CN112349388A - Data processing method and device for blood sugar, storage medium and electronic equipment - Google Patents
Data processing method and device for blood sugar, storage medium and electronic equipment Download PDFInfo
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- CN112349388A CN112349388A CN202011400046.4A CN202011400046A CN112349388A CN 112349388 A CN112349388 A CN 112349388A CN 202011400046 A CN202011400046 A CN 202011400046A CN 112349388 A CN112349388 A CN 112349388A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The embodiment of the disclosure provides a data processing method, a device, a storage medium and an electronic device for blood sugar, wherein the method comprises the steps of obtaining at least one blood sugar related parameter in a preset time based on a preset diet matching ratio; comparing the blood glucose related parameter with a corresponding reference value; based on the comparison, a recommended dietary ratio is determined. The embodiment of the disclosure can determine or evaluate the glucose metabolism characteristics of the subject according to the blood glucose data of the subject after the subject finishes the diet with the preset proportion, so that a personalized nutrition strategy can be provided for the subject, the increase of blood glucose related parameters is avoided, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of medical data processing, and in particular relates to a data processing method and device for blood sugar, a storage medium and an electronic device.
Background
The effects of different types and amounts of macronutrients and foods on human health are constantly controversial. In addition to other explanations of the inconsistency between different studies (e.g., study design, sample size, etc.), an important argument is the large individual variability in human response to dietary factors. Several milestone-bearing studies have been conducted in the prior art to capture or PREDICT human personalized responses to diet, including, for example, israel's personalized nutritional cohort study and the latest PREDICT 1 study, which challenge "one-break" dietary recommendations.
Most people are mainly in a postprandial state during non-sleep periods, and postprandial hyperglycemia is associated with a high risk of cardiometabolic diseases. Therefore, postprandial blood glucose is a good indicator for personalized response studies on the same meal. Although partial progress has been made in describing the individual's pattern of response to the same or different foods, it is currently unclear whether and how it should be applied clinically due to the lack of high levels of evidence (e.g., from the clinic).
The number of studies in the prior art that focus on personalized or accurate nutrition is limited, and no cost-effective method for the widespread use of individuals for a variety of conditions has been established. To date, personalized nutritional strategies either require a large algorithmic basis or lack an effective assessment method at the individual level.
Disclosure of Invention
In order to solve the above problems, embodiments of the present disclosure provide a data processing method and apparatus for blood glucose, a storage medium, and an electronic device, so as to solve the above problems in the prior art.
In order to solve the technical problem, the embodiment of the present disclosure adopts the following technical solutions:
the present disclosure provides a data processing method for blood glucose, comprising the steps of:
obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined dietary profile; comparing the blood glucose related parameter with a corresponding reference value; based on the comparison, a recommended dietary ratio is determined.
In some embodiments, obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined dietary profile comprises: a plurality of predetermined dietary formulations is determined, and at least one blood glucose related parameter of the user for each of the predetermined dietary formulations is obtained within a predetermined time.
In some embodiments, the predetermined dietary ratios include at least a high fat-low water ratio and a high carbohydrate-low fat ratio.
In some embodiments, comparing the blood glucose related parameter to a corresponding reference value comprises: comparing the blood glucose related parameter with a corresponding first blood glucose reference value to obtain a first proportional value based on the first blood glucose reference value; comparing the blood sugar related parameter with a corresponding second blood sugar reference value to obtain a second proportion value based on the second blood sugar reference value; comparing the first and second proportional values with a predetermined proportional reference value.
In some embodiments, the determining a recommended dietary ratio based on the comparison comprises: determining a diet propensity feature in the event that the first or second proportional value based on at least one of the blood glucose related parameters is greater than a corresponding predetermined proportional reference value; determining a recommended dietary allowance based on the dietary propensity profile.
In some embodiments, the blood glucose related parameter comprises at least one of a post-prandial maximum blood glucose value, an average magnitude of blood glucose value fluctuations, a total area under a blood glucose meter measurement curve.
In some embodiments, the dietary formulation comprises at least the following ingredients: fat, carbohydrate, protein.
The present disclosure also provides a data processing apparatus for blood glucose, comprising: an obtaining module for obtaining at least one blood glucose related parameter within a predetermined time based on a predetermined dietary proportion; a comparison module for comparing the blood glucose related parameter with a corresponding reference value; a determination module for determining a recommended dietary ratio based on the comparison.
