Microbial marker for schizophrenia with social anxiety disorder and prediction model thereof

Abstract

The invention provides a microbial marker and a prediction model for schizophrenia with social anxiety disorder. The microbial marker is a Bacteroides microorganism in saliva. The relevance between the relative abundance of Bacteroides microorganisms in saliva and social anxiety disorder is found for the first time. A Nomogram clinical prediction mode is formed by taking the specific microbial marker as a microbial marker in combination with a children bad experience scale and 6-hydroxymelatonin and 5-hydroxyindoleacetic acid in serum, so that objective biological indexes can be provided for early diagnosis and monitoring of whether schizophrenia is accompanied by social anxiety disorder or not; errors of subjective evaluation are reduced; moreover, the oral saliva sample has the obvious advantage of being easy to collect for psychiatric patients, the working difficulty of medical staff can be greatly reduced, and revolutionary changes of prediction, diagnosis and treatment of schizophrenia accompanied with social anxiety disorder are expected to be brought.

Description

Technical Field

[0001] The present invention relates to the field of biomarkers, and in particular to microbial markers of schizophrenia with social anxiety disorder and prediction models thereof. Background Art

[0002] Schizophrenia is prone to developing concurrent psychiatric disorders such as anxiety and depression, one of which is social anxiety disorder. Both genetic and environmental factors play a significant role in the development and progression of social anxiety disorder in schizophrenia. Social anxiety disorder can cause schizophrenia patients to fear returning to social life after hospitalization and can lead to functional impairment in daily life, resulting in a poor prognosis for many patients with schizophrenia. Therefore, early prevention and screening for social anxiety disorder in schizophrenia patients is urgent.

[0003] Traditional methods for predicting social anxiety disorder often require doctors to conduct on-site assessments of patients' social anxiety behaviors based on extensive psychiatric diagnostic guidelines. This not only requires doctors to possess advanced expertise and extensive treatment experience, which is inherently limited, but also presents challenges such as heavy medical burdens and stress on patients. Furthermore, schizophrenia has a complex etiology and unclear pathological mechanisms, and existing technologies rarely research or document biomarkers for schizophrenia with social anxiety disorder, resulting in a lack of objective indicators for the clinical diagnosis of schizophrenia with social anxiety disorder.

[0004] Based on this, exploring specific microbial markers and prediction models for the diagnosis of schizophrenia with social anxiety disorder is of great significance and value for the diagnosis and treatment of schizophrenia with social anxiety disorder. Summary of the Invention

[0005] One object of the present invention is to provide a microbial marker for schizophrenia with social anxiety disorder, which is a Bacteroidetes microorganism in saliva.

[0006] This study used microbiome analysis to identify changes in the expression of oral saliva microbiota in patients with schizophrenia. The study also used microbiome data to analyze saliva samples from two groups of patients: those with schizophrenia and social anxiety disorder, and those without. The researchers screened for differentially expressed microbiota between the two groups, ultimately identifying a Bacteroidetes microbiome. This study found that Bacteroidetes microbiota could serve as a potential microbial marker for schizophrenia with social anxiety disorder.

[0007] Prior studies investigating the relationship between microbes and anxiety have mostly focused on the gut microbiome. This is because the gut microbiome's primary function is to participate in digestion and absorption, and is closely linked to intestinal immunity, metabolism, and inflammatory responses. Imbalanced gut microbiota can affect neurotransmitters, inflammation, the HPA axis, short-chain fatty acids, the gut-brain axis, the immune system, and metabolites, ultimately leading to mood changes such as depression and anxiety. However, the oral and intestinal microbiota are distinct microbial communities within the human body, differing in species, abundance, and function. The oral microbiome is much smaller than the intestinal microbiome and is primarily composed of aerobic bacteria and facultative anaerobes (the intestinal microbiome is primarily composed of anaerobes). Furthermore, the oral microbiome's primary function is to participate in oral digestion and defense. Medical professionals generally believe that it is closely associated with the development of oral diseases such as dental caries and periodontal disease. Consequently, there are currently no studies or reports on the relationship between the oral microbiome and anxiety or schizophrenia with social anxiety disorder.

