Disease onset risk prediction device, prediction marker set, prediction method, program, and recording medium

Abstract

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting the onset risk of an endocrine disease or a metabolic disease from disease-related information regarding a prediction subject.　A disease onset risk prediction device according to the present disclosure includes an information acquisition unit, a prediction unit and an output unit. The information acquisition unit acquires disease-related information regarding a prediction subject. The disease is an endocrine disease or a metabolic disease. The disease-related information contains at least one of information acquired by a health diagnosis and information acquired by the prediction subject. The prediction unit predicts a disease onset risk of the prediction subject from the disease-related information, and the output unit outputs the disease onset risk.

Description

Disease onset risk prediction device, prediction marker set, prediction method, program, and recording medium

[0001] The present disclosure relates to a disease onset risk prediction device, a predictive marker set, a prediction method, a program, and a recording medium.

[0002] Patent Document 1 discloses a method for determining or predicting a subject's susceptibility to metabolic diseases and obesity, comprising measuring the level of circulating dermatopontin in a sample obtained from the subject; comparing the obtained level of circulating dermatopontin with the level of dermatopontin previously determined in a control; and determining the subject's susceptibility to metabolic diseases and obesity based on the difference between the level of circulating dermatopontin and the level of dermatopontin in the control.

[0003] Special table 2018-507853 publication

[0004] On the other hand, if it were possible to predict the risk of developing a disease from information that can be collected more easily than the information disclosed in Patent Document 1, this would be useful because it could lead to people improving their health awareness and reviewing their lifestyle habits.

[0005] Therefore, the present disclosure aims to provide a disease onset risk prediction device, a prediction marker set, a prediction method, a program, and a recording medium for predicting the risk of developing an endocrine disease or metabolic disease from disease-related information of a subject.

[0006] In order to achieve the above-mentioned object, the disease onset risk prediction device of the present disclosure includes an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of the person to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information includes at least one of information acquired through a health checkup and information acquired by the person to be predicted, the prediction unit predicts the disease onset risk of the person to be predicted from the disease-related information, and the output unit outputs the disease onset risk.

[0007] The disease onset risk prediction marker set disclosed herein includes at least two selected from information related to age, obesity, hypertension, dyslipidemia, hyperglycemia, and liver function, and serves as an indicator for predicting disease onset risk.

[0008] The disease onset risk prediction method disclosed herein includes an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information includes at least one of information acquired through a health checkup and information acquired by the subject to be predicted, the prediction step predicts the risk of developing the disease in the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease, and the disease onset risk prediction method is a disease onset risk prediction method in which each of the steps is performed by a computer.

[0009] The program of the present disclosure is a program for causing a computer to execute the steps of the method of the present disclosure as procedures.

[0010] The recording medium of the present disclosure is a computer-readable recording medium on which the program of the present disclosure is recorded.

[0011] According to the present disclosure, it is possible to predict the risk of developing a disease from disease-related information of a subject to be predicted.

[0012] FIG. 1 is a block diagram showing an example of the configuration of a disease onset risk prediction device of the present disclosure. FIG. 2 is a block diagram showing an example of the hardware configuration of a disease onset risk prediction device of the present disclosure. FIG. 3 is a flowchart showing an example of processing in the disease onset risk prediction device of the present disclosure. FIG. 4 is a diagram showing a breakdown of training data and evaluation data. FIG. 5 is a diagram showing an example of selection of data to be used for training data. FIG. 6 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to type 2 diabetes (E11). FIG. 7 is a diagram showing details of a trained model created for predicting the onset of type 2 diabetes (E11). FIG. 8 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to lipoprotein metabolism disorders and other lipemias (E78). FIG. 9 is a diagram showing details of a trained model created for predicting the onset of lipoprotein metabolism disorders and other lipemias (E78). FIG. 10 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to purine and pyrimidine metabolism disorders (E79). FIG. 11 shows details of the trained model for predicting the onset of purine and pyrimidine metabolism disorders (E79).

[0013] Embodiments of the present disclosure will be described with reference to the drawings. 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 mutually incorporated unless otherwise specified, and the configurations of the embodiments can be combined unless otherwise specified.

