A pancreatic islet cell function and insulin sensitivity calculation system

Abstract

The present application relates to a kind of islet cell function and insulin sensitivity computing system, belong to biomedical computing field.System includes: information acquisition module, for obtaining the basic information of human body to be detected, glucose plasma concentration measured value, insulin serum concentration measured value and C peptide serum concentration measured value;Determination curve generation module is used to generate glucose plasma concentration determination curve and C peptide serum concentration determination curve;Population nonlinear mixed effect model parameter calculation module includes population glucose oral micro model and population C peptide oral micro model;Wherein, based on the basic information, exogenous glucose input rate is constructed to obtain population glucose oral micro model;Population glucose oral micro model is used to fit glucose plasma concentration estimation curve and is approximated to glucose plasma concentration determination curve, and the insulin sensitivity of the human body to be detected is obtained;The population C peptide oral micro model is used to obtain islet cell function.

Description

Technical Field

[0001] This invention relates to the field of biomedical computing, and more particularly to a system for calculating pancreatic islet cell function and insulin sensitivity. Background Technology

[0002] Diabetes mellitus is a group of diseases characterized primarily by hyperglycemia, caused by a combination of genetic and environmental factors. It is a common endocrine and metabolic disorder in clinical practice and one of the most prevalent chronic non-communicable diseases worldwide. Insulin resistance (IR) and decreased beta cell function (BCF) are the fundamental pathological changes in the development of diabetes. When insulin cannot effectively promote glucose uptake by peripheral tissues and inhibit glucose output from the liver, it is called IR or decreased insulin sensitivity (SI). If pancreatic islet cells can produce enough insulin to compensate for insulin resistance, blood glucose can be maintained at a normal level; conversely, if pancreatic function is insufficient to compensate for the deficiency in insulin resistance, blood glucose will increase and gradually develop into diabetes.

[0003] To quantify insulin sensitivity and islet cell function, Cobelli's research team developed an Oral Minimal Model (OMM) and used SAAMII software for commercial applications, such as in innovative drug development as a pharmacodynamic indicator to assess the impact of drugs on patients' insulin sensitivity and islet cell function. However, because this model is an individual model, it requires collecting blood samples from each subject at least 11 time points to obtain complete blood glucose, insulin, and C-peptide-time curves in order to calculate insulin sensitivity and islet cell function. Clinically, 11 sampling points are too many, posing significant challenges in practical operation. Furthermore, when using the individual model, data needs to be calculated for each subject individually, and because individual differences are sometimes large, manual adjustments to software parameter settings are necessary, making batch processing generally impossible and time-consuming and labor-intensive. Summary of the Invention

[0004] Based on the above analysis, the present invention aims to provide a system for calculating pancreatic islet cell function and insulin sensitivity, in order to solve the problems of excessively high sampling point requirements and the need to manually set individual model parameters when using existing models to calculate pancreatic islet cell function and insulin sensitivity.

[0005] The objective of this invention is mainly achieved through the following technical solutions:

[0006] This invention provides a system for calculating pancreatic islet cell function and insulin sensitivity, comprising:

[0007] The information acquisition module is used to acquire basic information about the human body to be tested, as well as the measured values ​​of glucose plasma concentration, insulin serum concentration, and C-peptide serum concentration.

[0008] The measurement curve generation module is used to generate glucose plasma concentration measurement curves and C-peptide serum concentration measurement curves based on multiple glucose plasma concentration measurement values ​​and multiple C-peptide serum concentration measurement values, respectively.

[0009] The population nonlinear mixed-effects model parameter calculation module includes a population glucose oral mini-model and a population C-peptide oral mini-model; wherein, based on the basic information, the exogenous glucose input rate is constructed to obtain the population glucose oral mini-model; the population glucose oral mini-model is used to fit the glucose plasma concentration estimation curve and obtain the insulin sensitivity of the human subject by approximating the glucose plasma concentration estimation curve to the glucose plasma concentration measurement curve; the population C-peptide oral mini-model is used to obtain pancreatic islet cell function.