[0008] Because sampling the gut microbiota of schizophrenia patients is more difficult than in the general population, this study breaks with conventional methods by using oral saliva as a sample for screening potential microbial markers for schizophrenia with social anxiety disorder. This preliminary study explored the association between oral microbiota and schizophrenia with social anxiety disorder, and found significant differences in the abundance of Bacteroidetes microbes in saliva samples between patients with schizophrenia with social anxiety disorder and those without. Therefore, the inventors are the first to discover a correlation between the proportion of Bacteroidetes microbes in saliva and social anxiety disorder.

[0009] In addition, the present invention used the Wilcoxon signed-rank test to analyze the differential microbial communities in saliva samples from the schizophrenia-with-social anxiety disorder group and the schizophrenia-without-social anxiety disorder group. The relative abundance of the differential microbial communities obtained from the Wilcoxon signed-rank test was subjected to receiver operating characteristic (ROC) curve analysis. The ROC curve analysis showed that the area under the curve (AUC) for the diagnosis of schizophrenia-with-social anxiety disorder (SAD) by Bacteroidetes microbes in oral saliva was 0.623 (95% CI: 0.36-0.75). Generally speaking, an AUC value closer to 1 indicates better predictive performance. When the AUC value is 0.5, the diagnostic model's predictions are indistinguishable from random guesswork, indicating that the model has no predictive power. Therefore, Bacteroidetes microbes in oral saliva have a certain predictive value for determining whether schizophrenia patients have SAD and can serve as a specific microbial biomarker to aid in the diagnosis of SAD.

[0010] The present invention's research and discovery of oral-specific microbial markers for schizophrenia with social anxiety disorder is of great significance in many aspects. First, it can provide objective biological indicators for the predictive diagnosis of whether schizophrenia is accompanied by social anxiety disorder, reducing the error of subjective assessment. Secondly, compared with intestinal-specific biomarkers, oral saliva samples have obvious advantages such as easy collection for patients with mental illness, which can greatly reduce the difficulty of medical staff's work. In addition, the specific microbial marker can also be combined with traditional psychological scales and other biomarkers to form a more comprehensive diagnostic model. Therefore, the present invention's research and discovery of specific biomarkers for schizophrenia with social anxiety disorder is expected to bring about revolutionary changes in the predictive diagnosis and treatment of schizophrenia with social anxiety disorder.

[0011] A second object of the present invention is to provide a prediction model for schizophrenia with social anxiety disorder. The prediction model is a nomogram clinical prediction model constructed by performing regression analysis using the relative abundance of Bacteroidetes microorganisms in the saliva of schizophrenia patients as described in the first object of the present invention, the total score of the Adverse Childhood Experiences Scale, and the concentration of 6-hydroxymelatonin and 5-hydroxyindoleacetic acid in the serum as independent variables.

[0012] Furthermore, the unit of the relative abundance of Bacteroidetes microorganisms in saliva is %, the unit of the total score of the Adverse Childhood Experiences Scale is points, and the unit of the concentration of 6-hydroxymelatonin and 5-hydroxyindoleacetic acid is mg / ml. The corresponding relationship between the line segment scale of each factor indicator and the score line segment scale in the Nomogram clinical prediction model is as follows: The fractional line segment scale is a scale with a total scale interval of 0 to 100; The line scale of each factor indicator is divided into equal parts; Factor indicator 1: Adverse Childhood Experiences Scale, the total scale interval of its line segment scale is 10 to 32, and the scale interval on the corresponding score line segment scale is 0 to 46; Factor indicator 2: relative abundance of Bacteroidetes microorganisms in saliva, the total scale interval of the line segment scale is 24 to 2, and the scale interval on the corresponding score line segment scale is 0 to 65; Factor indicator 3: 6-hydroxymelatonin concentration in serum, the total scale interval of the line segment scale is 14 to 0, and the scale interval on the corresponding fractional line segment scale is 0 to 100; Factor indicator 4: 5-hydroxyindoleacetic acid concentration in serum, the total scale interval of the line segment scale is 0 to 5000, and the corresponding scale interval on the fractional line segment scale is 0 to 13; The corresponding relationship between the risk probability line scale and the total score line scale of schizophrenia with social anxiety disorder in the Nomogram clinical prediction model is as follows: The total score line segment scale is a scale with a total scale interval of 0 to 200; The risk probabilities of schizophrenia with social anxiety disorder are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 (i.e., 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%), and the corresponding total scores are 40, 50, 57, 64, 70, 76, 83, and 90, respectively.

