Group of biomarkers for diagnosing hypertension in children, test kit, and use thereof

Abstract

Provided are a group of biomarkers for diagnosing hypertension in children, a test kit, and a use thereof. By means of collecting tongue coating / intestinal samples of obese children with hypertension, obese children, and healthy people, performing metagenomic sequencing, and using bioinformatics to calculate statistics on sequencing data, disease-related tongue / intestinal flora is found, and the tongue / intestinal flora is integrated with disease information to obtain a combination of flora biomarkers. A binary classification prediction model constructed by means of said combination can maximally detect obese children with hypertension.

Description

A set of biomarkers for diagnosing hypertension in children, a kit and application thereof

[0001] Cross-reference to Related Applications

[0002] This application claims the benefit of Chinese application No. 2024112929046, filed on September 14, 2024. The application No. 2024112929046 is hereby incorporated by reference in its entirety. TECHNICAL FIELD

[0003] The present application relates to the field of medical detection, in particular, the present application relates to a set of biomarkers for diagnosing hypertension in children, a kit and application thereof. BACKGROUND

[0004] Childhood hypertension refers to a condition in which blood pressure is higher than normal in children and adolescents. With changes in lifestyle, the incidence of childhood hypertension has gradually increased, becoming an important public health problem in China. Childhood hypertension is divided into primary hypertension and secondary hypertension in terms of causes. Primary hypertension is usually related to genetic factors, unhealthy diet (high salt, high fat diet), lack of exercise, obesity and other lifestyle factors. Secondary hypertension is often caused by other diseases, such as kidney disease, heart problems, endocrine disorders, etc. Childhood hypertension not only causes target organ damage (such as left ventricular hypertrophy, increased carotid intima-media thickness, etc.), but also increases the risk of cardiovascular disease in adulthood. Therefore, strengthening the research on risk assessment and early diagnosis of childhood hypertension is conducive to the development of targeted prevention and intervention measures to alleviate the burden of adult cardiovascular disease from the source.

[0005] Currently, the main method for diagnosing childhood hypertension is through blood pressure measurement. Based on the blood pressure measurement results, the doctor may require further tests, such as urine analysis, blood tests, electrocardiogram or echocardiogram, to rule out the possibility of secondary hypertension. Generally, if a child's blood pressure is consistently above the 95th percentile, they can be diagnosed with hypertension. Early detection and intervention are crucial for preventing long-term complications.

[0006] The gut microbiota is closely related to the metabolism, immune regulation and development of diseases in the host. Dysbiosis of gut microbiota is closely related to the occurrence of obesity and hypertension. In particular, the role of gut microbiota in the pathogenesis of cardiovascular diseases such as hypertension has received increasing attention. Existing research has shown that there is a decrease in beneficial bacteria and an increase in harmful bacteria that increase inflammation and immune response in children with hypertension. A Meta study on the effect of probiotics on blood pressure found that compared with the control group, after consuming probiotics, the systolic blood pressure (SBP) was significantly reduced by 2.18 mmHg and the diastolic blood pressure (DBP) was reduced by 1.07 mmHg. Therefore, by targeting the gut microbiota of children, the blood pressure level in early life can be adjusted to reduce the risk of hypertension in adulthood.

[0007] Oral microbes can be transferred to the gut through multiple routes, including hematogenous spread and foodborne routes. It has been reported that about 40% of the flora exist in both the oral cavity and the gut, and 59% of the 40% flora exist in the oral cavity and the gut. The oral-gut flora transmission can participate in the pathogenesis of diseases in a synergistic or cooperative manner, and can also bring positive effects, such as improving intestinal health through probiotics. The research on oral flora as a health indicator is rapidly developing. However, no study has shown that hypertension can be diagnosed through oral flora. SUMMARY

[0008] To fill the gap in the prior art, the present application collects tongue / gut samples of obese children with hypertension, obese children and healthy people, performs metagenomic sequencing and uses bioinformatics to analyze the sequencing data, finds disease-related tongue / gut flora, and integrates tongue / gut flora and disease information to maximize the prediction of obese children with hypertension. Specifically, the present application provides the following technical solutions:

[0009] The first aspect of the present application provides a child hypertension intestinal / oral flora marker, and the flora marker is Streptococcus mitis, Fusobacterium mortiferum, Fusobacterium varium, Veillonella dispar, Veillonella atypica, klebsiella pneumoniae, Ruminococcus gnavus, Alistipes onderdonkii, Alistipes finegoldii, Alistipes shahii, oscillibacter valericigenes, Bicirculans, Ruminococcus champanellensis, Ruthenibacterium lactatiformans, intestinimonas butyriciproducens and / or Akkermansia muciniphila.

