Marker combination related to obesity donor, product and application thereof

Abstract

The invention relates to the field of treatment of obesity by flora transplantation, in particular to a marker combination related to an obesity donor, a product and application of the marker combination. The biomarker composition is prepared from the following components: Bifidobacterium adolescentis, Alistipes shaii, Odorobacter splanchnicus, and Bacteroides plebeius, and is characterized in that the biomarker composition is prepared from the following components: Bifidobacterium adolescentis, Alistipes shaii, Odorobacter splanchnicus, and Bacteroides plebeius. The invention provides a reagent for detecting the marker combination, which can be used for preparing a product for screening / evaluating flora transplantation treatment of obesity donors, a computer readable storage medium containing a donor curative effect judgment instruction, and a donor curative effect evaluation system containing a detection device and a comparison device. The invention discloses the association between the flora and the obesity donor for the first time, is non-invasive and high in accuracy, fills the blank of lack of specific markers for treating the obesity donor by flora transplantation, and provides a basis for obesity diagnosis and treatment and individualized treatment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fecal microbiota transplantation for obesity, in particular to a marker combination related to obesity donors, products and applications thereof. BACKGROUND

[0002] Obesity has become a global epidemic, causing heavy burden to human health and health systems. According to the latest prediction of NCD-RisC and the World Obesity Federation, by 2030, more than 2.9 billion adults (about 50% of adults) worldwide will be in a high BMI (BMI≥25kg / m 2 ) state, of which 1.1 billion (487 million men and 643 million women) will reach the level of obesity (BMI≥30kg / m 2 ). Not only that, obesity is also a risk factor for the incidence and death of non-communicable diseases such as type 2 diabetes, cardiovascular disease, fatty liver disease, etc.

[0003] Obesity is usually caused by genetic susceptibility, lifestyle factors (including diet, exercise, mental and psychological factors, sleep habits), disease and drug factors, environmental and social factors, etc. Its pathological mechanism involves adipose tissue dysfunction and inflammation, insulin resistance, blood lipid level changes, and leptin signal disorder. Current treatment is based on lifestyle intervention, combined with drugs (such as orlistat, bupropion, and GLP-1 receptor agonists, etc.) or surgical treatment, but the commonly used anti-obesity drugs mainly act through inhibition of central nervous system pathways, inhibition of dopamine and norepinephrine reuptake, etc. There is a risk of developing adverse neurological and psychiatric diseases. Moreover, drug treatment is usually accompanied by a high recurrence rate. Therefore, it is still necessary to explore safer and more effective weight loss methods and programs.

[0004] In recent years, the study of intestinal flora has brought new breakthroughs in the diagnosis and treatment of obesity. Clinical data shows that obese patients generally have intestinal flora imbalance, manifested as decreased flora diversity, imbalance of the ratio of Firmicutes and Bacteroidetes, and overproliferation of bacteria such as Escherichia coli and Faecalibacterium. Fecal microbiota transplantation (FMT) technology has unique advantages in improving intestinal barrier function, regulating immune response and inhibiting pathogenic bacteria colonization by reconstructing the intestinal microecosystem of patients. A number of clinical studies have confirmed that FMT can significantly improve the core indicators such as body weight and body fat rate of obese patients (a number of clinical studies show that after 6 months of intervention, the average BMI decreases by 1.5-2.0kg / m 2, body fat rate is reduced by 3%-5%), and can also relieve metabolic syndrome manifestations such as insulin resistance and abnormal blood lipids by remodeling intestinal flora metabolic pathways, and reduce the risk of complications such as fatty liver and type 2 diabetes. Compared with traditional weight loss drugs and surgery, FMT achieves metabolic improvement by gently adjusting the intestinal microecology, avoiding drug liver and kidney toxicity and surgical trauma risk, and showing significant advantages of long-term safety and good tolerance.

[0005] However, there are significant individual differences in the clinical efficacy of FMT. Studies have shown that the quality of donor flora is a key factor affecting the transplantation effect, and there are significant differences between high-efficiency donors and low-efficiency donors in terms of flora composition, metabolic function and ecological interaction network, which directly affects the efficiency of intestinal microecology remodeling in the recipient and the prognosis of the disease. At present, for obese patients, a specific biomarker system based on donor screening has not been established. Therefore, developing accurate and reliable biomarkers for donor evaluation and screening of obesity diseases has become a key requirement for improving the clinical value of FMT and promoting the technological innovation of obesity diagnosis and treatment. SUMMARY

[0006] In view of the above technical problems, the present application provides a marker combination related to an obesity donor, a product and an application thereof, which provides a new idea and approach for obesity donor screening and obesity treatment.