[0013] The prediction model of this invention can facilitate early intervention for high-risk patients, thereby reducing the economic and medical burden on society. It also breaks the limitations of traditional diagnosis and treatment based on individual physician expertise and clinical experience, providing auxiliary support for medical professionals in disease diagnosis and treatment. Furthermore, its high prediction accuracy can effectively assist medical staff in making rational decisions, thereby improving the efficiency of patient diagnosis and treatment.

[0014] Furthermore, the predictive model's factor indicators also include the relative abundance of Bacteroidetes microbes in the saliva of schizophrenia patients, for a total of five factor indicators. The combined use of Deinococcus and Bacteroidetes saliva microbes significantly improved the AUC value compared to using Bacteroidetes alone (from 0.623 to 0.701), improving the prediction of whether schizophrenia is accompanied by social anxiety disorder. This indicates that the predictive model's factor indicators, which increase the relative abundance of Bacteroidetes microbes in the saliva of schizophrenia patients, are beneficial for improving prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The ROC curve obtained by performing ROC curve analysis on Bacteroidetes microorganisms, Deinococcus microorganisms, and the combination of the two in saliva samples of two groups of patients with schizophrenia accompanied by social anxiety and without social anxiety; Figure 2 This is a schematic diagram of the construction principle of the prediction model for schizophrenia with social anxiety disorder in this application; Figure 3 This is a diagram of the prediction model for schizophrenia with social anxiety disorder in this application; Figure 4 The ROC curve obtained by performing ROC analysis on the prediction model of schizophrenia with social anxiety disorder of the present application; Figure 5This is the DCA decision curve for the clinical net benefit analysis of the prediction model for schizophrenia with social anxiety disorder of the present application, wherein the curved line corresponding to "LSAS scale ~ ACEs + Bacteroidetes + 6-hydroxymelatonin + 5-hydroxyindoleacetic acid" represents: the prediction model for schizophrenia with social anxiety disorder of the present invention, the slash line corresponding to "All" represents: the assumption that all schizophrenia patients are accompanied by social anxiety disorder behaviors, and the straight line corresponding to "None" represents: the assumption that all schizophrenia patients do not have social anxiety disorder behaviors. DETAILED DESCRIPTION

[0016] The following describes in detail preferred embodiments of the microbial markers for schizophrenia with social anxiety disorder and the prediction model thereof in conjunction with the accompanying drawings.

[0017] The microbial marker for schizophrenia with social anxiety disorder provided by the present invention is: Bacteroidetes microorganisms in saliva.

[0018] The screening process of microbial markers for schizophrenia with social anxiety disorder of the present invention is specifically as follows: 1. Research Subjects A total of 160 hospitalized patients with schizophrenia who participated in the above-mentioned epidemiological study on adverse childhood experiences and social anxiety disorder in schizophrenia patients at the Institute of Neuropsychiatric Prevention and Treatment of Fuzhou Second General Hospital from April 2023 to September 2024 were selected as research subjects (i.e., the first batch of research subjects).

[0019] Inclusion criteria: ① meeting the diagnostic criteria for schizophrenia in the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV); ② being diagnosed by two or more attending psychiatrists; ③ signing of the informed consent form by the patient and / or family member (guardian).

