Oral microbial marker composition and application thereof in preparation of kit for diagnosing high altitude headache

Abstract

The invention discloses an oral microbial marker composition and application thereof in preparation of a kit for diagnosing high altitude headache, and particularly relates to the technical field of molecular biotechnology detection. A diagnosis model constructed on the basis of oral flora finds that the provided oral flora marker combination can specifically recognize patients with high altitude headache from plateau healthy people. The kit is used for predicting the high-altitude headache illness condition and clinical auxiliary diagnosis, is good in detection specificity and high in sensitivity, can indicate the condition of the high-altitude headache tissue microbial flora, and can guide the clinical diagnosis mode of the high-altitude headache and the adjustment of medication so as to reduce the probability of misdiagnosis and missed diagnosis in clinical diagnosis. The oral flora marker has important application in the aspects of screening and diagnosis of high altitude headache and the like, so that health damage and life risks caused by diseases are reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of molecular biological technology detection, and relates to a combination of oral flora markers for diagnosing high-altitude headache, and further relates to application and a kit thereof. BACKGROUND

[0002] High-altitude headache (HAH) is also known as high-altitude headache, which is a neurological disease occurring in high-altitude environment. According to the definition of the International Headache Society (IHS), the disease occurs in people living in low-altitude areas for a long time, and shows mild to severe dull pain or pressure pain in the bilateral frontal and temporal regions within 24 hours after the people quickly enter the high-altitude area above 2500 m without adapting to the new environment (Yin X, Li Y, Ma Y, et al. Thickened retinal nerve fiber layers associated with high-altitude headache. Frontiers in Physiology, 2022, 13: 864222.). The incidence of HAH is quite high, and according to the ascending speed of the individual into the high-altitude area and the specific altitude, the incidence can reach 25% to 90% (Luks, A.M. and P.H. Hackett, Medical Conditions and High-Altitude Travel. The New England journal of medicine, 2022. 386(4): p. 364-373). The harmfulness of HAH cannot be ignored, which not only seriously affects the quality of life of people who quickly enter the high-altitude area and reduces their work ability or causes disability, and more seriously, if HAH cannot be effectively controlled and treated in time, it can even progress into high-altitude pulmonary edema (HAPE) and high-altitude cerebral edema (HACE) with high mortality, which endangers the life safety (Pham, K., K. Parikh and E.C. Heinrich, Hypoxia and Inflammation: Insights From High-Altitude Physiology. Frontiers in physiology, 2021. 12: p. 676782).

[0003] Currently, the diagnosis of HAH mainly relies on professional neurologists according to the intensity and characteristics of the relevant clinical symptoms exhibited by patients. The diagnosis is based on the International Classification of Headache Disorders, 3rd edition (ICHD-3) formulated by the International Headache Society (IHC). Specifically, the diagnosis of HAH requires the following conditions to be met: (1) the nature of the headache meets condition (3); (2) the patient ascends to an altitude of more than 2500 m and at least meets two of the following three conditions: (a) the occurrence of headache is related in time to the ascent of altitude, (b) meets one or two of the following two conditions: a) headache is significantly aggravated with continuous ascent of altitude, b) can be relieved within 24 hours of leaving the high altitude environment; (3) the headache at least meets two of the following three conditions: (a) bilateral headache, (b) mild to moderate headache, (c) force, movement, stretching, cough and / or bending movements will aggravate the headache (Headache Classification Committee of the International Headache Society (IHS). The International Classification of Headache Disorders. 3rd edition, Cephalalgia, 2018: 1-211). The diagnosis of HAH relies on the labor-intensive process of headache specialists, on the one hand, it is not conducive to the prevention and treatment of HAH to conduct large-scale population screening and disease monitoring of HAH. On the other hand, China's highland areas are also lack of professional headache specialists, which is not conducive to the correct diagnosis of HAH and the differentiation from other headache diseases, and seriously threatens the prevention and control of HAH. Therefore, it is urgent to find objective auxiliary diagnostic markers of HAH to meet the needs of early screening, accurate diagnosis and effective prevention and treatment of HAH in large-scale people who suddenly advance to high altitude.

