Methods for estimating glucose metabolism capacity

Abstract

According to the present invention, a biological information measurement system measures a peripheral blood circulation dynamics index value of a user before an event that affects the blood sugar level of the user and the peripheral blood circulation dynamics index value of the user after the event, and estimates the carbohydrate metabolism capacity of the user from the change in the peripheral blood circulation dynamics index value measured before and after the event.

Description

[Technical Field]

[0001] The present invention relates to a method for estimating a user's glucose metabolic capacity. [Background technology]

[0002] A pulse wave signal is a signal that captures, as a waveform, changes in the volume of blood vessels that occur as the heart pumps blood through them. Patent Document 1 describes a method for estimating the state of a user's glucose metabolism from indices calculated based on a pulse wave signal measured at a peripheral site of the user (subject). Examples of indices calculated based on the pulse wave signal include (1) pulse wave velocity (PWV) of the forward wave, (2) the magnitude of the reflected pulse wave, (3) the time difference between the forward and reflected pulse waves, and (4) an augmentation index (AI) that is expressed as the ratio of the magnitude of the forward and reflected pulse waves. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Retable 2016 / 174839 Summary of the Invention [Problem to be solved by the invention]

[0004] However, since PWV is calculated based on the propagation time difference between pulse wave signals measured at two locations on the user's body (for example, the arm and ankle) and the distance between the two locations, calculating PWV requires the user to be restrained to some extent, making it difficult to easily measure PWV without restraining the user.

[0005] Furthermore, while it is relatively easy to measure the magnitude of the reflected pulse wave in the user's arteries, the shape of the reflected pulse wave changes depending on the user's physical condition such as blood pressure or individual differences, making it difficult to detect the reflected wave. Similar problems can arise when measuring AI, which is expressed as the time difference between the forward and reflected pulse waves or the ratio of the magnitudes of the forward and reflected pulse waves.

[0006] Therefore, an object of the present invention is to solve such problems and to estimate a user's glucose metabolic capacity with high accuracy through simple measurements. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the method performed by the bioinformation measurement system of the present invention includes measuring a user's peripheral hemodynamic index values ​​before an event that affects the user's blood glucose level and the user's peripheral hemodynamic index values ​​after the event, and estimating the user's glucose metabolic capacity from the changes in the peripheral hemodynamic index values ​​measured before and after the event. [Effects of the Invention]

[0008] According to the method of the present invention, glucose metabolic capacity can be estimated non-invasively and simply. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an explanatory diagram showing the configuration of a biological information measuring system according to an embodiment of the present invention. [Figure 2] 1 is an explanatory diagram illustrating an external configuration of a sensing device according to an embodiment of the present invention. [Figure 3] FIG. 2 is an explanatory diagram showing an example of a posture of a user when measuring biological information. [Figure 4] FIG. 10 is an explanatory diagram of pulse wave feature amounts. [Figure 5] 10 is a graph showing the correlation between a peripheral blood pressure index value calculated from pulse wave feature values ​​with respect to time and systolic blood pressure. [Figure 6] 10 is a graph showing the correlation between a peripheral blood pressure index value calculated from pulse wave feature values ​​with respect to time and systolic blood pressure. [Figure 7] 1 is a graph showing the measurement results of changes in blood glucose levels over time after a glucose tolerance test. [Figure 8] 1 is a graph showing the results of measuring changes in peripheral blood pressure index over time after a glucose tolerance test. [Figure 9] 1 is a graph showing the rate of change in peripheral blood pressure index relative to the rate of change in blood glucose level. [Figure 10] 1 is a graph showing the measurement results of changes in blood glucose levels over time after a glucose tolerance test when the same subject underwent a glucose tolerance test on different days. [Figure 11] 1 is a graph showing the measurement results of changes over time in peripheral blood pressure index values ​​after a glucose tolerance test when the same subject underwent a glucose tolerance test on different days. [Figure 12] 1 is a graph showing the rate of change in peripheral blood pressure index relative to the rate of change in blood glucose level. [Figure 13] 1 is a graph showing the rate of change in peripheral blood pressure index relative to the rate of change in blood glucose level. [Figure 14] 1 is a graph showing a glucose metabolic capacity index relative to the blood glucose level change rate. [Figure 15] 1 is a graph showing the glucose metabolic capacity index relative to the rate of change in peripheral blood pressure index. [Figure 16] 1 is a flowchart showing the flow of processing in a method for estimating glucose metabolic capacity according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Here, the same reference numerals denote the same components, and redundant description will be omitted.

[0011] 1 is an explanatory diagram showing the configuration of a biological information measurement system 10 according to an embodiment of the present invention. The biological information measurement system 10 includes a sensing device 20 that measures biological information of a user (subject), and a computer 30 that is configured to be able to communicate with the sensing device 20.

[0012] The sensing device 20 is a wearable device having a structure that can be attached to, for example, a peripheral part (for example, a finger) of a user. The sensing device 20 includes a biosensor 21 that measures bioinformation from the peripheral part (for example, a finger) of the user, a control circuit 22 that controls the operation of the biosensor 21, a communication module 23 that transmits the measurement results of the sensing device 20 to a computer 30 via a wireless or wired line, and an acceleration sensor 24 that measures the movement acceleration of the sensing device 20.

[0013] The biosensor 21 includes, for example, a pulse wave sensor 211 that measures an index value indicating the user's peripheral blood pressure and a temperature sensor 212 that measures the skin temperature of the user's peripheral region. Peripheral blood pressure in this invention is defined as the blood pressure of peripheral capillaries and arterioles. The pulse wave sensor 211 may be, for example, a photoplethysmographic sensor or a piezoelectric pulse wave sensor. For example, a reflective photoplethysmographic sensor irradiates the user's body surface with infrared light, red light, or green light and measures the light reflected from the user's body surface using a photodiode or phototransistor. Oxygenated hemoglobin is present in arterial blood and has the property of absorbing incident light. Therefore, a pulse wave signal can be measured by chronologically sensing the blood flow rate (changes in blood vessel volume) that changes with the heartbeat.

