Healthcare Management Methods

Abstract

To provide a method for managing health care related to a blood sugar level, a method for providing health care information, a method for managing diabetes risk, and a method for preventing complications for diabetic patients.SOLUTION: A method for managing healthcare comprises: acquiring first data including a glycated albumin concentration and an albumin concentration in a subject's body fluid; generating output information related to a GA level based on the first data; and providing the generated output information to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a healthcare management method. [Background technology]

[0002] Several markers are used to indicate blood glucose control. For example, hospitals measure glycated hemoglobin (HbA1c) levels in diabetic patients. Glycated hemoglobin (HbA1c) levels indicate the degree of glycation of hemoglobin in red blood cells. Diabetic patients typically visit their doctor every one to three months. Because the half-life of red blood cells is approximately 36 days, this marker is an indicator of blood glucose levels over a one- to two-month period. HbA1c levels are measured every one to three months for diabetic patients and once a year for healthy individuals during health checkups. Glycated albumin (GA), an indicator of blood glucose control, is a marker that indicates the degree of glycation of albumin in the blood. Albumin has a half-life of approximately 17 days, indicating blood glucose levels over a two-week period. 1,5-anhydro-D-glucitol (1,5-AG) is another marker that indicates blood glucose levels over a period of several days. Summary of the Invention

[0003] The present disclosure provides a blood glucose level-related healthcare management method / healthcare information provision method / diabetes risk management method / method for preventing complications in diabetic patients, etc. In a further embodiment, first data including a glycoalbumin concentration and an albumin concentration in a body fluid (tears, saliva, blood, etc.) of a subject may be acquired. In a further embodiment, output information related to a GA value may be generated based on the first data. In a further embodiment, the generated output information may be provided to a user. [Brief explanation of the drawings]

[0004] [Figure 1] Graph showing the relationship between blood GA values ​​and tear GA values [Figure 2] Graph showing the relationship between blood GA values ​​and saliva GA values [Figure 3]1 is a flowchart of a method such as health management according to an embodiment of the present disclosure. [Figure 4] 1 is a flowchart of a method such as health management according to an embodiment of the present disclosure. [Figure 5] Graph showing weekly trends in 75th percentile blood glucose levels and blood GA levels [Figure 6] A graph showing the weekly trends in 90th percentile blood glucose levels and blood GA levels [Figure 7] Graph showing changes in median blood glucose levels and blood GA levels over a week [Figure 8] Graph showing the relationship between the 75th percentile blood glucose level and blood GA level for each subject every week [Figure 9] Graph showing the relationship between the 75th percentile blood glucose level and the relative value of blood GA level every week [Figure 10] A diagram showing a typical example of a normal dietary period [Figure 11] A diagram showing a typical example of a low-carbohydrate period [Figure 12] Graph showing the relationship between the 75th percentile blood glucose level and blood GA level for two subjects every week [Figure 13] Graph showing the relationship between blood GA values ​​corrected for uric acid levels and 75th percentile blood glucose levels [Figure 14] Figure showing the relationship between the total area within the 25th to 75th percentile blood glucose levels and blood GA values ​​for two subjects. [Figure 15] Comparison of blood GA values ​​corrected for uric acid levels and the total area within the 25th-75th percentile blood glucose levels [Figure 16] Graph showing changes in weight and blood GA levels over a week [Figure 17] Graph showing weekly changes in weight and 75th percentile blood glucose levels [Figure 18] A block diagram of a health management system according to an embodiment of the present disclosure. [Figure 19] 1 is a block diagram of a GA measuring device according to an embodiment of the present disclosure; [Figure 20] Schematic diagram showing the configuration of first data DETAILED DESCRIPTION OF THE INVENTION

[0005] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0006] Where a range of values ​​is provided, unless the context clearly dictates otherwise, the tenths of the units of the upper and lower limits of that range and any intervening values ​​within the stated range, or any other stated value, are encompassed within the scope of the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the scope of the invention, subject to any specifically excluded limit in the stated range. Where a stated range includes one or both of the limits, ranges excluding either of those included limits are also encompassed in the invention. It should be understood that the term "about" is not expressly stated to precede all numerical designations. All numerical designations, including ranges, such as length, volume, pH, temperature, time, concentration, and molecular weight, are approximations that vary up (+) or down (-) from the least significant digit of the numerical value. The term "about" also includes the exact value "X" as well as small increments of "X," such as "X+0.1" or "X-0.1." The reagents described herein are merely exemplary, and equivalents thereof are known in the art.

[0007] In the following description, numerous specific details are set forth to provide a more thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced with one or more of these specific details. In other instances, features and procedures well known to those skilled in the art are not described to avoid obscuring the present invention.

[0008] As used herein, the terms "comprises" and "comprising" are intended to mean that compositions and methods include the recited elements, but do not exclude others. Embodiments defined by each of these transition terms are within the scope of the present invention. Thus, methods, compositions, and compositions can "comprise" additional steps and components.

[0009] The present disclosure provides, without limitation, methods for healthcare management related to GA values ​​or blood glucose levels, healthcare information provision, diabetes risk management, measures to combat complications in diabetic patients, diabetes diagnosis, etc., programs for performing these methods, storage media for storing the programs, and data structures related thereto.

[0010] The present disclosure includes, but is not limited to: A method for diagnosing diabetes based on GA values. Primary data includes GA values ​​in body fluids such as tears, saliva, and blood, which are used to diagnose diabetes. Use of primary data including GA values ​​in body fluids for the diagnosis of diabetes. When diagnosing diabetes, a comprehensive judgment may be made by referring to secondary data such as blood glucose levels and HbA1c, diabetes type, symptoms, diabetic retinopathy status, and other diabetic symptoms, the contents of repeated tests, and the contents of the doctor's medical records.

[0011] Albumin is primarily produced in the liver and distributed throughout the body via the blood. It is also secreted into other body fluids by glands. For example, albumin is secreted from the blood via the lacrimal and salivary glands, and is present in tears and saliva.

[0012] The inventors have found that the GA value, which indicates the degree of glycation of albumin in body fluids, i.e., glycated albumin concentration / albumin concentration, is correlated with the GA value in blood. The correlation between the GA values ​​of tears and blood and the GA values ​​of saliva and blood that they discovered is shown below.

[0013] Example 1 <Comparison of tear GA values ​​and blood GA values> GA levels in tears (tear GA levels) and blood (blood GA levels) were measured. Sample solutions were prepared using tears and blood collected from 41 diabetic volunteers who provided informed consent. Blood GA levels were measured enzymatically using a commercially available kit, Lucica® GA-L (Asahi Kasei Pharma Corporation). Regarding tears, collected tears were first subjected to reduction and alkylation, followed by trypsin digestion overnight at 37°C. After processing, GA levels in tears were measured by liquid chromatography-mass spectrometry (LC-MS / MS). Figure 1 shows a plot of blood GA levels and tear GA levels. Each point represents the value for each sample.

[0014] The inventors compared the enzymatic method and LC-MS / MS analysis method for determining blood GA levels and confirmed that there is a very strong positive correlation between them (not shown). Therefore, the plots in Figures 1 and 2 use blood GA levels determined based on measurements by the enzymatic method, and tear and saliva GA levels determined based on measurements by LC-MS / MS analysis, respectively.

[0015] Figure 1 shows a graph showing the relationship between blood GA values ​​(X-axis) and tear GA values ​​(Y-axis). The correlation was found to have a correlation coefficient of r = 0.891, p < 0.01. In other words, the inventors discovered that there is a very strong positive correlation between tear GA values ​​and blood GA values. This result suggests that tear GA values ​​can be used as a biomarker to indicate average blood glucose levels over a 1-2 week period, instead of blood GA values.

[0016] Example 2 <Comparison of saliva GA values ​​and blood GA values> The GA levels of saliva (tear GA levels) and blood (blood GA levels) were measured. The sample solutions used were tears and blood collected from 41 diabetic volunteers who had given the same informed consent as in Example 1. The GA levels in blood were measured by an enzymatic method using a commercially available kit, Lucica GA-L (Asahi Kasei Pharma Corporation). Regarding saliva, the collected saliva was first subjected to reduction and alkylation treatment, followed by trypsin digestion overnight at 37°C. After treatment, the GA levels in saliva were measured by liquid chromatography-mass spectrometry (LC-MS / MS). Figure 2 shows a plot of blood GA levels and saliva GA levels. Each point represents the value for each sample.

[0017] Figure 2 shows a graph showing the relationship between blood GA values ​​(X-axis) and saliva GA values ​​(Y-axis). The correlation was found to be a correlation coefficient of r = 0.927, p < 0.01. In other words, the inventors discovered that there is a very strong positive correlation between saliva GA values ​​and blood GA values. This result suggests that saliva GA values ​​can be used as a biomarker to indicate average blood glucose levels over a 1-2 week period, instead of blood GA values.

[0018] All embodiments, aspects, examples, and numerical values ​​shown in this disclosure based on blood GA values ​​can be replaced with GA values ​​obtained for body fluids (including, for example, lacrimal fluid and tears). Even if only blood GA values ​​are described in this disclosure, embodiments in which they are replaced with body fluid (including, for example, lacrimal fluid and tears) GA values ​​are naturally included in this disclosure.

[0019] According to one embodiment of the present disclosure shown in FIG. 3, a healthcare management method includes: In step S101, first data including a glycated albumin concentration in the body fluid of a subject is acquired or collected. In step S102, output information related to a GA value is generated based on the first data. In step S103, the generated output information is provided to a user.

[0020] A "subject" may be a human or a terrestrial animal. A terrestrial animal may be a mammal. A terrestrial animal may be a companion animal, a livestock animal, a domestic animal, or a wild animal. A subject may be one subject (e.g., one person, one animal, one head, etc.), multiple subjects, at least one subject, or one or more subjects.

[0021] The test sample may be a solution. The solution may be a body fluid, a solution derived from a body fluid, or a diluted solution of a body fluid. The solution may be a solution that is not a body fluid (non-body fluid-derived), or a mixture of a body fluid or a body fluid-derived solution and a non-body fluid-derived solution. The solution may be a solution used for sample measurement, or a solution used for calibration measurement. For example, the solution may be a standard solution or a calibration solution.

[0022] "Body fluid" may be blood, serum, plasma, lymph, tissue fluid such as interstitial fluid, intercellular fluid, or interstitial fluid, or body cavity fluid, serous cavity fluid, pleural fluid, peritoneal fluid, pericardial fluid, cerebrospinal fluid (spinal fluid), joint fluid (synovial fluid), or aqueous humor (aqueous humor). Body fluid may be digestive fluid such as saliva, gastric juice, bile, pancreatic juice, or intestinal fluid, or may be sweat, tears, nasal mucus, urine, semen, vaginal fluid, amniotic fluid, or milk. Body fluid may be animal body fluid or human body fluid. "Body fluid" may be liquid in foods containing animal-derived protein (e.g., milk, dairy products, etc.). Body fluid may be plant body fluid, plant biofluid, or plant-derived liquid. For example, body fluid may be plant juice, nectar, or sap. "Body fluid" may also be a solution.

[0023] In some embodiments, the solution may include a physiological buffer solution. The solution may include a measurement target. The buffer solution may be a so-called Good's buffer. The buffer solution may include phosphate buffered saline (PBS) or N-tris(hydroxymethyl)methyl-2-aminoethanesulfonic acid buffer (TES). The buffer solutions were 2-morpholinoethanesulfonic acid (MES), 3-morpholinopropanesulfonic acid (MOPS), 2-hydroxy-3-morpholinopropanesulfonic acid (MOPSO), piperazine-1,4-bis(2-hydroxy-3-propanesulfonic acid) dihydrate (POPSO), N-(2-acetamido)iminodiacetic acid (ADA), piperazine-1,4-bis(2-ethanesulfonic acid) monosodium salt (PIPES), N-(2-acetamido)-2-aminoethanesulfonic acid (ACES), cholamine hydrochloride, N,N-bis(2-hydroxyethyl)-2-aminoethanesulfonic acid (BES), N-tris(hydroxymethyl)methyl-2-aminoethanesulfonic acid (TES), 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 2-[4- Sodium (2-hydroxyethyl)-1-piperazinyl]ethanesulfonate (HEPES-Na), Acetamidoglycine, Tricine, Glycinamide, Bicine, Bis(2-hydroxyethyl)iminotris(hydroxymethyl)methane (Bis-Tris), N-Cyclohexyl-3-aminopropanesulfonic acid (CAPS), N-Cyclohexyl-2-hydroxy-3-aminopropanesulfonic acid (CAPSO), N-Cyclohexyl-2-aminoethanesulfonic acid (CHES), 3-[N,N-Bis(2-hydroxyethyl)amino]-2-hydroxypropanesulfonic acid (DIPSO), 3-[4-(2-hydroxyethyl)-1-piperazinyl]propanesulfonic acid (HEPPS), 2-Hydroxy-3-[4-(2-hydroxyethyl)-1-piperazinyl] It may contain any one or a mixture of propanesulfonic acid monohydrate (HEPPSO), N-tris(hydroxymethyl)methyl-3-aminopropanesulfonic acid (TAPS), and 2-hydroxy-N-tris(hydroxymethyl)methyl-3-aminopropanesulfonic acid (TAPSO).

[0024] Acquiring or collecting a "glycated albumin concentration" or "albumin concentration" may be acquiring or collecting an output from a sensor, the "glycated albumin concentration" or "albumin concentration" itself, or a value related thereto, etc. Collecting a "glycated albumin concentration" or "albumin concentration" may include measuring the respective concentrations. Acquiring or collecting may include obtaining the albumin concentration and glycated albumin concentration by measurement in the subject's bodily fluid. Acquiring or collecting may include acquiring or collecting first data including the albumin concentration and glycated albumin concentration.

