Intestinal microbiota markers and applications for obstructive sleep apnea with obesity

Abstract

The application discloses an intestinal flora marker related to obstructive sleep apnea with obesity, a product and application thereof, and the intestinal flora marker combination is a marker combination composed of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena and Bacteroides finegoldii. The application of a reagent for detecting the intestinal flora marker combination in preparation of a product for diagnosing obstructive sleep apnea with obesity is characterized in that the reagent comprises cetyltrimethylammonium bromide. The application provides a new idea and approach for diagnosis and treatment of obstructive sleep apnea with obesity.

Description

Technical Field

[0001] This invention relates to the fields of biomedical detection, gut microbiome, and sleep apnea diagnosis, particularly gut microbiota biomarkers and their applications in obstructive sleep apnea with obesity. Background Technology

[0002] Obstructive sleep apnea (OSA) is a sleep-disordered breathing characterized by repeated upper airway collapse during sleep, leading to apnea or hypoventilation, and consequently intermittent hypoxia and sleep fragmentation. Obesity is not only a major driving factor of OSA, but OSA also exacerbates obesity and its metabolic disorders. The two exhibit a complex, two-way relationship. Obesity not only causes the accumulation of adipose tissue around the upper airway, mechanically compressing and affecting the upper airway, but also aggravates the pathophysiological process of OSA through multiple pathways such as systemic inflammation, metabolic disorders, and adipokines imbalance. Therefore, patients with "OSA and obesity" experience faster disease progression, a higher risk of cardiovascular complications (hypertension, coronary heart disease), and a greater prevalence of metabolic syndrome (such as insulin resistance, hyperglycemia, and hyperlipidemia), making it a major public health problem.

[0003] Currently, clinical diagnosis and classification of OSA (with or without obesity) mainly rely on polysomnography (PSG), portable sleep monitors (PM), and assessment scales (Epworth Sleepiness Scale, Stop Bang Questionnaire, NoSAS Questionnaire). These techniques have drawbacks such as high invasiveness, long testing cycles, insufficient specificity, and susceptibility to subjective factors (e.g., scale completion), failing to meet the needs of large-scale screening and early identification. Existing research has confirmed the association between gut microbiota and obesity and OSA (e.g., increased Firmicutes / Bacteroidetes ratio in obese individuals, and decreased gut microbiota diversity in OSA patients). However, current research mainly focuses on "OSA patients vs. healthy individuals" or "obese individuals vs. non-obese individuals," without conducting comparative studies on the key subgroup of "OSA with obesity" versus "OSA without obesity." Furthermore, previous studies have screened microbiota markers that are mostly single genus or microbiota diversity indicators, with limited diagnostic efficacy (AUC mostly below 0.8), and their specificity in the "OSA-obesity comorbidity" classification has not been verified. Summary of the Invention

[0004] The purpose of this invention is to address the above-mentioned technical problems by providing a gut microbiota biomarker, product, and its application related to obstructive sleep apnea with obesity, thereby providing a new approach and method for the diagnosis and treatment of obstructive sleep apnea with obesity.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a combination of gut microbiota markers associated with obstructive sleep apnea and obesity, characterized in that the combination of gut microbiota markers is a combination of markers composed of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii.

[0006] Secondly, the present invention provides an application of a reagent for detecting a combination of gut microbiota markers in the preparation of a product for diagnosing obstructive sleep apnea with obesity, characterized in that the reagent comprises hexadecyltrimethylammonium bromide, and the combination of gut microbiota markers is a marker combination composed of Bacteroides finegoldii, Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorealongicatena.

[0007] Thirdly, the present invention provides a computer program product for diagnosing obstructive sleep apnea with obesity, characterized in that: the computer program product is used to diagnose the risk of a subject having obstructive sleep apnea with obesity, including the following steps: Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii. The relative abundance values ​​of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii were substituted into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object. Calculate the probability P that the subject has OSA with obesity based on y, P=exp(y) / {1+exp(y)}, where exp() represents the natural exponential function; Based on the comparison of probability P with the reference value, the risk of the subject having obstructive sleep apnea with obesity or obstructive sleep apnea without obesity can be diagnosed or predicted.

