Methods, devices and systems for adjusting laboratory HbA1c levels

Abstract

1. A method for providing personalized therapy for a diabetic patient, comprising: a remote device configured to receive first data indicative of a subject's analyte level during a first time period, retrieve a first glycated hemoglobin level for the subject associated with the first time period, calculate a first individual apparent glycation ratio over the first time period using the received first data and the retrieved first glycated hemoglobin level, compare the calculated first individual apparent glycation ratio to a typical apparent glycation ratio, generate a recommendation based on the comparison, and display a graphical interface including the comparison between the calculated first individual apparent glycation ratio and the typical apparent glycation ratio.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 63 / 276,266, filed November 5, 2021, U.S. Provisional Patent Application No. 63 / 292,915, filed December 22, 2021, and U.S. Provisional Patent Application No. 63 / 326,231, filed March 31, 2022, which are incorporated by reference in their entireties for all purposes.

[0002] The subject matter described herein relates generally to improved methods, devices, and systems for regulating laboratory HbA1c levels. [Background technology]

[0003] Measurement of various analytes in an individual can sometimes be crucial to monitoring their health. During normal circulation of red blood cells in a mammal, such as the human body, glucose molecules are attached to hemoglobin, which is called glycosylated hemoglobin (also called glycated hemoglobin). The higher the amount of glucose in the blood, the higher the percentage of circulating hemoglobin molecules that have glucose molecules attached. Because glucose molecules remain attached to hemoglobin for the life of a red blood cell (usually about 120 days), the level of glycosylated hemoglobin reflects the average blood glucose level over that period.

[0004] Most hemoglobin is of a type called HbA. When a glucose molecule is attached to an HbA molecule, glycosylated HbA is formed, which is called HbA1. HbA1 has three components: HbA1a, HbA1b, and HbA1c. Because glucose binds more strongly and to a greater extent to HbA1c than to HbA1a and HbA1b, the level of HbA1c in the blood (HbA1c test) is frequently used as an indicator of a subject's average blood glucose level over a 120-day period (the average life span of a red blood cell). The HbA1c test is performed in the doctor's office by drawing a blood sample from the subject, which is then analyzed in a laboratory. The HbA1c test may be used as a screening and diagnostic test for prediabetes and diabetes. A subject's glucose exposure, as determined by the HbA1c level, is one of the main factors used in making diagnostic and / or treatment decisions. That is, normal or healthy glucose exposure is correlated to an HbA1c level or range assuming a 120 day red blood cell life span. A subject's laboratory HbA1c level (also referred to in the art as measured HbA1c) is compared to this normal or healthy range when diagnosing and / or treating the subject. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2018 / 0235524 [Patent Document 2] U.S. Provisional Patent Application No. 62 / 750,957 [Patent Document 3] U.S. Provisional Patent Application No. 62 / 939,956 [Patent Document 4] U.S. Patent No. 6,175,752 [Patent Document 5] US Patent Application Publication No. 2011 / 0213225 [Patent Document 6] US Patent Application Publication No. 2014 / 0188400 [Patent Document 7] US Patent Application Publication No. 2014 / 0350369 [Patent Document 8] US Patent Application Publication No. 2008 / 0009692 [Patent Document 9] US Patent Application Publication No. 2011 / 0319729 [Patent Document 10] US Patent Application Publication No. 2015 / 0018639 [Patent Document 11] US Patent Application Publication No. 2015 / 0025345 [Patent Document 12] US Patent Application Publication No. 2015 / 0173661 [Patent Document 13] US Patent Application Publication No. 2011 / 0193704 [Patent Document 14] U.S. Provisional Patent Application No. 63 / 196,677 [Patent Document 15] U.S. Provisional Patent Application No. 63 / 279,509 [Non-patent literature]

[0006] [Non-Patent Document 1] "Glucose management indicator (GMI): A new term for estimating A1C from continuous glucose monitoring", Diabetes 41(11), pp. 2275-2280, November 2018. [Non-Patent Document 2] "Translating the A1C assay into estimated average glucose values", Diabetes Care 31(8), pp. 1473-1478, August 2008, PMID:18540046 [Non-Patent Document 3] Xu Y, Dunn TC, Ajjan RA. "A kinetic model for glucose levels and hemoglobin A1C provides a novel tool for individualized diabetes management." J Diab Sci Tech. 2020, DOI:10.1177 / 1932296819897613 [Non-Patent Document 4] https: / / t1dexchange.org / pages / resources / clinic-network / studies / Summary of the Invention [Problem to be solved by the invention]

[0007] However, while HbA1c remains the reference biomarker for glycemic control, HbA1c levels can vary based on factors other than glycemia, including conditions that affect red blood cell RBC life span. For example, some of these factors can include race, age, sex, and pregnancy. Thus, diagnosis and glycemic control treatment may sometimes be based on inaccurate glucose exposure. [Means for solving the problem]

[0008] The objectives and advantages of the disclosed subject matter will be set forth in and will be obvious from the description which follows, as well as will be learned by practice of the disclosed subject matter. Additional advantages of the disclosed subject matter will be realized and attained by the methods and systems particularly pointed out in this document and claims hereof, as well as from the appended drawings.

[0009] To achieve these and other advantages and in accordance with the objectives of the disclosed subject matter, as embodied and broadly described, the disclosed subject matter relates to methods for managing and treating diabetes based on non-glycemic factors that exhibit wide inter-individual variability and are influenced by race, age, sex, and pregnancy.

[0010] As embodied herein, the disclosed subject matter relates to a method of providing personalized therapy. As embodied herein, the method can include receiving, by a remote device, first data indicative of an analyte level of a subject during a first time period. The method can also include retrieving, by the remote device, a first glycated hemoglobin level for the subject associated with the first time period. The remote device can also calculate a first individual apparent glycation ratio over the first time period using the received first data and the retrieved first glycated hemoglobin level. The remote device can then compare the calculated first individual apparent glycation ratio to a typical apparent glycation ratio. After comparing the apparent glycation ratios, the remote device can generate a recommendation based on the comparison. The remote device can then display a graphical interface including the calculated first individual apparent glycation ratio, the typical apparent glycation ratio, and the comparison.

[0011] As embodied herein, the method can also include generating an alert to prompt the subject to obtain a second glycated hemoglobin level associated with a second time period that is a predetermined time period after the first time period. In some embodiments, the predetermined time period can be 3 months, 6 months, 9 months, or 12 months.

[0012] As embodied herein, the method can further include receiving, by the remote device, second data indicative of the subject's analyte level during a second time period. The remote device can then derive a second glycated hemoglobin level. The remote device can also calculate a second individual apparent glycation ratio over the second time period using the received second data and the derived second glycated hemoglobin level.

[0013] As embodied herein, the exemplary apparent glycation ratio can be the apparent glycation ratio of a plurality of subjects having at least one demographic metric in common with the subject. As embodied herein, the at least one demographic metric can include age. As embodied herein, the at least one demographic metric can include gender. As embodied herein, the recommendation can include generating a personalized HbA1c target. As embodied herein, the recommendation can include generating a personalized HbA1c range.

[0014] As embodied herein, the first data can include data generated by an analyte sensor having an in vivo portion in contact with a bodily fluid of the subject. As embodied herein, the first data can include fasting plasma glucose.

[0015] As embodied herein, the first glycated hemoglobin level can be retrieved from at least one of an electronic medical record system, a cloud-based database, and a QR code. As embodied herein, the remote device can include at least one of a smartphone, a personal computer, and an electronic medical record system.

[0016] As embodied herein, the method may further include generating a notification when the calculated first individual apparent glycation ratio varies from the typical apparent glycation ratio by a predetermined amount. The notification may include at least one of a visual notification, an audio notification, an alarm, and a prompt. As embodied herein, the predetermined amount may be 20%.

[0017] The following figures are included to illustrate certain aspects of the present disclosure and should not be construed as limiting embodiments. The disclosed subject matter is capable of considerable modification, permutation, combination, and equivalents in form and function without departing from the scope of the present disclosure. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 illustrates that personal RBC life span can impact HbA1c and diabetes treatment, with 31% of laboratory HbA1c in this study potentially leading to misdiagnosis, resulting in under- or overtreatment. [Diagram 2] FIG. 1 shows an exemplary time series illustrating a collection of at least one HbA1c value and multiple glucose levels over a period of time. [Diagram 3] FIG. 1 illustrates an example of a physiological parameter analysis system for providing physiological parameter analysis in accordance with some of the disclosed embodiments of the present invention. [Figure 4] FIG. 1 illustrates an example of a physiological parameter analysis system for providing physiological parameter analysis in accordance with some of the disclosed embodiments of the present invention. [Diagram 5] FIG. 2 illustrates an example of a cHbA1c report that may be generated as an output by a physiological parameter analysis system according to some of the disclosed embodiments of the present invention. [Figure 6A] FIG. 1 illustrates an example method for determining individual-target glucose ranges according to some of the embodiments of the present disclosure. [Figure 6B]FIG. 2 illustrates an example of an individual-target glucose range report that may be generated as an output by a physiological parameter analysis system according to some of the disclosed embodiments of the present invention. [Figure 7] FIG. 2 illustrates an example of an individual-target average glucose report that may be generated as an output by a physiological parameter analysis system according to some of the disclosed embodiments of the present invention. [Figure 8] FIG. 2 illustrates an example of a glucose pattern insight report that may be generated as an output by a physiological parameter analysis system according to some of the disclosed embodiments of the present invention. [Figure 9] FIG. 1 illustrates an example of an in-vivo analyte monitor system according to some of the disclosed embodiments of the present invention. [Figure 10] FIG. 1 is a plot of glucose monitor data over 200 days (right y-axis), three HbA1c values ​​(left y-axis), and estimated HbA1c values ​​based on the 14-day eHbA1c model (left y-axis). [Figure 11] FIG. 11 is a plot of FIG. 10 with cHbA1c (left y-axis) over the first 100 days determined using kgly and kage by the methods described herein. [Figure 12] FIG. 12 is a plot of FIG. 11 with cHbA1c over the subsequent 100 days (extension from day 100 to day 200, left y-axis) using kgly and kage determined in relation to FIG. 10 by the method described herein. [Figure 13A] FIG. 11 is a cross-plot comparison of estimated HbA1c levels (by 14-day glucose model) compared with laboratory HbA1c levels. [Figure 13B] FIG. 1 shows a cross-plot comparison of cHbA1c levels (by the methods described herein) compared with laboratory HbA1c levels. [Figure 14] FIG. 1 is a plot of laboratory HbA1c compared to aHbA1c ("aA1C") by RBC lifetime. [Figure 15]FIG. 1 is a plot illustrating the distribution of RBC lifespan and adjustment of laboratory HbA1c by RBC lifespan for type 1 (n=51) and type 2 (N=80) diabetes, with the majority of subjects (69%) in this study falling into the mean RBC lifespan bin. [Figure 16A] FIG. 11 is a cross plot and correlation of mean 14-day intracellular glucose (I)G values ​​primed with aHbA1c. [Figure 16B] FIG. 1 is a cross plot of originally collected data for 14-day mean plasma glucose (PG) and laboratory HbA1c. [Figure 17A] FIG. 13 shows an example of a glucose pattern insight report for the same subject using measurement PG. [Figure 17B] FIG. 13 shows an example of a glucose pattern insight report for the same subject using PGeff. [Figure 18A] FIG. 1 shows an exemplary comparison of HbA1c-glucose relationships by race. [Figure 18B] FIG. 1 shows an exemplary comparison of HbA1c-glucose relationships by age. [Figure 19] 1 is a table summarizing key characteristics of exemplary study individuals. [Figure 20] FIG. 1 illustrates an exemplary relationship between HbA1c and mean glucose. [Figure 21] FIG. 1 shows an exemplary comparison of AGR and RDW between race, age, and gender groups. [Figure 22] FIG. 1 illustrates HbA1c targets adjusted by AGR for equalized mean glucose. [Figure 23] FIG. 1 illustrates the non-linear relationship between mean glucose and HbA1c. [Figure 24] FIG. 1 shows an exemplary relationship between steady state glucose and HbA1c change for a 10 unit AGR variation. [Diagram 25] 1 is a table summarizing baseline HbA1c and AGR characteristics for subjects. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] The present disclosure generally describes methods, devices, and systems for determining physiological parameters related to the dynamics of red blood cell glycation, clearance, and development in a subject's body. Such physiological parameters can be used, for example, to calculate, inter alia, calculated HbA1c (cHbA1c), adjusted HbA1c (aHbA1c), and / or individualized target glucose ranges for subject-specific diagnostic, therapeutic, and / or monitoring protocols with greater reliability.

[0020] As used herein, the terms "HbA1c level," "HbA1c value," and "HbA1c" are used interchangeably. As used herein, the terms "aHbA1c level," "aHbA1c value," and "aHbA1c" are used interchangeably. As used herein, the terms "cHbA1c level," "cHbA1c value," and "cHbA1c" are used interchangeably.

[0021] Dynamic Model High glucose exposure in certain organs (especially the eye, kidney, and nerves) is a risk factor for the development of diabetic complications. Although laboratory HbA1c (also referred to in the art as measured HbA1c) is routinely used to assess glycemic control, several studies have reported discrepancies between this glycemic marker and diabetic complications in some individuals. The exact mechanism by which laboratory HbA1c fails to predict diabetic complications is often unclear, but is likely related to inaccurate estimation of intracellular glucose exposure in affected organs in some cases.

[0022] Equation 1 illustrates the kinetics of red blood cell hemoglobin glycation (or simply referred to herein as red blood cell glycation), red blood cell clearance, and red blood cell development, where "G" is free glucose, "R" is non-glycated red blood cells, and "GR" is glycated red blood cell hemoglobin. The rate at which glycated red blood cell hemoglobin (GR) is formed is referred to herein as the red blood cell hemoglobin glycation rate constant (generally dL * mg -1* day -1 It has the unit kgly ) is called TIFF2024543824000002.tif47121 Chemical formula 1

[0023] Over time, red blood cell hemoglobin, including glycated red blood cell hemoglobin, is continually cleared from the subject's circulatory system, and new red blood cells containing hemoglobin are generated, typically at a rate of about 2 million per second. The rates associated with clearance and generation are each referred to herein as the red blood cell clearance constant (typically the daily -1 It has the unit k age ) and the erythrocyte development rate constant (generally M 2 k with unit / day gen ) The amount of red blood cells in the body is maintained at a stable level most of the time, so k age and k gen The ratio of should be an individual constant that is the square of the red blood cell concentration.

[0024] Regarding glycation, Equation 2 illustrates the mechanism in more detail, where glucose transporter 1 (GLUT1) facilitates the transport of glucose (G) into red blood cells. Intracellular glucose (GI) then interacts with hemoglobin (Hb) to produce glycated hemoglobin (HbG), where the hemoglobin glycation reaction rate constant is k g (Generally, dL * mg -1* day -1 A typical experimental measurement is k g The value is 1.2x10 -3 dL / mg / day. Hemoglobin glycation is a multi-step nonenzymatic chemical reaction, therefore k g should be a universal constant. The rate constant for transporting glucose into red blood cells and glycating Hb to HbG is k gly Furthermore, k age represents red blood cell clearance (with hemoglobin), also described herein as red blood cell turnover rate. TIFF2024543824000003.tif6394 Chemical formula 2

[0025] High intracellular glucose concentrations are the cause of diabetic complications, whereas extracellular hyperglycemia selectively damages cells with limited capacity to effectively regulate transmembrane glucose transport. HbA1c has been used as a biomarker for diabetes-associated intracellular hyperglycemia for two main reasons. First, glycation occurs within red blood cells (RBCs), and thus HbA1c is regulated by intracellular glucose levels. Second, RBCs do not have the ability to regulate glucose transporter GLUT1 levels and therefore cannot moderate their transmembrane glucose uptake rates, behaving similarly to cells selectively damaged by extracellular hyperglycemia. Thus, under conditions of fixed RBC life span and transmembrane glucose uptake rates, HbA1c faithfully reflects intracellular glucose exposure in organs affected by diabetic complications. However, considering the interindividual variability in both transmembrane glucose uptake rates and RBC life span, laboratory HbA1c may not always reflect intracellular glucose exposure. Variations in RBC transmembrane glucose uptake rates are likely to be relevant in estimating risk of diabetic complications in susceptible organs, whereas red blood cell life span is specific to RBCs and therefore unrelated to complication risk in other tissues. This explains the inability to clinically rely on laboratory HbA1c in individuals with hematological disorders characterized by abnormal RBC turnover, and provides a possible explanation for the apparent "discrepancy" between laboratory HbA1c and the development of complications in some diabetic individuals (Figure 1).

[0026] To overcome the limitations of laboratory HbA1c, we developed a measure of individual HbA1c that takes into account the individual variability of both RBC turnover and cellular glucose uptake rate. Current efforts aim to extend this model by adjusting for up to 100 days of standard RBC lifespan (equivalent to 1% RBC turnover rate per day or average RBC age for 50 days) to establish a new clinical marker, which we call adjusted HbA1c (aHbA1c). We propose that aHbA1c is the most appropriate glycemic marker for estimating the exposure of an organ to hyperglycemia and the subsequent risk of diabetes-related complications. As explained above, HbA1c is a commonly used analyte that indicates the fraction of glycated hemoglobin found in red blood cells. Thus, for example, a kinetic model can be used to derive a calculated HbA1c based on at least the glucose levels measured for the subject. However, this kinetic model can also be applied to HbA1. For simplicity, HbA1c is used throughout this specification, but it is believed that HbA1 can be substituted, except in cases where a specific HbA1c value is used, in which case it is believed that a similar equation can be derived using a specific HbA1 value.

[0027] Typically, when modeling the dynamics of a physiological process, assumptions are made to simplify some of the numerical calculations by focusing on the factors that most affect the physiological process.

[0028] The present disclosure uses only the following set of assumptions in kinetic modeling of the physiological processes exemplified in Equations 1 and 2. The following set of assumptions was made during model construction: 1. Absence of any abnormal red blood cells that may affect HbA1c measurement. 2. The widely accepted assumption is that the glycation process has a first-order dependence on the concentrations of both hemoglobin and intracellular glucose in red blood cells. 3. Newly produced red blood cells contain only small amounts of glycated hemoglobin. 4. Red blood cells are cleared from the circulation when they reach a subject-specific age. The individual's red blood cell clearance rate is approximated using a constant. Thus, the rate of glycated hemoglobin clearance is proportional to the product of the total red blood cell clearance rate and the HbA1c at that time.

[0029] Using these, the rates of change of glycated and non-glycated hemoglobin in red blood cells can be modeled by differential equations (1) and (2). TIFF2024543824000004.tif6150 equation (1) TIFF2024543824000005.tif6150 equation (2)

[0030] [HbG] and [Hb] are the concentrations of glycated and non-glycated hemoglobin, respectively, and [GI] is the intracellular glucose concentration. g is (concentration*time) -1 is the rate constant for the hemoglobin glycation reaction in units of Hb. C is the total hemoglobin concentration, where C=[Hb]+[HbG]. HbA1c is the fraction of glycated hemoglobin molecules. r is the rate of red blood cell removal in units of concentration / time. α has no unit of measurement and is a coefficient used to scale HbA1c for the fraction of glycated hemoglobin to be removed. All concentrations can have units such as mmol / l or mg / dL. Time units are hours or days.

[0031] The glucose transporter (GLUT1) on the red blood cell membrane follows Michaelis-Menten kinetics. M is the Michaelis constant, which relates to the affinity of an enzyme (e.g., GLUT1) for a substrate (e.g., glucose). K M is determined experimentally. K for GLUT1-glucose interaction M A variety of values ​​for K have been reported in the literature ranging from about 100 mg / dL to about 700 mg / L. Two specific exemplary values ​​are 306 mg / dL (17 mM) and 472 mg / dL (26.2 mM). MHowever, embodiments of the present disclosure may be modified to accommodate this particular K M Therefore, intracellular glucose is not limited to k c is the glucose consumption rate in red blood cells, d[GI] / dt=V max * [G] / (K M +[G])-k c * It can be modeled by [GI]. Maximum speed V max should be proportional to the GLUT1 level on the membrane. c and V max Both and can vary independently. Under equilibrium, equation (3) is derived. TIFF2024543824000006.tif10150 equation (3) Here, g=(K M * [G]) / (K M +[G]), k c is the rate constant for glucose consumption in red blood cells (generally 100 μg / day) -1 ), V max is the maximum glucose transport rate (generally mg * dL -1* day -1 has units of K and should be proportional to the GLUT1 level on the membrane. M is the Michaelis-Menten kinetic rate constant (typically having units of mM or mg / dL) for GLUT1, which transports glucose across the red blood cell membrane.