[0010] The second aspect of the present application provides the use of the flora marker of the first aspect of the present application in the preparation of a product for diagnosing child hypertension.

[0011] In one embodiment, the product comprises a reagent for detecting the intestinal / oral flora marker.

[0012] In a preferred embodiment, the agent is a primer, probe, antisense oligonucleotide, aptamer or antibody specific for the microbiota marker.

[0013] The third aspect of the present application provides a system for predicting hypertension in children, comprising:

[0014] 1. A nucleic acid sample separation unit for separating a microbiota nucleic acid sample from a detection subject;

[0015] 2. A detection unit for detecting the relative abundance of the separated microbiota nucleic acid sample to obtain the abundance value result of the gut / oral marker of the first aspect of the present application;

[0016] 3. A data processing unit for importing the obtained relative abundance value of the gut / oral marker into a hypertension risk warning system for children to obtain a prediction value;

[0017] 4. A result determination unit for comparing the prediction value obtained by the data processing unit with a set diagnosis value.

[0018] By comparing the obtained relative abundance value of the gut / oral marker with a predetermined critical value, it is determined whether the subject individual is a hypertensive individual or a healthy individual.

[0019] The fourth aspect of the present application provides a product for diagnosing hypertension in children, the product comprising an agent for detecting the abundance of the gut microbiota marker of the first aspect of the present application.

[0020] In an embodiment, the agent comprises a primer, probe, antisense oligonucleotide, aptamer or antibody specific for detecting the microbiota marker. Further, the product further comprises an agent for extracting microbial genomic DNA, microbial protein, and bacterial component.

[0021] The fifth aspect of the present application provides the use of the microbiota marker of the first aspect of the present application in constructing a computational model for predicting hypertension in children.

[0022] Compared with the prior art, the present application has the following remarkable advantages and beneficial effects:

[0023] The present application first discovers that 16 microorganisms are related to hypertension in children, and their abundance shows significant differences between children with hypertension and healthy people, children with hypertension and obese children. ROC curve analysis shows that they have high specificity and sensitivity as detection variables, and therefore can be used as detection markers for children with hypertension. Using 16 microorganisms as detection markers, it is completely non-invasive and highly accurate.

[0024] The 16 microorganism detection markers are:

[0025] Marker 1:

[0026] Streptococcus mitis: S. mitis is an important member of the VGS and is part of the normal microbiota of human skin and the oropharynx, gastrointestinal tract, and female genital tract. Although VGS are generally considered to have low pathogenic potential in immunocompetent individuals, in patients with immunocompromised or other risk factors, VGS can cause invasive disease, such as bloodstream infection, pneumonia, endocarditis, enteritis, and meningitis. In the literature, pediatric cases of meningitis caused by S. mitis have been primarily found in patients with leukemia, lymphoma, or neutropenia, and meningitis and other serious disease caused by S. mitis in healthy children are considered rare. (PMID: 37508318)

[0027] Marker 2:

[0028] Fusobacterium mortiferum: Fusobacterium; significantly enriched in the gut of hypertensive patients with insufficient sleep (PMID: 33785906); increased abundance in the gut of children with diarrhea (PMID: 28767339).

[0029] Marker 3:

[0030] Fusobacterium varium: can be used in combination with other flora to differentiate diabetic nephropathy from type 2 diabetic patients (PMID: 35863004).

