Application of Oral Microbiota in the Diagnosis of Heart Failure

Abstract

The present invention discloses the application of oral microbiota in the diagnosis of heart failure. The present invention has for the first time discovered a combination of biomarkers related to the diagnosis of heart failure, namely Streptococcus, Neisseria, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzae, Neisseria meningitidis, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis. By detecting the abundances of the biomarkers in the oral microbiota of a subject, heart failure can be diagnosed, thereby better intervening in the occurrence and development of heart failure and guiding clinicians to provide prevention or treatment plans for the subject.

Description

Technical Field

[0001] The present invention relates to the field of clinical medicine, and particularly to the application of oral microbiota in the judgment of heart failure and its severity. Background Art

[0002] Heart failure (hereinafter referred to as HF) refers to a group of clinical syndromes caused by various cardiac structural or functional abnormalities resulting in impaired ventricular filling or ejection ability, and is the end stage of various heart diseases. With the increasing aging of the population, the prevalence of chronic cardiovascular diseases such as coronary heart disease and hypertension has gradually increased, and the prevalence of HF has been on the rise year by year in China and even worldwide. According to statistics, the prevalence of HF in the adult population is about 1-2%, among which the prevalence of HF in people over 35 years old in China is about 1.3%, while the prevalence of HF in the elderly over 70 years old is as high as over 10%. Chronic HF leads to a decline in exercise tolerance, complications, etc., seriously affecting the quality of life of patients. In addition, HF has a high mortality rate. According to statistics, the one-year mortality rate of patients with congestive heart failure after discharge is about 16.5%, and the 5-year mortality rate of inpatients with HF exceeds 75%. Despite the continuous improvement of medical standards, the readmission rate and mortality rate of HF patients remain high. The increasing prevalence of HF and high mortality rate bring serious medical and economic burdens to patients and society, and have become a global public health problem. However, the current effective prevention and treatment methods for HF are still limited. Therefore, studying the pathogenesis of HF and finding its protective strategies are crucial for reducing the harm of HF.

[0003] In recent years, studies have found that there is a very close relationship between microbiota and inflammation. Oral microbiota has been repeatedly proven to be related to a variety of systemic inflammatory diseases. However, the evidence regarding the relationship between oral microbiota and the risk of HF is very limited, and most of it is based on the risk between periodontitis and HF. Therefore, the present invention takes oral microbiota as the starting point, and may fill this research gap and provide a new direction for the diagnosis and treatment of HF. Summary of the Invention

[0004] In order to make up for the deficiencies of the prior art, the purpose of the present invention is to provide markers related to heart failure through oral microbiota and their application in the diagnosis of heart failure.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a biomarker, which comprises one or more of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0007] Furthermore, the biomarker is a combination of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0008] The second aspect of the present invention provides the use of a reagent for detecting the abundance of the biomarker in a sample in the preparation of a product for diagnosing heart failure, wherein the biomarker comprises one or more of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0009] Further, the method for detecting the biomarker abundance includes any one or more of metagenomic sequencing, 16S-rRNA sequencing, ITS sequencing, qRT-PCR, Southern blotting, and in situ hybridization.

[0010] Further, the product includes, but is not limited to, reagents for detecting the biomarker abundance by any one or more of metagenomic sequencing, 16S-rRNA sequencing, ITS sequencing, qRT-PCR, Southern blotting, and in situ hybridization.

[0011] Further, the product also includes reagents for sample processing.

[0012] Further, the reagents for sample processing include cryoprotectants, buffer solutions, and storage media.

[0013] In an embodiment, the biomarker abundance is determined by amplifying the ASV sequences of the biomarker in the subject sample and then based on their proportion in the total sample, and the ASV sequences are any one or more of the sequences shown in SEQ ID NO: 1-10.

[0014] In an embodiment, the sample is a sample containing the oral microbiota of the subject to be tested.