[0020] Exclusion criteria: ① Other psychiatric illnesses; ② Neurological diseases such as brain trauma, Alzheimer's disease, Parkinson's disease, and intellectual disability; ③ Severe physical illnesses such as liver and kidney dysfunction or heart failure; ④ Psychoactive substance abuse; ⑤ Electroconvulsive therapy within 90 days; ⑥ Intravenous blood product infusion within 30 days; ⑦ No antibiotic use within one week. Subjects meeting any of the above criteria were excluded.

[0021] All subjects were assessed using the Diagnostic Score for Social Anxiety Disorder (DSM-III) criteria. They were then divided into two groups: a schizophrenia-with-social anxiety group (with a total score of 38 or greater) and a schizophrenia-without-social anxiety group (with a total score of <38). The schizophrenia-with-social anxiety group (referred to as the "case group") consisted of 64 patients, while the schizophrenia-without-social anxiety group (referred to as the "control group") consisted of 96 patients.

[0022] 2. Research Methods Saliva samples were collected from the subjects after 2 weeks of follow-up, and microbiome analysis was performed on the saliva samples to obtain bacterial flora marker data.

[0023] 1. Microbiome analysis (1) Genomic DNA extraction: Total genomic DNA was extracted from the collected saliva samples using cetyltrimethylammonium bromide (CTAB). DNA concentration and purity were monitored on a 1% agarose gel. Based on the original DNA concentration, the DNA was diluted to 1 ng / L using sterile water.

[0024] (2) PCR amplification: The samples were used according to the formal experimental conditions, with three replicates for each sample. The target region of the 16S rRNA gene (such as the V3-V4 region) was amplified by PCR to obtain PCR products. The PCR products of the same sample were mixed and detected by 2% agarose gel electrophoresis. The PCR products were recovered by gel excision using the AxyPrep DNA Gel Recovery Kit (AXYGEN) and eluted with Tris-HCl; then detected by 2% agarose gel electrophoresis.

[0025] (3) 16S library construction: Adapter sequences were added to the outer ends of the target region by PCR; PCR products were recovered by gel excision using a gel recovery kit; elution was performed with Tris-HCl buffer and detection was performed by electrophoresis on 2% agarose gel; and denaturation with sodium hydroxide was performed to generate single-stranded RNA fragments. Reagents used: TruSeq™ DNA Sample Prep Kit.

[0026] (4) On-machine sequencing: High-throughput sequencing of the 16S rRNA gene of single-stranded RNA fragments in the 16S library is performed to obtain the composition information of the microbial community.

[0027] (5) Microbiome bioinformation data processing: OTU (Operational Taxonomic Units) is an artificially set taxonomic unit marker for the convenience of analysis in phylogenetic or population genetics studies. It can represent a microbial population at a certain taxonomic level (such as strain, genus, species or group, etc.). In order to understand the number and composition of taxonomic information such as species and genera in the sample sequencing results, it is necessary to classify the sequences obtained after sequencing. Specifically, OTU analysis is to group the sequences according to their similarity, and each group is an OTU. Usually, the sequences are divided into OTUs at a similarity level of 97%, and subsequent bioinformatics statistical analysis is carried out based on this; The data processing flow is as follows: ① Raw data preprocessing: Quality control is performed on the raw data obtained by sequencing, including removing low-quality sequences, filtering impurity sequences (such as adapter sequences), and splicing sequences based on fragment overlap. The high-quality reads obtained after quality control processing are saved as clean reads for subsequent analysis; ②OTU clustering and analysis: Clean read sequences are clustered according to similarity to form OTUs. OTUs are annotated with species and compared with reference databases (such as Greengenes, SILVA, or NCBI) to determine the taxonomic information (such as phylum, class, order, family, genus, species, etc.) corresponding to each OTU. ③ Relative Abundance Calculation and Data Storage: Based on the distribution of OTUs (Operational Taxonomic Units), the relative abundance of each taxonomic unit (specifically, "phylum") was calculated (see Table 1). "Deinococcus" in Table 1 refers to microorganisms whose 16S rRNA gene sequences share at least 97% similarity with the core nucleotide sequences of Deinococcus microorganisms published in the NCBI database. The other taxa listed in Table 1 follow the same classification principles as "Deinococcus." The relative abundance of a microbial species is calculated as the number of clean reads per OTU representing that species in a sample divided by the total number of clean reads in the sample. The resulting relative abundance values ​​for each bacterial species at the phylum level were stored in the bioinformatics data processing software platform used in this study. Uparse (version 7.0.1090) software was used for OTU taxonomic analysis. This software efficiently clusters, annotates, and calculates abundance of sequencing data, providing reliable taxonomic information and relative abundance data for subsequent microbiome studies.