[0007] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: The present application provides a marker combination related to an obesity donor, which is composed of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius.

[0008] The present application also provides an application of a reagent for detecting the marker combination in the preparation of a product for screening / evaluating flora transplantation therapy for obesity donors, wherein the marker combination is composed of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius.

[0009] As a preferred technical solution of the present application, the abundance of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius is positively correlated with the effectiveness of the fecal microbiota transplantation for treating obesity.

[0010] As a preferred technical solution of the present application, the product is a detection kit, and the detection kit comprises reagents for detecting the abundance of the marker combination.

[0011] As a preferred technical solution of the present application, the detection sample is the feces of the subject.

[0012] The present application also provides a computer readable storage medium storing computer executable instructions for executing the following method: (1) obtaining the abundance of a marker combination in the feces sample of the subject after treatment, the marker combination comprising Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius; (2) detecting the abundance, and distinguishing the high-efficiency donor and the low-efficiency donor of the subject according to the abundance result.

[0013] As a preferred technical solution of the present application, in step (2), when the abundance of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius in the test sample is higher than the detection value, the subject is identified as a high-efficiency donor, otherwise, the subject is identified as a low-efficiency donor.

[0014] The present application also provides a system for screening / evaluating the therapeutic effect of fecal microbiota transplantation for treating obesity of a subject, comprising: The detection device is used for determining the abundance of a marker combination in a sample of a subject after treatment, and the marker combination is composed of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius. The comparison device distinguishes between high-efficiency donors and low-efficiency donors of the subject according to the abundance of the marker combination of the subject.

[0015] The present application is related to a marker combination associated with obese donors, products and applications thereof, and exhibits many aspects of significant beneficial effects: The present application first discloses that Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius have a significant correlation with the treatment potential of donors. Specifically, the relative abundance of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius in the high-efficiency donor group is significantly higher than that in the low-efficiency donor group. The difference characteristics are verified by a machine learning algorithm, and have stable group distinguishing ability, which proves that the four intestinal symbiotic bacteria groups can be used as a predictor for obese donors, filling the cognitive gap of the lack of specific biomarkers for obese donor screening.

[0016] At present, there is a lack of a standardized and high-accuracy prediction system and detection product for obese donor screening. The present application discloses a four-microbe marker combination as a clear target for developing a precise donor screening system and a rapid detection kit, avoiding the limitations of traditional donor selection relying on experience. The detection kit based on the marker combination (for detecting fecal samples) can be directly applied to clinical practice without complex operation and is completely non-invasive, with high patient compliance. The ROC curve verification shows that the AUC of the mimic marker (four-microbe combination) is 1.0, the sensitivity and specificity are both 100%, the accuracy of donor efficacy prediction is more than 99%, and it is significantly better than single bacteria or traditional indicators.

[0017] The marker detection method provided by the present application can detect the microbial abundance of donor samples of obese patients, and provide quantitative and objective donor screening basis for clinicians. The prediction model based on the marker combination can effectively evaluate the treatment potential of donors, provide scientific support for individualized formulation of fecal microbiota transplantation (FMT) treatment plan, and help to improve the effectiveness of treatment and the quality of patient prognosis. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The experimental analysis flow chart of the present application.

[0019] Figure 2 The abundance change chart of the four markers of the present application before and after treatment.

[0020] Figure 3 The box scatter plot of the four markers of the present application.

[0021] Figure 4 The ROC curve chart of the prediction score of the four markers of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below. If specific conditions are not indicated in the embodiments, the conventional conditions or the conditions suggested by the manufacturer are adopted. If the reagents or instruments used are not indicated by the manufacturer, they are all conventional products that can be purchased in the market.

[0023] In order to explore the feasibility of intestinal symbiotic flora as a screening marker for obese donors, the present application constructs a systematic screening model. By collecting fecal samples of high-efficiency donor population (obesity symptoms are significantly improved after fecal microbiota transplantation) and low-efficiency donor population (poor curative effect after transplantation) verified by clinical tests, the flora structure data are obtained by using metagenomic sequencing technology, and deep mining is carried out by combining bioinformatics analysis.

[0024] For obese donors, the present application constructs a screening system based on marker combination, and the specific technical path is as follows: For the clinical needs of obesity, the present application takes fecal samples as the research object, and selects microorganisms that are significantly related to the curative effect of donors through a standardized experimental detection process: four microorganisms highly related to high-efficiency donors are detected, which present characteristic high abundance distribution in the intestinal tract of donors, and can positively predict the treatment potential of fecal microbiota transplantation (FMT), and these microorganisms constitute the functional flora of high-efficiency donors.

