CN111378734A - Method for determining metabolism disease health food combination and readable storage medium thereof - Google Patents
Method for determining metabolism disease health food combination and readable storage medium thereof Download PDFInfo
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- CN111378734A CN111378734A CN201811619218.XA CN201811619218A CN111378734A CN 111378734 A CN111378734 A CN 111378734A CN 201811619218 A CN201811619218 A CN 201811619218A CN 111378734 A CN111378734 A CN 111378734A
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/04—Endocrine or metabolic disorders
- G01N2800/042—Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/04—Endocrine or metabolic disorders
- G01N2800/044—Hyperlipemia or hypolipemia, e.g. dyslipidaemia, obesity
Abstract
The invention provides a method for determining the combination of health care food for metabolic diseases and a readable storage medium thereof. The invention discloses a method for determining a metabolic disease health food combination and a non-transitory computer readable storage medium, which are used by individuals possibly suffering from metabolic diseases. The method comprises the following steps. The nucleic acid detecting step is to detect nucleic acid in the biological sample and judge the possibility of suffering metabolic diseases according to the detection result of the nucleic acid. The blood detection step is to detect the target and at least one health index in the blood sample and judge that the patient does not suffer from metabolic diseases according to the detection result of the target. The metabolic disease health food composition determining step determines the metabolic disease health food composition according to the possibility of suffering metabolic diseases and at least one health index, the metabolic disease health food composition comprises a plurality of health foods, and at least one health food is immunomodulatory protein or a compound of immunomodulatory protein.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to a method and a non-transitory computer readable storage medium for determining personalized metabolic disease health food combinations.
[ background of the invention ]
The metabolic diseases comprise a plurality of internal medicine important diseases such as metabolic syndrome, obesity, diabetes and the like, wherein the diabetes is one of ten causes of death of Chinese people, and the metabolic diseases can cause a plurality of numerical abnormalities of the body, such as triglyceride, high-density lipoprotein, blood pressure, blood sugar and the like, which easily cause other diseases with high mortality rate, such as cerebrovascular diseases, heart diseases and the like. It is often not quick and quick to start treatment when symptoms affecting the normal physiological functions of the patient appear. It follows that the threat of metabolic diseases to human health is not negligible.
In recent years, molecular medical science and technology have gradually developed metabolic disease gene markers for risk diagnosis and prediction of metabolic diseases, and gene detection can detect gene variation early to understand the possibility of an individual suffering from metabolic diseases. In addition, there are many different tests available to test whether an individual has suffered a metabolic disease, such as monitoring of body weight and waist circumference, blood tests, etc., and if the individual has suffered a metabolic disease, entering a metabolic disease course; if the subject does not suffer from a metabolic disease, no subsequent medical action is performed.
Although many health foods are available for metabolic disease health care, the health foods are mostly used for providing patient supportive health care effects after disease onset, and the prepositive health care is relatively less. However, it is often not quick to start providing health food until the patient actually gets ill. And each individual has a different constitution and health status, and even if the prepositive health care measures are taken at the non-onset stage, if the health care food is not provided according to individual differences only according to the metabolic disease types, the effect of prepositive health care can be exerted in practice to a very limited extent.
Therefore, it is an important object to provide a method for determining a personalized health food composition suitable for an individual based on the constitution of the individual when the individual is known to have the possibility or risk of metabolic diseases based on the correlation test.
[ summary of the invention ]
In view of the above, the present invention provides a method for determining a personalized health food composition for individuals who may suffer from metabolic diseases.
To achieve the above object, the method for determining a personalized health food composition for metabolic diseases according to the present invention comprises the following steps: (1) a nucleic acid detection step of detecting nucleic acid in a biological sample and judging the possibility of suffering from metabolic diseases based on the detection result of the nucleic acid; (2) a blood detection step, which is to detect a target and at least one health index in a blood sample and judge that the patient does not suffer from metabolic diseases according to the detection result of the target; and (3) a personalized metabolic disease health food combination determining step of determining a personalized metabolic disease health food combination based on the possibility of suffering from metabolic disease in the nucleic acid detecting step and at least one health index in the blood detecting step, the personalized metabolic disease health food combination including a plurality of health foods, wherein at least one of the health foods is an immunomodulatory protein or a complex of immunomodulatory proteins.
In one embodiment, the biological sample is a human bodily fluid, human blood, or human interstitial fluid.
In one embodiment, the immunomodulatory protein is a microsporogenous immunomodulatory protein (GMI).