[0028] 2. Microbiome bioinformatics data analysis: (1) Differential microbial community analysis: The Wilcoxon Signed-Rank Test was used to perform a non-parametric test on the relative abundance of the two groups of saliva samples to identify the microbial communities (such as bacterial species, bacterial genera, etc.) with significant differences between the groups. The relative abundance of the two groups of saliva samples and the results of the differential microbial community analysis are shown in Table 1. Table 1

[0029] As can be seen from Table 1, both Bacteroidetes and Deinococcus microorganisms meet the screening condition of "P value < 0.05" and are differential bacterial communities between groups.

[0030] ROC curve analysis: The relative abundance data of differential flora (Bacteroidetes microorganisms) screened out by the Wilcoxon signed-rank test (statistical analysis was performed using SPSS software (version 27.0.1)) were subjected to ROC curve analysis to evaluate the diagnostic efficacy of differential flora as biomarkers. The ROC curve is a broken line graph drawn with the true positive rate TPR (i.e., sensitivity) as the vertical axis and the false positive rate FPR (i.e., 1-specificity or 1-Specificity) as the horizontal axis. The AUC (i.e., area under the curve, generally 0.5 to 1) is used as the evaluation index. The larger the AUC value, the better the biomarker's predictive effect on the disease. The results showed that the AUC curves corresponding to Bacteroidetes microorganisms and Deinococcus microorganisms in saliva samples were as follows: Figure 1 As shown in the figure, the AUC values ​​were 0.623 (95% CI: 0.42-0.77) and 0.569 (95% CI: 0.36-0.75), respectively, both exceeding 0.5. Both Bacteroidetes and Deinococcus microorganisms in oral saliva can be used as marker flora to predict whether schizophrenia patients have social anxiety disorder, among which Bacteroidetes microorganisms have better prediction effect. The AUC curve corresponding to Bacteroidetes microorganisms in saliva samples and their combination with Deinococcus microorganisms (as shown in the figure) Figure 1 ), with AUC values ​​of 0.701 (95% CI: 0.53-0.85). This indicates that the combination of salivary Deinococcus and Bacteroidetes significantly improves the AUC value compared to Bacteroidetes alone, potentially improving the prediction of schizophrenia with social anxiety disorder.

[0031] In addition, the applicant also selected 40 schizophrenia patients from a psychiatric hospital in Fuzhou as research subjects (i.e., the second batch of research subjects) and evaluated them using the same diagnostic criteria as the first batch of research subjects. Among them, there were 23 patients in the schizophrenia with social anxiety group and 17 patients in the schizophrenia without social anxiety group. By conducting on-site surveys and multi-omics studies on the second batch of research subjects, relevant metabolic and microbial marker data were obtained to construct a prediction model for schizophrenia with social anxiety disorder.

[0032] The construction process of the schizophrenia with social anxiety disorder prediction model of the present invention is specifically as follows: 1. On-site survey on adverse childhood experiences: The Adverse Childhood Experiences scale (ACEs) was used to conduct an on-site questionnaire survey on adverse childhood experiences for the second batch of research subjects.

[0033] 2. Collection of multi-omics data: Obtain relevant metabolic and microbial biomarker data through metabolomics and microbiome studies on the second batch of research subjects.

[0034] The steps for obtaining microbiome data are the same as the research methods in the screening process of microbial markers for schizophrenia with social anxiety disorder mentioned above, and will not be repeated here.