[0015] Further, the sample containing the oral microbiota of the subject to be tested includes saliva, dental plaque, carious plaque, supragingival plaque, subgingival plaque, peri-implant submucosal plaque, endodontic plaque, dorsal tongue plaque, and other mucosal surface plaque samples from the subject to be tested.

[0016] Further, the sample is supragingival plaque.

[0017] The third aspect of the present invention provides a method for constructing a diagnostic heart failure model, and the method includes the step of using the biomarker described in the first aspect of the present invention for model construction.

[0018] Further, the model construction algorithm includes at least one of logistic regression, linear discriminant analysis, linear discriminant analysis of characteristic genes, support vector machine, random forest, cross-validation, receiver operating characteristic curve, recursive partitioning tree, XGBoost, ShrunkenCentroids, StepAIC, Kth-Nearest Neighbor, Boosting, neural network, Bayesian network, and hidden Markov model;

[0019] Further, the model construction algorithm includes random forest, cross-validation, and receiver operating characteristic curve.

[0020] The fourth aspect of the present invention provides a system / apparatus for diagnosing heart failure. Further, the system / apparatus includes an input unit, an analysis unit, and an output unit.

[0021] Among them, the input unit is used to input biomarker abundance data, and the biomarkers include one or more of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0022] Further, the data source includes: 1) obtained from the sample of the person to be tested, or 2) indirectly obtained from other readable storage media.

[0023] The analysis unit calculates the probability of the person to be tested having the disease by using the model obtained by the construction method described in the third aspect of the present invention based on the abundance of the biomarkers obtained in the input unit.

[0024] The output unit is used to output the probability of the disease output by the analysis unit.

[0025] The fifth aspect of the present invention provides a pharmaceutical composition for treating or delaying heart failure, characterized in that the pharmaceutical composition includes an inhibitor of one or more of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0026] Further, the inhibitor can specifically reduce the abundance of the biomarker.

[0027] Furthermore, the pharmaceutical composition further comprises a pharmaceutically acceptable carrier.

[0028] The sixth aspect of the present invention provides the use of biomarkers in screening candidate drugs for treating or delaying heart failure, wherein the biomarkers include one or more of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis;

[0029] Furthermore, the method for screening candidate drugs for treating or delaying heart failure is as follows: treating a flora or culture system containing the biomarker with a substance to be screened; detecting the abundance of the biomarker in the flora or culture system; wherein, when the substance to be screened reduces the abundance of one or more of the biomarkers, the substance to be screened is a candidate drug for treating or delaying heart failure;

[0030] Furthermore, in the method, the biomarker is a combination of all the above biomarkers.

[0031] Advantages and beneficial effects of the present invention:

[0032] The application of the present invention in clinical practice has the advantages of low cost and strong prospectivity, and can better intervene in the disease process of the occurrence and development of heart failure.

[0033] The present invention for the first time discovers biomarkers ASV10 (Streptococcus toyakuensis, streptococcus), ASV24 (Neisseria perflava, Neisseria), ASV37 (Neisseria subflava, Neisseria), ASV7 (Veillonella parvula, Veillonella), ASV215 (Neisseria cinerea, Neisseria), ASV1 (Haemophilus parainfluenzaes, Haemophilus), ASV127 (Neisseria sicca, Neisseria meningitidis), ASV443 (Geotrichum candidum, Geotrichum), ASV25 (Haemophilus influenzae, Haemophilus), ASV86 (Veillonella massiliensis, Veillonella), and the ASV sequences are the sequences shown in SEQ ID NO: 1-10. By detecting the relative abundance values of the biomarkers in the oral flora of a subject, heart failure can be diagnosed, thereby guiding clinicians to provide a prevention plan or a treatment plan for the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a random forest prediction result graph of the experimental set samples;

[0035] Figure 2 It is a classification result graph of the experimental set samples;

[0036] Figure 3 It is a ROC curve graph of the experimental set samples;

[0037] Figure 4 It is a classification result graph of the validation set samples;