The present disclosure also provides a storage medium storing a computer program, wherein the computer program is configured to implement the steps of any one of the above methods when executed by a processor.
The present disclosure also provides an electronic device, at least comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program on the memory.
The beneficial effects of this disclosed embodiment lie in: the embodiment of the disclosure can determine or evaluate the glucose metabolism characteristics of the subject according to the blood glucose data of the subject after the subject finishes the diet with the preset proportion, so that a personalized nutrition strategy can be provided for the subject, the increase of blood glucose related parameters is avoided, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic step diagram of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of the dietary formulation in the data processing method for blood glucose according to the embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating steps of a data processing method for blood glucose according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of a data processing device for blood glucose according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various aspects and features of the disclosure are described herein with reference to the drawings.
It will be understood that various modifications may be made to the embodiments of the present application. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
The embodiment of the present disclosure provides a data processing method for blood sugar, the data processing method is used for dietary intervention on a subject, so as to obtain and process blood sugar data of the subject subjected to dietary intervention, so as to recommend a corresponding dietary proportion to the subject based on the blood sugar data, as shown in fig. 1, the method includes the following steps:
s101, obtaining at least one blood sugar related parameter in a preset time based on a preset diet matching.
In this step, obtaining at least one blood glucose related parameter within a predetermined time based on a predetermined dietary ratio; specifically, during the dietary intervention of the subject, the subject is required to eat only the provided food or beverage with the predetermined dietary composition within a predetermined time, and at least one blood glucose related parameter based on the predetermined dietary composition is obtained for the subject based on the biological sample by continuously wearing a blood glucose monitor (CGMS) to collect the biological sample of the subject and obtain personal data. Note that, in this step, the predetermined time here may be set to, for example, 24 days; furthermore, in order to obtain a true and accurate blood glucose related parameter, the subject is required not to change his lifestyle or physical exercise program throughout a predetermined time, which aims to limit the factors affecting the blood glucose related parameter of the subject to only the dietary composition.
Further, in the step S101 of obtaining at least one blood glucose related parameter within a predetermined time based on a predetermined diet ratio, as shown in fig. 2, the method includes the following steps:
s201, determining a plurality of preset diet proportions.
In this step, it is necessary to first determine a plurality of predetermined dietary formulations, where the predetermined dietary formulations represent the distribution of different substances in the diet; this predetermination of the subject's dietary profile directly determines the manner in which dietary intervention is performed on the subject, where dietary intervention is by allowing the subject to consume an isocaloric diet of varying dietary profiles for a predetermined period of time (e.g., 2300 kcal per day for males and 1900 kcal per day for females). In particular embodiments, the predetermined dietary ratios include at least a high fat-to-low water ratio and a high carbohydrate-to-low fat ratio.
Specifically, the dietary composition of the isocaloric diet herein can be a high fat-low carbohydrate (HF-LC) composition and a low fat-high carbohydrate (LF-HC) composition, wherein the distribution of the main material components in the HF-LC composition diet is fat-based, and the composition can be that carbohydrate provides 25% of energy, protein provides 15% of energy and fat provides 60% of energy, although the dietary composition herein can be adjusted during dietary intervention, for example, the composition is adjusted to provide 15% of energy for carbohydrate, 15% of energy for protein and 70% of energy for fat; accordingly, the distribution of the main material components in the LF-HC formula diet is carbohydrate-based, and specifically, carbohydrate provides 65% of energy, protein 15% of energy and fat 20% of energy, although the formula may be adjusted, for example, to provide 75% of energy for carbohydrate, 15% of energy for protein and 10% of energy for fat.
For the dietary intervention period of the subject, the subject performs the cross-intervention of at least two diets of different proportions, and sometimes a washout period (e.g., 1-7 days) is required between the diets of different dietary proportions to reduce the residual effect of the food of the previous stage.
