Estimation method, estimation program, and estimation system

Abstract

This estimation method includes: a step for acquiring a measurement value of advanced glycation end products of a measured person; and a step for estimating the blood glucose spike frequency of the measured person on the basis of the measurement value of the advanced glycation end products of the measured person acquired in the acquiring step, using a correlation between the measurement value of advanced glycation end products and blood glucose spike frequency prepared beforehand.

Description

[Technical Field]

[0001] The present disclosure relates to an estimation method, an estimation program, and an estimation system for estimating the blood glucose spike frequency of a subject. Mu Regarding. [Background technology]

[0002] Advanced glycation end products (AGEs) are considered to be one of the substances that cause aging. Advanced glycation end products are a collective term for multiple compounds that are produced when sugars and proteins combine and react through oxidation, condensation, and dehydration. AGEs accumulate in the body due to lifestyle disorders such as eating habits, exercise habits, sleep habits, inflammation due to fever or injury, and stress, and are thought to cause lifestyle-related diseases (e.g., diabetes, dementia, etc.) or age-related diseases.

[0003] Patent Document 1 discloses a sensor that receives fluorescence excited by light irradiated onto the skin of a subject and measures the degree of accumulation of AGEs based on the intensity of the received fluorescence. Although there are individual differences, AGEs generally change over the course of several weeks, so subjects measure their AGEs, for example, once every several weeks.

[0004] Meanwhile, a known method for identifying lifestyle habits, particularly poor dietary habits, involves continuously measuring blood glucose levels (hereinafter, for convenience, glucose levels in interstitial fluid, which behave in a similar manner to blood glucose levels, will also be referred to as "blood glucose levels") over a certain period (e.g., two weeks). Blood glucose levels are indicated by the amount of glucose in the blood and can be significantly affected by the type and amount of nutrients contained in food. While fasting blood glucose levels are similar to those of healthy individuals, postprandial blood glucose levels may suddenly rise to the same level as those of diabetics, followed by a rapid drop. Such sudden fluctuations in blood glucose levels are also known as "blood glucose spikes." Blood glucose spikes have been reported to be a contributing factor to diseases such as arteriosclerosis, dementia, and cancer. Therefore, people who experience blood glucose spikes should reevaluate their lifestyle habits to prevent lifestyle-related or age-related diseases.

[0005] Patent Document 2 discloses a sensor that performs continuous glucose monitoring (CGM), which continuously measures blood glucose levels over a certain period of time by inserting a measurement needle disposed in a sensor unit into the subcutaneous tissue. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-223431 [Patent Document 2] Japanese Patent Application Laid-Open No. 2011-212118 Summary of the Invention [Problem to be solved by the invention]

[0007] Continuous blood glucose monitoring allows the subject to check whether or not blood glucose spikes are occurring, and if so, how frequently they occur (hereinafter also referred to as "blood glucose spike frequency"), which can provide an opportunity to reassess their lifestyle habits. However, when performing continuous blood glucose monitoring, the subject goes about their daily life while inserting a measurement needle subcutaneously for a certain period of time, so it is important to limit continuous blood glucose monitoring to the minimum necessary. Continuous blood glucose monitoring is also costly. For this reason, continuous blood glucose monitoring places a greater burden on the subject than AGE monitoring, which only requires measurement once every week or several weeks, and subjects tend to avoid it.

[0008] The present disclosure has been made to solve such problems, and its purpose is to provide a technology for determining the frequency of blood glucose spikes while minimizing the burden on the subject. [Means for solving the problem]

[0009] An estimation method according to an aspect of the present disclosure is a method in which a calculation device estimates the blood glucose spike frequency of a subject. The estimation method includes, as processing executed by the calculation device, a step of acquiring a measurement value of the subject's advanced glycation endproducts, and a step of estimating the blood glucose spike frequency of the subject based on the measurement value of the advanced glycation endproducts of the subject acquired in the acquiring step, using a correlation between the measurement value of the advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance. The correlation is generated based on the measured values ​​of advanced glycation end products for each of a plurality of subjects and the blood glucose spike frequency for each of the plurality of subjects, the plurality of subjects not including any subjects with a chronically hyperglycemic state as determined based on blood glucose measurements over a predetermined period of time.

[0010] An estimation program according to another aspect of the present disclosure is a program for estimating a blood glucose spike frequency of a subject. The estimation program causes a calculation device to execute the steps of acquiring a measurement value of the subject's advanced glycation endproducts and estimating the blood glucose spike frequency of the subject based on the measurement value of the advanced glycation endproducts of the subject acquired in the acquiring step, using a correlation between the measurement value of the advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance. The correlation is generated based on the measured values ​​of advanced glycation end products for each of a plurality of subjects and the blood glucose spike frequency for each of the plurality of subjects, the plurality of subjects not including any subjects with a chronically hyperglycemic state as determined based on blood glucose measurements over a predetermined period of time.

[0011] An estimation system according to another aspect of the present disclosure is a system for estimating a blood glucose spike frequency of a subject. The estimation system includes a measurement device that measures advanced glycation endproducts of the subject, an estimation device that estimates the blood glucose spike frequency of the subject based on measurements of the advanced glycation endproducts of the subject measured by the measurement device using a correlation between measurements of the advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance, and a display device that displays viewing information based on the blood glucose spike frequency of the subject estimated by the estimation device. The correlation is generated based on the measured values ​​of advanced glycation end products for each of a plurality of subjects and the blood glucose spike frequency for each of the plurality of subjects, the plurality of subjects not including any subjects with a chronically hyperglycemic state as determined based on blood glucose measurements over a predetermined period of time. [Effects of the Invention]

[0014] According to the present disclosure, it is possible to estimate the frequency of blood glucose spikes based on the measurement values ​​of the subject's advanced glycation end products without conducting continuous blood glucose measurements of the subject, allowing the user to understand the frequency of blood glucose spikes while minimizing the burden on the subject. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram illustrating an estimation system according to a first embodiment. [Figure 2] FIG. 4 is a diagram showing an example of a change in blood glucose level over time according to the first embodiment. [Figure 3] FIG. 1 is a diagram showing the correlation between the AGE score and the blood glucose spike frequency according to the first embodiment. [Figure 4] FIG. 1 is a diagram showing AGE scores relative to blood glucose spike frequency according to the first embodiment. [Figure 5] FIG. 1 is a diagram illustrating a configuration of an estimation device according to a first embodiment. [Figure 6] 4 is a diagram for explaining a user identification information table stored in the estimation device according to the first embodiment. FIG. [Figure 7] FIG. 3 is a diagram for explaining a browsing information table stored in the estimation device according to the first embodiment. [Figure 8] 3 is a diagram showing an example of a display screen of the display device according to the first embodiment. FIG. [Figure 9]3 is a diagram showing an example of a display screen of the display device according to the first embodiment. FIG. [Figure 10] 4 is a flowchart of an estimation process executed by the estimation device according to the first embodiment. [Figure 11] FIG. 10 is a diagram showing the correlation between the AGE score and the blood glucose spike frequency according to the second embodiment. [Figure 12] FIG. 10 is a diagram showing a density map summarizing blood glucose levels for each time period of the first subject according to the second embodiment. [Figure 13] FIG. 10 is a diagram showing a table summarizing the blood glucose levels of the first subject by time period according to the second embodiment. [Figure 14] FIG. 10 is a diagram showing a density map summarizing blood glucose levels for each time period of a second subject according to the second embodiment. [Figure 15] FIG. 10 is a diagram showing a table summarizing blood glucose levels for each time period of a second subject according to the second embodiment. [Figure 16] FIG. 10 is a diagram showing a density map summarizing blood glucose levels for each time period of a third subject according to the second embodiment. [Figure 17] FIG. 10 is a diagram showing a table summarizing the blood glucose levels for each time period of the third subject according to the second embodiment. [Figure 18] 11 is a flowchart of a determination process executed by an estimation device according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] The present embodiment will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and their description will not be repeated in principle.