[0010] Furthermore, the basic information includes the weight of the person being tested and the amount of oral glucose administered to the person being tested.

[0011] Furthermore, the zero-order absorption rate R is calculated based on the amount of oral glucose in the human body being tested using the following formula. in :

[0012]

[0013] Where D represents the amount of oral glucose administered to the human body being tested; f represents bioavailability; T abs Zero-order absorption time;

[0014] Based on the weight of the human body to be tested and the zero-order absorption rate R in The rate of exogenous glucose infusion was obtained.

[0015] Furthermore, the exogenous glucose infusion rate is obtained using the following formula:

[0016]

[0017] Where, k el The first-order elimination rate is represented by BW, which represents the body weight of the person being tested; T represents the body weight of the person being tested. abs t is the zero-order absorption time; t is the sampling time.

[0018] Furthermore, the structure of the population glucose oral administration mini-model is as follows:

[0019]

[0020] Where SI represents the insulin sensitivity of the human subject being tested; Q(t) represents the plasma glucose level at sampling time t; CL G This refers to both insulin-mediated and non-insulin-mediated central compartment glucose clearance when insulin levels are below baseline; CL I G(t) represents the insulin-mediated glucose clearance rate in the central compartment when insulin levels are above the baseline concentration; Q represents the glucose concentration at sampling time t. b This refers to the baseline value of glucose levels; G b The baseline glucose concentration is represented by Ra(t), which is the rate of exogenous glucose input at sampling time t; k is the base value of glucose concentration. out Peripheral tissue compartmental elimination rate of insulin; k ’ in It is the product of the distribution rate of insulin from the central compartment to the peripheral tissue compartments and the effector coefficient of insulin in eliminating glucose; It is the serum insulin concentration at sampling time t; b This represents the baseline serum insulin concentration; V G This represents the glucose distribution volume.

[0021] Furthermore, the population C-peptide oral micromodel is used to fit the C-peptide serum concentration estimation curve, and the C-peptide serum concentration estimation curve is approximated to the C-peptide serum concentration measurement curve to obtain the pancreatic islet cell function of the human body to be tested.

[0022] Furthermore, the structure of the population C-peptide oral micromodel is as follows:

[0023]

[0024] Wherein, ISR(t) is the C-peptide secretion rate at sampling time t that is higher than the baseline value; k 01 The elimination rate of the central compartment of C-peptide; k 12 k represents the distribution rate of C-peptide from the peripheral compartment to the central compartment. 21 y(t) represents the rate of C-peptide secretion from the central compartment to the peripheral compartment; q1(t) represents the amount of C-peptide in the central compartment at sampling time t; q2(t) represents the amount of C-peptide in the peripheral compartment at sampling time t; y(t) represents the static C-peptide secretion rate at sampling time t; K is the correction coefficient for glucose-stimulated dynamic C-peptide secretion in β cells; G(t) represents the glucose concentration at sampling time t; α represents the insulin secretion time delay rate; β represents the rate of C-peptide secretion in β cells stimulated by increased glucose concentration when glucose is above the C-peptide secretion threshold; h is the glucose concentration threshold for C-peptide secretion; c1(t) represents the C-peptide concentration in the central compartment at sampling time t; V is the C-peptide distribution volume in the central compartment; SR b This represents the baseline value for C-peptide secretion rate; CP b This represents the baseline serum concentration of C-peptide; G bThis is the baseline value for glucose concentration; For dynamic pancreatic islet cell function; This represents the static function of pancreatic islet cells; To support the basic function of pancreatic islet cells; The overall pancreatic islet cell function is represented by ΔG, which is the difference between the peak glucose concentration and the baseline glucose concentration. G(t) is the glucose concentration at sampling time t, where t is the sampling time.