[0032] By definition, HbA1c is the fraction of glycated hemoglobin found in red blood cells, i.e., HbA1c = [HbG] / C = (C-[Hb]) / C. At steady state, d[Hb] / dt = d[HbG] / dt = 0, and equation (1) states that C * k g / (α * r) = [HbG] / ([GI][Hb]). Combining this with equation (3) gives equation (4). TIFF2024543824000007.tif10150 equation (4)

[0033] By combining all the parameters associated with transmembrane glucose transport with the glycation from the right hand side of equation (4), the composite glycation rate constant is k gly =k g * V max / (k c * K M ), where k g and K M are the universal constants for nonenzymatic hemoglobin glycation and the affinity of glucose to GLUT1, respectively. Thus, k gly is k c and V max The remaining parameters for red blood cell turnover are k age = α * Reducing r / C, this equation leads to the definition of the apparent glycation parameter K according to equation (5). TIFF2024543824000008.tif6150 equation (5)

[0034] Under the assumed steady state of constant glucose levels, HbA1c should reach an equilibrium level, which we call "equilibrium HbA1c" (EA). Since C = [HbG] + [Hb], equation (5) becomes K = (C - [Hb]) / (g * Applying the definition HbA1c = (C - [Hb]) / C, we arrive at equation (6). TIFF2024543824000009.tif6150 equation (6)

[0035] The above relationships approximate the average glucose and HbA1c for an individual with a stable daily glucose profile. Equation (1) can be rearranged into Equation (7): TIFF2024543824000010.tif6150 equation (7)

[0036] Given the starting HbA1c (HbA1c0) and assuming a constant glucose level during a time interval t, the HbA1c value at the end of this time interval can be calculated by solving the differential equation in equation (7) and integrating from time 0 to t: t Equation (8) is derived for TIFF2024543824000011.tif6150 equation (8)

[0037] To address the time-varying glucose levels, the glucose history of each subject is approximated as a series of time intervals t with corresponding glucose levels [GI]. By recursively applying equation (8), the HbA1c value HbA1c z (At the end of the time interval t z ) can be expressed as equation (9) for numerical calculation. TIFF2024543824000012.tif11150 equation (9) Where: TIFF2024543824000013.tif6150. At the end of the time interval t z In the above, the value HbA1c is equal to the calculated HbA1c (cHbA1c), which is the preferred terminology introduced in the applicants' work. i and D i Both are k gly , k age Note that the D i is the time interval t i Depends on the length of

[0038] Equation (8) and equation (9) show that the change in HbA1c can be estimated using glucose levels and an individual kinetic constant k gly and k ageThe data section includes two HbA1c measurements, one at the beginning and one at the end of the period, with frequent glucose levels between these measurements. Also, cHbA1c is dependent on k gly and k age can be calculated at any time point assuming that HbA1c and frequent glucose measurements are available together. The purpose of frequent glucose measurements is to measure HbA1c at a given time interval (t i ) and the average glucose ([G i In this study, frequent glucose levels were measured at 15-minute intervals and the time interval between tests (t i ) were 3 hours, 6 hours, 12 hours, 24 hours, and 36 hours.

[0039] Calculation of physiological parameters from kinetic models Embodiments of the present disclosure provide for kinetic modeling of red blood cell glycation, clearance, and development within a subject's body.

[0040] Physiological parameter k gly , k age and / or K can be derived from equations described herein given at least one laboratory HbA1c value (also referred to as a measured HbA1c level) and a number of glucose levels (also referred to as measured glucose levels) over a period of time immediately preceding the HbA1c measurement.

[0041] FIG. 2 illustrates an example time series 200 showing a collection of at least one laboratory HbA1c value 202 a , 202 b , 202 c and a number of glucose levels 204 a over a period of time 206 .

[0042] k gly , k age The number of laboratory HbA1c values ​​202a, 202b, 202c needed to calculate K and / or K depends on the frequency and duration of multiple glucose levels and the dynamics of HbA1c values ​​and glucose levels over time.

[0043] In a first embodiment, a single laboratory HbA1c 202b is used in conjunction with multiple glucose measurements over a period of time 206 to determine k gly , k age , and / or K. Such an embodiment may be applicable to subjects who make regular daily glucose measurements over an extended period of time 206.

[0044] k gly and k age teeth, Since TIFF2024543824000014.tif10150 approaches zero over time, it can be calculated using equation (9) when glucose levels are measured over a sufficient amount of time (e.g., over about 200 days). Thus, an initial HbA1c level measurement is not necessarily required.

[0045] Since the initial HbA1c value is not measured, a longer period 206 of initial glucose level measurements with frequent measurements may be necessary to obtain an accurate representation of average glucose and reduce error. In this manner, steady glucose patterns over 100 days can be used to reduce error. Further lengths, such as 200 days or more or 300 days or more, further reduce error.

[0046] An embodiment in which one laboratory HbA1c value 202b can be used includes a period 206 of about 100 to 300 days (or longer) in which glucose levels are measured at least about 72 times per day (e.g., about every 20 minutes) to about 96 times per day (e.g., about every 15 minutes) or more frequently. Furthermore, in such an embodiment, the time between glucose level measurements can have some consistency, in which case the interval between two glucose level measurements should not exceed about 1 hour. When only one laboratory HbA1c value is used, some glucose measurement missing data is within the acceptable range. More missing data may lead to more errors.

[0047] Alternatively, in some embodiments where a single laboratory HbA1c value 202b is used, the time period 206 can be shortened if the subject has an existing glucose level monitoring history with a stable and consistent glucose profile. For example, in a subject who has been tested for a long time (e.g., six months or more), but perhaps not at a very frequent or tightly controlled frequency, the existing glucose level measurements can be used to determine and analyze a glucose profile. Then, if more frequent and tightly controlled glucose monitoring is performed over the time period 206 (e.g., from about 72 to about 96 or more times per day for about 14 days or more), followed by a measurement of HbA1c 202b, the three can be used in combination to determine one or more physiological parameters (k gly , k age , and / or K) can be calculated.

[0048] Alternatively, in some embodiments, two laboratory HbA1c values ​​can be used, a first laboratory HbA1c value 202a at the beginning of the time period 206 and a second laboratory HbA1c value 202b at the end of the time period 206, and multiple glucose levels 204a are measured during the time period 206. In these embodiments, equation (9) is used to calculate one or more physiological parameters (k gly , k age , and / or K) can be calculated. In such embodiments, multiple glucose levels 204a can be measured over a period of about 10 to about 30 days or more, with these measurements occurring on average from about 4 times each day (e.g., about every 6 hours) to about 24 times each day (e.g., about every hour) or more frequently.

[0049] The foregoing embodiments are not limited to the exemplary glucose level measurement duration and frequency ranges presented. Glucose levels can be measured over a period of time ranging from as little as a few days to about 300 days or more (e.g., about 1 week or more, about 10 days or more, about 14 days or more, about 30 days or more, about 60 days or more, about 90 days or more, about 120 days or more, etc.). The frequency of such glucose levels can be, on average, from about 14,400 times per day (e.g., about every 10 seconds) (or more frequently) to about 3 times per day (e.g., about every 8 hours) (e.g., 1,440 times per day (e.g., about every 1 minute), about 288 times per day (e.g., about every 5 minutes), about 144 times per day (e.g., about every 10 minutes), about 96 times per day (e.g., about every 15 minutes), about 72 times per day (e.g., about every 20 minutes), about 48 times per day (e.g., about every 30 minutes), about 24 times per day (e.g., about every hour), about 12 times per day (e.g., about every 2 hours), about 8 times per day (e.g., about every 3 hours), about 6 times per day (e.g., about every 4 hours), about 4 times per day (e.g., about every 6 hours), etc.). In some cases, less frequent monitors (such as once or twice daily) can be used in which glucose measurements are taken at approximately the same time each day (within about 30 minutes) to allow a more direct comparison of day-to-day glucose levels and reduce error in subsequent analyses.

[0050] The above embodiments may further include calculating an error or uncertainty associated with one or more physiological parameters. In some embodiments, the error may be used to determine whether or not another HbA1c value (not illustrated) should be measured near t1, whether or not one or more glucose levels 204b should be measured (e.g., near t1), whether or not the monitoring and analysis should be extended (e.g., to extend through a period 208 from t1 to t2, which includes measuring a glucose level 204b at time t2 and a measured HbA1c value 202c), and / or whether or not the frequency of glucose level measurements 204b within the extended period 208 should be increased relative to the frequency of glucose level measurements 204a during the period 206. In some embodiments, the error may be used to determine whether or not another HbA1c value (not illustrated) should be measured near t1, whether or not one or more glucose levels 204b should be measured near t1, whether or not the monitoring and analysis should be extended (e.g., to extend through a period 208 from t1 to t2, which includes measuring a glucose level 204b at time t2 and a measured HbA1c value 202c), and / or whether or not the frequency of glucose level measurements 204b within the extended period 208 should be increased relative to the frequency of glucose level measurements 204a during the period 206. gly , k ageand / or one or more of the above actions may be taken when the error associated with K (e.g., error from an HbA1c assay) is about 15% or greater, preferably about 10% or greater, preferably about 7% or greater, preferably about 5% or greater. Lower errors may be preferred when a subject has an underlying disease condition (e.g., cardiovascular disease) to have more rigorous monitoring and less error in the analyses described herein.

[0051] In some embodiments, instead or when the malfunction is within the acceptable range, one or more physiological parameters (k gly , k age , and / or K) may be used to determine one or more parameters or characteristics related to the subject's personalized diabetes management (e.g., cHbA1c at the end of time period 208, personalized-target glucose range, and / or treatment or treatment changes for the subject in the near future), each of which is further described in more detail herein. Optionally, the HbA1c value may be measured at time t2 and one or more physiological parameters may be recalculated and applied to a future time period (not illustrated).

[0052] One or more physiological parameters and / or one or more parameters or characteristics related to the subject's personalized diabetes management can be measured and / or calculated for two or more times (e.g., t1 and t2) and compared. For example, k at t1 gly and k at t2 gly In another example, the cHbA1c at t2 can be compared to the cHbA1c at a subsequent time. Some embodiments described herein further use such comparisons to (1) monitor the progress and / or effectiveness of the subject's personalized diabetes management and optionally modify the subject's personalized diabetes management, (2) identify abnormal or pathological physiological conditions, and / or (3) identify that the subject is taking nutritional supplements and / or medications that alter red blood cell production and / or metabolism.

[0053] One or more physiological parameters (k gly , k age In each of the exemplary methods, devices, and systems that utilize the individualized glucose target range, individualized glucose target average, cHbA1c, aHbA1c, and the like), one or more physiological parameters (k gly , k age , and K) and related analytical results can be updated periodically (e.g., about every three months to one year). The frequency of updates can depend, among other things, on the subject's glucose levels and diabetes history (e.g., how well the subject stays within defined thresholds), other disease conditions, and the like.

[0054] Regulatory HbA1c In the art of diabetes and red blood cell glycation, the generally accepted average RBC life span may vary. ref age Preferably, k reflects a baseline average RBC lifespan of 85 to 135 days, 85 to 110 days, 90 to 110 days, 95 to 125 days, or 110 to 135 days, although the baseline RBC lifespan may be outside these ranges. ref age reflects a baseline RBC life span of 85 to 110 days, or 90 to 110 days, or 100 days. ref age is 0.01 days -1 However, embodiments of the present disclosure may be modified to accommodate this particular k ref age Not limited to.

[0055] The aHbA1c of a subject is determined by the HbA1c level and k age And, k ref age and can be calculated using equation (10). TIFF2024543824000015.tif13150 equation (10) Here, HbA1c can be cHbA1c or laboratory HbA1c as described herein.

[0056] Usually K=k gly / k age requires only one section of data to make a decision with high confidence. Larger K values ​​are usually better than smaller k age Since it correlates with the value of k age K can be used to generate an approximate aHbA1c early on, when A is not yet available (Equation (11)). A typical K ref The value is, for example, 5.2 x 10 -4 However, embodiments of the present disclosure may be modified to accommodate this particular K ref Not limited to. TIFF2024543824000016.tif13150 equation (11) Here, HbA1c can be cHbA1c or laboratory HbA1c as described herein.

[0057] The subject's aHbA1c (based at least in part on the laboratory HbA1c and / or calculated HbA1c) can then be used for diagnostic, treatment, and / or monitoring protocols for the subject. For example, based at least in part on the aHbA1c described herein, the subject can be diagnosed as having diabetes, pre-diabetes, or another abnormal or pathological physiological condition. In another embodiment, the subject can be monitored and / or treated by self-monitoring and / or self-injection of insulin, continuous insulin monitoring and / or injection, and the like, based at least in part on the aHbA1c described herein. In yet another example, aHbA1c as described herein can be used to determine and / or administer personalized therapy for subject triage, to determine and / or administer personalized therapy for diabetic drug titration, to determine and / or administer personalized closed loop or hybrid closed loop control systems, to determine and / or administer personalized therapy using glycation drugs, to determine physiological age, to identify whether and / or which nutritional supplements and / or medications are present during testing, and for similar purposes, and any combination thereof.

[0058] By eliminating the interference from variations in RBC turnover rate, aHbA1c is a better personal biomarker than HbA1c for risk of complications in people with diabetes. aHbA1c can be higher or lower than laboratory HbA1c, making a significant difference in diabetic diagnosis and management. In individuals with faster than normal RBC turnover rates, HbA1c, which is typically observed in patients with kidney disease or after heart valve surgery, is artificially low, giving people the illusion of good glycemic control. Conversely, slower than normal RBC turnover can lead to artificially high HbA1c and overtreatment, potentially causing dangerous hypoglycemia.

[0059] In one example, 0.0125 days -1 k age(or 80 day RBC life span) and a laboratory HbA1c of 7% would lead to an aHbA1c of 8.6%. A laboratory HbA1c of 7% without adjustment for RBC turnover rate indicates good glycemic control. However, this HbA1c value is an underestimate, and in this case a more accurate value of 8.6% (aHbA1c) adjusted for RBC turnover rate would indicate a higher complication risk for that subject.

[0060] In another example, 0.0077 days -1 k age (or 130 day RBC life span) and a seemingly high laboratory HbA1c of 9% would lead to an aHbA1c of 7.1%. A seemingly high laboratory HbA1c of 9% would indicate poor glycemic control and a significant complication risk. However, with an aHbA1c of 7.1%, this individual has a low complication risk. Proceeding from the 9% laboratory HbA1c value, with an aHbA1c of 7.1%, the subject would be more likely to receive treatment that may put him or her at risk for hypoglycemia.

[0061] When only K is available, equation (11) can be used to estimate aHbA1c. For example, if laboratory HbA1c is 8% and 6 × 10 -4 day -1 When a high K value of 7% is determined, the aHbA1c estimate is 7%. This adjustment is usually conservative, so k age is not yet available. In this example, unnecessary and potentially harmful treatment may be provided based on the laboratory HbA1c value when treatment should not be provided based on the aHbA1c value.

[0062] In another example, if the laboratory HbA1c was 7% and the -4 day -1 When a low K value of 8.9% is determined, the estimated aHbA1c is 8.9%. In this case, treatment should be offered due to the high aHbA1c, whereas treatment may not be given when relying solely on the laboratory HbA1c value.

[0063] In this specification, k ref age is a predetermined value used as a reference average RBC turnover rate representing the RBC lifespan. The RBC turnover rate is k age divided by RBC life span to give a unit of 1% per day * 100 (or k age =(1 / RBC life) * 100). ref age is calculated in the same manner using the desired baseline mean RBC lifetime.

[0064] Subject's k age can be determined by a variety of methods, including but not limited to those described in U.S. Patent Application Publication No. 2018 / 0235524, U.S. Provisional Patent Application No. 62 / 750,957, and U.S. Provisional Patent Application No. 62 / 939,956, the entire contents of each of which are incorporated herein by reference for all purposes.

[0065] HbA1c can be measured in a laboratory and / or calculated based at least in part on glucose monitor data (e.g., as described herein as cHbA1c). Preferably, the glucose monitor data is continuous with nearly no missing readings to provide greater accuracy in the calculated HbA1c level. Although HbA1c is described herein as calculated HbA1c, the art may refer to HbA1c levels as calculated HbA1c levels or estimated HbA1c levels.When calculating (or estimating) HbA1c levels, the eAG / A1C Conversion Calculator provided by the American Diabetes Association, the glucose management indicator (GMI) method (e.g., the method described in “Glucose management indicator (GMI): A new term for estimating A1C from continuous glucose monitoring”, Diabetes 41(11), pp. 2275-2280, November 2018), “Translating the A1C assay into estimated average glucose values”, Diabetes Care 31(8), pp. 1473-1478, August 2008, PMID: 18540046, “Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes Some methods can be used, including but not limited to those described in U.S. Patent Application Publication No. 2018 / 0235524, U.S. Provisional Patent Application No. 62 / 750,957, and U.S. Provisional Patent Application No. 62 / 939,956, and the like, as well as any hybrids thereof. The entire contents of each of the above patent applications are incorporated herein by reference for all purposes.

[0066] The disclosed methods include determining a subject's HbA1c level (e.g., measured and / or calculated based on a glucose monitor) and determining the subject's RBC elimination rate constant (RBC turnover rate and k ageAlso called the Japanese -1 determining the HbA1c level and k age and a given criterion k age (k ref age and calculating an adjusted HbA1c value (aHbA1c) for the subject based on the aHbA1c. The subject can then be diagnosed, treated, and / or monitored based on the aHbA1c.

[0067] A non-limiting exemplary method of the present disclosure includes providing (or obtaining) a plurality of blood glucose measurements for a subject; calculating HbA1c for the subject based at least in part on the plurality of blood glucose measurements; and age and determining the HbA1c level and k age and k ref age and calculating the aHbA1c for the subject based on the aHbA1c. The subject can then be diagnosed, treated, and / or monitored based on the aHbA1c.

[0068] Another non-limiting exemplary method of the present disclosure includes providing (or measuring) HbA1c of a subject; age and determining the HbA1c level and k age and k ref age and calculating the aHbA1c for the subject based on the aHbA1c. The subject can then be diagnosed, treated, and / or monitored based on the aHbA1c.

[0069] Other factors One or more physiological parameters (k gly , k ageIn some of the embodiments described herein that apply K, , and / or K), one or more other subject-specific parameters can be used in addition to these one or more physiological parameters. Examples of subject-specific parameters can include, but are not limited to, underlying condition (e.g., cardiovascular disease, heart valve replacement, cancer, and systemic disorders such as autoimmune diseases, hormonal disorders, and blood cell disorders), family history of a condition, current treatment, age, race, sex, geographic location (e.g., where the subject grew up or where the subject currently lives), diabetes type, time since diabetes diagnosis, and the like, and any combination thereof.

[0070] system In some embodiments, one or more physiological parameters (k gly , k age The step of determining (i) and / or (ii) may be performed using a physiological parameter analysis system.

[0071] 3 illustrates an example physiological parameter analysis system 310 for providing physiological parameter analysis in accordance with some of the disclosed embodiments of the present invention. The physiological parameter analysis system 310 includes one or more processors 312 and one or more machine-readable storage media 314. The one or more machine-readable storage media 314 include a set of instructions for performing physiological parameter analysis routines that are executed by the one or more processors 312.

[0072] In some embodiments, these instructions are based on input 316 (e.g., one or more previously determined glucose levels, one or more HbA1c levels, one or more physiological parameters (k gly , k age , and / or K), one or more other subject-specific parameters, and / or one or more times associated with any of these), and an output 318 (e.g., one or more physiological parameters (k gly , k age, and / or K), an error associated with one or more physiological parameters, one or more parameters or characteristics related to the subject's personalized diabetes management (e.g., cHbA1c, aHbA1c, personalized-target glucose range, average target glucose level, dosage of nutritional supplement or drug, among other parameters or characteristics), and communicating an output 318. In some embodiments, the communication of the input 316 can be, for example, through a user interface (which can be part of the display), a data network, a server / cloud, another device, a computer, or any combination thereof. In some embodiments, the communication of the output 318 can be, for example, to a display (which can be part of the user interface), a data network, a server / cloud, another device, a computer, or any combination thereof.

[0073] The term "machine-readable medium" as used herein includes any mechanism that can store information in a form accessible by a machine (which may be, for example, a computer, a network device, a cellular telephone, a personal digital assistant (PDA), a manufacturing tool, any device having one or more processors, and the like). For example, machine-accessible media includes recordable / non-recordable media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and the like).

