Obesity-related intestinal flora marker, product and application thereof

Abstract

The invention discloses an intestinal flora marker related to obesity, a product and application of the intestinal flora marker. The invention relates to an intestinal flora marker, which is characterized in that the intestinal flora marker comprises Coprinus dexterous, Lacnospiraceae bacterium and Ruminococcus sp., and the intestinal flora marker comprises Coprinus dexterous, Lacnospiraceae bacterium, Ruminococcus sp., Lactobacillus plantarum, Ruminococcus sp., Lactobacillus plantarum, Lactobacillus plantarum, Lactobacillus plantarum, Lactobacillus plantarum and Ruminococcus sp. The invention provides a kit which comprises a detection reagent for detecting the intestinal flora marker. The invention also provides a product for diagnosing obesity and a computer program product related to obesity. The product provided by the invention has good feasibility and accuracy, can effectively evaluate the risk of obesity, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to obesity-related intestinal flora markers, products and applications thereof. Background Art

[0002] Obesity is a chronic metabolic disease characterized by excessive accumulation of body fat and abnormal body weight.

[0003] Obesity is mostly caused by the interaction of multiple factors such as genetics and environment. When the body consumes more calories than it consumes, the excess calories are stored in the body in the form of fat. The amount exceeds the normal physiological requirement, and when it reaches a certain value, it evolves into obesity. The presence of obesity can cause chronic non-communicable diseases such as hypertension, diabetes, cardiovascular and cerebrovascular diseases, and tumors, and is an important risk factor for these diseases.

[0004] Obesity can also be classified according to the pathogenesis and etiology, age of onset, adipose tissue pathology, and location of fat accumulation. For example, according to the pathogenesis and etiology, it can be divided into simple obesity and secondary obesity. Among them, primary obesity is the most common type of obesity, accounting for about 95% of the obese population, mainly caused by genetic factors and overnutrition; secondary obesity is a type of disease caused by endocrine disorders or metabolic disorders, which is rare in clinical practice, accounting for only 2% to 5% of obese people.

[0005] At present, there is still a lack of a clear and effective intestinal flora marker and related products to provide a new idea and approach for the diagnosis and treatment of obesity. Summary of the invention

[0006] In response to the above technical problems, the present invention provides an intestinal flora marker, product and application thereof related to obesity, so as to provide a new idea and approach for the diagnosis and treatment of obesity.

[0007] The technical solution provided by the present invention is as follows: In the first aspect, a marker of intestinal flora associated with obesity is provided, including Lacinospiraceae Lachnospiraceae bacteria , Ruminococcus Ruminococcus sp. Coprococcus Coprococcus cat.

[0008] In a second aspect, a reagent for detecting intestinal flora markers is provided for use in preparing a product for diagnosing or screening obesity, wherein the intestinal flora markers include Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus.

[0009] In a third aspect, a kit is provided, comprising a reagent for detecting the intestinal flora marker described in the first aspect.

[0010] In a fourth aspect, there is provided use of the kit described in the third aspect in preparing a product for detecting obesity, and / or use of the detection reagent in the kit described in the third aspect in preparing a kit for diagnosing obesity.

[0011] In a fifth aspect, a product for diagnosing obesity is provided, the product comprising primers, probes, antibodies, aptamers or chips that are specific to the intestinal flora markers described in the first aspect.

[0012] In a sixth aspect, a computer program product related to obesity is provided, wherein the computer program product is used to execute a method for diagnosing whether a subject to be tested has a risk of obesity, the method comprising: Obtaining the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; According to y, calculate the probability P that the subject to be tested is a healthy person, P = exp (y) / {1 + exp (y)}, exp () represents the natural exponential function; According to the comparison result of the probability P and the reference value, it is diagnosed or predicted whether the subject to be tested suffers from obesity or has the risk of suffering from obesity.

[0013] In one possible implementation, the formula of the binary logistic regression equation is:

[0014] Among them, A is the intercept term, B1~B3 are the regression coefficients of the independent variables; x1, x2 and x3 are the Lachnospiraceae bacteria , Ruminococcus Ruminococcus sp. Coprococcus Coprococcus cat The relative abundance value of .