[0025] Furthermore, the population nonlinear mixed-effects model parameter calculation module, based on the basic information of the human subject to be tested, the measured value of glucose plasma concentration, and the measured value of insulin serum concentration, uses NONMEM software and selects the FOCEI algorithm to fit and obtain the estimated curve of glucose plasma concentration and the estimated curve of C-peptide serum concentration.

[0026] Furthermore, the glucose plasma concentration measurement values ​​include glucose concentration values ​​collected at four sampling times: fasting and postprandial.

[0027] The insulin serum concentration measurements include insulin secretion concentration values ​​collected at four sampling times: fasting and postprandial.

[0028] The serum C-peptide concentration measurements included serum C-peptide concentrations collected at four sampling times: fasting and postprandial.

[0029] Furthermore, the four sampling times after the meal are any four time points: 30 minutes, 60 minutes, 90 minutes, 120 minutes, 150 minutes, and 180 minutes after the meal.

[0030] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0031] 1. This invention constructs a population glucose oral administration mini-model to measure the exogenous glucose input rate. Compared to existing glucose oral administration mini-models that require seven parameters to fit the rate curve of glucose absorption in the gastrointestinal tract, this invention uses only three parameters (first-order elimination rate k). el Zero-order absorption time T abs By fitting the rate curve of glucose absorption in the gastrointestinal tract with bioavailability (f), the computational difficulty of the oral glucose mini-model can be reduced, and the computational efficiency of the model can be improved.

[0032] 2. Compared with the prior art, which uses 11 sampling points to obtain the complete glucose plasma concentration estimation curve and C-peptide serum concentration estimation curve of the human body to be tested, the population nonlinear mixed effect model used in this invention can simulate two curves with only 5 sampling points, reducing the difficulty of actual operation.

[0033] 3. When using the population oral glucose mini-model for calculation, this invention adjusts some of the fixed parameter values ​​of the Caucasian population used in the original model calculation to use the model estimated parameters, thereby reducing the risk of calculation bias caused by racial differences.

[0034] 4. This invention uses a population nonlinear mixed-effects model to calculate human pancreatic islet cell function and insulin sensitivity. Compared with individual calculation models, it can simultaneously input multiple individuals and calculate and obtain the pancreatic islet cell function and insulin sensitivity of each individual, saving time and effort.

[0035] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0036] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0037] Figure 1 This is a schematic diagram of a pancreatic islet cell function and insulin sensitivity calculation system according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of the population glucose oral administration micromodel in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the structure of the population C-peptide oral micromodel in an embodiment of the present invention. Detailed Implementation

[0040] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0041] A specific embodiment of the present invention discloses a system for calculating pancreatic islet cell function and insulin sensitivity, such as... Figure 1 As shown, it includes:

[0042] The information acquisition module is used to acquire basic information about the human body to be tested, as well as the measured values ​​of glucose plasma concentration, insulin serum concentration, and C-peptide serum concentration.

[0043] The measurement curve generation module is used to generate glucose plasma concentration measurement curves and C-peptide serum concentration measurement curves based on multiple glucose plasma concentration measurement values ​​and multiple C-peptide serum concentration measurement values, respectively.

[0044] The population nonlinear mixed-effects model parameter calculation module includes a population glucose oral mini-model and a population C-peptide oral mini-model; wherein, based on the basic information, the exogenous glucose input rate is constructed to obtain the population glucose oral mini-model; the population glucose oral mini-model is used to fit the glucose plasma concentration estimation curve and obtain the insulin sensitivity of the human subject by approximating the glucose plasma concentration estimation curve to the glucose plasma concentration measurement curve; the population C-peptide oral mini-model is used to obtain pancreatic islet cell function.

[0045] Specifically, the basic information of the human body to be tested in the information acquisition module includes the weight of the human body to be tested and the amount of oral glucose administered to the human body to be tested.