[0031] Marker 4:

[0032] Veillonella dispar: Counts of Veillonella and Streptococcus in the oral cavity are closely related to recovery and progression of recurrent aphthous stomatitis (RAS) patients, especially in middle-aged patients. (PMID: 34167010) Dental caries remains the most common chronic disease in children, Veillonella dispar is more abundant in children with dental caries (PMID: 33248211). A decrease in pH to between pH 5.5 and 4.5 can enrich potential cariogenic species while leaving health-associated species relatively unaffected. Further reduction in pH (<pH 4.5) can not only enhance the competitiveness of dental pathogens but also inhibit the growth and metabolism of non-caries-associated species. Veillonella dispar is the most abundant organism at low pH (PMID: 9745120). According to the levels of Streptococcus mutans in the saliva of children (HS / LS: high / low Streptococcus mutans) and dental caries experience, four clinical groups were classified, disease-associated species such as Veillonella dispar, Streptococcus spp., and Prevotella spp. were significantly increased in the HS group and can contribute to dental caries progression along with Streptococcus mutans (PMID: 35250921). There is an important association between Veillonella increase and poor oral hygiene status in children, which tends to be higher in the poor oral hygiene group (PMID: 28934367). The presence and bacterial load of taxonomic groups associated with oral health in older adults (PMID: 33186726).

[0033] Marker 5:

[0034] Veillonella atypica: is an early colonizer of dental plaque biofilm (PMID: 19542285); in addition, Veillonella, Veillonella atypica, Veillonella denticariosi, Veillonella dispar, Veillonella parvula, Veillonella rogosae, and Veillonella tobetsuensis are referred to as early colonizers of oral biofilm formation. Is the major cultivable Veillonella species (Veillonella atypica, Veillonella dispar, and Veillonella rogosae) on the tongue surface of healthy adults (PMID: 18582335)

[0035] Marker 6:

[0036] Klebsiella pneumoniae: Enrichment of K. pneumoniae is a direct contributor to the pathogenesis of elevated blood pressure and hypertension (PMID: 36259407).

[0037] Marker 7:

[0038] Ruminococcus gnavus: R. gnavus was significantly more abundant in hypertensive women (PMID: 37073724). Relative to the hypertensive group, intestinal abundance was significantly lower in stroke patients.

[0039] Marker 8:

[0040] Alistipes onderdonkii: CAG-177 sp003538135 and CAG-127 sp900319515 were found to be associated with A. onderdonkii in the HT-T2DM group (PMID: 37689641)

[0041] Marker 9:

[0042] Alistipes finegoldii: Members of the Bacteroidetes phylum, represented by A. finegoldii, are prominent anaerobic, gram-negative residents of the gut microbiome. A. finegoldii was found to be decreased in abundance in pediatric IBD (PMID: 30102706); is a potential broad-spectrum target in obese populations (PMID: 36564713)

[0043] Marker 10:

[0044] Alistipes shahii: is a potential broad-spectrum target in obese populations (PMID: 36564713)

[0045] Marker 11:

[0046] oscillibacter valericigenes: O. valericigenes was higher in the T2DM group than in the healthy group (PMID: 36428566)

[0047] Marker 12:

[0048] bicirculans: Low-carbohydrate, high-fat, weight-reducing diets induce changes in the human gut microbiota: B. bicirculans increased.

[0049] Marker 13:

[0050] Ruminococcus champanellensis: Ruminococcus champanellensis sp. nov., a cellulose-degrading bacterium from the human gut microbiota (PMID: 21357460)

[0051] Marker 14:

[0052] Ruthenibacterium lactatiformans: increased abundance associated with cardiovascular risk (PMID: 38060843); increased abundance in feces of patients with periodontitis. (PMID: 38528960)

[0053] Marker 15:

[0054] intestinimonas butyriciproducens: SCFA-producing bacteria that increase upregulation of SCFA- glucagon-like peptide-1 (GLP-1) and peptide tyrosine tyrosine (PYY) to alleviate symptoms of type 2 diabetes (PMID: 32663709).

[0055] Marker 16:

probiotic

[0056] Akkermansia muciniphila: present in the human gut microbiota from infancy and gradually increasing in adulthood. Potential effects of A. muciniphila abundance have been studied in major cardiovascular diseases (PMID: 36153618), negative correlation between Akkermansia muciniphila abundance and overweight, obesity, untreated type 2 diabetes, or hypertension; (PMID: 31263284). BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the principles of the application. In the drawings:

[0058] Figure 1 is the alpha diversity results in Example 1 of the present application;

[0059] Figure 2 is the beta diversity results in Example 1 of the present application. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present application will be described herein below with reference to the accompanying drawings. It is to be understood that the preferred embodiments described herein are merely illustrative and explanatory of the application and are not intended to limit the application.

