. 1 is a block diagram showing the configuration of an example of a morbidity and mortality probability prediction device of the present disclosure. Fig. 2 is a block diagram showing an example of the hardware configuration of the morbidity and mortality probability prediction device of the present disclosure. Fig. 3 is a scatter plot showing the relationship between the hazard ratio and p-value for cardiovascular disease of blood proteins. Fig. 4 is an ROC curve showing the accuracy of a prediction model.

[0013] Unless otherwise specified, terms used in this specification can be used in the sense commonly used in the art.

[0014] As used herein, the term "morbidity" may mean, for example, a state of having a disease, or may mean developing the disease. Furthermore, as used herein, the term "morbidity and death" means at least one of morbidity and death, unless otherwise specified.

[0015] Hereinafter, embodiments of the present disclosure will be described. However, the present disclosure is not limited to the following embodiments. In the following drawings, the same parts are denoted by the same reference numerals. Furthermore, the descriptions of the embodiments can be used interchangeably unless otherwise specified. Furthermore, the configurations of the embodiments can be combined unless otherwise specified.

[0016] [Embodiment 1] (Mortality probability prediction model) First, the morbidity and mortality probability prediction model of the present disclosure will be described.

[0017] The morbidity and mortality probability prediction model of the present disclosure includes, as morbidity and mortality probability prediction markers, at least one of BNP (Natriuretic peptides B) and N-terminal pro-BNP (NT-proBNP), as well as ANTR2 (Anthrax toxin receptor 2), MACOI (Macoilin), SP-B (Pulmonary surfactant-associated protein B), and PLOD3 (Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3). The morbidity and mortality probability prediction markers serve as indicators for predicting at least one of the likelihood of developing cardiovascular disease and the likelihood of death due to the development of cardiovascular disease, and the quantification value of the morbidity and mortality probability prediction marker determined by an aptamer is used as input data to predict the likelihood of morbidity and mortality.The morbidity and mortality probability prediction model of the present disclosure may further use, as the morbidity and mortality probability prediction markers, ANP (Atrial natriuretic factor), CDHR3 (Cadherin-related family member 3), ASAH2 (Neutral ceramidase), BAGE2 (B melanoma antigen 2), LAP2B (Lamina-associated polypeptide 2, isoforms beta / gamma), Inhibin bB chain (Inhibin beta B chain), TFF3 (Trefoil factor 3), IP16 (Gamma-interferon-inducible protein 16), Pseudocholinesterase (Cholinesterase), PCOC2 (Procollagen C-endopeptidase enhancer 2), CRDL1 (Chordin-like protein 1), FBLN3 (EGF-containing fibulin-like extracellular matrix protein 1), HE4 (WAP four-disulfide core domain protein 2), b2-Microglobulin (Beta-2-microglobulin), C3d (Complement C3d fragment), and GBRAP (Gamma-aminobutyric acid receptor-associated protein).

[0018] The aptamer is not particularly limited as long as it specifically binds to the morbidity and mortality probability prediction marker, for example. The aptamer may be, for example, an aptamer that binds to BNP, such as the aptamer disclosed in Reference 1 below. Other morbidity and mortality probability prediction markers may also be aptamers disclosed in other documents. Reference 1: Bruno JG, Richarte AM, Phillips T. Preliminary Development of a DNA Aptamer-Magnetic Bead Capture Electrochemiluminescence Sandwich Assay for Brain Natriuretic Peptide. Microchem J. 2014 Jul 1;115:32-38. doi: 10.1016 / j.microc.2014.02.003. PMID: 24764602; PMCID: PMC3993981

[0019] The morbidity-mortality probability prediction marker can be obtained, for example, from a biological sample. The biological sample may be, for example, a biological sample containing blood. That is, the biological sample may be obtained only from blood, or may be obtained from other biological samples in addition to blood.

[0020] Examples of the cardiovascular disease include heart disease, stroke, etc. Examples of the heart disease include heart failure, myocardial infarction, etc.

[0021] By using the morbidity and mortality probability prediction model disclosed herein as an index for predicting at least one of the probability of developing cardiovascular disease and the probability of death due to developing cardiovascular disease, prediction accuracy is improved compared to, for example, predicting at least one of the probability of developing cardiovascular disease and the probability of death due to developing cardiovascular disease using clinical information, etc.