[0015] Furthermore, A is -0.5570, B1 is 8.2195, B2 is 134.2251, and B3 is 170.8060.

[0016] The beneficial effects of the present invention are as follows: 1. The intestinal flora marker combination of the present invention includes Lactospirillum Lachnospiraceae bacteria , Ruminococcus Ruminococcus sp.Coprococcus Coprococcus catus . It has been verified that there is a significant association between the three bacteria and obesity. Specifically: the three bacteria are significantly increased in healthy people. According to ROC curve analysis, the above three markers have high specificity and sensitivity as detection variables, so these three bacterial species can be used as detection markers for the prediction and diagnosis of obese patients.

[0017] 2. The kit of the present invention can predict or diagnose obesity using these three bacterial species as detection markers, which is completely non-invasive and highly accurate. Through metagenomic sequencing, a higher resolution is provided, so that the analysis of microbial communities can be carried out at the level of bacterial species or even strains, thereby improving the accuracy and reliability of diagnosis. The three bacterial species can also be used as target microorganisms for the development of these systems, filling the gap in this field.

[0018] 3. The present invention also provides a product for diagnosing obesity. The product can calculate the health probability based on the relative abundance of each bacterial species, and then compare it with the reference value to predict or diagnose whether the patient suffers from obesity or has the risk of obesity. The product has good feasibility and accuracy, can effectively assess the risk of obesity, and provides a new tool for clinical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the result diagram of linear discriminant analysis; Figure 2 Box scatter plots of intestinal flora markers; Figure 3 is the ROC curve. DETAILED DESCRIPTION

[0020] In order to evaluate whether the composition of intestinal symbiotic flora can be used as a predictor of obesity, the present invention collects samples from obese patients and healthy people, performs metagenomic sequencing, and uses bioinformatics to perform statistics on sequencing data, discovers intestinal flora associated with the disease, integrates intestinal flora with disease information, and predicts obese patients to the greatest extent. The present invention discovered Ruminococcus ( Ruminococcus sp. ), Coprococcus aegypti ( Coprococcus cat ), Lactospirillum Lachnospiraceae bacteria Correlation with obese patients. Three species of Ruminococcus ( Ruminococcus sp. ), Coprococcus aegypti ( Coprococcus catus )、Lac-Spiritaceae( Lachnospiraceae bacteria ) abundance was significantly lower than that in the control group, indicating that these species could serve as predictors of obesity.

[0021] In actual work, the staff numbered a strain of the genus Ruminococcus that they detected, and recorded it as Ruminococcus sp. CAG:177. The suffix [CAG:177] in Ruminococcus sp. CAG:177 was a number given by the staff according to their own experimental habits. The suffix [CAG:177] has no practical meaning. People can add a suffix after Ruminococcus sp. according to their preferences.

[0022] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods for which specific conditions are not specified in the examples are usually based on conventional conditions. Example 1: Sample collection Fecal samples were collected from 69 obese patients and 74 healthy subjects: The inclusion criteria for the obese group samples were as follows: 1. Age range 9 to 60 years old; 2. Stable vital signs; 3. BMI 25-34.9 or waist circumference >35 inches for women and >40 inches for men; 4. Diagnosed with obesity or obesity metabolic syndrome.

[0023] Exclusion criteria for the obese group: 1. People who are losing weight; 2. Those who have taken antibiotics in the past three months; 3. Those who have used probiotics, prebiotics or synbiotics in the past month; 4. Those who have used anti-inflammatory drugs, weight loss drugs or supplements in the past month; 5. Those with a personal history of cardiovascular, hypertension, cancer, type 1 or type 2 diabetes and inflammatory gastrointestinal diseases (such as Crohn's disease or colitis); 6. Smoking; 7. Drinking more than two units of alcohol per day; 8. Being pregnant or breastfeeding; 9. Irregular menstruation, menopause or hormone replacement therapy; 10. Exercising for more than 300 minutes per week.

