CN112768087A - Application of Somatode oral preparation in prevention of type II diabetes by using Mendelian randomization method - Google Patents
Application of Somatode oral preparation in prevention of type II diabetes by using Mendelian randomization method Download PDFInfo
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Abstract
The invention relates to the technical field of medicines, in particular to a somaglutide oral preparation which can be used as a medicine for treating type II diabetes and can also play a role in preventing type II diabetes. The research of the invention finds that the risk of healthy people suffering from type II diabetes can be obviously reduced by reducing the blood sugar level and the saccharified blood sugar level in vivo under the condition of ensuring the cardiovascular safety when the oral preparation of the Somalutide is taken. The invention mainly considers the crowd carrying specific genetic variation as the medicine-taking crowd taking the Somalutide oral preparation through Mendelian randomization, and calculates the disease risk ratio of the medicine-taking group to the non-medicine-taking group by a Cox proportional risk model. The health probability curve chart is drawn to discover that the probability of the people at high risk of type II diabetes mellitus to suffer from type II diabetes mellitus can be obviously reduced by taking the Somalutide oral preparation. Thus, suggesting that the oral formulation of the GLP-1 receptor agonist somaglutide is a regimen for preventing type ii diabetes in healthy people at high risk for type ii diabetes.
Description
Technical Field
The present application relates to the field of pharmaceutical technology, the field of observing the effects of drugs using a mendelian randomization method.
Background
Diabetes mellitus is a global epidemic that is becoming a serious health-threatening disease for humans. The number of people suffering from diabetes worldwide is increasing year by year, and the first global diabetes report issued by the world health organization shows that in 2014, 4.22 million people (or 8.5% of the population) suffer from diabetes worldwide and are mainly in developing countries. Diabetes mellitus directly causes 150 thousands of deaths in 2012, and the indirectly causes 220 thousands of deaths, estimated. It is predicted that diabetes will become the seventh leading cause of death worldwide by 2030. At present, the WHO (1999) diabetes etiological typing system is adopted in China and is divided into 4 types, namely type 1 diabetes, type 2 diabetes, special type diabetes and gestational diabetes, wherein the type 2 diabetes is the most common type in clinic. The symptoms of type II diabetes are usually hidden when the diabetes is started, and the diagnosis is difficult to obtain at the initial stage, but the prevalence rate is high and accounts for more than 90 percent of the diabetes. Type ii diabetes is a progressive disease, and the patient's condition deteriorates progressively over time, making it difficult to control blood glucose well for a long period of time even with conventional drug therapy. The british prospective diabetes study (UKPDS) showed that glycated hemoglobin (HbAlc) is difficult to reach continuously, regardless of metformin, sulfonylureas or insulin monotherapy. The reason for this is related to progressive decline in β cell function.
Glucagon-like peptide-1 (GLP-1) plays an important role in blood glucose regulation as an incretin secreted by intestinal cells under the stimulation of food intake. Many recent studies of Randomized Controlled Trials (RCT) have shown that GLP-1 receptor agonists not only enhance beta cell responses, but also better control of hyperglycemia by inhibiting glucagon secretion from islet alpha cells, as well as increasing insulin secretion and reducing weight in moderate amounts. Meanwhile, it also has the effects of delaying gastric emptying, alleviating hunger sensation, and reducing food intake, thereby reducing the load of beta cells [ zhangning qian, wangqing, etc.. research progress of GLP-1 receptor agonist/analog for treating type 2 diabetes [ J ] practical medicine and clinic, 2019, 22 (7): 677-679.]. However, these studies were performed in patients with type ii diabetes. Therefore, the efficacy and safety of GLP-1 receptor agonists in non-diabetic patients remain to be further explored.
At present, 9 GLP-1 receptor agonists are listed on the market globally, the varieties on the market in China include exenatide, liraglutide and the like, and the administration modes are subcutaneous injection. The oral formulation of somaglutide is the first GLP-1 receptor agonist oral formulation in the world and is approved by the U.S. FAD for marketing in 2019 for the treatment of type ii diabetes mellitus, 9 months. Compared with injection, the oral preparation has great advantages in curative effect, safety, applicable population and drug interaction, and can greatly increase the compliance of patients. At present, the medicine is not yet on the market in China. There is no systematic evaluation on the efficacy and safety of the oral somaglutide preparation in comparison with placebo or other hypoglycemic drugs for treating or preventing type two diabetes mellitus [ sterility, left daney, etc. ] in China, the systematic evaluation on the efficacy and safety of the oral somaglutide preparation in treating type 2 diabetes mellitus [ J ]. pharmacy, 2020, 31 (19): 2399-2405]. The U.S. food and drug administration approved the oral tablet of somaglutide for the control of blood glucose in adult type two diabetics on 2019, 9, month 20, along with diet and exercise therapy.