[0017] <First Embodiment> An estimation system 1 and an estimation device 50 according to a first embodiment will be described with reference to FIGS.

[0018] [Configuration of estimation system] 1 is a diagram showing an estimation system 1 according to embodiment 1. As shown in FIG. 1, the estimation system 1 includes an AGEs measurement device 10, a display device 30, and an estimation device 50.

[0019] The AGEs measurement device 10 is a device for measuring AGEs in a subject. Subjects include those suspected of developing lifestyle-related diseases or age-related diseases such as diabetes, those who have already developed lifestyle-related diseases or age-related diseases, and elderly people living in nursing homes. The AGEs measurement device 10 includes a measurement unit 11, a display 12, and a communication unit 13. The AGEs measurement device 10 may be configured integrally with the display 12 or separately from the display 12.

[0020] The measurement unit 11 non-invasively measures AGEs in a subject. Among the multiple compounds contained in AGEs, there are compounds that have the property of emitting fluorescence when irradiated with specific light. The measurement unit 11 measures AGEs in a subject by utilizing the properties of such compounds.

[0021] When the subject places their fingertip on the measurement unit 11, the measurement unit 11 irradiates light onto the skin from a light source (not shown). Note that the measurement unit 11 may also be configured to irradiate light onto skin other than the subject's fingertip (for example, the arm). The light irradiated by the measurement unit 11 is, for example, excitation light having a peak in a wavelength range of 410 nm or less. The measurement unit 11 receives fluorescence excited by the light irradiated onto the skin using a light-receiving element (not shown), and measures the degree of AGE accumulation based on the intensity of the received fluorescence. The display 12 displays the AGE measurement results obtained by the measurement unit 11. The measurement results include, for example, the intensity of the fluorescence received by the measurement unit 11 and a value obtained by converting the degree of AGE accumulation into a score. Note that the measurement results may also include a correction value obtained by correcting the intensity of the fluorescence received by the measurement unit 11 and the value obtained by converting the degree of AGE accumulation into a score.

[0022] The communication unit 13 transmits and receives data (information) to and from the estimation device 50 via wired or wireless communication. The communication unit 13 may be a component capable of communicating with the estimation device 50, such as a network adapter, or may be built into the AGEs measurement device 10. The communication unit 13 may also be an information terminal capable of communicating with the estimation device 50 via a network, such as a desktop personal computer (PC), laptop PC, smartphone, smartwatch, wearable device, or tablet PC, or may be separate from the AGEs measurement device 10.

[0023] The AGEs measurement device 10 is installed in various facilities such as pharmacies, medical institutions, nursing homes, and gyms. The AGEs measurement device 10 may be managed by a supporter who assists the person being measured. When the person being measured measures AGEs using the AGEs measurement device 10, the AGEs measurement value is transmitted from the AGEs measurement device 10 to the AGEs estimation device 50.

[0024] Although there are individual differences, AGE measurement values ​​generally change over the course of several weeks, so subjects should measure their AGEs, for example, once every two weeks.

[0025] The display device 30 is owned or used by a user. The display device 30 is an information terminal capable of communicating with the estimation device 50 via a network, such as a desktop PC, a laptop PC, a smartphone, a smartwatch, a wearable device, or a tablet PC. The user can directly or indirectly access the estimation device 50 using the display device 30 to obtain various information stored in the estimation device 50, such as advice information described below.

[0026] The user is a user of the service (hereinafter also referred to as "information provision service") provided by the estimation system 1. Specifically, the user may be the subject or a supporter of the subject. The user may also be a family member, relative, or person related to the subject (for example, an acquaintance) who has been granted permission by the subject or supporter to view the measurement results of the subject.

[0027] A supporter is someone who supports the person being measured, and includes staff at a nursing home, a life counselor at a nursing home, a doctor at a hospital, clinic, or corporate clinic, a nurse at a hospital, clinic, or corporate clinic, an instructor or nutrition advisor at a fitness gym, and a pharmacist at a pharmacy.

[0028] The estimation device 50 is managed by a service provider that provides an information provision service. The service provider may be the manufacturer of the AGEs measurement device 10, which lends the AGEs measurement device 10 to users such as subjects or supporters. The estimation device 50 functions as a cloud computer and communicates with both the AGEs measurement device 10 and the display device 30.

[0029] In the estimation system 1 having the above-described configuration, when a subject measures AGEs using the AGEs measurement device 10, the AGEs measurement device 10 outputs the AGE measurement value to the estimation device 50. When the estimation device 50 acquires AGE measurement values ​​from the AGEs measurement device 10, it stores the acquired AGE measurement values ​​together with previously acquired AGE measurement values ​​of the subject. In this way, by accumulating and observing the AGE measurement values ​​of the subject, the user of the estimation system 1 can prevent the onset of lifestyle-related diseases or age-related diseases based on changes in the AGE measurement values.

[0030] On the other hand, continuous glucose monitoring (CGM), which continuously measures blood glucose levels over a certain period of time (e.g., two weeks), is a well-known method for identifying lifestyle habits, particularly poor dietary habits. Continuous glucose monitoring allows subjects to determine whether or not they are experiencing blood glucose spikes, where blood glucose levels rise and fall suddenly, and, if they are, their frequency (blood glucose spike frequency), providing an opportunity to reassess their lifestyle. However, because continuous blood glucose monitoring requires a subcutaneous needle to be inserted into the subject's body for a certain period of time, it is important to limit continuous blood glucose monitoring to the minimum necessary. Continuous blood glucose monitoring is also expensive. For this reason, continuous blood glucose monitoring places a greater burden on subjects than AGE monitoring, which only requires testing once every week or several weeks, and subjects tend to avoid it.

[0031] Therefore, in the estimation system 1 according to the first embodiment, the estimation device 50 is configured to estimate the blood glucose spike frequency based on the AGE measurement values ​​of the subject. Furthermore, the estimation device 50 is configured to output, based on the estimation result of the blood glucose spike frequency, at least one piece of viewable information viewable by users such as the subject, supporter, and viewer: blood glucose spike information regarding the estimation result of the blood glucose spike frequency, diabetes risk information indicating the risk of diabetes (hereinafter, for convenience, the risk involved in the development of diabetic complications will also be referred to as "diabetes risk," and information indicating this diabetes risk will be referred to as "diabetes risk information"), and advice information indicating advice regarding the subject's lifestyle.

[0032] Specifically, the estimation device 50 estimates the blood glucose spike frequency of the subject based on the AGE measurement values ​​obtained from the AGE measurement device 10, and stores the estimated blood glucose spike frequency together with previously calculated estimated blood glucose spike frequency results of the subject.

[0033] The estimation device 50 generates blood glucose spike information based on the estimation result of the blood glucose spike frequency and outputs the blood glucose spike information as viewing information to the display device 30. The blood glucose spike information includes at least one of the following information: the current or past estimation result of the blood glucose spike frequency of the subject, a score converted from the estimation result of the blood glucose spike frequency, and the result of ranking the estimation result of the blood glucose spike frequency by a step-by-step evaluation.

[0034] The estimation device 50 generates diabetes risk information indicating the subject's risk of diabetes based on the estimated blood glucose spike frequency, and outputs the diabetes risk information as viewing information to the display device 30. The diabetes risk information includes information indicating the subject's current or past risk of diabetes (e.g., low risk, medium risk, high risk, etc.).

[0035] The estimation device 50 generates advice information that provides advice about the subject's lifestyle based on the estimated blood glucose spike frequency, and stores the advice information as viewing information. The advice information includes advice about at least one of the subject's eating habits, exercise habits, sleep habits, and mental health.