In a specific example, 28 healthy young adults were recruited as subjects to compare the effect of diets in the LF-HC ratio and the HF-LC ratio on the blood glucose values of the subjects after meal, 3 groups of dietary intervention activities were completed continuously during the dietary intervention, and each subject was provided with an isocaloric diet throughout the duration of the dietary intervention. The schedule of 3 dietary intervention sessions here is shown in figure 3, each session comprising 4 6 days of time, i.e. a total of 24 days. During each dietary intervention, each subject completed dietary interventions for both cross-experimental conditions (i.e., LF-HC ratio and HF-LC ratio). A washout period (6 days) may be set during the dietary intervention, this washout setting the gap between subjects consuming diets of different mix ratios to eliminate the effect of food carryover in the previous phase. It is noted that, among these, the sequence of interventions on HF-LC and LF-HC proportioned diets was formed in 3 intervention acts according to a computer-generated randomized schedule, and the sequence of interventions can be exemplified as follows: LF-HC, HF-LC, HF-LC, LF-HC, HF-LC, and LF-HC.
Specifically, the HF-LC matched diets included two groups of 3 day diets, where the first 3 day diet consisted of a total energy intake of fat (E) of 70%, a total energy intake of protein of 15% and a total energy intake of carbohydrate of 15%, and the other 3 day diet consisted of a total energy intake of fat of 60%, a total energy intake of protein of 15%, and a total energy intake of carbohydrate of 25%; the LF-HC matched diets included two groups of 3-day diets, the first 3-day diet having 20% energy intake from fat, 15% energy intake from protein and 65% energy intake from carbohydrate, and the other 3-day diet including 10% energy intake from fat, 15% energy intake from protein and 75% energy intake from carbohydrate. In addition, the 6 day washout diet formula included 30% of energy intake from fat, 15% of energy intake from protein and 55% of energy intake from carbohydrate.
S202, at least one blood sugar related parameter of the user aiming at each preset diet matching is obtained in preset time.
When a plurality of predetermined dietary ratios are determined through the above step S201, it is necessary to analyze continuous blood glucose data after the subject has consumed intervention diets having different ratios after the subject has received a dry diet. The blood glucose data corresponds to the adaptability of the blood glucose data to the predetermined dietary proportion, and is expressed by blood glucose related parameters, wherein the blood glucose related parameters are used for describing the condition of the blood glucose (PBG) of a subject after meal, and at least comprise a maximum blood glucose value (MPG) after meal, a blood glucose value fluctuation average amplitude (MAGE) and a total area under a blood glucose measurement curve (AUC 24).
Specifically, in order to evaluate the adaptability of the subject to different dietary formulations, the interstitial glucose level of the subject needs to be measured every 15 minutes by a device such as a blood glucose monitor, a blood glucose measurement curve is obtained, and blood glucose related parameters, especially blood glucose related parameters after the subject eats all diets with different formulations, are obtained based on the blood glucose measurement curve, wherein the blood glucose related parameters can be postprandial maximum blood glucose value (MPG), blood glucose value fluctuation average amplitude (MAGE) and total area under the blood glucose measurement curve (AUC24), wherein the maximum blood glucose value (MPG) is the peak value measured by the blood glucose monitor within 3 hours after each diet or the maximum value measured by the blood glucose monitor between two diets with an interval less than 3 hours; the mean amplitude of fluctuation (MAGE) of the blood glucose level is obtained by measuring the arithmetic mean of the differences of successive peaks and valleys; the total area under the blood glucose measurement curve (AUC24) refers to the total area under the blood glucose monitor measurement curve from 0:00 to 24:00 of the day.
S102, comparing the blood sugar related parameters with corresponding reference values.
After obtaining at least one blood glucose related parameter within a predetermined time based on a predetermined diet formula in step S101, in this step, the obtained blood glucose related parameter needs to be compared with a corresponding reference value, in order to obtain the adaptability of the subject to different diet formulas and determine the tendency of the subject to diet formulas under the condition that the blood glucose condition is ensured to be maintained within a relatively normal range, as shown in fig. 4, the method specifically includes the following steps:
s301, comparing the blood sugar related parameter with a corresponding first blood sugar reference value to obtain a first proportional value based on the first blood sugar reference value;
s302, comparing the blood sugar related parameter with a corresponding second blood sugar reference value to obtain a second proportion value based on the second blood sugar reference value;
s303, comparing the first ratio value and the second ratio value with a predetermined ratio reference value.
In this step, the effect of diets such as HF-LC and LF-HC formulas on Postprandial Blood Glucose (PBG) levels of each subject is further investigated by obtaining a comparison between said blood glucose related parameters of the subject and corresponding reference values. The method mainly adopts a Bayesian analysis model to calculate the posterior probability of the difference of the mean value and the clinically meaningful difference of the postprandial maximum blood glucose value (MPG), the average amplitude of blood glucose value fluctuation (MAGE) and the total area under a blood glucose measurement curve (AUC24) caused by different dietary modes of individual subjects.
