A microecological animal model for obstructive sleep apnea syndrome

Abstract

The present invention discloses a microecological animal model for obstructive sleep apnea syndrome, which includes steps such as constructing a fitting model for the changing trend of OSA events, selecting target animals and performing pretreatment, preparing an animal model, obtaining fecal samples, and DNA extraction and 16S rRNA high-throughput sequencing. By constructing a fitting model for the changing trend of respiratory sleep apnea events, the present invention obtains the fitting mathematical relationship between the number of apnea and time, and establishes an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply to establish an intestinal flora model for studying obstructive sleep apnea syndrome, which can simulate the actual physiological environment during sleep of patients. According to the changing trend of the duration of patients' respiratory sleep apnea events, a microecological animal model more consistent with the actual situation is established, making the detection result of the flora structure more accurate.

Description

Technical Field

[0001] The present invention relates to the field of animal model construction, and particularly relates to a microecological animal model for obstructive sleep apnea syndrome. Background Art

[0002] With the development of systems biology, the gut microbiota has received increasing attention. Subsequently, various biotechnologies have provided strong support for the study of the gut microbiota. The emergence of new sequencing technologies, such as metagenomic sequencing and 16S rRNA gene amplicon sequencing, enables researchers to conduct comprehensive and in-depth studies on the gut microbiota structure.

[0003] Multiple studies have reported that the imbalance of the gut microbiota structure is closely related to the occurrence and development of diseases such as diabetes, obesity, atherosclerosis, colorectal cancer, liver cirrhosis, and rheumatoid arthritis. Recent studies have shown that the gut microbiota plays a crucial role in regulating the risks of various chronic diseases and maintaining intestinal immunity and systemic homeostasis. It is of great significance for the occurrence of obesity, cardiometabolic disorders, inflammatory bowel disease, and mental diseases. In some IH and SF animal models simulating OSAHS, changes in the gut microbiota have been demonstrated. The gut microbial composition and diversity are related to IH combined with a high-fat diet, with an increase in the Firmicutes / Bacteroidetes ratio (F / B), an increase in lactic acid-producing bacteria, and a decrease in short-chain fatty acid-producing bacteria. And some studies have shown that the gut microbiota may participate in the regulation of sleep structure and blood pressure through the "gut-brain-axis" effect. Therefore, the role and interrelationship of the gut microbiota in the occurrence and development of OSAHS patients' diseases and their complications are worthy of further study.

[0004] OSAHS, namely obstructive sleep apnea hypopnea syndrome, refers to the repeated occurrence of more than 30 episodes of apnea during each night's sleep or a sleep apnea hypopnea index (AHI) ≥ 5 times / hour accompanied by clinical symptoms such as drowsiness. Apnea refers to the complete cessation of oronasal breathing airflow for more than 10 seconds during sleep; hypopnea refers to a reduction in the intensity (amplitude) of respiratory airflow during sleep by more than 50% compared to the baseline level, accompanied by a decrease in blood oxygen saturation by ≥ 4% or microarousal compared to the baseline level; the sleep apnea hypopnea index refers to the number of apneas plus hypopneas per hour of sleep time. It includes three categories: central (CSAS), obstructive (OSAS), and mixed (MSAS).

[0005] Models for studying the gut microbiota include animal models, in vitro membrane and cell models. In some IH and SF animal models simulating obstructive sleep apnea hypopnea syndrome, SD rats are very important experimental animal models and can be used to study physiological and pathogenic processes related to humans. By comparing and analyzing the gut microbial reference gene sets of humans, mice and rats, it was found that the proportion of shared genes between the rat gut gene set and the human gut gene set (2.47%) was higher than that between the mouse gut and the human gut (1.19%); relative to genes, the gut microbiota of the three were more similar in function; at the same time, 93.65% of the human gut microbiota KOs could be found in the rat gut microbiota, which was also higher than that in the mouse gut microbiota (80.03%). The research results show that SD rats are suitable as animal experimental models for studying the relationship between human diseases and gut microbiota, can provide important reference data for future research, the diet of the test animals can be effectively controlled, and experimental materials such as tissues and organs are relatively easy to obtain, and pharmacokinetics and other effects can be studied.

