Biomarkers for diagnosing hypertension in children, kit and application of biomarkers

Abstract

The invention relates to the field of medical detection, in particular to a group of biomarkers for diagnosing hypertension in children, a kit and application of the biomarkers. According to the method, tongue coating / intestinal tract samples of obese children hypertensive patients, obese children and healthy people are collected, metagenome sequencing is carried out, sequencing data statistics is carried out by using bioinformatics, tongue coating / intestinal tract florae related to diseases are found, the tongue coating / intestinal tract florae and disease information are integrated, a flora biomarker combination is obtained, and the method is applied to diagnosis and treatment of the hypertensive patients, the obese children and the healthy people. The hypertensive patients of obese children can be detected to the maximum extent through the binary classification prediction model constructed through the combination.

Description

Technical Field

[0001] The present invention relates to the field of medical detection, and in particular, to a group of biomarkers, a kit and applications thereof for diagnosing hypertension in children. Background Art

[0002] Childhood hypertension refers to a condition in which blood pressure in children and adolescents is higher than normal. With changes in lifestyle, the incidence of childhood hypertension has gradually increased and has become an important public health issue in my country. Childhood hypertension is divided into primary hypertension and secondary hypertension based on its 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 research on risk assessment and early diagnosis of childhood hypertension will help to formulate targeted prevention and intervention measures to alleviate the burden of cardiovascular disease in adults in my country from the source.

[0003] Currently, the diagnosis of hypertension in children is mainly through blood pressure measurement. Based on the blood pressure measurement results, the doctor may request further examinations, such as urine analysis, blood tests, electrocardiograms, or echocardiograms, to rule out the possibility of secondary hypertension. Usually, if a child's blood pressure is consistently above the 95th percentile, it can be diagnosed with hypertension. Early detection and intervention are crucial to prevent long-term complications.

[0004] The intestinal flora is closely related to the host's metabolism, immune regulation, and the occurrence and development of diseases. Intestinal flora imbalance is closely related to the occurrence of obesity and hypertension. In particular, the role of intestinal flora in the pathogenesis of cardiovascular diseases such as hypertension has received increasing attention. Existing studies have shown that beneficial bacteria are reduced in children with hypertension, while harmful bacteria that increase inflammation and immune responses are increased. A meta-study on the effect of probiotics on blood pressure found that compared with the control group, after taking probiotics, arterial systolic blood pressure SBP was significantly reduced by 2.18 mmHg and diastolic blood pressure DBP was reduced by 1.07 mmHg. Therefore, the risk of hypertension in adulthood can be reduced by targeting children's intestinal flora to regulate blood pressure levels in early life.

[0005] Oral microorganisms can be transferred to the intestines through a variety of pathways, including blood-borne and food-borne. Existing literature shows that about 40% of the flora exist in both the oral cavity and the intestine, and 59% of these 40% of the flora are ectopically transmitted between the oral cavity and the intestine. On the one hand, oral-intestinal flora transmission can participate in the pathogenesis of the disease in a synergistic or cooperative manner. On the other hand, it may also bring positive effects, such as improving intestinal health through probiotics. Research on oral flora as a health indicator is developing rapidly. However, no research has shown that hypertension can be diagnosed by oral flora. Summary of the invention

[0006] In order to fill the gap in the prior art, the present invention collects tongue coating / intestinal samples from obese children with hypertension, obese children and healthy people, performs metagenomic sequencing and uses bioinformatics to perform statistics on sequencing data, discovers tongue coating / intestinal flora related to the disease, integrates tongue coating / intestinal flora with disease information, and predicts obese children with hypertension to the greatest extent. Specifically, the present invention provides the following technical solutions:

[0007] The first aspect of the present invention provides a marker of intestinal / oral flora for children with hypertension, wherein the flora marker is Streptococcus mitis, Fusobacterium mortiferum, Fusobacterium varium, Veillonella dispar, Veillonella atypica, klebsiella pneumoniae, Ruminococcusgnavus, Alistipes onderdonkii, Alistipes finegoldii, Alistipes shahii, oscillibacter valericigenes, Rum bicirculans, Ruminococcus champanellensis, Ruthenibacterium lactatiformans, intestinimonas butyriciproducens and / or Akkermansia muciniphila.