The reference value here is determined in such a way that the difference between the mean levels of the postprandial maximum blood glucose values of healthy persons over the ages of 25-45 and 45, i.e. + -. 3mg/dL (. + -. 0.167mmol/L), is the reference value for the maximum blood glucose value; taking the difference value between the average levels of the blood glucose value fluctuation average amplitudes of healthy people of 25-45 years and 45 years old or more, namely +/-1.3 mg/dL (+/-0.072 mmol/L), as a reference value aiming at the blood glucose value fluctuation average amplitude; as for the total area under the blood glucose measurement curve, the difference between the average levels of the areas under the blood glucose measurement curves of healthy persons and those of pre-diabetic persons was. + -. 1.5X 104mg/dL.min (+ -13.889 mmol/L.h) is a reference value for the total area under the blood glucose measurement curve.
After determining corresponding blood glucose reference values for different blood glucose related parameters, further, for each blood glucose related parameter, it needs to be compared with a corresponding blood glucose reference value, specifically, the blood glucose related parameter is compared with a corresponding first blood glucose reference value to obtain a first proportion value based on the first blood glucose reference value, and simultaneously the blood glucose related parameter is compared with a corresponding second blood glucose reference value to obtain a second proportion value based on the second blood glucose reference value; the two proportional values are posterior probabilities representing that the subject exceeds the two blood glucose reference values, and further, the first proportional value and the second proportional value are compared with a predetermined proportional reference value so as to judge the tendency of the subject to diets with different proportions based on the comparison result. For example, as shown in Table 1, taking any subject as an example, the maximum blood glucose value difference is respectively compared with the first blood glucose reference value plus 0.167mmol/L and the second blood glucose reference value minus 0.167mmol/L to obtain a first proportion value greater than the first blood glucose reference value 4.5% and a second proportion value less than the second blood glucose reference value 35.43%, and/or comparing the average amplitude of blood glucose value fluctuation with +/-0.072 mmol/L as the first blood glucose reference value and-0.072 mmol/L as the second blood glucose reference value respectively to obtain a first proportion value 4.37% larger than the first blood glucose reference value and a second proportion value 83.17% smaller than the second blood glucose reference value, of course, the total area under the blood glucose measurement curve can be compared with + 13.889 mmol/L-h as the first blood glucose reference value and-13.889 mmol/L-h as the second blood glucose reference value respectively to obtain the first proportional value and the second proportional value.
Finally, the first and second proportional values for each of the blood glucose related parameters are compared to a predetermined proportional reference value. The predetermined proportional reference value here may be 80%.
TABLE 1
The comparing the blood glucose related parameter with the corresponding reference value in the above step S102 comprises the following steps:
and S103, determining a recommended dietary ratio based on the comparison result.
After comparing the blood glucose related parameter with the corresponding reference value through the above step S102, in this step, a recommended dietary formula is determined to the subject based on the comparison result so that the postprandial blood glucose level of the subject is within a relatively normal range. For example, if the subject's comparison is prone to higher blood glucose levels after they have consumed a high fat-low carbohydrate (HF-LC) formulated diet, then a low fat-high carbohydrate (LF-HC) formulated diet is recommended to the subject; conversely, if the subject's comparison is biased toward a higher blood glucose level after consuming a low fat-high carbohydrate (LF-HC) formulated diet, then a low fat-low carbohydrate (HF-LC) formulated diet is recommended to the subject.
In this example, the difference in the intervention effect of the subject between diets at LF-HC ratio and HF-LC ratio needs to be obtained to obtain a posterior probability that a subject not meeting the predetermined condition will be considered a non-responder if the subject's blood glucose condition after the intervention meets the predetermined condition will be defined as a high-fat responder or a high-carbohydrate responder. The subject's dietary propensity profile tags (e.g., high carbohydrate responders, high fat responders or non-responders) are the determining factors for their nutritional recommendations. For high carbohydrate responders, a HF-LC formulated diet is a better choice for controlling blood glucose, and vice versa. There was no difference in blood glucose correlation parameters between HF-LC and LF-HC diets for non-responders.