[0036] The estimation device 50 may generate the viewing information based on other information about the subject. The other information may include, for example, data about the subject's Skeletal Muscle Mass Index (SMI), inflammation, blood pressure, diet, exercise, vegetable intake, sleep, bone density, etc. The estimation device 50 may also analyze the subject's health condition based on the other information described above, and include analysis information indicating the analysis results of the health condition in the viewing information.

[0037] When a user requests viewed information using the display device 30, the estimation device 50 outputs the viewed information to the display device 30 in response to the request from the display device 30. The display device 30 displays the viewed information acquired from the estimation device 50.

[0038] This eliminates the need for the subject to perform continuous blood glucose measurements, and the user can grasp the frequency of blood glucose spikes while minimizing the burden on the subject using display device 30. Furthermore, the user can use display device 30 to obtain advice regarding the subject's risk of diabetes and the subject's lifestyle, which is generated based on the estimated blood glucose spike frequency.

[0039] [Correlation between AGEs and frequency of blood sugar spikes] The correlation between AGEs and the frequency of blood glucose spikes will be described with reference to FIGS. 2 to 4. FIG. 2 is a diagram showing an example of the transition of blood glucose levels over time according to the first embodiment. FIG. 2 shows a graph showing fluctuations in blood glucose levels, with time on the horizontal axis and blood glucose levels on the vertical axis. Generally, a blood glucose level below 126 mg / dL is considered normal, but a blood glucose level exceeding 200 mg / dL at any time is said to be grounds for a diagnosis of diabetes. As shown in FIG. 2, in the blood glucose level data when a blood glucose spike occurs, the fasting blood glucose level is no different from that of a healthy person, but the postprandial blood glucose level rises sharply to the same value as that of a diabetic patient, exceeding 200 mg / dL, and then immediately drops sharply, resulting in a rapid fluctuation in blood glucose levels.

[0040] The occurrence of blood glucose spikes as shown in Fig. 2 can be detected by performing continuous blood glucose measurements, but if it could be estimated based on the AGEs measured by the subject without performing continuous blood glucose measurements, the burden on the subject could be reduced. Fig. 3 is a diagram showing the correlation between the AGE score and the frequency of blood glucose spikes according to embodiment 1.

[0041] The correlation in Figure 3 was created based on the AGEs score and blood glucose spike frequency for each of multiple healthy subjects. Specifically, each subject first measured AGEs, and then measured blood glucose levels at predetermined intervals over a predetermined period of time after the AGEs measurement using continuous blood glucose monitoring, regardless of whether the blood glucose level was after meals, fasting, or while sleeping. In this example, each subject first measured AGEs, and then measured blood glucose levels at 1-minute intervals over a two-week period after the AGEs measurement using continuous blood glucose monitoring, regardless of whether the blood glucose level was after meals, fasting, or while sleeping. Note that AGEs change less than blood glucose levels and can generally change over the course of several weeks, so each subject only needs to measure AGEs at least once every one to two weeks. If AGEs are measured multiple times, the obtained AGEs measurements can be simply averaged. In this example, each subject measured AGEs only once every two weeks.

[0042] The designer of the estimation system 1 and estimation device 50 collects AGEs and blood glucose levels measured for each subject over a two-week period and calculates the AGEs score and blood glucose spike frequency for each subject. Specifically, the designer converts the acquired AGEs measurement values ​​for each subject into an AGEs score between 0 and 1.0. Furthermore, the designer calculates the number of blood glucose level data points exceeding 200 mg / dL among the multiple blood glucose level data points acquired at one-minute intervals over two weeks for each subject, and calculates the number of blood glucose spikes per day (blood glucose spike frequency) by dividing the calculated number of data points exceeding 200 mg / dL by 14 (i.e., the number of days in two weeks). The blood glucose measurement period is not limited to two weeks, but may be several days or one month.

[0043] Designers can create a graph showing the correlation between AGEs score and blood glucose spike frequency by plotting points at positions corresponding to each subject's AGEs score and blood glucose spike frequency on a graph with AGEs score on the horizontal axis and blood glucose spike frequency on the vertical axis, as shown in Figure 3. Each point in Figure 3 represents each subject's AGEs score and blood glucose spike frequency.

[0044] As shown in Figure 3, the higher the AGEs score, the greater the frequency of blood glucose spikes, and the lower the AGEs score, the lower the frequency of blood glucose spikes. For example, there is a correlation between AGEs score and blood glucose spike frequency with a correlation coefficient of 0.633. Here, the P value is the probability of observing a statistic that is significantly more contrary to the hypothesis than the statistic calculated from actual data under the null hypothesis. In the example in Figure 3, the P value is 0.0021, which is lower than 0.05, so it can be said that the correlation between AGEs score and blood glucose spike frequency has a certain degree of reliability that makes it unlikely to be due to chance. In this way, it can be said that there is a relatively strong correlation between AGEs score and blood glucose spike frequency.

[0045] 3, a regression line can be drawn for the correlation between the AGE score and blood glucose spike frequency, and the estimation device 50 can estimate the blood glucose spike frequency corresponding to the AGE score by using such a regression line. Specifically, the estimation device 50 can predict the blood glucose spike frequency of the subject by substituting the AGE measurement value of the subject into the regression line equation described above.

[0046] FIG. 4 is a diagram showing the AGE score versus blood glucose spike frequency according to embodiment 1. FIG. 4 shows a graph with the blood glucose spike frequency on the horizontal axis and the AGE score on the vertical axis. The graph in FIG. 4 shows the interquartile range of the AGE score for blood glucose spike frequencies of 0, 0.01 to 1, 1 to 2, and more than 2 per day. As shown in FIG. 4, no significant change in the AGE score is observed when the blood glucose spike frequency is 2 or less, but the AGE score increases significantly when the blood glucose spike frequency exceeds 2. In other words, a blood glucose spike frequency of more than 2 is considered to increase the likelihood of developing lifestyle-related diseases such as diabetes.

[0047] As described above, there is a correlation between AGEs and blood glucose spike frequency, and the estimation device 50 is configured to estimate blood glucose spike frequency based on the AGE measurement values ​​of the subject using data showing this correlation (hereinafter also referred to as "correlation data"). Specifically, the estimation device 50 converts the AGE measurement values ​​of the subject obtained from the AGE measuring device 10 into an AGE score regardless of whether the subject is postprandial, fasting, or asleep, and can estimate the blood glucose spike frequency of the subject based on the converted AGE score using the correlation data shown in FIG. 3. Note that the correlation data is not limited to the correlation between the AGE score and blood glucose spike frequency as shown in FIG. 3, but may also be data showing the correlation between AGE measurement values ​​and blood glucose spike frequency. In this case, the estimation device 50 may directly use the AGE measurement values ​​obtained from the AGE measuring device 10 to estimate blood glucose spike frequency based on the AGE measurement values.

[0048] [Configuration of the estimation device] The configuration of the estimation device 50 will be described with reference to Fig. 5 to Fig. 7. Fig. 5 is a diagram showing the configuration of the estimation device 50 according to Embodiment 1. As shown in Fig. 5, the estimation device 50 includes a calculation device 510, a storage device 520, and a communication device 530.

[0049] The arithmetic device 510 is a computer (computing entity) that executes various processes according to various programs. The arithmetic device 510 is configured as a computer such as a processor. The processor may be configured, for example, as a microcontroller, a central processing unit (CPU), or a micro-processing unit (MPU). The processor has the function of executing various processes by executing programs, but some or all of these functions may be implemented using dedicated hardware circuits such as an application-specific integrated circuit (ASIC), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The term "processor" is not limited to a processor in the narrow sense that executes processes using a stored program, such as a CPU or MPU, but may also include hardwired circuits such as an ASIC, GPU, or FPGA. Therefore, the term "processor" can also be interpreted as a processing circuitry whose processes are predefined by computer-readable code and / or hardwired circuits. The processor may be configured as a single chip or multiple chips. Furthermore, the processor and associated processing circuitry may be comprised of multiple computers interconnected by wire or wirelessly, such as via a local area network or a wireless network. The processor and associated processing circuitry may also be comprised of a cloud computer that performs remote calculations based on input data and outputs the results of the calculations to other devices at remote locations.