[0074] In some cases, the one or more processors 312 and the one or more machine-readable storage media 314 may reside within a single device (e.g., a computer, a network device, a cellular telephone, a PDA, an analyte monitor, and the like).

[0075] In some embodiments, the physiological parameter analysis system may include other components. Figure 4 illustrates another example of a physiological parameter analysis system 410 for providing physiological parameter analysis according to some of the embodiments of the present disclosure.

[0076] The physiological parameter analysis system 410 includes a health monitor device 420 having a subject interface 420A and an analysis module 420B, which is or can be operatively coupled to a data network 422. Also provided within the physiological parameter analysis system 410 are a glucose monitor 424 (e.g., an in vivo and / or ex vivo (external) device or system) and a data processing terminal / personal computer (PC) 426, each operatively coupled to the health monitor device 420 and / or the data network 422. Also shown in FIG. 4 is a server / cloud 428 operatively coupled to the data network 422 for bidirectional data communication with one or more of the health monitor device 420, the data processing terminal / PC 426, and the glucose monitor 424. A physiological parameter analysis system 410 within the scope of the present disclosure can exclude one or more of the server / cloud 428, the data processing terminal / PC 426, and / or the data network 422.

[0077] In certain embodiments, the analysis module 420B is programmed or configured to perform physiological parameter analysis and optionally other (e.g., cHbA1c, aHbA1c, individualized-target glucose range, and others, as described herein) analyses. As illustrated, the analysis module 420B is part of (e.g., executed by a processor residing therein) the health monitor device 420. However, instead, the analysis module 420B may be associated with one or more of the server / cloud 428, the glucose monitor 424, and / or the data processing terminal / PC 426. For example, one or more of the server / cloud 428, the glucose monitor 424, and / or the data processing terminal / PC 426 may include one or more machine-readable storage media having a set of instructions that cause one or more processors to execute a set of instructions corresponding to the analysis module 420B.

[0078] Although the health monitor device 420, the data processing terminal / PC 426, and the glucose monitor 424 are illustrated as each being operatively coupled to a data network 422 for communication to / from a server / cloud 428, one or more of the health monitor device 420, the data processing terminal / PC 426, and the glucose monitor 424 may be programmed or configured to bypass the data network 422 and communicate directly with the server / cloud 428. Modes of communication between the health monitor device 420, the data processing terminal / PC 426, and the glucose monitor 424 and the data network 422 may include one or more of wireless communication, wired communication, RF communication, BLUETOOTH® communication, WiFi data communication, radio frequency identification (RFID) enabled communication, ZIGBEE® communication, or any other suitable data communication protocol, optionally supporting data encryption / decryption, data compression, data decompression, and the like.

[0079] As described in further detail below, the physiological parameter analysis may be performed by one or more of the health monitor device 420, the data processing terminal / PC 426, the glucose monitor 424, and the server / cloud 428, and the resulting analysis output is shared within the physiological parameter analysis system 410.

[0080] In addition, although the glucose monitor 424, health monitor device 420, and data processing terminal / PC 426 are illustrated as each operatively coupled to one another through a communication link, they may be modules within a single integrated device (e.g., a sensor having a processor and a communication interface for transmitting / receiving and processing data).

[0081] Measurement of glucose and HbA1c levels The measurements of multiple glucose levels over different time periods described herein can be performed using in vivo and / or ex vivo (outside the body) methods, devices, or systems for measuring at least one analyte, such as glucose, in a bodily fluid, such as blood, interstitial fluid (ISF), subcutaneous fluid, dermal fluid, sweat, tears, saliva, or other bodily fluid. In some cases, a combination of in vivo and ex vivo methods, devices, or systems can be used.

[0082] An example of an in vivo method, device, or system is one in which at least a portion of the sensor and / or sensor control device is or can be positioned within the subject's body (e.g., beneath the subject's skin surface) to measure glucose levels and optionally other analytes in blood or ISF. Example devices include, but are not limited to, continuous analyte monitor devices and intermittent analyte monitor devices. Particular devices or systems are further described herein and can be found in U.S. Pat. No. 6,175,752 and U.S. Patent Application Publication No. 2011 / 0213225, the entire disclosures of each of which are incorporated herein by reference for all purposes.

[0083] Ex vivo methods, devices, or systems (including those that are completely non-invasive) include sensors that contact bodily fluids ex vivo to measure glucose levels. For example, an ex vivo system may use a metering device having a port for receiving an analyte test strip that can hold a subject's bodily fluid and be analyzed to determine the glucose level therein. Further devices and systems are described below.

[0084] As explained above, the frequency and duration over which glucose levels are measured can vary from an average of about 3 times per day (e.g., about every 8 hours) to about 14,400 times per day (e.g., about every 10 seconds) (or more frequently) and from a few days to more than about 300 days, respectively.

[0085] Once the glucose level is measured, it can be used to measure one or more physiological parameters (k gly , k age , and / or K) and optionally other analytical results (e.g., cHbA1c, aHbA1c, individualized-target glucose ranges, and others as described herein). In some cases, such analyses can be performed using a physiological parameter analysis system. For example, referring back to FIG. 4, in some embodiments, the glucose monitor 424 can include a glucose sensor coupled to electronics for (1) processing a signal from the glucose sensor and (2) communicating the processed glucose signal to one or more of the health monitor device 420, the server / cloud 428, and the data processing terminal / PC 426.

[0086] The measurement of one or more HbA1c levels at various times as described herein can be according to any suitable method. Typically, HbA1c levels are measured in a laboratory using a blood sample from a subject. Examples of laboratory tests include, but are not limited to, chromatography-based assays, antibody-based immunoassays, and enzyme-based immunoassays. HbA1c levels can also be measured using an electrochemical biosensor.

[0087] The frequency of HbA1c level measurements can vary from monthly to yearly on average (or less frequently if the subject's average glucose level remains stable).

[0088] Once the glucose level is measured, it can be used to determine one or more physiological parameters and, optionally, other analytical results as described herein. In some cases, such analyses can be performed using a physiological parameter analysis system. For example, referring back to FIG. 4, in some embodiments, the HbA1c level can be measured by a laboratory test, where the results are input from a testing entity, a medical professional, a subject, or other user to the server / cloud 428, the subject interface 420A, and / or the display. The HbA1c level can then be received by the health monitor device 420, the server / cloud 428, and the data processing terminal / PC 426 for analysis by one or more methods as described herein.

[0089] Calculation HbA1c (cHbA1c) One or more physiological parameters (k gly , k age After calculating the HbA1c level (k, k, k+1, and / or K), multiple glucose measurements can be taken over a subsequent period of time and used to calculate HbA1c during and / or at the end of the period. For example, referring back to FIG. 2, at time t1, one or more physiological parameters (k, k, k+1, and / or K) can be calculated based on multiple glucose level 204a measurements over a period of time 206, a laboratory HbA1c level 202b at the end of the period 206, and optionally a laboratory HbA1c level 202a at the beginning of the period 206. gly , k age , and / or K) can then be calculated. Then, multiple glucose levels 204b can be measured over a subsequent period of time 208. Additionally, during and / or at the end of period 204b, a cHbA1c value (HbA1c in Equation (9)) can be calculated using Equation (9). z), where HbA1c0 is the laboratory HbA1c level 202b at the end of time period 206 (which is the beginning of time period 208), and [G i ] is calculated based on the time t i or its average, and the physiological parameter (k gly , k age , and / or K) are used.

[0090] A subject's cHbA1c over several consecutive time periods is a function of one or more physiological parameters (kA1c) determined using the most recent laboratory HbA1c level and multiple glucose level measurements during the time period. gly , k age , and / or K). HbA1c can be measured periodically (e.g., every 6 months to every year) to recalculate one or more physiological parameters. The time between assessments of laboratory HbA1c may depend on (1) the consistency of glucose level measurements, (2) the frequency of glucose level measurements, (3) the subject's and corresponding family's diabetes history, (4) the length of time since the subject was diagnosed with diabetes, (5) changes to the subject's individualized diabetes management (e.g., medication / dosage changes, dietary changes, exercise changes, and the like), and combinations thereof. For example, a subject who exhibits consistent glucose level measurements (e.g., [G] with less than 5% variation) and who performs frequent glucose level measurements (e.g., continuous glucose monitor) may not need to measure HbA1c levels as frequently as a subject who exhibits consistent glucose level measurements and performs frequent glucose level measurements but who recently (e.g., within the last 6 months) changed the dosage of a glycosylation drug.

[0091] FIG. 5 shows an example of a cHbA1c report that can be generated as output 318 by the physiological parameter analysis system 310 of the present disclosure with reference to FIG. 3. The exemplary report shown includes a plot of average glucose levels over time. The report also includes the most recent laboratory HbA1c level (open circle) and the cHbA1c level calculated by the physiological parameter analysis system 310 (asterisk). Although two cHbA1c levels are illustrated, one or more cHbA1c levels can be displayed on the report, including a line that traces the cHbA1c continuously. Alternatively, the output 318 of the physiological parameter analysis system 310 can include a single numerical value for the current or most recently calculated cHbA1c, aHbA1c, a table corresponding to the data in FIG. 5, or any other report that presents at least one cHbA1c level to a subject, health care provider, or the like.

[0092] In some cases, cHbA1c can be compared to a previous cHbA1c level and / or a previous laboratory HbA1c level to monitor the effectiveness of the subject's personalized diabetes management. For example, if a diet and / or exercise plan is being implemented as part of the subject's personalized diabetes management, all other factors (e.g., medications and other diseases) being equal, a change in cHbA1c compared to a previous cHbA1c level and / or a previous laboratory HbA1c level can indicate whether the diet and / or exercise plan is effective, ineffective, or a transition therebetween.

[0093] In some cases, the cHbA1c can be compared to a previous cHbA1c level and / or a previous laboratory HbA1c level to determine if another HbA1c measurement should be obtained. For example, another laboratory HbA1c level can be tested if there has been a change of 0.5 percentage units or more compared to the previous cHbA1c level and / or previous laboratory HbA1c level in the absence of a significant glucose profile change (e.g., a change from 7.0% to 6.5% or a change from 7.5% to 6.8%).

[0094] In some cases, a comparison of cHbA1c to a previous cHbA1c level and / or a previous laboratory HbA1c level can indicate whether an abnormal or pathological physiological condition exists. For example, if a subject has maintained a cHbA1c level and / or a laboratory HbA1c level for an extended period of time and then a change in cHbA1c is identified without any other apparent cause, the subject may have an abnormal or pathological physiological condition. An indication of what a new abnormal or pathological physiological condition is may be provided by a change in one or more physiological parameters (k gly , k age , and / or K). Details of abnormal or pathological physiological conditions relating to one or more physiological parameters are discussed later in this specification.

[0095] Personalized-Target Glucose Ranges and Personalized Glucose Levels Typically, glucose levels in diabetic subjects are preferably maintained between 70 mg / dL and 180 mg / dL. However, the kinetic model described herein assumes that intracellular glucose levels are increased by k gly Furthermore, intracellular glucose levels are associated with hypoglycemic and hyperglycemic damage to organs, tissues, and cells. Thus, measured glucose levels may not actually correspond to the actual physiological state associated with diabetes management in a subject. For example, a higher than normal k gly A subject with a lower than normal k gly A subject with glaucoma does not get as much glucose into their cells, so at a glucose level of 70 mg / dL, their intracellular glucose levels are quite low, making them feel weak and potentially rendering them hypoglycemic in the long term.

[0096] As used herein, subject-specific k for glucose readings and / or corresponding individualized glucose ranges. gly Three methods for considering the upper and lower limits of acceptable normal glucose are given: gly (b) adjusting the subject's measured glucose level to an effective plasma glucose level that correlates with an acceptable upper and lower normal glucose limit; and (c) adjusting the subject's measured glucose level to an intracellular glucose level that correlates with an acceptable lower normal intracellular glucose limit (LIGL) and an acceptable upper normal intracellular glucose limit (UIGL).

[0097] In the first method, the lower acceptable normal glucose limit (LGL) and the upper acceptable normal glucose limit (AU) can be used to derive equations for the individualized lower glucose limit (GL) (Equation (12) and Equation (13)) and the individualized upper glucose limit (GU) (Equation (14) and Equation (15)). Equations (13) and (15) are rewrites of equations (12) and (14) for the case where both laboratory HbA1c and aHbA1c are available. TIFF2024543824000017.tif18150 equation (12) Where: TIFF2024543824000018.tif7150TIFF2024543824000019.tif6170 are the subject's k gly It is. TIFF2024543824000020.tif10150 equation (13) TIFF2024543824000021.tif18150 equation (14) TIFF2024543824000022.tif10150 equation (15)

[0098] Since the upper and lower glucose ranges are based on equivalent intracellular glucose levels, equations (12) and (14) can be expressed as mk gly It is based on.

[0099] The current acceptable limits for the above are LGL = 70 mg / dL, TIFF2024543824000023.tif7150=6.2 * 10 -6 dL * mg -1* day -1 , and AU=180 mg / dL.

[0100] 6A illustrates an example of a method for determining an individualized target glucose range 630. A desired glucose range 632 (e.g., a currently acceptable glucose range) having a lower limit 634 and an upper limit 636 is determined based on a physiological parameter k gly 638, can be personalized using equations (12) and (14), respectively, resulting in a personalized lower glucose limit (GL) 640 (equation (12) ±7%) and a personalized upper glucose limit (GU) 642 (equation (14) ±7%) that define the personalized-target glucose range 630. Alternatively or in addition to the above, the desired glucose range 632 (e.g., current acceptable glucose range) having lower and upper limits 634 and 636 can be personalized using the laboratory HbA1c and calculated aHbA1c 683, using equations (13) and (15), respectively. Thus, in general the method comprises: gly and / or (b) measuring HbA1c and determining an individualized-target glucose range after calculating aHbA1c, in which the lower glucose limit can be modified according to equation (12) (and / or equation (13)) ±7% and the upper glucose limit can be modified according to equation (14) (and / or equation (15)) ±7%. For example, 5.5 * 10 -6 dL * mg -1* day -1A subject with idiopathic pulmonary hypertension may have an individualized target glucose range of about 81±7 mg / dL to about 219±27 mg / dL, and therefore may have a different acceptable glucose level range than the currently implemented glucose range.

[0101] 6B shows an example of a personalized target glucose range report that can be generated as an output 318 by the physiological parameter analysis system 310 of the present disclosure with reference to FIG. 3. The exemplary report shown includes a day-long plot of glucose levels relative to the personalized target glucose ranges (shaded areas) described above. Alternatively, other reports can include, but are not limited to, an ambulatory glucose profile (AGP) plot, a numerical display of the personalized target glucose ranges with the most recent glucose level measurements, and the like, and any combination thereof.

[0102] In another example, 6.5 * 10 -6 dL * mg -1* day -1 k gly A subject with CKD may have an individualized target glucose range of about 66±5.5 mg / dL to about 167±18 mg / dL. With a significantly lower upper glucose level limit, this subject's individualized diabetes management may include more frequent glucose level measurements and / or dosing to remain substantially within this individualized target glucose range.

[0103] In yet another example, 5.0 * 10 -6 dL * mg -1* day -1 k glyA subject with CKD may have an individualized target glucose range of about 92±8 mg / dL to about 259±34 mg / dL. This subject may be sensitive to lower glucose levels and may feel weak, hungry, dizzy, etc. more frequently with currently implemented glucose ranges (70 mg / dL and 180 mg / dL).

[0104] All of the above examples include an individualized lower glucose limit and an individualized upper glucose limit, but alternatively, the individualized target glucose range may include only an individualized lower glucose limit or an individualized upper glucose limit, and the currently implemented lower glucose limit or upper glucose limit may be used as the other value in the individualized target glucose range.

[0105] A subject-specific k for the glucose reading and / or corresponding individual glucose range. gly The second method considers available plasma glucose (PG eff The subject's plasma glucose level (e.g., measured using an analyte sensor configured to measure glucose levels in a bodily fluid, which in this case may be part of a larger system) is calculated to provide a k gly It is personalized by equation (16) using TIFF2024543824000024.tif10150 equation (16) Where: The file is TIFF2024543824000025.tif12150.

[0106] PG eff The levels can be used in combination with the lower allowable normal glucose limit and / or the upper allowable normal glucose limit in diagnosing, monitoring, and / or treating a subject. eff Levels are interpreted relative to acceptable glucose limits, which are considered herein to be between 70 mg / dL and 180 mg / dL, but which may change based on new clinical and / or scientific data, and recommendations from health authorities.

[0107] For example, 6.5 * 10 -6 dL * mg -1* day -1 k gly A subject with a glucose level of 170 mg / dL may receive a measured glucose level of 183 mg / dL when equation (16) is applied, which is interpreted based on the acceptable glucose limits (70 mg / dL to 180 mg / dL). Thus, at this point, the subject would consider the 170 mg / dL measurement to be within the acceptable limits. However, the effective plasma glucose may actually be higher, which could affect the proper dose of insulin or other medication to be delivered.

[0108] A subject-specific k for the glucose reading and / or corresponding individual glucose range. gly In a third method, the subject's plasma glucose level (e.g., measured using an analyte sensor configured to measure glucose levels in a bodily fluid, which in this case may be part of a larger system) is calculated to be k to the intracellular glucose (IG) level. gly It is personalized by equation (17) using TIFF2024543824000026.tif14150 equation (17)

[0109] The subject's IG level can then be compared to an acceptable lower normal intracellular glucose limit (LIGL) and an acceptable upper normal intracellular glucose limit (UIGL). The current acceptable values ​​for LIGL and UIGL are 0.29 mg / dL and 0.59 mg / dL, respectively.

[0110] The individualized target glucose range and / or individualized glucose level (e.g., effective plasma glucose level or intracellular glucose level) can be determined and / or implemented within the physiological parameter analysis system. For example, an instruction set or program associated with a glucose monitor and / or health monitoring device that determines a therapy (e.g., insulin dosage) can use the individualized target glucose range and / or individualized glucose level in such an analysis. In some cases, a display or associated subject interface can display the individualized target glucose range and / or individualized glucose level.

[0111] The individualized target glucose range and / or individualized glucose level may be updated over time as one or more physiological parameters are recalculated.

[0112] Individual-Target Average Glucose Using equation (18), the individualized target average glucose level (GT) can be calculated from the reference glucose target (RG), which can take any value that the physician determines is appropriate, for example, 120 mg / dL. TIFF2024543824000027.tif18150 equation (18)

[0113] Equation (19) can be used instead of or in combination with equation (18) to calculate GT based on laboratory HbA1c and calculated aHbA1c. TIFF2024543824000028.tif10150 equation (19)

[0114] Equation (20) can be used instead of or in combination with equation (18) and / or equation (19) to calculate GT when the target HbA1c value (AT) is known. TIFF2024543824000029.tif10150 equation (20)

[0115] In some embodiments, the physiological parameter analyzing system can determine an average glucose level for the subject during the time period 208 and optionally display the average glucose level and / or the target average glucose level. The subject can self-monitor his / her progress over the time period 208 using the current average glucose level and the target average glucose level. In some cases, the current average glucose level can be transmitted (periodically or regularly) to a healthcare provider using the physiological parameter analyzing system for monitoring and / or analysis.

[0116] 7, with reference to FIG. 3, illustrates an example of a personal-target average glucose report that may be generated as an output 318 by the physiological parameter analysis system 310 of the present disclosure. The illustrated example report includes a plot of the subject's average glucose over time (solid line) and the personal-target average glucose (illustrated by the dashed line at 150 mg / dL). Alternatively, other reports may include, but are not limited to, a numerical display of the personal-target average glucose in conjunction with the subject's average glucose level over a given time frame (e.g., the last 12 hours), and the like, and any combination thereof.

[0117] The individualized target average glucose level may be updated over time as new relevant physiological parameters, relevant calculations, and / or relevant measurements for one or more of equations (18)-(20) are obtained.

[0118] Individualized Treatment - Patient Triage In subjects who require tight control of their glucose levels, a continuous glucose monitor can be used in conjunction with an insulin pump. As exemplified above, the target glucose range is k glyThus, in some cases, subjects with narrower individual-target glucose ranges may be good candidates for a combination continuous glucose monitor and insulin pump. Triage of subjects who are good candidates for a combination continuous glucose monitor and insulin pump is based on the width of the individual-target glucose range and k gly This may be based on:

[0119] The currently implemented spread between the lower and upper glucose limits is about 110 mg / dL. However, as illustrated above, gly It is believed that depending on the individual, this spread may narrow to about 60 mg / dL or less. Some embodiments can include triaging the subject to a continuous glucose monitor and insulin pump combination when the individual-target glucose range falls below a threshold below 110 mg / dL.