[0061] Example 1 Screening of tongue coating microbial community markers associated with childhood hypertension and establishment of early warning model

[0062] (1) Research object: Collect 100 cases of obese children with normal blood pressure (CO) and 100 cases of obese children with hypertension (HTN) tongue coating samples, and the clinical statistical data are shown in Table 1.

[0063] Table 1 Clinical data

[0064] (2) Metagenomic sequencing

[0065] DNA extraction: Genomic DNA was extracted using a genomic DNA extraction kit, and the operation steps were performed according to the instructions.

[0066] DNA sample purity and concentration determination: Genomic DNA was detected by 1% agarose gel electrophoresis.

[0067] PCR amplification and product purification: According to the specified sequencing region, specific primers with barcode were synthesized, or fusion primers with staggered bases were synthesized. PCR used TransGen AP221-02: TransStart Fastpfu DNA Polymerase; all samples were performed according to the formal experimental conditions, 3 repeats for each sample, the PCR products of the same sample were mixed and detected by 2% agarose gel electrophoresis, AxyPrep DNA gel recovery kit (AXYGEN company) was used to cut and recover the PCR products, and Tris-HCl was used for elution; 2% agarose electrophoresis detection.

[0068] Library construction: The qualified DNA samples were randomly broken into fragments with a length of about 350 bp by ultrasonic disrupter, and the construction of the whole library was completed through the steps of end repair, tailing, adapter addition, purification, and PCR amplification. After the library construction was completed, the size of each insert fragment in the library was detected using agilent 2100, and after meeting the expectations, the effective concentration of the library was accurately quantified by q-PCR method. When the library was qualified, the effective concentration of the library was > 3 nmol / L, and sequencing was performed on the Illumina PE150 platform.

[0069] Metagenome sequencing and assembly: The raw data was quality controlled and host filtered by Readfq (V8), removing low-quality bases with quality threshold ≤38, removing fragments with N-base length ≥10 bp, and removing fragments with more than 15 bp overlap with the adapter. The effective high-quality fragments were used for subsequent analysis to ensure the accuracy and reliability of the information analysis results. Then, the effective data after quality control was assembled for Metagenome, including assembly analysis of the effective data by SOAPdenovo (V2.04) software and mixed assembly of the unused fragments of each sample by SOAPdenovo (V2.04) / MEGAHIT (v1.0.4-beta) software.

[0070] Gene prediction and abundance analysis: MetaGeneMark was used to predict open reading frames for each sample and fragments with a length of ≥500 bp. CD-HIT software was used to remove redundancy from the prediction results. Bowtie2 software was used to align the effective data of each sample to the initial gene catalog, and the number of aligned fragments of each gene in each sample was calculated. Only genes with matching fragments ≥2 were considered related to the sample. Based on the number of aligned fragments and the length of the gene, the abundance information of each gene in each sample was calculated.

[0071] Species annotation and functional database annotation: Species annotation used DIAMOND software to align each gene with the bacterial, fungal, archaeal, and viral sequences extracted from the NR database of NCBI. For the alignment results of each sequence, the results with evalue ≤ minimum evalue x 10 were selected, and the lowest common ancestors (LCA) algorithm was used to determine the species annotation information of the sequence. Common functional data annotation used DIAMOND software to align each gene with the eggNOG functional database. For the alignment results of each sequence, the best matching results were selected for subsequent functional analysis.

[0072] Diversity analysis: Single-sample diversity (Alpha diversity) analysis reflects the richness and diversity of microbial communities. QIIME calculates the beta diversity distance matrix, and the R language vegan package performs non-metric multidimensional scaling (NMDS) analysis and plotting.

[0073] Differential flora analysis: LEfSe differential discriminant analysis obtains differential flora (screening criteria: P < 0.05, LDA > 3).

[0074] (3) Results

[0075] Species diversity analysis showed that there were significant differences in α diversity and β diversity.

[0076] Alpha diversity results: The alpha diversity of intestinal microorganisms in children with hypertension is lower than that in children with normal blood pressure (Figure 1).

[0077] Beta diversity results: There is a difference in beta diversity between children with hypertension and children with normal blood pressure (Figure 2).

[0078] Through species difference analysis results, it is found that there are 16 different bacterial groups with significant differences between the two groups (Table 2).