The oral formulation of somaglutide is the first GLP-1 receptor agonist drug approved in the united states for use without injection. Several clinical trials were conducted in japan and usa on the effectiveness and safety of oral formulations of somaglutide in reducing blood glucose in type 2 diabetic patients, two of which were placebo-controlled and several others were compared to other GLP-1 receptor agonist injection treatments. The oral preparation of somaglutide, as an independent therapy, can be used in combination with other diabetes treatment methods (including metformin, sulfonylureas (insulin secretion promoters), sodium-glucose cotransporter 2(SGLT-2) inhibitors, insulin and thiazolidinediones) in patients with type 2 diabetes. In the placebo-controlled study, the glycated hemoglobin assay showed that the oral formulation of somaglutide significantly reduced blood glucose (glycated hemoglobin) as an independent therapy compared to placebo. After 26 weeks of administration of the oral formulation of somaglutide, the investigators found: glycated hemoglobin was reduced by 7% in patients taking the oral formulation of somaltulin 7 mg once daily and in patients taking the oral formulation of somaltulin 14 mg once daily compared to glycated hemoglobin in patients taking placebo. The oral formulation of somaglutide should be taken at least 30 minutes before the start of the first meal, drink or other oral medication of the day. In addition, since the oral formulation of somaglutide can slow down digestion, patients should consult the medication scheme with the physician before starting to take the oral formulation of somaglutide, avoiding drug interactions. In addition, the most serious side effect of the oral preparation of the Somaloutide is cardiovascular adverse reaction, and a random control test with huge sample amount is still needed to verify the safety of the Somaloutide at present. (https:// www.fda.gov/news-events/press-associations/fda-imprves-first-oral-glp-1-transaction-type-2-diabetes).
The Random Control Test (RCT) is usually the gold standard for studying the efficacy and safety of drugs, but the random control test is not suitable for exploring the effect of the soromaglupeptide in preventing type ii diabetes. Over the past 70 years, randomized controlled trials have reshaped medical knowledge and clinical practice. Random control trials have great advantages in generating scientific evidence, in terms of their control of independent variables, the form of extremely standardized result generation, and the continued supervision of specialized personnel. However, the last seventeen years have also witnessed a number of limitations in this "gold standard". It involves dividing a group of individuals into two or more subgroups in a random manner, each of which receives a different treatment. However, if the test method is applied to the test of whether the soxhlet peptide can play a role in preventing diabetes in non-diabetic patients, a series of medical ethical problems can be caused, and mainly comprise that unknown side effects can cause unexpected and handling adverse reactions to a subject, such as adverse gastrointestinal reactions, renal dysfunction and the like [ Zhu assist, Yiyufei ] GLP-1 receptor agonist soxhlet, pharmacological and clinical evaluation of soxhlet, 2018, 16 (03): 17-21+27]. In addition, developing large sample RCTs requires a large amount of manpower, capital support and long time investment, and researchers have many difficulties in developing a high quality random contrast test; one of its limitations is that extrapolation of the population in random control trials is often limited. From a scientific point of view, the random control trial, which selects a specific population of a certain sample through a series of inclusion and exclusion criteria, cannot determine the generalizability in real clinical practice, and in addition, due to the design of control variables, rarely obtains information about accompanying diseases and accompanying treatments, and often takes more intervention measures in order to comply with the study protocol, which is not practical in clinical practice.
Mendelian Randomization (MR), however, can solve the dilemma encountered by partially randomized control trials [ wangyuezu, shenghill. 1231-1236]. Mendelian randomization was used in the present invention instead of the Random Control Test (RCT) to explore the prophylactic effect of the oral formulation of somaglutide in patients without diabetes. Mendelian Randomization (MR) is an effective method for causal inference that has become popular in recent years, using genetic variation in non-experimental data to estimate the causal relationship between exposure and outcome. "exposure" generally refers to a risk factor for a disease, which may be a biomarker (such as glycated hemoglobin), a drug therapy (such as taking an oral formulation of somaglutide), a anthropometric measure (such as blood glucose), or any other risk factor that may affect a disease. "outcome" generally refers to a disease. To investigate whether exposure is responsible for the outcome, tool variables were introduced. A tool variable (such as genetic variation) is a measurable value that is only related to the exposure (risk factor) that we are interested in, but not to other confounding factors (such as environment), and its effect on outcome (disease) can only be achieved by exposure (risk factor) [ assumption, validation and estimation of tool variable. Emergency epidemiology [ J ]2018, 15: 7.]. Genetic variation is the difference in DNA between individuals. In mendelian randomization, genetic variation was used as a tool variable, as shown in fig. 1. Individuals in the population may be divided into different subgroups based on their genetic variation, similar to the Random Control Test (RCT) in which the population is divided into groups for administration and groups for non-administration. Genetic variation is randomly distributed among the population, independent of the environment and other confounding factors. Genetic variation as a tool variable requires the following condition (1) genetic variation is associated with exposure (biomarker). (2) The genetic variation is not associated with any confounding factors (circumstances) associated with exposure-outcome. (3) This genetic variation does not directly affect the disease unless it is achieved by association with exposure (risk factors acting on the disease). A common method for selecting tool variables includes searching for genetic variations that are highly correlated with exposure, not correlated with outcome, by Genome-wide association assays (GWAS — see terminology). Individualized Mendelian Randomization (MR) has been successfully applied to assess potential drug targets for coronary heart disease [ renchler et al computer-aided drug design: nucleic acid studies 2019; 47: D886-D94.
Thus, after determining genetic variation as a tool variable, a population with the appropriate genetic variation can be considered as a drug-using population. With the emergence of more and more genome function researches, the research on drug effects and adverse reactions by using genetic variation of drug target genes to replace drugs is becoming more and more popular. Many studies in recent years have shown that drugs carrying a certain genetic variation can be considered as drugs when studying the adverse reactions and effects of hypertension drugs. [ Wacker et al Mendelian randomization: a new method to predict adverse drug events and opportunities for drug reuse international epidemiology 2017; 46: 2078-2089. doi: 10.1093/ije/dyx207 ]. In this study, genetic variations in human genes corresponding to common hypertensive pharmacological drug targets were first considered as a surrogate for drug use. The consistency of the Mendelian Randomization (MR) estimates of the risk of coronary heart disease and stroke for these drugs with the corresponding randomized control trial results was found to be feasible to study drug efficacy and adverse effects using this approach. Finally, researchers have also employed a variety of data sources and utilized multiple biomarkers to analyze the rationality of treating genetic variations as drugs in order to gain insight into the adverse effects of the drug and to reuse it for other therapeutic, prophylactic purposes. The results show that, as long as the genetic variation is properly selected, it is reasonable and efficient to regard the population with the genetic variation as a group of drugs.