[0004] Oral microbiota is a collection of microorganisms that colonize in human oral cavity, and performs physiological functions in the form of biofilm. When the ecological relationship between the oral microbiota and the host is imbalanced, it can induce various oral infectious diseases, which seriously endanger oral health. More importantly, oral microbiota is closely related to the incidence of oral tumors, diabetes, rheumatoid arthritis, cardiovascular disease, and premature birth and other systemic diseases; therefore, the structural characteristics of oral microbiota can be used as an emerging important marker for early warning of oral and systemic health status (Xin X, Junzhi H, Xuedong Z. Oral microbiota: a promising predictor of human oral and systemic diseases. 《West China Journal of Stomatology》. 2015 Dec 1;33(6):555-60.). In recent years, the relationship between oral microorganisms and headache diseases has also gradually become a research hotspot. A study on oral microbiota in migraine patients has shown that there are significant differences in the composition of oral microbiota between migraine patients and healthy people, and nitrate, nitrite and nitric oxide reducing bacteria can be used as auxiliary biomarkers for the diagnosis of migraine (Jiang W, Wang T, Liu C, Deng M, Ren X, Wang F, Zhang Y, Yu X, Yao L, Wang Y. A 16S rRNA gene sequencing based study of oral microbiota in migraine patients in China. 《Bioengineered.》2021 Jan 1;12(1):2523-33.). However, there is no report on the relationship between changes in oral microbiota and HAH, and no oral microbiota marker for the auxiliary diagnosis of HAH has been found. SUMMARY

[0005] Therefore, the purpose of the present application is to provide an oral microbiota marker combination for screening and early diagnosis of high-altitude headache (HAH), which can detect the oral microorganisms of the testee by non-invasive and non-intrusive means, quickly judge whether the testee has HAH, and has high application value in clinical practice.

[0006] In order to achieve the above purpose, the present application provides the following technical solutions: The present application provides a combination of oral microorganism markers for diagnosing high altitude headache (HAH), comprising Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas.

[0007] Further, the representative sequences of the 7 oral microorganisms are shown in SEQ ID No. 1~ SEQ ID No. 7.

[0008] The second aspect of the present application provides the use of the combination of oral microorganism markers in a HAH diagnostic reagent or kit, comprising Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas.

[0009] Further, the use specifically refers to detecting the relative abundance of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas in the oral cavity of the testee, and distinguishing HAH patients from healthy people on the plateau.

[0010] Further, the oral microorganism in the oral cavity of the testee is detected by detecting saliva, oral swab, throat swab samples of the testee.

[0011] Further, the relative abundance of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas is used to construct a joint diagnostic index equation to distinguish HAH patients from healthy people on the plateau.

[0012] Further, the joint diagnostic index judgment equation is: logit(P)=28.8*(Fusobacterium relative abundance)-3.0*(Rothia relative abundance)+121.8*(Treponema relative abundance)-658.3*(Lachnoanaerobaculum relative abundance)-475.5*(Kingella relative abundance)+40.0*(Actinomyces relative abundance)+23.4*(Porphyromonas relative abundance).

[0013] The third aspect of the present application is to provide a diagnostic kit for HAH, comprising reagents for detecting Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas in the oral cavity.

[0014] The present application has the following advantages: The present application finds the difference in oral flora composition structure between HAH patients and highland healthy controls, and performs machine learning method analysis (random forest analysis) to explore important characteristic oral flora of HAH. The present application finds that the oral flora of HAH patients changes significantly compared with the highland healthy control group. Further, based on the characteristic oral flora, a diagnostic model for HAH is constructed to find a new method for diagnosing HAH and perform early diagnosis and large-scale population screening.

[0015] The diagnostic model constructed based on oral flora in the present application finds that the oral flora marker including Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas can specifically identify HAH patients, and the oral flora marker has great diagnostic potential for HAH patients. This non-invasive operation method can early diagnose and large-scale population screen HAH patients. The abundance of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas in oral saliva is detected to screen and early diagnose HAH, guide disease prevention and treatment, and thus reduce the health damage and life risk caused by HAH. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to make the purposes, technical solutions and beneficial effects of the present application clearer, the present application is described below with the help of the following drawings.