[0014] The communication module 23 transmits the measurement results of the sensing device 20 (e.g., the pulse wave signal measured by the pulse wave sensor 211, the temperature value measured by the temperature sensor 212, and the movement acceleration of the sensing device 20 measured by the acceleration sensor 24) to the computer 30 via a wireless or wired line.

[0015] The acceleration sensor 24 measures the movement acceleration of the sensing device 20 when the user changes posture to measure the pulse wave signal. The reason for measuring the movement acceleration of the sensing device 20 will be described later.

[0016] The computer 30 is, for example, a multi-function mobile phone called a smartphone or a general-purpose computer (for example, a notebook computer, a desktop computer, a tablet terminal, a server computer, etc.). The computer 30 includes a communication module 31 that receives the measurement results of the biosensor 21 from the sensing device 20 via a wireless or wired line, and a signal processing device 32 that performs processing to estimate the user's biometric information from the measurement results of the biosensor 21. The signal processing device 32 includes a processor 321, a memory 322, and an input / output interface 323.

[0017] For example, signal processing device 32 can calculate pulse wave feature amounts from the pulse wave signal measured by pulse wave sensor 211 and estimate the user's blood pressure, blood glucose level, vascular resistance, peripheral blood pressure, peripheral blood flow rate, or degree of arteriosclerosis based on the pulse wave feature amounts. Signal processing device 32 can also estimate the heart rate (pulse rate) by determining the period of fluctuation from the pulse wave signal measured by pulse wave sensor 211. Signal processing device 32 can also estimate an index value of autonomic nervous function by performing power spectrum analysis on the frequency components of the periodic fluctuation of the heart rate.

[0018] FIG. 2 is an explanatory diagram showing the external configuration of a sensing device 20 according to an embodiment of the present invention. The sensing device 10 includes a ring-shaped housing 25 configured to be wearable on a user's finger. For example, in the example shown in FIG. 2, the housing 25 has a hollow cylindrical shape. When the sensing device 20 is worn on a user's finger, the biosensor 21 is attached to the inner circumferential surface of the housing 25 (the inner surface of the hollow cylinder) so that the pad of the user's finger faces the biosensor 21. Note that the shape of the housing 25 is not limited to a hollow cylindrical shape and may be, for example, a cylindrical shape that fits on the user's finger (for example, the shape of a fingerstall), and the cylinder may or may not have a bottom (the portion that the fingertip comes into contact with).

[0019] 3 shows an example of the posture of user 40 when measuring biometric information. In this example, biometric information is measured from the finger of user 40 with the finger wearing sensing device 20 held still at the position of heart 41. Note that the position (measurement position) of sensing device 20 when measuring biometric information is not limited to the position of heart 41 of user 40, and may be, for example, the position of the face or abdomen of user 40. Furthermore, the posture of user 40 when measuring biometric information may be a sitting position or a supine position.

[0020] Next, pulse wave feature quantities will be described with reference to FIG. 4. Reference numeral 51 denotes a velocity pulse wave signal obtained by first-order differentiation of the pulse wave signal. Reference numeral 52 denotes an acceleration pulse wave signal obtained by second-order differentiation of the pulse wave signal. The peaks (maximum peaks and minimum peaks) of the acceleration pulse wave signal 52 are called the a-wave, b-wave, c-wave, d-wave, and e-wave, respectively, as shown in FIG. 4. Reference numeral 53 denotes a volume pulse wave signal. Examples of pulse wave feature quantities that can be used include the peak time difference between the peaks (a-wave, b-wave, c-wave, d-wave, and e-wave), the height of each peak, the ratio of each peak time difference to the pulse interval, the peak half-width, the ratio of the positive area to the negative area of ​​the a-wave portion of the acceleration pulse wave signal 52, and the degree of match between the measured pulse wave waveform and a pulse waveform template. Furthermore, pulse wave feature quantities can be used not only for each pulse wave feature quantity but also the average value and standard deviation of pulse wave feature quantities over several to several tens of pulses.

[0021] Among pulse wave feature quantities, those that are easily affected by the contact state and pressure between the biosensor 21 and the skin include feature quantities related to signal strength, such as pulse wave height and the heights of the a, b, c, d, and e waves of the accelerated pulse wave. Compared to these pulse wave feature quantities, those that are less affected by the contact state and pressure between the biosensor 21 and the skin include time-related pulse wave feature quantities, such as the peak times of the a, b, c, d, and e waves. By calculating an index value indicating the level of the user's peripheral blood flow or peripheral blood pressure from these time-related pulse wave feature quantities, the influence of the contact state and pressure between the biosensor 21 and the skin can be reduced. In this specification, an index indicating the level of peripheral blood flow or peripheral blood pressure is referred to as a peripheral hemodynamic index. Unless otherwise specified, blood flow refers to peripheral blood flow.

[0022] Figures 5 and 6 show the correlation between peripheral blood pressure index values ​​calculated from pulse wave features over time and systolic blood pressure (SBP) measured with a wrist-cuff sphygmomanometer. Figure 5 shows graphs plotting the correlation between systolic blood pressure and peripheral blood pressure index values ​​for two subjects, A and B, when the height of the measurement site (e.g., finger) from the heart is changed. Measurement points 61A, 61B, and 61C show the relationship between peripheral blood pressure index values ​​measured at the forehead, chest, and navel of subject A, respectively, and systolic blood pressure. Graph 61 is obtained by regression analysis of these measurement points 61A, 61B, and 61C. Measurement points 62A, 62B, and 62C show the relationship between peripheral blood pressure index values ​​measured at the forehead, chest, and navel of subject B, respectively, and systolic blood pressure. Graph 62 is obtained by regression analysis of these measurement points 62A, 62B, and 62C. Generally, peripheral blood pressure drops relative to systolic blood pressure measured at the wrist due to vascular resistance between the wrist and the periphery. When the height of the measurement site from the heart is simply changed, the vascular resistance between the wrist and the periphery can be considered to be approximately constant, and therefore peripheral blood pressure is proportional to the systolic blood pressure at the wrist. Referring to Figure 5, it can be seen that when the height of the measurement site from the heart is simply changed, the peripheral blood pressure index is approximately proportional to the systolic blood pressure.