The UK Biobank (UK Biobank) is a planned 30 year old large scientific study initiated by the UK government and is one of the largest, most ambitious, health research projects in the UK to date. The purpose is as follows: the researchers who study "complex interactions of genetics and environment and risk of disease" are provided with the materials they collect. The study plan collects DNA samples of 50 ten thousand volunteers from 40 to 69 years old population in the United kingdom, collects gene information samples, lifestyle choices (including nutrition, lifestyle and drug use, etc.) and blood-related data on a large scale, and tracks and records health data of medical records in the rest of the year. The aim is to establish the largest information resource library of genes and environmental factors related to pathogenic or preventive diseases in the world, search the relationship among specific genes, life styles and health conditions, and improve the understanding of pathogenic genes of genetic diseases, including cancer, heart disease, diabetes and specific mental diseases.
The UK Biobank (UK Biobank) is the owner of DNA data, but researchers can apply for this valuable data and use the data to publish relevant studies. The uk bio-bank encourages and supports researchers to mine data and provides the applicant with a high degree of liberal usage rights. More importantly, the uk biostore announced to the public: all studies and inventions utilizing their data generation are attributed to the data applicants, while the data provider uk biobase does not possess any intellectual property rights. (English original: UK Biobank constants of the it with the bright no lights or the science to the organic Property lights in the relationship to the inorganic innovations or the fine modified by the application as a result of using the Materials "application-Generated references" -www.biobank.ac.uk)
In conclusion, the effect of the oral preparation of the somaglutide in preventing type II diabetes mellitus can be analyzed and potential adverse reactions can be evaluated by carrying out Mendel stochastic analysis on data of a British BioBank (UK Biobank).
Disclosure of Invention
The technical problem mainly solved by the invention is to provide a new application of the oral preparation of the Somalutide, and the background knowledge already explains that: the oral preparation of the Somalutide has the application of treating the diabetes, but has no prevention effect on the diabetes, and has not been proved by large-scale clinical random control experiments. In the invention, the inventor utilizes Mendelian randomization to show that the oral preparation of Somatobrut has the purpose of preventing healthy individuals from suffering from diabetes, and the oral preparation of Somatobrut has a prevention effect by reducing the value of glycated hemoglobin in people at risk of type II diabetes.
In order to achieve the above object, the present invention provides the following technical solutions:
the use of a Mendelian randomized Tausomalutide oral formulation for the prevention of type II diabetes mellitus, comprising the steps of:
the method comprises the following steps: randomizing the DNA sample data in the British biological database,
step two: follow-up visit to randomized data
Step three: regression analysis using Cox proportional Risk model
Step four: health probability analysis for population with high risk type two diabetes
As a further elaboration of the present invention, the first step specifically includes:
step one (1): analyzing and collating the whole blood sugar genome correlation research data to confirm whether genetic variation (tool variable) can be regarded as taking the oral preparation of the Somalutide;
step one (2): screening genetic variation (tool variable) and removing pleiotropic effect on the collected and sorted genetic variation data;
step one (3): determining the group of the used medicines (with genetic variation) and the group of the unused medicines (without genetic variation) in the British biological bank according to the screened genetic variation (tool variable);
step one (4): age, weight, waist, hip circumference, smoking and alcohol consumption were tested for differences using the variance test on samples from the drug group and the non-drug group
As a further elaboration of the present invention, the second step specifically includes:
step two (1): collecting follow-up information of the medicine group and the non-medicine group, wherein the follow-up information comprises oral self-describing diseases, hospitalization information and disease information filled in through a touch screen to obtain the first recording time and the disease occurrence time;
step two (2): and (4) sorting and summarizing the data in the British biological library by using the R language, and removing recording errors and invalid information.
As a further elaboration of the present invention, the third step specifically includes:
step three (1): the effect of the oral preparation of Somalide was evaluated using type II diabetes and diabetes as the results. Calculating a 95% confidence interval, and displaying a result by using a forest map;
step three (2): adverse reactions of the oral formulation of rumatin were judged as a result of hypertension, major cardiovascular adverse events, and all-cause death. Among the major adverse cardiovascular events are non-fatal myocardial infarction, non-fatal stroke, and cardiac death. The 95% confidence interval for the risk ratio was calculated and the results were presented using a forest map.
Step three (3): and (4) drawing a survival curve of the patients who finally suffer from diabetes in the medication group and the non-medication group.
As a further elaboration of the present invention, the step four specifically includes:
step four (1): dividing the population into high/low Risk type ii diabetes populations by Polygenic Risk Score (PRS-see terminology);
step four (2): utilizing R language to draw health probability curve
Compared with the prior art, the invention has the following advantages:
the new application of the oral preparation of the Somatobrut peptide based on the Mendelian randomized Probe in the prevention of diabetes mellitus, which is provided by the invention, simulates the use of the Somatobrut peptide oral preparation by people by taking genetic variation as a tool variable, can obviously improve the statistical efficacy of the effect of the Somatobrut peptide in the prevention of diabetes mellitus, simultaneously avoids the ethical problems possibly generated in a randomized control test, and provides a reliable result for developing the use of the oral preparation of the Somatobrut peptide in the prevention of diabetes mellitus.