[0046] It should be noted that the oral glucose dose of the human subject to be tested refers to the amount of glucose ingested orally after fasting sampling. The purpose is to obtain a gastrointestinal absorption rate curve for glucose, i.e., a glucose plasma concentration measurement curve. Preferably, the oral glucose dose is 75g.

[0047] Furthermore, the glucose plasma concentration measurement value in the information acquisition module includes glucose concentration values ​​collected at four sampling times: fasting and postprandial.

[0048] The insulin serum concentration measurements included insulin serum concentration values ​​collected at four sampling times: fasting and postprandial.

[0049] The C-peptide serum concentration measurements included C-peptide serum concentration values ​​collected at four sampling times: fasting and postprandial.

[0050] Specifically, a sample is taken once when the subject is fasting. After oral glucose administration, samples are taken at four randomly selected times: 30 minutes, 60 minutes, 90 minutes, 120 minutes, 150 minutes, and 180 minutes postprandial. The sampled data include plasma glucose concentration, serum insulin concentration, and serum C-peptide concentration.

[0051] It should be noted that the fasting plasma glucose concentration, serum insulin concentration, and serum C-peptide concentration are used as the baseline glucose concentration (G). b Baseline serum insulin concentration I b and baseline serum C-peptide concentration (CP) b Perform model calculations.

[0052] Furthermore, in the measurement curve generation module, based on the glucose plasma concentration measurement values ​​and C-peptide serum concentration measurement values ​​sampled at 5 time points, the glucose plasma concentration measurement curve and C-peptide serum concentration measurement curve of the current human body to be tested are constructed respectively.

[0053] Furthermore, the population nonlinear mixed-effects model parameter calculation module, based on the basic information of the human subject to be tested, the measured values ​​of glucose plasma concentration, insulin serum concentration, and C-peptide serum concentration, uses NONMEM software, selects the FOCEI algorithm to fit the estimated curves of glucose plasma concentration and C-peptide serum concentration, and adjusts the model parameters to approximate the measured curves of glucose plasma concentration and C-peptide serum concentration, respectively. The insulin sensitivity and pancreatic islet cell function of the human subject to be tested are calculated by Bayesian feedback.

[0054] Specifically, the Nonlinear Mixed Effect Model (NONMEM) is also known as a population model. This model is widely used in population pharmacokinetic (PK) and pharmacodynamic (PD) assessments for new drug development. NONMEM is based on classical PK / PD models, combining classic PK or PD models with population statistical models to estimate fixed and random effects.

[0055] Fixed effects are used to describe parameters that have a systematic impact on drug concentration, such as the clearance parameter in a PK model, which is generally represented by θ in NONMEM software.

[0056] Random effects are used to quantify the unexplained portion of parameter variation and are divided into first-order random effects and second-order random effects. First-order random effects quantify the difference between individual parameters and typical values ​​(i.e., random error at the parameter level), while second-order random effects quantify the difference between the observed dependent variable and its predicted value in the model (i.e., error at the observation level).

[0057] When describing the variation of a random effect, the parameter assumes a symmetric distribution with a mean of 0. The vector of individual estimates of a first-order random effect is called η, and the estimated parameter of the first-order random effect is the variance of the η-distribution, denoted by Ω. Second-order random effects represent the unexplained portion of the difference between the predicted and observed values ​​of the dependent variable; this is called residual variation, and it is the variance of the ε-distribution, denoted by σ. Both first-order and second-order random variations are assumed to be symmetrically distributed with a mean 0.

[0058] Fixed effects and random effects are described using structural / covariate models and statistical models, respectively. The combination of structural and statistical models is called a population-based model, which can describe the typical drug concentration / efficacy index level-time curves for a population.