In the step S103, determining the recommended dietary ratio based on the comparison result, as shown in fig. 5, the method includes the following steps:
s401, determining the diet tendency characteristics when the first proportion value or the second proportion value based on at least one blood sugar related parameter is larger than a corresponding preset proportion reference value.
In this step, in case the first or second ratio value based on at least one of the blood glucose related parameters is larger than the corresponding predetermined ratio reference value, the characteristic of the eating tendency of the subject is determined based on the first or second ratio value larger than the corresponding predetermined ratio reference value, where the characteristic of the eating tendency is expressed in the form of a responder to a substance. For example, taking 28 subjects in table 1 as an example, for example, among all proportional values of subject No. 1 for different said blood glucose related parameters, a second proportional value 83.17% smaller than-0.072 mmol/L is greater than 80% as a predetermined proportional reference value for the blood glucose related parameter of blood glucose value fluctuation average amplitude, then subject No. 1 is determined to be a high-fat responder, and among all proportional values of subject No. 5 for different said blood glucose related parameters, a first proportional value 93.97% larger than-0.072 mmol/L is greater than 80% as a predetermined proportional reference value for the blood glucose related parameter of blood glucose value fluctuation average amplitude, then subject No. 5 is determined to be a high-carbohydrate responder.
Through all the subjects, according to the above-identified method, for the blood glucose related parameter of the maximum blood glucose value after meal, there are 9 subjects defined as responders, respectively, subjects No. 7, No. 8, No. 11, No. 13, No. 17, No. 19, No. 22, No. 23 and No. 27, who are high-fat responders if the second proportional value of less than-0.167 mmol/L of subjects No. 8, No. 11 and No. 13 is considered to be greater than 80% based on the comparison between the first proportional value and the second proportional value of the maximum blood glucose value of these subjects and the predetermined proportional reference value, and the first proportional value of greater than +/-0.167 mmol/L of subjects No. 7, No. 17, No. 19, No. 22, No. 23 and No. 27 is greater than 80%, and these subjects are high-carbohydrate responders. Similarly, for the blood glucose related parameters of the average amplitude of blood glucose level fluctuation, high-fat responders were identified as nos. 1, 4 and 8, and high-carbohydrate responders as nos. 5, 19, 22, 25 and 27. It is readily apparent that for subject No. 8, both blood glucose related parameters are considered high fat responders, for test subjects No. 19, 22 and 27, and for both blood glucose related parameters are considered high carbohydrate responders.
Generally, in the case of high-carbohydrate responders, the ratio of the carbohydrate to the fat in the diet increases, and the ratio of the carbohydrate to the fat in the diet varies in proportion, so that the ratio of the carbohydrate to the blood glucose related parameter increases, whereas the ratio of the carbohydrate to the fat in the diet decreases in the case of high-fat responders.
S402, determining a recommended dietary proportion based on the dietary tendency characteristics.
In the above step, after determining the eating tendency characteristic in case that the first proportion value or the second proportion value based on at least one of the blood glucose related parameters is larger than the corresponding predetermined proportion reference value, the recommended dietary proportion may be determined based on the eating tendency characteristic. Specifically, for example, LF-HC diets are recommended for high-fat responders, HF-LC diets are recommended for high-carbohydrate responders to avoid elevated blood glucose related parameters, which is an accurate nutritional recommendation for the prevention of metabolic diseases such as diabetes.
A second embodiment of the present disclosure provides a data processing apparatus for blood glucose, the data processing apparatus is configured to perform dietary intervention on a subject, so as to obtain and process blood glucose data of the subject subjected to dietary intervention, so as to recommend a corresponding dietary matching to the subject based on the blood glucose data, as shown in fig. 6, which includes an obtaining module 10, a comparing module 20, and a determining module 30, which are coupled to each other, specifically:
an obtaining module 10 for obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined dietary allowance. Acquiring, by the acquiring module 10, at least one blood glucose related parameter within a predetermined time based on a predetermined dietary matching; specifically, during the dietary intervention of the subject, the subject is required to eat only the provided food or beverage with the predetermined dietary composition within a predetermined time, and at least one blood glucose related parameter based on the predetermined dietary composition is obtained for the subject based on the biological sample by continuously wearing a blood glucose monitor (CGMS) to collect the biological sample of the subject and obtain personal data. Note that the predetermined time here may be set to, for example, 24 days; furthermore, in order to obtain a true and accurate blood glucose related parameter, the subject is required not to change his lifestyle or physical exercise program throughout a predetermined time, which aims to limit the factors affecting the blood glucose related parameter of the subject to only the dietary composition.