[0050] Furthermore, the arithmetic device 510 may include a storage unit for storing program code, work memory, etc. when the processor executes various programs. The storage unit may be one or more non-transitory computer-readable media. The storage unit may include volatile memory such as dynamic random access memory (DRAM) and static random access memory (SRAM), or non-volatile memory such as read-only memory (ROM) and flash memory. The storage unit may also be one or more computer-readable storage media. Examples of the storage unit include storage devices such as hard disk drives (HDDs) and solid-state drives (SSDs).

[0051] The storage device 520 is one or more computer-readable storage media, and includes a hard disk drive (HDD) and a solid state drive (SSD), etc. The storage device 520 stores various programs and data, such as an estimation program 521 executed by the calculation device 510, user identification information 522 referenced by the calculation device 510, viewing information 523 viewable by the user using the display device 30, and advice information 524 prepared in advance.

[0052] The computing device 510 may include a media reader (not shown). The computing device 510 may receive one or more removable disks, which are computer-readable storage media, via the media reader, and obtain various programs and data, such as the estimation program 521, the user identification information 522, and the advice information 524, from the removable disks.

[0053] The estimation program 521 defines various commands that the calculation device 510 executes to estimate the blood glucose spike frequency based on the AGE measurement values ​​of the subject, using correlation data between the AGE score and blood glucose spike frequency as shown in Fig. 3. The correlation data showing the correlation between the AGE score and blood glucose spike frequency is stored in advance in the storage device 520.

[0054] The user identification information 522 includes information about the user, such as a user ID, a password, a username, etc. The estimation device 50 can use the user identification information 522 to identify the user.

[0055] The viewing information 523 includes subject information including information about the subject, AGEs information including information about the subject's AGE measurement values ​​obtained from the AGEs measurement device 10, blood glucose spike information including information about the subject's blood glucose spike frequency estimated based on the AGEs measurement values, diabetes risk information including information about the subject's diabetes risk generated based on the estimated blood glucose spike frequency, and advice information generated based on the estimated blood glucose spike frequency.

[0056] The advice information 524 includes multiple types of advice regarding the subject's lifestyle, and is stored in the storage device 520 so as to be selectable depending on the estimated blood glucose spike frequency. The computing device 510 selects at least one piece of advice from the multiple types of advice included in the advice information 524 based on the estimated blood glucose spike frequency, and includes it in the viewing information 523. The advice regarding the subject's lifestyle includes, for example, advice regarding the subject's eating habits, exercise habits, sleep habits, mental health, and inflammatory conditions caused by injury or illness, as shown in image 314 in Figures 8 and 9 (described below).

[0057] The communication device 530 receives, via wired or wireless communication, the AGE measurement values ​​from the AGE measurement device 10. Furthermore, the communication device 530 transmits the viewing information to the display device 30 via wired or wireless communication.

[0058] 6 is a diagram illustrating a user identification information table stored by the estimating device 50 according to Embodiment 1. The estimating device 50 stores the user identification information 522 using the user identification information table of FIG.

[0059] 6, the user identification information table stores various information about users, such as user IDs, passwords, and user names, as user identification information 522. Each user who uses the information providing service is identified by the user identification information 522. For example, a first user is assigned a user ID of "U1," and a second user is assigned a user ID of "U2."

[0060] Of the user identification information 522, the user ID, password, and user name are input by each user from the display device 30. The display device 30 outputs the input user identification information 522 to the estimating device 50. The estimating device 50 stores the user identification information 522 acquired from the display device 30 in the storage device 520 by storing it in a user identification information table.

[0061] 7 is a diagram illustrating a viewed information table stored by the estimating device 50 according to Embodiment 1. The estimating device 50 stores the viewed information 523 using the viewed information table of FIG.

[0062] As shown in FIG. 7, the view information table stores various types of information that can be viewed by users, such as subject information, AGEs information, blood glucose spike information, diabetes risk information, and advice information, in association with user IDs.

[0063] The AGE information includes at least one of the following: current or past AGE measurement values ​​obtained from the AGE measurement device 10; AGE scores converted from the AGE measurement values; and rankings of the AGE scores. The AGE scores include values ​​converted from the AGE measurement values ​​into scores between 0 and 1.0, as shown in Figures 3 and 4. The AGE score rankings include rankings of the AGE scores on a five-point scale of A to E, as shown in Figures 8 and 9, which will be described later.

[0064] The blood glucose spike information includes at least one of the following: an estimation result of current or past blood glucose spike frequency estimated based on AGE measurement values; a value obtained by converting the estimation result of blood glucose spike frequency into a score; and a ranking result of blood glucose spike frequency. For example, the blood glucose spike frequency score includes a value obtained by converting the estimation result of blood glucose spike frequency into a score between 0 and 10, as shown in Figures 8 and 9 described below. The blood glucose spike frequency rank includes a ranking result of blood glucose spike frequency on a five-point scale from A to E, as shown in Figures 8 and 9 described below.

[0065] The diabetes risk information includes information about the diabetes risk generated based on the estimation result of the blood glucose spike frequency. The estimation device 50 pre-stores the diabetes risk corresponding to the blood glucose spike frequency, and when it estimates the blood glucose spike frequency based on the AGE measurement value, it acquires the diabetes risk corresponding to the estimated blood glucose spike frequency and stores it in the viewing information table (viewing information 523). The diabetes risk information includes, for example, information for informing the user of the diabetes risk of the subject, as shown in image 313 in Figures 8 and 9 (described later).

[0066] [Example of viewing information] A display example of the viewing information will be described with reference to Fig. 8 and Fig. 9. Fig. 8 and Fig. 9 are diagrams showing an example of a display screen on the display device 30 according to the first embodiment.

[0067] When a user executes an application program for using an information provision service using the display device 30, the display device 30 displays a login screen (not shown) on the display 390. When the user enters a user ID and password on the login screen, the display device 30 outputs the user ID and password to the estimating device 50. When the estimating device 50 authenticates the user based on the user ID and password, the display device 30 displays a home screen 31 such as that shown in FIG. 8 on the display 390.

[0068] The home screen 31 includes an image 311 for viewing AGEs information, an image 312 for viewing blood glucose spike information, an image 313 for viewing diabetes risk information, and an image 314 for viewing advice information.

[0069] Image 311 shows, for example, the AGEs score corresponding to the most recently measured AGEs measurement value and the result of ranking the AGEs score. Here, the AGEs rank is a value indicating the evaluation of the AGEs score calculated by the estimation device 50 based on multiple, graded reference values. For example, the smaller the AGEs measurement value, the closer the AGEs evaluation rank to "A." An "A" AGEs evaluation rank indicates the highest evaluation of the AGEs measurement value. The larger the AGEs measurement value, the closer the AGEs evaluation rank to "E." An "E" AGEs evaluation rank indicates the lowest evaluation of the AGEs measurement value. In the example of FIG. 8 , image 311 shows a pre-improvement AGEs score of "0.55" measured on May 4, 2022, and a "C" AGEs rank. In the example of FIG. 9 , image 311 shows an improved AGEs score of "0.40" measured on June 24, 2022, and a "A" AGEs rank.

[0070] Although not shown in the figures, when the user selects image 311 (for example, by touching), display device 30 displays the most recently measured AGE measurement value, the time series changes in past AGE measurement values, comments on the AGE measurement values, etc. This allows the user to use display device 30 to view the AGE information of the subject.