[0014] In the present disclosure, the term "disease" refers to an endocrine disease or a metabolic disease, unless otherwise specified. More specifically, in the International Statistical Classification of Diseases and Related Health Problems (ICD-10), the endocrine disease includes type 2 diabetes (E11). Furthermore, the metabolic disease includes lipoprotein metabolism disorders and other lipemias (E78) and purine and pyrimidine metabolism disorders (E79).

[0015] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of a disease onset risk prediction device 10 (hereinafter also referred to as "the device 10") according to the present disclosure. As shown in Fig. 1, the device 10 includes an information acquisition unit 11, a prediction unit 12, and an output unit 13.

[0016] 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 system server, for example. 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. Furthermore, 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.

[0017] 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).

[0018] 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 an information acquisition unit 11, a prediction unit 12, and an output unit 13. The device 10 may include, as a computing device, other computing devices such as a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or the like, or may include a combination of a CPU and these.

[0019] The bus 103 can also be connected to, for example, external devices. Examples of the external devices include a user terminal, an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. 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.

[0020] 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).

[0021] 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, such as a hard disk drive (HDD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or 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.

[0022] 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, for example, disease-related information, etc., as described below. 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.

[0023] 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, or mouse; a keyboard; imaging means such as a camera or scanner; card readers such as an IC card reader or 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 or a liquid crystal display; audio output devices such as a speaker; a printer; etc. In the present disclosure 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated device, such as a touch panel display.

[0024] Next, an example of the disease onset risk prediction method of the present disclosure will be described based on the flowchart of Fig. 3. The disease onset risk prediction method of the present disclosure is carried out as follows, for example, using the present device 10 of Fig. 1 or Fig. 2. Note that the disease onset risk prediction method of the present disclosure is not limited to use with the present device 10 of Fig. 1 or Fig. 2.

[0025] First, the information acquisition unit 11 acquires disease-related information of the subject to be predicted (S11, information acquisition step). The disease-related information includes at least one of information acquired through a health checkup and information acquired by the subject to be predicted. The information acquired by the subject to be predicted is, for example, information acquired by a wearable device, a home health measurement device, or the like. The disease-related information may also include medical record information. Examples of the disease-related information include test result information, medical interview information, and subject attribute information (gender, age, etc.). The disease-related information may include, for example, at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function. The disease-related information may further include at least one of gender, blood test values, and medical interview information related to lifestyle habits. In addition, the disease-related information may further include height.

[0026] The obesity-related information includes, for example, abdominal circumference, weight, BMI (Body Mass Index), weight change over the course of a year, weight change since the age of 20, and the like.

[0027] Examples of the information related to hypertension include a medical history of hypertension, medication or administration information for hypertension, systolic blood pressure, diastolic blood pressure, etc. The medication or administration information for hypertension includes, for example, insulin administration information, antihypertensive drug administration information, etc. The systolic blood pressure is, for example, information that the systolic blood pressure is 140 mmHg or higher, etc. The diastolic blood pressure is, for example, information that the diastolic blood pressure is 90 mmHg or higher, etc.

[0028] Examples of the information related to hyperglycemia include a history of diabetes, medication or administration information for hyperglycemia, fasting blood glucose level, and HbA1c (hemoglobin A1c) level. The medication or administration information for hyperglycemia includes, for example, insulin administration information and hypoglycemic drug administration information. The fasting blood glucose level includes, for example, information that the fasting blood glucose level is 126 mg / dL or higher. The HbA1c level includes, for example, information that the HbA1c level is 6.5% or higher.

[0029] Examples of the information related to dyslipidemia include a medical history of dyslipidemia, medication or administration information for dyslipidemia, HDL (High Density Lipoprotein) value, LDL (Low Density Lipoprotein) value, TG (triglyceride) value, etc. The medication or administration information for dyslipidemia is, for example, medication information for drugs that lower cholesterol levels or drugs that lower triglyceride levels, etc. The HDL value is, for example, information that the HDL value is less than 40 mg / dL, etc. The LDL value is, for example, information that the LDL value is 140 mg / dL or higher, etc. The TG value is, for example, information that the TG value is 150 mg / dL or higher, etc.