[0017] The structures, proportions, sizes and the like shown in the present specification are only used to cooperate with the content disclosed in the present specification, to be understood and read by those skilled in the art, and do not have technical substantive significance, and any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effects and purposes that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0018] Figure 1 A Venn diagram of oral flora of HAH patients and plateau healthy people provided by the embodiment of the present application; Figure 2 A comparison chart of oral flora alpha diversity of HAH patients and plateau healthy people provided by the embodiment of the present application, A is a comparison chart of chao1 index of oral flora alpha diversity of two groups, B is a comparison chart of shannon index of oral flora alpha diversity of two groups, and C is a comparison chart of simpson index of oral flora alpha diversity of two groups; Figure 3 A comparison chart of oral flora beta diversity of HAH patients and plateau healthy people provided by the embodiment of the present application, A is a PCoA_binary_jaccard analysis chart of oral flora beta diversity of two groups, and B is a PCoA unweighted unifrac analysis chart of oral flora beta diversity of two groups; Figure 4 An analysis chart of oral flora door level composition of HAH patients and plateau healthy people provided by the embodiment of the present application; Figure 5 An analysis chart of oral flora genus level composition of HAH patients and plateau healthy people provided by the embodiment of the present application; Figure 6 An analysis chart of oral flora marker flora of HAH patients and plateau healthy people provided by the embodiment of the present application; Figure 7 An evolutionary branch chart of oral flora marker flora of HAH patients and plateau healthy people provided by the embodiment of the present application; Figure 8 An analysis chart of HAH characteristic bacteria (genus level) mined based on random forest analysis provided by the embodiment of the present application; Figure 9The diagnostic model constructed based on the top 7 HAH characteristic bacteria ranked by the importance measurement standard score provided by the embodiments of the present application is used for diagnosing HAH. DETAILED DESCRIPTION

[0019] The preferred technical solutions of the embodiments of the present application will be clearly and completely described in the embodiments of the present application combined with the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. The experimental methods not specified in the embodiments are usually performed according to the conventional conditions or the conditions suggested by the manufacturers.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0021] Example 1 Difference analysis of oral microflora of patients with high-altitude headache and highland healthy controls First, 22 patients with high-altitude headache (HAH) and 19 highland healthy people (Non-HAH) were compared and analyzed by 16s rRNA sequencing of oral flora to study the differences in oral flora composition between the two groups. (The clinical data of the oral flora specimens of the subjects are shown in Table 1).

[0022] Table 1 Clinical data of oral flora specimens

[0023] Note: HAH: patients with high-altitude headache; Non-HAH: highland healthy people; VAS: visual analogue scale (VAS) is used for pain assessment and is widely used worldwide. The basic method is to use a 10 cm long moving scale, one side of which is marked with 10 scales, and the two ends are "0" end and "10" end, respectively. 0 represents no pain, and 10 represents the most severe pain that is unbearable. Normally distributed data is expressed as mean ± standard deviation, and non-normally distributed data is expressed as median (interquartile range).

[0024] 1. Materials Saliva samples were collected from the people who were going to enter the plateau (about 4300 m above sea level) by plane quickly from the plain (about 308 m above sea level). Within 24 hours of entering the plateau, the HAH and Non-HAH populations were distinguished by professional headache specialists according to the HAH international diagnostic standard "International Classification of Headache Disorders (ICHD-3) "diagnostic system (Headache Classification Committee of the International Headache Society (IHS). The International Classification of Headache Disorders. 3rd edition, Cephalalgia, 2018: 1-211).

[0025] 2. Method: (1) Saliva sample collection steps: In the morning, 2 hours before saliva collection, fasting, water and mouthwash. Use a 20 ml centrifuge tube to collect 10 ml of saliva from the above-mentioned personnel within 24 hours of entering the plateau, number, and store at -80℃ for standby.

[0026] (2) DNA extraction and 16S rRNA gene PCR amplification The genomic DNA of the sample was extracted using MagPure Soil DNA LQ Kit (Magan, D6356-02) kit, and the detailed extraction method was carried out according to the kit instructions. The concentration and purity of DNA were detected by NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis, and the extracted DNA was stored at -20℃. The extracted genomic DNA was used as a template, and Tks Gflex DNA Polymerase high-fidelity enzyme kit (Takara, R060B) with Barcode-specific primers was used for PCR amplification of bacterial 16S rRNA gene. The universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3') were used to amplify the V3-V4 variable region of 16S rRNA gene for bacterial community diversity analysis.