In one embodiment, the aforementioned health index is an index comprising at least a blood cell index, a blood glucose index, a blood fat index, a liver function index, a kidney function index, or a pancreas function index.
In one embodiment, the metabolic disease is monogenic diabetes, type one diabetes, type two diabetes, or hyperlipidemia.
In one embodiment, the nucleic acid is GCK, HNF1A, HNF4A, HNF1B, HLA-DR3, HLA-DR4, HLA-DQ, TCF7L2, GLIS3, PEPD, FITM2, R3HDML, KCNK16, MAEA, GCC1, PAX4, PSMD6, ZFAND3, LDLR, APOB, PCSK-9, or a combination thereof.
In one embodiment, the target is blood glucose, an anti-ICA antibody, an anti-IAA antibody, an anti-GAD antibody, cholesterol, a triglyceride, or a combination thereof.
In one embodiment, when the aforementioned metabolic disease is monogenic diabetes, then the nucleic acid is GCK, HNF1A, HNF4A, HNF1B, or a combination thereof, and the target is blood glucose.
In one embodiment, when the metabolic disease is type I diabetes, the nucleic acid is HLA-DR3, HLA-DR4, HLA-DQ, or a combination thereof, and the target is an anti-ICA antibody, an anti-IAA antibody, an anti-GAD antibody, or a combination thereof.
In one embodiment, when the aforementioned metabolic disease is type ii diabetes, then the nucleic acid is TCF7L2, GLIS3, PEPD, FITM2, R3HDML, HNF4A, KCNK16, MAEA, GCC1, PAX4, PSMD6, ZFAND3, or a combination thereof, and the target is blood glucose.
In one embodiment, when the aforementioned metabolic disorder is hyperlipidemia, then the nucleic acid is LDLR, APOB, PCSK-9, or a combination thereof, and the target is cholesterol, triglyceride, or a combination thereof.
The present invention also provides a non-transitory computer readable storage medium storing a plurality of instructions executable by a processing unit of a computer device to determine detection data or detection data for each step of the method as described above and determine a personalized metabolic disease and health food composition.
In summary, the method for determining a personalized health food composition for metabolic diseases and the non-transitory computer readable storage medium for performing the method of the present invention can provide a personalized health food composition for metabolic diseases according to the possibility of metabolic diseases and the health status of an individual before the individual suffers from metabolic diseases.
[ description of the drawings ]
FIG. 1 is a flowchart illustrating a method for determining a personalized health food for metabolic diseases according to a first embodiment of the present invention.
Fig. 2 is a non-transitory computer readable storage medium for providing a personalized metabolic disease health food composition according to a second embodiment of the present invention.
[ detailed description ] embodiments
Various embodiments provided in accordance with the present invention will be described with reference to the accompanying drawings, wherein like components will be described with like reference numerals.
It should be noted that while the following examples of the present invention are provided to illustrate one possible combination of the disclosed components, the present invention is intended to include all possible combinations of the disclosed components. Thus, if one embodiment includes components A, B and C and a second embodiment includes components B and D, the present invention should be understood to include any combination of A, B, C and/or other species of D, even if not explicitly disclosed.
In certain embodiments described below, where numerical values are described that indicate quantities of ingredients, characteristics (e.g., concentrations, reaction conditions, etc.), it is to be understood that the term "about" is used in some instances to modify. Accordingly, in certain embodiments, the numerical parameters set forth in this specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, numerical parameters should be construed in light of the number of significant digits recited and the application of ordinary numerical reduction techniques. However, even though some of the examples show some approximations, the numerical ranges and numerical parameters set forth in the specific examples are reported as precisely as possible. The numerical values set forth herein may include certain errors resulting from the standard deviation found in the statistics of the various measurements.
Unless otherwise specified herein, all numerical ranges set forth herein are to be construed as inclusive of their endpoints, and open-ended ranges are to be construed as inclusive of commercially practicable values. Likewise, all numerical lists should be considered as containing the numerical values therein between unless otherwise specified herein. That is, recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated, each separate value within a range is incorporated into a portion of the disclosure of this specification as if it were individually recited herein.
The group of alternative components or embodiments disclosed herein should not be construed as limiting the invention. Each member of a group may be referred to and claimed individually or in any combination with other members of the group or with other components herein. For reasons of convenience and/or patentability, one or more members of the group may be included in or deleted from the group. When any of the foregoing additions or deletions occur, the specification is to be considered as including the modified group so as to satisfy the written description requirements of all markush groups used in the appended claims. And no language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
As used herein, the terms "individual", "subject" and "patient" can be a human or non-human mammal. Non-human mammals include, for example, domestic animals and pets, such as ovine, bovine, porcine, canine, feline, and murine mammals. Preferably, the subject is a human. The terms "individual" and "individual" may be used interchangeably.