[0071] Image 312 shows, for example, the estimated blood glucose spike frequency based on the most recently measured AGEs value and the ranking of the blood glucose spike frequency. Here, the blood glucose spike frequency rank is a value indicating the evaluation of the blood glucose spike frequency calculated by the estimation device 50 based on multiple, graded reference values. For example, the lower the blood glucose spike frequency, the closer the evaluation rank to "A." An "A" blood glucose spike frequency evaluation rank is the highest blood glucose spike frequency evaluation. The higher the blood glucose spike frequency, the closer the evaluation rank to "E." An "E" blood glucose spike frequency evaluation rank is the lowest blood glucose spike frequency evaluation. In the example of FIG. 8 , image 312 shows "6 times / day" as the estimated blood glucose spike frequency based on the pre-improvement AGEs score measured on May 4, 2022, and the blood glucose spike frequency rank is "D." In the example of Figure 9, image 312 shows "1 time / day" as the estimated blood glucose spike frequency based on the improved AGEs score measured on June 24, 2022, and "A" as the blood glucose spike frequency rank.

[0072] In this way, the blood glucose spike information shown in image 312 changes depending on the AGEs information shown in image 311. Specifically, the higher the AGEs score, the higher the blood glucose spike frequency, and the lower the AGEs score, the lower the blood glucose spike frequency. Also, the lower the rank of the AGEs score, the lower the rank of the blood glucose spike frequency, and the better the rank of the AGEs score, the better the rank of the blood glucose spike frequency.

[0073] Although not shown in the figures, when the user selects image 312 (for example, by touching), display device 30 displays the blood glucose spike frequency estimated based on the most recently measured AGE measurement values, the time series change in blood glucose spike frequency estimated based on past AGE measurement values, comments on the blood glucose spike frequency, etc. This allows the user to view the blood glucose spike information of the subject using display device 30.

[0074] Image 313 shows, for example, the diabetes risk corresponding to the estimated blood glucose spike frequency. In the example of Fig. 8, image 313 shows "medium risk" as the diabetes risk corresponding to the estimated blood glucose spike frequency based on the pre-improvement AGEs score measured on May 4, 2022. In the example of Fig. 9, image 313 shows "low risk" as the diabetes risk corresponding to the estimated blood glucose spike frequency based on the improved AGEs score measured on June 24, 2022.

[0075] In this way, the diabetes risk information shown in image 313 changes depending on the blood glucose spike information shown in image 312. Specifically, the higher the blood glucose spike frequency, the higher the diabetes risk, and the lower the blood glucose spike frequency, the lower the diabetes risk. Also, the lower the rank of the blood glucose spike frequency, the higher the diabetes risk, and the better the rank of the blood glucose spike frequency, the lower the diabetes risk.

[0076] Image 314 shows, for example, advice on eating habits, exercise habits, sleep habits, and mental health as advice corresponding to the estimated blood glucose spike frequency. In the example of Figure 8, image 314 shows advice corresponding to the estimated blood glucose spike frequency based on the pre-improvement AGEs score measured on May 4, 2022, such as to strive for a vegetable-based diet, to encourage exercise such as walking, to ensure sufficient sleep, and to undergo a stress check. In the example of Figure 9, image 314 shows advice corresponding to the estimated blood glucose spike frequency based on the improved AGEs score measured on June 24, 2022, such as to maintain a vegetable-based diet, to strive for a healthier body through daily exercise, to continue to ensure sufficient sleep, and to be careful about stress.

[0077] In this way, the advice information shown in image 314 changes depending on the blood glucose spike information shown in image 312. Specifically, the greater the frequency of blood glucose spikes, the more advice is given encouraging the subject to pay more attention to their health, such as by making significant improvements to their eating habits, exercise habits, sleep habits, and mental health. Conversely, the smaller the frequency of blood glucose spikes, the more advice is given recognizing the subject's good daily habits, such as by maintaining improved eating habits, exercise habits, sleep habits, and mental health.

[0078] In this way, the user can use the display device 30 to view AGE information, blood sugar spike information, diabetes risk information, advice information, and the like, and can also view changes in these information over time.

[0079] [Processing of the estimation device] The processing of the estimation device 50 will be described with reference to Fig. 10. Fig. 10 is a flowchart of the estimation processing executed by the estimation device 50 according to the first embodiment. The processing steps (hereinafter abbreviated as "S") shown in Fig. 10 are realized by the calculation device 510 executing the estimation program 521.

[0080] 10, the estimation device 50 acquires AGE measurement values ​​from the AGE measurement device 10 (S1). The estimation device 50 estimates the blood glucose spike frequency based on the acquired AGE measurement values ​​(S2).

[0081] The estimation device 50 generates diabetes risk information based on the estimation result of the blood glucose spike frequency (S3).The estimation device 50 generates advice information based on the estimation result of the blood glucose spike frequency (S4).

[0082] The estimation device 50 stores the AGEs information, blood glucose spike information, diabetes risk information, and advice information in the storage device 520 as viewing information 523 (S5). As a result, in response to a request from the display device 30, the estimation device 50 outputs the AGEs information, blood glucose spike information, diabetes risk information, and advice information stored as viewing information 523 to the display device 30, allowing the user to view this information.

[0083] As described above, the estimation device 50 according to the first embodiment can estimate the blood glucose spike frequency based on the AGE measurement values ​​obtained by the AGE measurement device 10 and provide the estimated blood glucose spike frequency to the user. This eliminates the need for the subject to perform continuous blood glucose measurements, and the user can grasp the blood glucose spike frequency using the display device 30 while minimizing the burden on the subject. Furthermore, the user can obtain, using the display device 30, advice regarding the subject's risk of diabetes and the subject's lifestyle, which is generated based on the estimated blood glucose spike frequency.

[0084] <Embodiment 2> An estimation system 1 and an estimation device 50 according to embodiment 2 will be described with reference to Figures 11 to 17. In the following, the estimation system 1 and the estimation device 50 according to embodiment 2 will be described in terms of differences from the estimation system 1 and the estimation device 50 according to embodiment 1, and descriptions of parts that are the same as those of the estimation system 1 and the estimation device 50 according to embodiment 1 may be omitted.

[0085] FIG. 11 shows the correlation between AGE scores and blood glucose spike frequency according to Embodiment 2. Similar to the correlation in FIG. 3 according to Embodiment 1, the correlation in FIG. 11 according to Embodiment 2 is created based on the AGE scores and blood glucose spike frequencies for multiple subjects. Specifically, each subject first measures their AGEs and then measures their blood glucose levels at one-minute intervals for two weeks after the AGE measurement, regardless of whether they are postprandial, fasting, or asleep. The designer of the estimation system 1 and estimation device 50 converts the acquired AGE measurement values ​​for each subject into an AGE score between 0 and 10.0. Furthermore, the designer calculates the number of blood glucose data points exceeding 200 mg / dL among the multiple blood glucose data points acquired at one-minute intervals over two weeks for each subject, and then divides the calculated number of data points exceeding 200 mg / dL by 14 (i.e., the number of days in two weeks) to calculate the number of blood glucose spikes per day (blood glucose spike frequency). The blood glucose measurement period is not limited to two weeks, and blood glucose levels can be evaluated over a period of several days or even one month.

[0086] Designers can create a graph showing the correlation between AGEs score and blood glucose spike frequency by plotting points at positions corresponding to each subject's AGEs score and blood glucose spike frequency on a graph with AGEs score on the horizontal axis and blood glucose spike frequency on the vertical axis, as shown in Figure 11. Each point in Figure 11 represents each subject's AGEs score and blood glucose spike frequency.