[0023] In Figure 6, measurement point 61D shows the relationship between peripheral blood pressure index values ​​and systolic blood pressure measured at chest height on subject A after cooling the area near the measurement site (e.g., finger) of subject A. Similarly, measurement point 62D shows the relationship between peripheral blood pressure index values ​​and systolic blood pressure measured at chest height on subject B after cooling the area near the measurement site (e.g., finger) of subject B. It can be seen that when the area near the measurement site is cooled, the systolic blood pressure increases slightly and the peripheral blood pressure index decreases significantly. This is thought to be because cooling causes blood vessels to constrict, significantly increasing vascular resistance, resulting in a significant decrease in peripheral blood pressure and an increase in systolic blood pressure.

[0024] The present inventors used these peripheral blood pressure indices to investigate the relationship between changes in blood glucose levels and peripheral blood pressure indices during a glucose tolerance test (75g OGTT). Blood glucose levels were measured using a commercially available blood sampling self-monitoring device.

[0025] Figure 7 shows the results of changes in blood glucose levels over time after a glucose tolerance test. Graphs 71, 72, and 73 show the changes in blood glucose levels over time measured for subjects C, D, and E, respectively, after a glucose tolerance test. Figure 8 shows the results of changes in peripheral blood pressure indices over time after a glucose tolerance test. Graphs 74, 75, and 76 show the changes in peripheral blood pressure indices over time measured for subjects C, D, and E, respectively. As can be seen from the results in Figure 7, subject C's blood glucose level increased significantly, temporarily exceeding 250 mg / dL. In contrast, subjects D and E's blood glucose levels had smaller changes and maximum values ​​than subject C. Furthermore, as can be seen from the results in Figure 8, the peripheral blood pressure indices of subjects D and E increased, while the peripheral blood pressure indices of subject C decreased.

[0026] The inventors calculated the rate of change in the measured values ​​(blood glucose level or peripheral blood pressure index value) based on the measured values ​​before the glucose tolerance test. Since the blood glucose level and peripheral blood pressure index value were measured intermittently and at irregular intervals, the rate of change in the measured values ​​was calculated according to the following calculation formula (1).

[0027]

number

[0028] Here, X indicates a measurement value (blood glucose level or peripheral blood pressure index value). i is a measurement number (an integer from 1 to n) indicating the number of times the measurement value was measured. X0 indicates a measurement value before the glucose tolerance test. Xi indicates a measurement value with measurement number i (the measurement value measured for the i-th time). t0 indicates the start time of the glucose tolerance test. ti indicates the measurement time of Xi.

[0029] Figure 9 shows a graph 80 of the rate of change in peripheral blood pressure index versus the rate of change in blood glucose level. Graph 80 was obtained by regression analysis of measurement points from different subjects, and each measurement point shows the relationship between the rate of change in blood glucose level and the rate of change in peripheral blood pressure index. The rate of change in blood glucose level and the rate of change in peripheral blood pressure index are each calculated using formula (1). Graph 80 shows that as the rate of change in blood glucose level increases, the rate of change in peripheral blood pressure index tends to decrease. In diabetes, a decline in glucose metabolism and a prolonged state of hyperglycemia damage blood vessels, leading to impaired vascular endothelial function and the progression of arteriosclerosis and peripheral vascular disorders. Vascular endothelial function refers to the function of vascular endothelial cells. Vascular endothelial cells are cells located in the innermost layer of blood vessels and play an important role in maintaining vascular health. Vascular endothelial cells release numerous vasoactive substances (factors that act on blood vessels), such as nitric oxide and endothelin, which regulate the contraction and relaxation of the vascular wall (hardness and flexibility of the blood vessels), as well as the adhesion of inflammatory cells to the vascular wall, vascular permeability, and the coagulation and fibrinolysis systems. It is assumed that a decline in glucose metabolic capacity tends to decrease peripheral blood flow. However, because the absolute value of blood flow varies greatly depending on diet, exercise, ambient temperature, blood pressure, and other factors, it is difficult to find a clear correlation between the absolute value of instantaneous blood flow and glucose metabolic capacity.

[0030] Therefore, the present inventors propose a method for estimating glucose metabolic capacity by focusing on changes in peripheral blood flow or peripheral blood pressure instead of the absolute value of peripheral blood flow. Vascular endothelial function includes contraction and relaxation of the vascular wall, and a decline in vascular endothelial function leads to a decline in the ability to regulate blood flow (peripheral hemodynamic regulation ability). It is presumed that a decline in peripheral hemodynamic regulation ability will also lead to a decline in changes in peripheral blood flow or peripheral blood pressure.

[0031] Although the peripheral blood pressure change rate calculated according to the calculation formula (1) is exemplified as the peripheral blood pressure change, it is not limited to this. For example, the maximum value of the peripheral blood pressure index change rate (the peripheral blood pressure index change rate at which the absolute value of the peripheral blood pressure index change rate is maximum), the maximum value of the peripheral blood pressure index change amount (the peripheral blood pressure index change amount at which the absolute value of the peripheral blood pressure index change amount is maximum), the (t i -ti-1 )) / (t n -t0) / X0 instead of (t i -t i-1 )) / (t n The amount of change in peripheral blood pressure index calculated using the variance (variation) of the rate of change in peripheral blood pressure index, the coefficient of variation, the time from glucose load at which the extreme value is reached, and the change pattern (classification of the time change in measurement value as a shape of a figure) may be used alone or in combination. In addition, to accurately capture changes in measurement value, continuous measurement is desirable, but intermittent measurement or measurement values ​​taken a certain time after glucose load (for example, 120 minutes after glucose load) may also be used.