[0006] Since the duration of obstructive apnea (OSA) events in patients with obstructive sleep apnea hypopnea syndrome is constantly changing during the whole sleep process, in the existing IH and SF animal models simulating obstructive sleep apnea hypopnea syndrome, there is a lack of research on the changing trend of the duration of OSA events, resulting in the environment of the animal model not matching the actual environment of the simulation object, so that the existing model cannot fully simulate the physiological environment of the human body, which will affect the subsequent microbiota structure and lead to deviations in the detection results of the microbiota structure. Therefore, we propose a microecological animal model for obstructive sleep apnea syndrome. Summary of the Invention

[0007] The main object of the present invention is to provide a microecological animal model for obstructive sleep apnea syndrome, which can effectively solve the problems in the background technology.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] A microecological animal model for obstructive sleep apnea syndrome, the gut microbiota model obtains the fitting mathematical relationship between the number of apneas and time by constructing a fitting model for the changing trend of OSA events, and establishes an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the number of apneas - time change, in order to establish a microecological animal model for studying obstructive sleep apnea syndrome. The construction steps of the microecological animal model are as follows:

[0010] Step 1: Construct a fitting model for the changing trend of OSA events

[0011] Through statistical analysis of the correlation influencing factors of OSAHS, obtain the fitting mathematical relationship between the number of apnea and time;

[0012] The specific steps are as follows: Design and establish an OSA event collection experiment for OSAHS patients, test a number of OSAHS patients, evenly divide the total recording time of all patients throughout the night into 4 segments: R, N1, N2, and N3 stages, calculate the correlation influencing factors for each sleep period, compare the changing trends of each indicator in different sleep periods, draw a curve of the number of apnea - time change for fitting analysis, and obtain the mathematical function relationship of the number of apnea - time change according to the analysis results;

[0013] The specific method of the OSA event collection experiment for OSAHS patients is as follows: Instruct the research subjects not to sleep during the day on the day before the monitoring, not to drink coffee, alcohol, strong tea, sedative hypnotics, or drugs affecting blood pressure, not to take a bath, wash hair, shave, or use chemical ornaments. Before the monitoring, a professional asks the examinee about relevant medical history and conducts a simple examination, measures the waist circumference, hip circumference, height, and weight, calculates the body mass index, and goes to bed around 22:00 on the night of the monitoring day. Empty the bladder and bowels before going to bed. Before going to sleep, the examinee sits on one side of the monitoring bed and installs a polysomnography monitor. After connecting each lead wire and EEG electrode patch, observe whether the image display is normal, perform various parameter calibrations, then measure the blood pressure before going to sleep and after waking up respectively. Take a blind method to issue a PSG monitoring report and input relevant sleep - breathing parameters. Continuously monitor with a polysomnography monitor for at least 7 hours and record the following indicators: oral and nasal airflow, SpO2, chest and abdominal respiratory movements, electrocardiogram, electroencephalogram, electro - oculogram, mentalis electromyogram, body position, leg movements, snoring, sleep structure analysis such as sleep staging and the percentage of time in each stage, and the number of awakenings; Calculate the apnea - hypopnea index, respiratory hypopnea index, mean oxygen saturation, lowest oxygen saturation, oxygen desaturation index, mean systolic blood pressure, and mean diastolic blood pressure respectively;

[0014] The method for fitting and analyzing the apnea times-time change curve is as follows: Using SPSS 25.0 statistical software, measurement data conforming to the normal distribution are expressed as mean ± standard deviation (±s), and measurement data not conforming to the normal distribution are expressed as M(Q1, Q3). One-way ANOVA is used for comparing multiple groups of measurement data that conform to the normal distribution and have homogeneous variances, and the Kruskal-Wallis rank sum test is used for measurement data not conforming to the normal distribution. The Fisher's exact probability method is used for comparing count data. Binary logistic regression analysis is used to divide the average apnea duration and the longest apnea duration into two groups above and below their medians respectively. Correlation analysis is performed on the average apnea duration, the longest apnea duration of all subjects, and age, gender, BMI, sleep apnea hypopnea index, obstructive apnea index, average apnea duration, the longest apnea duration, the percentage of oxygen saturation time in total sleep time, R, N1, N2, N3 sleep percentages in total sleep time, etc. P<0.05 indicates that the difference is statistically significant;

[0015] The steps for obtaining the mathematical function relationship of the apnea times-time change are as follows:

[0016] S1: According to the experimental data, obtain the coordinate point set () of the initial apnea times and the initial average apnea time during the patient process, and draw a scatter plot of the coordinate point set with as the horizontal and vertical coordinates respectively;

[0017] S2: Calculate the average value of all average apnea times y corresponding to the initial apnea times within the set area, where i = 2, 4, 6,..., n; i The average value of the numerical values, where i = 2, 4, 6,..., n;

[0018] S3: Through step two, obtain the coordinate point set () of the initial apnea times and the average value of the average apnea time during the patient process, and draw a scatter plot of the coordinate point set with as the horizontal and vertical coordinates again. Then, perform curve fitting on the scatter plot to obtain the mathematical function relationship of the apnea times-time change;

[0019] Step two: Select the target animals and perform pretreatment

[0020] The specific steps are as follows: Select 32 clean-grade SD rats at 5 to 6 weeks of age and weighing 250 ± 10 g as the target animals. 10 days before the start and during the 4-week research period, place the selected target animals in a room with temperature and light control, raise them in standard cages, and feed them with tap water and sterilized standard food;