[0059] The NONMEM method for calculating parameters relies on maximum likelihood estimation. The maximum likelihood of the population is defined as the sum of the contributions of all individuals. To minimize the objective function, i.e., to obtain the optimal values ​​of θ, Ω, and σ, NONMEM uses an iterative algorithm to calculate the minimum objective function. Population PK methods, during iteration, need to simplify the model to make it easier to converge. Commonly used estimation methods include First-order Estimation (FO), First-order Condition Estimation (FOCE), and First-order Condition Estimation Interaction (FOCEI) with interactions. The FO method uses a first-order Taylor approximation to linearize the experimental model, thereby obtaining the predicted concentrations related to η and ε. The FO method only provides typical values ​​of the parameters for the population; obtaining individual-specific parameter estimates requires Posthoc estimation. FOCE achieves the estimation of both population and individual parameters in one step by adjusting the individual-specific estimate of η during the minimization process. Building upon FOCE, INTER is added, incorporating the η-ε interaction term into the objective function calculation. FOCE and FOCEI are the most commonly used estimation methods. Population models are widely used in new drug development for drug evaluation of PK and PD due to their advantages, such as the ability to estimate population values, inter-individual variation, screen covariates that are meaningful to parameters, and the ability to handle sparse sampling data.

[0060] Furthermore, such as Figure 2 As shown, the population nonlinear mixed-effects model includes a population glucose oral administration mini-model. In this embodiment, based on the basic information of the human subject, 5 glucose plasma concentration measurements, and 5 insulin serum concentration measurements, the fixed-effects parameters of the population glucose oral administration mini-model are estimated using NONMEM software. A glucose plasma concentration estimation curve is fitted, and the glucose plasma concentration estimation curve is approximated to the glucose plasma concentration measurement curve to obtain the exogenous glucose input rate R. a And the insulin sensitivity (SI) of the human body to be tested.

[0061] Specifically, the structure of the population glucose oral administration mini-model is as follows:

[0062]

[0063] Where SI represents the insulin sensitivity of the human subject being tested; Q(t) represents the plasma glucose level at sampling time t; CL G This refers to both insulin-mediated and non-insulin-mediated central compartment glucose clearance when insulin levels are below baseline; CLI G(t) represents the insulin-mediated glucose clearance rate in the central compartment when insulin levels are above the baseline concentration; G(t) is the glucose concentration at sampling time t. b The baseline glucose concentration is represented by Ra(t), which is the rate of exogenous glucose input at sampling time t. Q represents the baseline glucose concentration. b This represents the baseline value for glucose levels; k out k' is the peripheral tissue compartmental elimination rate of insulin. in k is the distribution rate of insulin from the central compartment to the peripheral tissue compartments. in The effect coefficient k of insulin's elimination of glucose e The product of; I(t) is the serum insulin concentration at sampling time t; I b This represents the baseline serum insulin concentration; V G This represents the glucose distribution volume.

[0064] Specifically, the population oral glucose mini-model is similar to the classic one-compartment IVGTT minimal model, but it has a new parameter, namely the exogenous glucose infusion rate R. a As an input for oral doses, plasma glucose and insulin time progression were more stable during oral trials compared to IVGTT, thus the single-compartment model was able to accurately describe glucose kinetics.

[0065] Furthermore, the calculation yields the exogenous glucose infusion rate R. a The formula is as follows:

[0066]

[0067] Among them, R in The zero-order absorption rate; k el The first-order elimination rate is represented by BW, which represents the body weight of the person being tested; T represents the body weight of the person being tested. abs t is the zero-order absorption time; t is the sampling time.

[0068] Furthermore, the zero-order absorption rate R is calculated based on the amount of oral glucose in the human body being tested using the following formula. in :

[0069]

[0070] Where D represents the amount of oral glucose administered to the human body being tested; f represents bioavailability; T abs This is the zero-order absorption time.

[0071] Specifically, the fixed-effect parameters of the population oral glucose mini-model are: insulin-mediated glucose central compartment clearance and non-insulin-mediated glucose central compartment clearance CL when insulin levels are below the baseline concentration. GWhen insulin levels are above the baseline concentration, insulin-mediated glucose central clearance (CL) increases. I ; Baseline glucose level Q b Distribution rate k from the central compartment to the peripheral compartments in The effect coefficient k of insulin in eliminating glucose e Peripheral tissue compartmental elimination rate of insulin k out First-order elimination rate k el Zero-order absorption time T abs Bioavailability to glucose distribution volume ratio f / V G .