[0120] Some embodiments include gly 6.2 * 10 -6 dL * mg -1* day -1 The method may include triaging the subject to a continuous glucose monitor in combination with an insulin pump when the subject exceeds a higher threshold.

[0121] Some embodiments include gly is the threshold, e.g., 6.2 * 10 -6 dL * mg -1* day -1 The method may include placing the subject on a severe hypoglycemia prevention program when the blood glucose level is lower than 100 mg / kg.

[0122] In some embodiments, triaging a subject to a continuous glucose monitor in combination with an insulin pump can be a stepped triage in which the subject's glucose levels are first continuously monitored for a reasonable period of time (e.g., about 5 days, about 10 days, about 15 days, about 30 days, or more). This period of continuous monitoring can be used to assess whether the subject has the ability to effectively manage their glucose levels or whether an insulin pump would be more appropriate or required.

[0123] Whether the triage step is a direct transition to continuous glucose monitoring in combination with insulin pump or a stepwise triage with monitoring before insulin pump therapy is determined by indicators (i.e., the spread of individual-target glucose ranges, k gly , or any combination thereof). For example, k gly is about 6.4 * 10 -6 dL * mg -1* day -1 and the spread of the individual-target glucose range is about 103 mg / dL, then this subject may be suitable for tiered triage compared to another subject whose corresponding indicators suggest that an insulin pump should be used.

[0124] In some embodiments, the triage may be based on a look-up table (e.g., stored in the physiological parameter analysis system of the present disclosure). The look-up table may, for example, include a look-up table that includes ... gly , k age, and / or K), individual-target glucose range spreads, and / or other factors described herein such as, but not limited to, underlying medical conditions, family history of a medical condition, current treatment, age, race, sex, geographic location, diabetes type, time since diabetes diagnosis, and the like, and any combination thereof. The columns in the lookup table can, for example, define ranges or limits for the above parameters, and the rows can indicate suggested courses of action, which can be the output 318 of the physiological parameter analysis system 310 of FIG. 3. For example, two columns can be defined as k gly An upper and lower bound can be defined, where each row corresponds to a suggested course of action such as "Candidate for insulin pump", "Candidate for closed loop control system", "Candidate for basal / bolus insulin therapy", "Candidate for basal only insulin therapy", or any such treatment used to control diabetes or achieve glycation in the subject. In some cases, more than one course of action can be indicated. Thus, in this example, the subject triage report can simply display one (or more) suggested course of action.

[0125] Alternatively, the subject triage report may show a map of zones corresponding to course of action on a plot defined, for example, by one or more of the parameters described above in connection with a look-up table. Such zones may in some cases be defined by a look-up table that labels each zone with a recommendation, and show glycemic parameter points on the map to indicate the appropriate zone for the subject.

[0126] The above two subject triage reports are examples based on lookup tables, but instead, (1) one or more physiological parameters (k gly , k age, and / or K), the spread of individual-target glucose ranges, and / or other factors described herein, and (2) other correlations between action guidelines (e.g., mathematical algorithms or matrix analysis).

[0127] As will be explained, the glycation parameters of a subject can help healthcare providers and payers to better determine which therapeutic tools are most appropriate for which subjects. For example, closed-loop insulin pump systems are expensive to employ and maintain, but subjects with high glycation rates may have very narrow individual-target glucose ranges, in which case the safest treatment is to keep these subjects' glucose levels within such ranges using closed-loop insulin pump systems.

[0128] In some embodiments, the combination of a continuous glucose monitor and an insulin pump can be a closed loop system. In some embodiments, the combination of a continuous glucose monitor and an insulin pump can be a mixed loop system. For example, referring back to FIG. 4, the physiological parameter analysis system 410 can further include components therein, such as a glucose monitor 424 (e.g., a continuous glucose monitoring system) and one of the insulin pumps described above that can communicate with one or more of the health monitoring devices 420.

[0129] Personalizing Treatment – ​​Diabetes Drug Titration In some embodiments, one or more physiological parameters (k gly , k age, and K) can be used. For example, referring to FIG. 3, a physiological parameter analysis system 310 of the present disclosure can determine or have inputs of (1) one or more physiological parameters, (2) an individual-target glucose range, (3) an individual-target glucose level (e.g., effective plasma glucose level or intracellular glucose level), and / or (4) an individual-target average glucose. Then, when a subsequent glucose level is measured, the physiological parameter analysis system 310 can output a recommended diabetes drug dosage. An alternative or supplemental output 318 can be a glucose pattern insight report.

[0130] Examples of glucose pattern insight reports can be found in U.S. Patent Application Publication Nos. 2014 / 0188400 and 2014 / 0350369, each of which is incorporated herein by reference. The analyses and reports disclosed in the above applications may be used in conjunction with one or more physiological parameters (kcal) of the present disclosure. gly , k age , and K).

[0131] For example, FIG. 8 shows an example of a glucose pattern insight report that may be an output 318 of a physiological parameter analysis system 310 (e.g., an insulin titration system) with reference to FIG. 3. The illustrated glucose pattern insight report incorporates an AGP in conjunction with a table of glycemic control measures (or "traffic lights"). As illustrated, the report includes an individual-target average glucose of 120 mg / dL, an AGP plot over the analysis period (e.g., from about 1 month to about 4 months) illustrating the subject's average glucose level over the analysis period, the 25th to 75th percentiles of the subject's glucose level over the analysis period, and the 10th to 90th percentiles of the subject's glucose level over the analysis period. Optionally, the glucose pattern insight report may also or instead display the individual-target glucose range and / or the individual glucose level (e.g., effective plasma glucose level or intracellular glucose level) compared to the current tolerated glucose range. In addition, the glucose pattern insight report can optionally further include one or more of laboratory HbA1c levels, cHbA1c levels, adjusted HbA1c levels based on either laboratory HbA1c or glucose data, data ranges determined from average glucose and associated percentiles, and the like.

[0132] Beneath the AGP plot on the glucose pattern insight report is a table correlating one or more (illustrated as three) glycemic control measures with the subject's average glucose level over a given shortened time interval for that day during the analysis period. This correlation, in this example, represents a traffic light (e.g., green (good), yellow (mild), or high (red)) corresponding to the risk of a disease condition based on the glycemic control measures. Examples of glycemic control measures include, but are not limited to, likelihood of low glucose, likelihood of high glucose, proximity of average glucose to individual-target average glucose, adherence of glucose levels to individual-target glucose range and / or individual glucose level compared to the current acceptable glucose range, degree of average glucose variability below (or above) individual-target average glucose, degree of glucose level variability outside (below and / or above) individual-target glucose range and / or individual glucose level compared to the current acceptable glucose range, and the like.

[0133] In some embodiments, the glucose pattern insight reports can be used as part of a diabetic drug titration system, where the lights (or values ​​associated therewith) can drive logic to provide treatment modifications such as changing a basal dose of a diabetic drug or a bolus amount of a diabetic drug associated with a meal. For example, when used in conjunction with an automated or semi-automated system for titration, these lights driving logic can provide recommendations to the subject regarding dosage adjustments.

[0134] Glucose pattern insight reports and associated analyses incorporating the use of the kinetic models described herein can provide more targeted treatment recommendations for subjects with diabetes. In this example, as described above, 5.1 * 10 -6 dL * mg -1* day -1 k glyA subject with glucose level 100 may have an individualized target glucose range of about 90±8 mg / dL to about 250±32 mg / dL. This subject may be sensitive to lower glucose levels and may feel weak, hungry, dizzy, etc. more frequently with the currently implemented glucose ranges (70 mg / dL and 180 mg / dL). One or more physiological parameters (k gly , k age , and K), can include settings that define hypoglycemia risk as a traffic light for "potential low glucose." For example, if the potential low glucose indicates a low risk (e.g., green traffic light), it is deemed safe to increase insulin. If the potential low glucose indicates a medium risk (e.g., yellow traffic light), it is deemed that the current risk is within an acceptable range, but another increase in insulin should not be made. Finally, if the potential low glucose indicates a high risk, it is recommended that insulin should be decreased to bring glucose back up to an acceptable level. For subjects with high hypoglycemia risk due to a high lower glucose level threshold, the amount of risk associated with moderate and high risks (e.g., how far below the lower glucose level threshold) may be smaller than for subjects with a normal lower glucose level threshold.

[0135] In the above example, a glucose pattern insight report is described as the output 318, however, in other embodiments, other outputs can be used that use the same logic and analysis. For example, the output 318 can be a dosage recommendation value.

[0136] One or more physiological parameters (k gly , k age, and K) and associated analytical results (e.g., individualized target glucose ranges, individualized glucose levels, individualized target average glucose, cHbA1c, aHbA1c, and the like) can be updated periodically (e.g., about every three months to one year). The frequency of updates can depend, among other things, on the subject's glucose levels and diabetes history (e.g., how well the subject is staying within defined thresholds), other disease states, and the like.

[0137] The insulin titration system measures one or more physiological parameters (k gly , k age , and K) may also be optionally utilized. Error values ​​may be determined by one of skill in the art using standard statistical techniques and may be used as another set of parameters to configure a titration system. For example, a titration system may be implemented that may use a small risk amount for hypoglycemia (i.e., a small tolerance below the lower glucose level threshold for indicating moderate and high risk) when the lower glucose levels of an individualized-target glucose range of about 75 mg / dL are associated with an error of about 7% or less.

[0138] The dosage of the diabetic drug (eg, by titration) can be updated over time as one or more physiological parameters are recalculated.

[0139] Closed Loop and Hybrid Closed Loop Control Systems Closed-loop and hybrid closed-loop systems have been developed to recommend or administer insulin doses to a subject for insulin delivery based on near real-time glucose readings. These systems are often based on models that represent the subject's physiology, glucose sensor dynamics, and glucose sensor error characteristics. In some embodiments, the systems use one or more physiological parameters (k, k, k+, k+), similar to those described above for insulin titration, to more closely meet the subject's needs. gly , k age, and K) and associated analytical results (e.g., individual-target glucose range, individual-target glucose level, individual-target average glucose, cHbA1c, aHbA1c, and the like) can be incorporated into a closed loop system.

[0140] In many cases, the closed loop system is configured to "drive" the subject's glucose level within a target range and / or toward a single glucose target, which may be an individual-target glucose range, an individual-target glucose level, and / or an individual-target average glucose compared to an acceptable target glucose range as described herein. For example, a high k gly and a high lower glucose limit for the individual-target glucose range, the controller gly Based on this, the subject's glucose level can be driven to remain above the lower glucose limit, thereby avoiding lower glucose levels that would be more detrimental to these subjects than subjects with normal glucose ranges. Similarly, subjects with a low upper glucose limit relative to their individualized-target glucose range can have the controller of the closed-loop insulin delivery system and the hybrid closed-loop insulin delivery system drive their glucose to remain below their individualized upper glucose limit to mitigate hyperglycemic effects.

[0141] The metrics on which the closed-loop insulin delivery systems and hybrid closed-loop insulin delivery systems rely to determine insulin dosages can be updated over time when one or more physiological parameters are recalculated, for example, the individual-target glucose range, individual-target glucose level, and / or individual-target average glucose can be updated when one or more physiological parameters are recalculated.

[0142] Individualized Treatment - Glycosylation Drugs Diabetes is a disease caused by the inability of a subject's pancreas to produce enough insulin (or any insulin at all). However, in some cases, a subject's glycation process may be the cause of the body's failure to properly control intracellular glucose. Such subjects may be more responsive to treatment with glycation drugs (e.g., azathioprine, meloxicam, nimesulide, piroxicam, mefenamic acid, oxaprozin, D-penicillamine, penicillin G, trimethylphloroglucinol, ranitidine, phloroglucinol dihydrate, epinephrine bitartrate, pyridoxine HCl, topiramate, escitalopram, hydroquinone, tretinoin, colchicine, rutin, and the like) rather than previous diabetes treatments. The kinetic model of the present disclosure shows that k gly and / or K(k gly Thus, one or both of these physiological parameters can be used in identifying, treating, and / or monitoring a subject with a glycation disorder.

[0143] In some embodiments, the k value of a subject with respect to a glycosylation drug is gly and / or K, and optionally k gly and / or modifying the glycation drug dosage based on a change in K. For example, referring to FIG. 2, some embodiments may include modifying the glycation drug dosage based on a change in K at time t. gly1 and / or K1 and determine the corresponding k gly2 and / or K2 (as described above) and treating the subject with a glycosylation drug for a period of time 208. Then, k gly1 and / or K1 and the corresponding k gly2 and / or K2, the dosage and / or type of glycosylation drug for the subsequent time period may be altered based on the comparison with K3 and / or K4. Additionally, in some cases, the k3 and / or K5 corresponding to the end of the subsequent time period may be compared to one or more of the previously determined physiological parameters. gly3and / or K3 can be determined. The times between t1 and t2 and between t2 and t3 should be at least the expected time for the glycation drug to cause a measurable change in the monitored parameter, which may be drug and dose dependent.

[0144] In some embodiments, the output 318 of the physiological parameter analysis system 310 of FIG. 3 is the k gly and / or a glycation drug report including a glycation drug and / or dosage recommendation based on K. This output 318 can be displayed to the subject, a health care provider, and / or the like for review and adjustment of the glycation drug and / or dosage.

[0145] Alternatively, these dosage recommendations may suggest to the subject and / or to an automated drug delivery system the next dose to be administered. In this case, the system may guide the titration of the drug and allow the subject to start with the minimum dose or a recommended initial dose. The initial dose may be determined based on the subject's current condition, the subject's k gly1 and / or K1, as well as other factors described herein. After an adequate amount of time has elapsed to adequately determine the effect of the current drug dose, k is determined based on the new laboratory HbA1c level and the glucose level measured during drug administration. gly2 and / or K2 can be determined. Then, k gly2 and / or K2 is (1) k gly1 and / or K1, and / or (2) target k gly and / or a target K to determine whether the dosage needs to be changed. For example, if a hyperglycemic subject taking a drug intends to reduce the glycation rate, k gly2 If k is still higher than desired, the recommended dose can be increased according to (1) a standard titration schedule and / or (2) a system that takes into account how previous dose changes have affected the subject (known as control theory). gly2If K is low, the dosage can be reduced. It is believed that drugs could similarly be titrated to affect K or other parameters. In addition, it is believed that a similar process could be used to recommend non-pharmaceutical treatments such as transfusions or blood withdrawals by guiding the appropriate amount of blood to affect.

[0146] To monitor and titrate the effects of glycosylation drugs gly The use of and / or K may be beneficial to health care providers in treating subjects with abnormal glycation physiology.

[0147] The metrics relied upon in determining the dosage of the glycation drug can be updated over time as one or more physiological parameters are recalculated.

[0148] Identifying abnormal or pathological physiological conditions Kinetic modeling, in certain embodiments, involves the analysis of physiological parameters (e.g., k gly , k age (or k gen ), and / or K), where the same parameters are compared between different time periods to indicate an abnormal or pathological condition in the subject. gly , k age Variations in and / or K can provide an indication of an abnormal or pathological condition in a subject. gly , k age , and / or K varies across subjects but k for a single individual gly , k age , and / or the variation of K is small and slow. Therefore, k gly , k age Comparison of k and / or K provides information about the physiological state of the subject. gly , k age When clinically significant changes in and / or K are observed over time, it is possible and likely that an abnormal or pathological physiological condition exists.

[0149] For example, k glyHowever, when the changes significantly over time such that the variations are clinically significant, such clinically significant variations may indicate that glucose transporter levels or cell membranes have changed. Such biochanges may indicate underlying metabolic changes within the subject's body resulting from the subject's physiology undergoing a disease state.

[0150] k age and / or k gen However, when a change over time is significant enough to be clinically significant, such clinically significant fluctuations may indicate a change in the subject's immune system, because the immune system is designed to recognize cells that need to be eliminated.

[0151] k age and / or k gen Clinically significant variations in k may also or alternatively be related to oxygen sensing mechanisms in the body. age and / or k gen could indicate that the subject's body needs more red blood cells to carry oxygen or that the oxygen-sensing mechanism is not functioning properly, either of which could indicate a change in physiological condition, such as blood loss or a disease state.

[0152] In yet another example (in combination or in place of the above examples), k age and / or k gen Clinically significant variations in k can be associated with changes in the bone marrow. For example, if the bone marrow suddenly produces more oxygen-carrying red blood cells, the subject's body will respond by destroying or eliminating more red blood cells. age and / or k gen Clinically significant elevations of can be associated with bone marrow abnormalities.

[0153] In another example, hormonal disorders can cause age , k genHormones can affect heart rate, contractile strength, blood volume, blood pressure, and erythropoiesis. Stress hormones such as catecholamines and cortisol stimulate the release of reticulocytes from the bone marrow and in some cases also enhance erythropoiesis. Thus, large fluctuations in hormone levels can increase k age and / or k gen This may change, and therefore, change K.

[0154] In yet another example, k gly , k age Deviations from normal for K and / or K can be indicators of diabetes or prediabetes. gly , k age Using , and / or K may be more effective than standard fasting glucose testing and laboratory HbA1c. For example, subjects with laboratory HbA1c within the normal range and normal fasting glucose may have low k associated with high glucose values ​​during non-fasting periods. gly Thus, this subject may be a candidate for early diabetes intervention that may otherwise go unnoticed based on standard diabetes diagnostic methods.

[0155] In another example, for subjects with newly elevated laboratory HbA1c, standard diabetes treatments can be adopted to lower HbA1c in these subjects. gly Determining that is abnormal may indicate that there is a problem with the subject's glycation physiology rather than their pancreas, and may suggest other, more targeted forms of treatment.

[0156] The disclosed embodiment of the present invention is gly , k age , and / or K, their changes over time, and / or possible abnormal or pathological physiological conditions.

[0157] In accordance with embodiments of the present disclosure, physiological parameter analysis in the manner described herein provides an indication of an abnormal or diseased state of a subject, as well as a tool for analysis and / or monitoring of one or more parameters or characteristics related to the subject's personalized diabetes management.

[0158] Identification of nutritional supplements and / or medicines Some supplements and drugs interact with the kinetics of red blood cell glycosylation, elimination, and development in the body. For example, supplements and drugs used by athletes to dope include, but are not limited to, human growth hormone, supplements and drugs that increase metabolic levels, and the like. Human growth hormone increases red blood cell count, resulting in k age In another example, nutritional supplements and drugs that increase metabolic levels (e.g., exercise mimetics such as AMPK agonists) can increase k gly Therefore, some embodiments may be implemented by measuring one or more physiological parameters (k gly , k age , and / or K) can be used as indicators of doping.

[0159] In a first example, one or more physiological parameters (k gly , k age , and / or K) can be used as an indicator of doping in some cases.

[0160] In another example, one or more physiological parameters (k gly , k age Once K, Kb, and / or Kc have been determined, continuous monitoring over a period of 10 days or longer could identify a sudden change in a physiological parameter that could be indicative of doping. This change could be used alone or in combination with the above examples where one or more physiological parameters are outside of the normal range.

[0161] Physiological age Due to aging, the physiological parameter k age changes, and as a result, K changes. Therefore, k age and / or K(k gly A standard metabolic age can be calculated using the biomarkers k (assuming a stable or known change in k) over time. age decreases and K increases. k in healthy subjects age And / or the correlation between K and age can be used to calculate the metabolic age of the new subject. This metabolic age can then be used as an indicator of the new subject's risk for age-related degenerative pathologies, such as heart disease, Alzheimer's disease, or osteoporosis. The risk for age-related degenerative pathologies can be used in conjunction with a family history of age-related degenerative pathologies for pre-screening and / or preventative treatment. For example, a 54-year-old subject with a metabolic age of 65 and a family history of cardiovascular disease occurring later in life can be screened for signs and / or progression of cardiovascular disease more frequently than a 54-year-old subject with a metabolic age of 50 and a similar family history.

[0162] Sample monitor and monitoring system In general, the disclosed embodiments of the present invention are used in conjunction with or as systems, devices, and methods for measuring glucose and optionally at least one other analyte in a body fluid. The embodiments described herein can be used to monitor and / or process information about glucose and optionally at least one other analyte. Other analytes that can be monitored include, but are not limited to, glucose derivative HbA1c, reticulocyte count, RBC GLUT1 level, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxide, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. For example, concentrations of drugs such as antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin can also be monitored. In embodiments monitoring glucose and one or more analytes, each of the analytes can be monitored at the same or different times.