[0087] 11, as with the graph shown in FIG. 3 of the first embodiment, it can be seen that the higher the AGEs score, the higher the blood glucose spike frequency, and the lower the AGEs score, the lower the blood glucose spike frequency. For example, there is a correlation between the AGEs score and blood glucose spike frequency, with a correlation coefficient of 0.523. Furthermore, in the example of FIG. 11, the P value is 0.000825, which is lower than 0.05, so it can be said that the correlation between the AGEs score and blood glucose spike frequency has a certain degree of reliability that makes it unlikely to be a coincidence.

[0088] Furthermore, as shown in FIG. 11, a regression line can be drawn for the correlation between the AGEs score and the blood glucose spike frequency, and by using such a regression line, the estimation device 50 can estimate the blood glucose spike frequency corresponding to the AGEs score.

[0089] Here, the estimation device 50 according to the second embodiment is configured to find pre-diabetes subjects suspected of having diabetes using the correlation shown in FIG. 11. Therefore, when creating the correlation shown in FIG. 11, subjects with chronic hyperglycemia and subjects without blood glucose spikes are excluded from the measurement subjects, and only subjects in the gray zone suspected of having pre-diabetes are the measurement subjects when creating the correlation. The subjects when creating the correlation according to the second embodiment will be described with reference to FIGS. 12 to 17. Note that FIGS. 12 and 13 show the blood glucose level trends for the first subject, FIGS. 14 and 15 show the blood glucose level trends for the second subject, and FIGS. 16 and 17 show the blood glucose level trends for the third subject.

[0090] 12, 14, and 16 are diagrams showing density maps summarizing blood glucose levels by time period for the first, second, and third subjects, respectively, according to the second embodiment. In each of these graphs, the horizontal axis represents multiple time periods and the vertical axis represents blood glucose levels. The graphs show a lower limit for blood glucose levels corresponding to hypoglycemia and an upper limit for blood glucose levels corresponding to hyperglycemia. For example, 70 mg / dL is used as the lower limit, and 200 mg / dL is used as the upper limit. The multiple time periods include a sleep period from midnight to 6:00 AM, a breakfast period from 6:00 AM to 10:00 AM, a lunch period from 10:00 AM to 2:00 PM, a snack period from 2:00 PM to 7:00 PM, and a dinner period from 7:00 PM to midnight. A designer can create the density maps shown in FIGS. 12, 14, and 16 by aggregating multiple blood glucose level data collected at one-minute intervals over two weeks for each of the first, second, and third subjects.

[0091] Figures 13, 15, and 17 are diagrams showing tables summarizing blood glucose levels by time period for the first, second, and third subjects, respectively, according to embodiment 2. Figures 13, 15, and 17 show tables summarizing the number of blood glucose data points (Size), mean value (Mean), standard deviation (SD), difference between minimum and maximum value (Range), maximum value (Max), minimum value (Min), median value (Median), first quartile (Q1, 25%), and third quartile (Q3, 75%) for each time period.

[0092] As shown in FIG. 12, the blood glucose level of the first subject generally falls between the lower limit (70 mg / dL) and the upper limit (200 mg / dL) in all time periods. Furthermore, as shown in FIG. 13, the difference (range) between the minimum and maximum blood glucose levels of the first subject is a maximum of 123 between 10:00 and 14:00, which is less than a predetermined value (e.g., 130) preset by the designer. From this, it can be said that the first subject is not in a chronically hyperglycemic state. The predetermined value can be appropriately set by the designer. Furthermore, as shown in FIGS. 12 and 13, the blood glucose level of the first subject exceeds the upper limit (200 mg / dL) between 10:00 and 14:00. The blood glucose spike frequency of the first subject is 0.1 times per day. From this, it can be said that the first subject is susceptible to blood glucose spikes. Therefore, the designer assumes that the first subject is suspected of being at risk of developing diabetes, and employs the first subject as the measurement subject when creating the correlation shown in FIG.

[0093] On the other hand, as shown in FIG. 14, the blood glucose level of the second subject chronically exceeds the upper limit (200 mg / dL) in all time periods. Furthermore, as shown in FIG. 15, the difference (range) between the minimum and maximum blood glucose levels of the second subject exceeds a predetermined value (e.g., 130) preset by the designer in all time periods. Furthermore, the blood glucose spike frequency of the second subject is 32.1 times per day. From this, it can be said that the second subject is in a chronically hyperglycemic state. Therefore, the designer assumed that the second subject was a patient with chronic hyperglycemia and excluded the second subject from the measurement subjects when creating the correlation shown in FIG. 11.

[0094] Furthermore, as shown in FIG. 16, the blood glucose level of the third subject falls between the lower limit (70 mg / dL) and the upper limit (200 mg / dL) in all time periods. Furthermore, as shown in FIG. 17, the difference (range) between the minimum and maximum blood glucose levels of the third subject is a maximum of 130 between 7:00 PM and midnight, which is less than a predetermined value (e.g., 130) preset by the designer. From this, it can be said that the first subject is not in a chronically hyperglycemic state. However, as shown in FIGS. 16 and 17, the blood glucose level of the third subject does not exceed the upper limit (200 mg / dL) in all time periods. The third subject's blood glucose spike frequency is 0 times per day. From this, it can be said that the third subject is unlikely to experience blood glucose spikes. Therefore, the designer assumed that the third subject is a subject who does not experience blood glucose spikes and excluded the third subject from the measurement subjects when creating the correlation shown in FIG. 11.

[0095] Thus, subjects with chronic hyperglycemia (i.e., subjects who already have diabetes) tend to have a large difference (range) between the minimum and maximum blood glucose values ​​in any given time period. In light of this, the designer of the estimation device 50 assumes that subjects whose difference (range) between the minimum and maximum blood glucose values ​​in a specific time period exceeds a predetermined value (e.g., 130) are chronically hyperglycemic subjects and excludes them from the subjects when creating a correlation. In order to estimate whether a subject is at risk of prediabetes, it is desirable to create a correlation using the measurement results of subjects at risk of prediabetes. Therefore, when a correlation is created by excluding subjects with chronic hyperglycemia from the subjects for which the correlation is created, as described above, the accuracy of estimating whether a subject is at risk of prediabetes is improved compared to when a correlation is created without excluding subjects with chronic hyperglycemia from the subjects for which the correlation is created. The reason why subjects with chronic hyperglycemia are not distinguished from subjects without chronic hyperglycemia (i.e., subjects without diabetes) based on the frequency of blood glucose spikes is that, for example, even subjects without chronic hyperglycemia may experience frequent temporary blood glucose spikes after meals, and if the two were distinguished simply based on the frequency of blood glucose spikes, it would be impossible to identify subjects with "chronic" hyperglycemia.

[0096] Furthermore, among subjects who are not chronically hyperglycemic, identified based on a comparison of the difference (range) between the minimum and maximum blood glucose values ​​with a predetermined value, it is preferable to distinguish between subjects with prediabetes and subjects without prediabetes based on the frequency of blood glucose spikes per day, and to exclude subjects without prediabetes from the measurement subjects for which the correlation is created. This configuration ensures that only subjects with prediabetes are included in the measurement subjects for which the correlation is created, thereby further improving the accuracy of determining whether a subject is prediabetic or not. Note that distinguishing between subjects with prediabetes and subjects without prediabetes based on the frequency of blood glucose spikes per day is not limited to this; the distinction between the two may also be based on the frequency of blood glucose spikes over several days (multiple days). Furthermore, subjects who have experienced a predetermined number of blood glucose spikes may also be included in the non-prediabetic subjects.