[0022] The morbidity and mortality probability prediction model of the present disclosure may be a prediction model that is machine-learned based on, for example, the quantitative values ​​of the morbidity and mortality probability prediction markers of people who have developed the cardiovascular disease due to the aptamer, and the quantitative values ​​of the morbidity and mortality probability prediction markers of people who have not developed the cardiovascular disease due to the aptamer, and that predicts at least one of the probability of developing the cardiovascular disease and the probability of death due to the development of the cardiovascular disease.

[0023] [Embodiment 2] (Method for predicting likelihood of morbidity and mortality) Next, the method for predicting likelihood of morbidity and mortality of the present disclosure will be described.

[0024] The morbidity and mortality probability prediction method of the present disclosure includes a prediction step. The prediction step predicts at least one of the subject's likelihood of developing cardiovascular disease and the likelihood of death due to the cardiovascular disease based on the quantitative value of the aptamer-based morbidity and mortality probability prediction marker. The morbidity and mortality probability prediction marker is the same as the morbidity and mortality probability prediction marker described in the first embodiment. That is, the morbidity and mortality probability prediction marker includes at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3. An example of the precursor is N-terminal pro-BNP. The morbidity and mortality probability prediction marker of the present disclosure may further include, for example, at least one selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP.

[0025] The prediction step may, for example, predict at least one of the subject's likelihood of developing cardiovascular disease and the likelihood of death due to cardiovascular disease based on a quantitative value of the aptamer-based morbidity and mortality probability prediction marker and a prediction model. The prediction model may, for example, be the morbidity and mortality probability prediction model disclosed herein. The prediction model may, for example, be a prediction model described in the Examples below.

[0026] The morbidity-mortality probability prediction method of the present disclosure may further include a measurement step. The measurement step, for example, measures the quantitative value of the morbidity-mortality probability prediction marker by the aptamer in the subject's biological sample. The method for measuring the amount of the morbidity-mortality probability prediction marker (i.e., the quantification method) is not particularly limited and may be, for example, a method commonly used to quantify a marker (e.g., a protein) in a biological sample. Examples of the quantification method include a quantification method using SomaScan (registered trademark) from Somalogic, Inc.

[0027] The biological sample is, for example, a biological sample containing blood. That is, the biological sample may be obtained from blood alone, or may be obtained from other biological samples in addition to blood. The subject is, for example, a Mongoloid. Furthermore, the subject is preferably, for example, an Asian. The Asian is preferably, for example, a Japanese.

[0028] [Embodiment 3] (Mortality probability prediction device) Next, the morbidity and mortality probability prediction device of the present disclosure will be described.

[0029] 1 is a block diagram showing an example of the configuration of a morbidity-mortality probability prediction device 10 according to an embodiment of the present disclosure. As shown in FIG. 1, the device 10 includes a prediction unit 11. The device 10 may further include a measurement unit 12.

[0030] The device 10 may be, for example, a single device including the above-mentioned components, or a device in which the components can be connected via a communication network. The device 10 may also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and may be any known network, for example, wired or wireless. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), local 5G, and LPWA. The wireless communication may be a form in which each device communicates directly (ad hoc communication), infrastructure communication, indirect communication via an access point, or the like. The device 10 may be incorporated into a server as a system. The device 10 may also be, for example, a personal computer (PC, e.g., desktop or laptop) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, or the like. The device 10 may be, for example, in the form of cloud computing or edge computing, in which at least one of the components is located on a server and the other components are located on a terminal.

[0031] 2 is a block diagram illustrating an example of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, an output device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0032] The central processing unit 101 operates in cooperation with other components via a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as a prediction unit 11. The central processing unit 101 may further function as a measurement unit 12. The central processing unit 101 may include, as a computing device, a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0033] The bus 103 can also be connected to, for example, external devices. Examples of the external devices include external storage devices such as external databases, printers, external input devices, external display devices, and external imaging devices. The device 10 can be connected to an external network (the communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0034] The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from the memory 102 and executes the programs. The main memory may be, for example, a RAM (random access memory). Alternatively, the memory 102 may be, for example, a ROM (read only memory).