[0027] 1) The first round of PCR reaction system and parameters are shown in Table 2 and Table 3 Table 2 First round of PCR reaction system

[0028] Table 3 First round of PCR reaction parameters

[0029] 2) The second round of PCR reaction system and parameters are shown in Table 4 and Table 5 Table 4 Second round of PCR reaction system

[0030] Table 5 Second round of PCR reaction parameters

[0031] (3) Library construction and sequencing The PCR amplification products were detected by agarose gel electrophoresis. Then purified using AMPure XP beads magnetic beads, and the purified products were used as templates for the second round of PCR amplification. And purified again using magnetic beads, and the purified second round products were quantified by Qubit, and then adjusted to a certain concentration for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform (Illumina, USA), and 250 bp double-end reads were generated.

[0032] (4) Bioinformatics and statistical analysis The original data is in FASTQ format. After the data is downloaded, the raw data sequence is first cut off the primer sequence using Cutadapt software. Then using DADA2 software and its default parameters (Callahan BJ, Mcmurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 2016), the qualified double-end raw data in the previous step is filtered, denoised, spliced and dechimered according to the QIIME 2 default parameters for quality control analysis to obtain the representative sequence (Amplicon Sequence Variant, ASV). Using QIIME 2 software (Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 2019 37: 852-857.) to pick up each ASV, and all ASV sequences are compared and annotated with Silva (version 138) database (https: / / www.arb-silva.de / documentation / release-138 / ). The species comparison and annotation use q2-feature-classifier software (https: / / github.com / qiime2 / q2-feature-classifier.git) default parameters for analysis. QIIME 2 software is used for α and β diversity analysis. The α diversity of the sample is evaluated using α diversity including Simpson index and Shannon index. The unweighted Unifrac distance matrix calculated by R is used for unweighted Unifrac principal coordinate analysis (PCoA) to evaluate the β diversity of the sample. Based on the R package, the Kruskal Wallis statistical method is used for difference analysis, and LEfSe is used for difference analysis of species abundance spectrum. As Figure 1 As shown in the Venn diagram, the results showed that 2098 microbial features were detected in the HAH group, 1765 microbial features were detected in the Non-HAH group, and 976 microbial features were common to both groups.

[0033] 3、Results: (1) Comparison of clinical data between HAH patients and healthy people on the plateau There was no significant difference in age, body mass index, heart rate, and other aspects between the two groups. The blood oxygen saturation of the HAH group was significantly lower than that of the healthy people on the plateau (see Table 1).

[0034] (2) Analysis of oral flora diversity of HAH patients and healthy people on the plateau Chao1, Simpson, and Shannon indices were used to evaluate the alpha diversity of oral flora in the two groups, and the comparison results are shown in Figure 2 There was no significant difference in alpha diversity of oral flora between the two groups. The beta diversity of oral flora in the two groups was evaluated by PCoA_binary_jaccard and PCoAunweighted unifrac, and the comparison results are shown in Figure 3 The results showed that there was a significant difference in beta diversity between the two groups, indicating that the overall oral flora in the two groups had changed significantly.

[0035] (3) Analysis of oral flora species composition at the door level of HAH patients and healthy people on the plateau Figure 4 The door level composition analysis chart of oral flora provided by the embodiments of the present application is shown in the figure; among them, Firmicutes, Proteobacteria, Bacteroidota, Fusobacteriota, Actinobacteriota, Campilobacterota, Spirochaetota, Patescibacteria, Desulfobacterota, Elusimicrobiota, Myxococcota, Deferribacterota, and Sva0485 are the main composition of the oral flora in the two groups, and the average proportion is more than 99%.

[0036] (4) Analysis of oral flora species composition at the genus level of HAH patients and healthy people on the plateau Figure 5 The genus level composition analysis chart of oral flora provided by the embodiments of the present application is shown in the figure; among them, Streptococcus, Haemophilus, Prevotella, Neisseria, Fusobacterium, Leptotrichia, Bergeyella, Alloprevotella, Granulicatella, Aggregatibacter, Porphyromonas, Rothia, Gemella, Capnocytophaga, and Campylobacter are the main composition of the oral flora in the two groups, and the average proportion is more than 88%.