[0021] Step three: Prepare the animal model

[0022] The specific steps are as follows: The selected SD rats are randomly and evenly divided into two groups, namely the conventional group and the control group. The SD rats in the conventional group are placed in a conventional breeding room, and the rats in the control group are placed inside a control chamber for breeding. The two groups of rats are given the same feeding time, drinking water time, and feeding amount. Among them, the control chamber is equipped with an environmental control system for controlling the oxygen content and oxygen supply time inside the chamber. And the environmental control system gives periodic oxygen supply according to the mathematical function relationship between the apnea times and time changes obtained in Step 1 during the time period from 9 am to 9 pm when the rat sleep phase is dominant, so that the oxygen content inside the control chamber corresponds to the mathematical function relationship between the oxygen supply period and the apnea times - time changes, and the continuous period is 4 weeks. The oxygen supply inside the breeding room of the conventional group is kept the same as the indoor environment;

[0023] Step 4: Obtain fecal samples

[0024] The specific steps are as follows: After making the oxygen content inside the control chamber correspond to the mathematical function relationship between the oxygen supply period and the apnea times - time changes and ending the periodic oxygen supply after continuous treatment for 4 weeks, collect the fresh feces of each rat and put them into pre-labeled 1.5 mL sterile centrifuge tubes, and store them at -80 °C for later use;

[0025] Step 5: DNA extraction and 16S rRNA high-throughput sequencing

[0026] The specific steps are as follows: Use a DNA extraction kit to extract the total DNA from the rat feces, use a spectrophotometer to measure the concentration of the DNA, use 1.0% ethidium bromide agarose gel electrophoresis to measure the DNA quality, then use the extracted and separated fecal whole-genome DNA as a sample to amplify the V3 / V4 variable region of the bacterial 16S ribosomal RNA gene in the feces, use 2% agarose gel electrophoresis to detect the PCR amplification product, use the AxyPrep DNA Gel Extraction Kit to cut and recover the target fragment, use a microfluorometer to quantitatively detect the purified fragment, mix each sample in the corresponding proportion according to the quantitative results, use a sequencer for sequencing, and perform species annotation based on the quantitative analysis and database comparison of microbial ecological bioinformatics, and perform taxonomic classification of the sample species based on the species annotation results, so as to classify the fecal microbiota of the fecal sequencing samples of 32 SD rats;

[0027] The bioinformatics analysis method is as follows: Measurement data are expressed as mean plus or minus standard deviation (±s), and Welch ,The t-test was used to statistically analyze the differences in sample Alpha diversity indices between the two groups. The ANOSIM analysis was used to test the significance of the β-diversity differences between groups. The KW rank sum test was used to detect the significant abundance difference characteristics between the two groups, and taxa with significant abundance differences were found. Linear discriminant analysis was used to estimate the magnitude of the influence of each component abundance on the differential effect. A P value < 0.05 was considered statistically significant.

[0028] Further, in step S3 of step one, the fitting degree index R of the fitting curve 2 is not less than 0.8.

[0029] Further, the correlation influencing factors include, but are not limited to, the apnea-hypopnea index AHI, the obstructive apnea index OAI, the mean apnea duration MAD, the longest apnea duration LAD, the percentage of oxygen saturation time in total sleep time, the rapid eye movement period, the sleep period, and the percentages of N1, N2, and N3 sleep stages in non-rapid eye movement sleep in total sleep time.

[0030] Further, in step five, universal primers were designed based on the highly variable V3 / V4 fragment of the 16S rRNA gene of bacteria, and the sequencing region was 338F-06R, where:

[0031] The forward primer sequence is: 5-ACTCCTACGGGAGGCAGCAG-3;

[0032] The reverse primer sequence is: 5-GGACTACHVGGGTWTCTAAT-3.

[0033] Further, the PCR reaction system was 20 μL, including 10 μL of 2×PCR Master Mix Solution, 5 μM of forward and reverse primers, and 10 ng of template DNA. The program parameters of the PCR amplification reaction were 95 °C for 3 min; 95 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; 27 cycles; 72 °C for 10 min.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) By constructing a fitting model for the changing trend of OSA events, the fitting mathematical relationship between the number of apnea and time is obtained, and an animal model with SD rats is established. According to the obtained fitting mathematical relationship, periodic oxygen supply is given to the animal model in the experimental group, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the number of apnea - time changes, which can simulate the actual physiological environment of patients during sleep. According to the changing trend of the duration of patients' sleep apnea events, a microecological animal model more consistent with the actual situation is established, making the detection results of the flora structure more accurate;