[0008] The second aspect of the present invention provides the use of the microbiota marker described in the first aspect of the present invention in the preparation of a product for diagnosing hypertension in children.

[0009] In one embodiment, the product comprises a reagent for detecting a marker of intestinal / oral flora.

[0010] In a preferred embodiment, the reagent is a primer, a probe, an antisense oligonucleotide, an aptamer or an antibody specific to the bacterial flora marker.

[0011] A third aspect of the present invention provides a system for predicting hypertension in children, comprising:

[0012] 1. A nucleic acid sample separation unit, used to separate bacterial nucleic acid samples from the test object;

[0013] 2. A detection unit, used to perform relative abundance detection on the separated bacterial nucleic acid sample to obtain the abundance value result of the intestinal / oral marker described in the first aspect of the present invention;

[0014] 3. A data processing unit, which imports the relative abundance values ​​of the intestinal / oral markers obtained into the children's hypertension risk warning system to obtain a predicted value;

[0015] 4. A result determination unit, used to compare the predicted value obtained by the data processing unit with the set diagnosis value.

[0016] By comparing the obtained relative abundance value of the intestinal / oral marker with a predetermined critical value, it is determined whether the subject individual is a hypertensive individual or a healthy individual.

[0017] The fourth aspect of the present invention provides a product for diagnosing hypertension in children, the product comprising a reagent for detecting the abundance of the intestinal flora marker described in the first aspect of the present invention.

[0018] In one embodiment, the reagent includes a specific primer, probe, antisense oligonucleotide, aptamer or antibody for detecting the bacterial flora marker. Further, the product also includes a reagent for extracting microbial genomic DNA, microbial protein, and bacterial components.

[0019] The fifth aspect of the present invention provides the use of the microbiota marker described in the first aspect of the present invention in constructing a computational model for predicting childhood hypertension.

[0020] Compared with the prior art, the present invention has the following significant advantages and beneficial effects:

[0021] The present invention discovered for the first time that 16 microorganisms are associated with childhood hypertension, and their abundances showed significant differences between childhood hypertension patients and healthy people, and between childhood hypertension and obese children. ROC curve analysis showed that they have high specificity and sensitivity as detection variables. Therefore, the 16 microorganisms can be used as detection markers for early warning of childhood hypertension. Using 16 microorganisms as detection markers is completely non-invasive and highly accurate.

[0022] The 16 microbial detection markers are:

[0023] Marker 1:

[0024] Streptococcus mitis: Streptococcus mitis is an important member of VGS and is part of the normal microbiota of human skin as well as the oropharynx, gastrointestinal tract, and female genital tract. Although VGS is generally considered to have low pathogenic potential in immunocompetent individuals, in patients with immunocompromise or other risk factors, VGS can cause invasive diseases such as bloodstream infections, pneumonia, endocarditis, enteritis, and meningitis. In the literature, pediatric cases of meningitis caused by S. mitis are mainly reported in patients with leukemia, lymphoma, or neutropenia, while meningitis and other serious diseases caused by S. mitis are considered rare in healthy children. (PMID: 37508318)

[0025] Marker 2:

[0026] Fusobacterium mortiferum: Fusobacterium; significantly enriched in the intestines of hypertensive patients with insufficient sleep (PMID:33785906); increased abundance in the intestines of children with diarrhea (PMID:28767339).

[0027] Marker 3:

[0028] Fusobacterium varium: can be used in combination with other bacterial species to differentiate diabetic nephropathy from patients with type 2 diabetes (PMID: 35863004).