Further, in the obtaining of the at least one blood glucose related parameter within the predetermined time based on the predetermined diet matching by the obtaining module 10, the obtaining module 10 includes the following parts:
a determining unit for determining a plurality of predetermined dietary formulations.
A plurality of predetermined dietary formulations, which represent the distribution of different substances in the diet, are first determined by the determining unit; this predetermination of the subject's dietary profile directly determines the manner in which dietary intervention is performed on the subject, where dietary intervention is by allowing the subject to consume an isocaloric diet of varying dietary profiles for a predetermined period of time (e.g., 2300 kcal per day for males and 1900 kcal per day for females). In particular embodiments, the predetermined dietary ratios include at least a high fat-to-low water ratio and a high carbohydrate-to-low fat ratio.
An obtaining unit for obtaining at least one blood glucose related parameter of the user for each of the predetermined dietary formulations within a predetermined time. After a plurality of predetermined dietary ratios are determined by the above determination unit, when a subject receives a diet dry state, it is necessary to analyze continuous blood glucose data of the subject after eating an intervention diet having a different ratio. The blood glucose data corresponds to the adaptability of the blood glucose data to the predetermined dietary proportion, and is expressed by blood glucose related parameters, wherein the blood glucose related parameters are used for describing the condition of the blood glucose (PBG) of a subject after meal, and at least comprise a maximum blood glucose value (MPG) after meal, a blood glucose value fluctuation average amplitude (MAGE) and a total area under a blood glucose measurement curve (AUC 24).
A comparison module 20 for comparing the blood glucose related parameter with a corresponding reference value.
After obtaining at least one blood sugar related parameter within a predetermined time based on a predetermined diet ratio by the obtaining module 10, the comparing module 20 needs to compare the obtained blood sugar related parameter with a corresponding reference value, in order to obtain the adaptability of the subject to different diet ratios and determine the tendency of the subject to diet ratios under the condition that the blood sugar condition is ensured to be maintained within a relatively normal range, which specifically includes the following parts:
a first scaling unit for comparing the blood glucose related parameter with a corresponding first blood glucose reference value, obtaining a first scaling value based on the first blood glucose reference value;
a second proportion unit for comparing the blood sugar related parameter with a corresponding second blood sugar reference value to obtain a second proportion value based on the second blood sugar reference value;
a comparison unit comparing the first and second proportional values with a predetermined proportional reference value.
The effect of diets such as HF-LC and LF-HC formulas on Postprandial Blood Glucose (PBG) levels of each subject is further investigated by a comparison unit by obtaining a comparison between said blood glucose related parameters of the subject and corresponding reference values. The comparison unit mainly adopts a Bayesian analysis model to calculate the posterior probability of the difference of the maximum postprandial blood glucose value (MPG), the average amplitude of fluctuation of blood glucose value (MAGE) and the total area under the blood glucose measurement curve (AUC24) in the mean value and the clinically meaningful difference caused by different diet modes of individual subjects.
A determination module 30 for determining a recommended dietary ratio based on the comparison result.
After the blood glucose related parameter is compared with the corresponding reference value by the comparing module 20, the determining module 30 determines the recommended dietary matching to the subject based on the comparison result, so that the postprandial blood glucose level of the subject is in a relatively normal range. For example, if the subject's comparison is prone to higher blood glucose levels after they have consumed a high fat-low carbohydrate (HF-LC) formulated diet, then a low fat-high carbohydrate (LF-HC) formulated diet is recommended to the subject; conversely, if the subject's comparison is biased toward a higher blood glucose level after consuming a low fat-high carbohydrate (LF-HC) formulated diet, then a high fat-low carbohydrate (HF-LC) formulated diet is recommended to the subject.
The determination module 30 comprises the following parts: a characteristic determination unit for determining a diet propensity characteristic in case the first or second proportional value based on at least one of the blood glucose related parameters is larger than a corresponding predetermined proportional reference value. A ratio determination unit for determining a recommended dietary ratio based on the dietary propensity characteristic.