[0036] (2) By constructing a fitting model for the changing trend of OSA events, a collection experiment of OSA events in patients with sleep apnea syndrome is designed and established. A number of OSAHS patients are tested to obtain data on the relevant influencing factors for evaluating sleep in each sleep period, and evaluation indicators such as the apnea - hypopnea index, respiratory hypopnea index, average blood oxygen saturation, lowest blood oxygen saturation, oxygen desaturation index, average systolic blood pressure, and average diastolic blood pressure are calculated. This is beneficial to constructing a database of OSA events in OSAHS patients, thereby understanding the disease conditions of OSAHS patients, evaluating the severity of the disease, and designing targeted treatment plans. Brief Description of the Drawings

[0037] Figure 1 It is a flowchart for constructing the microecological animal model of the present invention. Detailed Embodiments

[0038] The present invention will be further described below in conjunction with the detailed embodiments. Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as limiting the present invention. In order to better illustrate the detailed embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product.

[0039] Example 1

[0040] As Figure 1 shown, a microecological animal model for obstructive sleep apnea syndrome. The intestinal flora model obtains the fitting mathematical relationship between the number of apnea and time by constructing a fitting model for the changing trend of OSA events, and establishes an animal model with SD rats. According to the obtained fitting mathematical relationship, periodic oxygen supply is given to the animal model in the experimental group, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the number of apnea - time changes, in order to establish a microecological animal model for studying obstructive sleep apnea syndrome. The construction steps of the microecological animal model are as follows:

[0041] Step 1: Construct a fitting model for the changing trend of OSA events

[0042] By statistically analyzing the correlation influencing factors of OSAHS, the fitting mathematical relationship between the number of apneas and time is obtained;

[0043] The steps to obtain the mathematical function relationship of the number of apneas - time change are as follows:

[0044] S1: According to the experimental data, obtain the coordinate point set () of the initial number of apneas and the initial average apnea time during the patient's process, and draw a scatter plot of the coordinate point set with the horizontal and vertical coordinates respectively;

[0045] S2: Calculate the average value of all average apnea times y i corresponding to the initial number of apneas within the set area, where i = 2, 4, 6,..., n;

[0046] S3: Through step two, obtain the coordinate point set () of the initial number of apneas and the average value of the average apnea time during the patient's process, and draw a scatter plot of the coordinate point set with the horizontal and vertical coordinates again. Then, perform curve fitting on the scatter plot to obtain the mathematical function relationship of the number of apneas - time change;

[0047] Step two: Select the target animals and perform pretreatment;

[0048] Step three: Prepare the animal model;

[0049] Step four: Obtain fecal samples;

[0050] Step five: DNA extraction and 16S rRNA high - throughput sequencing.

[0051] The specific steps of step one are: Design and establish an OSA event collection experiment for OSAHS patients, test several OSAHS patients, evenly divide the total overnight recording time of all patients into 4 segments: R, N1, N2, and N3 periods, calculate the correlation influencing factors for each sleep period, compare the change trends of each index in different sleep periods, draw a curve of the number of apneas - time change for fitting analysis, and obtain the mathematical function relationship of the number of apneas - time change according to the analysis results;

[0052] The specific steps of step two are: Select 32 clean - grade SD rats at 5 - 6 weeks old and weighing 250 ± 10 g as the target animals. 10 days before the start and during the 4 - week research period, place the selected target animals in a room with temperature and light control, raise them in standard cages, and feed them with tap water and sterilized standard food.

[0053] The specific steps of Step 3 are as follows: The selected SD rats are randomly and evenly divided into two groups, namely the conventional group and the control group. The SD rats in the conventional group are placed in a conventional breeding room, and the rats in the control group are placed inside a control chamber for breeding. The same feed, drinking water time, and feeding amount are given to the two groups of rats. Among them, the control chamber is equipped with an environmental control system for controlling the oxygen content and oxygen supply time in the chamber. And during the time period from 9 am to 9 pm when the rat sleep phase is dominant, according to the mathematical function relationship between the apnea times and time changes obtained in Step 1, periodic oxygen supply is given, so that the oxygen content inside the control chamber corresponds to the mathematical function relationship between the oxygen supply period and the apnea times - time changes, and the duration of the cycle is 4 weeks. The oxygen supply inside the breeding room of the conventional group is kept the same as the indoor environment.

[0054] The specific steps of Step 4 are as follows: After making the oxygen content inside the control chamber correspond to the mathematical function relationship between the oxygen supply period and the apnea times - time changes and continuously processing for 4 weeks with the periodic oxygen supply ended, the fresh feces of each rat are collected and put into a pre-labeled 1.5 mL sterile centrifuge tube, and stored at -80 °C for standby.