[0029] Marker 4:

[0030] Veillonella dispar: The counts of Veillonella and Streptococcus in the oral cavity are closely related to the recovery and progression of patients with recurrent aphthous stomatitis (RAS), especially in middle-aged patients. (PMID: 34167010) Dental caries remains the most common chronic disease in children, and Veillonella dispar has a higher abundance in children with dental caries (PMID: 33248211). A decrease in pH to between pH 5.5 and 4.5 may enrich potential cariogenic species while leaving health-related species relatively unaffected. Further decreasing the pH (<pH 4.5) can not only enhance the competitiveness of dental pathogens but also inhibit the growth and metabolism of non-caries-related species. Veillonella dispar is the organism with the highest number at low pH values (PMID: 9745120). According to the levels of Streptococcus mutans in children's saliva (HS / LS: high / low Streptococcus mutans) and caries experience, they are divided into four clinical groups. Disease-related species, such as Veillonella dispar, Streptococcus spp., and Prevotella spp., are significantly increased in the HS group and may contribute to the progression of dental caries together with Streptococcus mutans (PMID: 35250921). There is an important association between the increase in Veillonella and poor oral hygiene in children, and it tends to increase in the poor oral hygiene group (PMID: 28934367). The presence and bacterial load of taxa related to oral health in the elderly (PMID: 33186726).

[0031] Marker 5:

[0032] Veillonella atypica: It is an early colonizing member of dental plaque biofilms (PMID: 19542285); in addition, oral Veillonella, Veillonella atypica, Veillonella denticariosi, Veillonella dispar, Veillonella parvula, Veillonella rogosae, and Veillonella tobetsuensis are known as early colonizers of oral biofilm formation. It is the main cultivable genus of Veillonella on the surface of the tongues of healthy adults (Veillonella atypica, Veillonella dispar, and Veillonella rogosae) (PMID: 18582335)

[0033] Marker 6:

[0034] klebsiella pneumoniae: Enrichment of Klebsiella pneumoniae is a direct contributor to elevated blood pressure and the pathogenesis of hypertension (PMID: 36259407).

[0035] Marker 7:

[0036] Ruminococcus gnavus: Ruminococcus gnavus was significantly more abundant in hypertensive women (PMID: 37073724). Compared with the hypertensive group, the relative abundance of Ruminococcus gnavus in the intestinal tract of stroke patients was significantly reduced.

[0037] Marker 8:

[0038] Alistipes onderdonkii: In the HT-T2DM group, CAG-177sp003538135 and CAG-127sp900319515 were found to be associated with Alistipes onderdonkii. (PMID:37689641)

[0039] Marker 9:

[0040] Alistipes finegoldii: Members of the phylum Bacteroidetes, represented by Alistipes finegoldii, are prominent anaerobic Gram-negative residents of the gut microbiome. Reduced abundance of Alistipes finegoldii is a major feature of pediatric IBD (PMID: 30102706); a potential broad-spectrum target in obese individuals (PMID: 36564713)

[0041] Marker 10:

[0042] Alistipes shahii: a potential broad-spectrum target for obese people (PMID: 36564713)

[0043] Marker 11:

[0044] Oscillibacter valericigenes: The expression of Oscillibacter valericigenes in the T2DM group was higher than that in the healthy group (PMID: 36428566)

[0045] Marker 12:

[0046] bicirculans: Low-carbohydrate, high-fat, weight-loss diets induce changes in the human gut microbiota: an increase in Ruminococcus bicirculans.

[0047] Marker 13:

[0048] Ruminococcus champanellensis: Ruminococcus champanellensis sp. nov., a cellulose-degrading bacterium from the human gut microbiota (PMID: 21357460)

[0049] Marker 14:

[0050] Ruthenibacterium lactatiformans: increased abundance is associated with cardiovascular risk (PMID:38060843); increased abundance in feces of patients with periodontitis (PMID:38528960)

[0051] Marker 15:

[0052] intestinimonas butyriciproducens: a SCFA-producing bacterium that increases and upregulates SCFAs glucagon-like peptide-1 (GLP-1) and peptide tyrosine yrosine (PYY) to alleviate symptoms of type 2 diabetes (PMID: 32663709).