[0097] As described above, the estimation device 50 according to the second embodiment is configured to estimate the blood glucose spike frequency of the subject based on the AGE measurement values ​​acquired from the AGE measuring device 10, using the correlation shown in Fig. 11 created for subjects identified based on a comparison of blood glucose values ​​that differ between subjects with and without diabetes and predetermined values. Specifically, the estimation device 50 is configured to estimate the blood glucose spike frequency of the subject based on the AGE measurement values ​​acquired from the AGE measuring device 10, using the correlation shown in Fig. 11 created for subjects suspected of having prediabetes, whose difference (range) between the minimum and maximum blood glucose values ​​is equal to or less than a predetermined value (e.g., 130). As a result, the estimation device 50 can estimate the blood glucose spike frequency of the subject using the correlation shown in Fig. 11 created for subjects suspected of having prediabetes, and can therefore estimate whether the subject is at risk of prediabetes based on the estimated blood glucose spike frequency.

[0098] The correlation between AGEs measurement values ​​and blood glucose spike frequency may be created using a subject whose difference (range) between the minimum and maximum blood glucose values ​​is equal to or less than a predetermined value in any time period, or may be created using a subject whose difference (range) between the minimum and maximum blood glucose values ​​is equal to or less than a predetermined value in any time period as in embodiment 2. Furthermore, the correlation may be created using a subject whose average difference (range) between the minimum and maximum blood glucose values ​​over multiple time periods is equal to or less than a predetermined value.

[0099] Furthermore, the correlation is not limited to the difference (range) between the minimum and maximum blood glucose levels, and may be created using data of the measurement target determined based on other blood glucose level trends. For example, in light of the fact that subjects with chronic hyperglycemia have higher blood glucose median values ​​than subjects who are not in a hyperglycemic state, the correlation may be created using subjects whose blood glucose median is equal to or less than a predetermined value as the measurement target. Furthermore, the correlation may be created using subjects whose difference between the median and maximum blood glucose levels is equal to or less than a predetermined value as the measurement target when creating the correlation. Furthermore, the measurement target when creating the correlation may be determined based on both the difference (range) between the minimum and maximum blood glucose levels and the above-mentioned blood glucose median.

[0100] Generally, if a subject's fasting blood glucose level is 126 mg / dL or higher, the subject is said to have diabetes, so subjects with fasting blood glucose levels of 126 mg / dL or higher may be excluded from measurement subjects when creating a correlation.Furthermore, if a subject's hemoglobin level (HbA1c) is 6.5% or higher, the subject is said to have diabetes, so subjects with hemoglobin levels (HbA1c) of 6.5% or higher may be excluded from measurement subjects when creating a correlation.

[0101] <Third Embodiment> An estimation system 1 and an estimation device 50 according to embodiment 3 will be described with reference to Fig. 18. Below, the estimation system 1 and the estimation device 50 according to embodiment 3 will be described in terms of differences from the estimation system 1 and the estimation device 50 according to embodiment 1 and embodiment 2, and descriptions of parts that are the same as those of the estimation system 1 and the estimation device 50 according to embodiment 1 and embodiment 2 may be omitted.

[0102] 18 is a flowchart of a determination process executed by the estimation device 50 according to embodiment 3. The processing steps (hereinafter abbreviated as “S”) shown in FIG. 18 are realized by the calculation device 510 executing the estimation program 521.

[0103] 18, the estimation device 50 acquires the AGE measurement value of the subject from the AGE measurement device 10 (S11). The estimation device 50 compares the acquired AGE measurement value of the subject with the standard AGE measurement value of a healthy subject (S12). The estimation device 50 stores the standard AGE measurement value of a healthy subject in advance in the storage device 520.

[0104] If the AGE measurement value of the subject is higher than the standard AGE measurement value of a healthy person (YES in S12), the estimation device 50 determines that the subject's blood glucose spike frequency is higher than that of a healthy person (S13).On the other hand, if the AGE measurement value of the subject is equal to or lower than the standard AGE measurement value of a healthy person (NO in S12), the estimation device 50 determines that the subject's blood glucose spike frequency is equal to or lower than that of a healthy person (S13).

[0105] As described above, the estimation device 50 can determine the blood glucose spike frequency of the subject by comparing the AGE measurement values ​​of the subject obtained by the measurement device 10 with standard AGE measurement values ​​of healthy individuals. This allows the estimation device 50 to estimate the blood glucose spike frequency based on the AGE measurement values ​​of the subject without having to measure the subject's blood glucose continuously, allowing the user to understand the blood glucose spike frequency while minimizing the burden on the subject.

[0106] The estimation system 1 and the estimation device 50 according to each of the first to third embodiments have been described above, but the configurations and functions (processing) of the estimation system 1 and the estimation device 50 according to each embodiment can be combined.

[0107] In the estimation system 1 and estimation device 50 according to each of the first to third embodiments, the AGEs of the subject are used to estimate the blood glucose spike frequency of the subject. Therefore, the AGEs of the subject function as a marker for estimating the blood glucose spike frequency of the subject.

[0108] <Aspect> It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0109] (Item 1) An estimation method according to one embodiment includes, as processing performed by a calculation device, a step of acquiring a measurement value of the subject's advanced glycation end products, and a step of estimating the blood glucose spike frequency of the subject based on the measurement value of the subject's advanced glycation end products acquired in the acquiring step, using a correlation between the measurement value of advanced glycation end products and the blood glucose spike frequency that has been prepared in advance.

[0110] According to the estimation method described in paragraph 1, the subject does not need to perform continuous blood glucose measurements, and the user can easily grasp the frequency of blood glucose spikes using the subject's advanced glycation end products while reducing the burden on the subject.

[0111] (Item 2) In the estimation method according to item 1, the correlation is generated based on the measurement values ​​of advanced glycation end products of each of a plurality of subjects and the blood glucose spike frequency of each of the plurality of subjects.

[0112] According to the estimation method described in paragraph 2, the blood glucose spike frequency of the subject is estimated using a correlation generated based on the measurement values ​​of advanced glycation end products of each of the multiple subjects and the blood glucose spike frequency of each of the multiple subjects, so the blood glucose spike frequency of the subject can be easily estimated using a correlation prepared in advance.

[0113] (Item 3) In the estimation method described in Item 1 or 2, the correlation is a regression line generated based on the measurement values ​​of advanced glycation end products of each of multiple subjects and the blood glucose spike frequency of each of the multiple subjects. The estimating step includes a step of using the regression line to estimate the blood glucose spike frequency of the subject based on the measurement values ​​of advanced glycation end products of the subject acquired in the acquiring step.

[0114] According to the estimation method described in paragraph 3, the blood glucose spike frequency of a subject can be easily estimated using a regression line generated based on the measurement values ​​of advanced glycation end products of each of multiple subjects and the blood glucose spike frequency of each of the multiple subjects.

[0115] (4) In the estimation method described in any one of paragraphs 1 to 3, the blood glucose spike frequency for each of a plurality of subjects is obtained by calculating the number of times that the blood glucose measurement values ​​for each of the plurality of subjects, obtained at predetermined intervals over a predetermined period of time, exceed a predetermined value.

[0116] According to the estimation method described in paragraph 4, the number of blood glucose measurement values ​​obtained at specified intervals over a specified period that exceed a specified value is used as the blood glucose spike frequency for generating a correlation, so the subject can estimate the blood glucose spike frequency over a specified period based on the measurement values ​​of advanced glycation end products.

[0117] (Item 5) In the estimation method according to any one of items 1 to 4, the measurement values ​​of advanced glycation end products for each of a plurality of subjects are obtained by measuring at least once in a predetermined period of time.

[0118] According to the estimation method described in paragraph 5, the measurement value of advanced glycation end products measured at least once in a specified period is used as the measurement value of advanced glycation end products to generate a correlation, so that the subject can easily estimate the frequency of blood glucose spikes by similarly measuring advanced glycation end products at least once in a specified period.

[0119] (Item 6) In the estimation method described in any one of Items 1 to 5, the multiple subjects are subjects suspected of being at risk of developing diabetes, as determined based on a comparison of blood glucose values ​​that differ between subjects with diabetes and subjects without diabetes with a predetermined value.