[0024] Inclusion criteria for the control group: 1. Age range 9-60 years old; 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 antibiotics (such as neomycin, rifaximin) or probiotics and prebiotics were taken before and during the study.

[0025] The exclusion criteria were the same as those of the obese group.

[0026] The above data are derived from stool samples collected by Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology.

[0027] Example 2: DNA extraction, library construction and sequencing The CTAB (cetyltrimethylammonium bromide) method was used to extract the microbial genomic DNA of the sample, and the extracted genomic DNA was quality checked to screen out genomic DNA samples with qualified quality. The selected DNA samples were randomly interrupted, end-repaired, connected to the A base, added with adapters and indexes, and fragment selection to obtain a library of about 300bp. Finally, the paired-end method was used to sequence the inserted fragments on the BGI platform.

[0028] Example 3: LEfSe analysis and screening of biomarkers 1. Divide the data set In the samples of Example 1, as shown in Table 1, samples are randomly selected as training sets, and the remaining samples are used as validation sets.

[0029] Table 1 Sample information table

[0030] 2. Screening of intestinal flora markers KneadData software was used to filter, denoise and splice the data from the machine to obtain high-quality sequencing data. The Identity (sequence consistency) standard was set to 100% for clustering and ASV (Amplicon Sequence Variant) feature sequences were divided. The feature sequence was annotated using the feature-classifierclassify-sklearn plugin of the open source software package qiime2 to generate taxonomic abundance data. 80% of the abundance data was randomly selected for analysis using LEfSe software, and the default setting of LDA Score (Linear Discriminant Analysis Score) was 2.5. The results are as follows: Figure 1 shown.

[0031] The linear discriminant analysis scores of the three bacteria in the figure are greater than 2.5, and the linear discriminant analysis scores are a kind of analytical method that can mine and interpret biomarkers from high-dimensional data, and have significant statistical significance and biological relevance.

[0032] Therefore, three microorganisms, Ruminococcus, which were significantly reduced in obese patients, were screened out. Ruminococcus sp. ), Coprococcus aegypti ( Coprococcus catus ) and Lactospirillum ( Lachnospiraceae bacteria ). The box plots of the three markers are shown in Figure 2 These three species are the first to be found to be associated with obesity, but Ruminococcus ( Ruminococcus sp.) has a much higher predictive ability for obesity than other bacteria species, and the predictive effect of the combination of the three bacteria is the highest.

[0033] Example 4: Construction and verification of binary regression equation 4.1. Construction of binary regression equation 4.11. Based on the biomarkers selected above, the probability of predicting illness or health for each sample of three bacteria was calculated using the binary logistic regression algorithm in SPSS software, which facilitated the subsequent evaluation of the performance of the binary regression model for each bacteria separately.

[0034] 4.12. Construction of binary logistic regression equation for relative abundance of mimicry markers (three bacteria association) The formula for the binary logistic regression equation is:

[0035] Among them, A is the intercept term, B1~B3 are the regression coefficients of the independent variables; x1, x2 and x3 are the Lachnospiraceae bacteria , Ruminococcus Ruminococcus sp. Coprococcus Coprococcus cat The relative abundance value of .

[0036] Through data fitting, the values ​​of each variable are obtained, and then the following formula is obtained:

[0037]

[0038] On this basis, the health probability of the subject to be tested can be calculated and the subject to be tested can be diagnosed or predicted whether it suffers from obesity or has the risk of obesity, as follows: Obtain the relative abundance value of each single bacterial species in the three intestinal flora markers of the tested subject; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; According to y, calculate the probability P that the subject to be tested is a healthy person, P = exp (y) / {1 + exp (y)}, exp () represents the natural exponential function; Based on the comparison between the probability P and the reference value, it is diagnosed or predicted whether the subject suffers from obesity or has the risk of suffering from obesity.

[0039] 4.2. Binary Logistic Regression Equation Verification 4.21. Validation set data calculation and statistical analysis Based on the data of the validation set, the relative abundance of the mimicry markers (combination of three bacteria) in the disease group and the healthy group of each sample was calculated, and then the logarithm y of the advantage of the object to be tested was obtained using the aforementioned binary logistic regression method, and then the probability that the sample to be tested was a healthy person was calculated. The results are shown in Tables 2 and 3.