[0032] The coefficient of determination for graph 80 shown in Figure 9 is approximately 0.33, which is a poor fit of the estimated regression equation. One possible reason for the poor coefficient of determination is that the rate of change in blood glucose levels resulting from the glucose tolerance test is affected by the subject's physical condition at the time, food and drink ingested within a few hours of the glucose tolerance test, and so on.

[0033] Figure 10 shows the results of changes in blood glucose levels over time after glucose tolerance tests when the same subject F performed glucose tolerance tests on different days. Graph 91 shows changes in blood glucose levels over time after a glucose tolerance test when subject F performed the first glucose tolerance test on a certain day. Graph 92 shows changes in blood glucose levels over time after a glucose tolerance test when subject F performed the second glucose tolerance test on a different day.

[0034] Figure 11 shows the results of changes over time in peripheral blood pressure index values ​​after glucose tolerance tests when the same subject F performed glucose tolerance tests on different days. Graph 93 shows the changes over time in peripheral blood pressure index values ​​after the first glucose tolerance test when subject F performed the first glucose tolerance test on a certain day. Graph 94 shows the changes over time in peripheral blood pressure index values ​​after the second glucose tolerance test on a different day.

[0035] The results in Figure 10 show that in the first glucose tolerance test, there was a large change in blood glucose level over time, and in the second glucose tolerance test, there was a small change in blood glucose level over time. In the first glucose tolerance test, subject F ingested coffee several hours before the test, whereas in the second glucose tolerance test, subject F measured his blood glucose level without consuming coffee. Although it is unclear to what extent coffee consumption affected the change in blood glucose level over time, it shows that the change in blood glucose level over time differs greatly depending on whether or not coffee was consumed.

[0036] Figure 12 shows a graph 100 of the rate of change in peripheral blood pressure index versus the rate of change in blood glucose level, obtained by regression analysis of measurement points when the same subject F underwent glucose tolerance tests on different days. Graph 100 reveals that the greater the rate of change in blood glucose level, the greater the rate of change in peripheral blood pressure index, and that the rate of change in blood glucose level is approximately proportional to the rate of change in peripheral blood pressure index. The trend in the rate of change in peripheral blood pressure index versus the rate of change in blood glucose level differs between graph 80 shown in Figure 9 and graph 100 shown in Figure 12. The reason for this difference is presumably that there was no significant difference in subject F's glucose metabolic capacity, and that although the same amount of glucose was ingested on different days, the amount of glucose absorbed was different.

[0037] FIG. 13 shows a graph 110 of the rate of change in peripheral blood pressure index versus the rate of change in blood glucose level, obtained by regression analysis of the measurement results of FIG. 9, with measurement point 110A showing the measurement results of subject F's first glucose tolerance test (measurement results when coffee was consumed). Measurement point 110B shows the measurement results of subject F's second glucose tolerance test (measurement results when coffee was not consumed). The coefficient of determination of graph 110 shown in FIG. 13 was approximately 0.25, and it was found that the addition of the measurement results when coffee was consumed reduced the coefficient of determination from approximately 0.33 to approximately 0.25.

[0038] If the purpose of estimating glucose metabolic capacity is to detect signs of diabetes, it is important to capture changes over months or years, eliminating the influence of the subject's current physical condition and the food and drink ingested within a few hours. In glucose tolerance tests conducted in medical institutions, food and drink intake is prohibited for more than 10 hours, thereby eliminating the influence of food and drink intake as much as possible, but the influence of physical condition cannot be eliminated, and besides being possible when diagnosed at a hospital, it is difficult to strictly conduct this type of glucose tolerance test in everyday life.

[0039] In light of this situation, the present inventors propose a novel index: "peripheral blood pressure index change rate / blood glucose level change rate." A small value of this novel index means that peripheral blood pressure does not increase (or decreases) despite a large blood glucose level change rate, which is thought to indicate a decline in glucose metabolic capacity. Figure 14 shows graph 120 obtained by plotting this novel index against the blood glucose level change rate instead of the peripheral blood pressure index change rate and performing regression analysis on the plotted measurement points. The coefficient of determination for graph 120 is approximately 0.38, demonstrating a better correlation than graph 110. Note that measurement point 120A represents the measurement results of subject F's first glucose tolerance test (measurement point obtained by plotting the novel index described above against the blood glucose level change rate instead of the peripheral blood pressure index change rate). Measurement point 120B represents the measurement results of subject F's second glucose tolerance test (measurement point obtained by plotting the novel index described above against the blood glucose level change rate instead of the peripheral blood pressure index change rate).

[0040] However, as can be seen from the examples of FIGS. 10 to 12, the blood glucose level change rate is not necessarily an appropriate index for glucose metabolic capacity.

[0041] Therefore, the present inventors have proposed a method for treating the "peripheral blood pressure index change rate / blood glucose level change rate" as an index indicating glucose metabolic capacity, and estimating this index. In this specification, the "peripheral blood pressure index change rate / blood glucose level change rate" is referred to as the "glucose metabolic capacity index."

[0042] Figure 15 shows a graph 130 of the glucose metabolic capacity index versus the rate of change in peripheral blood pressure index, obtained by replacing the rate of change in blood glucose level in the measurement results shown in Figure 14 with the rate of change in peripheral blood pressure index. Note that measurement point 130A shows the measurement result of subject F's first glucose tolerance test (measurement result obtained by replacing the rate of change in blood glucose level at measurement point 120A with the rate of change in peripheral blood pressure index). Measurement point 130B shows the measurement result of subject F's second glucose tolerance test (measurement result obtained by replacing the rate of change in blood glucose level at measurement point 120B with the rate of change in peripheral blood pressure index).