[0035] The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, and examples include a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, and memory card. The storage device 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0036] In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store information such as user information and work information. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0037] The device 10 further includes, for example, an input device 105 and an output device 106. Examples of the input device 105 include pointing devices such as a touch panel, track pad, and mouse; a keyboard; imaging means such as a camera and scanner; card readers such as an IC card reader and a magnetic card reader; and audio input means such as a microphone. Examples of the output device 106 include display devices such as an LED display and a liquid crystal display; audio output devices such as a speaker; a printer; and the like. In the first embodiment, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may also be configured as an integrated device, such as a touch panel display.

[0038] In the present device 10, the prediction step in the morbidity and mortality probability prediction method of the present disclosure is performed by the prediction unit 11. Furthermore, if the present device 10 includes a measurement unit 12, the measurement step in the morbidity and mortality probability prediction method of the present disclosure is performed. Note that the descriptions of the components of the present device 10 can be derived from the descriptions of the morbidity and mortality probability prediction model of the present disclosure and the descriptions of the steps in the morbidity and mortality probability prediction method of the present disclosure.

[0039] [Embodiment 4] The program of this embodiment is a program for causing a computer to execute each step in the morbidity and mortality probability prediction method of the present disclosure as a procedure. Specifically, the program of this embodiment is a program for causing a computer to execute a prediction procedure. Furthermore, the program may be a program for causing a computer to execute a measurement procedure.

[0040] The program of the present embodiment can also be referred to as a program that causes a computer to function as a prediction procedure, and can also be referred to as a program that causes a computer to function as a measurement procedure.

[0041] The program of this embodiment can incorporate the descriptions in the morbidity and mortality probability prediction model and the morbidity and mortality probability prediction method of this disclosure. For example, the "procedure" in each of the steps can be replaced with "processing." The program of this embodiment may also be recorded on a computer-readable recording medium. The recording medium may be, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples thereof include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., USB flash memory, SD / SDHC card, etc.), optical disks (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disks (MO), floppy disks (FD), etc. The program of this embodiment (e.g., a programming product or program product) may also be distributed from an external computer. The "distribution" may be, for example, distribution via a communication network or distribution via a device connected via a wire. The program of this embodiment may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0042] Next, examples of the present disclosure will be described, but the present disclosure is not limited to the following examples.

[0043] In this example, a marker set for predicting at least one of the likelihood of developing cardiovascular disease and the likelihood of death due to the development of cardiovascular disease was identified, and a morbidity and mortality probability prediction model based on the marker set was created. The prediction accuracy of the created prediction model was also verified.

[0044] Examples 1 and 2: Using a subset of participants in the Chronic Heart Failure Analysis and Registry in the Tohoku District-2 Study (CHART-2 Study), blood proteins were identified as predictors of cardiovascular disease incidence and mortality based on the occurrence of the primary outcome. The primary outcomes were flags for all-cause mortality, myocardial infarction, stroke, and heart failure, as well as the time to onset of these events. Proteins significant for cardiovascular disease incidence were selected from approximately 5,000 blood proteins using Cox Proportional Hazard Regression. The significance level was set at 0.01, and Bonferroni correction was applied. As a result, 73 proteins were selected. The results are shown in Figure 3. Figure 3 is a scatter plot showing the relationship between the hazard ratio and p-value for blood proteins with respect to cardiovascular disease. In Figure 3, the vertical axis represents the p-value, and the horizontal axis represents the hazard ratio.

[0045] Using the selected significant proteins, stepwise selection was performed to select the proteins best suited to predicting morbidity and mortality. The Bayesian Information Criterion (BIC) and Akaike's Information Criterion (AIC) were applied to the stepwise selection. A prediction model was created using the proteins selected using BIC (Stepwise BIC) and the proteins selected using AIC (Stepwise AIC) as marker sets for predicting morbidity and mortality in Examples 1 and 2.

[0046] [Comparative Example 1] As a comparative example, a portion of the participants in the CHART-2 study was used as the target population to search for clinical variables (clinical information) that could be used as predictors of morbidity and mortality. For all included clinical information, stepwise selection was used to select the clinical information that was most suitable for predicting morbidity and mortality. AIC was applied to the stepwise selection. A prediction model was created in which the selected clinical information was used as a predictor of morbidity and mortality in Comparative Example 1.