[0040] Table 3 shows the relative abundance mean and standard deviation of each bacterial species. The relative abundance mean determines the central position of the data distribution, while the standard deviation reflects the degree of 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 disease group and the healthy group.

[0041] Table 2 Relevant data of validation set markers

[0042] Note: In Table 2, 6.10E-05 means .

[0043] Table 3. Statistical data of the relative abundance of the validation set markers

[0044] Note: In Table 3, the mean refers to the relative abundance mean, and the same applies to the standard deviation.

[0045] 4.3 Verification Results Based on the data in Table 2, the receiver operating curve test (ROC curve) analysis was performed to obtain the cutoff value (optimal cutoff value). The specificity and sensitivity calculation and ROC curve drawing were completed using IBM SPSS Statistics (v27) statistical software. The software first calculates the threshold of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold, specificity (true negative rate) = TN / (TN+FP), sensitivity (true positive rate) = TP / (TP+FN), and the ROC curve can be constructed by 1-specificity and sensitivity. The integral of the ROC curve is AUC (Area Under the Curve, the area covered under the ROC curve). In order to calculate the specificity and sensitivity of a certain indicator, the Youden coefficient (Youden index = sensitivity + specificity - 1) is first calculated in this embodiment. The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of a certain indicator.

[0046] The relative abundance values ​​of individual microbial markers were directly analyzed by receiver operating characteristic curve test (ROC curve) to obtain the cutoff value (optimal cutoff value). The AUC of the three-bacteria combined prediction scoring method was 93.3%, the optimal cutoff value was 0.4378, the sensitivity was 0.933, and the specificity was 0.857. The ROC curve of the prediction score is shown in Figure 2. Figure 3 The AUC, optimal cutoff value, sensitivity, and specificity of the three-bacteria combined and single-bacteria prediction scoring methods are shown in Table 4.

[0047] Table 4 ROC diagnostic curve results

[0048] From the above results, it can be seen that the above three intestinal flora markers are found to be associated with obesity for the first time, among which Lactospirillum has the highest single-bacteria prediction effect on obesity, followed by Ruminococcus and Coprococcus.

[0049] As shown in Table 4, the AUC of the mimicry marker for predicting obesity is 93.3%, the optimal cutoff value is 0.4378, the sensitivity is 0.933, and the specificity is 0.857, which are higher than other biomarkers. This also proves that the performance of the three-bacteria combined model is higher than other models, and it has good feasibility and accuracy. The optimal cutoff value of the mimicry marker is calculated to be 0.4378. The larger the value, the higher the probability of health. Through this binary logistic regression equation, the risk of obesity can be effectively assessed, providing a new tool for clinical diagnosis.

[0050] Example 5 Based on the above embodiments, this embodiment provides a computer program product related to obesity, wherein the computer program product is used to execute a method for diagnosing whether a subject to be tested has a risk of obesity, comprising the following steps: 1) Obtain the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species is Lactospiraceae Lachnospiraceae bacteria , Ruminococcus sp., and Coprococcus catus; 2) Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation;

[0051] Wherein, x1, x2 and x3 are the relative abundance values ​​of Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus respectively; 3) Calculate the probability P that the subject to be tested is a healthy person based on y, P = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y; 4) Diagnosing or predicting the risk of the subject suffering from obesity based on the comparison of the probability P value of a healthy person with a reference value.

[0052] In actual work, when the P value is greater than 0.5, it means that the probability of the subject suffering from obesity is low; when the P value is less than 0.5, it means that the probability of the subject suffering from obesity is high; when the P value is 0.5, it means that the subject to be tested may be a healthy person or an obese patient. At this time, other means are needed for further testing, such as blood routine test and physical signs. Furthermore, the closer the P value is to 0.5, the more other means are needed for testing.