[0043] The regression equation that approximates the relationship between the rate of change in a peripheral blood pressure index and the glucose metabolic capacity index can be approximated to a first order, and its intercept is very close to 0. Therefore, by determining the slope of the regression equation of graph 130 in advance, it becomes possible to determine the glucose metabolic capacity index from the rate of change in a peripheral blood pressure index. Furthermore, because the slope of the regression equation of graph 130 can be regarded as a constant, there is a one-to-one correspondence between the rate of change in a peripheral blood pressure index and the glucose metabolic capacity index, and therefore the rate of change in a peripheral blood pressure index can be treated as an index showing glucose metabolic capacity.

[0044] Information defining the relationship between the peripheral blood pressure index change rate and the glucose metabolic capacity index (e.g., information defining the regression equation of graph 130) is stored in advance in memory 322 of signal processing device 32. When processor 321 of signal processing device 32 receives measurement results from sensing device 20 (e.g., the pulse wave signal measured by pulse wave sensor 211, the temperature value measured by temperature sensor 212, and the movement acceleration of sensing device 20 measured by acceleration sensor 24), processor 321 calculates the peripheral blood pressure index change rate from the pulse wave signal measured by pulse wave sensor 211, and estimates the glucose metabolic capacity index (i.e., estimates glucose metabolic capacity) based on the calculated peripheral blood pressure index change rate and information stored in memory 322 (i.e., information defining the relationship between the peripheral blood pressure index change rate and the glucose metabolic capacity index (e.g., information defining the regression equation of graph 130)).

[0045] In the process of estimating the glucose metabolic capacity index from the peripheral blood pressure index change rate, the change in the peripheral blood pressure index (for example, the maximum value of the peripheral blood pressure index change rate, the maximum value of the peripheral blood pressure index change amount, (t i -t i-1 )) / (t n -t0) / X0 instead of (t i -t i-1 )) / (t n -t0), the amount of change in peripheral blood pressure index, the variance of the rate of change in peripheral blood pressure index, the coefficient of variation, the time from glucose load at which the extreme value is reached, the change pattern, etc. may also be used.

[0046] Furthermore, information defining the relationship between the rate of change in the blood flow index and the glucose metabolic capacity index may be stored in advance in the memory 322 of the signal processing device 32. When the processor 321 of the signal processing device 32 receives the measurement results of the sensing device 20 (for example, the pulse wave signal measured by the pulse wave sensor 211, the temperature value measured by the temperature sensor 212, and the movement acceleration of the sensing device 20 measured by the acceleration sensor 24), the processor 321 calculates the rate of change in the blood flow index from the pulse wave signal measured by the pulse wave sensor 211, and can estimate the glucose metabolic capacity index (i.e., estimate the glucose metabolic capacity) based on the calculated rate of change in the blood flow index and the information stored in the memory 322 (i.e., information defining the relationship between the rate of change in the blood flow index and the glucose metabolic capacity index).

[0047] In addition to glucose tolerance tests, meals are another event that can affect a user's blood glucose level. Unlike glucose tolerance tests, meals vary in the type, amount, and combination of ingredients, as well as the time and duration required for eating, making them uncontrollable. It is also not possible to prohibit food and drink intake for more than 10 hours before a meal. Therefore, it is difficult to estimate a user's glucose metabolism capacity by measuring only blood glucose levels before and after a meal.

[0048] The rate of change in a glucose metabolic capacity index or a peripheral blood pressure index before and after a meal is considered to be a more suitable index for estimating glucose metabolic capacity than the rate of change in blood glucose levels, but if the amount of glucose absorbed is small, it is still difficult to accurately estimate glucose metabolic capacity. Therefore, since a single measurement of the rate of change in a peripheral blood pressure index may result in low accuracy in estimating glucose metabolic capacity, the accuracy of estimating glucose metabolic capacity can be improved by using the results of measurements of the rate of change in a peripheral blood pressure index after multiple meals (for example, on a daily or weekly basis) to determine the maximum, average, minimum, variance, coefficient of variation, etc. of the rate of change in a peripheral blood pressure index, and then estimating glucose metabolic capacity from these values.

[0049] In order to estimate glucose metabolic capacity from the rate of change in peripheral blood pressure indexes before and after a meal, it is necessary to distinguish between before and after a meal. Methods for distinguishing between before and after a meal include, for example, a method in which the user inputs the distinction between before and after a meal into computer 30 when measuring a pulse wave signal, or a method in which computer 30 distinguishes between before and after a meal based on the user's activity level, skin temperature, and blood circulation status (for example, a method in which the user is determined to have eaten if the user's pulse rate increases, blood circulation improves, and skin temperature rises, even when the user's activity level has not increased).

[0050] The computer 30 can estimate the user's activity level from the movement acceleration of the sensing device 20 measured by the acceleration sensor 24. The user's blood circulation state can be estimated, for example, from the user's pulse wave feature values. The blood circulation state is correlated with vascular age, and vascular age can be estimated from the waveforms of the b wave and d wave of the acceleration pulse wave signal. Therefore, the computer 30 can estimate the blood circulation state using, for example, the pulse wave feature values ​​of the b wave and d wave. The computer 30 can detect the user's skin temperature from the temperature measured by the temperature sensor 212.

[0051] Alternatively, the biological information measurement system 10 may periodically measure the user's peripheral blood pressure index without determining whether the user has had a meal, calculate the variance and coefficient of variation of the measurement results, estimate the user's ability to regulate peripheral hemodynamics based on the calculation results, and estimate the user's glucose metabolic ability from the estimated result of the user's ability to regulate peripheral hemodynamics. This method eliminates the need to determine whether the user has had a meal.

[0052] However, the user's peripheral hemodynamics changes depending on the height of the measurement site (e.g., finger) from the heart. Therefore, it is desirable that the biological information measurement system 10 measures the user's peripheral blood pressure index at a position at a constant height from the heart, or corrects the peripheral blood pressure index taking into account the difference in height of the measurement site (e.g., finger) from the heart.