[0047] <Results> (Prediction markers and prediction model) The morbidity / mortality probability prediction marker set and its prediction model in Example 1 are shown in Table 1 below. The calculation formula for the prediction model is shown in Equation 1 below. Note that the coefficients in Table 1 below are values ​​rounded to three decimal places. In Equation 1 below, when t = 1 (year), Λ 0 (1) = 0.03084816, S 0 (1) = 0.9696228, and S0 (t) = exp (-Λ 0 (t)).

[0048]

[0049]

[0050] Next, the morbidity / mortality probability prediction marker set and its prediction model in Example 2 are shown in Table 2 below. The calculation formula for the prediction model is shown in Equation 2 below. Note that the coefficients in Table 2 below are values ​​rounded to three decimal places. In Equation 2 below, when t = 1, Λ 0 (1) = 0.01626379, S 0 (1) = 0.9838678, and S0 (t) = exp (-Λ 0 (t)).

[0051]

[0052]

[0053] Next, the morbidity and mortality probability prediction marker set and its prediction model of Comparative Example 1 are shown in Table 3 below.

[0054]

[0055] (Accuracy of prediction model) The occurrence of the primary outcome one year later was evaluated using AUC (Area Under the Curve). The results are shown in Figure 4 and Table 4. Figure 4 is a receiver operating characteristic (ROC) curve showing the accuracy of the prediction model. In Figure 4, the vertical axis represents sensitivity and the horizontal axis represents specificity. In Table 4, "NRI" stands for "net reclassification improvement." As shown in Figure 4 and Table 4, the prediction models using the morbidity and mortality probability prediction marker sets of Examples 1 and 2 showed higher accuracy than the prediction model using the morbidity and mortality probability prediction marker set of Comparative Example 1.

[0056]