According to the invention, the causal effect analysis is firstly carried out on the glycosylated hemoglobin and the diabetes by a Mendel randomization method system, and the glycosylated hemoglobin with the protection effect on the diabetes is effectively found to be reduced in vivo by a GLP-1 receptor stimulant. In addition, the research of the inventor discovers that the population with special genetic variation can be similar to a drug group taking the GLP-1 receptor stimulant, and the risk of suffering from the diseases can be obviously reduced after the drug group and a non-drug group are subjected to Cox proportional risk model regression. In addition, the side effect of the GLP-1 receptor stimulant is evaluated by using the same method, the risk-free effect on cardiovascular adverse events and other diseases is found, and the fact that the oral preparation of the GLP-1 receptor stimulant somaglutide can be applied to prevention of type II diabetes is further determined. In the process of drawing a health probability curve, the inventor also finds that the incidence of high-risk type II diabetes mellitus people can be effectively reduced by taking the oral preparation of the GLP-1 receptor agonist somaglutide. Thus, suggesting that healthy people with a high risk of type two diabetes may be a regimen for preventing type two diabetes by taking GLP-1 receptor agonists.
Term of art
[ genetic variation ]: the genetic information (or genome) of many organisms consists of a long string of genetic codes in the form of DNA (deoxyribonucleic acid), a molecule that encodes a life, packaged into chromosomes. There are 23 pairs of chromosomes in a human, one from the mother and one from the father in each pair of chromosomes. Chromosomes contain genes that are localized regions of the genetic code, encoding a unit of heritable information, but not all genetic sequences belong to a gene region, and most of the chromosomes are composed of intermediate genetic material called non-coding DNA. Each chromatid has two strands, each strand consisting of a nucleotide sequence that can be represented by the letters A (adenine), T (thymine), C (cytosine), and G (guanine). These nucleotide strands pair in a complementary fashion (A pairs with T and C pairs with G) such that each strand contains the same information and therefore only one of the strands is considered. Assuming that the DNA sequence at a given locus in a chromosome is: .. ATTACGCTTCCGAGCTTCGCAG.; and the same loci on the paired chromosomes are shown as: ... attacgcctccgagcttccgcag. It exists in various forms. All individuals contain many genetic variations whose DNA codes differ from those that occur commonly in the population, in that a single nucleotide base at a particular locus has been replaced with a different nucleotide, and the different possible nucleotides present at each locus are called alleles. For example, at the loci highlighted above, the letter for one chromosome is T, and the letter for the other chromosome is C: thus T and C are alleles of this particular genetic variation.
[ Single nucleotide polymorphism, Single nucleotide polymorphism ploymorphism, SNP ]: in one of the most common forms of genetic variation, genetic variation at different sites typically has its unique number, e.g., rs 10305492. The sequence numbers are followed by genetic codes such as A/T/G/C, indicating that the sites of genetic variation are A (adenine), T (thymine), C (cytosine) or G (guanine). Generally divided into conventional SNPs, which are genotypes that most people have, such as rs10305492-G, and effector SNPs; the effect SNPs are genotypes analyzed in the study, e.g., rs10305492-a, which are less frequent in the population, but have a different effect than conventional SNPs.
[ genome-wide association analysis ]: correlation analysis is a simple and practical analysis technique that finds correlations or correlations that exist in a large number of data sets, describing the laws and patterns of simultaneous occurrence of certain attributes in an object. Genome-wide association analysis (GWAS) is a new strategy for finding genetic variation affecting traits (such as blood sugar) by using millions of Single Nucleotide Polymorphisms (SNPs) in a Genome as molecular genetic markers, performing control analysis or correlation analysis on the Genome-wide level, and comparing the genetic variations.
[ weak tool variable bias ]: in the mendelian randomization study, there is a very important problem with weak tool variable bias (weak instrument bias). Weak tool variables refer to genetic variations that account for the lower potency of exposure, which correlates with exposure, but the strength of this correlation is not very high. Typically, the main reason for the weak tool variable bias is insufficient sample size. The F statistic is generally used to evaluate weak tool variable effects and its specific formula is as follows:
(sample number-tool variable number-1)/tool variable number) × (regression fitting coefficient/1-regression fitting coefficient)
From traditional experience, when the F statistic is less than 10, the genetic variation used is a weak tool variable, which may bias the result to some extent, and the interpretation of the result requires great care.