The term "biological sample" refers to a sample obtained from an organism or a component of an organism (e.g., a cell). The sample may be any biological tissue or fluid. Samples include, but are not limited to, bone marrow, sputum, whole blood, serum, plasma, lymph fluid, corpuscular cells (e.g., red blood cells), urine, peritoneal fluid, pleural fluid, or cells from these samples. The biological sample may also include portions of tissue, such as frozen sections taken for histological purposes. Preferably, the biological sample is a body fluid, blood or interstitial fluid.
The term "blood sample" includes, but is not limited to, whole blood, serum, plasma, lymph fluid, or corpuscular cells (e.g., red blood cells). Preferably, the blood sample is whole blood.
The term "nucleic acid," such as DNA or RNA, refers to an isolated nucleic acid. The term "isolated" refers to a DNA or RNA molecule that is correspondingly isolated from other DNA or RNA present in the natural source of the macromolecule.
The term "health index" refers to an index generally examined by routine examination of blood, including, but not limited to, a blood cell index, a blood glucose index, a blood fat index, a liver function index, a kidney function index, and a pancreas function index.
The term "mutation" includes, but is not limited to, a Single Nucleotide Polymorphism (SNP), insertion, deletion, rearrangement or point mutation.
The term "target" includes, but is not limited to, blood glucose, anti-ICA antibodies, anti-IAA antibodies, anti-GAD antibodies, cholesterol, triglycerides or other targets that can be used to detect metabolic diseases.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for determining a personalized metabolic disease health food composition according to a first embodiment of the present invention. As shown in FIG. 1, S1 is a nucleic acid detecting step, S2 is a blood detecting step, S3 is a personalized metabolic disease health food combination determining step, and S4 is an ending step.
As shown in fig. 1, the method for determining a personalized health food composition for metabolic diseases according to the first embodiment of the present invention is provided for individuals who may suffer from metabolic diseases. The method comprises the following steps. Nucleic acid detection step S1: in this step, nucleic acid is detected in a biological sample, which may be an isolated (or non-living) sample isolated from a human individual, such as a body fluid (e.g., sweat or urine), blood, or tissue fluid. Meanwhile, whether the individual corresponding to the biological sample has the possibility of suffering from metabolic diseases or not is judged according to the detection result of the nucleic acid. The detection method may be performed by a gene chip, PCR, quantitative PCR, Next Generation Sequencing (NGS), or the like. If the subject has a possibility of suffering from a metabolic disease, go to step S2; if the subject does not have a possibility of suffering from a metabolic disease, step S4 is entered to end the method provided herein. In this step, if the subject does not have a possibility of suffering from the corresponding metabolic disease according to the detection result, it indicates that the subject is at a low risk of suffering from the corresponding metabolic disease, so that it may not be necessary to further determine whether the subject actually suffers from the corresponding metabolic disease.
Next, when the subject has a possibility of suffering from metabolic diseases, a blood detection step S2 is performed: in this step, the target and at least one health index in the blood sample of the subject are detected, and whether the subject has suffered from metabolic disease is determined according to the detection result of the target. The target corresponds to the metabolic disease. If the subject does not suffer from the metabolic disease, go to step S3; if the subject already suffers from a metabolic disease, step S4 is entered to end the method provided herein. Thus, through steps S1 and S2 of the method provided in this embodiment, individuals who have a possibility of having a metabolic disease but who have not yet had a metabolic disease have been identified. In view of the above, the steps S1 and S2 of the method of this embodiment can establish the time period that the subject is suitable for receiving the personalized health food combination for metabolic diseases.