[0055] The specific steps of Step 5 are as follows: Take the frozen fresh feces of rats obtained in Step 4, thaw them, extract the total DNA in the rat feces using a DNA extraction kit, and measure the concentration of DNA using a spectrophotometer. Use 1.0% ethidium bromide agarose gel electrophoresis to measure the DNA quality. Then, using the extracted and separated fecal genomic DNA as a sample, amplify the V3 / V4 variable region of the bacterial 16S ribosomal RNA gene in the feces, detect the PCR amplification product using 2% agarose gel electrophoresis, use the AxyPrep DNA Gel Extraction Kit to cut and recover the target fragment, use a micro-fluorometer to quantitatively detect the purified fragment, mix each sample in the corresponding proportion according to the quantitative results, perform sequencing using a sequencer, and perform species annotation based on the quantitative analysis of microbial ecological bioinformatics and database comparison. Based on the species annotation results, taxonomic classification is carried out on the sample species, so as to classify the fecal microbiota of all the SD rat sequencing samples.

[0056] The fitting degree index R of the fitting curve in Step S3 of Step 1 2 is not less than 0.8.

[0057] By adopting the above technical solutions: By constructing a fitting model for the changing trend of OSA events, obtaining the fitting mathematical relationship between the number of apnea and time, and establishing an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the change of the number of apnea - time, which can simulate the actual physiological environment of patients during sleep. According to the changing trend of the duration of patients' sleep apnea events, a micro - ecological animal model more in line with the actual situation is established, making the detection results of the flora structure more accurate. When obtaining the mathematical function relationship of the change of the number of apnea - time, since the scatter plot of the coordinate point set plotted again with as the horizontal and vertical coordinates is mostly in a discrete state, when performing curve fitting on the scatter plot, its function expression can be in various forms, and it can be fitted by drawing software, and the fitting degree index R of the fitting curve is selected. 2 The curve function relationship with R not less than 0.8 is used as the fitting result, where the fitting degree index R 2 ranges between, and the larger the value, the higher the fitting degree of the curve. When the fitting degree index R 2 is greater than 0.8, its fitting degree can meet the accuracy requirements of the experimental process.

[0058] Example 2

[0059] As Figure 1 shown, a micro - ecological animal model for obstructive sleep apnea syndrome. The intestinal flora model constructs a fitting model for the changing trend of OSA events, obtains the fitting mathematical relationship between the number of apnea and time, and establishes an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the change of the number of apnea - time, in order to establish a micro - ecological animal model for studying obstructive sleep apnea syndrome. The construction steps of the micro - ecological animal model are as follows:

[0060] Step 1: Construct a fitting model for the changing trend of OSA events

[0061] By statistically analyzing the correlation influencing factors of OSAHS, the fitting mathematical relationship between the number of apnea and time is obtained;

[0062] The steps for obtaining the mathematical function relationship of the change of the number of apnea - time are as follows:

[0063] S1: According to the test data, obtain the coordinate point set () of the initial number of apnea and the initial average apnea time during the patient's process, and draw a scatter plot of the coordinate point set with as the horizontal and vertical coordinates respectively;

[0064] S2: Calculate all average apnea times y corresponding to the initial apnea counts within the set region, which is the average value of the numerical values, where i = 2, 4, 6,..., n; i

[0065] S3: Obtain the set of coordinate points (, ) of the initial apnea counts and the average value of the average apnea times during the patient process through Step 2. Then, plot the scatter plot of the set of coordinate points again with the and as the horizontal and vertical coordinates respectively. Next, perform curve fitting on the scatter plot to obtain the mathematical function relationship of the apnea count - time variation;

[0066] Step 2: Select the target animals and conduct pretreatment;

[0067] Step 3: Prepare the animal model;

[0068] Step 4: Obtain the fecal samples;

[0069] Step 5: DNA extraction and 16S rRNA high - throughput sequencing.

[0070] The specific steps of Step 1 are as follows: Design and establish an OSA event collection test for OSAHS patients. Test several OSAHS patients. Divide the total overnight recording time of all patients evenly into 4 segments: R, N1, N2, and N3. Calculate the correlation influence factors for each sleep period, compare the change trends of each index in different sleep periods, plot the apnea count - time change curve for fitting analysis, and obtain the mathematical function relationship of the apnea count - time variation based on the analysis results;

[0071] The specific steps of Step 2 are as follows: Select 32 clean - grade SD rats aged five to six weeks with a body weight of 250 ± 10 g as the target animals. 10 days before the start and during the 4 - week research period, place the selected target animals in a room with temperature and light control, keep them in standard cages, and feed them with tap water and sterilized standard food.