[0057] Although the present disclosure has been described above with reference to the embodiments and examples, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0058] <Supplementary Notes> Some or all of the above embodiments and examples may be described as, but are not limited to, the following supplementary notes: (Supplementary Note 1) A morbidity and mortality probability prediction model comprising, as morbidity and mortality probability prediction markers, at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3, wherein the morbidity and mortality probability prediction markers are indicators for predicting at least one of the morbidity and mortality probability of cardiovascular disease and the morbidity and mortality probability attributable to the morbidity and mortality probability of cardiovascular disease, and wherein the quantified value of the morbidity and mortality probability prediction marker by an aptamer is used as input data to predict the morbidity and mortality probability. (Supplementary Note 2) The morbidity and mortality probability prediction model according to Supplementary Note 1, wherein the morbidity and mortality probability prediction marker further comprises at least one selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP. (Supplementary Note 3) The morbidity and mortality probability prediction model according to Supplementary Note 1 or 2, wherein the cardiovascular disease is heart disease or stroke. (Supplementary Note 4) The morbidity and mortality probability prediction model according to Supplementary Note 3, wherein the heart disease is heart failure or myocardial infarction. (Supplementary Note 5) A method for predicting a subject's likelihood of developing cardiovascular disease and / or a likelihood of death due to the development of cardiovascular disease based on a quantitative value of a marker for predicting likelihood of morbidity and mortality determined by an aptamer, wherein the markers for predicting likelihood of morbidity and mortality include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3, wherein the step is carried out by a computer. (Supplementary Note 6) The method for predicting likelihood of morbidity and mortality according to Supplementary Note 5, wherein the markers for predicting likelihood of morbidity and mortality further include at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP.(Appendix 7) The method for predicting a likelihood of morbidity and mortality according to Appendix 5 or 6, further comprising a measuring step, wherein the measuring step measures the amount of the aptamer-based marker for predicting a likelihood of morbidity and mortality in a biological sample from the subject. (Appendix 8) The method for predicting a likelihood of morbidity and mortality according to Appendix 7, wherein the biological sample is a biological sample containing blood. (Appendix 9) The method for predicting a likelihood of morbidity and mortality according to any of Appendixes 5 to 8, wherein the subject is a mongoloid. (Appendix 10) The method for predicting a likelihood of morbidity and mortality according to any of Appendixes 5 to 9, wherein the cardiovascular disease is heart disease or stroke. (Appendix 11) The method for predicting a likelihood of morbidity and mortality according to Appendix 10, wherein the heart disease is heart failure or myocardial infarction. (Supplementary Note 12) An morbidity and mortality probability prediction device comprising: a prediction unit that predicts at least one of a subject's morbidity and mortality probability attributable to morbidity of cardiovascular disease based on a quantitative value of an aptamer-based morbidity and mortality probability prediction marker, wherein the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3. (Supplementary Note 13) The morbidity and mortality probability prediction device according to Supplementary Note 12, wherein the morbidity and mortality probability prediction markers further include at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP. (Supplementary Note 14) The morbidity and mortality probability prediction device according to Supplementary Note 12 or 13, further comprising a measurement unit, wherein the measurement unit measures the amount of the morbidity and mortality probability prediction marker determined by the aptamer in a biological sample from the subject. (Supplementary Note 15) The morbidity and mortality probability prediction device according to Supplementary Note 14, wherein the biological sample is a biological sample containing blood. (Supplementary Note 16) The morbidity and mortality probability prediction device according to any of Supplementary Notes 12 to 15, wherein the subject is a mongoloid. (Supplementary Note 17) The morbidity and mortality probability prediction device according to any of Supplementary Notes 12 to 16, wherein the cardiovascular disease is heart disease or stroke.(Supplementary Note 18) The morbidity and mortality probability prediction device according to Supplementary Note 17, wherein the cardiac disease is heart failure or myocardial infarction. (Supplementary Note 19) A program for causing a computer to execute the procedure, comprising: a prediction step for predicting at least one of a subject's likelihood of developing cardiovascular disease and a likelihood of death due to the development of the cardiovascular disease based on a quantitative value of a morbidity and mortality probability prediction marker determined by an aptamer; and the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3. (Supplementary Note 20) The program according to Supplementary Note 19, wherein the morbidity-mortality probability prediction marker further comprises at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP. (Supplementary Note 21) The program according to Supplementary Note 19 or 20, further comprising a measurement step of measuring the amount of the morbidity-mortality probability prediction marker determined by the aptamer in a biological sample from the subject. (Supplementary Note 22) The program according to Supplementary Note 21, wherein the biological sample is a biological sample containing blood. (Supplementary Note 23) The program according to any of Supplements 19 to 22, wherein the subject is a mongoloid. (Supplementary Note 24) The program according to any one of Supplementary Notes 19 to 23, wherein the cardiovascular disease is heart disease or stroke. (Supplementary Note 25) The program according to Supplementary Note 24, wherein the heart disease is heart failure or myocardial infarction. (Supplementary Note 26) A computer-readable recording medium having recorded thereon a program for causing a computer to execute the steps, comprising a prediction step of predicting at least one of a subject's likelihood of developing cardiovascular disease and a likelihood of death attributable to the development of said cardiovascular disease based on a quantitative value of an aptamer-based morbidity and mortality probability prediction marker, wherein the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3.(Appendix 27) The recording medium according to Appendix 26, further comprising at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP as the morbidity and mortality probability prediction marker. (Appendix 28) The recording medium according to Appendix 26 or 27, further comprising a measurement step of measuring the amount of the morbidity and mortality probability prediction marker determined by the aptamer in a biological sample from the subject. (Appendix 29) The recording medium according to Appendix 28, wherein the biological sample is a biological sample containing blood. (Appendix 30) The recording medium according to any of Appendices 26 to 29, wherein the subject is a mongoloid. (Appendix 31) The recording medium according to any one of Appendices 26 to 30, wherein the cardiovascular disease is heart disease or stroke. (Appendix 32) The recording medium according to Appendices 31, wherein the heart disease is heart failure or myocardial infarction.

[0059] This application claims priority based on Japanese Patent Application No. 2024-020658, filed February 14, 2024, the disclosure of which is incorporated herein in its entirety by reference.

[0060] As described above, according to the present disclosure, it is possible to provide a morbidity and mortality probability prediction model, a morbidity and mortality probability prediction method, a morbidity and mortality probability prediction device, a program, and a recording medium for improving the prediction accuracy of the morbidity of cardiovascular disease and the probability of mortality due to the morbidity of cardiovascular disease. The applications of the present disclosure are not particularly limited, and can be used for a wide range of applications.