[0035] The steps for obtaining metabolomics data include: (1) Sample collection: Peripheral blood samples were collected from the subjects after 2 weeks of follow-up; (2) Metabolite extraction: Take out peripheral blood samples from -80℃ environment, slowly thaw, add 4 times the volume of extraction buffer MeOH / ACN (1:1, v / v), vortex thoroughly and then ultrasonically lyse. Place at -20℃ for precipitation for 1 hour, centrifuge at 4℃, 18000g for 15 minutes, remove protein precipitate, transfer the supernatant to a new centrifuge tube, use a concentrator to drain, add an equal volume of ACN:H2O (1:1, v / v) and ultrasonically re-dissolve. Centrifuge at 4℃, 18000g for 15 minutes, transfer the supernatant to a new centrifuge tube and store at -80℃; (3) LC-MS analysis: The supernatant obtained in step (2) was separated using a Waters ACQUITY UPLC ultra-high performance liquid chromatography system coupled with a Waters ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 mm × 100 mm). The injection volume was 10 µL, the elution was performed at a flow rate of 400 µl / min, and the column temperature was 40°C. Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile-water solution containing 0.1% formic acid. The liquid phase gradient was set as follows: 0-11 min, 5%-90% B; 11.0-12.0 min, 90% B; 12.0-12.1 min, 90%-5% B; 12.1-15.0 min, 5% B. After separation by the ultra-high performance liquid chromatography system, the supernatant was injected into the ESI ion source for ionization; and then analyzed by the timsTOF Pro mass spectrometer. The ion source voltage was set to 1.6 kV, and the peptide precursor ions and their secondary fragments were detected and analyzed using a high-resolution TOF. The mass spectrometer scan range was set to 20–1300 m / z; (4) Mass spectrometry data acquisition: LC-MS mass spectrometry data acquisition was performed using the parallel accumulation serial fragmentation (PASEF) mode; (5) Metabolomics data analysis: The mass spectrometry data of the saliva samples of the second batch of research subjects obtained in step (3) were used to perform differential metabolite analysis (P < 0.05) to screen marker metabolites; The analysis results of the specific adverse childhood experiences scale and the screened metabolomics and microbiome data of the second batch of study subjects in the two groups of patients with schizophrenia with social anxiety disorder and without social anxiety disorder are shown in Table 2.

[0036] Table 2

[0037] Table 2 shows that significant differences were found between the schizophrenia group with social anxiety disorder and the schizophrenia group without social anxiety disorder in the Adverse Childhood Experiences Scale (ACEs) total score, the relative abundance of Bacteroidetes microbes, and 6-hydroxymelatonin and 5-hydroxyindoleacetic acid concentrations. Bacteroidetes microbes were identified as a marker bacterial community through microbiome analysis. 6-hydroxymelatonin and 5-hydroxyindoleacetic acid were identified as marker metabolites through metabolomics analysis.

[0038] 3. Establishment and validation of the nomogram clinical prediction model: (1) Establishment of Nomogram Clinical Prediction Model: R software and RMS package were used to establish Nomogram clinical prediction model based on the above-mentioned marker metabolites, marker flora and total score data of Adverse Childhood Experiences Scale (e.g. Figure 2 The corresponding relationship between the line segment scale and the score line segment scale of each factor index in the Nomogram clinical prediction model is shown in Table 3.

[0039] Table 3

[0040] The corresponding relationship between the risk probability line segment scale and the total score line segment scale of schizophrenia with social anxiety disorder in the Nomogram clinical prediction model is shown in Table 4. Table 4

[0041] The risk probabilities of schizophrenia with social anxiety disorder in Table 4 are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8, which correspond to 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively.

[0042] The fractional line segment scale is a scale with a total scale interval of 0 to 100; The line scale of each factor indicator is divided into equal parts; The total score line segment scale is a scale with a total scale interval of 0 to 200.