[0072] The specific steps of Step 3 are as follows: Randomly and evenly divide the selected SD rats into two groups: the conventional group and the control group. Place the SD rats in the conventional group in the conventional breeding room, and place the rats in the control group inside the control chamber. Give the two groups of rats the same feeding time, drinking water time, and feeding amount. The control chamber is equipped with an environmental control system for controlling the oxygen content and oxygen supply time in the chamber. During the 9 am - 9 pm period when the rat sleep phase is dominant, according to the mathematical function relationship of the apnea count - time variation obtained in Step 1, give periodic oxygen supply so that the oxygen content inside the control chamber corresponds to the apnea count - time variation mathematical function relationship in terms of the oxygen supply period. The continuous period is 4 weeks. Keep the oxygen supply inside the breeding room of the conventional group the same as the indoor environment.

[0073] ​The specific steps of Step 4 are as follows: Make the oxygen content inside the control cabin correspond to the mathematical function relationship between the oxygen supply period and the apnea times-time change. After continuously processing for 4 weeks and ending the periodic oxygen supply, collect the fresh feces of each rat, put them into pre-labeled 1.5 mL sterile centrifuge tubes, and store them at -80 °C for standby.

[0074] The specific steps of Step 5 are as follows: Take the frozen fresh feces of rats obtained in Step 4, thaw them, extract the total DNA in the rat feces using a DNA extraction kit, measure the DNA concentration using a spectrophotometer, and measure the DNA quality using 1.0% ethidium bromide agarose gel electrophoresis. Then, take the extracted and separated fecal genomic DNA as a sample, amplify the V3 / V4 variable region of the bacterial 16S ribosomal RNA gene in the feces, detect the PCR amplification product using 2% agarose gel electrophoresis, use the AxyPrep DNA Gel Extraction Kit to cut and recover the target fragment, use a micro-fluorometer to quantitatively detect the purified fragment, mix each sample in the corresponding proportion according to the quantitative results, perform sequencing using a sequencer, and perform species annotation based on the quantitative analysis of microbial ecological bioinformatics and database comparison. Based on the species annotation results, conduct taxonomic classification of the sample species, so as to classify the fecal microbiota of all SD rat sequencing samples.

[0075] The correlation influencing factors include but are not limited to the apnea-hypopnea index AHI, obstructive apnea index OAI, mean apnea duration MAD, longest apnea duration LAD, percentage of oxygen saturation time in total sleep time, rapid eye movement period, sleep period, and percentages of N1, N2, and N3 stages of non-rapid eye movement sleep in total sleep time.

[0076] In Step 5, universal primers are designed according to the highly mutated V3 / V4 fragment of the bacterial 16S rRNA gene, and the sequencing region is 338F-06R, where:

[0077] The forward primer sequence is: 5-ACTCCTACGGGAGGCAGCAG-3;

[0078] The reverse primer sequence is: 5-GGACTACHVGGGTWTCTAAT-3.

[0079] The PCR reaction system is 20 μL, including 10 μL of 2×PCR Master Mix Solution, 5 μM forward and reverse primers, and 10 ng of template DNA. The program parameters of the PCR amplification reaction are 95 °C for 3 min; 95 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; 27 cycles; 72 °C for 10 min

[0080] By adopting the above technical solutions, the specific method for the OSA event collection test of OSAHS patients is as follows: The research subjects are instructed not to sleep during the day on the day before the monitoring, not to drink coffee, alcohol, strong tea, sedative hypnotics, or drugs that affect blood pressure, not to take a bath, wash their hair, shave, or use chemical ornaments. Before the monitoring, a professional asks the examinee about relevant medical history and conducts a simple examination, measures the waist circumference, hip circumference, height, and weight, calculates the body mass index, and goes to bed around 22:00 on the night of the monitoring day. Before going to bed, the examinee empties the bladder and bowels, sits on one side of the monitoring bed before going to sleep, installs a polysomnography monitor, connects each lead wire and electroencephalogram electrode patch, observes whether the image display is normal, calibrates various parameters, then measures the blood pressure before going to bed and after waking up respectively, adopts a blind method to issue a PSG monitoring report, and enters relevant sleep breathing parameters. The polysomnography monitor is used to continuously monitor for at least 7 hours, and the following indicators are recorded: oral and nasal airflow, SpO2, thoracic and abdominal respiratory movements, electrocardiogram, electroencephalogram, electrooculogram, mental myogram, body position, leg movement, snoring, sleep structure analysis such as sleep stages and the percentage of time in each stage, and the number of awakenings; calculate the apnea-hypopnea index, respiratory hypopnea index, mean oxygen saturation, lowest oxygen saturation, oxygen desaturation index, mean systolic blood pressure, and mean diastolic blood pressure respectively;