[ multigene risk score]: a method for assessing an individual's risk of developing a disease by obtaining a value for the effect of each genetic variation SNP on the disease by linear regression. Followed byThe number of SNPs is multiplied by the effect value, and each SNP is summed up to obtain a polygene risk score for an individual suffering from a disease. And (4) calculating the genotype effect value of the statistical data. The calculation formula is as follows PRS ═ effect value1*SNP1Number +. + effect valuei*SNPiNumber of
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 illustrates the work flow of Mendelian randomization in the present invention:
the leftmost "tool variable" (genetic variation) needs to satisfy the arrow (r) i.e. the hypothesis (r): the tool variables (genetic variation) and exposure factors (glycated hemoglobin) are strongly correlated; arrow (c) assumes that (c): the tool variables (genetic variation) and confounding factors (external environment) are not related, and the specific explanation hypothesis is two: the genetic variation of each individual was well established before birth, so the tool variables (genetic variation) and confounding factors (external environment) were not correlated; arrow c is assumed to be: the tool variables (genetic variation) are not directly related to outcome (type II diabetes), i.e. the effect of genetic variation on diabetes can only be achieved by affecting glycated hemoglobin. Specific explanation hypothesis (c): in the invention, the inventor selects rs10305492-A as a tool variable through layer-by-layer screening, and the tool variable (genetic variation) has low association degree with type II diabetes mellitus but has strong association with glycosylated hemoglobin, so the rs10305492-A can be selected as the tool variable. In the figure, the term "exposure" refers to a putative causal risk element, sometimes also referred to as an intermediate phenotype, which may be a biomarker (glycated hemoglobin), a anthropometric measure (blood glucose) or any other risk element that may affect the outcome. Exposure (glycated hemoglobin) is strongly associated with outcome (type ii diabetes), and confounders (environment) may affect exposure (glycated hemoglobin). In the figure, "outcome" is disease (type II diabetes in the present invention), and confounding factors (environment) can have an effect on the outcome.
FIG. 2 shows the workflow of this study
1) During the randomization process, the inventors divided the samples into two groups according to whether they possessed the genetic variation rs10305492-a, and the population without the genetic variation rs10305492-a was regarded as a control group without using the oral preparation of the GLP-1 receptor agonist somaglutide (sample number: 264, 659); the population with the genetic variation rs10305492-a is considered as a group of drugs taking oral formulations of the GLP-1 receptor agonist somaglutide (sample No. 8242). 2) In the follow-up stage, diseases, hospitalization information and disease information data filled in by a touch screen are orally taken by a patient to judge the disease onset condition of the patient, and the disease onset time is judged according to the first recording time and the disease onset recording time. 3) Survival analysis was performed using the Cox proportional hazards model using the data above.
Figure 3 shows a forest plot of mendelian randomization in diabetic versus adverse events, left half of the plot: the disease is listed as five diseases, and the risk ratio is the ratio of the risk of the drug group to the risk of the non-drug group to the disease. The highest risk ratio is the highest value taken within the 95% confidence interval of the risk ratio and the lowest risk ratio is the lowest value taken within the 95% confidence interval of the risk ratio. From the left half, the following conclusions can be drawn: 1) when the crowd results in the type II diabetes and the type II diabetes, the medicine group reduces the blood sugar/glycosylated hemoglobin level by taking the Somalutide oral preparation, thereby reducing the risk of developing the disease (diabetes: risk ratio 0.87[0.80, 0.94], p 6.72 x 10-4 is very significant/diabetes: risk ratio 0.88[0.81, 0.96], p 4.05 x 10-3 is very significant) 2) when the population results in hypertension, major cardiovascular adverse effects and adverse effects of all-cause death, the three side effects are basically the same in the risk of the medicine group and the non-medicine group (hypertension: risk ratio is 0.97[0.94, 1.00], p is 3.68 x 10-2/diabetes: risk ratio is 0.94[0.86, 1.02], p is 0.14 non-significant/all-cause death risk ratio is 0.98[0.88, 1.09], p is 0.72 non-significant). Right half of the figure: the results are a forest plot with black squares representing the risk ratio for the disease, e.g. diabetes, 0.87, i.e. the abscissa of the black squares is 0.87, and the solid grey lines represent the fluctuation range with 95% confidence interval from 0.8 to 0.94. Several other disease interpretations are consistent with diabetes. In conclusion, compared with the control group, the drug group has lower risk of suffering from diabetes and type II diabetes, and does not increase the risk of suffering from hypertension, cardiovascular adverse reaction and all-cause death.
FIG. 4 shows the health probabilities of high-risk diabetic patients in different age groups of the drug group and the non-drug group
The figure shows the health probability of the group without drug for the non-group without type II diabetes, the P value of the health probability curve of the group with drug and the non-group with drug is 0.0268, which has statistical significance, and shows that the difference of the health probability curve is not the difference caused by sampling. The health probability curve results show that the health probability of the medicine group is gradually higher than that of the non-medicine group after the follow-up time exceeds 15 years. In the figure, the vertical axis "health probability" refers to the probability that the sample is healthy without type II diabetes, and the horizontal axis "follow-up time" refers to the follow-up time of whether the population is observed to have type II diabetes. In the invention, the inventor observes the onset of type II diabetes of a patient once every five years by collecting follow-up information (oral self-describing diseases, hospitalization information and disease information filled in through a touch screen to obtain the first recorded time and the disease occurrence time) of a medicine group and a non-medicine group, and calculates the health probability of the patient in the age group.
Detailed Description
The invention discloses application of Mendelian randomization in exploring a GLP-1 receptor agonist somaglutide oral preparation in preventing type II diabetes, and a person skilled in the art can appropriately improve process parameters by referring to the content. It is expressly intended that all such similar substitutes and modifications which would be obvious to those skilled in the art are deemed to be included within the invention. While the methods and applications of this invention have been described in terms of preferred embodiments, it will be apparent to those of ordinary skill in the art that variations and modifications in the methods and applications described herein, as well as other suitable variations and combinations, may be made to implement and use the techniques of this invention without departing from the spirit and scope of the invention.