[0038] Figure 5 It is a ROC curve analysis graph of the validation set samples. DETAILED DESCRIPTION OF THE INVENTION

[0039] In the present invention, the abundance of all species in the oral microbiota of two groups of heart failure patients and healthy individuals is subjected to differential testing by machine learning algorithms, and species with significant differences between the two groups are screened out. Classification modeling is performed on the preliminarily screened differential species to select biomarkers that can be used for inter-group classification. After analysis, 10 key strains are found, namely ASV10 (Streptococcus toyakuensis, streptococcus), ASV24 (Neisseria perflava, neisseria), ASV37 (Neisseria subflava, subflavine neisseria), ASV7 (Veillonella parvula, Veillonella parvula), ASV215 (Neisseria cinerea, grey neisseria), ASV1 (Haemophilus parainfluenzaes, Haemophilus parainfluenzae), ASV127 (Neisseria sicca, Neisseria meningitidis), ASV443 (Geotrichum candidum, Geotrichum candidum), ASV25 (Haemophilus influenzae, Haemophilus influenzae), ASV86 (Veillonella massiliensis, Veillonella massiliensis), and the ASV sequences are the sequences shown in SEQ ID NO: 1-10. Further research finds that the individual diagnostic AUC and the combined diagnostic AUC of ASV10 (Streptococcus toyakuensis, streptococcus), ASV24 (Neisseria perflava, neisseria), ASV37 (Neisseria subflava, subflavine neisseria), ASV7 (Veillonella parvula, Veillonella parvula), ASV215 (Neisseria cinerea, grey neisseria), ASV1 (Haemophilus parainfluenzaes, Haemophilus parainfluenzae), ASV127 (Neisseria sicca, Neisseria meningitidis), ASV443 (Geotrichum candidum, Geotrichum candidum), ASV25 (Haemophilus influenzae, Haemophilus influenzae), and ASV86 (Veillonella massiliensis, Veillonella massiliensis) in the validation sample set are all greater than 0.7, showing significant effectiveness in distinguishing the two groups.

[0040] In the present invention, the term "oral microbiota" refers to the collection of microorganisms colonizing the human oral cavity, and its composition includes bacteria, fungi, viruses, etc. Many of these species have a low detection rate and abundance in the healthy human oral cavity. Therefore, specific species can be used as biomarkers to distinguish patients from healthy individuals.

[0041] In the present invention, the term "biomarker" refers to a specific strain in the oral microbiota. However, in some embodiments, the term also encompasses a measurable entity, such as the abundance of the biomarker, that has been determined to be a target indicating a target output, such as one or more diagnoses, prognoses, prediction of drug sensitivity, and / or treatment output.

[0042] Biomarkers can differentially exist at any level, but generally exist at a differential level that is increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally exist at a level that is decreased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent). Preferably, the biomarker has a statistical difference (P < 0.05).

[0043] In the present invention, the term "abundance" refers to the ratio of the number of species of the biomarker in a sample to the total number of species of all microorganisms in the sample. This ratio is usually expressed as a percentage.

[0044] In the present invention, ASV is an abbreviation for Amplicon Seauence Varant, which refers to the determination and analysis of DNA sequences in a microbial community by high-throughput sequencing technology to obtain the ASV number of each microorganism. The sequence corresponding to the ASV number has sufficient nucleotide diversity to distinguish the genus and species of the biomarker.

[0045] In the context of the present invention, the term "sample" refers to a composition obtained from or derived from a subject (e.g., an individual of interest) that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical, and / or physiological characteristics. For example, a sample refers to any sample derived from a subject of interest that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell cultures, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymph fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, sputum, tears, sweat, mucus, saliva, dental plaque, carious plaque, supragingival plaque, subgingival plaque, subgingival plaque around implants, intracanal plaque, dorsal tongue plaque, and other mucosal surface plaque samples, tissue culture media, tissue extracts, homogenized tissue, cell extracts, and combinations thereof. In a specific embodiment of the present invention, the sample is supragingival plaque.