[0030] Examples of the information related to liver function include γGTP (γ-glutamyl transpeptidase) values, AST (aspartate aminotransferase) values, and ALT (alanine aminotransferase) values. The γGTP value is, for example, information that the γGTP value is 65 U / L or more for men and 33 U / L or more for women. The AST value is, for example, information that the AST value is 31 U / L or more. The ALT value is, for example, information that the ALT value is 43 U / L or more and 24 U / L or more for women.

[0031] The blood test value may be, for example, a uric acid level, etc. The uric acid level may be, for example, information that the uric acid level is 43 U / L or more for men and 24 U / L or more for women.

[0032] Examples of the lifestyle-related information include smoking, exercise, walking speed, drinking frequency (occasionally, always, etc.), amount of alcohol consumed (1-2 go, 2-3 go, 3 go or more per day, etc.), quality of sleep, snacking, eating a late dinner, skipping breakfast, etc.

[0033] The disease-related information is not limited to these, and may include other information, as long as it provides information necessary for predicting the risk of developing a disease. The disease-related information may be, for example, information selected based on the results of analyzing the association with a disease using a Cox proportional hazards model. The disease-related information may be, for example, a disease development risk prediction marker set described later in the present disclosure. Methods for acquiring the disease-related information include, but are not limited to, acquiring it from a connected external database, acquiring it via a communication line, or directly inputting required items into the device 10.

[0034] Next, the prediction unit 12 predicts the disease development risk of the prediction subject from the disease-related information (S12, prediction step). The disease development risk is predicted, for example, based on the association between the disease-related information and the disease. The disease development risk prediction may be, for example, a prediction of the disease development risk within an arbitrary period of time.

[0035] The prediction unit 12 may be a trained model (disease onset risk prediction model). In this case, the disease onset risk prediction model predicts the disease onset risk of the prediction subject based on the disease-related information. The disease onset risk prediction model is, for example, a trained model that outputs the disease onset risk of the prediction subject when the disease-related information is input. The disease onset risk prediction model can also be referred to as a trained model that is machine-learned using, for example, multiple pieces of disease-related information as training data and causes a computer to function to predict the disease onset risk of the prediction subject. The multiple pieces of disease-related information may or may not include the disease-related information of the prediction subject. The disease onset risk prediction model may be, for example, a trained model using a Cox proportional hazards model. The covariates may be selected, for example, by stepwise selection and Akaike's Information Criterion (AIC). The trained model may, for example, evaluate the AUC (area under curve) for disease onset within an arbitrary period using ROC analysis. The trained model may be, for example, a trained model that predicts the risk of developing a disease within an arbitrary period of time. In this case, the probability of developing a disease within an arbitrary period of time can be calculated, for example, by the following formula (1): In the formula, t is the arbitrary period of time, S ０ is the baseline survival function, X is the test value, β is the regression coefficient of the prediction model, and the X bar represents the mean of the test value.

[0036] Thereafter, the output unit 13 outputs the disease development risk (S13, output step), and the process ends. The disease development risk to be output is not particularly limited as long as it can grasp the disease development risk of the prediction subject, and may be, for example, an absolute evaluation, a relative evaluation, a numerical value, an evaluation result based on a threshold, etc. The output may be, for example, output to the output device 106 included in the present apparatus 10, or may be output to an output device included in a device other than the present apparatus 10.

[0037] In this way, the disease onset risk prediction device and disease onset risk prediction method of the present disclosure can predict the disease onset risk from, for example, only disease-related information, making it possible to easily predict the disease onset risk.

[0038] [Embodiment 2] Next, a disease onset risk prediction marker set that serves as an index for predicting the disease onset risk will be described.

[0039] The disease onset risk prediction marker set of the present disclosure includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function, and serves as an index for predicting the risk of developing a disease. The disease onset risk prediction marker set may further include at least one of gender, blood test values, and lifestyle-related questionnaire information. Additionally, the disease onset risk prediction marker set may further include height. The description of the disease onset risk prediction device described above can be used to describe the disease onset risk prediction marker set of the present disclosure.