[0025] In some embodiments, acquiring or collecting may include time information of measurements of albumin concentration and glycated albumin concentration, or time information relating to collection of or collection of the bodily fluid (e.g., tears, saliva, blood) being measured. In some embodiments, acquiring or collecting may include collecting first data including time information of albumin concentration and glycated albumin concentration and their measurement. In some embodiments, time information relating to secretion, collection, or measurement of the bodily fluid of the subject being measured may be acquired. In some embodiments, the time of measurement may be recorded by a sensor or measuring device as the time information. In cases where there is a time interval between collection and measurement, the time of collection, the time of measurement, or both may be acquired as time information.

[0026] "Obtaining or collecting" may include collecting a bodily fluid from a subject. In one aspect, tears or saliva may be collected from a subject. In one aspect, blood may be collected from a subject. In some embodiments, the sample may be collected non-invasively. In some embodiments, the sample may be collected invasively.

[0027] The tear fluid sample may be collected from the surface of the subject's eye, eyelid, cornea, conjunctiva, or the vicinity thereof. The tear fluid may be collected from the lacrimal duct, such as the lacrimal punctum, lacrimal canaliculus, or lacrimal sac, or from the nose. The saliva sample may be collected from the subject's saliva or drool, or from the oral cavity. Blood may be collected by a conventional blood collection method.

[0028] In some embodiments, bodily fluid may be collected from a subject and measured substantially immediately. For example, a sensor or the like may be brought close to the eye or the like and measurement may be performed instantly. In some embodiments, the bodily fluid may be collected once in a container or sensor and then measured. In some embodiments, measurement may be performed after a certain time has elapsed after collection. In some embodiments, the bodily fluid may be stored between collection and measurement. The storage format may be frozen. For example, bodily fluid may be collected multiple times, stored for a certain period of time, and then measured together.

[0029] In some embodiments, measurements of albumin and glycated albumin concentrations may be made in bodily fluids (e.g., tears, saliva, blood) collected at substantially the same time, or secreted or collected at substantially the same time. In some embodiments, the measurements may be made simultaneously with collection of the specimen or at separate times. In some embodiments, the measurements may be made using the same sensor or multiple sensors. Albumin and glycated albumin concentrations may be measured at separate times, in bodily fluids (e.g., tears, saliva, blood) collected or secreted at separate times, or in separate bodily fluids.

[0030] "Glycated albumin concentration" or "albumin concentration" refers to the concentration of glycoalbumin or albumin, respectively, in a collected or target sample (tear or saliva). When a sensor is used, an output (such as a current, voltage, or optical signal) corresponding to or related to the "glycated albumin concentration" or "albumin concentration" may be obtained, and the respective concentrations may be determined by converting the output based on the correlation. The acquired or collected data may be the "glycated albumin concentration" or "albumin concentration" directly, the corresponding sensor output value, or an intermediate value during the conversion, i.e., some value, signal, information, or data corresponding to the "glycated albumin concentration" or "albumin concentration" based on the sensor output.

[0031] Methods for measuring GA levels include enzymatic methods, high-performance liquid chromatography, mass spectrometry, and isotope dilution mass spectrometry. Enzymatic methods are primarily used for clinical testing. The enzymatic method involves decomposing glycoalbumin into amino acids using protease, then treating it with ketoamine oxidase, which reacts only with glycated amino acids, and measuring the H2O2 generated by this process to determine glycoalbumin concentration. Albumin may also be reacted with bromocresol purple (BCP) reagent, and the albumin concentration measured from the change in absorbance of the blue conjugate. The GA value can be calculated as [glycated albumin concentration / albumin concentration] x 100 (%).

[0032] In some embodiments, a glycated protein sensor comprising an immobilized protease, an immobilized ketoamine oxidase, and a hydrogen peroxide detector may be used to measure the concentration of glycated albumin.

[0033] For example, by using such a sensor, the concentration of glycoalbumin in tears or saliva can be measured noninvasively, and thus the GA value can be measured without imposing physical or mental stress on the subject or with the burden of such stress on the subject being minimized.

[0034] In some embodiments, obtaining or collecting the albumin and glycated albumin concentrations of a bodily fluid (e.g., tears, saliva, blood, etc.) may include obtaining or collecting the albumin and glycated albumin concentrations in the bodily fluid multiple times. In some embodiments, obtaining or collecting may include collecting the albumin and glycated albumin concentrations in multiple bodily fluids in a time series. In some embodiments, a data stream may be generated having time or time information corresponding to the measurements of the glycated albumin concentrations.

[0035] In some embodiments, obtaining or collecting albumin and glycated albumin concentrations in bodily fluids multiple times may involve collecting glycated albumin levels in tears twice.

[0036] In some embodiments, obtaining or collecting albumin and glycated albumin concentrations in a body fluid multiple times may mean collecting albumin and glycated albumin concentrations in tears three or more times. In this case, the amount of glycated albumin in tears may be collected three or more times with a constant or variable time interval between them. When collecting multiple albumin and glycated albumin concentrations, the interval between measurements or the interval between the collection of tears to be measured may be constant or may vary. The time interval between obtaining or collecting albumin and glycated albumin concentrations may vary each time, or if there are several intervals, only a portion of the interval may vary.

[0037] The frequency of obtaining, collecting, or measuring a specimen may be substantially once per month, twice per month, or more. The frequency may be less than or equal to 20 times per month, 10 times per month, 8 times per month, or once per month. The frequency may be substantially twice per month, three times per month, four times per month, or even 0.5 times per week or once per week.

[0038] The frequency of acquisition, collection or measurement may be substantially once every 17 days in some embodiments, and may be as frequent as once every two weeks in some embodiments.

[0039] The value of the frequency of acquisition, collection or measurement may be converted into a collection interval before use.

[0040] The period of acquisition or collection (hereinafter referred to as collection) and the period for which the collection frequency and other collection characteristics are calculated may be 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 11 weeks, 12 weeks, 1 month, 2 months, 3 months, etc.

[0041] The collection or measurement may be performed at intervals of an average of 7 days, an average of 10 days, an average of 14 days, or more. The collection or measurement may be performed at intervals of an average of 21 days, an average of 24 days, an average of 28 days, or less.

[0042] In some embodiments, the first data may be collected by a computer, a computer system, and in some embodiments, the collected location data may be input to, and further saved or stored in, a computer, a computer system, a memory, or a storage medium.

[0043] The output information is generated based on the first data. In some embodiments, the output information may be generated by a computer or computer system based on the first data.

[0044] In some embodiments, the output information is related to a GA value. In some embodiments, the output information may be a GA value. Generally, a GA value can be calculated as glycated albumin concentration (mg / dL) / albumin concentration (mg / dL). GA value (%) = glycated albumin (mg / dL) / albumin concentration (mg / dL) × 100

[0045] A GA value obtained from the concentration of glycated albumin in tears or saliva may be referred to as a tear GA value or a saliva GA value. A GA value obtained from the concentration of glycated albumin in a non-invasively collected body fluid may be referred to as a non-invasive GA value. A GA value obtained from the concentration of glycated albumin in blood may be referred to as a blood GA value. A GA value obtained from the concentration of glycated albumin in an invasively collected body fluid may be referred to as an invasive GA value.

[0046] The "output information" generated and provided to the user may be the GA value at each measurement, the most recent GA value, the trend of past GA values, or one or more GA values.

[0047] Furthermore, output information related to the GA value includes, but is not limited to, the following: Difference from previous value = previous GA value - current GA value Converted HbA1c value (%) = GA value x 0.245 + 1.73 (other conversion formulas may also be used) Estimated average blood glucose level (HbA1c equivalent: average for the past 36 days) = 28.7 x converted HbA1c - 46.7 - Estimated average blood glucose level (GA conversion: average for the past 17 days) is shown in the data table of GA values ​​and average blood glucose levels.

[0048] The half-life of the GA value is said to be 17 days. The actual interval between sample collection or measurement may not be exactly the half-life. In some embodiments, the average blood glucose level may be calculated by taking into account or correcting the difference between the measurement interval and the half-life. In some embodiments, the average blood glucose level corresponding to the half-life from the measurement interval may be calculated using the decay curve (negative exponential curve) of the average blood glucose value.

[0049] In some embodiments, the average GA value or blood glucose value for the period from n-1th measurement to nth measurement may be calculated. In some embodiments, the average GA value or blood glucose value for the period may be calculated by converting GA values ​​calculated from two consecutive measurements. In some embodiments, the average GA value or blood glucose value for the period may be calculated from GA values ​​calculated from multiple measurements. In some embodiments, the average GA value or blood glucose value may be calculated by dividing the sum of the GA values ​​calculated from multiple measurements by the number of measurements. In some embodiments, the average blood glucose value may be calculated as a weighted function of the ratio calculated for each measurement, and as an example, the weighting may be set so that the ratio calculated for a more recent measurement is greater.

[0050] Generating the output information may use data other than the first data (second data, secondary data). The second data includes data related to non-subjects. In some embodiments, the second data may be a collection of data related to individual non-subjects (individual-specific data). In some embodiments, the second data may be data extracted or processed from data related to multiple subjects. In some embodiments, the second data may be data including trends, characteristics, traits, etc. related to a specific population of subjects (population-specific data).

[0051] The second data may be classified into data groups such as, for example but not limited to, personal data, health-related data, lifestyle data, and population data. The following are examples of the data groups of the second data and the respective second data items: Personal data: personal ID (My Number), age, gender, marital status, number of children, address, type of residence, car ownership, motorcycle ownership, bicycle ownership, occupation, job type, position, annual income, genome information, religion, health insurance number, fingerprints, voiceprints, facial recognition information, iris recognition information, personal thoughts, emotions, desires, and requests that change over time, input and usage information for smart devices, etc. Group data: race, nationality, region, area, seasonal factors (seasonal temperature and humidity changes, etc.), holiday periods (including New Year's, spring break, summer vacation, etc.), commuting method, religion, etc. Health-related data: weight, height, body fat, systolic blood pressure, diastolic blood pressure, heart rate, number of steps, activity level, blood oxygen saturation, body temperature, room temperature, solar radiation, UV radiation, average blood glucose level, standard deviation and AGP obtained from continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) devices, health checkup data (which may include HbAlc levels), medical history, prescription medications, medication information, data from medical institutions, food and other allergies, medical history and health-related data of family members, etc. Lifestyle data: Eating habits (including meat-eating preferences, vegetarianism, etc.), favorite foods, meal menu, meal photos, snack photos, exercise habits, wake-up time, bedtime, sleep duration, sleep depth, sleep score, active or passive rest time, eating speed, chewing frequency, speed and characteristics, food allergies, dietary rules related to religious beliefs (including Ramadan, etc.), whether or not you exercise, type of exercise, exercise duration, exercise frequency, calories burned during exercise, hobbies, etc.

[0052] The second data items, definitions of data groups, and how each second data item is assigned to a data group are not limited to those described above. In some embodiments, a computer or artificial intelligence may be used to create a data group based on characteristics discovered in existing second data, or existing second data may be classified into a new data group. In some embodiments, information or data regarding correlations or associations within existing second data may be classified as second data, stored, or used.

[0053] In some embodiments, the second data may include time-related information. In some embodiments, the second data may include information regarding the order in which meals are eaten, the timing of exercise and meals. In some embodiments, the second data may include the timing of medication intake. In some embodiments, the second data may include timing of insulin intake, depending on whether the insulin is fast-acting or slow-acting, as well as timing and time-related information regarding meals and exercise.

[0054] In some embodiments, the second data may be acquired from the subject, collector, or user. In some embodiments, the second data may be acquired from other individuals, organizations, etc. In some embodiments, the second data provider may be asked to indicate whether or not they wish to opt in (consent to use of personal data). In some embodiments, the second data may be acquired and collected from the subject, collector, or user, or a person related thereto, using an application.

[0055] In some embodiments, the subject, user, other individual, business entity, etc. may be prompted to provide, inquire about, question, etc. the second data or information regarding the second data.

[0056] 4 illustrates a method for generating and providing output information regarding a GA value using the second data in addition to the first data, in some embodiments. In some embodiments, these steps are performed using a computer system.

[0057] First, in step S201, the collector is notified that a body fluid of the subject will be collected and measured.

[0058] Next, in step S202, first data relating to the GA value in the collected body fluid is acquired or collected. For example, the concentration of glycated albumin (GA) in the body fluid is measured, and the concentration is collected as the first data. In some embodiments, the concentration of albumin in the same body fluid may also be measured. The GA value can be calculated from the ratio of the concentration of glycated albumin to the concentration of albumin.

[0059] In step S203, the collected first data and second data are referenced and analyzed. In some embodiments, data other than the first data and second data may be used.

[0060] In step S207, second data about the subject may be acquired from the subject. That is, data about the subject may be treated as the second data. Alternatively, the second data may be acquired from a person other than the subject, i.e., a non-subject, in step S208.

[0061] Next, in step S204, a relationship or correlation between the first data and the second data is searched for from the analysis in step S203. In some embodiments, if any relationship is found, it may be generated and stored as third data (tertiary data). In some embodiments, even if no clear relationship is found, if an event suggesting a possible relationship is discovered, or even if there is no relationship at all, if any data is generated, it may be stored as other data. If this data is useful or as a routine, it may be returned to step S203 for further analysis.

[0062] Next, in step S205, output information to be provided to the user is generated.

[0063] Then, in step S206, the output information is provided to the user. If the user is a collector, the output information is provided to the collector. The output information may be provided to the collector simultaneously with the notification to the collector that measurement should be performed, at a time related to the timing of the notification, or at a time unrelated to the notification.

[0064] In some embodiments, the second data may include information on blood glucose control status at or associated with the GA measurement, and may be analyzed using artificial intelligence, including machine learning and deep learning. In some embodiments, the analysis may predict the future risk of developing diabetes, its complications, or other diseases.

[0065] In some embodiments, the subject's health data, which is part of the second data, may be used to check for and determine whether the GA value obtained by measurement is a false high value or a false low value. In some embodiments, based on these checks for and determinations of false diagnosis, warning information may be provided to the user. Some examples are described below.