[0037] (5) Oral microbiota signature analysis of HAH patients Figure 6 For LEfSe analysis of the oral microbiota signature of the two groups, LEfSe analysis showed that (log10LDA>2 and p<0.05 as the standard), the oral microbiota signature of the HAH group was: g__Lentimicrobium, g__Rikenellaceae_RC9_gut_group, g__Absconditabacteriales__SR1, f__Bacteroidaceae, c__Gracilibacteria, o__Sphingobacteriales, g__Peptostreptococcus, f__Lentimicrobiaceae, g__Muribaculaceae, g__Moraxella, g__Bacteroides, g__Fusobacterium, f__Muribaculaceae, g__Helicobacter, o__Pseudomonadales, g__Filifactor, f__Fusobacteriaceae, p__Patescibacteria, f__Peptostreptococcaceae, o__Absconditabacteriales__SR1, f__Absconditabacteriales__SR1_, g__Faecalibaculum, f__Moraxellaceae and f__Helicobacteraceae, and the signature bacteria phylogenetic tree is shown in Figure 6. Figure 7 .

[0038] LEfSe analysis showed that the oral flora marker flora of the Non-HAH group was: g__Lachnoanaerobaculum, o__Micrococcales, g__Oscillibacter, p__Actinobacteriota, g__Oribacterium, g__Leptotrichia, g__Actinomyces, f__Actinomycetaceae, f__Caulobacteraceae, c__Actinobacteria, o__Lactobacillales, f__Micrococcaceae, g__Rothia, g__Veillonella, g__Kingella, o__Actinomycetales and o__Caulobacterales, and the marker flora evolution branch tree is shown in Figure 1. Figure 7 .

[0039] Example 2: Study on diagnostic efficiency of oral flora markers for patients with high-altitude headache (HAH) Based on the above research, this embodiment intends to find the characteristic bacteria (genus level) markers for diagnosing high-altitude headache (HAH) by machine learning method (random forest analysis) on the oral flora data obtained in Example 1, and construct a joint diagnostic index by binary Logestic regression analysis, and finally evaluate the diagnostic efficiency of the characteristic bacteria joint diagnostic index for HAH through ROC curve. In order to further evaluate the diagnostic value of oral flora markers for screening HAH, an independent verification population is established according to the standard of Example 1.

[0040] 1. Statistical analysis method Machine learning method (random forest analysis) is based on RandomForest_R language package. Binary Logestic regression analysis is performed by using statistical software SPSS 19.0 to construct a joint diagnostic index. ROC curve analysis is performed by MedCalc 19.20. Kruskal Wallis statistical method is used for comparison between groups for difference analysis. When P <0.05, it is considered that there is a statistically significant difference. Among them, AUC reflects the diagnostic efficiency (AUC=0.5, no diagnostic efficiency; 0.5

[0041] 2. Results analysis (1) Random forest analysis method to find characteristic bacteria for diagnosing HAH Figure 8For HAH oral biomarker random forest analysis, the random forest analysis showed that 30 feature bacteria were found, and according to the importance measure standard score (MeanDecreaseGini) ranking, 7 feature bacteria were selected according to the MeanDecreaseGini>0.85 selection standard: Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas. The sequences of the feature bacteria are shown in Table 6.