[0040] Third Embodiment Next, the selection of disease-related information (covariates) for predicting the risk of developing a disease and the creation of a trained model will be described with reference to FIGS.

[0041] 4 is a diagram showing the breakdown of training data and evaluation data. In creating a trained model, as shown in FIG. 4, for example, 50% of the plurality of pieces of disease-related information are randomly selected and used as training data, 25% are used as evaluation data, and the remaining 25% can be used for final evaluation.

[0042] The training data may be, for example, data selected based on an arbitrary condition. Fig. 5 is a diagram showing an example of the selection of data to be used as training data. The arbitrary condition may be, for example, as shown in Fig. 5, excluding data (3) in the case where the disease developed before the start of observation (initial, baseline), and using the other data (1, 2, 4, 5) as training data.

[0043] Here, an example of a method for selecting disease-related information (covariates) for predicting the risk of developing a disease based on selected training data will be described. The disease-related information can be obtained, for example, using a Cox proportional hazards model. The Cox proportional hazards model may be used to analyze the association between the disease-related information and the disease.

[0044] Next, an example of creating a disease onset prediction model using disease-related information is shown. The disease onset risk prediction model utilizes a Cox proportional hazards model, and for example, covariates can be selected from the disease-related information by applying stepwise selection and AIC. The created prediction model can be evaluated by evaluating the AUC (Area Under Curve) for disease onset within a given period using ROC analysis. The created trained model can predict the risk of developing the disease.

[0045] Below, the association between the disease-related information and the disease analyzed using the Cox proportional hazards model, as well as an example of a trained model, are shown for each disease. Note that the "corrected p-value" in Tables 1 to 3 refers to the p-value after Bonferroni correction. Figures 6, 8, and 10 are scatter plots showing the relationship between the hazard ratio and p-value for disease-related information related to type 2 diabetes (E11), lipoprotein metabolism disorders and other lipemias (E78), and purine and pyrimidine metabolism disorders (E79), respectively. In Figures 6, 8, and 10, the vertical axis represents -log(p-value) and the horizontal axis represents the hazard ratio. Figures 7, 9, and 11 are diagrams showing details of trained models for predicting the onset of type 2 diabetes (E11), lipoprotein metabolism disorders and other lipemias (E78), and purine and pyrimidine metabolism disorders (E79), respectively.

[0046] <Type 2 diabetes (E11)>

[0047] When predicting the risk of developing type 2 diabetes (E11), it is preferable to include disease-related information with a small corrected p-value (large -log(p)) and a large or small hazard ratio as a covariate, as shown in Table 1 and Figure 6. It can also be seen that the disease-related information shown in Figure 7 can be an indicator of the risk of developing the disease. Therefore, it is also possible to propose lifestyle improvement measures based on these indicators.

[0048] <Lipoprotein Metabolism Disorders and Other Lipidemias (E78)>

[0049] When predicting the risk of developing lipoprotein metabolism disorders and other lipemias (E78), it is preferable to include disease-related information with a small corrected p-value (large -log(p)) and a large or small hazard ratio as a covariate, as shown in Table 2 and Figure 8. It can also be seen that the disease-related information shown in Figure 9 can be an indicator of the risk of developing a disease. Therefore, it is also possible to propose measures to improve lifestyle habits based on these indicators.

[0050] <Purine and pyrimidine metabolism disorders (E79)>

[0051] When predicting the risk of developing purine and pyrimidine metabolism disorders (E79), it is preferable to include disease-related information with a small corrected p-value (large -log(p)) and a large or small hazard ratio as a covariate, as shown in Table 3 and FIG. 10. It can also be seen that the disease-related information shown in FIG. 11 can be an index of the risk of developing the disease. Therefore, it is also possible to propose lifestyle improvement measures based on these indexes.

[0052] [Embodiment 4] The program of the present disclosure is a program for causing a computer to execute each of the steps of the present disclosure described above. Specifically, the program of the present disclosure is a program for causing a computer to execute, for example, an information acquisition procedure, a prediction procedure, and an output procedure.