In addition, the health index detected in this step S2 may be or include at least one index among a blood cell index, a blood glucose index, a blood fat index, a liver function index, a kidney function index, or a pancreas function index. A hematocrit index is, for example, but not limited to, white blood cells, red blood cells, hemoglobin, average corpuscular volume, platelets, or other hematocrit index known to those skilled in the art. Glycemic indices such as, but not limited to, pre-prandial blood glucose, post-prandial blood glucose, glycated hemoglobin, or other glycemic indices known to those of skill in the art. The blood fat index is such as, but not limited to, cholesterol, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, or other blood fat indices known to those skilled in the art. Liver function index such as but not limited to GOT (glutamic acid oxalate transferase), GPT (pyruvate transferase), GGT (glutamic acid transferase), ALK-P (alkaline phosphatase), T-BIL (total bilirubin) or other known liver function index by those skilled in the art. Renal function indices such as, but not limited to, BUN (urea nitrogen), CREA (creatinine), UA (uric acid), or other renal function indices known to those skilled in the art. Pancreatic function index such as, but not limited to, AMY (amylase), LIPASE (LIPASE) or other pancreatic function index known to those skilled in the art. It should be noted that, for example, when blood glucose is a target for blood test in diabetes (monogenic diabetes and type ii diabetes), the health index detected by diabetes is other than blood glucose, and the detailed description of the target for diabetes is as follows and will not be repeated herein.
In summary, if the individual does not suffer from the metabolic disease, the personalized metabolic disease health food combination determination step S3 is performed: in this step, if the individual does not have the metabolic disease and has a possibility of having the metabolic disease according to the results of steps S1 and S2, the personalized health food combination for metabolic disease is determined according to at least one of the possibility of having the metabolic disease in the nucleic acid detecting step and the health index in the blood detecting step. The personalized health food composition for metabolic diseases comprises a plurality of health foods, wherein at least one health food is immunomodulatory protein or a compound of immunomodulatory proteins. It is further noted that in this step, the health food items other than the immunomodulatory protein or the immunomodulatory protein complex in the personalized metabolic disease health food composition are determined based on the health index measured in step S2. That is, the health index measured in step S2 is used to determine the metabolic disease health food suitable for the constitution and/or current health condition of the subject. In addition, the methods of providing personalized metabolic disease health food combinations of the present invention are non-therapeutic and/or suitable for providing further health care to individuals who are not ill.
In this embodiment, the aforementioned immunomodulatory protein may be Ganoderma microsporum immunomodulatory protein (GMI). The immunomodulatory proteins used in this embodiment can reduce the transcriptional activity of nuclear factor-activated B cell kappa light chain (NF-kB), reduce insulin resistance, reduce adiponectin (adiponectin) in serum, further improve blood glucose regulation, improve hepatic triglyceride accumulation, and have anti-inflammatory effects, so the immunomodulatory proteins are suitable for health food for metabolic diseases.
Therefore, after the subject receives the personalized health food combination for metabolic diseases containing the immunomodulatory protein (which may also be a compound or compound product of the immunomodulatory protein) determined in step S3 and starts to take the composition, the subject can obtain the personalized health food combination (i.e. required by the physical fitness and/or the health status of the subject) due to the metabolic disease health food determined according to the health index in the blood detection step in addition to the health effect of the immunomodulatory protein itself.
In this embodiment, the metabolic disease may be monogenic diabetes, type I diabetes, type II diabetes, or hyperlipidemia. The nucleic acid is or can comprise genes such as GCK, HNF1A, HNF4A, HNF1B, HLA-DR3, HLA-DR4, HLA-DQ, TCF7L2, GLIS3, PEPD, FITM2, R3HDML, KCNK16, MAEA, GCC1, PAX4, PSMD6, ZFAND3, LDLR, APOB or PCSK-9, or any combination of the foregoing genes. Targets are or may include targets for blood glucose, anti-ICA antibodies, anti-IAA antibodies, anti-GAD antibodies, cholesterol, triglycerides, or the like, or any combination of the foregoing.
For example, if the metabolic disease is a single-gene diabetes, the nucleic acid to be detected in the nucleic acid detecting step S1 is or may include a nucleic acid of at least one gene of GCK, HNF1A, HNF4A or HNF1B, and the target is or may include blood glucose. For example, the nucleic acid GCK (glucokinase) is a glucokinase gene. The nucleic acids HNF1A (HNF1 homobox A) and HNF1B (HNF1 homobox B) are related genes of carbohydrate metabolism. The nucleic acid HNF4A (hepatocyte factor 4alpha) is a nuclear transcription factor and can regulate the expression of genes related to liver function. When the mutation occurs in the gene, the possibility of suffering from monogenic diabetes in an individual can be improved. In addition, the target is blood glucose, which indicates that the subject may have suffered from monogenic diabetes when the concentration of the target in the blood sample is increased. If the subject detects a mutation in the gene and no target is detected in the blood sample at a higher concentration than the average value of other subjects not suffering from haplotypic diabetes, other tests (e.g., abdominal obesity, body weight, blood pressure, etc.) can be performed to assist in determining whether the subject suffers from haplotypic diabetes, and if not, the subject is likely to suffer from haplotypic diabetes but not yet suffer from haplotypic diabetes, and a personalized health food combination for metabolic disease can be determined based on the haplotypic diabetes and the health index of the subject. The health food composition for metabolic diseases at least comprises immunomodulating protein or a complex of immunomodulating protein, and diabetes health food such as but not limited to Vitamin B group (Vitamin B complex), green tea extract, bioflavonoid (bioflavonoid), citrus extract or other health food known to those skilled in the art to be useful for diabetes, or any combination of the foregoing. In addition, personalized health foods are provided according to the health indexes, for example, when the liver index in the health index of the tested individual is too high, the metabolic disease health food combination can further comprise health foods of livers such as schisandra chinensis, sesamin and lucid ganoderma.