[0072] It is worth noting that the exogenous glucose infusion rate R a This model uses three parameters to fit the rate curve of glucose absorption in the gastrointestinal tract, which reduces the computational difficulty and improves computational efficiency compared to the existing technology that uses seven parameters to fit the curve.

[0073] In the prior art, the bioavailability f is related to the glucose distribution volume V. G To fix the parameters, which are fixed to Caucasian parameter values ​​rather than model-estimated parameters, this embodiment uses the ratio of bioavailability to glucose distribution volume (f / V). G This reduces the risk of bias in the calculation of insulin sensitivity SI due to racial differences, as a model estimate.

[0074] Furthermore, the population nonlinear mixed-effects model calculation module also includes fitting the C-peptide serum concentration estimation curve based on 5-point glucose concentration measurements using a population C-peptide oral micro-model, and approximating it to the C-peptide serum concentration measurement curve to obtain the pancreatic islet cell function of the human body to be tested.

[0075] Specifically, such as Figure 3 As shown, the structure of the population C-peptide oral micromodel is as follows:

[0076]

[0077] Wherein, ISR(t) is the C-peptide secretion rate at sampling time t that is higher than the baseline value; k 01 The elimination rate of the central compartment of C-peptide; k 12 k represents the distribution rate of C-peptide from the peripheral compartment to the central compartment. 21y(t) represents the rate of C-peptide secretion from the central compartment to the peripheral compartment; q1(t) represents the amount of C-peptide in the central compartment at sampling time t; q2(t) represents the amount of C-peptide in the peripheral compartment at sampling time t; y(t) represents the static C-peptide secretion rate at sampling time t; K is the correction coefficient for dynamic C-peptide secretion in β cells stimulated by glucose; G(t) represents the glucose concentration at sampling time t; α is the insulin secretion time delay rate, usually expressed as 1 / T, where T is the insulin secretion delay time; β is the C-peptide secretion rate stimulated by increased glucose concentration in β cells when glucose is above the C-peptide secretion threshold; h is the glucose concentration threshold for C-peptide secretion; c1(t) represents the C-peptide concentration in the central compartment at sampling time t; V is the C-peptide distribution volume in the central compartment; SR b This represents the baseline value for C-peptide secretion rate; CP b This represents the baseline serum concentration of C-peptide; G b This is the baseline value for glucose concentration; For dynamic pancreatic islet cell function; This represents the static function of pancreatic islet cells; To support the basic function of pancreatic islet cells; The overall pancreatic islet cell function is represented by ΔG, which is the difference between the peak glucose concentration and the baseline glucose concentration. G(t) is the glucose concentration at sampling time t, where t is the sampling time.

[0078] Furthermore, the population C-peptide oral micromodel can represent the relationship between the output serum C-peptide concentration and the observed changes in plasma glucose concentration.

[0079] It should be noted that, in this embodiment, the standard parameter of C-peptide clearance rate is obtained by using the age, height, and weight of the human body to be tested and the following formula. The standard parameter of C-peptide clearance rate includes the elimination rate k of the C-peptide central chamber. 01 The distribution rate k of C-peptide from the peripheral compartment to the central compartment 12 The secretion rate k of C-peptide from the central compartment to the peripheral compartment 21 .

[0080] Specifically, the formula is as follows:

[0081]

[0082] A body mass index (BMI) value exceeding 30 is considered obese. BMI = weight (kg) / height (m) 2 .