[0163] The analyte monitors and / or analyte monitoring systems (collectively referred to herein as analyte monitoring systems) used in conjunction with or as the systems, devices, and methods for measuring glucose and optionally one or more analytes in a bodily fluid can be in vivo analyte monitoring systems or in vitro analyte monitoring systems. In some cases, the systems, devices, and methods of the present disclosure can use both in vivo and in vitro analyte monitoring systems.

[0164] An in-vivo analyte monitoring system includes an analyte monitoring system in which at least a portion of an analyte sensor is or can be positioned within a subject's body to obtain information regarding at least one analyte in the body. The in-vivo analyte monitoring system can operate without requiring factory calibration. Examples of in-vivo analyte monitoring systems include, but are not limited to, continuous analyte monitoring systems and intermittent analyte monitoring systems.

[0165] For example, a continuous analyte monitoring system (eg, a continuous glucose monitoring system) is an in-vivo system that can transmit data repeatedly or continuously without prompting (eg, automatically according to a schedule) from a sensor control device to a reader device.

[0166] For example, an intermittent analyte monitor system (or intermittent glucose monitor system or simply an intermittent system) is an in-vivo system that can transmit data from a sensor control device using near field communication (NFC) protocols or radio frequency identification (RFID) protocols, etc., in response to a scan or data request by a reader device.

[0167] An in vivo analyte monitor system may include a sensor that contacts a subject's bodily fluid while positioned in vivo to sense the level of one or more analytes contained therein. The sensor may be part of a sensor control device that resides on the subject's body, the sensor control device housing the electronics and power source that enable and control the sensing of the analytes. Sensor control devices and variations thereof may also be referred to as "sensor control units," "body-worn electronics" devices or units, "body-worn" devices or units, or "sensor data communication" devices or units, to name a few. As used herein, these terms are not limited to devices having analyte sensors, but include devices having other types of sensors, whether biometric or non-biometric. The term "body-worn" refers to any device that resides directly on the body or in close proximity to the body, such as a wearable device (e.g., eyeglasses, watch, wristband or bracelet, neckband or necklace, etc.).

[0168] The in-vivo analyte monitoring system may further include one or more reader devices that receive the sensed analyte data from the sensor control device. These reader devices may process and / or display the sensed analyte data to the subject in any number of forms. These devices and variations thereof may be referred to as "handheld reader devices," "reader devices" (or simply "readers"), "handheld electronic devices" (or handheld), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply receivers), "relay" devices or units, or "remote" devices or units, to name a few. Additionally, other devices such as personal computers have been utilized or incorporated into in-vivo and in-vitro monitoring systems.

[0169] 4, the sensor or portion thereof of the in vivo analyte monitoring system may be a glucose monitor 424 and the reader device may be a health monitor device 420. In an alternative embodiment, the in vivo analyte monitoring system as a whole may be a glucose monitor 424 that transmits data to a health monitor device 420, a data network 422, a data processing terminal / PC 426, and / or a server / cloud 428.

[0170] In vivo analyte monitoring systems include one or more physiological parameters (e.g., k gly , k age (or k gen ), and / or K), and / or other analytical results described herein may be performed within the in-vivo analyte monitor system in some cases. For example, only physiological parameters may be determined within the in-vivo analyte monitor system and transmitted to appropriate other components of the physiological parameter analysis system that may perform other analyses described herein. In some embodiments, the in-vivo analyte monitor system may generate only an output signal corresponding to the glucose level, which is received by another component of the physiological parameter analysis system. In such cases, one or more of the other components of the physiological parameter analysis system may determine the one or more physiological parameters (e.g., k gly , k age (or k gen ), and / or K), and optionally perform one or more of the other analyses described herein.

[0171] 9 illustrates an example in-vivo analyte monitor system 960. In an embodiment of the present disclosure, the example in-vivo analyte monitor system 960 monitors glucose and optionally one or more other analytes.

[0172] The in-vivo analyte monitor system 960 includes a sensor control device 962 (which may be at least a part of the glucose monitor 424 of FIG. 4 ) and a reader device 964 (which may be at least a part of the health monitor device 420 of FIG. 4 ) that communicate with each other over a local communication pathway (or communication link) 966, which may be wired or wireless and unidirectional or bidirectional. In embodiments where the pathway 966 is wireless, a near field communication (NFC) protocol, an RFID protocol, a BLUETOOTH® protocol or a BLUETOOTH® low energy protocol, a WiFi protocol, a proprietary protocol, or the like, including communication protocols existing as of the date of this application or later developed variations, may be used.

[0173] The reader device 964 (e.g., a dedicated reader, a cellular phone or PDA running an app, or the like) is likewise capable of wired, wireless, or combinations of communication with a computer system 968 (which may be at least a portion of the data processing terminal / PC 426 of FIG. 4) over a communication path (or link) 970 and with a network 972, such as the Internet or a cloud (which may be at least a portion of the data network 422 and / or the server / cloud 428 of FIG. 4), over a communication path (or link) 974. Communication with the network 972 may include communication with a trusted computer system 976 within the network 972 or communication through the network 972 to the computer system 968 over a communication link (or path) 978. The communication paths 970, 974, and 978 may be wireless, wired, or both, may be unidirectional, bidirectional, and may be part of a long distance communication network such as a Wi-Fi network, a local area network (LAN), a wide area network (WAN), the Internet, or other data network. In some cases, the communication paths 970 and 974 may be the same path. All communications over the paths 966, 970, and 974 may be encrypted, and each of the sensor control device 962, the reader device 964, the computer system 968, and the trusted computer system 976 may be configured to encrypt and decrypt these incoming and outgoing communications.

[0174] Variations of devices 962 and 964, as well as other components of an in vivo based analyte monitor system for use in the embodiments of the systems, devices, and methods described herein, are described in U.S. Patent Application Publication No. 2011 / 0213225 (the '225 publication), the entire contents of which are incorporated herein by reference for all purposes.

[0175] The sensor control device 962 can include a housing 980 that houses the in vivo analyte monitor circuitry and a power source. In this embodiment, the in vivo analyte monitor circuitry is electrically coupled to an analyte sensor 982 that extends through an adhesive patch 984 and protrudes away from the housing 980. The adhesive patch 984 includes an adhesive layer (not shown) for attachment to a skin surface of a subject's body. Other forms of physical attachment to the body can be used in addition to or instead of an adhesive.

[0176] The sensor 982 is adapted to be at least partially inserted into the subject's body, where it is in fluid contact with the subject's bodily fluids (e.g., subcutaneous (subdermal) fluid, dermal fluid, or blood) and can measure analyte-related data of the subject in conjunction with an in vivo analyte monitor circuit. The sensor 982 and any associated sensor control electronics can be affixed to the body in any desired manner. For example, an insertion device (not shown) can be used to position all or a portion of the analyte sensor 982 through the outer surface of the subject's skin and into contact with the subject's bodily fluids. In doing so, the insertion device can position the sensor control device 962 on the skin using an adhesive patch 984. In other embodiments, the insertion device can first position the sensor 982 and then later couple the associated sensor control electronics to the sensor 982, either manually or using a mechanical device. Examples of insertion devices are described in U.S. Patent Application Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, the entire contents of all of which are incorporated herein by reference for all purposes.

[0177] After collecting the raw data from the subject's body, the sensor control device 962 can apply analog signal conditioning to the data and convert the data into conditioned raw data in digital form. In some embodiments, this conditioned raw digital data can be coded for transmission to another device (e.g., a reader device 964) that then algorithmically processes the digital raw data into a final form representing the measured biometric of the subject (e.g., a form that is readily processed for display to the subject or readily used by the analysis module 420B of FIG. 4). This algorithmically processed data can then be formatted or graphically processed for digital display to the subject. In other embodiments, the sensor control device 962 algorithmically processes the digital raw data into a final form representing the measured biometric of the subject (e.g., an analyte level), then codes and wirelessly communicates the data to the reader device 964 that can then format or graphically process the received data for digital display to the subject. In other embodiments, the sensor control device 962 may graphically process the data in its final form so that it is ready for display, and display this data on the display of the sensor control device 962 or transmit it to the reader device 964. In some embodiments, the biometric data in its final form is used by the system (e.g., incorporated into a diabetes monitoring plan) without processing for display to the subject (prior to graphical processing). In some embodiments, the sensor control device 962 and the reader device 864 transmit the raw digital data to another computer system for algorithmic processing and display.

[0178] The reader device 964 may include a display 986 for outputting information to the subject (e.g., an output such as one or more physiological parameters or cHbA1c derived therefrom) and / or for accepting input from the subject, and optional input components 988 (or more) such as buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog dials, or the like for inputting data, commands, or otherwise controlling operation of the reader device 964. In certain embodiments, the display 986 and the input components 988 may be integrated into a single component, for example, in which case the display may measure the presence and location of physical contact touches on the display, such as a touch screen subject interface (which may be at least a portion of the subject interface 420A of FIG. 4). In certain embodiments, the input components 988 of the reader device 964 may include a microphone, and the reader device 964 may include software configured to analyze audio input received from the microphone, such that functions and operation of the reader device 964 may be controlled by voice commands. In certain embodiments, the output components of the reader device 964 include a speaker (not shown) for outputting the information as an audible signal. Similar audio-enabled components such as a speaker, microphone, and software routines for generating, processing, and storing audio drive signals can be included within the sensor control device 962.

[0179] The reader device 964 may further include one or more data communication ports 990 for wired data communication with an external device, such as a computer system 968. Exemplary data communication ports 990 include, but are not limited to, a USB port, a mini USB port, a USB Type-C port, a USB Micro-A port and / or a USB Micro-B port, an RS-232 port, an Ethernet port, a Firewire port, or other similar data communication ports configured to connect to a corresponding data cable. The reader device 964 may further include an integral or attachable in vitro glucose meter that includes an in vitro test strip port (not shown) for accepting an in vitro test strip to perform an in vitro blood glucose measurement.

[0180] The reader device 964 can display the measured biometric data wirelessly received from the sensor control device 962, and can also be configured to output alarms (e.g., visual alarms on a display, audible alarms, or a combination thereof), alert notifications, glucose levels, etc., which can be visual, audible, tactile, or any combination thereof. Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011 / 0193704, the entire contents of which are incorporated herein by reference for all purposes.

[0181] The reader device 964 can act as a data conduit to transfer measurement data from the sensor control device 962 to the computer system 968 or the trusted computer system 976. In certain embodiments, data received from the sensor control device 962 can be stored (permanently or temporarily) in one or more memories of the reader device 964 before being uploaded to the computer system 968, the trusted computer system 976, or the network 972.

[0182] The computer system 968 may be a personal computer, a server terminal, a laptop computer, a tablet, or other suitable data processing device. The computer system 968 may be (or include) software for managing and analyzing data, and communicating with components within the analyte monitor system 960. The computer system 968 may be used by a subject, a medical professional, or other user to display and / or analyze the biometric data measured by the sensor control device 962. In some embodiments, the sensor control device 962 may communicate the biometric data directly to the computer system 968 without an intermediary such as the reader device 964, or indirectly using an internet connection (also optionally without first sending the data to the reader device 964). The operation and use of the computer system 976 is described in more detail in the '225 publication, which is incorporated herein. The analyte monitor system 960 may also be configured to cooperate with a data processing module (not shown), which is also described in the incorporated '225 publication.

[0183] The trusted computer system 976 may be owned by the manufacturer or vendor of the sensor control device 962, connected through a secure connection, either physically or virtually, and may be used to perform authentication of the sensor control device 962 for secure storage of the subject's biometric data and / or as a server providing data analysis programs (e.g., accessible via a web browser) for performing analysis on the subject's measurement data.

[0184] An in vivo analyte monitor system may be used in conjunction with or be part of an integrated diabetes management system. For example, an integrated diabetes management system may include an in vivo analyte monitor system and a nutritional supplement / drug delivery system, more specifically an in vivo glucose monitor system and an insulin delivery system (e.g., an insulin pump). An integrated diabetes management system may be closed loop, open loop, or a hybrid of these. A closed loop system provides full control over analyte monitoring times and nutritional supplement / drug dosage and time. An open loop system allows the subject full control over analyte monitoring times and nutritional supplement / drug dosage and time. A hybrid system relies primarily on a closed loop system approach but may allow for subject intervention.

[0185] In vitro analyte monitoring systems contact bodily fluids outside the body. In some cases, an in vitro analyte monitoring system includes a metering device having a port for receiving a subject's bodily fluid (e.g., on an analyte test strip / swab or by drawing the bodily fluid) that can be analyzed to determine the subject's analyte level.

[0186] Exemplary embodiments A first non-limiting exemplary embodiment of the present disclosure uses a model that takes into account transmembrane glucose transport and glycation to calculate a red blood cell glycation rate constant (k) based on (1) a plurality of first glucose levels and (2) a laboratory HbA1c level (e.g., one or more laboratory HbA1c levels). gly ), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age), and an apparent glycation constant (K); receiving (and / or measuring) a plurality of second glucose levels for the subject over a period of time; and deriving (e.g., using equation (9)) a calculated HbA1c (cHbA1c) level for the subject based on the at least one physiological parameter and the plurality of second glucose levels. A first non-limiting exemplary embodiment is characterized in that: Element 1: the method further includes a step of diagnosing, treating, and / or monitoring the subject based on the cHbA1c level; Element 2: Element 1, and the step of treating the subject is performed, including a step of administering and / or adjusting insulin administration, glycosylation drug administration, exercise plan, dietary intake, or a combination thereof; Element 3: the method further includes a step of displaying the cHbA1c level (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like); Element 4: the method further includes a step of displaying the cHbA1c level and k age and a given criterion k age (k ref age Element 5: the method further comprises the step of calculating (e.g., using equation (10)) the adjusted HbA1c (aHbA1c) of the subject based on the cHbA1c level, K and a predetermined reference K (K refElement 6: element 5 or element 6 and the method further comprises a step of diagnosing, treating, and / or monitoring the subject based on the aHbA1c level; element 7: element 6 and the step of treating the subject is performed and includes administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; element 8: element 5 or element 6 and the method further comprises a step of calculating (e.g., using equation (11)) an adjusted HbA1c (aHbA1c) for the subject based on the aHbA1c level; Element 9: element 5 or element 6 and the method further includes a step of indicating (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels are measured, or the like), deriving a personalized-target glucose range based on the aHbA1c level and the laboratory HbA1c level (e.g., using equations (13) and (15)), deriving a personalized glucose upper limit (e.g., using equation (15)), and / or Element 10: Element 9 and the method further includes a step of diagnosing, treating, and / or monitoring the subject based on the individual-target glucose range, the individual-upper glucose limit, and / or the individual-lower glucose limit; Element 11: Element 10 and the step of treating the subject is performed and includes a step of administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; Element 12: Element 9 and the method further includes a step of diagnosing, treating, and / or monitoring the subject based on the individual-target glucose range, the individual-upper glucose limit, and / or the individual-lower glucose limit; and displaying (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like), element 13: receiving a glucose level for the subject after element 9 and the method have derived the individual-target glucose range, individual upper glucose limit, and / or lower glucose limit; and the glucose level being:and displaying an alarm (visually, audibly, and / or tactilely (for touch)) when the glucose is outside the individual-target glucose range, when the individual-target glucose upper limit is exceeded, and / or when the individual-target glucose lower limit is exceeded; element 14: element 5 or element 6 and the method further includes deriving an individual-target average glucose (e.g., using equation (18), equation (19), or equation (20)); element 15: element 14 and the method further includes deriving an individual-target average glucose based on the individual-target average glucose. Element 16: Element 15, and the step of treating the subject is performed and includes administering and / or adjusting insulin administration, glycosylation drug administration, exercise plan, dietary intake, or a combination thereof; Element 17: Element 14 and the method further includes displaying the individual-target average glucose (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or ... Element 18: element 5 or element 6 and the method further includes one or more of the steps of deriving a personalized therapy for subject triage based at least in part on aHbA1c level, deriving a personalized therapy for diabetic drug titration, deriving a personalized closed-loop or hybrid closed-loop control system, deriving a personalized therapy using glycation drugs, identifying abnormal or pathological physiological conditions, identifying nutritional supplements and / or drugs present during the test, and identifying physiological age. element 19: the method further comprises one or more of the steps of deriving a personalized therapy for subject triage based at least in part on the cHbA1c level, deriving a personalized therapy for diabetic drug titration, deriving a personalized closed loop or hybrid closed loop control system, deriving a personalized therapy using glycation drugs, identifying an abnormal or pathological physiological condition, identifying nutritional supplements and / or medications present during the test, and identifying physiological age; element 20: the method further comprises k, gly and a given criterion k gly (k refgly ), deriving an individualized target glucose range (e.g., using equation (12) and equation (14)) based on the individualized glucose upper limit (e.g., using equation (14)), and / or deriving an individualized glucose lower limit (e.g., using equation (12)); element 21: element 20 and the method further include diagnosing, treating, and / or monitoring the subject based on the individualized target glucose range, the individualized upper glucose limit, and / or the individualized lower glucose limit; element 22: element 21 and the step of treating the subject is performed, and includes administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; element 23: element 20 and the method further include diagnosing, treating, and / or monitoring the subject based on the individualized target glucose range, the individualized glucose upper limit, and / or the individualized lower glucose limit; displaying (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels are measured, or the like); element 24: receiving the subject's glucose level after element 20 and the method have derived the individual-target glucose range, the individual-target glucose upper limit, and / or the individual-target glucose lower limit, and displaying (visually, audibly, and / or tactilely (for touch)) an alarm when the glucose level is outside the individual-target glucose range, above the individual-target glucose upper limit, and / or below the individual-target glucose lower limit; element 25: the method further comprises: gly and a given criterion k gly (k ref gly) and the measured glucose level (e.g., using equation (16) or equation (17)); element 26: element 25 and the method further includes diagnosing, treating, and / or monitoring the subject based on the individualized glucose level (e.g., the individualized glucose level compared to a current acceptable glucose range or the intracellular glucose level compared to a current acceptable intracellular glucose level range (i.e., LIGL-UIGL)); element 27: element 26 and the step of treating the subject are performed, administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof. and / or adjusting the individual glucose level; element 28: element 25 and the method further including a step of displaying the individual glucose level (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like); element 29: element 25 and the method further including a step of displaying an alarm (visually, audibly, and / or tactilely (for touch)) when the individual glucose level is outside of the current respective acceptable glucose range.

[0187] A second non-limiting exemplary embodiment of the present disclosure includes receiving (and / or measuring) a plurality of first glucose levels for a subject over a first time period, receiving (and / or measuring) a first glycated hemoglobin (HbA1c) level for the subject corresponding to an end of the first time period, and determining, using a model that accounts for transmembrane glucose transport and glycation, a red blood cell glycation rate constant (k) based on (1) the plurality of first glucose levels and (2) the first laboratory HbA1c level. gly ), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age), and an apparent glycation constant (K); receiving (and / or measuring) a plurality of second glucose levels for the subject over a second period of time; and deriving (e.g., using equation (9)) a calculated HbA1c (cHbA1c) level based on the at least one physiological parameter and the plurality of second glucose levels. Measuring the glucose level may involve sampling bodily fluid from the subject with an analyte sensor and measuring a plurality of first glucose levels with the analyte sensor. A second non-limiting exemplary embodiment may further include one or more of elements 1-29.

[0188] A third non-limiting exemplary embodiment of the present disclosure is an analyte sensor configured to measure glucose levels in a bodily fluid and a monitoring device, the monitoring device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the first or second non-limiting exemplary embodiment, optionally including one or more of elements 1 through 29.

[0189] A fourth non-limiting exemplary embodiment of the present disclosure is a closed-loop insulin pump system including an analyte sensor configured to measure glucose levels in a bodily fluid, an insulin pump, and a monitor device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the first or second non-limiting exemplary embodiment (optionally including one or more of elements 1 to 29), wherein, when a therapy is administered, the therapy includes a step of administering an insulin dose by the closed-loop insulin pump system.