[0042] Table 6 7 oral feature bacteria representative sequences Oral flora SEQ Fusobacterium TGGGGAATATTGGACAATGGACCAAGAGTCTGATCCAGCAATTCTGTGTGCACGATGAAGTTTTTCGGAATGTAAAGTGCTTTCAGTTGGGAAGAAAAAAATGACGGTACCAACAGAAGAAGTGACGGCTAAATACGTGCCAGCAGCCGCGGTAATACGTATGTCACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGTAGCAAAC SEQ ID No. 1 Rothia TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTGTTAGCAGGGAAGAAGAGAGATTGACGGTACCTGCAGAGAAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGGCCGGGGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGAAGCGAAAGCATGGGGAGCGAAC SEQ ID No. 2 Treponema CTAAGAATATTCCGCAATGGACGAAAGTCTGACGGAGCGACGCCGCGTGGATGAAGAAGGCTGAAAAGTTGTAAAATCCTTTTGTTGATGAAGAATAAGGATAAGAGGGAATGCTTATCTGATGACGGTAATCAGCGAATAAGCCCCGGCTAATTACGTGCCAGCAGCCGCGGTAACACGTAAGGGGCGAGCGTTGTTCGGAATTATTGGGCGTAAAGGGCATGTAGGCGGTTATGTAAGCCTGATGTGAAATCCTAGAGCTTAACTCTAGAATAGCATTGGGTACTGTATAACTTGAATTACGGAAGGGAAACTGGAATTCCAAGTGTAGGGGTGGAATCTGTAGATATTTGGAAGAACACCGGTGGCGAAGGCGGGTTTCTGGCCGATAATTGACGCTGAGATGCGAAAGTGTGGGGATCGAAC SEQ ID No. 3 Lachnoanaerobaculum TGGGGAATATTGGACAATGGGGGAAACCCTGATCCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCTGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGACGGCGAAGCAAGTCTGAAGTGAAATGCATGGGCTCAACCCATGAATTGCTTTGGAAACTGTTTGGCTTGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGAGGCGAAGGCGGCTTACTGGACGACAACTGACGTTGAGGCTCGAAGGCGTGGGGAGCAAAC SEQ ID No. 4 Kingella TGGGGAATTTTGGACAATGGGCGCAAGCCTGATCCAGCCATGCCGCGTGTCTGAAGAAGGCCTTCGGGTTGTAAAGGACTTTTGTTAGGGAAGAAAAGGATAGTGTTAATACCATTATCTGCTGACGGTACCTAAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGAGCGCAGACGGTTTATTAAGCAAGATGTGAAATCCCCGAGCTTAACTTGGGAACTGCGTTTTGAACTGGTAAGCTAGAGTATGTCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGATAATACTGACGTTCATGCTCGAAAGCGTGGGTAGCAAAC SEQ ID No. 5 Actinomyces TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGAGGGATGGAGGCCTTCGGGTTGTGAACCTCTTTCGCCAGTGAAGCAGGCCTGTCCCTTGTGGGTGGGTTGACGGTAGCTGGATAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGCTGGTCGCGTCTGTCGTGAAATCCTCTGGCTTAACTGGGGGCTTGCGGTGGGTACGGGCCGGCTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAAC SEQ ID No. 6 Porphyromonas TGAGGAATATTGGTCAATGGGCGAGAGCCTGAACCAGCCAAGTCGCGTGAAGGATGACTGTCTTATGGATTGTAAACTTCTTTTGTAGGGGAATAAAGAGGGGCACGTGTGCCTCAGTGAATGTACCCTACGAATAAGCATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTGTTAAGTCAGCGGTGAAATCTAGGAGCTTAACTCCTAAATTGCCATTGATACTGGCGGGCTTGAGTGTAGATGAGGTAGGCGGAATGCGTGGTGTAGCGGTGGAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAGGTACAACTGACGCTGAAGCACGAAAGCGTGGGTATCAAAC SEQ ID No. 7 Note: Fusobacterium: Fusobacterium; Rothia: Rothia; Treponema: Treponema; Lachnoanaerobaculum: Lachnoanaerobaculum; Kingella: Kingella; Actinomyces: Actinomyces; Porphyromonas: Porphyromonas.

[0043] (2) Construction of combined diagnostic indicators based on 7 feature bacteria The relative abundance of the two groups of 7 feature bacteria was input into SPSS19.0 software, and a combined diagnostic indicator was constructed by binary Logestic regression analysis, and the combined diagnostic indicator equation was: logit(P)=28.8×(Fusobacterium relative abundance)−3.0×(Rothia relative abundance)+121.8×(Treponema relative abundance)−658.3×(Lachnoanaerobaculum relative abundance)−475.5×(Kingella relative abundance)+40.0×(Actinomyces relative abundance)+23.4×(Porphyromonas relative abundance).

[0044] The relative abundance value distribution of the 7 feature bacteria in HAH and highland healthy control groups is shown in Table 7.

[0045] Table 7 HAH patient and healthy control oral bacterial flora biomarker abundance expression data

[0046] Note: HAH: patients with high altitude headache; Non-HAH: healthy people on the plateau; Fusobacterium: Fusobacterium; Rothia: Rothia; Treponema: Treponema; Lachnoanaerobaculum: Lachnoanaerobaculum; Kingella: Kingella; Actinomyces: Actinomyces; Porphyromonas: Porphyromonas; Normally distributed data is expressed as mean ± standard deviation, and non-normally distributed data is expressed as median (interquartile range).