[0008] In some possible implementations, the relative abundance value of each individual bacterial species in the feces of the test subject is obtained. Individual bacterial species include *Streptococcus thermophilus*, *Faecalibacterium prausnitzii*, *Dorea longicatena*, and *Bacteroides finegoldii*. This is further specifically preceded by the following steps: Microbial genomic DNA was extracted from the samples using the CTAB cetyltrimethylammonium bromide method. The extracted genomic DNA underwent quality control, and samples meeting quality standards were selected. These selected DNA samples were then randomly fragmented, end-repaired, and A-base ligated. Adapters and indexes were added, and fragment selection yielded a library of approximately 300 bp. The inserted fragments were then sequenced using the BGI platform using a paired-end method. The sequencing data were filtered, denoised, and assembled to obtain high-quality data; ASV feature sequences were divided by clustering based on 100% Identity; the feature sequences were annotated using the feature-classifier classify-sklearn plugin of QIIME 2 to generate taxonomic abundance data; 80% of the abundance data were randomly selected and analyzed using LEfSe software.

[0009] In some possible implementations, the formula for the binary logistic regression equation is: y=A+B1 x1+B2 x2+B3 x3+B4 x4 Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1, x2, x3 and x4 are the relative abundance values ​​of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena and Bacteroides finegoldii, respectively.

[0010] In some possible implementations, A is 0.1676, B1 is 78.8714, B2 is -13.3382, B3 is -76.8137, and B4 is 1451.8136.

[0011] Fourthly, the present invention provides a drug or biological agent for the adjunctive treatment of obstructive sleep apnea with obesity, based on the markers described in the first aspect, wherein the drug or biological agent contains an active ingredient that promotes an increase in the abundance of *Streptococcus thermophilus*, *Faecalibacterium prausnitzii*, and *Dorea longicatena*, and inhibits a decrease in the abundance of *Bacteroides finegoldii*.

[0012] Fifthly, a method for producing or screening the drug or biological agent described in the fourth aspect, comprising producing or screening a substance capable of modulating the marker described in the first aspect as a core component of the drug or biological agent.

[0013] The beneficial effects of this invention are as follows: This invention collects samples from subjects with "OSA with obesity" and "OSA without obesity", performs 16S genome sequencing and uses bioinformatics to statistically analyze the sequencing data, discovers the gut microbiota associated with the disease, integrates the gut microbiota with disease information, and distinguishes the two groups of patients with the greatest accuracy. (1) This invention is the first to discover four significantly different gut microbiota, namely, Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii, as specific markers for obstructive sleep apnea with obesity. Differential bacterial and linear discriminant analysis of the training set data revealed that the relative abundance of Bacteroides finegoldii was significantly reduced in the "OSA with obesity" group, while the abundance of Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorea longicatena was significantly increased in the "OSA without obesity" group. This indicates that these bacteria can serve as differential markers between "OSA with obesity" and "OSA without obesity". ROC curve analysis showed that they have high specificity and sensitivity as detection variables. Therefore, these four bacteria can be used as detection markers for the diagnosis of OSA with obesity. Using these four bacteria as detection markers is completely non-invasive and highly accurate. (2) High-throughput sequencing can process multiple samples in batches, shortening the single sample detection cycle to 24-48 hours. Compared with the "single person, single test" detection mode of PSG, the unit sample detection cost is greatly reduced. The fecal microbiota test can quickly distinguish between "OSA without obesity" and "OSA with obesity", which can help identify high-risk patients in the early stage and meet the needs of large-scale screening of high-risk populations. (3) Current studies mostly focus on "OSA patients vs. healthy people" or "obese people vs. non-obese people", and have not conducted comparative studies on the key sub-group of "OSA with obesity" and "OSA without obesity"; and the microbiota markers screened in previous studies are mostly single genera or microbiota diversity indicators, with limited diagnostic efficacy (AUC is mostly below 0.8) and have not verified their specificity in the "OSA-obesity comorbidity" subtype. This patent uses a larger sample size for verification, which makes the prediction effect of OSA with obesity better. Secondly, it uses 16S genomic sequencing to provide higher resolution, which allows the analysis of microbial communities to go deep into the level of species or even strains, improving the accuracy and reliability of diagnosis.(4) The changes in the abundance of these four types of bacteria can be combined to help determine the severity of OSA with obesity (such as in conjunction with the Apnea-Hypopnea Index (AHI) and Body Mass Index (BMI), providing a basis for the formulation of personalized treatment plans. By dynamically tracking the changes in the abundance of the patient's bacteria, the effects of interventions such as weight loss, ventilator therapy, and probiotic regulation can be evaluated in real time, and the treatment plan can be adjusted in a timely manner to improve treatment efficiency. (5) This invention also discloses the application of these bacteria in the research and development of targeted intervention drugs or functional foods to alleviate OSA with obesity, as well as in the gut microbiota matching system to improve the effectiveness of gut microbiota transplantation in alleviating OSA with obesity-related symptoms, providing a new direction for the treatment of OSA with obesity, and possessing high clinical translational value and market prospects. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the experimental method for obtaining a combination of gut microbiota biomarkers associated with obstructive sleep apnea and obesity, provided by the present invention. Figure 2 A schematic diagram of the workflow of the computer program product for diagnosing obstructive sleep apnea with obesity provided by the present invention; Figure 3 LEfSe score graph (sleep apnea with obesity and sleep apnea without obesity); Figure 4 Box plot of gut microbiota biomarkers; Figure 5 This is a schematic diagram of the ROC diagnostic curve; Figure 6 Table 2 shows the relative abundance values ​​of the markers in the validation set; Figure 7 Table 3 shows the correlation abundance statistics of the markers in the validation set.