[0025] Based on the above-mentioned four functional flora of high-efficiency donors, an analysis model is constructed by a preset experimental method (see Example 1): the relevant quantitative data of microorganisms are input into a binary regression equation to obtain a simulated microorganism data, and the multi-sample data are statistically analyzed: the average data, difference fold and variable weight value of all samples are obtained by summing and averaging multiple samples, and the diagnostic model constructed based on the difference fold and variable weight value has a high prediction accuracy for the curative effect of obese donors, the prediction accuracy of a single microorganism is more than 80%, the prediction accuracy of a compound simulated marker is more than 99%, and the high-efficiency / low-efficiency donors can be accurately distinguished, which provides an objective and quantitative judgment basis for donor selection in FMT treatment and breaks through the limitations of traditional experience-based donor selection.

[0026] Example 1 Experimental analysis procedure as follows Figure 1 As shown, the process of screening, detecting, identifying, and validating gut microbiota in obese donors was carried out.

[0027] I. Sample Collection A high-efficiency donor group and a low-efficiency donor group were set up, with 120 fecal samples to be collected in each group. By comparing the differences in the gut microbiota between the two groups, the characteristics of the donor microbiota that have a positive impact on the treatment effect of obesity were explored.

[0028] The donor samples were sourced from Wuhan Central Hospital and the selection criteria were as follows: They were recruited from Wuhan Central Hospital.

[0029] Inclusion criteria: 1. Age > 18 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 use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study; 7. Stool samples were collected at one-week intervals within 3 months (a total of 12 time points, with one stool sample collected at each time point).

[0030] Exclusion criteria: 1. Women who are pregnant or plan to become pregnant during the study; 2. Volunteers who have any concerns about this study or have other risk history; 3. Volunteers who are in the terminal stage of an illness or may die during the study; 4. Volunteers who have participated in other clinical trials; 5. Volunteers with diabetes or other metabolic diseases; 6. Volunteers with depression or other neurological disorders; 7. Volunteers with irritable bowel syndrome or gastrointestinal diseases; 8. Volunteers with other immune system diseases or who are not immunodeficient; 9. Volunteers who have participated in any other gut microbiota therapy before enrollment; 10. Volunteers who take antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics during the study; 11. Circumstances identified by the investigator during the study that may affect volunteer compliance and / or completion of study-related procedures.

[0031] Table 1 Sample Information Table II. DNA extraction, library construction, and sequencing Microbial genomic DNA was extracted from the samples using the CTAB (hexadecyltrimethylammonium bromide) method, followed by PCR amplification. The amplification primers were used to construct the library using the TruSeq@DNA PCR-Free Sample Preparation Kit, and the library was then sequenced using an Illumina Miseq PE250.

[0032] III. Data statistics and analysis The original data was quality controlled (based on Trimmomatic) and dehosted (based on Bowtie2) using KneadData software. Kraken2 alignment was used to calculate the number of sequences of each species contained in the sample, and Bracken was used to estimate the actual abundance of species in the sample. Randomly selected 80% of the data was analyzed using LEfSe software, with a default setting of LDAScore filter value of 2, and the results are shown in Figure 2 .

[0033] As can be seen from Figure 2 , the staff screened out four markers that changed significantly (increased or decreased) in the high-efficiency donor group, including Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus, and Bacteroides plebeius. The box scatter plot of these four markers is shown in Figure 3 .

[0034] As can be seen from Figure 3 , these four biomarkers were first discovered and associated with high-efficiency donors, but Bifidobacterium adolescentis had the highest predictive effect on high-efficiency donors, followed by Odoribacter splanchnicus, Bacteroides plebeius, and finally Alistipes shahii.

[0035] The biomarkers mined from 80% of the data were used to construct a logistic regression formula for the training model using the logistic regression algorithm in R software: Formula 1. Logistic regression training model formula for intestinal symbiotic flora; logistic regression equation to predict the probability of high-efficiency donor group; X1: Bifidobacterium adolescentis; X2: Alistipes shahii; X3: Odoribacter splanchnicus; X4: Bacteroides plebeius.

[0036] IV. Marker screening method and experimental results 4.1, Experimental method S1: Extract sample DNA; quality inspection is performed on the extracted genomic DNA, and qualified genomic DNA samples are screened out.

[0037] S2: Library construction and sequencing: the DNA samples screened out are subjected to random fragmentation, end repair, A base ligation, adapter and index addition, and fragment selection to obtain a library of about 300 bp. The inserted fragments are sequenced by a double-end (Paired-End) method.