[0190] A fifth non-limiting exemplary embodiment includes steps of receiving (and / or measuring) a laboratory HbA1c level (e.g., one or more laboratory HbA1c levels) of a subject, and determining a red blood cell turnover rate (k age ) (e.g., using a model that takes into account transmembrane glucose transport and glycation), and determining HbA1c levels and k age and a given criterion k age (k ref age) and calculating (e.g., using equation (10)) an adjusted HbA1c (aHbA1c) for the subject based on the aHbA1c level. Yet another embodiment is a method comprising the steps of: element 30: the method further comprising diagnosing, treating, and / or monitoring the subject based on the aHbA1c level; element 31: element 30, and treating the subject is performed, comprising administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; element 32: the method comprises displaying the aHbA1c level (e.g., on system 310, on system 410, on a glucose measurement location where the plurality of first and / or second glucose levels were measured); Element 33: the method further comprises deriving an individual-target glucose range based on the aHbA1c level and the laboratory HbA1c (e.g., using equation (13) and equation (15)), deriving an individual-specific upper glucose limit (e.g., using equation (15)), and / or deriving an individual-specific lower glucose limit (e.g., using equation (13)); Element 34: the method further comprises deriving an individual-target glucose range based on the aHbA1c level and the laboratory HbA1c (e.g., using equation (14)), deriving an individual-specific upper glucose limit (e.g., using equation (15)), and / or deriving an individual-specific lower glucose limit (e.g., using equation (13)); Element 35: the feature where element 34 and the step of treating the subject is performed and includes administering and / or adjusting insulin administration, glycosylation drug administration, exercise plan, dietary intake, or a combination thereof; element 36: element 33 and the method further includes displaying (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like) the individual-target glucose range, the individual-upper glucose limit, and / or the individual-lower glucose limit; element 37: element 36 and the method further includes receiving the glucose level of the subject after deriving the individual-target glucose range, the individual-upper glucose limit, and / or the individual-lower glucose limit, and when the glucose level is outside the individual-target glucose range, when the glucose level is above the individual-upper glucose limit,and / or displaying (visually, audibly, and / or tactilely (for touch)) an alarm when the individual glucose lower limit is exceeded; element 38: the method further includes deriving an individual-target average glucose (e.g., using equation (18), equation (19), or equation (20)); element 39: element 38 and the method further includes diagnosing, treating, and / or monitoring the subject based on the individual-target average glucose; element 40: element 39 and the step of treating the subject is performed, and includes administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; element 41: element 38 and the method further includes displaying the individual-target average glucose (e.g., for example, on system 310, on system 410, on a glucose measuring device and / or a closed loop insulin pump system where the plurality of first and / or second glucose levels are measured, or the like; element 42: the method further includes one or more of the steps of deriving a personalized therapy for subject triage based at least in part on the aHbA1c level, deriving a personalized therapy for diabetic drug titration, deriving a personalized closed loop or hybrid closed loop control system, deriving a personalized therapy using glycation drugs, identifying an abnormal or pathological physiological condition, identifying nutritional supplements and / or medications present during the test, and identifying physiological age; element 43: the method further includes k, gly and a given criterion k gly (k ref gly) and the measured glucose level; element 44: element 43 and the method further includes diagnosing, treating, and / or monitoring the subject based on the individualized glucose level (e.g., the individualized glucose level compared to a current acceptable glucose range or the intracellular glucose level compared to a current acceptable intracellular glucose level range (i.e., LIGL-UIGL)); element 45: element 44 and the step of treating the subject is performed, and the step of administering and / or administering an insulin administration, a glycation drug administration, an exercise plan, a dietary intake, or a combination thereof. and / or adjusting the individual glucose level; element 46: element 43 and the method further including displaying the individual glucose level (e.g., on system 310, on system 410, on a glucose measuring device and / or closed loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like); element 47: element 43 and the method further including displaying an alarm (visually, audibly, and / or tactilely (for touch)) when the individual glucose level is outside of the current respective acceptable glucose range.

[0191] A sixth non-limiting exemplary embodiment includes the steps of receiving (and / or measuring) a laboratory HbA1c level (e.g., one or more laboratory HbA1c levels) of a subject, determining an apparent glycation constant (K) for the subject (e.g., using a model that takes into account transmembrane glucose transport and glycation), and comparing the HbA1c level, K, and a predetermined reference K (K ref and calculating (e.g., using equation (11)) adjusted HbA1c (aHbA1c) for the subject based on the HbA1c (aHbA1c) and the HbA1c (aHbA1c) of the subject based on the HbA1c (aHbA1c). The sixth non-limiting exemplary embodiment can further include one or more of elements 30 to 47.

[0192] A seventh non-limiting exemplary embodiment includes receiving (and / or measuring) a plurality of first glucose levels for a subject over a first time period, receiving (and / or measuring) a first glycated hemoglobin (HbA1c) level for the subject corresponding to an end of the first time period, and determining, using a model that accounts for transmembrane glucose transport and glycation, a red blood cell glycation rate constant (k) based on (1) the plurality of first glucose levels and (2) the first laboratory HbA1c level. gly ), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age ), and apparent glycation constant (K); determining at least one physiological parameter of the subject selected from the group consisting of HbA1c level and k age and a given criterion k age (k ref age ) and calculating (e.g., using equation (10)) an adjusted HbA1c (aHbA1c) for the subject based on the HbA1c (aHbA1c) and the glucose level (aHbA1c). Measuring the glucose level may involve sampling bodily fluid from the subject with an analyte sensor and measuring a plurality of first glucose levels with the analyte sensor. A second non-limiting exemplary embodiment may further include one or more of elements 30 through 47.

[0193] An eighth non-limiting exemplary embodiment includes receiving (and / or measuring) a plurality of first glucose levels for a subject over a first time period, receiving (and / or measuring) a first glycated hemoglobin (HbA1c) level for the subject corresponding to an end of the first time period, and determining, using a model that accounts for transmembrane glucose transport and glycation, a red blood cell glycation rate constant (k) based on (1) the plurality of first glucose levels and (2) the first laboratory HbA1c level. gly ), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age determining at least one physiological parameter of the subject selected from the group consisting of HbA1c level, K and a predetermined reference K (K ref) and calculating (e.g., using equation (11)) an adjusted HbA1c (aHbA1c) for the subject based on the HbA1c (aHbA1c) and the glucose level (aHbA1c). Measuring the glucose level may involve sampling bodily fluid from the subject with an analyte sensor and measuring a plurality of first glucose levels with the analyte sensor. A second non-limiting exemplary embodiment may further include one or more of elements 30 through 47.

[0194] A ninth non-limiting exemplary embodiment of the present disclosure is an analyte sensor and monitoring device configured to measure glucose levels in a bodily fluid, the monitoring device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the fifth, sixth, seventh, or eighth non-limiting exemplary embodiment (optionally including one or more of elements 30 through 47).

[0195] A tenth non-limiting exemplary embodiment of the present disclosure is a closed-loop insulin pump system including an analyte sensor configured to measure glucose levels in a bodily fluid, an insulin pump, and a monitor device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the fifth, sixth, seventh, or eighth non-limiting exemplary embodiment (optionally including one or more of elements 30 through 47), wherein, when a therapy is administered, the therapy includes a step of administering an insulin dose by the closed-loop insulin pump system.

[0196] An eleventh non-limiting exemplary embodiment of the present disclosure uses a model that takes into account transmembrane glucose transport and glycation to calculate a red blood cell glycation rate constant (k) based on (1) a plurality of first glucose levels and (2) a laboratory HbA1c level (e.g., one or more laboratory HbA1c levels). gly), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age determining at least one physiological parameter of the subject selected from the group consisting of a glucose constant (K), an apparent glycation constant (K), and an apparent glycation constant (K), receiving (and / or measuring) a plurality of second glucose levels of the subject over a period of time; gly and a given criterion k gly (k ref gly ) and deriving an individualized glucose level based on the measured glucose level (e.g., using equation (16) or equation (17)). An eleventh non-limiting exemplary embodiment can further include one or more of the following features: element 50: the method further includes a step of diagnosing, treating, and / or monitoring the subject based on the individual glucose level (e.g., the individual glucose level compared to a current acceptable glucose range or the intracellular glucose level compared to a current acceptable intracellular glucose level range (i.e., LIGL-UIGL)); element 51: the feature where element 50 and the step of treating the subject are performed, including a step of administering and / or adjusting insulin administration, glycation drug administration, exercise plan, dietary intake, or a combination thereof; element 52: the method further includes a step of displaying the individual glucose level (e.g., on system 310, on system 410, on a glucose measuring device and / or closed-loop insulin pump system where the plurality of first and / or second glucose levels were measured, or the like); element 53: the method further includes a step of displaying an alarm (visually, audibly, and / or tactilely (for touch)) when the individual glucose level is outside the current respective acceptable glucose range.

[0197] A twelfth non-limiting exemplary embodiment of the present disclosure includes receiving (and / or measuring) a plurality of first glucose levels for a subject over a first time period, receiving (and / or measuring) a first glycated hemoglobin (HbA1c) level for the subject corresponding to an end of the first time period, and determining, using a model that accounts for transmembrane glucose transport and glycation, a red blood cell glycation rate constant (k) based on (1) the plurality of first glucose levels and (2) the first laboratory HbA1c level. gly ), red blood cell formation rate constant (k gen ), red blood cell exclusion constant (k age determining at least one physiological parameter of the subject selected from the group consisting of an apparent glycation constant (K), an apparent glycation constant (K), and receiving (and / or measuring) a measured glucose level; gly and a given criterion k gly (k ref gly deriving an individualized glucose level based on the measured glucose level (e.g., using equation (16) or equation (17)). Measuring the glucose level may involve sampling bodily fluid from the subject with an analyte sensor and measuring a plurality of first glucose levels with the analyte sensor. A second non-limiting exemplary embodiment may further include one or more of elements 50-53.

[0198] A thirteenth non-limiting exemplary embodiment of the present disclosure is an analyte sensor configured to measure glucose levels in a bodily fluid and a monitoring device, the monitoring device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the eleventh or twelfth non-limiting exemplary embodiment, optionally including one or more of elements 50 through 53.

[0199] A fourteenth non-limiting exemplary embodiment of the present disclosure is a closed-loop insulin pump system including an analyte sensor configured to measure glucose levels in a bodily fluid, an insulin pump, and a monitor device including one or more processors and a memory operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of the eleventh or twelfth non-limiting exemplary embodiment (optionally including one or more of elements 50 to 53), wherein, when a therapy is administered, the therapy includes a step of administering an insulin dose by the closed-loop insulin pump system.

[0200] Unless otherwise indicated, all numbers expressing quantities and similar in the specification and appended claims are to be understood as being modified in all instances by the term "about." Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and appended claims are approximations that may vary depending on the desired properties sought to be obtained by the embodiments of the present disclosure. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed by applying ordinary rounding techniques in light of the number of reported significant digits.

[0201] One or more exemplary embodiments incorporating various features are presented herein. For purposes of clarity, this application does not describe or show all features of a physical embodiment. It should be understood that in developing a physical embodiment incorporating an embodiment of the present disclosure, numerous embodiment-specific decisions must be made to reach the developer's goals, such as compliance with system-related, commercial, governmental, and other constraints that vary from embodiment to embodiment and change from time to time. While the developer's endeavor may be time-consuming, such an endeavor is nevertheless a routine undertaking for one of ordinary skill in the art having the benefit of the present disclosure.

[0202] Although various systems, tools, and methods are described herein in terms of "comprising" various components or steps, the systems, tools, and methods may also be "essentially composed of" or "consisting of" the various components and steps.

[0203] As used herein, the phrase "at least one of" following a series of items where the word "and" or "or" separates any of the items modifies the list as a whole, rather than each member (i.e., each item) of the list. The phrase "at least one of" is given a meaning including at least one of any of the items, at least one of any combination of the items, and / or at least one of each of the items. By way of example, "at least one of A, B, and C" or "at least one of A, B, or C" means, respectively, A only, B only, or C only, any combination of A, B, and C, and / or at least one of each of A, B, and C.

[0204] In order to facilitate a clearer understanding of the embodiments of the present invention, the following examples of preferred or exemplary embodiments are presented. The following examples should not be read in any way as limiting or defining the scope of the present invention.

[0205] Working Example EXAMPLES

[0206] The model described herein was verified using 200 days of glucose monitor data and three HbA1c values ​​for a single patient. Figure 10 is a plot of 200 days of glucose monitor data (right y-axis), three HbA1c values ​​(left y-axis), and estimated HbA1c values ​​based on the 14-day eHbA1c model (left y-axis). As illustrated, the estimated HbA1c derived from the 14-day HbA1c model changed very dramatically over time. However, it is unlikely that HbA1c can change this quickly.

[0207] FIG. 11 shows the results of the method described herein. gly and k age FIG. 11 is a plot of the FIG. 10 plot with cHbA1c (left y-axis) over the first 100 days determined using

[0208] FIG. 12 shows k determined in relation to FIG. 11 by the method described herein. gly and k age FIG. 12 is a plot of FIG. 11 with cHbA1c over the subsequent 100 days (extending from day 100 to day 200, left y-axis) using the method described herein. The third HbA1c value was not taken into account in this method, and the model described herein predicted the measured third HbA1c value, thereby demonstrating that the model described herein closely matches reality.

[0209] The same procedure was performed on the larger data set of Table 1, with the addition of a 14-day glucose model to estimate HbA1c levels. Figure 13A shows a cross-plot comparison of estimated HbA1c levels (by the 14-day glucose model) compared to laboratory HbA1c levels, and Figure 13B shows a cross-plot comparison of cHbA1c levels (by the methods described herein) compared to laboratory HbA1c levels. The 14-day glucose model had an R 2 value, whereas the method described herein has an R 2 values, which showed a reduction in variability of about 50%.

[0210] (Table 1) TIFF2024543824000030.tif41162 EXAMPLES

[0211] Continuous glucose monitor (CGM) and laboratory HbA1c data from 139 type 1 and 148 type 2 diabetes patients enrolled in two previous European clinical studies were used to calculate HbA1c as detailed below. Both studies were conducted after appropriate ethical approval and participants provided informed consent. A total of 6 months of CGM data was collected using a sensor-based intermittent glucose monitoring system (FREESTYLE LIBRE, Abbott Diabetes Care, Witney, UK), while HbA1c was measured by a central laboratory (ICON Laboratories, Dublin, Ireland) at months 0, 3, and 6 of the study. Analyses were performed with a minimum of 80% CGM coverage and no gaps in glucose data longer than 12 hours.

[0212] RBC removal due to aging and eryptosis is a complex process known to vary within and between individuals. Past efforts have attempted to account for variations in mean RBC age to accurately reflect HbA1C. However, these efforts did not adjust for potential differences in RBC transmembrane glucose uptake rates. By applying a recently published model (Xu Y, Dunn TC, Ajjan RA. A kinetic model for glucose levels and hemoglobin A1C provides a novel tool for individualized diabetes management. J Diab Sci Tech. 2020, DOI:10.1177 / 1932296819897613, referred to herein as "Xu Y et al. 2020"), we constructed a model that accounts for both RBC turnover rate and RBC transmembrane glucose uptake rate. For all analyses, we used the Python / SciPy software package to calculate the RBC glucose transmembrane uptake rate (k gly) and RBC turnover (k age We then adapted this model for potential clinical use by constructing aHbA1c that takes into account RBC turnover rate according to Equation 1 above.

[0213] Under the assumption of a constant RBC life span for each individual, the RBC turnover rate (k age ) and RBC life span (L RBC ) and mean RBC age (MA RBC ) can be expressed by a simple equation: Interconversions can be made using TIFF2024543824000031.tif11150. Thus, a standard RBC turnover rate of 1% / day corresponds to an RBC lifespan of 100 days and a mean RBC age of 50 days. Of note, the regulation is not symmetric; shortening the RBC lifespan corresponds to a greater aHbA1c regulation than an equivalent increase in RBC lifespan.

[0214] FIG. 14 is a plot of laboratory HbA1c compared to aHbA1c by RBC lifetime ("aA1C"). Each individual (circle: type 1 diabetes, n=18; diamond: t, n=32) is represented by two dots, one hollow (laboratory HbA1c) and one solid (aHbA1c). The hollow squares represent similar laboratory HbA1c but different aA1c with variable RBC lifetime (solid squares). Conversely, the hollow stars show different laboratory HbA1c but similar aHbA1c (solid star).

[0215] Data sets from 50 individuals (18 had type 1 diabetes and 32 had type 2 diabetes) met the specified criteria for calculating RBC lifespan. The mean age of participants was 54 years (ranging from 21 to 77 years), and 18 of these participants (36%) were women. The mean RBC lifespan was 92 days, ranging from 56 to 166 days. Among the individuals studied, 68% had aHbA1C values ​​that differed from laboratory HbA1c by more than 1.0% (11.0 mmol / mol) (Figure 14). At the individual level, two similar laboratory HbA1c (7.7% and 7.6% squares) showed aHbA1C of 6.5% and 10.2%, respectively (with different RBC lifespans), indicating different future diabetic complication risks. In contrast, individuals with different laboratory HbA1c (8.8% and 6.6% asterisks) showed equal aHbA1C of 7.9%, exposing these individuals to similar diabetic complication risks, but potentially different hypoglycemic risks with potentially intensified treatment in patients with 8.8% laboratory HbA1c. In general, individuals with an RBC lifespan of 86-113 days showed relatively small differences between adjusted HbA1c and laboratory HbA1c (<1.0% when laboratory HbA1c was <8%). However, in individuals with an RBC lifespan of <83 days, aHbA1c was higher than laboratory HbA1c by a median of 2.6%, indicating that these individuals may be undertreated and therefore at higher risk for persistent hyperglycemia and diabetic complications. Conversely, individuals with an RBC life span >113 days had aHbA1c that was a median 1.4% lower than the laboratory value, thus placing a subset of these patients at risk for overtreatment and acute onset of hypoglycemia (Figure 14).

[0216] Variations in RBC life span and transmembrane glucose uptake rates between individuals may lead to different laboratory HbA1c despite similar hyperglycemic exposure of affected organs by diabetic complications. To personalize care and assess individual risk of hyperglycemic complications, laboratory HbA1c levels must be adjusted to take into account RBC turnover variations by aHbA1c as proposed by the inventors. Without this adjustment, there is a risk of overestimation of glucose levels, which may lead to hypoglycemia due to unnecessary intensification of diabetic treatment, or alternatively underestimation, which may lead to insufficient treatment and subsequent higher risk of complications. In addition, the variable RBC life span across individuals may lead to misclassification if the diagnosis is based solely on laboratory HbA1c, with possible consequences for the diagnosis of prediabetes and diabetes.

[0217] In conclusion, quantitative aHbA1c derived from laboratory HbA1c and CGM readings has the potential to more accurately assess intracellular glycemic exposure, providing safer and more effective glycemic guidance for the management of diabetic individuals. In this study, we chose a standard RBC lifespan of 100 days when adjusting laboratory HbA1c, but further efforts are needed to improve the accuracy of this adjustment and establish the best measure. Clinical studies with a larger number of individuals are needed to further validate the accuracy of the model and correlate aHbA1C with diabetic complications and hypoglycemic exposure. EXAMPLES

[0218] To calculate aHbA1c, we evaluated continuous glucose monitor (CGM) and laboratory HbA1c data from 139 type 1 and 148 type 2 diabetes patients enrolled in two previous European clinical studies [10, 11], as detailed below. Both studies were conducted after appropriate ethical approval, and participants provided informed consent. A total of 6 months of CGM data was collected using a sensor-based intermittent glucose monitoring system (FreeStyle Libre®, Abbott Diabetes Care, Witney, UK), while HbA1c was measured by a central laboratory (ICON Laboratories, Dublin, Ireland) at months 0, 3, and 6 of the study. For T1D participants, the mean age was 44 years (ranging from 18 to 70 years), and 17 (33%) of these participants were women. For T2D, the mean age was 59 years (range 33-77 years), and 28 (35%) of these participants were women.

[0219] To support high quality estimation of kinetic model parameters, the analysis required a minimum of 70% CGM coverage and no gaps in glucose data longer than 48 hours. Each had at least one data measurement consisting of two HbA1c measurements connected by CGM data. Parameters were successfully estimated for individuals with sufficient daily glucose variability, as evidenced by convergence of model fits of RBC life span between 50 and 180 days.

[0220] RBC clearance due to aging and red blood cell eryptosis is a complex process known to vary within and between individuals. Past efforts have attempted to account for variations in mean RBC age to accurately reflect HbA1C. However, these efforts did not adjust for potential differences in RBC transmembrane glucose uptake rates. We constructed a model that accounts for both RBC turnover rate and RBC transmembrane glucose uptake rate by adapting our recently published model. We used the Python / SciPy software package for all analyses to estimate the RBC transmembrane glucose uptake rate (k gly ) and RBC turnover (k age ) was determined. Next, the inventors determined that HbA1c (%) is laboratory HbA1c and k age is the individual's RBC turnover rate (% / day), and k ref age This model was adapted for prospective clinical use by constructing aHbA1c that takes into account the RBC turnover rate as in equation (1) above, where a is the standard RBC turnover rate (1% / day).