[0015] in: Figure 7 In Table 3, e-05 indicates 10 -5 For example, 4.57e-05 means 4.57 10 -5 . Detailed Implementation

[0016] To assess whether gut microbiota composition can aid in the diagnosis of obstructive sleep apnea with obesity, this invention collects samples from subjects with both "OSA with obesity" and "OSA without obesity," performs 16S genome sequencing, and uses bioinformatics to statistically analyze the sequencing data. This reveals disease-related gut microbiota, integrates gut microbiota with disease information, and precisely differentiates the two groups of patients. A schematic diagram of the experimental method for obtaining a combination of gut microbiota biomarkers associated with obstructive sleep apnea and obesity is shown below. Figure 1The diagram shows the experimental workflow for "biomarker screening," which encompasses the overall technical approach from sample collection, DNA extraction, sequencing, and data analysis to ultimately screen out four gut microbiota biomarkers. Figure 2 illustrates the workflow for "biomarker-based diagnosis," which involves using a computer program to perform actual diagnosis using the four identified biomarkers. This includes inputting bacterial abundance, substituting it into a regression equation, calculating probabilities, and outputting diagnostic results; essentially, a schematic diagram of the workflow for a computer program diagnosing obstructive sleep apnea with obesity. This invention, through 16S genome sequencing, has for the first time discovered four significantly different gut microbiota—Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii—as specific biomarkers for OSA with obesity. Differential bacterial and linear discriminant analyses of the training set data revealed that the relative abundance of Bacteroides finegoldii was significantly reduced in the "OSA with obesity" group, while the abundance of Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorea longicatena was significantly increased in the "OSA without obesity" group. This suggests that these bacterial species can serve as differential markers between "OSA with obesity" and "OSA without obesity".

[0017] The workflow of the computer program product for diagnosing obstructive sleep apnea with obesity in this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, in the description of the embodiments of this application, the terms "first," "second," etc., are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0018] Example 1: Sample Collection Stool samples were collected from 65 patients who underwent polysomnography (PSG): The inclusion criteria for the OSA with obesity group are as follows: 1. Age distribution: 9-70 years; 2. Stable vital signs; 3. BMI ≥ 28 kg / m² 2 4. Being diagnosed with obesity or obesity metabolic syndrome.

[0019] Exclusion criteria for OSA with obesity: 1. People who are currently losing weight; 2. People who have taken antibiotics within the past three months; 3. People who have used probiotics, prebiotics, or synbiotics within the past month; 4. People who have used anti-inflammatory drugs, weight loss drugs, or supplements within the past month; 5. People with a personal history of cardiovascular disease, hypertension, cancer, type 1 or type 2 diabetes, or inflammatory gastrointestinal diseases (such as Crohn's disease or colitis); 6. People who smoke; 7. People who drink more than two units of alcohol per day; 8. People who are pregnant or breastfeeding; 9. People with irregular menstruation, menopause, or hormone replacement therapy; 10. People who exercise more than 300 minutes per week.

[0020] Inclusion criteria for the control group: 1. Age range of 9-60 years; 2. No diabetes or other metabolic diseases; 3. No depression or other neurological diseases; 4. No irritable bowel syndrome or gastrointestinal diseases; 5. No other immune system diseases or not in an immunodeficient state; 6. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics and prebiotics before and during the study.