[0046] The term "RT-PCR method" is also known as "reverse transcription polymerase chain reaction" or "retroviral polymerase chain reaction" and is a technique that combines the reverse transcription (RT) of RNA and the polymerase chain amplification (PCR) of cDNA. First, cDNA is synthesized from RNA by the action of reverse transcriptase, and then the target fragment is amplified and synthesized under the action of DNA polymerase using the cDNA as a template. The RT-PCR technique is sensitive and versatile and can be used to detect the gene expression level in cells, the content of RNA viruses in cells, and directly clone the cDNA sequence of a specific gene.

[0047] The term "qRT-PCR" is also known as "quantitative real-time polymerase chain reaction" and refers to the use of changes in fluorescence signals to detect the changes in the amount of amplification products in each cycle of a PCR amplification reaction in real time, and finally perform precise quantitative analysis of the starting template.

[0048] The present invention provides a method for constructing a diagnostic heart failure model, using Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, Veillonella massiliensis for model construction. As known to those skilled in the art, the steps of associating marker abundances with a certain likelihood or risk can be implemented and achieved in different ways. Mathematically combine the abundance measurements of the marker and one or more other markers, and associate the combined value with the underlying diagnostic problem. The determination of the relative abundance values of the markers can be combined by any suitable prior art mathematical method, such as at least one of logarithmic regression, linear discriminant analysis, linear discriminant analysis of characteristic genes, support vector machines, random forests, cross-validation, receiver operating characteristic curves, recursive partitioning trees, XGBoost, Shrunken Centroids, StepAIC, Kth-Nearest Neighbor, Boosting, neural networks, Bayesian networks, hidden Markov models.

[0049] The present invention provides a system or device programmed to implement the methods of the present invention. The system or device can regulate various aspects of the microbiota analysis of the present invention, such as, for example, matching data against known sequences. The system can be the user's electronic device or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

[0050] The present invention provides a pharmaceutical composition for diagnosing heart failure, and the pharmaceutical composition comprises inhibitors of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis.

[0051] In the present invention, the term "inhibitor" refers to an inhibitor that inhibits any function or activity of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, Veillonella massiliensis, including nucleic acid inhibitors, compound inhibitors, etc., but not limited thereto. Among them, the nucleic acid inhibitors are selected from: those targeting Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, Veillonella massiliensis or their transcripts, and capable of inhibiting the gene expression or gene transcription of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, Veillonella massiliensis, including: shRNA (small hairpin RNA), small interfering RNA (siRNA), dsRNA, microRNA, antisense nucleic acid, or constructs capable of expressing or forming the shRNA, small interfering RNA, dsRNA, microRNA, antisense nucleic acid.

[0052] In the present invention, the term "pharmaceutical composition" refers to a composition comprising at least one bioactive compound. The pharmaceutical compositions of the present invention can be administered orally, parenterally, by inhalation spray, topically, rectally, nasally, buccally, vaginally, or by implantation of a depot device. The pharmaceutical compositions of the present invention may contain any conventional non-toxic pharmaceutically acceptable carriers, adjuvants or excipients. In certain cases, pharmaceutically acceptable acids, bases or buffers may be used to adjust the pH of the formulation to enhance the stability of the formulated compound or its dosage form. The term parenteral as used herein includes subcutaneous, intradermal, intravenous, intramuscular, intra-articular, intra-arterial, intrasynovial, intrasternal, intrathecal, intracapsular, and intracranial injection or infusion techniques. The pharmaceutical compositions of the present invention can be administered to a recipient by any route as long as the target tissue can be reached.