[0047] (3) Analysis of the diagnostic efficiency of the combined diagnostic index of 7 characteristic bacteria for HAH In another independent verification cohort, the diagnostic efficiency of the oral flora marker was verified, and the clinical data of the verification population is shown in Table 8, the relative abundance value distribution of the 7 characteristic bacteria in the two groups of the verification cohort is shown in Table 9, the working characteristic curve of the oral flora marker is shown in Figure 9 Table 10, and the ROC curve is shown in Table 10. The results show that the combined diagnostic index of 7 characteristic bacteria has very accurate diagnostic value for HAH (AUC = 0.909), the sensitivity is 81.82%, and the specificity is 94.74%, indicating that the diagnostic efficiency of the combined index is strong, and the sensitivity and specificity are good. At the same time, the diagnostic discriminant value is given, that is, when logit (P) > 0.598, it can be judged as high altitude headache (HAH).

[0048] Table 8 Clinical data of the verification population

[0049] Note: HAH: patients with high altitude headache; Non-HAH: healthy people on the plateau; VAS: visual analogue scale (VAS) is used for pain assessment, which is widely used in the world. The basic method is to use a 10 cm long moving scale, one side of which is marked with 10 scales, and the two ends are "0" end and "10" end, respectively. 0 represents no pain, and 10 represents the most severe pain that is difficult to endure; normally distributed data is expressed as mean ± standard deviation, and non-normally distributed data is expressed as median (interquartile range).

[0050] Table 9 Oral flora marker abundance expression data of the verification population in two groups

[0051] Note: HAH: patients with high altitude headache; Non-HAH: healthy people on the plateau; Fusobacterium: Fusobacterium; Rothia: Rothia; Treponema: Treponema; Lachnoanaerobaculum: Lachnoanaerobaculum; Kingella: Kingella; Actinomyces: Actinomyces; Porphyromonas: Porphyromonas; Normally distributed data is expressed as mean ± standard deviation, and non-normally distributed data is expressed as median (interquartile range).

[0052] Table 10: Receiver operating characteristic curve data of oral flora markers

[0053] Note: HAH: patients with high altitude headache; Non-HAH: healthy people on the plateau; Oral flora marker combination: combination of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas.

[0054] The inventors found significant differences in oral flora between HAH and Non-HAH groups through oral flora 16S sRNA detection analysis and bioinformatics statistics. Further, machine learning method was used to screen characteristic bacteria for diagnosing HAH, and a combined diagnostic index of 7 characteristic bacteria and its calculation equation were constructed. The results showed that the combined index of 7 characteristic bacteria had very accurate value for the diagnosis of HAH. This invention helps early diagnosis and large-scale population screening of HAH, and thus reduces the health damage to the plateau population caused by high incidence of HAH.

[0055] Finally, it should be pointed out that the above preferred embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present application.

Claims

1. A combination of oral microbial markers for diagnosing high altitude headache disease, characterized by, The oral microorganism marker combination is composed of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas.

2. Use of the oral microorganism marker combination of claim 1 in the preparation of a reagent or kit for diagnosing high-altitude headache.

3. Use according to claim 2, characterized in that, The application is specifically for detecting the relative abundance of oral microorganism markers in the subject, and the reagent is for detecting the relative abundance of each oral microorganism.

4. Use according to claim 3, characterized in that, The relative abundance of each oral microorganism is mainly up-regulated in patients with high-altitude headache.

5. Use according to claim 3, characterized in that, The reagent or kit is used for detecting saliva, oral swab, throat swab samples of the subject.

6. Use according to claim 3, characterized in that, The relative abundance of Fusobacterium, Rothia, Treponema, Lachnoanaerobaculum, Kingella, Actinomyces and Porphyromonas is used to construct a joint diagnostic index equation to distinguish patients with high-altitude headache from healthy people on the plateau.

7. Use according to claim 6, characterized in that, The joint diagnostic index equation is logit (P) = 28.8 x (relative abundance of Fusobacterium) - 3.0 x (relative abundance of Rothia) + 121.8 x (relative abundance of Treponema) - 658.3 x (relative abundance of Lachnoanaerobaculum) - 475.5 x (relative abundance of Kingella) + 40.0 x (relative abundance of Actinomyces) + 23.4 x (relative abundance of Porphyromonas).

8. A diagnostic kit for high altitude headache disease, characterized by, The reagent includes the oral microorganism marker combination of claim 1.

9. The diagnostic kit according to claim 8, characterized in that, The reagent is for detecting the relative abundance of each oral microorganism.

10. The diagnostic kit according to claim 8, characterized in that, The reagent is a primer, probe or sequencing reagent for measuring each oral microorganism in the oral microorganism marker combination.

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