Taking type i diabetes as an example, if the metabolic disease is type i diabetes, the nucleic acid to be detected in the nucleic acid detecting step S1 is or may include a nucleic acid of at least one gene of HLA-DR3, HLA-DR4 or HLA-DQ, and the target is or may include at least one target of anti-ICA antibody, anti-IAA antibody or anti-GAD antibody. For example, the nucleic acids HLA-DR3 (master histocompatibility complex, class II, DR beta 3), HLA-DR4 (master histocompatibility complex, class II, DR beta 4), HLA-DQ (DQ) are major histocompatibility complex genes encoding Human Leukocyte Antigens (HLA). When the subject carries the aforementioned gene or the aforementioned gene is mutated, it is possible to increase the possibility that the subject suffers from type I diabetes. In addition, the target anti-ICA antibody is a islet cell antibody. The target anti-IAA antibody is an insulin antibody. The target anti-GAD antibody is a glutamate decarboxylase antibody. When the concentration of the target in the blood sample is increased, it indicates that the subject may have suffered from type i diabetes. If the subject detects the presence or mutation of the gene and no target is detected in the blood sample at a higher concentration than the average value in other subjects not suffering from type i diabetes, other tests (e.g., abdominal obesity, body weight, blood pressure, etc.) can be performed to assist in determining whether the subject suffers from type i diabetes, and if not, indicating that the subject is likely to suffer from type i diabetes but not yet suffering from type i diabetes, and then a personalized health food combination for metabolic disease can be determined based on the type i diabetes and the health index of the subject. The metabolic disease health food composition at least comprises an immunomodulatory protein or a complex of immunomodulatory proteins, and a diabetic health food, such as but not limited to Vitamin B group (Vitamin Bcomplex), green tea extract, bioflavonoids (bioflavonoids), citrus extract or other health foods known to those skilled in the art to be useful for diabetes, or any combination of the foregoing. In addition, personalized health foods are provided according to the health indexes, for example, when the liver index in the health index of the tested individual is too high, the health food combination for metabolic diseases can also comprise health foods of livers such as schisandra chinensis, sesamin, lucid ganoderma and the like.
Taking the second type diabetes as an example, if the metabolic disease is the second type diabetes, the nucleic acid to be detected in the nucleic acid detecting step S1 is or may include a nucleic acid of at least one gene selected from TCF7L2, GLIS3, PEPD, FITM2, R3HDML, HNF4A, KCNK16, MAEA, GCC1, PAX4, PSMD6, or ZFAND3, and the target is or may include blood glucose. For example, the nucleic acid HNF4A is a nuclear transcription factor that can regulate expression of genes associated with liver function. The nucleic acid TCF7L2(transcription factor 7like 2) is a transcription factor gene. The nucleic acid GLIS3(GLIS family zinc finger 3) is a gene involved in pancreatic cell development and insulin expression. The nucleic acid PEPD (Peptidase D) is a proteolytic enzyme D gene. The nucleic acid FITM2(fat storage inducing transmembrane protein 2) is a gene for promoting lipid storage transmembrane protein. The nucleic acids R3HDML (R3H domain linking like), KCNK16 (specific two-dimensional domain channel K member 16), GCC1(GRIP and linked-domain linking 1), PSMD6 (proteosom 26S subustit, non-ATPase 6), ZFAND3(zinc finger AN1-type linking 3) are diabetes related genes. The nucleic acid MAEA (macrophage erythroblastia ttatacher) is a macrophage erythroblast attachment gene. The nucleic acid PAX4(paired box 4) is a paired box gene. When the subject carries the aforementioned gene or the aforementioned gene is mutated, it is possible to increase the possibility that the subject suffers from type II diabetes. In addition, the target is blood glucose, which indicates that the subject may have suffered from type ii diabetes when the concentration of the target in the blood sample is increased. If the subject detects the presence or mutation of the gene and no target is detected in the blood sample at a higher concentration than the average value in other subjects not suffering from type ii diabetes, other tests (e.g., abdominal obesity, body weight, blood pressure, etc.) can be performed to assist in determining whether the subject suffers from type ii diabetes, and if not, the subject is likely to suffer from type ii diabetes but not yet suffer from type ii diabetes, and a personalized health food combination for metabolic disease can be determined based on the type ii diabetes and the health index of the subject. The health food composition for metabolic diseases at least comprises immunomodulating protein or a complex of immunomodulating protein, and diabetes health food such as but not limited to Vitamin B group (Vitamin B complex), green tea extract, bioflavonoid (bioflavonoid), citrus extract or other health food known to those skilled in the art to be useful for diabetes, or any combination of the foregoing. In addition, personalized health foods are provided according to the health indexes, for example, when the liver index in the health index of the tested individual is too high, the health food combination for metabolic diseases can also comprise health foods of livers such as schisandra chinensis, sesamin, lucid ganoderma and the like.