[0053] Marker 16: [Probiotics]

[0054] Akkermansia muciniphila: present in the human gut microbiota from infancy and gradually increases in adulthood. The potential impact of A. muciniphila abundance has been studied in major cardiovascular diseases (PMID: 36153618), and there is an inverse association between Akkermansia muciniphila abundance and overweight, obesity, untreated type 2 diabetes or hypertension; (PMID: 31263284). BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 is the alpha diversity result in Example 1 of the present invention;

[0057] Figure 2 This is the β diversity result in Example 1 of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] Example 1 Screening of tongue coating flora markers associated with childhood hypertension and establishment of an early warning model

[0060] (1) Research subjects: Tongue coating samples were collected from 100 obese children with normal blood pressure (CO) and 100 obese children with hypertension (HTN). The clinical statistical data are shown in Table 1.

[0061] Table 1 Clinical data

[0062]

[0063]

[0064] (2) Metagenomic sequencing

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

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

[0067] PCR amplification and product purification: According to the designated sequencing region, specific primers with barcodes or fusion primers with misplaced bases were synthesized. PCR was performed using TransGen AP221-02: TransStart Fastpfu DNA Polymerase; all samples were performed according to the formal experimental conditions, with 3 replicates for each sample. The PCR products of the same sample were mixed and tested by 2% agarose gel electrophoresis. The PCR products were cut and recovered using the AxyPrepDNA gel recovery kit (AXYGEN) and eluted with Tris-HC1; and tested by 2% agarose gel electrophoresis.

[0068] Library construction: The qualified DNA samples were randomly broken into fragments of about 350 bp in length by ultrasonic crusher, and the entire library was constructed through end repair, tailing, linker addition, purification, PCR amplification and other steps. After the library construction was completed, the size of each insert fragment in the library was detected using agilent 2100. If it met expectations, the effective concentration of the library was accurately quantified using the q-PCR method. When the library was qualified and the effective concentration of the library was >3nmol / L, it was sequenced on the Illumina PE150 platform.

[0069] Metagenome sequencing and Metagenome assembly: Readfq (V8) was used to perform quality control and host filtering on the raw data, and fragments with low-quality bases exceeding 40 bp with a quality threshold of ≤38, fragments with N base length values ​​≥10 bp, and fragments with overlaps with the adapter for more than 15 bp were removed to obtain effective and high-quality fragments for subsequent analysis to ensure the accuracy and reliability of the information analysis results. Then, the effective data after quality control was assembled into Metagenome, including the assembly analysis of effective data by SOAPdenovo (V2.04) software and the mixed assembly of 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 the open reading frames of each sample and the fragments of mixed assembly length ≥500bp, and CD-HIT software was used to remove redundancy in the prediction results. Bowtie2 software was used to match the valid data of each sample to the initial gene catalog, and the number of fragments of the gene in each sample was calculated. Only genes with matching fragments ≥2 were considered to be related to the sample. Based on the number of fragments and gene length in the match, the abundance information of each gene in each sample was calculated.

[0071] Species annotation and functional database annotation: The species annotation was performed using DIAMOND software to compare each gene with the bacterial, fungal, archaeal and viral sequences extracted from the NCBI NR database. For each sequence comparison result, the result with evalue ≤ minimum evalue × 10 was selected, and the lowest common ancestors (LCA) algorithm was used to determine the species annotation information of the sequence. Common functional data annotation was performed using DIAMOND software to compare each gene with the eggNOG functional database. For each sequence comparison result, the best match result was selected for subsequent functional analysis.

[0072] Diversity analysis: The richness and diversity of the microbial community were reflected by the diversity analysis of a single sample (Alpha diversity). QIIME calculated the beta diversity distance matrix, and the R language vegan package performed non-metric multidimensional scaling (NMDS) analysis and mapping.

[0073] Differential bacterial flora analysis: LEfSe differential discriminant analysis was used to obtain differential bacterial 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] α diversity results: The α diversity of intestinal microorganisms in children with hypertension was lower than that in children with normal blood pressure ( Figure 1 ).

[0077] β diversity results: There were differences in β diversity between hypertensive children and normotensive children ( Figure 2 ).

[0078] The results of species difference analysis showed that there were 16 different bacterial communities with significant differences between the two groups (Table 2).