[0066] ·GA false high price check In some embodiments, if the second data includes information on liver cirrhosis, hypothyroidism, or the use of thyroid hormone synthesis inhibitors, output information may be generated containing a warning that the acquired GA value may be a falsely high value. The GA value obtained by measurement may be higher than the true value due to the effects of disease or the administration or use of therapeutic drugs. For example, in cases of liver cirrhosis or hypothyroidism, or when taking thyroid hormone synthesis inhibitors (such as Mercazole (registered trademark)), protein synthesis slows. That is, the production and metabolism of albumin are delayed, and the half-life of albumin is extended. As a result, the GA value increases.

[0067] GA false low value check In some embodiments, when the second data includes information on nephrotic syndrome, protein-losing enteropathy, hyperthyroidism, or thyroid hormone therapeutic agents, a notice indicating that the obtained GA value may be a false low value may be generated as output information. The GA value obtained by measurement may be lower than the true value due to the influence of diseases, therapeutic agent administration, or medication. For example, this may occur in the case of nephrotic syndrome, protein-losing enteropathy, etc. In such cases, when protein is excreted outside the body, biosynthesis is enhanced to compensate for albumin deficiency, and as a result, the half-life of albumin is shortened. That is, the denominator of the GA value becomes higher, and the GA value becomes lower. Also, for example, in the case of hyperthyroidism or when taking thyroid hormones (such as Thyralin S (registered trademark)), metabolism increases, so the half-life of albumin is shortened. As a result, the GA value becomes lower.

[0068] ·Check of GA-converted HbA1c value In some embodiments, when the second data includes the actually measured HbA1c value at a medical institution or the like, it is compared with the GA-converted HbA1c value of the most recent first data. When the actually measured HbA1c value ≥ GA-converted HbA1c value, information indicating that the average blood glucose level has improved may be generated as output information. In some embodiments, when the second data includes the actually measured HbA1c value at a medical institution or the like, it is compared with the GA-converted HbA1c value of the most recent first data. When the actually measured HbA1c value < GA-converted HbA1c value, information indicating that the average blood glucose level has deteriorated may be generated as output information.

[0069] ·Check and correlation between GA value and continuous / continuous blood glucose measurement In some embodiments, the data from the continuous glucose monitoring device included in the second data may be compared with the measured GA value to generate a correlation between them. In some embodiments, the continuous glucose monitoring may include a method for estimating a blood glucose trend from blood or interstitial fluid glucose concentrations (glucose values) continuously measured over several days. In some embodiments, the second data may include an Ambulatory Glucose Profile (AGP) based on the continuous glucose monitoring. In some embodiments, a correlation or conversion formula may be determined between the measured GA value and the average glucose value obtained by the CGM. CGM data obtained from measurements for several days during a certain measurement period may be graphed in the form of daily profiles from midnight to midnight overlaid for each day. In some embodiments, glucose fluctuation data or statistical values ​​(e.g., mean value, fluctuation range, standard deviation (SD)) obtained from continuous glucose monitoring performed over a certain period, e.g., two weeks, may be calculated. The fluctuation range may be the deviation of the glucose value at each time point from the mean value (which may be a statistical or mathematical value indicating the median or other center) in the data overlaid as a daily profile. The fluctuation range may be the maximum and / or minimum values ​​from the average glucose value at each time, or the 10-90% value, the 25-75% value (25-75%tile) of the total fluctuation, or the standard deviation (SD). In some embodiments, the relationship may be determined with the most recently measured GA value or a GA value measured at a time point associated with the continuous glucose monitoring period.

[0070] Example 3 <Comparison of blood glucose level and blood GA level> We investigated the relationship between blood glucose levels and blood GA levels. Four healthy volunteers, who provided informed consent, wore a FreeStyle Libre (registered trademark, Abbott Japan Co., Ltd.) and continuously measured interstitial fluid glucose levels for three months. The FreeStyle Libre was reworn approximately every two weeks. It is known that the absolute values ​​measured with the FreeStyle Libre fluctuate above and below the true value. Therefore, during the measurement period, blood glucose levels were measured by SMBG using the Glutest i (registered trademark, Sanwa Kagaku Kenkyusho Co., Ltd., Arkray Factory Co., Ltd.) at a certain frequency in addition to the Libre measurements. The interstitial fluid glucose values ​​indicated by the Libre were corrected based on the SMBG blood glucose measurements and converted to blood glucose levels (also known as CGM-corrected blood glucose levels). Blood samples were also drawn weekly to measure blood GA levels.

[0071] During the continuous monitoring period, healthy volunteers were asked to follow a normal diet, a low-glycemic diet, and then a normal diet again every four weeks for three months, and CGM-corrected blood glucose values ​​were collected.

[0072] Figure 5 shows the weekly trends in blood GA values ​​and the 75th percentile of CGM-corrected blood glucose values ​​obtained from one healthy volunteer. The trends in blood GA values ​​and the 75th percentile of blood glucose values ​​closely matched, revealing a very strong positive correlation between the two (correlation coefficient r = 0.749). A strong positive correlation was also confirmed in other healthy volunteers.

[0073] Figure 6 shows the weekly trends in blood GA values ​​and the 90th percentile of CGM-corrected blood glucose values. Similar to the results for the 75th percentile blood glucose values, the trends in blood GA values ​​and 90th percentile blood glucose values ​​over a three-month period were quite consistent, with a very strong positive correlation between the two (correlation coefficient r = 0.786).

[0074] Figure 7 shows the weekly trends in blood GA values ​​and median CGM-corrected blood glucose levels. Similar to the results for the 75th and 90th percentile blood glucose levels, it was revealed that there was a very strong positive correlation between blood GA values ​​and median blood glucose levels (correlation coefficient r = 0.801).

[0075] As can be seen from the above results, the trend in blood glucose levels over a week can be estimated from the blood GA values ​​on a weekly basis, without the need for continuous monitoring using a CGM or other device.

[0076] Figure 8 shows plots of the 75th percentile values ​​of weekly blood GA values ​​and CGM-corrected blood glucose values ​​for each subject, based on data obtained over 12 weeks for four healthy volunteers (ID: 001 to ID: 004).

[0077] It has been found that the relationship between weekly blood GA values ​​and the 75th percentile value varies from subject to subject. In some embodiments, the coefficients of the correlation equation between the blood GA value and the 75th percentile value (e.g., when approximating with a linear function, a and b in y=ax+b) may be set for each subject. In some aspects, the coefficients may be set for each subject by referring to some or all of the information included in the secondary data.

[0078] Figure 9 shows a plot of the weekly blood GA values ​​and the 75th percentile CGM-corrected blood glucose values ​​for four healthy volunteers, divided by the 12-week average and converted to relative values ​​(normalized). The correlation was found to be a correlation coefficient of r = 0.711, p < 0.01. In other words, by relativizing or normalizing the data, the inventors discovered a very strong positive correlation between fluctuations in blood GA values ​​and fluctuations in the 75th percentile values.

[0079] In some embodiments, the 75th percentile value or a related value for the CGM may be estimated or calculated based on the GA value over a period of time (eg, one week).

[0080] Example 4 <Comparison of postprandial hyperglycemia peak frequency and blood GA values> As in Example 3, blood GA values ​​and CGM-corrected blood glucose values ​​were collected over a three-month period from four healthy volunteers who had given informed consent.

[0081] We compared blood GA values ​​and the number of times blood glucose levels exceeded the threshold per day over a one-week period. Table 1 shows the correlation coefficients between blood GA values ​​obtained from each healthy volunteer (ID: 0001 to ID: 0004) and the number of times CGM-corrected blood glucose levels exceeded each threshold (unit: mg / dL).

[0082] [Table 1] A table showing the correlation coefficient when comparing the number of blood glucose levels above the threshold per day with the trend in blood GA values.

[0083] In the healthy volunteer ID:001, the correlation coefficient exceeded 0.481 at blood glucose levels of 110 mg / dL or higher, and 0.628 at blood glucose levels of 120 mg / dL or higher, indicating a strong positive correlation.The correlation coefficient exceeded 0.738 at blood glucose levels of 130 mg / dL or higher, indicating an even stronger positive correlation (Table 1).

[0084] In the healthy volunteer ID:004, the correlation coefficient exceeded 0.600 from blood glucose levels of 80 mg / dL or higher to blood glucose levels of 140 mg / dL or higher, indicating a strong positive correlation. On the other hand, the correlation coefficient decreased at blood glucose levels of 150 mg / dL or higher, suggesting a weak positive correlation (Table 1).

[0085] Thus, the relationship between the value of hyperglycemic peaks and the number or frequency of occurrence may vary from person to person. In some embodiments, the blood glucose range of hyperglycemic peaks (including, but not limited to, postprandial hyperglycemic peaks) may be determined by measurement for each individual, and may be appropriately set. In some embodiments, data on the relationship between the frequency of hyperglycemic peaks and GA values ​​may be accumulated. In one aspect, the GA value one week later may be predicted from data over a time range shorter than one week, for example, three days. In some embodiments, the frequency of hyperglycemia (e.g., postprandial hyperglycemic peaks) may be predicted from the measured GA values. In one aspect, a health forecast may be generated based on the data, and advice regarding diet, eating habits, and lifestyle may be provided. Analysis related to the above may be performed using AI.

[0086] Example 5 <Comparison of blood glucose level and blood GA level 3> As in Example 3, blood GA values ​​and CGM-corrected blood glucose values ​​were collected over a three-month period from four healthy volunteers who had given informed consent.

[0087] Table 2 shows the correlation coefficients between the blood GA values ​​obtained from each healthy volunteer (ID: 0001 to ID: 0004) and the integrated area (unit: mg / dL * time) of the area exceeding each threshold, based on data calculated from all data for one week of CGM-corrected blood glucose values.

[0088] [Table 2] Table showing the correlation coefficient when comparing the area under the curve of blood glucose levels above the threshold per day with the trend of blood GA values.

[0089] In the healthy volunteer ID:001, the correlation coefficient exceeded 0.400 at blood glucose levels above 50 mg / dL, was close to 0.500 at blood glucose levels above 110 mg / dL, and exceeded 0.600 at blood glucose levels above 120 mg / dL. A strong positive correlation was also observed at these thresholds. Thus, it was revealed that the integrated area of ​​blood glucose levels had a strong positive correlation with blood GA values ​​above any threshold (Table 2).

[0090] In the healthy volunteer ID:004, the correlation coefficient exceeded 0.750 for blood glucose levels above 50 mg / dL and exceeded 0.700 for blood glucose levels above 110 mg / dL. The correlation coefficient also exceeded 0.500 for blood glucose levels above 130 mg / dL. At these thresholds, a strong positive correlation was observed. However, at thresholds of 140 mg / dL and above, a strong positive correlation was not observed (Table 2).

[0091] Thus, the relationship between blood GA values ​​and the integrated area of ​​daily blood glucose levels may vary from person to person. In some embodiments, the blood glucose range of the hyperglycemic peak (including, but not limited to, a postprandial hyperglycemic peak) may be measured and appropriately set for each individual. In some embodiments, data on the relationship between the integrated area and GA values ​​may be accumulated. In one aspect, the GA value one week later may be predicted from data related to the integrated area over a time range shorter than one week, for example, three days. In some embodiments, the integrated area of ​​blood glucose levels may be predicted from the measured GA values. In one aspect, a health forecast may be generated based on the data, and advice regarding diet, eating habits, and lifestyle may be provided. Analysis related to the above may be performed using AI.

[0092] Example 6 <Comparison of blood glucose AGP and blood GA values> As in Example 3, blood GA values ​​and CGM-corrected blood glucose values ​​were collected over a three-month period from four healthy volunteers who had given informed consent.

[0093] AGPs were created using blood glucose levels continuously monitored weekly. Figure 10 shows a representative example of the AGP during the normal diet period, and Figure 11 shows a representative example of the AGP during the low-carbohydrate diet period. The area between the two lines in the graph (the area between the curves) was calculated. Table 3 shows the correlation coefficient between this area and blood GA values. It was found that the changes in the area within each percentile over the course of a week closely matched the changes in blood GA values ​​in many areas. Non-limiting examples of the correlations revealed strong positive correlations for all four healthy volunteers, particularly the areas between the 25th and 75th percentiles, the areas between the 10th and 75th percentiles, and the areas between the 2nd and 75th percentiles.

[0094] [Table 3] A table showing the correlation coefficient when comparing the area between the curves of each percentile blood glucose level and the transition of blood GA levels based on AGP

[0095] On the other hand, no strong positive correlation was observed in some areas, such as the area between the 2nd and 10th percentiles, the area between the 2nd and 25th percentiles, and the area between the 90th and 98th percentiles.

[0096] It is suggested that the change in the area within each percentile when an AGP is created can be estimated from the blood GA value every week. In some embodiments, the area within each percentile when an AGP is created either before or after the blood collection, or both, may be estimated or calculated from the blood GA. In some aspects, a percentile or its range to be adopted may be set for each individual.

[0097] Example 7 <Comparison of blood GA values ​​corrected for uric acid levels and blood glucose levels> As in Example 3, blood GA values ​​and CGM-corrected blood glucose values ​​were collected over a three-month period from four healthy volunteers who had given informed consent.

[0098] In some embodiments, the correlation between GA values ​​and blood glucose levels can be improved by correction. In one aspect, the correlation between GA values ​​and blood glucose levels can be improved by correcting blood GA values ​​using uric acid levels.

[0099] <Example: Correlation between blood GA values ​​corrected for uric acid levels and the 75th percentile of CGM-corrected blood glucose levels> Figure 12 shows the relationship between the blood GA value and the 75th percentile value of the CGM-corrected blood glucose value for each healthy volunteer every week.