Taking hyperlipidemia as an example, if the metabolic disease is hyperlipidemia, the nucleic acid to be detected in the nucleic acid detection step S1 is a nucleic acid of at least one gene of LDLR, APOB or PCSK-9, and the target is or may include at least one target of cholesterol or triglyceride. For example, the nucleic acid ldlr (low density lipoprotein receptor) is a low density lipoprotein receptor gene. Nucleic acid APOB (apolipoprotein B) is apolipoprotein B gene. The nucleic acid PCSK-9 (protein convertase subtilisin/kexin type 9) is proprotein convertase subtilisin 9 gene. When the gene is mutated, the possibility of suffering from hyperlipidemia of a subject can be improved. Additionally, the target may be or comprise cholesterol or a triglyceride. When the concentration of the target in the blood sample increases, it indicates that the subject may have suffered from hyperlipidemia. If the subject detects the mutation of the gene and does not detect that the concentration of the target in the blood sample is higher than the average value of other subjects not suffering from hyperlipidemia, other tests (such as abdominal obesity, body weight, blood pressure, etc.) can be performed to assist in determining whether the subject suffers from hyperlipidemia, and if not, the subject has the possibility of suffering from hyperlipidemia but does not suffer from hyperlipidemia, and then the personalized health food combination for metabolic diseases is determined according to hyperlipidemia and the health index of the subject. The health food composition for metabolic diseases at least comprises immunomodulating protein or a compound of immunomodulating protein, and hyperlipidemia health food, such as but not limited to vitamin B group (vitamin B complex), green tea extract, bioflavonoid (bioflavonoid), citrus extract or other health food known by those skilled in the art to be applicable to hyperlipidemia, or any combination of the above. In addition, personalized health foods are provided according to the health indexes, for example, when the liver index in the health index of the tested individual is too high, the health food combination for metabolic diseases can also comprise health foods of livers such as schisandra chinensis, sesamin, lucid ganoderma and the like.
In addition, the present invention also provides a second embodiment, which is a non-transitory computer readable storage medium for providing a personalized metabolic disease health food composition.
As shown in fig. 2, the non-transitory computer readable storage medium of the present embodiment is executed by the computer device 10 and is used for determining and providing personalized metabolic disease and health food combinations. The computer device 10 of the present embodiment includes a storage unit 101, one or more processing units 102, a communication unit 103, a display unit 104, an input unit 105, and a housing (not shown), wherein the storage unit 101, the processing unit 102, and the communication unit 103 are disposed in the housing of the computer device 10. In the embodiment, the drawings are simplified, and the number of the processing units 102 is illustrated as one example.
The processing unit 102 is coupled to the memory unit 101, the communication unit 103, the display unit 104 and the input unit 105, and is configured to execute instructions (e.g., program codes) to perform the method for providing personalized metabolic disease health food as described above. The processing unit 102 is, for example, a processor capable of executing instructions (e.g., program code), each of which may include one or more cores. The memory unit 101 may be a random access memory, a non-volatile computer-readable storage medium such as a hard disk, a Solid State Disk (SSD), a flash memory, an optical disk, a computer tape, or any combination thereof. The Memory may include a Read Only Memory (ROM), a Flash Memory (Flash Memory), a Field Programmable Gate Array (FPGA), or other forms of non-transitory Memory. The memory unit 101 stores instructions 101a (e.g., program codes) executable by the processing unit 102. for simplicity, fig. 2 illustrates an example of one instruction 101a in the memory unit 101, which is not intended to limit the present invention. The communication unit 103 is a device capable of providing network connection, such as a network card, a network chip, and a modem. The display unit 104 includes a display adapter, a display chip, a display, and the like, and the input unit 105 is, for example, a keyboard, a mouse, a touch screen, or the like.