[0043] (2) ROC method validation: Receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of the nomogram clinical prediction model. The AUC of the nomogram clinical prediction model for the test diagnosis of social anxiety disorder increased to 0.717 (95% CI: 0.538-0.895), see Figure 4 The closer the AUC value is to 1, the better its performance in predicting schizophrenia and social anxiety disorder. When the AUC is 0.5, the diagnostic model's predictions are no different from random guessing, indicating that the model has no predictive power. Therefore, the models of the present invention all have high accuracy and discrimination, demonstrating strong predictive power.

[0044] (3) Clinical net benefit analysis: Install and load the "RMDA" program package, use multiple groups of biomarkers (including the marker metabolites and the marker flora) and the total score of the Childhood Adverse Experience Scale to predict the net benefit rate of social anxiety disorder in schizophrenia patients as the vertical axis, and the high-risk threshold as the horizontal axis, draw a decision curve, and analyze the predicted net benefit. The decision curve (DCA) analysis results show that when the threshold probability is between 0.01 and 0.99, the Nomogram clinical prediction model can produce better clinical benefits (see Figure 5 ), which has certain clinical significance.

[0045] Furthermore, the predictive model can also include the relative abundance of Bacteroidetes microbes in the saliva of schizophrenia patients, for a total of five factor indicators. Table 1 shows that the combination of Deinococcus and Bacteroidetes microbes significantly improves the AUC value compared to Bacteroidetes microbes alone. Therefore, adding the relative abundance of Bacteroidetes microbes in saliva as a factor indicator to the predictive model will inevitably lead to further improvement in the accuracy of the predictive model in assessing whether schizophrenia is accompanied by social anxiety disorder.

Claims

1. Microbial markers for schizophrenia with social anxiety disorder, characterized by: The microbial marker is Bacteroidetes microorganisms in saliva.

2. A prediction model for schizophrenia with social anxiety disorder, characterized by: A nomogram clinical prediction model was constructed by using the relative abundance of Bacteroidetes microorganisms in the saliva of schizophrenia patients as claimed in claim 1, the total score of the Adverse Childhood Experiences Scale, and the concentration of 6-hydroxymelatonin and 5-hydroxyindoleacetic acid in serum as independent variables for regression analysis.

3. The prediction model for schizophrenia with social anxiety disorder according to claim 2, characterized in that: The unit of the relative abundance of Bacteroidetes microorganisms in saliva is %, the unit of the total score of the Adverse Childhood Experiences Scale is points, and the unit of the concentration of 6-hydroxymelatonin and 5-hydroxyindoleacetic acid is mg / ml. The correspondence between the line segment scale of each factor indicator and the score line segment scale in the Nomogram clinical prediction model is as follows: The fractional line segment scale is a scale with a total scale interval of 0 to 100; The line scale of each factor indicator is divided into equal parts; Factor indicator 1: the total score of the Adverse Childhood Experiences Scale, the total scale interval of its line segment scale is 10 to 32, and the scale interval on the corresponding score line segment scale is 0 to 46; Factor indicator 2: relative abundance of Bacteroidetes microorganisms in saliva, the total scale interval of the line segment scale is 24 to 2, and the scale interval on the corresponding score line segment scale is 0 to 65; Factor indicator 3: 6-hydroxymelatonin concentration in serum, the total scale interval of the line segment scale is 14 to 0, and the scale interval on the corresponding fractional line segment scale is 0 to 100; Factor indicator 4: 5-hydroxyindoleacetic acid concentration in serum, the total scale interval of the line segment scale is 0 to 5000, and the corresponding scale interval on the fractional line segment scale is 0 to 13; The corresponding relationship between the risk probability line scale and the total score line scale of schizophrenia with social anxiety disorder in the Nomogram clinical prediction model is as follows: The total score line segment scale is a scale with a total scale interval of 0 to 200; The risk probabilities of schizophrenia with social anxiety disorder are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8, corresponding to total scores of 40, 50, 57, 64, 70, 76, 83, and 90, respectively.

4. The prediction model for schizophrenia with social anxiety disorder according to claim 2, characterized in that: The factor indicators also include the relative abundance of Bacteroidetes microorganisms in the saliva of schizophrenia patients, with a total of five factor indicators.

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