[0053] The term "pharmaceutically acceptable carrier" refers to any pharmaceutical carrier that does not itself induce the production of antibodies harmful to the individual receiving the composition and can be administered without undue toxicity. Suitable carriers can be large, slowly metabolized macromolecules such as proteins, polysaccharides, polylactic acid, polyglycolic acid, polymeric amino acids, and amino acid copolymers. Such carriers are well known to those of ordinary skill in the art. Pharmaceutically acceptable carriers in therapeutic compositions can include fluids such as water, saline, glycerol, and ethanol. Auxiliary substances such as wetting or emulsifying agents, pH buffering substances, etc. may also be present in such vehicles.

[0054] The pharmaceutical compositions of the present invention can also be used in combination with other drugs for diagnosing heart failure. Other compounds for diagnosing heart failure can be administered simultaneously with the main active ingredient (e.g., an inhibitor of Streptococcus toyakuensis), or even simultaneously in the same composition. Other therapeutic compounds can also be administered separately in a separate composition or in a dosage form different from the main active ingredient. Partial doses of the main ingredient (e.g., an inhibitor of Streptococcus toyakuensis) can be administered simultaneously with other compounds for diagnosing heart failure, while other doses can be administered separately.

[0055] The present invention further provides the use of a biomarker in screening for candidate drugs for treating or delaying heart failure. The method for screening candidate drugs for treating or delaying heart failure is as follows: treating a microbial community or a culture system containing the biomarker with a substance to be screened; detecting the abundance of the biomarker in the microbial community or the culture system; wherein, when the substance to be screened reduces the abundance of one or more of the biomarkers, the substance to be screened is a candidate drug for treating or delaying heart failure.

[0056] In the present invention, the method further includes: further testing the candidate drug obtained in the above steps for its effect in diagnosing heart failure. If the test compound has a significant difference in effect between heart failure patients and normal people, it indicates that the candidate drug is a candidate drug for diagnosing heart failure.

[0057] AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifier with a higher AUC has a higher ability to correctly classify unknowns between the two target groups. The ROC curve is useful for depicting the performance of a specific feature (e.g., any biomarker described in the present invention and / or any entry of additional biomedical information) when differentiating between two populations (e.g., individuals responsive and non-responsive to a therapeutic agent). Generally, feature data is selected across the entire population in ascending order based on the value of a single feature. Then, for each value of the feature, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with a value higher than that of the feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with a value higher than that of the feature and dividing by the total number of controls. Although this definition refers to the situation where the feature is increased in cases compared to controls, this definition also applies to the situation where the feature is lower in cases compared to controls (in which case, samples with a value lower than that of the feature will be counted). The ROC curve can be generated for an individual feature and can also be generated for other individual outputs. For example, combinations of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single summed value, and this single summed value can be plotted in the ROC curve. Additionally, any combination of multiple features whose combinations are derived from individual output values can be plotted in the ROC curve. These combinations of features can include assays. The ROC curve is a plot of the true positive rate (sensitivity) of the assay against the false positive rate (specificity) of the assay.

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. Simple improvements made to the present invention according to the essence of the present invention all fall within the scope claimed by the present invention.

[0059] Example 1 Screening and validating biomarkers related to heart failure in oral microbiota

[0060] I. Experimental materials and experimental procedures

[0061] 1. Experimental samples

[0062] 94 samples. There were 40 samples in the experimental group (17 HC and 23 HF), and 54 samples in the validation group (14 HC and 40 HF).