To confirm how the oral formulation of the GLP-1 receptor agonist somaglutide is applied to the treatment and prevention of diabetes, the inventors consulted the information promulgated by the U.S. food and drug administration. The oral formulation of somaglutide is the first GLP-1 receptor agonist drug approved in the united states for use without injection. Several clinical trials were conducted in japan and usa on the effectiveness and safety of oral formulations of somaglutide in reducing blood glucose in type 2 diabetic patients, two of which were placebo-controlled and several others were compared to other GLP-1 receptor agonist injection treatments. The oral preparation of somaglutide can be used as an independent therapy, and can be combined with other diabetes treatment methods (including metformin, sulfonylureas drugs (insulin secretion promoters), sodium-glucose cotransporter 2(SGLT-2) inhibitors, insulin and thiazolidinediones) to be applied to patients with type 2 diabetes. In the placebo-controlled study, the glycated hemoglobin assay showed that the oral formulation of somaglutide significantly reduced blood glucose (glycated hemoglobin) as an independent therapy compared to placebo. After 26 weeks of administration of the oral formulation of somaglutide, the investigators found: glycated hemoglobin was reduced by 7% in patients taking the oral formulation of somaltulin 7 mg once daily and in patients taking the oral formulation of somaltulin 14 mg once daily compared to glycated hemoglobin in patients taking placebo. The oral formulation of somaglutide should be taken at least 30 minutes before the start of the first meal, drink or other oral medication of the day. In addition, since the oral formulation of somaglutide can slow down digestion, patients should consult the medication scheme with the physician before starting to take the oral formulation of somaglutide, avoiding drug interactions. In addition, the most serious side effect of the oral preparation of the Somalou peptide is the adverse cardiovascular effect, and a random control test with huge sample amount is still needed to verify the safety of the oral preparation of the Somalou peptide at the present stage. (https:// www.fda.gov/news-events/press-associations/fda-imprves-first-oral-glp-1-transaction-type-2-diabetes).
In the invention, through a Mendelian randomization study of an individual level, the applicant simulates and verifies the incidence probability of adverse reactions of long-term administration of the Somatobrut and finds the preventive effect of the Somatobrut oral preparation on diabetes. The inventors evaluated the prophylactic value of the GLP-1 receptor agonist somaglutide oral formulation for type ii diabetes and its safety for cardiovascular adverse events by analyzing data in the uk biobank (UKBiobank).
The invention is further illustrated below with reference to the implementation methods:
example 1 comparison of risks of combinations and non-combinations of drugs in the Total study
Background: GLP-1 is a short peptide, is mainly secreted by intestinal epithelial cells, and the secreted GLP-1 can be combined with a GLP-1 receptor to activate a downstream biological pathway for reducing blood sugar. The oral preparation of the GLP-1 receptor agonist somaglutide can activate GLP-1 receptor to receive GLP-1 signal in vivo, so as to play the same biological function as GLP-1. In the population, some people carry genetic variation related to the GLP-1 receptor, and the GLP-1 receptor of people with the variation is in an activated state for a long time, namely, the blood sugar reduction pathway in the body of people is more active than that of normal people, so that the blood sugar of people is reduced. According to biological data, the genetic variation rs10305492-A is positioned on the fifth transmembrane domain of the GLP-1 receptor, and the allele A of rs10305492 enables the hydrophobic alanine to be replaced by the hydrophilic threonine, so that the GLP-1 receptor is activated all the year round. Therefore, the inventor considers that the population with rs10305492-A genetic variation can be regarded as the user of the GLP-1 receptor agonist somaglutide oral preparation. Therefore, people with genetic variation rs10305492-A can be regarded as the patients who use the GLP-1 receptor agonist somaglutide oral preparation for a long time.
Step 1.1: verification of the rationality of the selection of the genetic variation rs10305492-A as a tool variable
The method comprises the following steps: the inventor constructs a linear regression equation by taking blood sugar and glycosylated hemoglobin as dependent variables and the number of genetic variations rs1030549-A as independent variables: dependent variables (blood sugar/glycosylated hemoglobin) to independent variables (the number of genetic variations rs 1030549-A), and obtaining effect values obtained by linear regression (each having 1 genetic variation rs1030549-A, which causes the magnitude of the change of the dependent variable). In addition, in order to verify the relevance of rs1030549-A and diabetes, the inventor constructs a logistic regression equation: dependent variables (diabetes/non-diabetes) independent variables (number of genetic variations rs 1030549-a), effect values from logistic regression (ratio of risk of diabetes to individuals with genetic variation rs 1030549-a) were obtained.
Furthermore, to verify that genetic variation rs1030549-a is a valid tool variable that is not subject to weak tool variable bias, the inventors performed an F-test (see terminology) that can be considered as a better tool variable when the F statistic is greater than 10.
As a result: see table one
TABLE I correlation of rs1030549-A with dependent variables
In table one, the inventors selected the genetic variation rs1030549-a in the GLP-1 receptor gene as a tool variable (genetic variation) for mendelian randomization analysis. The effect SNP is the genotype analyzed in the study, in the present invention rs 1030549-A. Conventional SNPs are genotypes that most people have, in the present invention, rs 1030549-G. The frequency of the effect SNP is the frequency of rs1030549-A in the population, 0.02 indicates that 100 rs1030549 genotypes in each detected population, and 2 rs1030549-A genes can be detected by the detector. The dependent variables are values used for regression analysis, in the present invention, blood glucose and glycated hemoglobin. The effect SNP effect value is the influence of each effect SNP on a dependent variable, and the regression effect value is-0.05 and-0.12, namely, each rs1030549-A genetic variation is capable of reducing free blood sugar by 0.05mmol/L and reducing glycosylated hemoglobin by 0.12 mmol/L. The associated P value shows that the genetic variation has high correlation with dependent variable, the free blood sugar associated analysis P value is 1.7E-21, the glycosylated hemoglobin associated analysis P value is 3.6E-4, and both the free blood sugar associated analysis P value and the glycosylated hemoglobin associated analysis P value are obviously lower than 0.05, namely the genetic variation has statistical significance. The results of the F test showed that the F values of rs10305492 and free blood glucose were 99.02, and the F value of glycated hemoglobin was 12.61, both of which were greater than 10. In summary, the F-test in which the genetic variation satisfies the bias of a weak tool variable also satisfies the use assumption of the tool variable. It can be concluded that: it is reasonable to select rs10305492-a as a tool variable (genetic variation) for mendelian randomization in the present invention, patients with the genetic variation are classified as a group of drugs, and patients without the genetic variation are classified as a group of non-drugs.