[0081] The method for fitting analysis of the apnea count-time change curve is as follows: The SPSS 25.0 statistical software is used. Measurement data that conforms to the normal distribution is expressed as mean plus or minus standard deviation (±s), and measurement data that does not conform to the normal distribution is expressed as M(Q1, Q3). One-way analysis of variance is used for the comparison of multiple groups of measurement data that conform to the normal distribution and have homogeneous variances, and the Kruskal-Wallis rank sum test is used for measurement data that does not conform to the normal distribution. The Fisher exact probability method is used for the comparison between count data. Binary logistic regression analysis is used to divide the average apnea duration and the longest apnea duration into two groups above and below their medians respectively, and the average apnea duration, the longest apnea duration, age, gender, BMI, apnea-hypopnea index, obstructive apnea index, average apnea duration, the longest apnea duration, the percentage of oxygen saturation time in the total sleep time, R, N1, N2, N3 sleep stages in the total sleep time percentage, etc. of all subjects are analyzed for correlation. P<0.05 indicates that the difference is statistically significant; The bioinformatics analysis method is as follows: Measurement data is expressed as mean plus or minus standard deviation (±s), and Welch ,The t-test was used to statistically analyze the differences in sample Alpha diversity indices between the two groups. The ANOSIM analysis was used to test the significance of the differences in β diversity between groups. The KW rank sum test was used to detect the significant abundance difference characteristics between the two groups, and taxa with significant differences in abundance were found. Linear discriminant analysis was used to estimate the magnitude of the influence of each component abundance on the differential effect. A P value < 0.05 was considered statistically significant.

[0082] Example 3

[0083] As Figure 1 shown, a microecological animal model for obstructive sleep apnea syndrome. The intestinal flora model constructs a fitting model for the changing trend of OSA events, obtains the fitting mathematical relationship between the number of apneas and time, and establishes an animal model using SD rats. According to the obtained fitting mathematical relationship, the animal model in the experimental group is given periodic oxygen supply, so that the oxygen content inside the control cabin corresponds to the mathematical function relationship between the oxygen supply period and the number of apneas-time change, in order to establish a microecological animal model for studying obstructive sleep apnea syndrome. The construction steps of the microecological animal model are as follows:

[0084] Step 1: Construct a fitting model for the changing trend of OSA events

[0085] By statistically analyzing the correlation influencing factors of OSAHS, the fitting mathematical relationship between the number of apneas and time is obtained;

[0086] The steps for obtaining the mathematical function relationship of the number of apneas-time change are as follows:

[0087] S1: According to the experimental data, obtain the coordinate point set () of the initial number of apneas and the initial average apnea time during the patient process, and draw a scatter plot of the coordinate point set with the respectively as the horizontal and vertical coordinates;

[0088] S2: Calculate the average value of all average apnea time y i values corresponding to the initial number of apneas within the set area, where i = 2, 4, 6,..., n;

[0089] S3: Through Step 2, obtain the coordinate point set () of the initial number of apneas and the average value of the average apnea time during the patient process, and draw a scatter plot of the coordinate point set again with the respectively as the horizontal and vertical coordinates, and then perform curve fitting on the scatter plot to obtain the mathematical function relationship of the number of apneas-time change;

[0090] Step 2: Select the target animal and perform pretreatment;

[0091] Step 3: Prepare the animal model;

[0092] Step 4: Obtain fecal samples;

[0093] Step 5: DNA extraction and high-throughput sequencing of 16S rRNA.

[0094] By adopting the above technical solution: by constructing a fitting model for the changing trend of OSA events, designing and establishing a collection test of OSA events for patients with sleep apnea syndrome, testing a number of OSAHS patients, obtaining data on the relevant influencing factors for evaluating sleep in each sleep period, and calculating evaluation indicators such as apnea-hypopnea index, respiratory hypopnea index, average blood oxygen saturation, lowest blood oxygen saturation, oxygen desaturation index, average systolic blood pressure, and average diastolic blood pressure, it is beneficial to construct a database of OSA events for OSAHS patients, so as to understand the disease conditions of OSAHS patients, evaluate the severity of the disease, and design targeted treatment plans.