[0053] The program of the present disclosure can also be said to be a program that causes a computer to function as, for example, an information acquisition procedure, a prediction procedure, and an output procedure.

[0054] The program of the present disclosure can be implemented by invoking the descriptions of the disease onset risk prediction device and disease onset risk prediction method according to the first and second embodiments of the present disclosure. For example, the "procedure" in each of the steps can be replaced with "processing." The program of the present disclosure may 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., solid state drive (SSD), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. Furthermore, the program of the present disclosure (e.g., a programming product or a program product) may be distributed from an external computer, for example. The "distribution" may be, for example, distribution via a communication network or distribution via a device connected via a wire. The program of the present disclosure may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0055] Although the present disclosure has been described above with reference to the embodiments, 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.

[0056] <Supplementary Notes> Some or all of the above embodiments can be described as in the following supplementary notes, but are not limited to them. (Supplementary Note 1) A disease onset risk prediction device including an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of a subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired through a health checkup and information acquired by the subject to be predicted, the prediction unit predicting a risk of the disease of the subject to be predicted from the disease-related information, and the output unit outputting the risk of the disease. (Supplementary Note 2) The disease onset risk prediction device according to Supplementary Note 1, wherein the disease-related information includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function. (Supplementary Note 3) The disease onset risk prediction device according to Supplementary Note 2, wherein the disease-related information further includes at least one of gender, blood test results, and lifestyle questionnaire information. (Supplementary Note 4) The disease onset risk prediction device according to any one of Supplementary Notes 1 to 3, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder. (Supplementary Note 5) A disease onset risk prediction marker set, which includes at least two selected from information related to age, obesity, hypertension, dyslipidemia, hyperglycemia, and liver function, and serves as an index for predicting the risk of disease onset, and the disease is an endocrine disease or a metabolic disease. (Supplementary Note 6) The disease onset risk prediction marker set according to Supplementary Note 5, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder.(Supplementary Note 7) A disease onset risk prediction method comprising an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired through a health checkup and information acquired by the subject to be predicted, the prediction step predicting the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputting the risk of developing the disease, wherein each of the steps is performed by a computer. (Supplementary Note 8) The disease onset risk prediction method according to Supplementary Note 7, wherein the disease-related information includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function. (Supplementary Note 9) The disease onset risk prediction method according to Supplementary Note 8, wherein the disease-related information further includes at least one of gender, blood test values, and questionnaire information regarding lifestyle habits. (Supplementary Note 10) The disease onset risk prediction method according to any one of Supplementary Notes 7 to 9, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder. (Supplementary Note 11) A disease onset risk prediction program for causing a computer to execute each of the steps, including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired by a health checkup and information acquired by the subject to be predicted, the prediction step predicting the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputting the risk of developing the disease. (Supplementary Note 12) The disease onset risk prediction program according to Supplementary Note 11, wherein the disease-related information includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function. (Appendix 13) The disease onset risk prediction program according to Appendix 12, wherein the disease-related information further includes at least one of gender, blood test results, and lifestyle-related interview information.(Appendix 14) The disease onset risk prediction program according to any one of Appendices 11 to 13, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder. (Appendix 15) A computer-readable recording medium having recorded thereon a disease onset prediction program for causing a computer to execute each of the steps, including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired by a health checkup and information acquired by the subject to be predicted, the prediction step predicting the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputting the risk of developing the disease. (Appendix 16) The recording medium according to Appendices 15, wherein the disease-related information includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function. (Appendix 17) The recording medium according to appendix 16, wherein the disease-related information further includes at least one of gender, blood test values, and lifestyle-related questionnaire information. (Appendix 18) The recording medium according to any one of appendices 15 to 17, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolism disorder and other lipemia, or purine and pyrimidine metabolism disorder.

[0057] This application claims priority based on Japanese Patent Application No. 2024-022231, filed February 16, 2024, the disclosure of which is incorporated herein in its entirety by reference.