[0221] Under the assumption of a constant RBC life span for each individual, the RBC turnover rate (k age ) and RBC life span (L RBC ) and mean RBC age (MA RBC ) can be expressed by a simple equation: Interconversions can be made using TIFF2024543824000032.tif11150. Thus, a standard RBC turnover rate of 1% / day corresponds to an RBC lifespan of 100 days and a mean RBC age of 50 days. Of note, the regulation is not symmetric; shortening the RBC lifespan corresponds to a greater aHbA1c regulation than an equivalent increase in RBC lifespan.

[0222] Of the 287 subjects in the original study, 218 had sufficient CGM coverage between at least two HbA1c measurements. Of these subjects, 131 individuals (51 had type 1 diabetes and 80 had type 2 diabetes) had sufficient glucose variability for the model to determine estimates for RBC lifespan and transmembrane glucose transport rates. The mean (median, range) RBC lifespan was 94 (100, 57-125) days in individuals with T1D and 92 (100, 56-151) in individuals with T2D (Figure 15). In this cohort, the mean difference between aHbA1c and laboratory HbA1c was 6.6 mmol / mol (0.60%) in T1D subjects and 9.7 mmol / mol (0.88%) in T2D subjects. The corresponding standard deviations were 17 mmol / mol (1.5%) and 19 mmol / mol (1.7%), respectively.

[0223] Putting these results into a clinical context, Figure 15 shows the adjustment for laboratory HbA1c with different RBC life spans. Near the boundaries of the interquartile range, two subjects with equal laboratory HbA1c of 63mmol / mol (7.9%) but different RBC life spans of 84 days and 101 days would have RBC life span-adjusted aHbA1c values ​​of 78mmol / mol (9.3%) and 62mmol / mol (7.8%), respectively, and would have different future diabetic complication risks. In contrast, several individuals with different laboratory HbA1c of 60mmol / mol (7.6%) and 75mmol / mol (9.0%) and RBC life spans of 84 days and 101 days would have equal aHbA1c values ​​of 74mmol / mol (8.9%). Thus, these individuals may be at similar risk for diabetic complications, but at different risks for hypoglycemia with the potential for intensified treatment in patients with higher laboratory HbA1c. In general, individuals with an RBC life span of approximately 86-113 days showed a relatively small difference between adjusted HbA1c and laboratory HbA1c (<11 mmol / mol or 1% when laboratory HbA1c was <64 mmol / mol or 8%). In this cohort, 90 subjects (69%) were within this RBC life span range. However, at the more extreme RBC life spans, greater adjustment is possible. In individuals with an RBC life span of <83 days, aHbA1c was a median 35 mmol / mol (3.2%) higher than laboratory HbA1c, indicating that these individuals may be undertreated and therefore at higher risk for persistent hyperglycemia and diabetic complications. Conversely, individuals with an RBC life span >113 days had a median aHbA1c that was 13 mmol / mol (1.2%) lower than the laboratory value, thus placing some of these patients at risk for overtreatment and acute onset of hypoglycemia.

[0224] Variations in RBC life span and transmembrane glucose uptake rates between individuals may lead to different laboratory HbA1c despite similar hyperglycemic exposure of affected organs by diabetic complications. In order to tailor treatment to each individual and assess individual risk of hyperglycemic complications, laboratory HbA1c levels must be adjusted to take into account RBC turnover variations by our proposed aHbA1c. Without this adjustment, there is a risk of overestimation of glucose levels, which may lead to hypoglycemia due to unnecessary intensification of diabetic treatment, or alternatively underestimation, which may lead to insufficient treatment and subsequent higher risk of complications. In addition, the variable RBC life span across individuals may lead to misclassification if the diagnosis is based solely on laboratory HbA1c, with possible consequences for the diagnosis of prediabetes and diabetes.

[0225] Several numerical models have been developed to estimate laboratory HbA1c from glucose or TIR, highlighting the importance of the field. The unique advantage of our model is the explicit inclusion of individual-specific RBC lifespan and glycation rate in the calculation. Thus, this method allows the estimation of RBC lifespan from CGM and HbA1c data without interference from glycation rate variations due to individual GLUT1 levels. We have presented an equation to calculate adjusted HbA1c from laboratory HbA1c and RBC lifespan. RBC lifespan can be measured directly, but this measurement requires complex RBC labeling and tracking, a difficult process to implement in routine clinical practice (6). In this study, we applied a previously published kinetic model (9) to estimate RBC lifespan using high-quality CGM and HbA1c data.

[0226] In conclusion, quantitative aHbA1c derived from laboratory HbA1c and CGM readings has the potential to more accurately assess glycemic exposure in various organs, providing safer and more effective glycemic guidance for the management of diabetic individuals. In this study, we chose a standard RBC lifespan of 100 days when adjusting laboratory HbA1c, but further efforts are needed to improve the accuracy of this adjustment and establish the best measure in various populations. Clinical studies with a larger number of individuals are needed to further validate the accuracy of the model and correlate aHbA1C with diabetic complications and glycemic exposure. EXAMPLES

[0227] Continuous glucose monitor (CGM) and laboratory HbA1c data were obtained from 31 type 1 diabetes patients. All of these individuals had type 1 diabetes managed by a sensor-equipped pump system. The data set included an average of approximately 10 laboratory HbA1c values ​​for each individual spaced approximately one month apart, along with continuous glucose monitor values ​​throughout. A total of 304 laboratory HbA1c values ​​paired with CGM over a 14-day period were available for analysis. Intracellular glucose (IG) was determined throughout using equation (17).

[0228] Figure 16A is a cross plot and correlation of 14-day IG mean values ​​when aHbA1c was provided, and Figure 16B is a cross plot of the originally collected data for 14-day mean plasma glucose (PG) and laboratory HbA1c. The IG method had an R 2 value for the unadjusted data, whereas the unadjusted data had an R 2 This illustrates a significant reduction in variability. EXAMPLES

[0229] Continuous glucose monitor (CGM) and laboratory HbA1c data were obtained from 31 type 1 diabetes patients. All of these individuals had type 1 diabetes managed by a sensor-equipped pump system. The data set included an average of approximately 10 laboratory HbA1c values ​​for each individual spaced approximately one month apart, along with the full-time continuous glucose monitor values. A total of 304 laboratory HbA1c values ​​paired with CGM over a 14-day period were available for analysis. Effective plasma glucose (PG) was calculated using equation (16) throughout. eff ) was decided.

[0230] 17A and 17B show the measured plasma glucose (PG) and PG eff This is an example of a glucose pattern insight report for the same subject (individual with stage 2 mild kidney loss) using PG eff indicates the extent of excess glucose exposure in organs and tissues and therefore a potential cause of renal damage. The time above the 180 mg / dL target ranges from 6.7% of the PG to PG eff The mean glucose control zones changed from 0.01 to 0.37% and the time below the 70 mg / dL target decreased from 3.3% to 0.7%. These changes change the clinical interpretation of glucose control zones that need to be addressed when optimizing short-term and long-term risk reduction attributable to diabetes. EXAMPLES

[0231] Three months of continuous glucose monitor and laboratory HbA1c data from 31 type 1 diabetes patients were evaluated to understand HbA1c differences between racial groups. Calculation of the individual apparent glycation ratio (AGR) will help develop individualized HbA1c targets and optimize glycemic control. Specifically, using the kinetic model described above, the following equation was employed to calculate the AGR: TIFF2024543824000033.tif10150 equation (21) where A is the CGM acquired average glucose and K is the Mis the glucose affinity for GLUT1. AGR was compared across various ethnic and age groups.

[0232] 18A and 18B are exemplary comparisons of HbA1c-glucose relationships by race and age, respectively. The lines are the mean steady-state glucose-HbA1c relationships by group. The numbers of black and white individuals were similar, 106 and 110, respectively (120 women and 96 men). The mean age (range) was 30 years (8-72 years), with n=94 younger than 19 years, n=78 between 19-50 years, and n=44 older than 50 years. Overall calculations K M The mean glucose concentration was 464 mg / dL, and AGR (mean ± SD) was different between white and black populations at 69.9 ± 5.8 ml / g and 74.2 ± 7.1 ml / g, respectively (p<0.001). AGR was highest in those aged >50 years at 75.4 ± 6.9 ml / g, decreasing to 73.2 ± 7.8 ml / g in those aged 19-50 years, and further to 71.0 ± 5.8 ml / g in the youngest age group (p<0.05). In contrast, AGR values ​​were similar in men and women at 71.5 ± 7.5 ml / g and 72.5 ± 6.6 ml / g (p=0.27). Both race and age, but not sex, influenced the relationship between mean glucose and HbA1c, and there was large individual variability within the groups. EXAMPLES

[0233] It is widely accepted that good glycemic control in diabetes reduces the risk of microvascular complications and long-term macrovascular disease. Although HbA1c is a good marker of complication risk at the group level, differences exist between individuals that may lead to inappropriate management decisions when based on HbA1c alone. The glycation gap represents the difference between predicted and actual HbA1c. Importantly, the glycation gap has shown a relationship with the risk of diabetic complications and adverse outcomes, thereby moving it from a biochemical concept to a clinical risk marker. A recent comprehensive study using HbA1c, blood glucose (BG), and continuous glucose monitors (CGM) demonstrated that HbA1c overestimated mean glucose control in black individuals by 0.4 percentage points compared to white individuals. Notably, no racial differences were found in albumin or fructosamine glycation, indicating that the differences observed were specific to HbA1c. The variable nature of the glucose-HbA1c relationship suggests that the observed changes are related to individual variations in the rate of glucose uptake by RBCs and / or RBC life span, due to hemoglobin glycation occurring within red blood cells (RBCs). Understanding the causes of inter-individual differences between HbA1c and mean glucose levels will allow the development of reliable individual glycemic measures that more accurately reflect tissue glucose exposure in organs susceptible to diabetic complications.

[0234] To validate and characterize the relationship between HbA1c and glucose for age, race, and gender groups, we evaluated 3 months of continuous glucose monitor (CGM) and laboratory HbA1c data from 216 type 1 diabetes patients. Understanding the variability in AGR can help personalize HbA1c targets and thus provide optimal glycemic control for each individual, thereby minimizing the risk of both hyperglycemic and hypoglycemic complications.

[0235] Publicly available data from a previous study on racial differences in the relationship between mean glucose and HbA1c for people with type 1 diabetes (https: / / t1dexchange.org / pages / resources / clinic-network / studies / ) were used to obtain CGM and central laboratory HbA1c measurements. More specifically, specialized CGM data and up to six central laboratory HbA1c measurements were collected. Red blood cell indices, including red blood cell distribution width (RDW), were also collected. The analysis included 216 individuals with at least 100 days of CGM and two HbA1c readings.

[0236] Laboratory HbA1c is regulated by mean glucose level, red blood cell life span, and cellular glucose uptake rate, which is mediated by glucose transporter 1 (GLUT1). Individual-specific apparent glycation ratio (AGR) was estimated using equation (21), where AGR (mL / g) is the individual's glycation propensity expressed as the product of glucose uptake rate and RBC life span, and the population predicted value was 65.1 mL / g based on a mean RBC life span of 105 days and an RBC glucose uptake rate of 0.62 mL / g / day. AG (mg / dL) is mean glucose, HbA1c (NGSP%) is mean HbA1c, and K M (mg / dL) is the Michaelis constant for glucose on the RBC membrane and GLUT1, which was assumed to be a universal parameter. M Mean glucose, mean HbA1c, and AGR values ​​were calculated for all individuals, assuming a universal constant. We then analyzed the effects of race, sex, and age on AGR values ​​and evaluated the relationship of this effect to red cell distribution width (RDW), which can reflect RBC life span. The relationship of RDW to AGR values ​​was evaluated across groups. Group mean RDW should follow the same trend as AGR assuming similar within-group apparent hemoglobin glycation rate distributions.

[0237] The patient population was divided according to race into non-Hispanic black / African American individuals and non-Hispanic white individuals. Separate analyses were performed according to sex and age. Age analyses were performed according to within-group tertiles and further subcategory by three clinically significant groups: i) young (18 years or younger), ii) adults (18-50 years), and iii) older adults (over 50 years).

[0238] The mean glucose concentration for each individual was calculated from the average of all available glucose concentrations. Similarly, the mean HbA1c was calculated from all available central laboratory HbA1c values ​​(point-of-care HbA1c values ​​were excluded from the analysis). In the primary analysis, Deming regression was used to obtain the best-fit AGR for the subject group. Deming regression (detailed in the Supplementary Material) was used to minimize the deviation in both mean glucose and HbA1c. K M Values ​​were set to a universal value (464 mg / dL, see Supplementary Material) and between-group comparisons were made on group mean AGR values.

[0239] Based on an expected AGR of 70 ml / g in this dataset and an overall SD=7 ml / g, a sample size of 86 individuals per group would be required to detect 3 ml / g (approximately 5% expected AGR) with 80% power and a significance level of 0.05. Applicants' dataset contained 216 individuals with over 100 individuals in each of the two main ethnic groups. Between-group analysis was performed using one-way ANOVA and unpaired two-sample t-tests in Python / Scipy with a significance level of p<0.05 for comparisons.

[0240] FIG. 19 is a table showing the characteristics of the study individuals. The grey rows list the p-values ​​of the within-group analysis of variance. For all pairwise comparisons between age groups using t-tests, the younger age group was significantly different from the other two age groups with p-values ​​<0.05, whereas the adult and older adult age groups were not significantly different from each other with p-values ​​>0.05. Good quality CGM and HbA1c data were obtained for all 216 individuals with T1D, including 96 males and 120 females. In total, 110 were non-Hispanic African American individuals and 106 were non-Hispanic White American individuals. The age tertile groups had 72 subjects in each group, with mean ages of 12.9 years (range 8.5-16 years), 25.8 years (range 16-38 years), and 52.6 years (range 38-72.3 years). For clinically significant age groups, there were 90, 82, and 44 individuals in the young (<18 years), adult (18-50 years), and older adult (>50 years) age groups with mean ± SD ages of 13.7 ± 2.5, 34.0 ± 9.1, and 57.9 ± 6.6 years, respectively (Figure 19). Each individual had 5826 ± 1728 glucose readings and 4.8 ± 0.7 laboratory HbA1c readings collected over 85 ± 15.5 days. Figure 19 also includes the RDW distribution as a rough indicator of RBC longevity in our analysis.

[0241] FIG. 20A plots the regression lines using a linear function (green) and equation (1) (black) for the full cohort study. As can be seen in FIG. 20A, the relationship between HbA1c and mean glucose was evaluated using the total least squares method with linear regression and curvilinear regression based on equation (21). Estimated K M and cohort AGR were 464 mg / dL and 72.5 ± 7.0 ml / g, respectively, showing considerable interindividual variability. M This value is close to the literature reported value of 472 mg / dL. Therefore, the K value of 464 mg / dL Mwas used as a universal constant throughout. Importantly, our model demonstrated that the relationship between mean glucose and HbA1c is nonlinear. Specifically, FIG. 23 illustrates composite binning of subjects and comparison of group means to regression lines. For example, FIG. 23 illustrates (A) subjects in odd bins and subjects in even bins colored blue and red, group means plotted with solid circles, regression lines from a linear function and regression lines from Equation (1) plotted as black and blue lines, (B) residuals of the linear regression line to group means, and (C) residuals of the regression line of Equation (1) to group means.

[0242] 20B-20C depict the differences in glycation trends between various demographic groups. For FIG. 20B, the individual steady-state glucose A1c curves (solid line) and group mean steady-state glucose A1c curves (dashed line) for the white and black racial groups are plotted in blue and red. For FIG. 20C, the individual steady-state glucose A1c curves (solid line) and group mean steady-state glucose A1c curves (dashed line) for the young (≦18 years), adult (19-50 years), and elderly (>50 years) age groups are plotted in blue, gray, and red, respectively. As can be seen in FIG. 20B-20C, the individual AGR plot curves demonstrate a wide range of glycation trends, including differences between the two racial groups (FIG. 20B) and between multiple age groups (FIG. 20C). FIG. 20D depicts the relationship between steady-state glucose and HbA1c under different AGR values. As can be seen in FIG. 20D, a typical AGR curve is plotted, including a reference curve with an AGR value of 65.1 ml / g calculated from baseline RBC life span and glucose uptake rate for non-diabetic individuals. Compared to the baseline AGR of 65.1 ml / g, T1D individuals in this data set had a higher glycation tendency as evidenced by a higher average AGR value of 72.5 ml / g. In general, a 5-unit increase in AGR results in an increase in HbA1c of about 0.5% (about 5 mmol / mol) with an average glucose of 154 mg / dL (8.6 mmol / l). Thus, having an HbA1c of 7.0% compared to a baseline AGR of 65.1 ml / g is associated with an average glucose of 154 mg / dL, while another individual with an AGR of 80 ml / g would be expected to have an HbA1c of 8.5% with an average glucose of 154 mg / dL. Higher mean glucose corresponds to a larger change in HbA1c for the same difference in AGR.

[0243] The data show that AGR was adjusted for race (p<0.001) with mean ± SD AGR values ​​of 74.2 ± 7.1 ml / g and 69.9 ± 6.2 ml / g for the black and white groups, respectively. As can be seen in Figure 21, age, but not sex, had an effect on AGR. Notably, mean AGR values ​​increase in the older age groups. The highest AGR was observed in those aged >50 years at 75.4 ± 6.9 ml / g, decreasing to 73.2 ± 7.8 ml / g in those aged 19-50 years, and further decreasing to 71.0 ± 5.8 ml / g in the youngest age group (p<0.05). Comparing the youngest and oldest age groups, the AGR difference is 4.5 ml / g or 6%. The AGR differences between the racial groups are of similar magnitude, corresponding to an HbA1c difference of about 0.45% (about 5 mmol / mol) at a mean glucose of 154 mg / dL. The effects of race and age appear to be additive, with older black individuals having the highest AGR of 76.4±5.0 ml / g compared to younger white individuals having the lowest AGR of 69.2±5.4 ml / g. In contrast to race and age, males and females have similar AGRs at 72.5±6.6 and 71.5±7.5 ml / g, respectively (p=0.30). As can be seen in FIG. 21A, the differences between these groups, except between the sexes, are statistically and clinically significant, but relatively smaller than the AGR variation between individuals. More specifically, the between-individual variance within each group is at least four times larger than the average between-group variance. This difference can be seen by comparing the within-group variance with the between-group variance. For example, as can be seen in FIG. 21B, the smallest within-group AGR standard deviations (SD) are 6.2 ml / g and 5.8 ml / g for the racial and age groups, respectively. These SD values ​​are consistent with the variance of 38.4 ml / g. 2 / g 2 and 33.6 ml 2 / g 2 These figures correspond to 9.2 ml for each racial and age group. 2 / g 2 and 3.2 ml 2 / g 2 This is four times larger than the mean between-group variance.

[0244] FIG. 21 is a diagram of the comparison of AGR and RDW among racial, age, and gender groups. Specifically, FIG. 21 depicts (A) the mean (red), standard deviation (light blue), and 95% confidence section (blue) of AGR and RDW in different subgroups, and (B) their numerical comparison. As can be seen in FIG. 21, RDW shows a similar trend as AGR among age, gender, and racial groups, suggesting a relationship between these two measures. An additive effect of race and age was observed on AGR and RDW, evidenced by similar mean values ​​in the oldest white and youngest black groups. RDW can reflect RBC lifespan / turnover, but is also influenced by physiological status. Since AGR is the product of RBC lifespan and apparent hemoglobin glycation rate, AGR and RDW should be related to each other. FIG. 21 shows good agreement at the group mean level. Regression analysis produced a weak positive correlation of R=0.2.

[0245] The nonlinear relationship observed between fasting glucose and HbA1c was originally attributed to a Michaelis-Menten saturation type reaction between glucose and hemoglobin. As the importance of good glucose control in diabetes has been firmly established, the observed dynamic range of glucose has narrowed and various linear approximations have been utilized to characterize the relationship between glucose and HbA1c. However, the Michaelis constants (K) for glucose and GLUT1, which affect the curvature of the relationship between glucose and HbA1c, have not been fully understood. M ) has been measured at about 472 mg / dL. M is estimated to be 464 mg / dL, which is in close agreement with experimentally reported values ​​and further validates the equations used to perform the calculations disclosed herein. Furthermore, the methodology of the present disclosure is consistent with the non-linear relationship between glucose and HbA1c, thus avoiding the mean glucose distortions that occur when using linear relationship models.