[0021] Exclusion criteria were the same as for the OSA with obesity group.

[0022] The above data comes from fecal samples collected by Meiyitian Biopharmaceutical (Wuhan) Co., Ltd. and Wuhan Central Hospital.

[0023] Example 2: DNA extraction, library construction, and sequencing: Microbial genomic DNA was extracted from the samples using the CTAB (hexadecyltrimethylammonium bromide) method. The extracted genomic DNA underwent quality control, and samples meeting quality standards were selected. These selected DNA samples underwent random fragmentation, end repair, A base ligation, adapter and index addition, and fragment selection to obtain a library of approximately 300 bp. Finally, the inserted fragments were sequenced using the BGI platform using a paired-end sequencing method.

[0024] Example 3: LEfSe analysis for screening biomarkers 1. Split the dataset In the samples of Example 1, as shown in Table 1, samples were randomly selected as the training set, and the remaining samples were used as the validation set.

[0025] Table 1 Sample Information Table 2. Screening for gut microbiota biomarkers The sequencing data was filtered, denoised, and assembled to obtain high-quality data. ASV feature sequences were segmented using 100% Identity as the criterion. Feature sequences were annotated using the QIIME 2 feature-classifier classify-sklearn plugin to generate taxonomic abundance data. 80% of the abundance data was randomly selected and analyzed using LEfSe software. The default LDA Score filter value was set to 2.5, and the results are as follows: Figure 3 As shown. Three microorganisms—Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorea longicatena—were significantly reduced in patients with sleep apnea and obesity, while one microorganism—Bacteroides finegoldii—was significantly increased. Box plots of the four markers are shown in [reference needed]. Figure 4 These four bacterial species are the first to be screened and validated as specific biomarkers for OSA accompanied by obesity.

[0026] Example 4: Construction and Validation of Bivariate Regression Equations Construction of the binary regression equation: Based on the four gut microbiota biomarkers screened in Example 3, the binary logistic regression algorithm in IBM SPSS Statistics (v27) software was used to fit the training set data (24 cases of OSA with obesity + 23 cases of OSA without obesity), calculate the regression coefficient of each microbiota, and construct a diagnostic model. The formula for the binary logistic regression equation is: y=A+B1 x1+B2 x2+B3 x3+B4 x4 Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1, x2, x3 and x4 are the relative abundance values ​​of Bacteroides finegoldii, Streptococcus thermophilus, Faecalibacterium prausnitzii and Dorea longicatena, respectively. In this embodiment, A is 0.1676, B1 is 78.8714, B2 is -13.3382, B3 is -76.8137, and B4 is 1451.8136; The formula for calculating the probability of OSA with obesity is: P, P=exp(y) / {1+exp(y)}, where exp() represents the natural exponential function.

[0027] Validation of the binary logistic regression equation: Validation set data calculation and statistical analysis: Based on the validation set data, the relative abundance values ​​of mimicry markers 1, 2, 3, and 4, and individual bacterial communities for each sample in the obese and non-obese groups were calculated. Then, the logarithm y of the dominance of the test subject was obtained using the aforementioned binary logistic regression method, and the probability of the test sample being OSA with obesity was calculated. The results are shown in Table 2 (see...). Figure 6 ) and Table 3 (see Figure 7 ).

[0028] Table 3 (see Table 3) Figure 7 The relative abundance mean and standard deviation of each bacterial species are given. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The q-value is a statistic calculated using the formula of the rank-sum test. The lower the q-value, the greater the difference between the OSA with obesity group and the OSA without obesity group.

[0029] Model performance validation and ROC curve analysis: The validation set data (8 cases of OSA with obesity + 10 cases of OSA without obesity) were first subjected to binary logistic regression, followed by receiver operating characteristic (ROC) curve analysis to obtain the cut-off value (optimal cutoff value). IBM SPSS Statistics (v27) statistical software was used to calculate specificity and sensitivity and plot the ROC curve. The software first calculates the threshold of the actual measured value, and then calculates the corresponding number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Specificity (true negative rate) = TN / (TN+FP), sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by subtracting specificity and sensitivity from 1. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, we first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of that indicator.