Step 1.2 randomization of the study object
The method comprises the following steps: the inventors have selected 272, 901 unrelated individuals from the uk biobase, whose ancestors were europeans, as subjects of study. 22655 cases of diabetes (8.30%), 20375 cases of type II diabetes (7.46%), 142240 cases of hypertension (52.14%), 10959 cases of myocardial infarction (4.02%), 18046 cases of angina (6.62%), 7364 cases of stroke (2.70%). A mendelian randomization study was performed as in fig. 2. The specific process is as follows (1) dividing the research object into a medicine group and a control group according to whether the research object has rs10305492-A allele. Wherein 8242 samples with GA and AA genotypes on the genetic variation rs10305492 are used as a drug group, and 264659 samples with GG genotypes on the genetic variation rs10305492 are used as a non-drug group. (2) The student's two-tailed t test is used to test the difference of the continuous characters of the population, and the variance test is used to test the difference of the binary characters of the population, thereby eliminating the influence of mixed factors (environment and the like) on the medicine group and the non-medicine group.
As a result: see table two
TABLE II comparison of the differences between the group and the group
As can be seen from the data in the table, the mean value of the free blood sugar level in the drug group is 5.03mmol/L, while the mean value in the non-drug group is 5.12mmol/L, the difference is 0.09mmol/L, and the two results of the variance test show that the P value is 1.8E-11 and is far less than 0.05, which has statistical significance and can be shown as follows: the blood sugar value of the medicine group is reduced due to the use of the oral preparation of the Somalutide; the mean value of the glycosylated hemoglobin in the drug group is 35.63mmol/L, the mean value in the non-drug group is 35.94mmol/L, the difference value is 0.31mmol/L, and the two results of the variance test show that the P value is 3.2E-5 and is far less than 0.05, and the glycosylated hemoglobin level has statistical significance and is obviously different between the drug group and the control group. In data of age, height-weight ratio, waist circumference, hip circumference, smoking and drinking, the difference between a medicine group and a non-medicine group is not obvious, and the factors have no influence on the type II diabetes.
Step 1.3 Risk comparison between medication and non-medication groups
The method comprises the following steps: the disease results of the crowd in a Cox risk regression model, namely type II diabetes and diabetes, are used for judging the action of the GLP-1 receptor agonist somaglutide oral preparation. The adverse reactions of the oral preparation of the GLP-1 receptor agonist somaglutide are judged by taking hypertension, major cardiovascular adverse events and all-cause death as the results of adverse reactions. Among the major adverse cardiovascular events are non-fatal myocardial infarction, non-fatal stroke, and cardiac death. The influence of the use of the Somalutide oral preparation on the medication group and the non-medication group is analyzed by carrying out Cox proportional risk model regression on the medication group and the non-medication group by using an R language open source software package 'survivval'. The Cox proportional hazards model is a common statistical tool in the clinic that can simultaneously analyze the impact of several factors on disease. It is examined how specific factors (herein referred to as glycated hemoglobin and free blood glucose) influence the incidence of specific events (herein referred to as type II diabetes and its adverse reactions) that occur at specific time points. The results obtained are generally referred to as risk ratios. When the risk ratio is equal to 1, it indicates that the specific factor (glycated hemoglobin and free blood glucose) has no effect, when the risk ratio is greater than 1, it indicates that the specific factor (glycated hemoglobin and free blood glucose) increases the risk, and when the risk ratio is less than 1, it indicates that the specific factor (glycated hemoglobin and free blood glucose) decreases the risk.
The results are shown in FIG. three: during the course of the study, the inventors found that the risk of major cardiovascular adverse events and the risk of all-cause mortality were the same in both the drug and non-drug groups, except that the drug group was significantly reduced in the risk of developing diabetes. The results are shown in fig. three, and three conclusions can be obtained: 1) when the population results in type II diabetes and type II diabetes, the drug group reduces the blood sugar/glycosylated hemoglobin level by taking the oral preparation of the Somalutide, thereby reducing the risk of developing the disease (diabetes: risk ratio 0.87[0.80, 0.94], p 6.72 x 10-4 is very significant/diabetes: risk ratio 0.88[0.81, 0.96], p 4.05 x 10-3 is very significant) 2) when the population results in hypertension, major cardiovascular adverse effects and adverse effects of all-cause death, the three side effects are basically the same in the risk of the medicine group and the non-medicine group (hypertension: risk ratio is 0.97[0.94, 1.00], p is 3.68 x 10-2/diabetes: risk ratio is 0.94[0.86, 1.02], p is 0.14 non-significant/all-cause death risk ratio is 0.98[0.88, 1.09], p is 0.72 non-significant).