[0063] The samples were selected from 63 patients hospitalized at Peking Union Medical College Hospital from April 2022 to March 2023. These patients were diagnosed with HF by at least two experienced cardiologists according to the 2021 ESC heart failure guidelines. Detailed demographic, medical history, laboratory test results, and echocardiogram data were obtained through PUMCH's electronic medical record system. The New York Heart Association (NYHA) functional classification was used to evaluate exercise tolerance. According to the clinical manifestations of the patients, we divided them into (1) chronic HF (CHF): patients who had received appropriate anti-HF treatment and had stable clinical manifestations for at least 1 month; (2) acute HF (AHF): patients with new-onset or acute decompensated heart failure; NYHA class II to IV at enrollment; ≥1 symptom (such as worsening dyspnea, orthopnea) or ≥1 sign (such as rales, peripheral edema, serous effusion) at admission; and NT-proBNP (N-terminal of pro-brain natriuretic peptide) > 300 pg / ml or B-type natriuretic peptide (BNP) > 100 pg / ml. According to the left ventricular ejection fraction (LVEF) evaluated by echocardiogram, they were divided into (1) heart failure with reduced ejection fraction (HFrEF): LVEF ≤ 40%; (2) heart failure with mid-range ejection fraction (HFmrEF): LVEF of 41% - 49%; (3) heart failure with preserved ejection fraction (HFpEF): LVEF ≥ 50%; (4) heart failure with improved ejection fraction (HFimpEF): LVEF ≤ 40% at baseline, improved by up to 40% from 1 month to 1 year after discharge, and increased by at least ≥10%. A total of 31 subjects without heart disease or HF-related symptoms or signs were included in the control group. Subjects with one or more of the following conditions were excluded: 1) evidence of acute myocardial infarction; 2) received cardiac surgery within the previous 6 months; 3) systemic diseases such as systemic lupus erythematosus and malignancies; 4) end-stage renal disease (eGFR < 15 ml / kg / 1.73 m2, CKD-EPI); or 5) used antibiotics for more than 3 days within the previous 3 months. This study protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (K23C0687) and was conducted in accordance with the principles of the Declaration of Helsinki. All subjects signed a written informed consent form agreeing to participate in this study.

[0064] 2. Sample collection

[0065] Sample type: supragingival plaque

[0066] Specific steps: The supragingival plaque of all subjects was collected using sterile swabs. All subjects rinsed their mouths with 10 ml of 0.9% sodium chloride solution for 15 seconds before sampling to remove residual food debris in the oral cavity, and none of them had eaten, drunk, brushed their teeth, or used any oral cleaning solution within two hours before sampling. The collected samples were stored frozen at -80°C.

[0067] 3. DNA extraction

[0068] Extract the genomic DNA of the flora in the sample using the CTAB extraction method.

[0069] 4. Sequencing

[0070] Amplify the 16S rRNA and ITS rRNA genes using the 16S V3-4 region and the ITS1-1F region respectively. According to the manufacturer's recommendations, use NEB Ultra TM II FSDNA PCR-Free Library Preparation Kit (New England Biolabs, USA, Catalog #: E7430L) to generate the sequencing library. Use Qubit and real-time PCR to check the library for quantification, and use a bioanalyzer for size distribution detection. Based on the PE250 strategy, the quantified libraries were pooled and sequenced on the Illumina NovaSeq 6000. All of the above procedures were completed at Novogene Bioinformatics Technology Co., Ltd. The ASV sequences of the biomarkers described in the present invention are shown in Table 1.

[0071] Table 1. SEQ ID corresponding ASV sequence table

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] 5. Species diversity analysis

[0078] Perform species diversity analysis using the Wilcoxon rank sum test. The specific steps are as follows: Through ranking, replace the original data information with rank order for testing. Mix the data of the two populations and sort them, assign values in order and calculate the average value of the two populations. If the average ranks of the two populations are not very different, it means that there is no significant difference between the two populations; otherwise, further calculate the P value of the statistic and screen out the species with a P value less than 0.05, that is, the species with significant differences.

[0079] 6. Construct a classification model

[0080] The present invention constructs a random forest classifier by using microbial species and clinical index variables. Five times of 10-fold cross-validation is used to select microbial indicators to construct the final classifier, and a receiver operating characteristic curve (AUC) is established to evaluate the performance of the classifier.

[0081] 1) Random Forest analysis (RF)

[0082] It belongs to the machine learning algorithm and is a classifier containing multiple decision trees, which can efficiently and quickly select the most important species categories for sample classification.