[0120] According to the estimation method described in paragraph 6, a correlation created for subjects identified as measurement subjects based on a comparison of blood glucose values ​​that show differences between subjects with diabetes and subjects without diabetes with a predetermined value is used, so that, for example, subjects with chronic hyperglycemia and subjects who do not experience blood glucose spikes can be excluded from the correlation measurement subjects. This makes it possible to estimate the frequency of blood glucose spikes in a subject using a correlation created for subjects at risk of diabetes, and therefore to estimate whether or not the subject is at risk of diabetes.

[0121] (Item 7) In the estimation method described in any one of items 1 to 6, the value related to the blood glucose level includes at least one of the difference between the minimum blood glucose level and the maximum blood glucose level, the median blood glucose level, and the difference between the median blood glucose level and the maximum blood glucose level.

[0122] According to the estimation method described in Section 7, the user can exclude subjects with chronically high blood sugar levels and subjects who do not experience blood sugar spikes from the subjects for which correlation is to be measured.

[0123] (Item 8) In the estimation method according to any one of items 1 to 7, the measurement value of the subject's advanced glycation end products is obtained by non-invasive measurement.

[0124] According to the estimation method described in paragraph 8, the subject can easily estimate the frequency of blood glucose spikes by non-invasively measuring advanced glycation end products.

[0125] (Item 9) An estimation program according to one embodiment causes a calculation device to execute the steps of acquiring measurement values ​​of advanced glycation end products of the subject, and estimating the blood glucose spike frequency of the subject based on the measurement values ​​of advanced glycation end products of the subject acquired in the acquiring step, using a correlation between the measurement values ​​of advanced glycation end products and the blood glucose spike frequency that has been prepared in advance.

[0126] According to the estimation program described in paragraph 9, the subject does not need to perform continuous blood glucose measurements, and the user can easily grasp the frequency of blood glucose spikes using the subject's advanced glycation end products while reducing the burden on the subject.

[0127] (Item 10) An estimation system according to one embodiment includes a measurement device that measures the advanced glycation end products of a subject, an estimation device that estimates the subject's blood glucose spike frequency based on the measurement values ​​of the advanced glycation end products of the subject measured by the measurement device using a correlation between the measurement values ​​of the advanced glycation end products and the blood glucose spike frequency that has been prepared in advance, and a display device that displays viewing information based on the subject's blood glucose spike frequency estimated by the estimation device.

[0128] According to the estimation system described in paragraph 10, the subject does not need to perform continuous blood glucose measurements, and the user can easily grasp the frequency of blood glucose spikes using the subject's advanced glycation end products while reducing the burden on the subject.

[0129] (Item 11) A determination method according to one embodiment includes, as processing performed by a calculation device, a step of acquiring a measurement value of advanced glycation end products of the subject, a step of comparing the measurement value of advanced glycation end products of the subject acquired by the acquiring step with a standard measurement value of advanced glycation end products of a healthy subject, and a step of determining that the blood glucose spike frequency of the subject is higher than the blood glucose spike frequency of a healthy subject if the measurement value of advanced glycation end products of the subject is higher than the standard measurement value of advanced glycation end products.

[0130] According to the determination method described in paragraph 11, the subject does not need to perform continuous blood glucose measurements, and the user can easily grasp the frequency of blood glucose spikes using the subject's advanced glycation end products while reducing the burden on the subject.

[0131] (Item 12) A predictive marker according to one embodiment is a marker consisting of advanced glycation end products for predicting blood glucose spike frequency.

[0132] According to the determination method described in paragraph 12, the subject does not need to perform continuous blood glucose measurements, and the user can easily grasp the frequency of blood glucose spikes using the subject's advanced glycation end products while reducing the burden on the subject. [Explanation of symbols]

[0133] 1 estimation system, 10 measuring device, 11 measuring unit, 12,390 display, 13 communication unit, 30 display device, 31 home screen, 50 estimation device, 311, 312, 313, 314 image, 510 computing device, 520 storage device, 521 estimation program, 522 user identification information, 523 browsing information, 524 advice information, 530 communication device.

Claims

1. A method for estimating a blood glucose spike frequency of a subject by a computing device, comprising: The processing executed by the arithmetic unit includes: obtaining a measurement value of advanced glycation end products of the subject; and a step of estimating the blood glucose spike frequency of the subject based on the measurement value of advanced glycation endproducts of the subject acquired in the acquiring step, using a correlation between the measurement value of advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance, the correlation is generated based on the measurement values ​​of advanced glycation end products of each of a plurality of subjects and the blood glucose spike frequency of each of the plurality of subjects; A method for estimating that the plurality of subjects does not include subjects who are in a chronically hyperglycemic state as determined based on blood glucose level measurements over a predetermined period of time.

2. The estimation method described in claim 1, wherein the subject in a chronically hyperglycemic state is determined based on a comparison of the difference between the minimum blood glucose value and the maximum blood glucose value during a specific time period included in the specified period with a specified value.

3. the correlation is a regression line generated based on the measurement values ​​of advanced glycation end products of each of the plurality of subjects and the blood glucose spike frequency of each of the plurality of subjects; The estimation method according to claim 1, wherein the estimating step includes a step of estimating the blood glucose spike frequency of the subject based on the measurement values ​​of advanced glycation end products of the subject obtained by the obtaining step using the regression line.

4. 2. The estimation method according to claim 1, wherein the blood glucose spike frequency for each of the plurality of subjects is obtained by calculating the number of times that the blood glucose measurement values ​​for each of the plurality of subjects, obtained at predetermined intervals during the predetermined period, exceed a predetermined value.

5. The estimation method according to claim 4 , wherein the measurement values ​​of advanced glycation end products of each of the plurality of subjects are obtained by measuring at least once during the predetermined period.

6. The estimation method described in claim 1, wherein the multiple subjects are subjects suspected of being at risk of developing diabetes, as determined based on a comparison of blood glucose levels that differ between subjects with diabetes and subjects without diabetes with a predetermined value.

7. The estimation method according to claim 6 , wherein the value related to the blood glucose level includes at least one of a difference between a minimum blood glucose value and a maximum blood glucose value, a median blood glucose value, and a difference between the median blood glucose value and a maximum blood glucose value.

8. The method of claim 1 , wherein the measurement value of the subject's advanced glycation end products is obtained by non-invasive measurement.

9. An estimation program for estimating a blood glucose spike frequency of a subject, The computing device obtaining a measurement value of advanced glycation end products of the subject; and a step of estimating the blood glucose spike frequency of the subject based on the measurement value of advanced glycation endproducts of the subject acquired in the acquiring step, using a correlation between the measurement value of advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance. the correlation is generated based on the measurement values ​​of advanced glycation end products of each of a plurality of subjects and the blood glucose spike frequency of each of the plurality of subjects; A prediction program in which the plurality of subjects does not include subjects who are in a chronically hyperglycemic state as determined based on blood glucose level measurements over a predetermined period of time.

10. An estimation system for estimating a blood glucose spike frequency of a subject, comprising: a measuring device for measuring advanced glycation end products of the subject; an estimation device that estimates the blood glucose spike frequency of the subject based on the measurement value of advanced glycation endproducts of the subject measured by the measurement device, using a correlation between the measurement value of advanced glycation endproducts and the blood glucose spike frequency that has been prepared in advance; a display device that displays viewing information based on the blood glucose spike frequency of the subject estimated by the estimation device, the correlation is generated based on the measurement values ​​of advanced glycation end products of each of a plurality of subjects and the blood glucose spike frequency of each of the plurality of subjects; An estimation system in which the plurality of subjects does not include subjects who are in a chronically hyperglycemic state as determined based on blood glucose level measurements over a specified period of time.

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