[0100] In the data shown in Figure 12, among healthy volunteers, the correlation between the weekly fluctuations in blood GA values ​​and the 75% blood glucose values ​​of the CGM-corrected blood glucose values ​​was a correlation coefficient of r = 0.343, p = 0.101.

[0101] Figure 13 shows the relationship between blood GA values ​​corrected by uric acid levels and the 75th percentile blood glucose levels of CGM-corrected blood glucose levels for each healthy volunteer. The correlation was found to have a correlation coefficient of r = 0.823, p < 0.01. In other words, the inventors discovered that correcting GA values ​​by uric acid levels strengthens the correlation between blood GA values ​​and CGM-corrected blood glucose levels across multiple subjects.

[0102] <Example: Correlation between blood GA value corrected for uric acid level and area within the 25th to 75th percentile> Figure 14 shows the relationship between the weekly blood GA values ​​of each healthy volunteer and the total area between the 25th and 75th percentiles of CGM-corrected blood glucose values ​​based on AGP. The correlation coefficient was r = 0.319, p = 0.129.

[0103] Figure 15 shows the relationship between the weekly blood GA values ​​corrected for uric acid levels for each healthy volunteer and the total area between the 25th and 75th percentiles of CGM-corrected blood glucose levels based on AGP. The correlation was found to have a correlation coefficient of r = 0.771, p < 0.01. In other words, the inventors discovered that correcting for uric acid levels strengthened the positive correlation between blood GA values ​​and the integrated area of ​​blood glucose levels calculated from AGP across multiple subjects.

[0104] These results demonstrate that the correlation between fluctuations in blood GA levels and fluctuations in blood glucose levels can be strengthened by correcting the blood GA levels with uric acid levels. In one embodiment, the correction of uric acid levels may be a function of uric acid levels. The correction function for uric acid levels may be set depending on whether or not a subject is diabetic, the type of diabetes, the level of postprandial hyperglycemia, the pattern of blood glucose level fluctuations, the state of renal function, the state of liver function, metabolic function, insulin resistance, obesity level, etc. In a further embodiment, correction may be performed by multiplying the blood GA level by the uric acid level and then multiplying by a coefficient. For a group of subjects selected using a portion of certain secondary data, the uric acid level may be used for correction. Correction is not limited to the use of uric acid levels or uric acid levels. Correction may also be performed using other chemicals, biological substances, or values ​​or functions related thereto. In some embodiments, correction can strengthen the correlation between fluctuations in blood GA levels and fluctuations in blood glucose levels across multiple subjects. In some embodiments, correction may be performed using different measurements performed on the same subject. In one embodiment, the correction may be performed across multiple measurements at multiple locations taken at the same time. In one embodiment, the correction may be performed across multiple measurements taken at different times. In one example, if the measured values ​​on a certain day show results that deviate from this correlation, it may be determined that this suggests that the uric acid level may be abnormal. Physiological or mathematical values ​​or functions, such as uric acid levels, may be used for the correction.

[0105] Example 8 <Comparison of weight and blood GA value or blood glucose level> As in Example 3, blood GA values ​​and CGM-corrected blood glucose values ​​were collected over a three-month period from four healthy volunteers who had given informed consent.

[0106] In some embodiments, weight fluctuations may be estimated from the GA value or fluctuations in the GA value. In some embodiments, the relationship between the GA value and weight may be accumulated as data. Future weight fluctuations may be estimated, including a time lag between the GA value fluctuations and the weight fluctuations. In one aspect, the time lag may be accumulated as data. The relationship between the GA value or fluctuations in the GA value and weight fluctuations may be set according to the characteristics of the individual subject.

[0107] Figure 16 shows the weekly changes in blood GA levels and body weight obtained from one healthy volunteer. Although blood GA levels appear to change slightly ahead of body weight, a strong positive correlation was found between the two (correlation coefficient r = 0.568). A strong positive correlation was also confirmed in other healthy volunteers.

[0108] In some embodiments, weight fluctuations may be estimated from fluctuations in blood glucose levels or statistical values ​​related to blood glucose levels. In some embodiments, the relationship between blood glucose levels, etc. and weight may be accumulated as data. Future weight fluctuations may be estimated, including a time lag between fluctuations in blood glucose levels, etc. and weight fluctuations. In one aspect, this time lag may be accumulated as data. The relationship between fluctuations in blood glucose levels, etc. and weight fluctuations may be set according to the characteristics of the individual subject.

[0109] Figure 17 shows the weekly trends in the 25th percentile of CGM-corrected blood glucose levels and body weight for a healthy volunteer. It was revealed that there was a very strong positive correlation between the trends in the 25th percentile blood glucose levels and the trends in body weight (correlation coefficient r = 0.782). A very strong positive correlation was also confirmed in other healthy volunteers.

[0110] As the above results show, weight gain or loss can be predicted from fluctuations (trends) in blood GA levels, blood glucose levels, etc. For subjects who need weight control, monitoring GA levels, blood glucose levels, and related values ​​can be used to motivate them to maintain or improve their lifestyle habits.

[0111] In some embodiments, GA values ​​(invasive or non-invasive glycoalbumin measurements) may be obtained multiple times over a period of time, either continuously or non-continuously, and continuous blood glucose monitoring may be performed less frequently than the number of GA values. For example, GA values ​​may be measured every two weeks for 10 years, while AGPs may be obtained from CGM measurements every two weeks each year, and glucose fluctuation data and statistics for the 10-year period may be calculated from the GA values. In some embodiments, GA values ​​may be obtained multiple times over a period of time, and a relationship table or correlation may be created that shows the relationship between these GA values ​​and statistical values, such as the average glucose value and fluctuation range for the AGP corresponding to or associated with each GA value. In some embodiments, continuous blood glucose measurements may be performed repeatedly, and a relationship table or correlation may be created that shows the relationship between statistical values, such as the average glucose value and fluctuation range for the AGP, and the GA values ​​obtained corresponding to or associated with the continuous blood glucose measurement period. In some embodiments, based on the correlation between GA values ​​and statistical values ​​of glucose values ​​(e.g., average glucose values) obtained by AGP, GA values ​​may be obtained for periods other than those during which continuous blood glucose monitoring was actually performed, or for periods during which continuous blood glucose monitoring was not actually performed, and a conversion formula may be created to calculate the average glucose value from the obtained GA values.

[0112] In some embodiments, machine learning or deep learning may be used to search for or find associations between GA values ​​(first data) or statistical values ​​(e.g., fluctuation ranges) in continuous glucose monitoring (CGM) and all or part of the subject's past first data or second data related to lifestyle habits, etc. For example, it may be possible to find the presence or absence of association, or the strength of association, between a certain factor or item of personal data, health-related data, or lifestyle habit data (sometimes simply referred to as lifestyle habit data, etc. in this specification) and a certain value, behavior, or change in the fluctuation range of a certain GA value or CGM.

[0113] In some embodiments, the system or electronic device may store fluctuation data or statistical values ​​of glucose levels corresponding to or converted from GA values. In some embodiments, correlations (relationships) may be determined or generated between fluctuation data or statistical values, such as the fluctuation range of glucose levels obtained based on or estimated from GA values, and changes in those statistical values, and other second data. In some embodiments, fluctuation data or statistical values ​​of glucose levels obtained by estimation from measured GA values, and correlations between those changes and other second data, may be saved as third data.

[0114] The search for correlations between GA values, CGM fluctuations, and other lifestyle data may be performed on the same subject, across different subjects, or across multiple subjects. These searches may also use artificial intelligence. For example, even if individuals have the same constitution, dietary habits, and population, some individuals may have the same GA value or average glucose level, but others may have a large fluctuation range in their CGM glucose levels. It is expected that correlations or patterns may be found, such as subjects with a large fluctuation range experiencing faster vascular hardening and a higher risk of complications. Large-scale genome analysis has identified genes related to diabetes by race. For example, mutations in GLP-1R, which are unique to Japanese people, may be identified. Genomic testing may be used to set target GA values, HbA1c levels, BMI, blood pressure, etc., or may be used as parameters for calculating various complication risks and life expectancy.

[0115] In some embodiments, correlations already found by machine learning or the like may be used to determine health points, risk factors such as complication risk, and risk levels from statistical values ​​such as newly acquired GA values ​​and continuous glucose monitoring (CGM) glucose values ​​and fluctuation ranges. In some embodiments, the GA value and fluctuation ranges in continuous glucose monitoring may be referenced to generate a health forecast or a percentage risk of developing complications or other diseases. In some embodiments, other secondary data may also be referenced. Continuous glucose monitoring may be performed, for example, once or multiple times a year. Continuous glucose monitoring may be performed seasonally or in connection with life events such as New Year's, Christmas, and long holidays. GA value acquisition or invasive or non-invasive measurement of glycoalbumin concentration may be performed more frequently than continuous glucose monitoring, etc.

[0116] In some embodiments, a relationship table may be created between fluctuation data or statistical values ​​(e.g., fluctuation range) of glucose levels estimated based on the measured GA values ​​obtained multiple times in a time series, and changes in those values, and parameters related to health forecasts, such as the risk percentage of each complication including medical signs and diseases such as arteriosclerosis and the estimated time until a complication occurs, or the relationship between them may be generated. In some embodiments, the lifetime loss amount from the onset of a complication to the average life expectancy may be calculated.

[0117] In some embodiments, newly acquired GA values, glucose values ​​from CGM, and updated information on lifestyle habits, etc. may be added to the existing first data and second data, and analysis may continue using artificial intelligence, thereby improving or updating the accuracy of the related information (third data).

[0118] In some embodiments, continuous glucose monitoring (CGM) may be performed, and based on the AGP or by referencing the AGP data (e.g., mean glucose value or change in mean value and range of fluctuation or change in range of fluctuation) and the first data, a user (e.g., a person with prediabetes, a healthy individual, etc.) may be presented with a % risk of diabetes and an estimated time until developing diabetes. Prediabetes and healthy individuals may take actions to improve their lifestyle. In some embodiments, continuous glucose monitoring (CGM) may be performed, and based on the AGP or by referencing the AGP data (e.g., mean glucose value or change in mean value and range of fluctuation or change in range of fluctuation) and the first data, a health forecast including lifestyle habits, treatment, disease % risk, estimated time until developing a certain disease, and health points based thereon may be generated as output information. Such output information may be provided to a user (a diabetic, a person with prediabetes, or a healthy individual). In some embodiments, the disease or condition related to the % risk of disease may be diabetes, which occurs in people who do not have diabetes. In some embodiments, the time until developing diabetes may be estimated. In some embodiments, the disease or illness associated with the disease risk % may be a complication of a diabetic patient or a disease that develops at the same time as diabetes. In the present disclosure, "complication" includes an actual medical diagnosis of a complication, but broadly includes new or recurrent diseases in addition to diabetes, regardless of whether such a diagnosis is present. The risk of developing or experiencing such a complication is referred to as the complication risk %. In some embodiments, the time until a complication develops may be estimated. This allows, for example, the subject or user to realize the impact on their diabetes risk % and health points. The subject or user can realize that their own actions, such as lifestyle improvements, result in an increase or decrease in their diabetes risk %, estimated time until diabetes, and health points. Similarly, diabetic patients can realize the impact of their own actions on the progression of their diabetes, thereby maintaining or increasing their motivation to prevent or reduce the worsening of their condition and even improve it. The present disclosure can, for example, help raise awareness of health and disease status and maintain or increase motivation for next actions, or for ongoing or discontinuous actions.

[0119] In some embodiments, a subject's drug responsiveness or physiological effects may be determined based on continuous blood glucose measurements or measurements using a Sensor Augmented Pump (SAP), and drug prescriptions or drug administration recommendations may be made. In some embodiments, GA measurements may be performed substantially simultaneously with continuous blood glucose measurements. In some embodiments, GA measurements may be performed substantially continuously or discontinuously after continuous blood glucose measurements. In some embodiments, lifestyle or treatment recommendations may be made based on the GA value during continuous blood glucose measurements (initial GA value), subsequent GA values, or their changes or fluctuation characteristics (which may be simply referred to as "changes in GA value").

[0120] In some embodiments, the medication information in the second data may be referenced to suggest the next medication or prescription depending on the GA value, the trend of the GA value, values ​​related thereto, or a combination thereof.

[0121] In some embodiments, the second data may reference type 2 diabetes medication information. In some aspects, the second data may reference medical history.

[0122] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of sulfonylureas, a type of oral hypoglycemic drug, when the patient's GA value exceeds the limit value and the postprandial blood glucose level is determined to be high, the candidates may be suggested in the following order: α-GI, DPP-4 inhibitor (to alert for hypoglycemia), and SGLT2 inhibitor.

[0123] In the present disclosure, suggesting in order may include, in one example, suggesting candidate drugs in order of safety, such as the likelihood of hypoglycemia or other side effects.

[0124] In some examples, the limit value of the GA value may be determined based on conditions such as age, gender, history of diabetes, complications, genomic information, HbA1c value, converted HbA1c value, CGM data, fasting blood glucose level, postprandial blood glucose level, insulin resistance (HOMA-R), insulin secretion ability, and physical condition.

[0125] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of a sulfonylurea drug, which is a type of oral hypoglycemic drug, when the patient's GA value exceeds the limit value and their fasting blood glucose level is high, additional prescription medications may be suggested that are safe and inexpensive to administer in the following order: a biguanite drug, followed by a DPP-4 inhibitor (with a warning about hypoglycemia).

[0126] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of biguanite drugs, when the GA limit determined by reference to the second data is exceeded and postprandial blood glucose levels are high, additional medications may be suggested in the following order: α-GI, glinide drugs, DPP-4 inhibitors (with a warning about hypoglycemia), and SGLT2 inhibitors; and when fasting blood glucose levels are high, thiazoline drugs, DPP-4 inhibitors (with a warning about hypoglycemia), and SGLT2 inhibitors.

[0127] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of a thiazoline drug, when the GA limit determined by reference to the second data is exceeded and the postprandial blood glucose level is high, it may be suggested to administer an α-GI drug and then a glinide drug in that order.