[0030] The relative abundance values ​​of individual microbial biomarkers were directly analyzed using receiver operating characteristic (ROC) curves to determine the cut-off value. The AUC of the predictive scoring method for the four-bacterial combination (x1+x2+x3+x4) was 1, with an optimal cut-off value of 0.486987976, a sensitivity of 1, and a specificity of 1. The AUC of the predictive scoring method for the three-bacterial combination (x1+x2+x3) was 1, with an optimal cut-off value of 0.583700415, a sensitivity of 1, and a specificity of 1. The AUC of the predictive scoring method for the two-bacterial combination (x1+x2) was 1, with an optimal cut-off value of 0.000408875, a sensitivity of 1, and a specificity of 1. The AUC of the predictive scoring method for the two-bacterial combination (x1+x3) was 1, with an optimal cut-off value of 0.000272584, a sensitivity of 1, and a specificity of 1. The ROC curves for the predictive scores are shown below. Figure 5 As shown in Table 4, the AUC, optimal cutoff value, sensitivity, and specificity of the four-strain, three-strain, two-strain combined, and single-strain prediction scoring methods are presented.

[0031] Table 4. ROC diagnostic curve results The results show that these four gut microbiota markers are the first to be found to be associated with OSA and obesity. Among them, Streptococcus thermophilus has the highest single-bacterial predictive effect for OSA and obesity, followed by Dolceae longi and Bacteroides fenestrate.

[0032] Table 4 shows that: the AUC of the prediction scoring method for mimicry marker 1 (four bacteria combined (x1+x2+x3+x4)) is 1, the optimal cutoff value is 0.486987976, the sensitivity is 1, and the specificity is 1. The AUC of the prediction scoring method for mimicry marker 2 (three bacteria combined (x1+x2+x3)) is 1, the optimal cutoff value is 0.583700415, the sensitivity is 1, and the specificity is 1. The AUC of the prediction scoring method for mimicry marker 3 (two bacteria combined (x1+x2)) is 1, the optimal cutoff value is 0.000408875, the sensitivity is 1, and the specificity is 1. The AUC of the prediction scoring method for mimicry marker 4 (two bacteria combined (x1+x3)) is 1, the optimal cutoff value is 0.000272584, the sensitivity is 1, and the specificity is 1. The levels were higher than other individual biomarkers, confirming that the multi-bacterial combined model outperformed the single-bacterial model, demonstrating good feasibility and accuracy. This binary logistic regression equation can effectively assess the risk of OSA with obesity, providing a new tool for clinical diagnosis.

[0033] Example 5: Medications or biological agents for the adjunctive treatment of obstructive sleep apnea with obesity This embodiment provides a drug or biological agent for the adjunctive treatment of obstructive sleep apnea accompanied by obesity. Based on the biomarkers screened in Example 3, the drug or biological agent contains an active ingredient that promotes an increase in the abundance of *Streptococcus thermophilus*, *Faecalibacterium prausnitzii*, and *Dorealongicatena*, and inhibits a decrease in the abundance of *Bacteroides finegoldii*. This active ingredient is selected from at least one of the following: 1. Live probiotics: including freeze-dried Streptococcus thermophilus powder, freeze-dried Bacillus prednioides powder, and freeze-dried Dolorella longiformis powder. 2. Prebiotics: selectively promote the proliferation of the above three types of probiotics. 3. Targeted inhibitory ingredients: specifically inhibit the growth and reproduction of Bacteroides fenestration, without significantly inhibiting the target probiotics.