In conclusion, in three steps, it can be concluded that the oral formulation of the GLP-1 receptor agonist somaglutide is effective and safe in the prevention of type ii diabetes.
Example 2 Risk comparison of medicinal versus non-medicinal groups in high-risk type II diabetics
The method comprises the following steps: the population is divided into high-risk type II diabetes population and low-risk type II diabetes population according to the polygene risk score. The polygenic risk score uses 266 SNPs (P value less than 5e-5, significant in the whole genome range) that are strongly associated with type ii diabetes, and is calculated by the formula: PRS ═ Effect value1*SNP1Number +. + effect valuei*SNPiAnd (4) obtaining the multi-gene risk score values of all the people. And selecting high-risk type II diabetes mellitus crowd, and dividing the crowd into a medicinal group and a non-medicinal group according to whether the crowd contains rs10305492-A allele. The same Cox proportional hazards modeling regression as in example one was performed on the high risk population to determine whether the population would be at a reduced risk of developing type two diabetes after taking the oral formulation of somaglutide.
In addition, 4130 samples with extremely high risk (1.5% before the multigene risk score) are also divided into a medicinal group and a non-medicinal group according to whether rs10305492-A allele is contained, and a health probability curve is drawn on the samples, wherein the method for drawing the health probability curve comprises the following steps: bringing a patient with high-risk diabetes mellitus into a health probability curve, observing the onset condition of type II diabetes mellitus of the patient every five years by collecting follow-up information (oral self-describing diseases, hospitalization information and disease information filled in through a touch screen to obtain the first recorded time and the disease occurrence time) of the medicine group and the non-medicine group, and calculating the health probability at the follow-up time point. For example: the total number of samples in the non-drug group at visit time 0 was 3999, and all samples were not affected by type II diabetes when they were entered into the group, and therefore, the probability of health on the vertical axis was 1. The health probability calculation method of the medicine group is the same as the above. Along with the extension of follow-up time, the number of the patients with type II diabetes mellitus increases, the number of healthy people in the medication group and the non-medication group is in a downward trend, but the health probability of the medication group is higher at the same follow-up time point. The R language open source software package "survivval" was used to plot drug administration versus non-drug administration curves to further evaluate the effectiveness of the somaglutide oral formulations at different age groups.
As a result: the Polygenic Risk Score (PRS) results for type ii diabetes show: the median multigene risk score was 0.029. The population with the multigene risk score greater than the median is classified as high risk, and 4141 samples of the medication group and 132, 310 samples of the non-medication group are in the high risk group. In type II diabetic patients with high risk, the oral administration of the oral formulation of somniferin significantly reduces the risk of developing type II diabetes (risk ratio ═ 0.86[0.78, 0.95], p ═ 3.80X 10-3).
The inventor classifies the first 1.5% of the population with multigene risk score as the extremely high risk, and the extremely high risk group comprises 131 medicine group samples and 3999 non-medicine group samples. The inventor carries out the drawing of the health curve on 131 medicine groups and 3999 non-medicine groups, the result is shown in figure 4, the result shows that the P value of the health probability curve of the medicine groups and the non-medicine groups is 0.0268 and is less than 0.05, and the statistical significance is achieved. The health probability curve results show that the health probability of the medicine group is gradually higher than that of the non-medicine group after the follow-up time exceeds 15 years. The number of samples used to plot the health curve is shown in table three.
TABLE III health probability of each follow-up time point of the group of drugs and the group of non-drugs
In conclusion, in the population with high risk of type II diabetes, the risk of the type II diabetes can be effectively reduced by taking the Somalutide oral preparation.
According to the above two examples, the inventors found by the method of mendelian randomization: the oral preparation of the GLP-1 receptor agonist Somalutide taken by healthy people with high-risk type II diabetes can play a role in preventing type II diabetes, and does not increase the risks of cardiovascular adverse events and death.
Claims (7)
1. The use of somaglutide can be safely used for preventing diabetes.
2. The use according to claim 1, wherein the somaglutide is a glucagon-like peptide-1 receptor agonist.
3. The use according to claim 1, wherein the somaglutide is administered orally and the oral dosage is an orally safe dosage of somaglutide.
4. The use according to claim 1, wherein diabetes is type 2 diabetes.
5. The use according to claim 1, wherein the safety profile is characterized by the absence of changes in the incidence of hypertension, incidence of major cardiovascular adverse effects, and all-cause mortality in the population following administration of the somaglutide compared to the population without the administration of the somaglutide.
6. The use according to claim 1, wherein the subject is at high risk for diabetes in modern medicine.
7. The high risk population of claim 6, having a significantly reduced incidence of diabetes compared to the population not taking the thaumatin.
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| US20150190474A1 (en) * | 2012-07-01 | 2015-07-09 | Novo Nordiskk A/S | Use of long-acting glp-1 peptides |
| CN109498559A (en) * | 2018-11-30 | 2019-03-22 | 复旦大学 | A kind of oral preparation and preparation method thereof loading treating diabetes polypeptide |
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| US20150190474A1 (en) * | 2012-07-01 | 2015-07-09 | Novo Nordiskk A/S | Use of long-acting glp-1 peptides |
| CN109498559A (en) * | 2018-11-30 | 2019-03-22 | 复旦大学 | A kind of oral preparation and preparation method thereof loading treating diabetes polypeptide |
Non-Patent Citations (1)
| Title |
|---|
| 覃渝等: "口服索马鲁肽治疗2型糖尿病疗效和安全性的系统评价", 《中国药房》, pages 2399 - 2405 * |
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