[0128] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of a thiazoline drug, it may be suggested to administer a biguanide drug as an additional medication when the GA limit determined by reference to the second data is exceeded and the fasting blood glucose level is high.

[0129] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of a DPP-4 inhibitor, when the GA limit determined by reference to the second data is exceeded and the postprandial blood glucose level is high, it may be suggested to administer an α-GI drug and then a glinide drug in that order.

[0130] In some embodiments, if the medication information in the second data of the type 2 diabetes patient includes a history of administration of a DPP-4 inhibitor, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, it may be suggested to administer additional medications in the following order: a biguanide, a thiazoline, and an SGLT2 inhibitor.

[0131] In some embodiments, if the medication information of the second data of the type 2 diabetes patient includes a history of administration of an SGLT2 inhibitor, it may be suggested to administer an α-GI when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high.

[0132] In some embodiments, if the medication information in the second data of the type 2 diabetes patient includes a history of administration of an SGLT2 inhibitor, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, it may be suggested to administer additional medications in the following order: a biguanide, followed by a DPP-4 inhibitor (with caution against hypoglycemia).

[0133] In some embodiments, if the medication information of the second data of a type 2 diabetes patient includes a history of administration of a GLP-1 receptor agonist, it may be suggested to administer an α-GI when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high.

[0134] In some embodiments, if the medication information in the second data of the type 2 diabetes patient includes a history of administration of a GLP-1 receptor agonist, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, it may be suggested to administer additional medications in the order of a biguanide drug and an SGLT2 inhibitor.

[0135] In some embodiments, the second data may refer to medication information for type 1 diabetes or type 2 diabetes.

[0136] In some embodiments, for patients with type 1 or type 2 diabetes who, in addition to oral hypoglycemic agents, are injecting long-dissolving insulin (glargine or detemir) once a day at dinner or before bedtime, when the GA value exceeds the GA limit determined with reference to the second data and the postprandial blood glucose level is high, Administration of alpha-GI, glinide medications may be suggested.

[0137] In some embodiments, for patients with type 1 or type 2 diabetes who, in addition to an oral hypoglycemic agent, are receiving a once-daily injection of long-acting insulin (glargine or detemir) at dinner or before bedtime, when the GA value exceeds the GA limit determined with reference to the second data and the fasting blood glucose level is high, it may be suggested that additional medications be administered in this order: a biguanide, followed by a DPP-4 inhibitor. In this case, a warning about nocturnal hypoglycemia may also be given.

[0138] In some embodiments, for patients with type 1 or type 2 diabetes who, in addition to an oral hypoglycemic agent, are receiving a once-daily morning injection of long-acting dissolving insulin (glargine or detemir), administration of an α-GI, glinide drug may be suggested when the GA value exceeds the GA limit determined with reference to the second data and postprandial blood glucose levels are high.

[0139] In some embodiments, for patients with type 1 or type 2 diabetes who, in addition to an oral hypoglycemic agent, are receiving a once-daily morning injection of long-dissolving insulin (glargine or detemir), when the GA value exceeds the GA limit determined with reference to the second data and the fasting blood glucose level is high, it may be suggested to administer a biguanide drug and then a DPP-4 inhibitor.

[0140] In some embodiments, for patients with type 1 or type 2 diabetes who are receiving a once-daily morning injection of long-acting insulin (glargine or detemir) in addition to an oral hypoglycemic agent, if the GA value exceeds the GA limit determined with reference to the second data and both fasting and postprandial blood glucose levels are high, it may be recommended that a DPP-4 inhibitor be administered as an additional medication. For example, if the patient is also receiving an SU drug, a warning about hypoglycemia may also be provided.

[0141] It has been revealed that the genomic information of approximately 80 genetic mutations related to diabetes varies between races and between individuals. One theory is that these differences in genomic information are the cause of individual differences in the effectiveness of various diabetes drugs, particularly those with different mechanisms of action.

[0142] On the other hand, it is believed that various indicators of diabetes status (e.g., insulin secretion capacity, insulin resistance, HbA1c level), compliance with exercise and dietary therapy, and physical conditions such as sick days all interact to affect the fluctuation range of blood glucose levels. Daily blood glucose levels and their fluctuations can be monitored by measuring them using a CGM. As a result, the inventors have discovered that various indicators obtained from CGM (e.g., AGP, area under the blood glucose curve, frequency of postprandial hyperglycemia) are correlated with the GA value. The limit value for the GA value is in the range of 15.6 to 25.0%, and values ​​such as 25.0%, 22.5%, 18.3%, 16.5%, and 15.6% are selected depending on the state of blood glucose control obtained from the CGM.

[0143] As described above, by suggesting and alerting doctors and medical professionals to hypoglycemic medications, it is possible, for example, to safely bring GA values ​​closer to the normal range for healthy individuals (less than 15.6%) or to help bring them closer.

[0144] In some embodiments, if the GA value or its change or fluctuation characteristics during continuous blood glucose measurement are within a predetermined or appropriate range, output information may be generated or the user may be notified that the condition is good and that the next continuous blood glucose monitoring (CGM) measurement does not yet need to be performed, or no notification may be given regarding the need for the next continuous blood glucose measurement. In some embodiments, if the GA value or its change or fluctuation characteristics after continuous blood glucose measurement are not within a predetermined or appropriate range, output information may be generated or the user may be notified that the subject should undergo continuous blood glucose monitoring again. In some embodiments, if a change such as a rapid decrease in GA value is observed after continuous blood glucose measurement, the user may be notified with a warning that the average blood glucose level may be changing rapidly or that the blood glucose level during hypoglycemia may be extremely low, or may be notified that the next continuous blood glucose measurement should be performed to obtain an AGP and re-examine blood glucose changes. In some embodiments, if the GA value changes suddenly, such as decreasing, the user may be notified of health and lifestyle warnings and suggestions, such as the possibility of a subsequent weight loss or change, a loss of muscle mass that may occur in conjunction with weight loss, and caution against locomotive syndrome, injury, or illness.

[0145] Continuous glucose monitoring, or AGP, is an invasive method that places a significant mental and financial burden on subjects, making it difficult to continue for long periods of time. On the other hand, if statistical values ​​(such as mean and standard deviation) of glucose levels could be calculated from non-invasive GA measurements, the burden on subjects could be reduced.

[0146] In some embodiments, if the second data regarding the subject includes information regarding pregnancy, dialysis, anemia, administration of erythropoietin, treatment for anemia, renal dysfunction, high urea levels, iron deficiency, vitamin B12 deficiency, folate deficiency, hemoglobin abnormalities, blood transfusion, metabolic acidosis, hemolysis of the sample during actual HbA1c measurement, etc., the HbA1c value may be abnormal, so a warning mark may be added to output information such as misdiagnosis checks (judgments, output information providing warning information, etc.).

[0147] In some embodiments, the output information may be information that can influence the behavior or behavioral change of a subject or user. The GA value has an average half-life of 17 days and is an index that is particularly likely to influence human motivation maintenance and behavioral change.

[0148] In some embodiments, the first data and the second data may be used to diagnose a blood glucose level-related disease. In some embodiments, the first data and the second data may be used to generate advice regarding a blood glucose level-related disease or health condition. In some embodiments, the diagnosis of the disease or the generation of advice regarding the disease or health condition may be performed using a diagnostic algorithm performed or as would be performed by a physician. In some embodiments, the diagnosis of the disease or the advice regarding the disease or health condition may be generated as output information or may be further provided to a user.

[0149] In some embodiments, the output information may include the GA value and a graph of changes in the GA value, the GA-converted HbA1c value, the difference from the previous value, the average blood glucose level, dietary information, a health forecast, suggested measures, health points, etc. In some embodiments, these may be generated by a computer system using AI (artificial intelligence) to analyze the first data and the second data, or further analyze the third data.

[0150] The dietary information may include the nutritional balance, calorie intake, carbohydrate intake, and glycemic index (GI value). In some embodiments, a meal menu or a photograph of the meal may be sent to the computer system as second data of the subject. In some embodiments, the computer system may analyze these inputs to generate the nutritional balance, calorie intake, carbohydrate intake, and GI value of the meal.

[0151] The health forecast may include the % risk of developing diabetes, the % risk of diabetic complications (% risk of cardiovascular events, % risk of nephropathy, % risk of neurological disease, % risk of blindness), % risk of developing cancer, % risk of developing dementia, as well as the estimated time to onset, lifetime loss, etc. In some embodiments, these may be generated by a computer system using AI to analyze first data and second data collected from the subject, or even third data.

[0152] In some embodiments, the proposed countermeasures may be generated by a computer system using AI or the like to analyze past changes in GA values, dietary information, seasons, etc. In some embodiments, the proposed countermeasures may be used to maintain or improve the subject's health and to increase or maintain the subject's motivation to continue measuring GA values. In some embodiments, when GA values ​​are on the rise, measures to prevent postprandial hyperglycemia that lead to improvements in GA values ​​may be provided in accordance with the subject's lifestyle habits.

[0153] Health points may be generated as output information to improve or maintain the subject's health. In some embodiments, health points may be awarded to the subject or a person associated with the subject, such as a caregiver or family member, in response to application operation, GA value measurement, health checkup data input, meal photo input, or illness- or health-related actions. For example, the minimum score may be 1 point for launching and viewing the app, and the maximum score may be 50 points for measuring the GA value. Alternatively, points may be awarded or added depending on the magnitude of the action that reduces the GA value, such as 10 points for a 0.1 decrease in the GA value, or confirmation of compliance with the above-mentioned countermeasures, such as 2 points for walking 8,000 steps, 4 points for 12,000 steps, or 1 point for reducing a meal by 80 kcal.

[0154] The "user" receiving the output information may or may not be a subject, and may include both subjects and non-subjects. A "user" may be a person who actually uses a sensor, a person who is measuring their own or another person's sensor, a person who plans to measure, or a person who is required or recommended to measure. A "user" may be a doctor, medical institution, pharmacist, pharmacy, local government, government, employer, service provider, insurance carrier, family member, other bodily fluid provider (subject), or other individual or corporation, company, or organization that requires, shares, or purchases health data, and may be one person or entity, or multiple people or entities.

[0155] The output information may be generated in a form that can be recognized by the user's five senses, sensors, or a computer. The output information may be in the form of visual information such as numbers, text, graphs, and images, auditory information such as sound, music, voice, and voice messages, tactile information such as Braille and Braille displays, olfactory information, taste information, or a combination thereof. The output information may be electronic, optical, electromagnetic, or a combination thereof.

[0156] In some embodiments, the visual output information may be displayed as numbers and characters for each measurement, based on the user's selection from the above GA value, the difference from the previous value, the converted HbA1c value, the converted average blood glucose value (HbA1c conversion: average over 36 days), and the estimated average blood glucose value (GA conversion: average over the past 17 days).

[0157] For example, various display items (icons) may be prepared on the display of a smartphone or the like. Items corresponding to output information related to the first data and second data collected by the user may be set to be displayable, and the user may select and display any item of their choice within that range. For example, the output information may be provided using an application.

[0158] In some embodiments, the output information may be provided to the user based on the user's expression of intent. In some embodiments, the output information may be configured to be accessible, acquired, or viewed by the user via a terminal or electronic device, or via a server. In some embodiments, the output information may be provided without the user expressing their intent each time it is provided. In some embodiments, the output information may be provided based on a time, conditions, or the like that are preset or agreed upon by the user. In some embodiments, the output information may be provided independently of the user's intent, or may be provided based on the provider's judgment. In some embodiments, the provider may make a human or computer-based judgment when deciding whether to provide the output information. In some embodiments, the provider may make a decision to provide the output information based on a comprehensive combination of the user's intent and the provider's judgment.

[0159] In some embodiments, the user may be notified, for example, with a warning that glycoalbumin should be measured.

[0160] The notification may be performed at a predetermined or appropriate timing. For measurements after the first one, notification may be performed at regular or irregular intervals. The interval between any two measurements may be 3 days, 4 days, 5 days, 6 days, 7 days, 10 days, 15 days, 20 days, 30 days, 40 days, 50 days, 60 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, or other intervals. For measurements of glycated albumin, notification may be performed at intervals of 7 days, 10 days, 15 days, 20 days, 1 week, 2 weeks, 3 weeks, etc.

[0161] In some embodiments, a notification may be given that the time to collect body fluids is approaching, that the time has come, or that the time has passed. The subject, collector, or user may be notified that the time to collect body fluids is approaching, for example, one day or two days before the time to measure, or at a date and time preset by the subject, collector, or user. If the time to collect body fluids has passed but the collection and measurement has not been performed, a notification to that effect may be given.

[0162] In some embodiments, a computer may be used to search for, discover, calculate, or generate associations or correlations as third data or information.

[0163] Figure 18 shows a block diagram of a medical information or healthcare management system 100 related to GA values ​​according to some embodiments. In the system 100 shown in Figure 18, each device is connected by a communication network 110. The computer that transmits information and performs calculations may be a single CPU, but in Figure 18, multiple CPUs are arranged in each device, and these cooperate to collect, manage, store, calculate, etc. data.

[0164] Subjects 121 and 122 measure their own body fluids using GA measuring devices 131 and 132, respectively. In FIG. 18, subjects 121 and 122 use smart devices 141 and 142 as display devices or communication devices. These smart devices 141 and 142 are directly connected to the GA measuring devices 131 and 132, i.e., wirelessly or by wire, without via a communication network. Therefore, subjects 121 and 122 can know the GA values ​​as measurement results and the trends in the GA values ​​directly via smart devices 141 and 142.

[0165] The GA values ​​obtained by measuring the subjects 121, 122 and time information on the measurement times are transmitted as first data from the GA measuring devices 131, 132 or smart devices 141, 142 to the memory 150 via the communication network 110 and stored in the memory 150. Second data is also stored in the memory 150.

