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

Abstract

The purpose of the present invention is to provide a disease onset risk prediction device for predicting the risk of onset of a skeletal disease or joint disease from disease-related information of a prediction target individual.　The disease onset risk prediction device according to the present disclosure comprises an information acquisition unit, a prediction unit, and an output unit, wherein: the information acquisition unit acquires disease-related information of a prediction target individual; the disease is a skeletal disease or a joint disease; the disease-related information includes at least one of information acquired through a medical checkup and information acquired by the prediction target individual; the prediction unit predicts the risk of onset of the disease in the prediction target individual 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 predicting the risk of bone disease based on bone density, which is characterized by measuring apolipoprotein E (ApoE) polymorphisms in a human specimen and correlating the results with bone disease based on bone density, and a method for predicting the risk of bone disease based on bone density, which is characterized by measuring apolipoprotein E (ApoE) phenotype gene polymorphisms in a human specimen and correlating the results with bone disease based on bone density.

[0003] Japanese Patent Application Publication No. 09-299337

[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 a skeletal disease or a joint 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 a skeletal disease or a joint 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 of the present disclosure includes at least two selected from information related to age, obesity, and information related to hypertension, and serves as an index 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 a skeletal disease or a joint 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 the configuration of an example 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 the 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 gout (M10). FIG. 7 is a diagram showing details of a trained model created for predicting the onset of gout (M10). FIG. 8 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to knee osteoarthritis (M17). FIG. 9 is a diagram showing details of a trained model created for predicting the onset of knee osteoarthritis (M17). FIG. 10 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to osteoporosis without pathological fractures (M81). FIG. 11 shows details of the trained model created to predict the onset of osteoporosis without pathological fractures (M81).

[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 a skeletal disease or a joint disease, unless otherwise specified. More specifically, in the International Statistical Classification of Diseases and Related Health Problems (ICD-10), examples of the skeletal disease include osteoporosis without pathological fractures (M81). Examples of the joint disease include knee osteoarthritis (M17) and gout (M10).

[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 (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. The information acquired by the subject may be, for example, information acquired using 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, and information related to hypertension. The disease-related information may also include at least one of gender, height, blood test values, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and medical interview information related to lifestyle habits.

[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 in men and 33 U / L or more in 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 in men and 24 U / L or more in 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 information related to age, obesity, and hypertension, 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, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information regarding lifestyle habits. The description of the disease onset risk prediction device described above can be used to explain 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 for gout (M10), knee osteoarthritis (M17), osteoporosis, and disease without pathological fracture (M81), 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 gout (M10), knee osteoarthritis (M17), osteoporosis, and disease without pathological fracture (M81), respectively.

[0046] <Gastrointestinal (M10), men only>

[0047] When predicting the risk of developing gout (M10), 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] <Knee osteoarthritis (M17)>

[0049] When predicting the risk of developing knee osteoarthritis (M17), 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 the disease. Therefore, it is also possible to propose lifestyle improvement measures based on these indicators.

[0050] <Osteoporosis and pathological fractures not present (M81)>

[0051] When predicting the risk of developing osteoporosis without pathological fractures (M81), 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 Figure 10. It can also be seen that the disease-related information shown in Figure 11 can be an indicator of the risk of developing a disease. Therefore, it is also possible to propose lifestyle improvement measures based on these indicators.