[0079] Table 216 Differential bacteria

[0080]

[0081]

[0082] (4) ROC analysis verifies the accuracy of the screened biomarker and early warning binary classification model

[0083] R software completes the calculation of specificity and sensitivity and the drawing of ROC curve. The software first calculates the threshold of the actual measurement value, and then calculates the true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN+FP), sensitivity (true positive rate) = TP / (TP+FN), and the ROC curve can be constructed by 1-specificity and sensitivity. The integral of the ROC curve is AUC. In order to calculate the specificity and sensitivity of a certain indicator, the Youden coefficient (Youden index = sensitivity + specificity-1) is first calculated. The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of a certain indicator.

[0084] The receiver operating characteristic curve test (ROC curve) was analyzed by the relative abundance values ​​of single or multiple microbial markers to obtain the cutoff value (optimal cutoff value). From the analysis results (Table 3), when 16 bacteria were combined, the AUC was 0.9765, the optimal cutoff value was 0.1825, the sensitivity was 92.73%, and the specificity was 94.12%.

[0085] Table 3 AUC values ​​of 16 different bacteria for early warning of hypertension in children

[0086]

[0087] Example 21 Validation of the effectiveness of 16 biomarkers for the diagnosis of hypertension in children

[0088] In order to further verify the effectiveness of the early warning binary classification model, we re-collected tongue coating samples from 100 children with hypertension and 100 children with normal blood pressure, detected the expression levels of 16 bacteria in the samples, and randomly selected 50 children with hypertension and 50 children with normal blood pressure as the validation set. The ROC curve was drawn according to the abundance of each strain in the sample, and the area under the curve (AUC) was calculated to evaluate the classification effect and diagnostic value of the 16 differential bacteria for childhood hypertension. The results are shown in Table 4.

[0089] According to the data in Table 4, the results of the validation set and the aforementioned sample set data are highly consistent, indicating that the model has a good prediction effect.

[0090] Table 4

[0091]

[0092] In addition, we also compared the model diagnosis with the clinical expert diagnosis for the remaining 50 children with hypertension and 50 children with normal blood pressure. The comparison showed that the diagnostic accuracy of the model was as high as 98%, indicating that the model has excellent accuracy in diagnosing hypertension in children.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A marker of intestinal / oral flora for hypertension in children, 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, Rum bicirculans, Ruminococcus champanellensis, Ruthenibacterium lactatiformans, intestinimonas butyriciproducens and / or Akkermansia muciniphila.

2. Use of the microbiota marker as claimed in claim 1 in the preparation of a product for diagnosing hypertension in children.

3. The use according to claim 2, characterized in that The product comprises a reagent for detecting intestinal / oral flora markers.

4. The use according to claim 3, characterized in that The reagent is a primer, a probe, an antisense oligonucleotide, an aptamer or an antibody that is specific to the bacterial flora marker.

5. A system for predicting hypertension in children, comprising: (1) a nucleic acid sample separation unit, used to separate a bacterial nucleic acid sample from a test object; (2) a detection unit, used to perform relative abundance detection on the separated bacterial nucleic acid sample to obtain the abundance value result of the intestinal / oral marker as claimed in claim 1; (3) a data processing unit, used to import the relative abundance values ​​of the intestinal / oral markers obtained as claimed in claim 1 into a children's hypertension risk warning system to obtain a predicted value; (4) a result determination unit, used to compare the predicted value obtained by the data processing unit with the set diagnosis value; By comparing the obtained relative abundance value with a predetermined critical value, it is determined 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 a reagent for detecting the abundance of the intestinal / oral marker as claimed in claim 1.

7. The product according to claim 6, characterized in that The reagents include specific primers, probes, antisense oligonucleotides, aptamers or antibodies for detecting the bacterial flora markers.

8. The product according to claim 7, characterized in that The product also includes reagents for extracting microbial genomic DNA, microbial proteins, and bacterial components.

9. Use of the microbiota marker as described in claim 1 in constructing a computational model for predicting childhood hypertension.

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