[0095] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A microecological animal model for obstructive sleep apnea syndrome, by constructing a fitting model of the change trend of OSA events, obtaining the fitting mathematical relationship between the number of apnea and time, and establishing an animal model with SD rats. According to the obtained fitting mathematical relationship, the animal model of the experimental group is given periodic oxygen supply, so that the internal oxygen content corresponds to the mathematical function relationship between the oxygen supply period and the number of apnea - time change, to establish a microecological animal model for studying obstructive sleep apnea syndrome, characterized in that: The construction steps of the microecological animal model are as follows: Step 1: Construct a fitting model for the changing trend of OSA events Through statistical analysis of the correlation influencing factors of OSAHS, obtain the fitting mathematical relationship between the number of apnea and time; The specific steps are as follows: Design and establish an OSA event collection experiment for OSAHS patients, test several OSAHS patients, evenly divide the total overnight recording time of all patients into 4 segments: R, N1, N2, and N3 stages, calculate the correlation influencing factors of each sleep period, compare the changing trends of each index in different sleep periods, draw a curve of the number of apnea - time changes for fitting analysis, and obtain the mathematical function relationship of the number of apnea - time changes according to the analysis results; The said correlation influencing factors include the apnea - hypopnea index AHI, obstructive apnea index OAI, mean apnea duration MAD, longest apnea duration LAD, percentage of time of blood oxygen saturation in total sleep time, rapid eye movement period, sleep period, and percentages of N1, N2, and N3 stages of non - rapid eye movement sleep in total sleep time; The said indexes include the apnea - hypopnea index, respiratory hypopnea index, mean blood oxygen saturation, lowest blood oxygen saturation, oxygen desaturation index, mean systolic blood pressure, and mean diastolic blood pressure; The steps for obtaining the mathematical function relationship of the number of apnea - time changes are as follows: S1: Obtain the coordinate point set of the patient's initial apnea times and initial average apnea time according to the test data ( ), and draw a scatter plot of the coordinate point set with as the horizontal and vertical coordinates respectively; S2: Calculate the average apnea time y corresponding to all the initial apnea times within the set area i average value of the numerical values , where i = 2, 4, 6,..., n; S3: Obtain the average value of the initial apnea times and the average apnea duration of the patient through step S2 of the coordinate point set ( ), and plot the scatter plot of the coordinate point set again with as the horizontal and vertical coordinates respectively. Then perform curve fitting on the scatter plot to obtain the mathematical function relationship of the apnea times - time change; Step 2: Select the target animals and conduct pretreatment The specific steps are as follows: Select 32 clean - grade SD rats at 5 - 6 weeks old with a body weight of 250 ± 10 g as the target animals, start a 4 - week study, place the selected target animals in a room with temperature and light control, raise them in standard cages, and feed them with sterilized standard food; Step 3: Prepare the animal model The specific steps are as follows: Randomly divide the selected SD rats into two groups, namely the conventional group and the control group. The SD rats in the conventional group are placed in a conventional breeding room, and the rats in the control group are placed inside a control chamber for breeding. Give the two groups of rats the same feeding time, amount of drinking water, and feeding amount. Among them, the control chamber is equipped with an environmental control system for controlling the oxygen content and oxygen supply time in the chamber. And the environmental control system gives periodic oxygen supply according to the mathematical function relationship of the number of apnea - time changes obtained in Step 1 during the period from 9 am to 9 pm when the rats' sleep time is dominant, so that the oxygen content inside the control chamber corresponds to the oxygen supply period and the mathematical function relationship of the number of apnea - time changes, and the duration of the cycle is 4 weeks. The oxygen supply inside the breeding room of the conventional group remains the same as the indoor environment; Step 4: Obtain fecal samples The specific steps are as follows: Make the oxygen content inside the control chamber correspond to the oxygen supply period and the mathematical function relationship of the number of apnea - time changes, and after 4 weeks of continuous periodic oxygen supply, collect the fresh feces of each rat and put them into pre - labeled 1.5 mL sterile centrifuge tubes, and store them at - 80 °C for standby; Step 5: DNA extraction and 16S rRNA high - throughput sequencing The specific steps are as follows: Take the fresh frozen feces of rats obtained in Step 4, thaw them, extract the total DNA in the rat feces using a DNA extraction kit, measure the concentration of the DNA using a spectrophotometer, measure the DNA quality using 1.0% ethidium bromide agarose gel electrophoresis, then take the isolated fecal genomic DNA as a sample, amplify the V3 / V4 variable region of the bacterial 16S rRNA gene in the feces, detect the PCR amplification product using 2% agarose gel electrophoresis, use the AxyPrep DNA Gel Extraction Kit to cut and recover the target fragment, use a microfluorometer to quantitatively detect the purified fragment, mix each sample in the corresponding proportion according to the quantitative results, perform sequencing using a sequencer, and perform species annotation by quantitative analysis and comparison with the database, and conduct sample species classification based on the species annotation results, so as to classify the fecal microbiota of all SD rat sequencing samples.

2. The microecological animal model for obstructive sleep apnea syndrome according to claim 1, characterized in that: The fitting degree index R of the fitting curve in step S3 of step one 2 is not less than 0.

8.

3. The microecological animal model for obstructive sleep apnea syndrome according to claim 1, wherein: In Step 5, universal primers are designed according to the V3 / V4 highly variable fragment of the bacterial 16S rRNA gene, and the sequencing region is 338F-06R, where: The forward primer sequence is: 5-ACTCCTACGGGAGGCAGCAG-3; The reverse primer sequence is: 5-GGACTACHVGGGTWTCTAAT-3.

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