[0079] Table 2 16 different bacteria

[0080] (4) ROC analysis to verify the accuracy of the screened biomarkers and early warning binary classification model

[0081] R software is used to calculate specificity and sensitivity and draw ROC curve. The software first calculates the threshold value of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold value. The specificity (true negative rate) = TN / (TN+FP), and the sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by 1-specificity and sensitivity, and the integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of a certain index, the Youden coefficient (Youden index = sensitivity + specificity - 1) is calculated first. The specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of the certain index.

[0082] The relative abundance values of single or multiple microbial markers are used for receiver operating characteristic curve (ROC curve) analysis to obtain the cutoff value (optimal cutoff value). From the analysis results (Table 3), when 16 bacteria are combined, the AUC is 0.9765, the optimal cutoff value is 0.1825, the sensitivity is 92.73%, and the specificity is 94.12%.

[0083] Table 3 AUC values of 16 different bacteria for early warning of childhood hypertension

[0084] Example 2 Verification of the effectiveness of 16 biomarkers for childhood hypertension diagnosis

[0085] In order to further verify the effectiveness of the early warning binary classification model, 100 tongue fur samples of hypertensive children and 100 tongue fur samples of normal blood pressure children were re-collected, the expression amounts of 16 bacteria in the samples were detected, 50 samples of hypertensive children and 50 samples of normal blood pressure children were randomly selected as a verification set, the ROC curve was drawn according to the abundance of each strain in the samples, and the area under the curve AUC was calculated to evaluate the classification effect and diagnostic value of 16 differential bacteria on children's hypertension, and the results are shown in Table 4.

[0086] According to the data in Table 4, the results of the verification set and the aforementioned sample set data have strong consistency, indicating that the prediction effect of the model is good.

[0087] Table 4

[0088] In addition, we also compared the model diagnosis and clinical expert diagnosis of the remaining 50 samples of hypertensive children and 50 samples of normal blood pressure children, and the comparison showed that the diagnosis accuracy of the model was as high as 98%, indicating that the model has excellent accuracy in diagnosing children's hypertension.

[0089] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A child hypertension gut / oral flora marker characterized in that, The microbial community markers are Streptococcus mitis, Fusobacterium mortiferum, Fusobacterium varium, Veillonella dispar, Veillonella atypica, klebsiella pneumoniae, Ruminococcus gnavus, Alistipes onderdonkii, Alistipes finegoldii, Alistipes shahii, oscillibacter valericigenes, bicirculans, Ruminococcus champanellensis, Ruthenibacterium lactatiformans, intestinimonas butyriciproducens, and / or Akkermansia muciniphila.

2. Use of the bacterial community marker of claim 1 in the manufacture of a product for diagnosing hypertension in children.

3. Use according to claim 2, wherein the compound is ###0002### The product comprises reagents for detecting the intestinal / oral bacterial community marker.

4. The use according to claim 3, wherein the compound is ###0002### The reagents are primers, probes, antisense oligonucleotides, aptamers or antibodies specific to the bacterial community marker.

5. A system for predicting hypertension in children, comprising: (1) a nucleic acid sample separation unit for separating a bacterial community nucleic acid sample from a test subject; (2) a detection unit for detecting the relative abundance of the separated bacterial community nucleic acid sample to obtain the abundance value results of the intestinal / oral marker of claim 1; (3) a data processing unit for importing the obtained relative abundance value of the intestinal / oral marker of claim 1 into a hypertension risk warning system for children to obtain a prediction value; (4) a result determination unit for comparing the prediction value obtained by the data processing unit with a set diagnostic value; The obtained relative abundance value is compared with the predetermined critical value to determine whether the subject individual is a hypertensive individual or a healthy individual.

6. A product for diagnosing hypertension in children, characterized in that The product comprises reagents for detecting the intestinal / oral marker of claim 1.

7. The product of claim 6, wherein, The reagents comprise primers, probes, antisense oligonucleotides, aptamers or antibodies specific to the bacterial community marker.

8. The product of claim 7, wherein, The product further comprises reagents for extracting microbial genomic DNA, microbial proteins, and bacterial components.

9. Use of the bacterial community marker of claim 1 in constructing a computational model for predicting hypertension in children.

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