[0083] Furthermore, in this embodiment, based on the 5-point glucose plasma concentration measurements, the fixed-effect parameters of the population C-peptide oral micromodel are estimated using NONMEM software, the C-peptide serum concentration estimation curve is fitted, and the C-peptide serum concentration estimation curve is approximated to the C-peptide serum concentration measurement curve to obtain the pancreatic islet cell function parameter BCF of the human body to be tested.

[0084] Specifically, the fixed-effect parameters of the population C-peptide oral micromodel are: glucose-stimulated dynamic C-peptide secretion correction coefficient K, insulin secretion time delay rate α, C-peptide secretion rate β of β cells stimulated by glucose concentration increase when glucose is higher than the C-peptide secretion threshold, glucose concentration threshold h for C-peptide secretion, and C-peptide central compartment distribution volume V.

[0085] Furthermore, the BCF parameter of pancreatic islet cell function in the human body to be tested includes baseline pancreatic islet cell function. Static pancreatic islet cell function and overall pancreatic islet cell function

[0086] In summary, the pancreatic islet cell function and insulin sensitivity calculation system of this invention has the following beneficial effects:

[0087] 1. This invention constructs a population glucose oral administration mini-model to measure the exogenous glucose input rate. Compared to existing glucose oral administration mini-models that require seven parameters to fit the rate curve of glucose absorption in the gastrointestinal tract, this invention uses only three parameters (first-order elimination rate k). el Zero-order absorption time T abs By fitting the rate curve of glucose absorption in the gastrointestinal tract with bioavailability (f), the computational difficulty of the oral glucose mini-model can be reduced, and the computational efficiency of the model can be improved.

[0088] 2. Compared with the prior art, which uses 11 sampling points to obtain the complete glucose plasma concentration estimation curve, insulin serum concentration estimation curve and C-peptide serum concentration estimation curve of the human body to be tested, the population nonlinear mixed effect model used in this invention can simulate three curves with only 5 sampling points, reducing the difficulty of actual operation.

[0089] 3. When using the population oral glucose mini-model for calculation, this invention adjusts some of the fixed parameter values ​​of the Caucasian population used in the original model calculation to use the model estimated parameters, thereby reducing the risk of calculation bias caused by racial differences.

[0090] 4. This invention uses a population nonlinear mixed-effects model to calculate human pancreatic islet cell function and insulin sensitivity. Compared with individual calculation models, it can simultaneously input multiple individuals and calculate and obtain the pancreatic islet cell function and insulin sensitivity of each individual, saving time and effort.