The embodiment of the disclosure can determine or evaluate the glucose metabolism characteristics of the subject according to the blood glucose data of the subject after the subject finishes the diet with the preset proportion, so that a personalized nutrition strategy can be provided for the subject, the increase of blood glucose related parameters is avoided, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
A third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program that, when executed by a processor, implements the method provided by the first embodiment of the present disclosure, including the following steps S11 to S13:
s11, acquiring at least one blood sugar related parameter in a preset time based on a preset diet matching ratio;
s12, comparing the blood sugar related parameters with corresponding reference values;
and S13, determining the recommended dietary proportion based on the comparison result.
Further, the computer program realizes the other methods provided by the first embodiment of the present disclosure when executed by the processor.
The embodiment of the disclosure can determine or evaluate the glucose metabolism characteristics of the subject according to the blood glucose data of the subject after the subject finishes the diet with the preset proportion, so that a personalized nutrition strategy can be provided for the subject, the increase of blood glucose related parameters is avoided, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
A fourth embodiment of the present disclosure provides an electronic device, a schematic structural diagram of the electronic device may be as shown in fig. 7, and the electronic device at least includes a memory 901 and a processor 902, where the memory 901 stores a computer program, and the processor 902 implements the method provided in any embodiment of the present disclosure when executing the computer program on the memory 901. Illustratively, the electronic device computer program steps are as follows S21-S23:
s21, acquiring at least one blood sugar related parameter in a preset time based on a preset diet matching ratio;
s22, comparing the blood sugar related parameters with corresponding reference values;
and S23, determining the recommended dietary proportion based on the comparison result.
Further, the processor 902 also executes a computer program to implement the other methods in the first embodiment described above.
The embodiment of the disclosure can determine or evaluate the glucose metabolism characteristics of the subject according to the blood glucose data of the subject after the subject finishes the diet with the preset proportion, so that a personalized nutrition strategy can be provided for the subject, the increase of blood glucose related parameters is avoided, and an accurate nutrition strategy for preventing metabolic diseases such as diabetes is provided.
The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the passenger computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.
Claims (10)
1. A data processing method for blood glucose, comprising the steps of:
obtaining at least one blood glucose related parameter for a predetermined time based on a predetermined dietary profile;
comparing the blood glucose related parameter with a corresponding reference value;
based on the comparison, a recommended dietary ratio is determined.
2. The data processing method according to claim 1, wherein the obtaining of the at least one blood glucose related parameter for a predetermined time based on a predetermined dietary profile comprises the steps of:
determining a plurality of predetermined dietary ratios;
obtaining at least one blood glucose related parameter of the user for each of the predetermined dietary formulations within a predetermined time.
3. The data processing method of claim 2, wherein the predetermined dietary ratios include at least a high fat-low carbon water ratio and a high carbohydrate-low fat ratio.
4. The data processing method of claim 1, wherein the comparing the blood glucose related parameter with a corresponding reference value comprises the steps of:
comparing the blood glucose related parameter with a corresponding first blood glucose reference value to obtain a first proportional value based on the first blood glucose reference value;
comparing the blood sugar related parameter with a corresponding second blood sugar reference value to obtain a second proportion value based on the second blood sugar reference value;
comparing the first and second proportional values with a predetermined proportional reference value.
5. The data processing method of claim 1, wherein determining a recommended dietary formula based on the comparison comprises:
determining a diet propensity feature in the event that the first or second proportional value based on at least one of the blood glucose related parameters is greater than a corresponding predetermined proportional reference value;
determining a recommended dietary allowance based on the dietary propensity profile.
6. The data processing method of claim 1, wherein the blood glucose related parameter comprises at least one of a post-prandial maximum blood glucose value, an average magnitude of blood glucose value fluctuations, a total area under a glucometer measurement curve.
7. The data processing method of claim 1, wherein the dietary formulation comprises at least the following components: fat, carbohydrate, protein.
8. A data processing device for blood glucose, characterized by comprising the following parts:
an obtaining module for obtaining at least one blood glucose related parameter within a predetermined time based on a predetermined dietary proportion;
a comparison module for comparing the blood glucose related parameter with a corresponding reference value;
a determination module for determining a recommended dietary ratio based on the comparison.
9. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 7 when executed by a processor.
10. An electronic device comprising at least a memory, a processor, the memory having a computer program stored thereon, wherein the processor, when executing the computer program on the memory, is adapted to carry out the steps of the method of any of claims 1 to 7.
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