[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, but are not limited to, the following supplementary notes. (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 a skeletal disease or a joint 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, and information related to hypertension. (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, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, 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 skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis. (Supplementary Note 5) A disease onset risk prediction marker set including at least two selected from information related to age, obesity, and information related to high blood pressure, which serve as indicators for predicting the risk of disease onset, and the disease is a skeletal disease or a joint disease. (Supplementary Note 6) The disease onset risk prediction marker set according to Supplementary Note 5, wherein the skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis. (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 person to be predicted, the disease being a skeletal disease or a joint disease, the disease-related information including at least one of information acquired by a health checkup and information acquired by the person to be predicted, the prediction step predicting the risk of developing the disease of the person 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 executed by a computer.(Appendix 8) The method for predicting a disease onset risk according to Appendix 7, wherein the disease-related information includes at least two selected from age, information related to obesity, and information related to hypertension. (Appendix 9) The method for predicting a disease onset risk according to Appendix 8, wherein the disease-related information further includes at least one of sex, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information regarding lifestyle habits. (Appendix 10) The method for predicting a disease onset risk according to any of Appendices 7 to 9, wherein the skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis. (Supplementary Note 11) A disease onset risk prediction program for causing a computer to execute each of the steps, the program 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 a skeletal disease or a joint 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 a risk of the subject to be predicted from the disease-related information, and the output step outputting the risk of 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, and information related to hypertension. (Supplementary Note 13) The disease onset risk prediction program according to Supplementary Note 12, wherein the disease-related information further includes at least one of gender, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information regarding lifestyle habits. (Appendix 14) The disease onset risk prediction program according to any one of Appendices 11 to 13, wherein the skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis.(Supplementary Note 15) A computer-readable recording medium having recorded thereon a disease onset prediction program for causing a computer to execute each of the steps, the program including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being a skeletal disease or a joint 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 a risk of the subject to be predicted having the disease from the disease-related information, and the output step outputting the risk of the disease. (Supplementary Note 16) The recording medium of Supplementary Note 15, wherein the disease-related information includes at least two selected from age, information related to obesity, and information related to hypertension. (Supplementary Note 17) The recording medium of Supplementary Note 16, wherein the disease-related information further includes at least one of gender, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information regarding lifestyle habits. (Appendix 18) The recording medium according to any one of Appendices 15 to 17, wherein the skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis.

[0057] This application claims priority based on Japanese Patent Application No. 2024-022232, 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 a skeletal disease or a joint 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 development 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 a skeletal disease or a joint 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, and high blood pressure.

3. A disease onset risk prediction device as described in claim 2, wherein the disease-related information further includes at least one of gender, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information related to lifestyle habits.

4. A disease onset risk prediction device according to any one of claims 1 to 3, wherein the skeletal disease is not accompanied by osteoporosis or pathological fracture, and the joint disease is gout or knee osteoarthritis.

5. A marker set for predicting the risk of developing a disease, comprising at least two selected from information related to age, obesity, and information related to high blood pressure, and serving as an indicator for predicting the risk of developing a disease, wherein the disease is a skeletal disease or a joint disease.

6. The disease onset risk prediction marker set according to claim 5, wherein the skeletal disease is osteoporosis or one not accompanied by pathological fracture, and the joint disease is gout or knee osteoarthritis.

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 a skeletal disease or a joint disease, the disease-related information including at least one of information acquired by a health checkup and information acquired by the person to be predicted, the prediction step predicting the risk of developing the disease in the person 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.

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. A method for predicting the risk of developing a disease as described in claim 8, wherein the disease-related information further includes at least one of gender, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, 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 skeletal disease is not accompanied by osteoporosis or pathological fractures, and the joint disease is gout or knee osteoarthritis.

11. A disease onset risk prediction program for causing a computer to execute each of the above 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 a skeletal disease or a joint 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 predicting the risk of developing the disease of the person to be predicted from the disease-related information, and the output step outputting 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 information related to age, information related to obesity, and information related to high blood pressure.

13. A disease onset risk prediction program as described in claim 12, wherein the disease-related information further includes at least one of gender, height, blood test results, information related to hyperglycemia, information related to dyslipidemia, information related to liver function, and questionnaire information regarding lifestyle habits.

14. A disease onset risk prediction program according to any one of claims 11 to 13, wherein the skeletal disease is not accompanied by osteoporosis or pathological fractures, and the joint disease is gout or knee osteoarthritis.

15. A computer-readable recording medium having recorded thereon a disease onset prediction program for causing a computer to execute each of the above 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 a skeletal disease or a joint disease, the disease-related information including at least one of information acquired by a health checkup and information acquired by the person to be predicted, the prediction step predicting the risk of developing the disease of the person to be predicted from the disease-related information, and the output step 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, and high blood pressure.

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

18. The recording medium according to any one of claims 15 to 17, wherein the skeletal disease is osteoporosis or one not accompanied by pathological fracture, and the joint disease is gout or knee osteoarthritis.

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