[0083] 2) Cross-validation analysis

[0084] Using five times of 10-fold cross-validation (K-folder Cross-validation, K-folder CV), the combinations of key species selected by the random forest method are traversed, in order to construct an efficient classifier with the lowest error rate using the optimal species combination.

[0085] 3) Receiver Operating Characteristic curve (ROC) analysis

[0086] It is used to verify the classification performance of the classification model. According to the classification test results, the upper and lower limits, group interval and cut-off point of the measured values are determined, and the sensitivity and specificity of all cut-off points are calculated respectively. The larger the area under the curve (Area Under the Curve, AUC), the better the performance of the classification model.

[0087] II. Experimental results

[0088] 1. Screening results

[0089] In the experimental group, 270 species with significant differences between the two groups were selected through the results of the difference test, and a random forest classification model was trained with 270 species, as shown in Figure 1 Figure A.

[0090] After feature selection based on 5-fold 10-times cross-validation, 10 signature species were retained with the best performance, namely: ASV10 (Streptococcus toyakuensis, Streptococcus), ASV24 (Neisseria perflava, Neisseria), ASV37 (Neisseria subflava, Subflavine Neisseria), ASV7 (Veillonella parvula, Veillonella parvula), ASV215 (Neisseria cinerea, Neisseria cinerea), ASV1 (Haemophilus parainfluenzaes, Haemophilus parainfluenzae), ASV127 (Neisseria sicca, Neisseria sicca), ASV443 (Geotrichum candidum, Geotrichum candidum), ASV25 (Haemophilus influenzae, Haemophilus influenzae), ASV86 (Veillonella massiliensis, Veillonella massiliensis). The ranking of these 10 species according to the importance is as shown in Figure 1 Figure B.

[0091] The ROC curve further verified the classification performance of the classification model, as shown in Figure 2 Figure, with the AUC as high as 0.96, indicating good classification performance. The ROC curves were separately plotted for each of the 10 selected species, as shown in Figure 3 Figure, and the classification performance of individual species was not as high as that of the combined species.

[0092] 2. Verification Results

[0093] The 10 species selected from the experimental sample set were used to classify and verify the validation group. As shown in Figure 4 Figure, according to the ROC curve, the AUC was as high as 0.96, indicating good classification performance. The ROC curves were separately plotted for each of the 10 selected species, as shown in Figure 5 Figure, and the classification performance of individual species was not as high as that of the combined species, but the AUC of each ROC curve was greater than 0.7.

[0094] The description of the above embodiments is only for understanding the method of the present invention and its core idea. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting the abundance of a biomarker in a sample in the preparation of a product for diagnosing heart failure, characterized in that, The biomarker is Streptococcus toyakuensis or a combination of Streptococcus toyakuensis, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Geotrichum candidum, Haemophilus influenzae, and Veillonella massiliensis; The reagent for detecting the abundance of the biomarker is a primer or probe capable of specifically amplifying the ASV sequence of the biomarker; The sample is a sample containing the oral flora of the subject to be tested; The abundance of the biomarker is determined by amplifying the ASV sequence of the biomarker in the subject's sample and then according to its proportion in the total sample; The ASV sequence is the sequence shown in SEQ ID NO: 1-10; 2. The application according to claim 1, wherein The method for detecting the abundance of the biomarker includes any one or more of metagenomic sequencing, 16S-rRNA sequencing, ITS sequencing, qRT-PCR method, Southern blotting, and in situ hybridization; 3. The application according to claim 1, characterized in that, The product also contains reagents for sample processing; 4. The application according to claim 3, characterized in that, The reagents for sample processing include cryoprotectants, buffer solutions, and storage media; 5. The application according to claim 1, characterized in that The sample containing the oral flora of the subject to be tested includes saliva, dental plaque, carious plaque, supragingival plaque, subgingival plaque, subperi-implant mucosal plaque, endodontic plaque, dorsal tongue plaque, and other mucosal surface plaque samples; 6. The application according to claim 1, wherein The sample containing the oral flora of the subject to be tested is supragingival plaque.

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