[0038] S3: Raw data is preprocessed: FastUniq is used to remove PCR products in raw reads, Trimmomatic is used to remove adapters and quality filter, and KneadData v0.10.0 is used to remove contaminated sequences from humans and hosts. After processing, clean reads are obtained.

[0039] S4: Use MEGAHIT to assemble clean reads to generate small fragments (Contig). Discard Contigs with a length of less than 500 bp.

[0040] S5: Gene prediction: use Prodigal to identify CDS present on contigs, and filter CDS with a length of less than 100 bp.

[0041] S6: Construct a non-redundant gene set and abundance statistics: use CD-HIT to cluster the genes obtained in each sample and remove redundancy. The threshold is coverage > 90% and identity > 95%. Use Salmon to calculate the normalized expression (TPM) value of each gene in each sample. Take the TPM value as the relative abundance, and the subsequent species abundance and functional abundance are obtained by adding the TPM value.

[0042] S7: According to the results of step S6, the overall relative abundance of biomarkers in the high / low efficiency donor group is calculated, and the discrimination ability between the two groups, the mean and standard deviation of the relative abundance of obesity prediction or diagnosis are output.

[0043] 4.2, Experimental results The relevant statistical data of the validation set markers are shown in Table 2, wherein the mean determines the center position of the data distribution, and the standard deviation reflects the dispersion degree of the data relative to the mean.

[0044] Table 2: Relevant statistical data of the validation set markers The mean calculation formula is: where ∑ represents summation, xi represents each data, μ represents mean, and N represents the number of data.

[0045] The calculation formula of standard deviation is: where ∑ represents summation, xi represents each data, μ represents mean, N represents the number of data, and σ is the standard deviation.

[0046] The p value is calculated by using the formula of rank sum test, and the calculation formula of the statistic is as follows: where R1 is the rank sum of the first group, and n1 and n2 are the sample sizes of the first group and the second group, respectively.

[0047] The data of the validation set of the correlation abundance is shown in Table 3. The binary logistic regression equation is calculated in R software, and then the values of the five bacteria are subjected to likelihood ratio test.

[0048] The calculation formula of the binary logistic regression equation is as follows: In the above formula, X1~X4 are the correlation abundance data of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius, respectively, β0 is the intercept term, and β1~βm are the regression coefficients of the independent variables. In actual work, the staff only needs to input the correlation abundance data of X1~X4 in the binary logistic regression equation of R software, and the correlation abundance values of the four bacteria in Table 3 can be obtained.

[0049] Table 3 Correlation abundance data of the validation set of the marker From the data of Table 2 and Table 3, it can be seen that the four strains of the application as detection markers are completely non-invasive and have high accuracy. The application uses a larger sample size for verification, which makes the prediction effect of the high-efficiency donor group good, significantly improves the screening efficiency of obese donors. And using metagenomic sequencing means, it provides higher resolution, so that the analysis of microbial community can go deep to the strain level, and improve the accuracy and reliability of diagnosis. This research focuses on the FMT treatment of obesity, analyzes the intestinal flora of different donors and the corresponding treatment effect, accurately identifies the "high-efficiency strain characteristic spectrum" through metagenomic sequencing and functional flora targeted detection, customizes the personalized FMT donor screening scheme for obese patients, fills the gap of the lack of objective quantitative evaluation system in this field, breaks through the limitations of traditional treatment, promotes the precision of microecological therapy, and provides a new path for long-term relief and life quality improvement of this complex functional disease.

[0050] 4.3, ROC verification results Based on the data of Table 2 and Table 3, the receiver operating characteristic curve (ROC curve) analysis was carried out to obtain the cutoff value (optimal cutoff value).

[0051] The R software was used to calculate the specificity and sensitivity and draw the ROC curve. The software first calculates the threshold value 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 value. The specificity (true negative rate) = TN / (TN+FP), the 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 the AUC.

[0052] In order to calculate the specificity and sensitivity of a certain index, the Youden coefficient (Youden index = sensitivity + specificity - 1) is calculated first. The specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of a certain index.

[0053] The expression value of a single microbial marker was directly subjected to receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value). The ROC curve of the prediction score is shown in Figure 4 The AUC, optimal cutoff value, sensitivity, and specificity of the mimic marker and each single microbial prediction score method are shown in Table 4.

[0054] In the above method, it is found through analysis that Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius are obviously increased in the high-efficiency donor group. Further verification set data shows that the four bacteria can be used as detection markers for screening of obese donors through ROC curve analysis of their specificity and sensitivity as detection variables.