[0166] Each component of the system in FIG. 18 is connected to an AI analysis center (or simply, analysis center) 160 via a communication network 110. The AI ​​analysis center 160 has a function of collecting the first data and the second data stored in the memory 150 and performing AI analysis using machine learning. The AI ​​analysis center 160 accesses the memory 150, analyzes the first data and the second data, and generates the analysis results as output information. This output information is transmitted to the smart devices 141, 142 of the subjects 121, 122 via the communication network 110. As a result, the output information is provided to the subjects 121, 122.

[0167] The artificial intelligence of the AI ​​analysis center 160 can calculate huge amounts of first data and second data using artificial intelligence, machine learning, and deep learning. The AI ​​analysis center 160 can also legally access memories (not shown) outside the management system network and refer to the second data stored therein.

[0168] The AI ​​analysis center 160 can search for and find associations or correlations between the first data and the second data. Information or data about the correlations found is stored in the memory 150 as third data. The third data is used for output information.

[0169] The output information including the first data, second data, and third data may be transmitted to users 171, 172 who are not subjects, such as medical institutions, public institutions, patient organizations, insurance companies, and data companies, via the communication network 110. The output information and information or data generated in generating the output information are stored in the memory 150.

[0170] FIG. 19 shows a block diagram of the GA measuring device 230. In the GA measuring device 230 shown in FIG. 19, a processing (calculation) unit 232, a memory 233, a power supply 234, a display unit 235, and a communication unit 236 are arranged in a main body 231 and are connected to each other. A sensor 237 is attached to the GA measuring device main body in a replaceable or detachable manner. The processing unit 232 controls the sensor 237 and receives an output signal from the sensor 237. The processing unit 232 performs calculations on the signal from the sensor 237 and sends the calculation results to the display unit 235, the communication unit 236, and the memory 233. The display unit 235 displays the received signals to the user of the GA measuring device 230. The communication unit 236 transmits signals received from the processing unit 232 to the outside wirelessly (FIG. 19) or via a wired connection (not shown). Furthermore, the communication unit 236 transmits signals received from the outside to the processing unit 232. The memory 233 stores signals or data in an electrical, optoelectronic, electromagnetic, or other format. The memory 233 stores signals received from the process unit 232 and provides data, etc. to the process unit 232 in response to commands from the process unit 232. Data in the memory 233 is added, deleted, changed, etc. in response to commands from the process unit 232. The power supply 234 may be a rechargeable or replaceable battery (FIG. 19), or may be a power input element from an external power source (not shown). The power supply 234 provides power to the process unit 232. The power supply 234 also supplies power to the display unit 235, communication unit 236, sensor 237, and memory 233 via the process unit 232 (FIG. 19) or directly (not shown).

[0171] In addition to measuring glycated albumin concentration or albumin concentration, the sensor 237 also acquires time information about the collection or measurement of body fluids. It also has a function for managing sensors that are in use or have been used. The GA measuring device 230 acquires time information such as the year, month, day, hour, and minute of measurement or tear collection, event information such as measurement values ​​including glycated albumin concentration and albumin concentration, and management data.

[0172] FIG. 20 schematically shows the structure or configuration of data (first data, primary data) collected by a GA measuring device, or the structure of the first data. The first data is stored as a data array. The data (first data, primary data) collected by a GA measuring device includes information 310 for identifying the subject and measurement-related information 320. The information 310 for identifying the subject includes, for example, the subject's name 311 and the subject's ID 312. The measurement-related information 320 includes measurement time information (measurement time) 323, glycoalbumin concentration 325, and albumin concentration 326. In addition to this, the first data may include information such as a measurement number 321, a sensor number (sensor management information) 322, whether the measurement was valid or an error (measurement valid / invalid information) 324, and other measurement-related information 327. The first data is collected for each subject or in a format associated with the subject.

[0173] The GA measuring instrument 230 shown in FIG. 19 can temporarily record these data in a memory 233 inside the main body.

[0174] The time and event information may be saved, stored or recorded as a data string or data matrix.

[0175] 19 can also manage and record other management data. The management data may include, but is not limited to, the measurement device ID, measurement device serial number, sensor serial number, sensor lot number, sensor manufacturing date, sensor calibration value, sensor expiration date, remaining number of sensor measurements, temperature during measurement, error history, remaining battery life, communication code, etc.

[0176] 19 has a calculation and analysis block (processing unit) 232 and is capable of performing calculation and analysis. The GA meters 131, 132, 230 or smart devices 141, 142 may perform relatively simple calculation and analysis, while the AI ​​analysis center 160 may perform more advanced calculation and analysis, such as calculation and analysis using artificial intelligence or machine learning on big data. The calculation and analysis block 232 in the GA meter shown in FIG. 19 can calculate, for example, the GA value, converted HbA1c value, comparison with the previous value, and average blood glucose level using the following formulas: GA value (%) = glycated albumin concentration (mg / dL) / albumin concentration (mg / dL) × 100 (%) GA value in tears (%) + estimated blood GA value (%) = a × [GA value in tears (%)] α + b Difference from previous value = previous GA value - current GA value Converted HbA1c value (%) = GA value x 0.245 + 1.73 Estimated average blood glucose level (HbA1c equivalent: average for the past 36 days) = 28.7 x converted HbA1c - 46.7 - Estimated average blood glucose level (GA conversion: average for the past 17 days) is displayed from the data table of GA values ​​and average blood glucose levels. The above-mentioned calculation formula is an example, and other functions that define the correlation between the respective parameters may be used, and other values ​​may be calculated.

[0177] The calculation and analysis block 232 can also calculate the average blood glucose level for the past two weeks from the GA value. Such calculation and analysis can also be performed by the smart devices 141 and 142 or the AI ​​analysis center 160.

[0178] 19 includes a means (communication unit) 236 for communicating the above data with the communication network 110, smart devices 141, 142, etc. (see FIG. 18) periodically or arbitrarily as needed. The transmitted data may be displayed on an electronic device terminal with a display function, such as a personal computer or smart devices 141, 142 (see FIG. 18). The measurement results may be displayed on a display unit 235 of the GA measuring device main body 231.

[0179] For example, output information may be generated based on real-time or time-varying individual thoughts, emotions, and desires obtained from a smart device or the like. For example, when a subject desperately wants to eat ramen, the smart device can warn the subject before eating that eating ramen may have a negative impact on their health. Alternatively, information such as the subject should refrain from eating the ramen or should not finish it can be provided. Furthermore, the manner in which information is provided may be direct or gentle, and can be appropriately adjusted according to the user's preferences. Furthermore, future changes in health status, i.e., a health forecast, can be provided based on the first data or the first data and the second data. Alternatively, coaching on lifestyle habits or notifications, contacts, and information provision to increase and maintain motivation can be provided. Alternatively, a visit to a hospital can be encouraged.

[0180] This system collects the second data, aligns it with the first data, and analyzes it using machine learning based on the blood glucose control state at the time of GA measurement to predict the risk of developing diabetes, its complications, or other diseases in the future. Health data may be used as the second data.

[0181] The operations, actions, calculations, analyses, information transmissions, etc. included in this disclosure may be performed by a computer or computer system. The computer, etc. may include an input means, a processor, a memory means for storing a database, and a display means. The computer itself, the processor, the memory, or the storage medium may each be arranged as a single component, may be configured with multiple components, may be installed in one location, or may be installed in multiple locations and connected via a communication means such as a network, or may be configured to be connected as needed.

[0182] The system may notify the collector of actions to be taken regarding the collection or measurement of bodily fluids, such as the number of the sensor to be used, how to use the sensor, precautions to take during measurement, and how to transmit and store the measurement data after measurement.

[0183] The subject measures the GA value of their body fluid. The system acquires the measurement value from the sensor or GA meter along with the measurement time information. By measuring the GA value one or more times, first data including glycated albumin concentration, albumin concentration (GA-related information), and time-related information can be collected. The first data is stored in memory by the computer system.

[0184] The second data may be provided by the subject, but the second data may also be collected from persons other than the subject (non-subjects). A "non-subject" may be a diabetic patient, a person with pre-diabetes, a person with diabetic complications, a subject, a user, their family or other related person or person with power of attorney, a medical institution, a pharmacy, or other individual, organization, or company. The second data may be collected from a non-subject, from a subject, or from both a subject and a non-subject.

[0185] The system may reference first data and second data. The system may reference second data items related to the first data of interest. In some embodiments, the system may reference the second data to evaluate the first data or the measured GA value to check for misdiagnosis. In some embodiments, the system may reference the second data during AI analysis. During analysis, the system may refer to third data indicating the relationship between the first data and second data, as described below, and other data, in addition to the first and second data. The third data may be stored in memory.

[0186] The system may generate output information based on the first data and the second data. In some embodiments, the system may explore associations or correlations between the first data and the second data. In some embodiments, this association may be stored in memory as third data and provided to a user as output information.

[0187] In some embodiments, a measurement or analyte notification or a request for new secondary data may be made when, or before or after, the output information is provided to the user.

[0188] In some embodiments, the acquisition of the second data and the provision of the output information may occur without necessarily being linked to the acquisition of the first data.

[0189] The second data may be acquired in response to a notification or request from the system, or may be collected voluntarily or automatically from the subject, collector, user, or other individual or organization. In some embodiments, events in the subject's daily life, such as meals or exercise, may be acquired as the second data. In some embodiments, the subject may voluntarily input the second data into the smart device. In some embodiments, when an event occurs when the subject uses the smart device or communicates while using the smart device, the system may respond and provide the subject, collector, or user with information or suggestions that may be appropriate or useful. In some embodiments, the system may acquire the second data by referencing healthcare data stored in or connected to the subject's smart device or the like. In some embodiments, the system may request access to the data or consent from the subject or the like. In some embodiments, the dietary information may be generated by the subject taking photos of meals using the smart device or the like and calculating nutrition, sugar content, calories, etc. from the photos. In some embodiments, the system may acquire the subject's exercise status through the smart device or the like. In some embodiments, the system may acquire exercise-related information at a sports club such as a gym or pool.

[0190] In some embodiments, the system may generate output information such as a five-level health forecast, diabetes risk percentage, cardiovascular event risk percentage, nephropathy risk percentage, neurological disease risk percentage, blindness risk percentage, lifetime loss equivalent, etc., and notify the user.

[0191] The system may also suggest ways to efficiently reduce these risks based on the recipient's individual characteristics, group characteristics, and lifestyle habits. For example, it may suggest exercise related to meals, such as walking slowly 5,000 steps within 30 minutes after a meal. Or, it may suggest a meal plan, such as eating 100g of certain vegetables first, followed by 80g of rice slowly over 10 minutes.

[0192] In some embodiments, if the next GA measurement shows an effect, the displayed risk is improved and health points are awarded. Health points may be calculated in accordance with lifestyle and other factors, taking into account changes in GA values. For example, rather than simply reflecting lifestyle items in health points, it is possible to actually monitor GA values ​​and determine the degree of impact of each lifestyle item in accordance with each individual's constitution and habits.

[0193] In some embodiments, health points, other medical data, or the first data, second data, third data, etc. may be shared, in whole or in part, with entities such as insurance companies, local governments, medical institutions, pharmacies, etc. via a computer system.

[0194] By displaying the results of machine learning analysis to service recipients, various services can be provided that motivate them to prevent or slow the progression of disease.

[0195] A program or computer program according to some embodiments of the present disclosure may cause a computer to execute each step included in the measurement method described herein, or may be read and executed by a computer. A computer-readable storage medium according to some embodiments of the present disclosure may store a program or computer program for causing a computer to execute each step included in the measurement method described herein.

[0196] Some embodiments of the present disclosure are data structures comprising data related to GA values ​​or an array of such data, and the data structure may be provided so that a computer or computer system can reference the first data and the second data to generate output information and provide it to a user.

[0197] In some embodiments, the first data may include glycated albumin and albumin concentrations in a plurality of bodily fluids and the times at which they were measured or the bodily fluids were collected. In some embodiments, the first data may be configured to allow for the addition of measurements for each measurement of glycated albumin or GA value.

[0198] In some embodiments, the data structure is a data structure that a computer or the like can perform by referring to the first data and the second data: GA value, converted HbA1c value, comparison with previous value, average blood glucose value; an estimate of their values ​​during one or more past measurement periods; A forecast of their values ​​for one or more future measurement periods; Past lifestyle trend information; future risk of developing diabetes or its complications or other diseases; lifestyle recommendations; generating output information including at least one of The output information can be provided to a user. It may be configured as follows.

[0199] In some embodiments, the data structure may be configured to allow third data to be added as it is generated, and in some embodiments, the third data may be configured to allow a computer or the like to access and reference the third data and generate output information based thereon and provide it to a user.

[0200] According to some embodiments of the present disclosure, the use of non-invasively obtained GA data potentially reduces the burden on the subject compared to invasive methods, allowing for the acquisition and analysis of larger amounts of data over the medium to long term, and providing useful information to users over a longer period of time rather than just one-off.

[0201] In some embodiments of the present disclosure, data based on GA values ​​is likely to contribute to maintaining and improving the motivation of subjects, etc. to manage their health. It is easy to understand compared to other indices (markers) that indicate the state of control of blood glucose levels.

[0202] For example, hospitals measure glycated hemoglobin (HbA1c) levels in blood samples taken by diabetic patients or as part of blood tests during health checkups. Glycated hemoglobin (HbA1c) levels are an indicator of the degree of glycation of hemoglobin in red blood cells. Diabetic patients typically visit their doctor at intervals of one to three months. Because the half-life of red blood cells is approximately 36 days, this marker is an indicator of blood glucose levels over a one- to two-month period. HbA1c levels are measured every one to three months for diabetic patients at each visit, and even in healthy individuals, they are measured once a year during health checkups and other similar events. Because HbA1c measurement is invasive and requires intervals of several months, it is physiologically and psychologically unsuitable for maintaining the subject's motivation between visits.