[0053] In addition to glucose tolerance tests and meals, exercise is another event that can affect a user's blood glucose level. Exercise, particularly anaerobic exercise, increases glucose metabolism and blood flow. Although it depends on the exercise intensity and environmental temperature, the increase in blood flow is generally greater than after eating a meal, and is considered suitable for estimating glucose metabolic capacity. Because blood flow is affected by acceleration caused by exercise (for example, swinging your arm back and forth increases or decreases the blood flow in your fingers), it is desirable to measure peripheral blood pressure indicators in a resting state after exercise.

[0054] In order to estimate glucose metabolic capacity from the rate of change in peripheral blood pressure indexes before and after exercise, it is necessary to distinguish between before and after exercise. Methods for distinguishing between before and after exercise include, for example, a method in which the user inputs the distinction between before and after exercise into computer 30 when measuring a pulse wave signal, or a method in which computer 30 distinguishes between before and after exercise based on the user's activity level, pulse rate, skin temperature, and blood circulation state (for example, a method in which the user is determined to have exercised when the user's activity level increases, pulse rate increases, blood circulation improves, and skin temperature rises).

[0055] Events that affect a user's blood glucose level include a glucose tolerance test, meals, exercise, and sleep. It is known that blood flow also changes during sleep. Generally, peripheral blood flow increases during sleep, causing peripheral skin temperature to rise and core body temperature to fall. It is said that the greater the increase in peripheral skin temperature, the greater the level of sleepiness. Therefore, changes in peripheral blood flow during sleep are related to sleep quality, and it can be inferred that a greater change in peripheral blood flow during sleep indicates higher quality of sleep. Furthermore, it can be inferred that a lower peripheral hemodynamic regulation ability results in smaller changes in peripheral blood pressure indexes during sleep.

[0056] However, because changes in peripheral blood pressure indices during sleep are affected not only by peripheral hemodynamic regulatory ability but also by the time elapsed since eating, drinking, bathing, or exercise, environmental temperature, and physical condition, the accuracy of estimating peripheral hemodynamic regulatory ability from measurements taken on a single day is poor. Therefore, it is desirable to estimate peripheral hemodynamic regulatory ability by calculating the maximum, average, minimum, variance, and coefficient of variation of changes in peripheral blood pressure indices during sleep over a period of one day or more, such as one week or one month. If peripheral hemodynamic regulatory ability can be estimated, glucose metabolic ability can also be estimated, as described above.

[0057] In order to estimate glucose metabolic capacity from the rate of change in peripheral blood pressure indexes before and after sleep, it is necessary to distinguish between before and after sleep. Methods for distinguishing between before and after sleep of a user include, for example, a method in which the user inputs into computer 30 the distinction between before falling asleep and after waking up when measuring a pulse wave signal, or a method in which computer 30 distinguishes between before falling asleep and after waking up based on changes in the user's activity level over time (for example, a method in which the user is determined to have fallen asleep when the activity level decreases for a certain period of time).

[0058] To improve the accuracy of measuring changes in peripheral blood pressure indices, factors that affect peripheral blood pressure, such as peripheral skin temperature, ambient temperature, and autonomic nervous function, are easy to measure simultaneously when measuring peripheral blood pressure.

[0059] Because blood flow is affected by the ambient temperature, changes in blood flow are also affected by the ambient temperature. Whether the skin temperature is low or high, the change in blood flow is small, and so are the changes in peripheral blood pressure indices. By estimating the influence of ambient temperature from the peripheral skin temperature and correcting the peripheral blood pressure indices, the accuracy of estimating glucose metabolic capacity and peripheral hemodynamic regulation capacity can be improved.

[0060] The same applies when using outside air temperature instead of skin temperature, but even when the outside air temperature is the same, skin temperature varies greatly due to individual differences, the amount of time spent in that environment, clothing, physical condition, etc., and the accuracy of estimating skin temperature from outside air temperature is poor. Therefore, the accuracy of correcting peripheral blood pressure indices is worse than when skin temperature is measured directly.

[0061] For example, when the height of the measurement site from the heart decreases, vascular resistance remains almost unchanged, peripheral blood pressure increases, and blood flow increases. However, this increase in peripheral blood pressure is almost unrelated to peripheral hemodynamic regulation ability and glucose metabolic ability. Therefore, the change in peripheral blood pressure in such cases may be corrected by reducing its contribution to the estimation of peripheral hemodynamic regulation ability and glucose metabolic ability.

[0062] Because peripheral hemodynamics is affected by autonomic nervous function (sympathetic nervous activation causes blood vessels to constrict), changes in peripheral blood pressure indices are also affected by autonomic nervous function. A well-known method for estimating autonomic nervous function is to use frequency analysis of heart rate (pulse rate) fluctuations. For example, in a state of extreme tension, sympathetic nervous activation increases, heart rate increases, heart rate variability decreases, and hands become cold. In such cases, peripheral blood flow decreases, blood flow changes become smaller, and peripheral blood pressure indices also change less. Such temporary conditions may be corrected by reducing their contribution to the estimation of peripheral hemodynamic regulation ability and glucose metabolic ability.

[0063] FIG. 16 is a flowchart showing the flow of processing in the method for estimating glucose metabolic capacity according to an embodiment of the present invention.

[0064] In step 1601, the pulse wave sensor 211 measures a pulse wave signal from the user's peripheral region before an event that affects the user's blood glucose level (e.g., a glucose tolerance test, a meal, or exercise), and a pulse wave signal from the user's peripheral region after the event that affects the user's blood glucose level (e.g., a glucose tolerance test, a meal, or exercise). At this time, the pulse wave sensor 211 may measure the pulse wave signals from the user's peripheral region at the same measurement position at the same height from the user's heart. The temperature sensor 212 measures the skin temperature of the user's peripheral region. The acceleration sensor 24 measures the movement acceleration of the sensing device 20.