[0055] Table 4 ROC diagnostic curve results V. Implementation effect Single microorganism prediction effect: Bifidobacterium adolescentis has the highest prediction effect on high-efficiency donors, followed by Odoribacter splanchnicus and Bacteroides plebeius, with a prediction effect of more than 75%, and Alistipes shahii has the lowest prediction effect.

[0056] Camouflage marker prediction effect: The camouflage marker (a marker formed by combining multiple biomarkers) has the highest accuracy of more than 99% and the best prediction effect, which is significantly better than single markers. It can provide more accurate and precise identification of high-efficiency donors, and through quantitative evaluation model, it provides a standardized solution for clinical individual donor screening, greatly improving the accuracy and reliability of obesity diagnosis and treatment.

[0057] Embodiment 2 Based on a general inventive concept, the embodiment provides a computer-readable storage medium storing computer-executable instructions for executing the following method: (1) obtaining the abundance of a marker combination in a fecal sample of a subject after treatment, the marker combination consisting of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius; (2) comparing the abundance with the abundance before treatment of the same subject, and distinguishing the high-efficiency donor and low-efficiency donor of the subject according to the comparison result.

[0058] In step (2), when the abundances of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius in the test sample are higher than the pre-treatment abundances from the same subject, the subject is identified as a high-efficiency donor, and vice versa, the subject is identified as a low-efficiency donor.

[0059] Embodiment 3 Based on a general inventive concept, the embodiment provides a system for screening / evaluating the efficacy of fecal microbiota transplantation in treating obesity in a subject, comprising: a detection device for determining the abundances of a marker combination in the sample of the subject after treatment, the marker combination consisting of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius; a comparison device for comparing the abundances with the pre-treatment abundances from the same subject, and distinguishing the high-efficiency donor and the low-efficiency donor of the subject according to the comparison results. The comparison process can be performed according to Embodiment 2.

[0060] In summary, the present application first found that the expression of Bifidobacterium adolescentis, Odoribacter splanchnicus, Bacteroides plebeius and Alistipes shahii microorganisms in the high-efficiency donor group is significantly higher than that in the low-efficiency donor group. Through ROC curve analysis, the above four markers have high specificity and sensitivity as detection variables, and therefore, the four microorganisms can be used as detection markers and can be accurately applied to the screening of donors for personalized fecal microbiota transplantation (FMT) in obese patients. Using the four microorganisms as detection markers is completely non-invasive and highly accurate.

[0061] The above is only a preferred embodiment of the present application, and it should be noted that the above preferred embodiment should not be regarded as a limitation of the present application, and the protection scope of the present application should be limited by the scope defined in the claims. For ordinary skilled persons in the art, several improvements and refinements can be made without departing from the spirit and scope of the present application, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A combination of biomarkers associated with obese donors, characterized in that, The biomarker combination consists of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus, and Bacteroides plebeius.

2. The application of a reagent for detecting a combination of biomarkers in the preparation of products for screening / evaluating gut microbiota transplantation therapy for obese donors, characterized in that, The biomarker combination consists of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus, and Bacteroides plebeius.

3. The application according to claim 2, characterized in that, The abundance of *Bifidobacterium adolescentis*, *Alistipes shahii*, *Odoribacter planchnicus*, and *Bacteroides plebeius* was positively correlated with the effectiveness of microbial transplantation in treating obese donors.

4. The application according to claim 2, characterized in that, The product is a detection kit, which includes reagents for detecting the abundance of biomarker combinations.

5. The application according to claim 4, characterized in that, The test sample was the subject's feces.

6. A computer-readable storage medium, characterized in that, The system stores computer-executable instructions for performing the following methods: (1) Obtain the abundance of a biomarker combination in the fecal sample of the subject, the biomarker combination consisting of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus and Bacteroides plebeius. (2) The abundance is detected, and the subjects are distinguished as high-efficiency donors and low-efficiency donors based on the abundance results.

7. The computer-readable storage medium according to claim 6, characterized in that, In step (2), when the abundance of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus, and Bacteroides plebeius in the test sample is higher than the detection value, they are identified as highly efficient donors of the subject; otherwise, they are identified as inefficient donors of the subject.

8. A system for screening / evaluating the efficacy of microbiome transplantation in treating obesity in subjects, characterized in that, include: A detection device for determining the abundance of a biomarker combination in a post-treatment subject sample, the biomarker combination consisting of Bifidobacterium adolescentis, Alistipes shahii, Odoribacter splanchnicus, and Bacteroides plebeius. The comparison device distinguishes between high-efficiency and low-efficiency donors based on the abundance of the biomarker combinations described by the subjects.

Images