The processing unit 102 reads the instruction (e.g., program code) 101a from the storage unit 101 and executes it to perform the following operations: (1) after the nucleic acid detection result determining step of detecting the nucleic acid in the biological sample of the individual to obtain the detection data (or result) of the nucleic acid by the method described above and the processing unit 102 receives the detection data (or result) of the nucleic acid inputted via the input unit 105, the processing unit 102 performs the nucleic acid detection result determining step of: the processing unit 102 determines whether the individual has a possibility of suffering from a metabolic disease according to the detection result of the nucleic acid; (2) after the target and at least one health index in the blood sample of the individual are detected by the method to obtain the target detection data (or result) and the health index, and the processing unit 102 receives the detection data (or result) of the target and the health index inputted through the input unit 105, the processing unit 102 performs the blood detection result determination step: the processing unit 102 determines whether the subject has suffered from a metabolic disease according to the detected data (or result) of the target. And the target corresponds to the aforementioned metabolic disease. And (3) if the processing unit 102 determines that the individual does not have the metabolic disease and has a possibility of having the metabolic disease, the processing unit 102 performs a personalized metabolic disease health food combination determination step of determining the personalized metabolic disease health food combination according to the possibility of having the metabolic disease in the nucleic acid detection step and at least one health index in the blood detection step. The personalized health food composition for metabolic diseases comprises a plurality of health foods, wherein at least one health food is immunomodulatory protein or a compound of immunomodulatory proteins. For example, the method of nucleic acid detection may be performed by a gene chip, PCR, quantitative PCR, Next Generation Sequencing (NGS), etc., and the step of determining the result of nucleic acid detection in the embodiment determines whether the subject has the possibility of suffering from metabolic diseases according to the result (e.g., data) detected by the method; in the present embodiment, the step of determining the blood detection result is performed to determine whether the individual has suffered from the metabolic disease according to the result (e.g., data) detected by the method.
Other technical features of the non-transitory computer readable storage medium of this embodiment are the same as those described in the previous embodiment, and reference may be made to the related description of the method for providing personalized health food in the first embodiment of the present invention, which will not be repeated herein.
In summary, the present invention provides a method for determining a personalized metabolic disease and health food composition and a non-transitory computer readable storage medium for performing the method. It can provide a personalized metabolic disease health food combination aiming at the possibility of suffering metabolic diseases of individuals and the health condition thereof.
The foregoing is by way of example only, and not limiting. It is intended that all equivalent modifications or variations not departing from the spirit and scope of the present invention shall be included in the appended claims.
Claims (12)
1. A method for determining a personalized health food composition for use by individuals likely to suffer from a metabolic disease, comprising:
a nucleic acid detection step of detecting a nucleic acid in a biological sample and judging the possibility of suffering from the metabolic disease based on the detection result of the nucleic acid;
a blood detection step, namely detecting a target and at least one health index in a blood sample, and judging that the metabolic disease is not suffered according to the detection result of the target; and
a personalized metabolic disease health food combination determining step of determining a personalized metabolic disease health food combination including a plurality of health foods, wherein at least one health food is an immunomodulatory protein or a complex of immunomodulatory proteins, based on the likelihood of suffering from the metabolic disease in the nucleic acid detecting step and the at least one health index in the blood detecting step.
2. The method of claim 1, wherein the biological sample is a human bodily fluid, human blood, or human interstitial fluid.
3. The method of claim 1, wherein the immunomodulatory protein is microsporo ganoderma immunomodulatory protein (GMI).
4. The method of claim 1, wherein the health index is an index comprising at least a blood cell index, a blood glucose index, a blood fat index, a liver function index, a kidney function index, or a pancreas function index.
5. The method of claim 1, wherein the metabolic disease is monogenic diabetes, type I diabetes, type II diabetes, or hyperlipidemia.