[0065] In step 1602, the sensing device 20 transmits the measurement results of the sensing device 20 (e.g., the pulse wave signal measured by the pulse wave sensor 211, the temperature value measured by the temperature sensor 212, and the movement acceleration of the sensing device 20 measured by the acceleration sensor 24) to the computer 30.

[0066] In step 1603 , the computer 30 receives the measurement results of the sensing device 20 .

[0067] In step 1604, computer 30 calculates the user's peripheral hemodynamic index value. For example, computer 30 calculates pulse wave feature values ​​from the pulse wave signal measured by pulse wave sensor 211, and calculates the user's peripheral hemodynamic index value from the calculated pulse wave feature values.

[0068] Thus, the step of measuring the user's peripheral hemodynamic index value includes step 1601 of measuring a pulse wave signal, step 1602 of transmitting the measurement result of the pulse wave signal, step 1603 of receiving the measurement result of the pulse wave signal, and step 1604 of calculating the peripheral hemodynamic index value from the pulse wave signal.

[0069] In step 1605, the computer 30 performs a correction process for the peripheral hemodynamic index value of the user.

[0070] For example, the computer 30 may correct the peripheral hemodynamic index value according to the skin temperature measured by the temperature sensor 212 .

[0071] For example, the computer 30 may estimate the pulse interval by determining the period of fluctuation from the pulse wave signal measured by the pulse wave sensor 211, calculate an index value of autonomic nervous function by performing power spectrum analysis on the frequency components of the periodic fluctuation of the heartbeat from the estimated pulse interval, and correct the peripheral hemodynamic index value according to the index value of autonomic nervous function.

[0072] In step 1606, the computer 30 calculates the change in the index value indicating the user's peripheral blood pressure measured before and after the event that affects the user's blood glucose level (for example, the peripheral blood pressure index change rate, the maximum peripheral blood pressure index change rate, the maximum peripheral blood pressure index change amount, (t i -t i-1 )) / (t n -t0) / X0 instead of (t i -t i-1 )) / (t n The user's glucose metabolic capacity is estimated from the change in the peripheral blood pressure index, the variance of the peripheral blood pressure index change rate, the coefficient of variation, the time from glucose load at which the extreme value is reached, the change pattern, etc., calculated using (t0-t0). For example, the computer 30 may determine that the user's glucose metabolic capacity is low when the change in the user's peripheral blood flow rate or the change in the peripheral blood pressure is small.

[0073] In step 1601, the pulse wave sensor 211 may measure the pulse wave signal of the user's peripheral location multiple times continuously or intermittently from before an event that affects the user's blood glucose level (e.g., a glucose tolerance test, a meal, or exercise) to after the event that affects the user's blood glucose level (e.g., a glucose tolerance test, a meal, or exercise).

[0074] Furthermore, in the above description, the events that affect the user's blood glucose level are exemplified as being either a glucose tolerance test, a meal, or exercise, but the event that affects the user's blood glucose level may also be sleep.

[0075] If the event that affects the user's blood glucose level is sleep, the pulse wave sensor 211 may continuously or intermittently measure the pulse wave signal of the user's peripheral site multiple times while the user is sleeping in step 1601. Furthermore, in step 1606, the computer 30 may estimate the user's glucose metabolic capacity, peripheral hemodynamic regulation capacity, or sleep quality from changes in the peripheral hemodynamic index values ​​measured while the user is sleeping.

[0076] Note that the processing of steps 1602 to 1605 when the event that affects the user's blood glucose level is sleep is the same as the processing of steps 1602 to 1605 when the event that affects the user's blood glucose level is any one of a glucose tolerance test, a meal, or exercise.

[0077] Although the above description describes a method for estimating a user's glucose metabolic capacity from changes in the user's peripheral hemodynamic index values, the present invention is not limited to this. For example, the biological information measurement system 10 may measure the user's peripheral hemodynamic index values ​​and blood glucose levels before and after an event that affects the user's blood glucose level, and estimate the user's glucose metabolic capacity from the changes in the peripheral hemodynamic index values ​​and blood glucose levels measured before and after the event that affects the user's blood glucose level. The ease of glucose absorption varies depending on the type, amount, and combination of ingredients eaten, physical condition, etc., and glucose metabolic capacity cannot be accurately determined without knowing the amount of absorbed sugar. By simultaneously measuring blood glucose levels and peripheral hemodynamic index values, glucose metabolic capacity can be accurately estimated.

[0078] The computer 30 can calculate the blood glucose level from the pulse wave signal measured by the pulse wave sensor 211. When estimating the user's glucose metabolic capacity from changes in peripheral hemodynamic index values ​​and changes in blood glucose levels measured before and after an event that affects the user's blood glucose level, information defining the relationship between the peripheral blood pressure index change rate and the glucose metabolic capacity index (for example, information defining the regression equation of graph 130) is not required.

[0079] According to the embodiment of the present invention, glucose metabolic capacity can be estimated non-invasively and simply by measuring a pulse wave signal using pulse wave sensor 211.

[0080] Furthermore, changes in peripheral hemodynamics after an event that affect the user's blood glucose level occur within approximately 0 to 2 hours, but the pattern of these changes is not consistent due to individual differences, etc. By measuring the pulse wave signal multiple times during this period, the pattern of peripheral hemodynamic changes can be understood, improving the accuracy of glucose metabolic capacity estimation.

[0081] Furthermore, because the amount of sugar intake varies depending on the ingredients and amount of food, it is difficult to determine sugar metabolic capacity by measuring before and after a single meal. However, measuring changes in peripheral hemodynamic indices after multiple meals makes it possible to estimate sugar metabolic capacity more accurately.

[0082] Furthermore, while there are no events (e.g., glucose tolerance tests, meals, exercise, etc.) that significantly affect blood glucose levels during sleep, blood flow to the brain generally decreases and blood flow in peripheral blood vessels increases. This change in peripheral hemodynamics can be used to estimate glucose metabolic capacity.