[0034] Example 6: Method for producing or screening the drug or biological agent of Example 5 Specifically, this includes producing or screening substances that can adjust the markers screened in Example 3 as core components of the drug or biological agent, including the following steps: 1. Screening of candidate active ingredients: Based on the four gut microbiota biomarkers (Bacteroides finegoldii, Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorea longicatena) screened in Example 3, substances capable of regulating the abundance of these biomarkers were screened using in vitro culture or computational simulation methods. A library of potential active substances, such as probiotic strains, prebiotics, and targeted inhibitory components, were selected as candidate active ingredients. The regulation included promoting the proliferation of Streptococcus thermophilus, Faecalibacterium prausnitzii, and Dorea longicatena, and inhibiting the growth of Bacteroides finegoldii. These biomarkers were co-cultured with in vitro gut microbiota simulation systems containing the four microbiota species described in the first aspect. 2. Detection of the regulatory effect of biomarkers: The abundance growth rate of Streptococcus thermophilus, Bacillus prednioides, and Dolceae longiformis and the abundance reduction rate of Bacteroides fenestration were detected by 16S or metagenomic sequencing; the effect of candidate active ingredients on the abundance of the four bacterial species was evaluated; changes in bacterial community composition were detected to verify their regulatory effect. 3. Animal Model Validation: An animal model of obstructive sleep apnea with obesity (such as a high-fat diet-induced obese mouse model) was constructed. After administration of the candidate active ingredient, fecal samples were collected, and genomic DNA was extracted. The DNA was extracted using the CTAB method described in Example 2, a library was constructed, and 16S or metagenomic sequencing was performed to analyze the abundance changes of the four biomarkers. At the same time, physiological parameters such as animal body weight, apnea index, and inflammatory markers were monitored to further validate the in vivo regulatory effect and safety through animal models. 4. Formulation preparation and optimization: Combine validated active ingredients with pharmaceutically acceptable carriers or excipients to prepare oral formulations (such as capsules, tablets, powders, or liquids); or use them as core ingredients in probiotic, prebiotic, synbiotic, or postbiotic functional foods.

Claims

1. A combination of gut microbiota markers for obstructive sleep apnea accompanied by obesity, characterized in that, The intestinal flora biomarker combination is a biomarker combination consisting of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii.

2. The application of a reagent for detecting a combination of gut microbiota markers in the preparation of products for diagnosing obstructive sleep apnea with obesity, characterized in that, The reagent includes hexadecyltrimethylammonium bromide, and the intestinal flora marker combination is a combination of markers consisting of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii.

3. A computer program product for diagnosing obstructive sleep apnea with obesity, characterized in that: The computer program product is used to diagnose the risk of a subject having obstructive sleep apnea with obesity, including the following steps: Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii. The relative abundance values ​​of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena, and Bacteroides finegoldii were substituted into the binary logistic regression equation to calculate the logarithm y of the dominance of the test object. Calculate the probability P that the subject has OSA with obesity based on y, P=exp(y) / {1+exp(y)}, where exp() represents the natural exponential function; Based on the comparison of probability P with the reference value, the risk of the subject having obstructive sleep apnea with obesity or obstructive sleep apnea without obesity can be diagnosed or predicted.

4. The computer program product according to claim 3, characterized in that: The relative abundance values ​​of each individual bacterial species in the feces of the test subject were obtained. Individual bacterial species included *Streptococcus thermophilus*, *Faecalibacterium prausnitzii*, *Dorea longicatena*, and *Bacteroides finegoldii*. The preceding steps also included: Microbial genomic DNA was extracted from the samples using the CTAB cetyltrimethylammonium bromide method. The extracted genomic DNA underwent quality control, and samples meeting quality standards were selected. These selected DNA samples were then randomly fragmented, end-repaired, and A-base ligated. Adapters and indexes were added, and fragment selection yielded a library of approximately 300 bp. The inserted fragments were then sequenced using the Paired-End method on the BGI platform. The sequencing data was filtered, denoised, and assembled to obtain high-quality data. ASV feature sequences were clustered using 100% identity as the standard. Feature sequences were annotated using the QIIME 2 feature-classifier classify-sklearn plugin to generate taxonomic abundance data. 80% of the abundance data were randomly selected and analyzed using LEfSe software.

5. The computer program product according to claim 3, characterized in that: The formula for the binary logistic regression equation is: y = A + B1 x1+B2 x2+B3 x3+B4 x4 Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1, x2, x3 and x4 are the relative abundance values ​​of Streptococcus thermophilus, Faecalibacterium prausnitzii, Dorea longicatena and Bacteroides finegoldii, respectively.

6. The computer program product according to claim 5, characterized in that: The values ​​are: A = 0.1676, B1 = 78.8714, B2 = -13.3382, B3 = -76.8137, and B4 = 1451.8136.

7. A drug or biological agent for the adjunctive treatment of obstructive sleep apnea with obesity, based on the biomarker of claim 1, characterized in that, The drug or biological agent contains an active ingredient that promotes an increase in the abundance of *Streptococcus thermophilus*, *Faecalibacterium prausnitzii*, and *Dorealongicatena*, and inhibits a decrease in the abundance of *Bacteroides finegoldii*.

8. A method for producing or screening the drug or biological agent of claim 6, comprising screening for substances that can modulate the marker of claim 1 as a core component of the drug or biological agent.

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