[0058] According to the present disclosure, it is possible to predict the risk of developing an endocrine or metabolic disease from the disease-related information of a prediction subject. The fields to which the present disclosure can be applied are not limited, and the present disclosure can be applied to a wide range of fields using a disease onset risk prediction device.

[0059] REFERENCE SIGNS LIST 10 Disease onset risk prediction device 11 Information acquisition unit 12 Prediction unit 13 Output unit 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. A disease onset risk prediction device comprising an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of a subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired through a health checkup and information acquired by the subject to be predicted, the prediction unit predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output unit outputs the risk of developing the disease.

2. The disease onset risk prediction device according to claim 1, wherein the disease-related information includes at least two selected from information related to age, obesity, hypertension, dyslipidemia, and hyperglycemia.

3. The disease onset risk prediction device according to claim 2, wherein the disease-related information further includes at least one of gender, blood test results, and lifestyle-related interview information.

4. A disease onset risk prediction device according to any one of claims 1 to 3, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder.

5. A marker set for predicting the risk of developing a disease, comprising at least two selected from information related to age, obesity, hypertension, dyslipidemia, and hyperglycemia, and serving as an indicator for predicting the risk of developing a disease, wherein the disease is an endocrine disease or a metabolic disease.

6. The marker set for predicting a disease onset risk according to claim 5, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemias, or purine and pyrimidine metabolic disorder.

7. A disease onset risk prediction method comprising an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the person to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information includes at least one of information acquired through a health checkup and information acquired by the person to be predicted, the prediction step predicts the risk of developing the disease of the person to be predicted from the disease-related information, and the output step outputs the risk of developing the disease, and wherein each of the steps is performed by a computer.

8. The method for predicting a disease onset risk according to claim 7, wherein the disease-related information includes at least two selected from information related to age, information related to obesity, and information related to high blood pressure.

9. The method for predicting the risk of developing a disease according to claim 8, wherein the disease-related information further includes at least one of gender, blood test results, and questionnaire information regarding lifestyle habits.

10. A method for predicting the risk of developing a disease according to any one of claims 7 to 9, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is a lipoprotein metabolic disorder and other lipemias, or a purine and pyrimidine metabolic disorder.

11. A disease onset risk prediction program for causing a computer to execute each of the steps, the program comprising an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the person to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired through a health checkup and information acquired by the person to be predicted, the prediction step predicts the risk of developing the disease of the person to be predicted from the disease-related information, and the output step outputs the risk of developing the disease.

12. The disease onset risk prediction program according to claim 11, wherein the disease-related information includes at least two selected from age, information related to obesity, information related to hypertension, information related to dyslipidemia, information related to hyperglycemia, and information related to liver function.

13. The disease onset risk prediction program according to claim 12, wherein the disease-related information further includes at least one of gender, blood test results, and lifestyle-related interview information.

14. A disease onset risk prediction program according to any one of claims 11 to 13, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is lipoprotein metabolic disorder and other lipemia, or purine and pyrimidine metabolic disorder.

15. A computer-readable recording medium having recorded thereon a disease onset prediction program for causing a computer to execute each of the above procedures, the program comprising an information acquisition procedure, a prediction procedure, and an output procedure, wherein the information acquisition procedure acquires disease-related information of a subject to be predicted, the disease being an endocrine disease or a metabolic disease, the disease-related information including at least one of information acquired through a health checkup and information acquired by the subject to be predicted, the prediction procedure predicting the risk of developing the disease of the subject to be predicted from the disease-related information, and the output procedure outputting the risk of developing the disease.

16. The recording medium according to claim 15, wherein the disease-related information includes at least two selected from information related to age, obesity, hypertension, dyslipidemia, hyperglycemia, and liver function.

17. The recording medium according to claim 16, wherein the disease-related information further includes at least one of gender, blood test results, and questionnaire information regarding lifestyle habits.

18. The recording medium according to any one of claims 15 to 17, wherein the endocrine disease is type 2 diabetes, and the metabolic disease is a lipoprotein metabolic disorder and other lipemias, or a purine and pyrimidine metabolic disorder.

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