[0083] Furthermore, because peripheral hemodynamics is affected by outside temperature, changes in peripheral hemodynamic indices are also affected by outside temperature. Estimating the influence of outside temperature from skin temperature and correcting peripheral hemodynamic indices can improve the accuracy of estimating glucose metabolic capacity and peripheral hemodynamic regulation capacity.

[0084] Furthermore, because peripheral hemodynamics is affected by autonomic nervous function (for example, sympathetic nervous activation causes vasoconstriction, resulting in a decrease in blood flow), changes in peripheral hemodynamics are also affected by autonomic nervous function. By analyzing autonomic nervous function from pulse interval variability and correcting changes in peripheral hemodynamic indices based on the results, it is possible to improve the accuracy of estimating glucose metabolic capacity and peripheral hemodynamic regulation capacity.

[0085] The pulse wave sensor 211 is small and can be worn on a finger, wrist, ear, etc., and therefore has the advantage of being able to be incorporated into a wearable device. Wearable devices include, in addition to ring-type devices worn on a user's finger, wristband-type devices worn on the wrist, wristwatch-type devices, earphone-type devices worn in the ear, and patch-type devices attached to the skin. Furthermore, the sensing device 20 does not necessarily have to be a wearable device, and may be, for example, a portable device (such as a multi-function mobile phone called a smartphone) or a stationary device configured to measure a pulse wave signal by placing a finger on the pulse wave sensor.

[0086] Laser Doppler blood flow measurement or ultrasonic Doppler blood flow measurement may be used instead of pulse wave sensor 211. Laser Doppler blood flow measurement can be made smaller and has good measurement accuracy, but ultrasonic Doppler blood flow measurement is more difficult to make smaller and has poorer measurement accuracy than laser Doppler blood flow measurement. A method of calculating peripheral hemodynamic indicators from pulse wave feature quantities of a pulse wave signal measured by pulse wave sensor 211 allows for miniaturization of sensing device 20 and is suitable for reducing costs.

[0087] Laser Doppler blood flow measurement is highly accurate in measuring blood flow velocity, but blood flow volume is easily affected by factors such as the state of contact with the skin and pressure. Meanwhile, methods for estimating blood flow volume from features of the photoplethysmographic waveform are also affected by factors such as the state of contact with the skin and pressure, but this can be reduced by selecting the features used. For example, while pulse wave amplitude is strongly affected when used as a feature, features such as the peak time of the pulse waveform are less affected. Therefore, selecting appropriate features can improve the stability and reproducibility of peripheral hemodynamics estimation.

[0088] The above-described embodiments are intended to facilitate understanding of the present invention and are not intended to limit the scope of the present invention. The present invention may be modified or improved without departing from its spirit, and equivalents are also included within the scope of the present invention. In other words, even designs modified appropriately by a person skilled in the art are encompassed within the scope of the present invention as long as they incorporate the characteristics of the present invention. Furthermore, the elements of the embodiments can be combined to the extent technically possible, and such combinations are also encompassed within the scope of the present invention as long as they incorporate the characteristics of the present invention. [Explanation of symbols]

[0089] 10...Biometric information measuring system 20...Sensing device 21...Biometric sensor 22...Control circuit 23...Communication module 24...Acceleration sensor 25...Housing 211...Pulse wave sensor 212...Temperature sensor 30...Computer 31...Communication module 32...Signal processing device 321...Processor 322...Memory 323...Input / output interface 40...User 41...Heart

Claims

1. 1. A method performed by a biometric measurement system, comprising: measuring a peripheral hemodynamic index value of the user before an event affecting the user's blood glucose level and a peripheral hemodynamic index value of the user after the event, respectively; estimating the user's glucose metabolic capacity from changes in the peripheral hemodynamic index values ​​measured before and after the event; estimating the pulse interval of the user; calculating an index value of the user's autonomic nervous function from the fluctuation of the pulse interval; correcting the peripheral hemodynamic index value according to the index value of the autonomic nervous function; A method comprising:

2. 1. A method performed by a biometric measurement system, comprising: measuring a peripheral hemodynamic index value of the user continuously or intermittently multiple times while the user is sleeping; estimating the user's glucose metabolic ability, peripheral hemodynamic regulation ability, or sleep quality from changes in the peripheral hemodynamic index values ​​measured during the user's sleep; estimating the pulse interval of the user; calculating an index value of the user's autonomic nervous function from the fluctuation of the pulse interval; correcting the peripheral hemodynamic index value according to the index value of the autonomic nervous function; A method comprising:

3. 1. A method performed by a biometric measurement system, comprising: measuring a user's peripheral hemodynamic index value continuously or intermittently multiple times over a period of one day or more; estimating the user's glucose metabolic ability or peripheral hemodynamic regulation ability from the change in the measured peripheral hemodynamic index value; estimating the pulse interval of the user; calculating an index value of the user's autonomic nervous function from the fluctuation of the pulse interval; correcting the peripheral hemodynamic index value according to the index value of the autonomic nervous function; A method comprising:

4. 1. A method performed by a biometric measurement system, comprising: measuring a peripheral hemodynamic index value and a blood glucose value of the user before an event affecting the user's blood glucose level, and a peripheral hemodynamic index value and a blood glucose value of the user after the event, respectively; estimating the glucose metabolic capacity of the user from changes in the peripheral hemodynamic index value and the blood glucose level measured before and after the event; A method comprising:

5. The peripheral hemodynamic index value is calculated from pulse wave feature values, 5. The method according to claim 1, wherein the pulse wave feature amount is any one of the peak time differences of the a-wave, b-wave, c-wave, d-wave, and e-wave of the acceleration pulse wave signal, the height of each peak, the ratio of each peak time difference to the pulse interval, the peak half-width, the ratio of the area on the positive side to the area on the negative side of the peak of the acceleration pulse wave signal, and the degree of match between the measured pulse waveform and a pulse waveform template.

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