[0203] The methods, systems, data structures, etc. disclosed herein are based on GA values ​​obtained by non-invasive measurement, which minimizes the physiological and psychological burden on subjects and makes it easy to continue measurements over the medium to long term. Furthermore, because GA has a half-life of approximately two weeks, it is ideal as a marker for observing or tracking fluctuations over the medium to long term. Therefore, the present disclosure minimizes the physiological and psychological burden on subjects and can maintain and increase their motivation to maintain and improve their health.

[0204] According to some embodiments of the present disclosure, it is possible to provide a service that potentially or exemplarily understands the status of diseases related to the lifestyle habits of individual service recipients, and presents disease risks based on the individual's temporal fluctuation characteristics, characteristics of the country or region, characteristics of groups similar to the individual, the individual's family history, characteristics of groups with a genetic information background, etc., and also presents optimal countermeasures, thereby increasing motivation to improve blood glucose control.

[0205] According to some embodiments of the present disclosure, potentially or exemplarily, it is possible to provide a user with their blood glucose control status based on first data of time series of GA measurements in tears or saliva that can be collected non-invasively, and to integrate the first data with other collectable health-related information and analyze it using AI or the like to provide services including methods for preventing diabetes, methods for improving and progressing diabetes, methods for preventing complications, and useful information and suggestions regarding healthcare.

[0206] The present disclosure also includes the following embodiments. A01a A blood glucose level-related healthcare management method / healthcare information provision method / diabetes risk management method / method for preventing complications in diabetic patients, collecting first data from at least one human or at least one mammal, the first data including albumin concentration and glycated albumin concentration in a body fluid; generating output information related to the collected first data; providing the generated output information to a user; A method for providing A01b A blood glucose level-related healthcare management method / healthcare information provision method / diabetes risk management method / method for preventing complications in diabetic patients, collecting first data including albumin concentration and glycated albumin concentration measured in the subject's body fluid and time information of the measurements, and inputting the first data into a computer system; generating, using the computer system, output information related to a GA value based on the first data; providing the generated output information from the computer system to a user; A method for providing A01c A blood glucose level-related healthcare management method / healthcare information provision method / diabetes risk management method / method for preventing complications in diabetic patients, collecting first data comprising a concentration of glycated albumin in a body fluid of the subject and inputting the first data into a computer system; generating, using the computer system, output information related to a GA value based on the first data; providing the generated output information from the computer system to a user; A method for providing A01 or A01d A blood glucose level-related healthcare management method / healthcare information provision method / diabetes risk management method / method for preventing complications in diabetic patients, acquiring first data including a glycoalbumin concentration and an albumin concentration in a body fluid of the subject; generating output information related to a GA value based on the first data; providing the generated output information to a user; A method for providing A02 obtaining the first data includes measuring a concentration of glycated albumin in a bodily fluid of the subject; The method of claim A01. A03 obtaining the first data includes sampling a bodily fluid of the subject; The method of embodiment A01 or A02. A03b The body fluid includes at least one of blood, tears, and saliva. A method according to any one of claims A01a to A03. A03c Sampling the subject's bodily fluid includes non-invasively collecting tears or saliva. The method of embodiment A03. A04 obtaining the first data includes obtaining a concentration of albumin in a body fluid of the subject; A method according to any one of embodiments A01 to A03c. A05 acquiring the first data includes acquiring time information related to secretion, collection, or measurement of the subject's bodily fluid that is the subject of measurement; A method according to any one of embodiments A01 to A04. A11 obtaining the first data includes measuring the glycated albumin concentration and the albumin concentration in the tears or saliva multiple times; A method according to any one of embodiments A01 to A05. A12 obtaining the first data includes measuring glycated albumin levels in tears or saliva multiple times over a period ranging from two weeks to six months; A method according to any one of embodiments A01 to A11. A13 collecting the first data includes measuring glycated albumin levels in tears or saliva multiple times, with an average interval of 7 to 28 days; A method according to any one of embodiments A01 to A11. A21 obtaining the first data includes measuring the glycated albumin concentration and the albumin concentration in tears or saliva multiple times, with an average interval of 7 to 28 days; generating the output information includes generating information indicating at least one of the GA value, a graph of changes in the GA value, a converted HbA1c value, a comparison with a previous value, an average blood glucose value, a fluctuation in the average blood glucose value, a health forecast, a countermeasure, and health points for some or all of the measurements; A method according to any one of embodiments A01 to A13. A22 The output information is GA value, converted HbA1c value, comparison with previous value, and estimated average blood glucose value for part or all of the period during which the measurement was taken; Predicted values ​​of GA, converted HbA1c, comparison with previous values, and average blood glucose levels for one or more future periods or time points; Past lifestyle trend information; future risk of developing diabetes or its complications or other diseases; lifestyle recommendations; Medication recommendations; Contains at least one of A method according to any one of embodiments A01 to A21. A31 further comprising collecting second data. A method according to any one of embodiments A01 to A22. A39 the second data includes data relating to a non-subject; The method of embodiment A31. A401 generating output information related to the amount of glycated albumin, referencing second data, the second data comprising at least one of personal data, health-related data, lifestyle data, and population data; generating output information using a computer based on the first data and the second data. A method according to any one of embodiments A01 to A31. A402 The second data is Personal ID (My Number), age, gender, marital status, number of children, address, type of residence, car ownership, motorcycle ownership, bicycle ownership, occupation, job type, position, annual income, genome information, religion, health insurance number, fingerprints, voiceprints, facial recognition information, iris recognition information, personal thoughts, emotions, desires, and requests that change over time, input and usage information for smart devices, Race, nationality, region, area, seasonal factors (such as temperature and humidity changes), holiday periods (including New Year's holidays, spring break, summer vacation, etc.), commuting means, religion, Weight, height, body fat, systolic blood pressure, diastolic blood pressure, heart rate, number of steps, activity level, blood oxygen saturation, body temperature, room temperature, solar radiation, UV radiation, average blood glucose level, standard deviation and AGP obtained from continuous and continuous blood glucose monitoring (CGM, FGM) devices, health checkup data (including HbAlc values), medical history, prescription drugs, medication information, data from medical institutions, food and other allergies, medical history and health-related data of family members, Eating habits (including meat-eating preferences, vegetarianism, etc.), favorite foods, meal menu, meal photos, snack photos, exercise habits, wake-up time, bedtime, sleep duration, sleep depth, sleep score, active or passive rest time, eating speed, chewing frequency, speed and characteristics, food allergies, dietary rules related to religious beliefs (including Ramadan, etc.), whether or not you exercise, type of exercise, exercise duration, exercise frequency, calories burned during exercise, hobbies, at least one of A method according to any one of embodiments A01 to A401. A403 generating output information using a computer includes performing machine learning or deep learning on data including the first data and the second data using artificial intelligence; The method of any one of embodiments A401 or A402. A404 the second data includes data about the subject; A method according to any one of embodiments A401 to A403. A405 generating the output information When the second data regarding the subject includes information on cirrhosis, hypothyroidism, or taking a thyroid hormone synthesis inhibitor, generating a caution indicating that there is a possibility of a false high value as output information. The method according to any one of Embodiments A401 to A404. A406 Generating the output information When the second data regarding the subject includes information on nephrotic syndrome, protein-losing enteropathy, hyperthyroidism, or a thyroid hormone therapeutic agent, generating a caution indicating that there is a possibility of a false low value as output information. The method according to any one of Embodiments A401 to A405. A407 Generating the output information In the second data regarding the subject When there is an actually measured HbA1c value at a medical institution or the like, comparing it with the GA-converted HbA1c value of the most recent first data When the actually measured HbA1c value ≥ GA-converted HbA1c value, generating as output information that the average blood glucose level has improved When the actually measured HbA1c value < GA-converted HbA1c value, generating as output information that the average blood glucose level has deteriorated In the second data regarding the subject, when there is information regarding at least one of pregnancy, hemodialysis, anemia, under erythropoietin administration, during anemia treatment, renal dysfunction, high urea value, iron deficiency, vitamin B12 deficiency, folic acid deficiency, hemoglobin abnormality, blood transfusion, metabolic acidosis, and hemolysis of the sample at the time of actually measuring HbA1c, attaching a caution mark to the output information including The method according to any one of Embodiments A401 to A406. A408 The second data includes fluctuation data of glucose values obtained by continuous blood glucose measurement. The method according to any one of Embodiments A31 to A407. A409 The second data includes an AGP obtained by continuous blood glucose measurement. A method according to any one of claims A31 to A407. A410 As the first data, a GA value is acquired substantially continuously multiple times over a certain period of time; As the second data, continuous blood glucose measurement is performed a number of times less than the number of times the GA value is acquired, and glucose value fluctuation data is acquired; Further comprising: The method of any one of embodiments A31 to A407. A411 The method further comprises determining a correlation between an average value of fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value. The method of embodiment A410. A412 determining the correlation includes optimizing the correlation using machine learning or deep learning with reference to all or a portion of the second data regarding the subject; The method of embodiment A411. A413 and estimating, based on the correlation, from the GA value, an average value of the glucose value for a period other than the period during which the continuous blood glucose measurement was actually performed. The method of embodiment A411 or A412. A409b and determining a correlation between the fluctuation data of the glucose value obtained by the continuous blood glucose measurement and the GA value. The method of embodiment A408. A409c The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the 75th percentile value of the glucose value of the continuous blood glucose measurement, the fluctuation data and the GA value. The method of embodiment A409b. A409c The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the 90% tile value of the glucose value of the continuous blood glucose measurement, the fluctuation data and the GA value. The method of embodiment A409b. A409d The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the median and fluctuation data of the glucose values ​​of the continuous blood glucose measurements and the GA value. The method of embodiment A409b. A409e The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the frequency and fluctuation data of postprandial hyperglycemia and the GA value is included. The method of embodiment A409b. A409f The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the area under the curve of the glucose value obtained by the continuous blood glucose measurement and the GA value. The method of embodiment A409b. A409g The correlation between the fluctuation data of glucose values ​​obtained by the continuous blood glucose measurement and the GA value is as follows: The correlation between the area under the curve of the glucose value obtained by the continuous blood glucose measurement and the GA value is included. The method of embodiment A409b. A409h The area under the curve of the glucose values ​​obtained by the continuous blood glucose measurement is the area between the 25th and 75th percentile curves, The area between the 10th percentile curve and the 75th percentile curve, or Area between the 2% and 75% tile curves That is, The method of embodiment A409g. A409i Obtaining a correlation between the fluctuation data of the glucose value obtained by the continuous blood glucose measurement and the GA value correcting the glucose value obtained by the continuous blood glucose measurement with a function of the uric acid level; Equipped with The method of any one of embodiments A409b to A409g. A409j Obtaining a correlation between the fluctuation data of the glucose value obtained by the continuous blood glucose measurement and the GA value determining a correlation between the glucose correction value based on the uric acid value and the GA value; Further comprising: The method of embodiment A409i. A409k The correlation is obtained as second data. The method of any one of embodiments A409b to A409i. A409A Further comprising determining a correlation between body weight and GA value. The method of embodiment A402 A409B Estimating changes in body weight from GA values ​​or changes in GA values, Method A414 according to embodiment A402 or A409B further comprising storing the correlation as third data. The method of any one of embodiments A411 to A413. A415 The correlation includes at least one of the mean value and the fluctuation range of the glucose value. The method of any one of embodiments A411 to A414. A416 determining whether or not the next continuous blood glucose measurement should be performed based on the change in the GA value obtained after the continuous blood glucose measurement, and generating the output information including the determination; The method of any one of embodiments A411 to A415. A417 determining whether a next continuous blood glucose measurement should be performed in consideration of the GA value obtained substantially simultaneously with the continuous blood glucose measurement; The method of embodiment A416. A418 When the rate of change in the GA value obtained after the continuous blood glucose measurement is greater than a predetermined value, output information is generated to indicate that the next continuous blood glucose measurement should be performed. The method of embodiment A417. A419 generating a prescription for a drug or a suggestion for administration as output information in response to the AGP after the continuous blood glucose measurement; The method of any one of embodiments A416 to A418. A419b and generating, as output information, a suggestion regarding the next prescribed drug and / or medication, in accordance with the GA value or a trend of the GA value, a value related thereto, or a combination thereof, by referring to the prescription drug and / or medication information in the second data. The method of any one of embodiments A01a to A419. A420 generating, as output information, suggestions regarding lifestyle habits or treatment, by referring to the first data and the average value and fluctuation range of the glucose values ​​of the continuous blood glucose measurements; The method of embodiment A409 or A10. A421 and providing output information including the lifestyle or treatment suggestions to a diabetic patient, a person at risk of diabetes, or a healthy person. The method of embodiment A420. A422 generating output information indicating a diabetes risk percentage or an estimated period until onset of diabetes by referring to the first data and the average glucose value and fluctuation range of the continuous blood glucose measurements; The method of embodiment A409 or A10. A423 and providing output information including the diabetes risk percentage or the estimated time until developing diabetes to a person at risk of diabetes or a healthy person. The method of embodiment A422. A424 generating output information indicating a risk percentage of diabetes complications or an estimated period until a complication occurs, by referring to the first data and the average value and fluctuation range of the glucose values ​​of the continuous blood glucose measurements; The method of embodiment A409 or A10. A425 and providing the diabetic patient with output information including the diabetic complication risk percentage or the estimated time until the diabetic complication occurs. The method of embodiment A424. A51 The method further comprises notifying a person collecting the bodily fluid that collection should be performed. The method of any one of embodiments A31 to A419. A52 The informing includes informing the subject that the body fluid should be collected substantially once every 17 days or once every two weeks. The method of embodiment A51. A53 The notifying includes notifying that the time to collect the body fluid is approaching, that the time has come, or that the time has passed. The method of embodiment A51 or A52. A54 The notifying includes notifying an action to be taken regarding collection or measurement of bodily fluid. The method of any one of embodiments A51 to A53. A61 Further comprising: searching for a correlation between the first data and the second data; and generating third data having the correlation as an attribute. The method of any one of embodiments A31 to A54. A62 the output information includes the third data. The method of embodiment A61. A71 Medication information in secondary data B01 Software for performing the method according to embodiments A01 to A62. C01 A storage medium having software recorded thereon for performing the methods described in embodiments A01 to A62. D01 configured to be connected to a sensor for measuring GA values ​​in body fluids; a memory configured to store first data including glycated albumin concentrations and albumin concentrations in a plurality of body fluids measured by the sensor and the time of measurement thereof, and second data including at least one of personal data, health-related data, lifestyle data, and population data; an analysis center configured to analyze the first data and the second data; a computer configured to provide useful healthcare-related information to a user based on the analysis results by the analysis center; A healthcare management system comprising: E01 a first data array configured to allow addition of first data including the glycoalbumin concentration and albumin concentration in the body fluid measured by the glycoalbumin sensor and the measurement time thereof for each measurement; a second data array including second data including at least one of personal data, health-related data, lifestyle data, and population data; Equipped with By computer, With reference to the first data and the second data, GA value, converted HbA1c value, comparison with previous value, average blood glucose value; an estimate of their values ​​during one or more past measurement periods; A forecast of their values ​​for one or more future measurement periods; Past lifestyle trend information; future risk of developing diabetes or its complications or other diseases; lifestyle recommendations; generating output information including at least one of The output information can be provided to a user. A data structure for healthcare management, configured as follows: E02 a third data array including third data including information on the association between the first data and the second data; 10. The data structure for healthcare management according to claim 1, wherein:

[0207] Several embodiments and examples of the present disclosure have been described above, but these embodiments and examples exemplify the present disclosure. For example, the above embodiments have been described in detail to clearly explain the present disclosure, and additional changes in dimensions, configurations, materials, and circuits may be made as necessary. Note that embodiments that combine any one or more features of the present disclosure described above are also within the scope of the present disclosure. The claims encompass numerous modifications to the embodiments without departing from the technical spirit of the present disclosure. Therefore, the embodiments and examples disclosed herein are provided for illustrative purposes and should not be considered to limit the scope of the present disclosure. [Explanation of symbols]

[0208] 100 Medical information or healthcare management systems 110 Communication Network 121,122 subjects 131,132 GA measuring instrument 141,142 smart devices 171,172 Non-subject users 150 memory 160 AI Analysis Center 230 GA measuring instrument 231 GA measuring instrument body 232 Processor / Calculation Circuit Block 233 memory 234 Power supply 235 Display section 236 Communications Department 237 Sensors 300 First Data Structure 310 Subject identification information 311 Subject name 312 Subject ID 320 Measurement-related information 321 Measurement Number 322 Sensor number (sensor management information) 323 Measurement time (measurement time information) 324 Measurement valid / invalid information 325 Glycated Albumin Concentration 326 Albumin Concentration 327 Other related information

Claims

1. 1. A method of managing health care, comprising: Obtaining data on the fluctuation of the subject's glucose levels obtained by continuous blood glucose measurements over several days; obtaining a GA value, which is the ratio of the glycated albumin concentration to the albumin concentration in the body fluid, which is the blood, tears, or saliva, of the subject, for the several days during which the fluctuation data was obtained and for a period other than the several days; determining a correlation between the fluctuation data of the glucose value obtained by the continuous blood glucose measurement over the several days and the GA value; estimating an average value of the glucose value for a period other than the several days from the GA value for a period other than the several days during which the continuous blood glucose measurement was actually performed; Detecting a risk using any one of the GA value, the average value of the glucose value for a period other than the several days estimated from the GA value, and an AGP (Ambulatory Glucose Profile) obtained based on the fluctuation data of the glucose value of the continuous blood glucose measurement for the several days; and If the risk is detected, automatically output information indicating the risk to notify the user; A method for providing the above.

2. The AGP of the fluctuation data includes a fluctuation range of the fluctuation data. The method of claim 1.

3. optimizing the correlation using machine learning or deep learning with reference to all or part of the fluctuation data of the glucose values ​​of the continuous blood glucose measurements for the subject; The method of claim 1.

4. Obtaining a correlation between the fluctuation data of the glucose value obtained by the continuous blood glucose measurement and the GA value includes: correcting the glucose value obtained by the continuous blood glucose measurement with a function of the uric acid level; determining a correlation between the glucose corrected value based on the uric acid value and the GA value; The method of claim 1.

5. and determining whether or not a next continuous blood glucose measurement should be performed based on a change in GA value obtained after the continuous blood glucose measurement, and outputting the determination. The method of claim 1.

6. determining whether to perform a next successive blood glucose measurement in consideration of a GA value obtained substantially simultaneously with the successive blood glucose measurements; The method of claim 5.

7. and outputting a message indicating that the next continuous blood glucose measurement should be performed when the rate of change in the GA value obtained after the continuous blood glucose measurement is greater than a predetermined value. The method of claim 5.

8. generating and outputting a drug prescription or administration suggestion in response to the AGP after the continuous blood glucose measurements. The method of claim 1.

9. and generating a lifestyle or treatment suggestion by referring to the GA value and the average value and fluctuation range of the glucose values ​​of the continuous blood glucose measurements. The method of claim 8.

10. providing the lifestyle or treatment suggestions to the user.

10. The method of claim 9.

11. The subject is a diabetic, a prediabetic, or a healthy individual, The user is the subject, a doctor, a medical institution, a pharmacist, a pharmacy, a local government, a government, an insurance company, a family member, an employer, or a service provider. The method of claim 10.

12. The risk includes diabetes risk. The method of claim 1.

13. providing the user with the estimated diabetes risk or estimated time to developing diabetes. The method of claim 12.

14. The risk includes a risk of diabetes complications. The method of claim 1.

15. The method further comprises providing the user with the risk of a diabetes complication or an estimated time until the user develops a complication.

15. The method of claim 14.

16. The GA values ​​of the subject's body fluids include GA values ​​of tears or saliva measured multiple times over a period ranging from 2 weeks to 6 months. The method of claim 1.

17. The GA values ​​of the body fluids of the subject include GA values ​​of tears or saliva measured multiple times at an average interval of 7 to 28 days. The method of claim 1.

18. generating information indicating at least one of the following for some or all of the measurements among the plurality of measurements: GA value, a graph of changes in GA value, a converted HbA1c value, a comparison with a previous value, an average blood glucose value, a fluctuation in the average blood glucose value, a health forecast, a countermeasure, and health points; and notifying the user of the information; Further comprising:

18. The method of claim 17.

19. GA value, converted HbA1c value, comparison with previous value, and estimated average blood glucose value for part or all of the period during which the measurements were taken; Predicted values ​​of GA value, converted HbA1c value, comparison with previous value, and average blood glucose value for one or more future periods or time points; Past lifestyle trend information; future risk of developing diabetes or its complications or other diseases; lifestyle recommendations; and Medication recommendations; and providing at least one of the following to the user. The method of claim 1.

20. The method further comprises notifying the user of a warning that the GA value may be a falsely high value when the subject has liver cirrhosis, hypothyroidism, or is taking a thyroid hormone synthesis inhibitor. The method of claim 1.

21. and if the subject has nephrotic syndrome, protein-losing gastroenteropathy, hyperthyroidism, or is taking a thyroid hormone therapeutic drug, notifying the user of a warning that the GA value may be a falsely low value. The method of claim 1.

22. If the subject has an actual HbA1c value measured at a medical institution, compare it with the most recent GA-converted HbA1c value; When the actual measured HbA1c value is greater than or equal to the GA-converted HbA1c value, the average blood glucose level has improved. When the actual HbA1c value is less than the GA-converted HbA1c value, the average blood glucose level is worsening. Or, If the subject falls under at least one of the following conditions: pregnancy, dialysis, anemia, administration of erythropoietin, treatment for anemia, renal dysfunction, high urea level, iron deficiency, vitamin B12 deficiency, folic acid deficiency, hemoglobin abnormality, blood transfusion, metabolic acidosis, and hemolysis of the sample during actual HbA1c measurement, the user is notified of a warning regarding the corresponding item. Further comprising: The method of claim 1.

23. Further comprising determining a correlation between body weight and GA value. The method of claim 1.

24. Further comprising estimating a change in body weight from the GA value or a change in the GA value.

24. The method of claim 23.

25. and outputting to the user a suggestion regarding the next prescribed drug and / or medication, based on the GA value or a trend of the GA value, a value related thereto, or a combination thereof, by referring to prescription drug and / or medication information of the subject.

25. The method according to any one of claims 1 to 24.

26. further comprising referencing the subject's medical history and medication information.

26. The method of claim 25.

27. The proposal is: In the case where the subject is a type 2 diabetes patient and the medication information includes a history of administration of a sulfonylurea drug, which is a type of oral hypoglycemic drug, when the GA value of the patient exceeds the limit value and the postprandial blood glucose level is determined to be high, the device proposes candidates in the order of α-GI, DPP-4 inhibitor, and SGLT2 inhibitor; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a sulfonylurea drug, which is a type of oral hypoglycemic drug, when the patient's GA value exceeds the limit value and the fasting blood glucose level is high, the method proposes additional prescription drugs that are safe and inexpensive to add in the order of biguanite drugs and DPP-4 inhibitors; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a biguanite drug, when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high, additional medications are suggested in the order of α-GI, glinide drug, DPP-4 inhibitor, and SGLT2 inhibitor, and when the fasting blood glucose level is high, additional medications are suggested in the order of thiazoline drug, DPP-4 inhibitor, and SGLT2 inhibitor; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a thiazoline drug, when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high, suggesting the administration of an α-GI drug and a glinide drug in that order; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a thiazoline drug, suggesting administration of a biguanide drug as an additional drug when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a DPP-4 inhibitor, when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high, suggesting that an α-GI drug and a glinide drug be administered in this order; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a DPP-4 inhibitor, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, suggest administering additional drugs in the order of a biguanide drug, a thiazoline drug, and an SGLT2 inhibitor; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of an SGLT2 inhibitor, suggesting administration of α-GI when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of an SGLT2 inhibitor, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, suggesting that additional medications be administered in the order of a biguanide drug and a DPP-4 inhibitor; If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a GLP-1 receptor agonist, suggesting administration of α-GI when the GA limit determined with reference to the second data is exceeded and the postprandial blood glucose level is high; and If the subject is a type 2 diabetes patient and the medication information includes a history of administration of a GLP-1 receptor agonist, when the GA limit determined with reference to the second data is exceeded and the fasting blood glucose level is high, suggesting that additional medications be administered in the order of a biguanide drug and an SGLT2 inhibitor; At least one of 27. The method of claim 26.

28. The proposal is: If the subject is a type 1 or type 2 diabetic, in addition to an oral hypoglycemic agent, To propose to administer an α-GI or glinide drug to a patient who is receiving a once-daily injection of long-acting insulin at dinner or before bedtime when the GA value exceeds the GA limit determined by referring to the second data and the postprandial blood glucose level is high; For patients who, in addition to oral hypoglycemic agents, are receiving a once-daily injection of long-dissolving insulin at dinner or before bedtime, when the GA value exceeds the GA limit determined by referring to the second data and fasting blood glucose levels are high, we propose administering additional medications in the order of a biguanide and a DPP-4 inhibitor; To propose administration of α-GI and glinide drugs to patients who, in addition to oral hypoglycemic agents, are receiving a once-daily injection of long-dissolving insulin in the morning when the GA value exceeds the GA limit determined by referring to the second data and the postprandial blood glucose level is high; For patients who, in addition to oral hypoglycemic agents, receive a once-daily injection of long-dissolving insulin in the morning, when the GA value exceeds the GA limit determined with reference to the second data and the fasting blood glucose level is high, suggest administering a biguanide drug and then a DPP-4 inhibitor; and To propose administering a DPP-4 inhibitor as an additional medication to patients who are receiving a once-daily morning injection of long-dissolving insulin in addition to oral hypoglycemic agents when their GA value exceeds the GA limit determined by reference to secondary data and their blood glucose levels are high both in the fasting and after meals; At least one of 28. The method of claim 26 or 27.

29. further comprising informing the subject that the body fluid should be collected. The method of claim 1.

30. Notifying the user that the bodily fluid should be collected includes notifying the user that the bodily fluid should be collected substantially once every 17 days or once every two weeks.

30. The method of claim 29.

31. Notifying that the body fluid should be collected includes notifying that the time to collect the body fluid is approaching, that the time has come, or that the time has passed.

30. The method of claim 29.

32. The user is the subject. The method of claim 1.

33. 1. A healthcare management system, comprising: a computer that acquires a GA value, which is the ratio of the glycated albumin concentration to the albumin concentration in the body fluid of the subject, such as blood, tears, or saliva, and fluctuation data of the glucose level of the subject obtained by continuous blood glucose measurement over several days; a memory for storing the fluctuation data of the acquired GA value and the glucose value; determining a GA value of the body fluid of the subject for the several days during which the fluctuation data was acquired and for a period other than the several days; determining a correlation between the fluctuation data of the glucose level obtained by the continuous blood glucose measurement for several days and the GA value; estimating an average glucose value for a period other than the several days from the GA value for the period other than the several days; detecting a risk using any one of the GA value, the average value of the glucose value for a period other than the several days, and an AGP (Ambulatory Glucose Profile) obtained based on the fluctuation data of the glucose value in continuous blood glucose measurements for the several days; an analysis center that, if a risk is detected, generates output information indicating that the risk exists; The computer is configured to automatically output the output information and notify a user. A system comprising:

34. configured to be connected to a sensor that measures the GA value of the body fluid of the subject; Acquire the GA value measured by the sensor and store it in the memory; The measurement time of the GA value is further acquired and stored in the memory. It was configured as follows:

34. The healthcare management system of claim 33.

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