[0091] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A system for calculating pancreatic islet cell function and insulin sensitivity, comprising: The information acquisition module is used to acquire basic information of the human body to be tested, plasma glucose concentration, serum insulin concentration, and serum C-peptide concentration; wherein, the basic information includes the weight of the human body to be tested and the amount of oral glucose administered to the human body to be tested. The measurement curve generation module is used to generate glucose plasma concentration measurement curves and C-peptide serum concentration measurement curves based on multiple glucose plasma concentration measurement values ​​and multiple C-peptide serum concentration measurement values, respectively. The population nonlinear mixed-effects model parameter calculation module includes a population glucose oral mini-model and a population C-peptide oral mini-model. The population glucose oral mini-model is obtained by constructing the exogenous glucose input rate based on the aforementioned basic information. The population glucose oral mini-model is used to fit the glucose plasma concentration estimation curve and to obtain the insulin sensitivity of the human subject by approximating the glucose plasma concentration estimation curve to the glucose plasma concentration measurement curve. The population C-peptide oral mini-model is used to obtain pancreatic islet cell function. The zero-order absorption rate R is calculated based on the amount of oral glucose taken by the human subject using the following formula. in : Where D represents the amount of oral glucose administered to the human body being tested; f represents bioavailability; T abs Zero-order absorption time; Based on the weight of the human body to be tested and the zero-order absorption rate R in The rate of exogenous glucose infusion was obtained; The exogenous glucose infusion rate is obtained using the following formula: Where, k el The first-order elimination rate is represented by BW, which represents the body weight of the person being tested; T represents the body weight of the person being tested. abs t is the zero-order absorption time; t is the sampling time. The structure of the population glucose oral administration mini-model is as follows: Where SI represents the insulin sensitivity of the human subject being tested; Q(t) represents the plasma glucose level at sampling time t; CL G This refers to both insulin-mediated and non-insulin-mediated central compartment glucose clearance when insulin levels are below baseline; CL I G(t) represents the insulin-mediated glucose clearance rate in the central compartment when insulin levels are above the baseline concentration; Q represents the glucose concentration at sampling time t. b This refers to the baseline value of glucose; G b The baseline glucose concentration is represented by Ra(t), which is the rate of exogenous glucose input at sampling time t; k is the base value of glucose concentration. out Peripheral tissue compartmental elimination rate of insulin; k ’ in It is the product of the distribution rate of insulin from the central compartment to the peripheral tissue compartments and the effector coefficient of insulin in eliminating glucose; It is the serum insulin concentration at sampling time t; b This represents the baseline serum insulin concentration; V G This refers to the volume of glucose distribution. The structure of the oral micromodel of population C peptide is as follows: Wherein, ISR(t) is the C-peptide secretion rate at sampling time t that is higher than the baseline value; k 01 The elimination rate of the central compartment of C-peptide; k 12 k represents the distribution rate of C-peptide from the peripheral compartment to the central compartment. 21 y(t) represents the rate of C-peptide secretion from the central compartment to the peripheral compartment; q1(t) represents the amount of C-peptide in the central compartment at sampling time t; q2(t) represents the amount of C-peptide in the peripheral compartment at sampling time t; y(t) represents the static C-peptide secretion rate at sampling time t; K is the correction coefficient for glucose-stimulated dynamic C-peptide secretion in β cells; G(t) represents the glucose concentration at sampling time t; α represents the insulin secretion time delay rate; β represents the rate of C-peptide secretion in β cells stimulated by increased glucose concentration when glucose is above the C-peptide secretion threshold; h is the glucose concentration threshold for C-peptide secretion; c1(t) represents the C-peptide concentration in the central compartment at sampling time t; V is the C-peptide distribution volume in the central compartment; SR b This represents the baseline value for C-peptide secretion rate; CP b This represents the baseline serum concentration of C-peptide; G b This is the baseline value for glucose concentration; For dynamic pancreatic islet cell function; This represents the static function of pancreatic islet cells; To support the basic function of pancreatic islet cells; The overall pancreatic islet cell function is represented by ΔG, which is the difference between the peak glucose concentration and the baseline glucose concentration. G(t) is the glucose concentration at sampling time t, where t is the sampling time.

2. The system according to claim 1, characterized in that, The population C-peptide oral mini-model is used to fit the C-peptide serum concentration estimation curve, and the C-peptide serum concentration estimation curve is approximated to the C-peptide serum concentration measurement curve to obtain the pancreatic islet cell function of the human body to be tested.

3. The system according to claim 1, characterized in that, The population nonlinear mixed-effects model parameter calculation module, based on the basic information of the human subject to be tested, the measured value of glucose plasma concentration, and the measured value of insulin serum concentration, uses NONMEM software and selects the FOCEI algorithm to fit and obtain the estimated curve of glucose plasma concentration and the estimated curve of C-peptide serum concentration.

4. The system according to claim 1, characterized in that, The measured glucose plasma concentration values ​​include glucose concentration values ​​collected at four sampling times: fasting and postprandial. The insulin serum concentration measurements include insulin secretion concentration values ​​collected at four sampling times: fasting and postprandial. The C-peptide serum concentration measurements included C-peptide serum concentration values ​​collected at four sampling times: fasting and postprandial.

5. The system according to claim 4, characterized in that, The four sampling times after a meal are any four time points selected from 30 minutes, 60 minutes, 90 minutes, 120 minutes, 150 minutes, and 180 minutes after the meal.

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