[0053] Example 6 Based on the product and method of Example 5, the health probabilities of healthy people and obese patients in the verification set are checked, and the specific steps are as follows: 1) Collect stool samples from the persons to be tested and detect the relative abundance of a single strain in the stool; the single strain is any one of Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus; 2) Calculate the logarithm y of the odds of the subject to be tested based on the binary logistic regression equation;

[0054] Wherein, x1, x2 and x3 are the relative abundance values ​​of Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus respectively; 3) Calculate the probability P that the subject to be tested is a healthy person based on y, P = exp(y) / {1+exp(y)}; exp(y) is the natural exponential function of y; 4) Diagnosing or predicting the risk of the subject suffering from obesity based on the comparison of the probability P value of a healthy person with a reference value.

[0055] In actual work, when the P value is greater than 0.5, it means that the probability of the subject suffering from obesity is low; when the P value is less than 0.5, it means that the probability of the subject suffering from obesity is high; when the P value is 0.5, it means that the subject may be healthy or obese. At this time, other means are needed for further testing, such as blood routine test and physical signs. Furthermore, the closer the P value is to 0.5, the more other means are needed for testing.

[0056] Conclusion and explanation: 1. By Figure 1~3 As shown in Tables 2 to 4, Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus can be used as relevant markers of obesity.

[0057] 2. As can be seen from Table 2, when testing fecal samples, only one or several species of bacteria are detected, which is a normal phenomenon. This is because individuals are different. The probability values ​​of the three species are calculated, that is, even if a sample only contains a single species of bacteria, this application can calculate the probability that the sample to be tested suffers from obesity.

[0058] 3. Single bacteria prediction effect: Lactospirillum has the highest single bacteria prediction effect on obesity, followed by Ruminococcus and Coprococcus; 4. Prediction effect of mimicry markers: The combination of three bacteria, Lactospiraceae, Ruminococcus and Coprococcus can all be used as biomarkers for predicting obesity, and the AUC of mimicry markers for predicting obesity is 93.3%, the best cutoff value is 0.4378, the sensitivity is 0.933, and the specificity is 0.857. It has the best prediction effect and can provide more accurate obesity prediction.

[0059] The above description is only a preferred specific implementation manner of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the scope of protection of the invention.

Claims

1. A marker of intestinal flora associated with obesity, characterized in that: The intestinal flora markers are Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus.

2. Use of a reagent for detecting intestinal flora markers in the preparation of a product for diagnosing or screening obesity, wherein the intestinal flora markers include Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus.

3. A kit, characterized in that: The kit comprises a reagent for detecting the intestinal flora marker according to claim 1.

4. Use of the kit according to claim 3 in the preparation of a product for detecting obesity, and / or use of the detection reagent in the kit according to claim 3 in the preparation of a kit for diagnosing obesity.

5. A product for diagnosing obesity, characterized in that: The product includes primers, probes, antibodies, aptamers or chips that are specific to the intestinal flora markers described in claim 1.

6. A computer program product related to obesity, characterized in that: The computer program product is used to diagnose the risk of a subject suffering from obesity, comprising the following steps: Obtaining the relative abundance value of each single bacterial species in the feces of the test subject; the single bacterial species include Lachnospiraceae bacterium, Ruminococcus sp. and Coprococcus catus; Calculate the logarithm y of the odds of the subject to be tested according to the binary logistic regression equation; According to y, calculate the probability P that the subject to be tested is a healthy person, P = exp (y) / {1 + exp (y)}, exp () represents the natural exponential function; According to the comparison result of the probability P and the reference value, it is diagnosed or predicted whether the subject to be tested suffers from obesity or has the risk of suffering from obesity.

7. The computer program product according to claim 6, characterized in that: The formula for the binary logistic regression equation is: Among them, A is the intercept term, B1~B3 are the regression coefficients of the independent variables; x1, x2 and x3 are the Lachnospiraceae bacteria , Ruminococcus Ruminococcus sp. Coprococcus Coprococcus catus The relative abundance value of .

8. The computer program product according to claim 7, characterized in that: A is -0.5570, B1 is 8.2195, B2 is 134.2251, and B3 is 170.8060.

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