[0246] The data presented herein show that AGR values ​​are proportional to HbA1c at a given glucose level. The use of AGR can explain previously reported racial differences between HbA1c and glucose levels. Furthermore, these findings provide insight into the cause of ethnic-specific variation in HbA1c despite similar mean glucose levels. An additional and clinically significant consideration is that age has a similar effect as race, and the two have additive effects. Regression analyses show independent relationships between AGR and age and between AGR and race, further supporting the specific role of each in determining the relationship between mean glucose and HbA1c. Similarly, age and race are also individually and independently associated with RDW. Because RDW correlates with RBC lifespan, race and age likely influence AGR through changes in RBS lifespan. Clinically, HbA1c can be >0.7% (7 mmol / mol) higher in older black adults than in younger white children with comparable glucose exposure. For example, higher HbA1c in black adults may lead to overtreatment and hypoglycemic episodes that are associated with adverse outcomes, especially in the elderly. Conversely, a relatively "good HbA1c" in white children may provide a false sense of security and lead to undertreatment even when early glycemic control is clearly crucial in this population. It should be noted that this is not just a "group effect," as we have demonstrated herein that the interindividual variability in the HbA1c-mean glucose relationship is greater than the between-group differences.

[0247] The T1D cohort had a median AGR of 72.0 ml / g, which is higher than the norm of 65.1 ml / g for non-diabetic individuals, thereby indicating that our patient cohort had approximately 0.7% higher HbA1c at a mean glucose of 154 mg / dL. This suggests that hyperglycemia itself or other unidentified factors may be altering the mean glucose-HbA1c relationship. One potential mechanism is altered GLUT1 activity with hyperglycemic exposure of RBCs.

[0248] Given the AGR differences between groups, as well as the individual differences within each group, addressing glycemic control according to a uniform HbA1c target may lead to inappropriate management decisions. For example, with a target HbA1c of 7%, an individual with an AGR of 80 mg / dL will have a mean glucose of 117 mg / dL, whereas another individual with an AGR of 60 mg / dL will have a significantly higher mean glucose level of 172 mg / dL. This predisposes this second individual to diabetic complications at a significantly higher risk due to the high glucose exposure of the organs. This calls for the development of an individualized HbA1c target to optimize glycemic care. Specifically, FIG. 22 shows how an individual's HbA1c should be adjusted by AGR based on Equation (1). Given the various issues with the accuracy of HbA1c, one may argue that this glycemic marker should be entirely replaced by a CGM-derived metric. However, there are two caveats to this CGM technique. First, the ideal in-range dwell time has only received limited validation, and more robust support for CGM glycemic markers is needed. Second, financial constraints make it difficult to make CGM available to all diabetic patients. Our methodology will help estimate AGR and sufficient HbA1c levels using intermittent CGM, which may be available without significant expenditure. However, we must acknowledge that our individualized HbA1c target values ​​will require validation with prospective clinical outcomes before this measure can be widely adopted in routine clinical practice.

[0249] Assuming that AGR represents the product of individual glucose uptake rate and RBC lifespan, we confirmed the correlation between RDW and age and demonstrated a relationship with age but not with sex. Furthermore, AGR and RDW showed similar trends across multiple age groups, and a significant correlation was detected between these two measures.

[0250] The strengths of this approach include its novel technique, its ease of computation, and its sufficient detection power to detect small differences in AGR. [Example 7 Supplement]

[0251] The relationship between steady-state glucose and HbA1c described herein can be transformed into equation (22), equation (23), and equation (24), which are different forms of this same relationship. TIFF2024543824000034.tif15150 equation (22) TIFF2024543824000035.tif14150 equation (23) TIFF2024543824000036.tif20150 equation (24)

[0252] AGR is an individual's apparent glycation ratio (AGR = RBC glucose uptake rate / RBC turnover rate = k gly / k age ) and K M is the Michaelis constant for glucose and GLUT1 on the RBC. K M It has been reported that K is in the range of about 400-500 mg / dL under various conditions and methods, with the most appropriate being 472 mg / dL. M AGR is likely to be a universal parameter in humans, since it reflects the binding affinity of glucose to GLUT1 on RBCs. Thus, the steady-state glucose-HbA1c relationship is controlled by the AGR value. Individual AGR values ​​explain the discrepancies with any regression relationship at the individual level that are often seen in clinical practice.

[0253] The default units in equation (22), equation (23), and equation (24) are A1c (%), PG (mg / dL), AGR (dL / mg), and K. M (mg / dL). The preferred unit sets according to DCCT are A1c (%), PG (mg / dL), K (ml / g), and K M When (mg / dL) is used, these equations become: TIFF2024543824000037.tif15150 equation (25) TIFF2024543824000038.tif20150 equation (26) TIFF2024543824000039.tif14150 equation (27)

[0254] The preferred unit sets according to the IFCC and SI are A1c (mmol / mol), PG (mmol / L), K (ml / g), and K M When (mg / dL) is used, these equations become: TIFF2024543824000040.tif18150 equation (28) TIFF2024543824000041.tif20150 equation (29) TIFF2024543824000042.tif14150 equation (30)

[0255] The mean glucose and HbA1c were used as approximations of steady-state glucose and HbA1c in equation (21).

[0256] The subject-level mean glucose and HbA1c data points were fitted to a linear function and equation (21) using Deming regression. Specifically, the sum of absolute relative deviations (SARD) was optimized / minimized. TIFF2024543824000043.tif9150 equation (31)

[0257] This regression is M This was done by minimizing the average SARD by scanning the entire range of values ​​(200-800 mg / dL in 1 mg / dL increments). M The step size was calculated by aggregating the average AGR from the individual AGR values ​​calculated using equation (1). Equations (22) and (23) were then used to determine the step size K M The sum of SARD can be calculated using AGR and K M The optimal pair with has the minimum sum of SARD. M was 464 mg / dL, very close to the value of 472 mg / dL previously reported from the study.

[0258] A nonlinear relationship between steady-state glucose and HbA1c was proposed as early as the late 1970s and early 1980s, but since then this relationship has been primarily modeled using linear functions. Under the assumptions presented herein, when subjects are grouped with various glucose and HbA1c levels, the group mean AGR values ​​should approach the cohort mean as the group size increases. Thus, the group mean glucose and HbA1c should approximate the relationship between steady-state glucose and HbA1c under cohort K. Assuming this approximation, the group means should follow the curve that follows equation (22) under the cohort AGR. Thus, equation (22) can be tested by examining the curvature at the group means. To identify a more accurate function to describe the data, this model was tested by comparing the accuracy of Deming regression fits using linear functions and equation (22), particularly by comparing residuals versus independent variable plots.

[0259] To reduce the mean regression effect by binning, subjects were grouped by a composite index of both mean glucose and HbA1c. The composite index was calculated as the sum of quadratic ranks using glucose and HbA1c. Subjects were then grouped by this composite index into 6, 7, 8, and 9 equally sized bins.

[0260] (Table 2) TIFF2024543824000044.tif47138 Table 2. Grouping based on composite index

[0261] Composite binning is illustrated in Figure 23A. Residuals plotted against group means are shown in Figures 23B and 23C. The curve of Equation (22) produced a smaller residual range (-0.18%, 0.1%) than the linear function (-0.29%, 0.2%). In addition, a consistent V-shaped steepening pattern was observed with the linear function suggesting that the underlying relationship was not linear. Furthermore, Equation (22) was observed to have smaller and more stable residuals and therefore a more accurate function for relating glucose to HbA1c.

[0262] The AGR value reflects an individual's propensity for glycation. By definition, AGR is the ratio or equilibrium point between hemoglobin glycation rate and RBC regeneration rate, controlling two opposing dynamic processes for HbA1c. A higher hemoglobin glycation rate increases HbA1c, whereas a higher RBC regeneration rate decreases HbA1c. The steady-state HbA1c change when the AGR value changes can be derived from equation (21). The difference in AGR has a larger effect on HbA1c in the absence of adequate diabetes control, as depicted in FIG. 24. TIFF2024543824000045.tif18150 EXAMPLES

[0263] As discussed above, HbA1c is a useful biomarker for glycemic control at a population level, but can be improved for determining glycemic control at an individual level. Fasting plasma glucose ("FPG") is a measurement of plasma glucose levels after a subject has fasted for a set period of time. The relationship between HbA1c and FPG can form the basis of calculations for determining intrapersonal AGR, which can further indicate the glucose uptake rate and longevity of RBCs within an individual by characterizing the individual-specific relationship between glucose and HbA1c.

[0264] A recent exemplary study calculated AGR using FPG and HbA1c for different subgroups of individuals based on age, race, and sex. Specifically, subjects were divided between (1) men and women, (2) black, Hispanic, white, and other race individuals, and (3) individuals under 21 years of age, between 21-50 years of age, and older than 50 years of age. Results from this study are summarized in FIG. 25.

[0265] As can be seen in Figure 25, women tend to have higher Diabetes Diagnostic Criteria ("DDC") HbA1c and AGR levels compared to men. For example, women averaged (i) FPG DDC HbA1c of 6.7 (6.6-6.7)% and (ii) AGR levels of 71.7 (71.5-71.8) mL / g compared to men who averaged (i) FPG DDC HbA1c of 6.5 (6.4-6.5)% and (ii) AGR levels of 69.3 (69.2-69.5) mL / g. The study results also showed that black individuals generally have higher FPG DDC HbA1c and AGR levels compared to white individuals. For example, black individuals, on average, had (i) FPG DDC-referenced HbA1c of 6.5 (6.5-6.5)% and (ii) AGR levels of 69.8 (69.6-70.0) mL / g compared with white individuals. Finally, the findings showed that younger individuals generally had lower FPG DDC-referenced HbA1c and AGR levels compared with older individuals. For example, individuals younger than 21 years of age had, on average, (i) FPG DDC Reference HbA1c of 6.57 (6.6-6.6)% and (ii) AGR levels of 70.8 (70.6-71) mL / g compared to individuals older than 50 years of age. FIG. 25 shows a complete results table. Similar results can be observed when using average glucose measurements rather than FPG. According to embodiments, average glucose measurements can be obtained using the continuous glucose monitor system disclosed herein, by way of example and not limitation.

[0266] In view of the above findings in diabetes treatment, according to embodiments of the present disclosure, a method of providing personalized treatment is disclosed herein. In some embodiments, the method can include receiving data indicative of a subject's analyte level during a particular time period at a remote device. By way of example and not limitation, the remote device can receive the data indicative of the analyte level from a continuous glucose monitor system, a cloud-based database storing patient data, an electronic medical record management system, or the like. The remote device can be a smartphone, a personal computer, an electronic medical record system, or any other type of suitable system including executable software code to perform the enumerating step. The remote device can retrieve a first glycated hemoglobin level for the subject associated with a predetermined time period. By way of example and not limitation, the predetermined time period can be 3 months, 6 months, 9 months, 12 months, or any other suitable time period. Additionally or alternatively, the predetermined time period can be determined by a healthcare provider, a subject, or a caregiver.

[0267] In some embodiments, the first glycated hemoglobin level can be associated with the start of a particular time period, or alternatively or additionally, with the time period itself or the end of the time period. Additionally, the first glycated hemoglobin level can be retrieved from at least one of an electronic medical record system, a cloud-based database, and a QR code. In some embodiments, the HbA1c level can be from a laboratory measurement. Further details regarding receiving data indicative of the analyte level and retrieving the glycated hemoglobin level are disclosed in U.S. Provisional Patent Application No. 63 / 196,677, filed June 3, 2021, the entire contents of which are incorporated by reference.

[0268] According to an embodiment, the remote device can be configured to use the received data and the retrieved glycated hemoglobin level to calculate a first individual AGR over a particular time period as disclosed above. The remote device can then compare the calculated individual AGR to a typical AGR. According to an embodiment, the typical AGR can be the AGR of a plurality of subjects, each of which has at least one demographic metric in common with each other and with the subject. By way of example and not by way of limitation, the at least one common demographic metric can include age, race, and / or gender, as previously described. By way of example and not by way of limitation, as can be seen in FIG. 25, if the subject is female, the at least one demographic metric can be gender, and the average typical AGR of the several subjects is 71.7 ml / g, the standard deviation of the typical AGR of the several subjects is 6.35 ml / g, the 95% confidence interval is 71.5-71.8 ml / g, and the AGR range is 59.7-89.5 ml / g. Similarly, as can be seen in FIG. 25, if the subjects are black individuals, the at least one demographic metric can be race, the mean typical AGR for the several subjects is 72.7 ml / g, the standard deviation of the typical AGR for the several subjects is 7.08 ml / g, the 95% confidence interval is 72.4-72.9 ml / g, and the AGR range is 57.8-86.5 ml / g. Similarly, as can be seen in FIG. 25, the at least one demographic metric can be age. For example, if the subjects are young individuals (i.e., younger than 21 years old), the mean typical AGR for the several subjects is 70.3 ml / g, the standard deviation of the typical AGR for the several subjects is 6.01 ml / g, the 95% confidence interval is 70.1-70.5 ml / g, and the AGR range is 58.9-81.9 ml / g.

[0269] According to the disclosed embodiment of the present invention, one more demographic metric can be used to compare the measured AGR. For example, as can be seen in FIG. 21A-21B, by way of example and not limitation, when the subject has demographic metrics of young and white, two demographic metrics skewed toward relatively low AGR levels, the average typical AGR is 69.2 ml / g with a standard deviation of 5.4 ml / g, in contrast to when the subject is old and black, two demographic metrics skewed toward relatively high AGR levels, the average typical AGR is 76.4 mg / l with a standard deviation of 5.0 ml / g. Similarly, typical AGRs for certain other demographic metrics are presented in FIG. 21A-21B. Therefore, it is desirable to consider these demographic metrics when measuring and analyzing HbA1c levels.

[0270] As disclosed above, despite the shortcomings of HbA1c readings, HbA1c remains the standard for diagnosing diabetes. Thus, in light of the shortcomings disclosed herein, HbA1c readings can be improved by considering AGR, which represents the demographic metrics of the subject. For example, FIG. 22 shows an "adjusted" HbA1c measurement based on an individualized AGR measurement. Presenting this information to subjects and health care providers can help these parties make more accurate and informed diabetes diagnoses and treatments based at least on the subject's personal demographic metrics.

[0271] Specifically, according to embodiments disclosed herein, the remote device may generate recommendations based on the comparison and display a graphical interface including the calculated personal AGR, the typical AGR, and / or one or more of the comparison of the two. According to embodiments, the recommendation may include generating at least one of an individualized HbA1c target or range or an "adjusted" HbA1c as described above.

[0272] Further, according to an embodiment, the method described herein can include generating an alert prompting the subject to obtain a second glycated hemoglobin level associated with a second time period that is a predetermined time period after the first time period, which can also be 3 months, 6 months, 9 months, 12 months, or any other time period. In some embodiments, it can be beneficial to notify the subject or another individual when the calculated first AGR differs from the typical AGR by a predetermined amount, such as, for example and without limitation, 20%. In this embodiment, the notification can be any one of a visual notification, an audio notification, an alarm, and / or a prompt, or any combination thereof. Further exemplary graphical interfaces, as well as notifications, alarms, and alerts, are disclosed in U.S. Provisional Patent Application No. 63 / 279,509, filed November 15, 2021, the entire contents of which are incorporated by reference.

[0273] According to an embodiment, the method may also include receiving, using the remote device, additional data indicative of the subject's analyte level during a second time period, deriving a second glycated hemoglobin level, and calculating a second individual AGR for the second time period based on the received second data and the derived second glycated hemoglobin level.

[0274] The data received by the remote device can be generated by an analyte sensor configured to have an in vivo portion in contact with a bodily fluid of the subject, and further, the data can be indicative of a fasting plasma glucose level in the subject.

[0275] Thus, the systems, tools, and methods of the present disclosure are well suited to achieve the above-mentioned goals and advantages, as well as those inherent therein. The specific embodiments disclosed above are illustrative only, as the teachings of the present disclosure may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit thereof. Moreover, no limitations to the details of construction or design shown herein are intended, other than as set forth in the following claims. Thus, it is apparent that the specific exemplary embodiments disclosed above may be altered, combined, or modified, and all such variations are deemed to be within the scope of the present disclosure. The systems, tools, and methods illustratively disclosed herein may be suitably practiced without any element not specifically disclosed herein and / or any optional element disclosed herein. Although the systems, tools, and methods are described in terms of "comprising," "containing," or "including" various components or steps, the systems, tools, and methods may also "consist essentially of" or "consist of" various components and steps. All numbers and ranges disclosed above may vary by some amount. Whenever a numerical range having a lower and upper limit is disclosed, every number falling within that range and every encompassed range is specifically disclosed. In particular, all value ranges disclosed herein (in the form of "about a to about b" or, equivalently, "about a to b" or, equivalently, "about a to b") should be understood to represent every number and range encompassed within that broad range. Moreover, the terms in the claims have their plain and ordinary meaning unless expressly and clearly defined otherwise by the patent owner. Moreover, the indefinite article "a" or "an" when used in the claims is defined herein to mean one or more of the elements it introduces. In the event of any discrepancy between the use of a word or term in this specification and the use of the word or term in one or more patents or other documents that may be incorporated herein by reference, the consistent definition in this specification shall prevail. [Explanation of symbols]

[0276] RBC Red blood cell HbA1c: A component of HbA1, which is glycosylated HbA

Claims

1. A method for providing a comparison between an individual apparent glycation rate and a typical apparent glycation rate, comprising: receiving, by a remote device, first data indicative of an analyte level in the subject during a first time period; retrieving, by the remote device, a first glycated hemoglobin level for the subject associated with the first time period; calculating, by the remote device, a first personal apparent glycation ratio for the first time period using the received first data and the retrieved first glycated hemoglobin level; comparing, by the remote device, the calculated first individual apparent glycation rate with a typical apparent glycation rate; generating, by the remote device, a recommendation based on the comparison; displaying, by the remote device, a graphical interface including the calculated first individual apparent glycation ratio, the typical apparent glycation ratio, and the comparison; A method comprising:

2. 10. The method of claim 1, further comprising generating an alert prompting the subject to obtain a second glycated hemoglobin level associated with a second period that is a predetermined period after the first period.

3. The method of claim 2 , wherein the predetermined period of time comprises 3 months, 6 months, 9 months, or 12 months.

4. receiving, by a remote device, second data indicative of an analyte level in the subject during the second time period; retrieving, by the remote device, the second glycated hemoglobin level; calculating, by the remote device, a second individual apparent glycation ratio for the second time period using the received second data and the retrieved second glycated hemoglobin level; The method of claim 2 further comprising:

5. 2. The method of claim 1, wherein the typical apparent glycation rate is the apparent glycation rate of a plurality of subjects having at least one demographic metric in common with the subject.

6. The method of claim 5 , wherein the at least one demographic metric includes age.

7. The method of claim 5 , wherein the at least one demographic metric includes gender.

8. The method of claim 1 , wherein the recommendation includes generating a personalized HbA1c target.

9. The method of claim 1 , wherein the recommendation includes generating a personalized HbA1c range.

10. 10. The method of claim 1, wherein the first data comprises data generated by an analyte sensor having an in vivo portion configured to be positioned in contact with a bodily fluid of the subject.

11. The method of claim 1 , wherein the first data comprises fasting plasma glucose.

12. The method of claim 1 , wherein the first glycated hemoglobin level is retrieved from an electronic medical record system.

13. The method of claim 1 , wherein the first glycated hemoglobin level is retrieved from a cloud-based database.

14. The method of claim 1 , wherein the first glycated hemoglobin level is derived from a QR code.

15. The method of claim 1 , wherein the remote device comprises a smartphone.

16. The method of claim 1 , wherein the remote device comprises a personal computer.

17. The method of claim 1 , wherein the remote device comprises an electronic medical record system.

18. 10. The method of claim 1, further comprising generating a notification if the calculated first individual apparent glycation rate varies from the typical apparent glycation rate by a predetermined amount.

19. The method of claim 18 , wherein the notification comprises a visual notification.

20. The method of claim 18 , wherein the notification comprises an audio notification.

21. The method of claim 18 , wherein the notification is an alarm.

22. The method of claim 18 , wherein the notification is a prompt.

23. 20. The method of claim 18, wherein the predetermined amount is 20%.

24. A computer system for providing personalized therapy, comprising: one or more processors; and one or more machine-readable storage media; The computer system, wherein the one or more machine-readable storage media comprises instructions for execution by the one or more processors to perform the method of any one of claims 1 to 23.

Images