[0061] REFERENCE SIGNS LIST 10 Morbidity and Mortality Probability Prediction Device 11 Prediction Unit 12 Measurement Unit 101 CPU 102 Memory 103 Bus 104 Storage Device 105 Input Device 106 Output Device 107 Communication Device

Claims

1. A morbidity and mortality probability prediction model comprising, as morbidity and mortality probability prediction markers, at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3, wherein the morbidity and mortality probability prediction markers are indicators for predicting at least one of the likelihood of developing cardiovascular disease and the likelihood of death due to the development of the cardiovascular disease, and wherein the quantified values ​​of the morbidity and mortality probability prediction markers determined by an aptamer are used as input data to predict the likelihood of morbidity and mortality.

2. The morbidity and mortality probability prediction model according to claim 1, further comprising at least one selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP as the morbidity and mortality probability prediction marker.

3. The morbidity and mortality probability prediction model according to claim 1 or 2, wherein the cardiovascular disease is heart disease or stroke.

4. The morbidity and mortality probability prediction model according to claim 3, wherein the heart disease is heart failure or myocardial infarction.

5. A method for predicting morbidity and mortality probability, comprising a prediction step of predicting at least one of a subject's morbidity and mortality probability due to the morbidity and mortality probability caused by the morbidity and mortality probability caused by the morbidity and mortality probability predicted by an aptamer, wherein the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3, and wherein the step is carried out by a computer.

6. The method for predicting the likelihood of morbidity and mortality according to claim 5, wherein the marker for predicting the likelihood of morbidity and mortality further comprises at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP.

7. A method for predicting the likelihood of morbidity and mortality according to claim 5 or 6, further comprising a measuring step, wherein the measuring step measures the amount of the marker for predicting the likelihood of morbidity and mortality determined by the aptamer in the biological sample of the subject.

8. The method for predicting the likelihood of morbidity and mortality described in claim 7, wherein the biological sample is a biological sample containing blood.

9. The method for predicting the likelihood of morbidity and mortality according to claim 5 or 6, wherein the subject is a Mongoloid.

10. A method for predicting the likelihood of mortality according to claim 5 or 6, wherein the cardiovascular disease is heart disease or stroke.

11. The method for predicting the likelihood of morbidity and mortality according to claim 10, wherein the heart disease is heart failure or myocardial infarction.

12. A morbidity and mortality probability prediction device comprising a prediction unit that predicts at least one of a subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of said cardiovascular disease based on a quantitative value of an aptamer-based morbidity and mortality probability prediction marker, and the morbidity and mortality probability prediction markers include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3.

13. The morbidity and mortality probability prediction device according to claim 12, wherein the morbidity and mortality probability prediction marker further comprises at least one biomarker selected from the group consisting of ANP, CDHR3, ASAH2, BAGE2, LAP2B, Inhibin bB chain, TFF3, IP16, Pseudocholinesterase, PCOC2, CRDL1, FBLN3, HE4, b2-Microglobulin, C3d, and GBRAP.

14. The morbidity and mortality probability prediction device according to claim 12 or 13, further comprising a measurement unit, which measures the amount of the morbidity and mortality probability prediction marker due to the aptamer in the subject's biological sample.

15. The morbidity and mortality probability prediction device according to claim 14, wherein the biological sample is a biological sample containing blood.

16. The morbidity and mortality probability prediction device according to claim 12 or 13, wherein the subject is a Mongoloid.

17. The morbidity and mortality probability prediction device according to claim 12 or 13, wherein the cardiovascular disease is heart disease or stroke.

18. The morbidity and mortality probability prediction device according to claim 17, wherein the heart disease is heart failure or myocardial infarction.

19. A program for causing a computer to execute the procedure, comprising a prediction step of predicting at least one of a subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of said cardiovascular disease based on the quantitative value of a marker for predicting likelihood of morbidity and mortality determined by an aptamer, wherein the markers for predicting likelihood of morbidity and mortality include at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B, and PLOD3.

20. A computer-readable recording medium having recorded thereon a program for causing a computer to execute the procedure, the procedure including a prediction procedure for predicting at least one of a subject's likelihood of developing cardiovascular disease and the likelihood of death due to the development of said cardiovascular disease based on the quantitative value of a marker for predicting likelihood of morbidity and mortality determined by an aptamer, the marker for predicting likelihood of morbidity and mortality including at least one of BNP and N-terminal pro-BNP, as well as ANTR2, MACOI, SP-B and PLOD3.

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