6. The method of claim 5, wherein the nucleic acid is GCK, HNF1A, HNF4A, HNF1B, HLA-DR3, HLA-DR4, HLA-DQ, TCF7L2, GLIS3, PEPD, FITM2, R3HDML, KCNK16, MAEA, GCC1, PAX4, PSMD6, ZFN 3, LDLR, APOB, PCSK-9, or a combination thereof.
7. The method of claim 5, wherein the target is blood glucose, an anti-ICA antibody, an anti-IAA antibody, an anti-GAD antibody, cholesterol, a triglyceride, or a combination thereof.
8. The method of claim 5, wherein when the metabolic disease is monogenic diabetes, the nucleic acid is GCK, HNF1A, HNF4A, HNF1B, or a combination thereof, and the target is blood glucose.
9. The method of claim 5, wherein when the metabolic disease is type I diabetes, the nucleic acid is HLA-DR3, HLA-DR4, HLA-DQ, or a combination thereof, and the target is an anti-ICA antibody, an anti-IAA antibody, an anti-GAD antibody, or a combination thereof.
10. The method of claim 5, wherein when the metabolic disease is type II diabetes, the nucleic acid is TCF7L2, GLIS3, PEPD, FITM2, R3HDML, HNF4A, KCNK16, MAEA, GCC1, PAX4, PSMD6, ZFAND3, or a combination thereof, and the target is blood glucose.
11. The method of claim 5, wherein when the metabolic disease is hyperlipidemia, the nucleic acid is LDLR, APOB, PCSK-9, or a combination thereof, and the target is cholesterol, triglyceride, or a combination thereof.
12. A non-transitory computer readable storage medium storing a plurality of instructions for execution by a processing unit of a computer device to determine inspection data or inspection data for the steps of the method of any one of claims 1 to 11 and determine the personalized metabolic disease health food composition.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200726775A (en) * | 2006-01-06 | 2007-07-16 | Biosoltech Internat Company Ltd | An immunomodulatory protein cloned from ganoderma microsporum |
| CN102317786A (en) * | 2007-04-18 | 2012-01-11 | 特提斯生物科学公司 | Diabetes correlativity biological marker and method of application thereof |
| CN105825049A (en) * | 2009-10-19 | 2016-08-03 | 提拉诺斯公司 | Integrated health data capture and analysis system |
| US20170009299A1 (en) * | 2015-07-07 | 2017-01-12 | Capitalbio Ehealth Science & Technology (Beijing) Co., Ltd. | Compositions and methods for detecting snp(s) associated with diabetes |
| US20170137968A1 (en) * | 2015-09-07 | 2017-05-18 | Global Gene Corporation Pte. Ltd. | Method and System for Diagnosing Disease and Generating Treatment Recommendations |
| WO2019074388A1 (en) * | 2017-10-09 | 2019-04-18 | Владимир Валерьевич ВОЛОБУЕВ | Method for developing individual dietary recommendations on the basis of dna analysis |
-
2018
- 2018-12-28 CN CN201811619218.XA patent/CN111378734A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200726775A (en) * | 2006-01-06 | 2007-07-16 | Biosoltech Internat Company Ltd | An immunomodulatory protein cloned from ganoderma microsporum |
| CN102317786A (en) * | 2007-04-18 | 2012-01-11 | 特提斯生物科学公司 | Diabetes correlativity biological marker and method of application thereof |
| CN105825049A (en) * | 2009-10-19 | 2016-08-03 | 提拉诺斯公司 | Integrated health data capture and analysis system |
| US20170009299A1 (en) * | 2015-07-07 | 2017-01-12 | Capitalbio Ehealth Science & Technology (Beijing) Co., Ltd. | Compositions and methods for detecting snp(s) associated with diabetes |
| US20170137968A1 (en) * | 2015-09-07 | 2017-05-18 | Global Gene Corporation Pte. Ltd. | Method and System for Diagnosing Disease and Generating Treatment Recommendations |
| WO2019074388A1 (en) * | 2017-10-09 | 2019-04-18 | Владимир Валерьевич ВОЛОБУЕВ | Method for developing individual dietary recommendations on the basis of dna analysis |
Non-Patent Citations (3)
| Title |
|---|
| HAI-BING C等: "Biomarkers for diabetes prediction, diagnosis and personalized therapy", 《中华医学杂志》 * |
| ZEEVI D等: "Personalized nutrition by prediction of glycemic responses", 《CELL》 * |
| 陈谊颖等: "探讨小孢子灵芝免疫调节蛋白GMI在STZ诱导糖尿病小鼠之伤口癒合治疗效果", 《台北医学大学医学科学研究所学位论文》 * |
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