A set of biomarkers for diagnosing hypertension in children, kits and applications thereof

Abstract

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

Description

Technical Field

[0001] This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. Background Technology

[0002] Childhood hypertension refers to blood pressure levels higher than normal in children and adolescents. With changing lifestyles, the incidence of childhood hypertension is gradually increasing, becoming a significant public health issue in my country. Childhood hypertension is categorized into primary and secondary hypertension based on its causes. Primary hypertension is typically associated with lifestyle factors such as genetics, unhealthy diets (high-salt, high-fat diets), lack of exercise, and obesity. Secondary hypertension is often caused by other diseases, such as kidney disease, heart problems, and endocrine disorders. Childhood hypertension not only causes target organ damage (such as left ventricular hypertrophy and increased carotid intima-media thickness) but also increases the risk of cardiovascular disease in adulthood. Therefore, strengthening research on risk assessment and early diagnosis of childhood hypertension is beneficial for developing targeted prevention and intervention measures, thereby alleviating the cardiovascular burden on adults in my country at its source.

[0003] Currently, the primary method for diagnosing hypertension in children is through blood pressure measurement. Based on the measurement results, doctors may order further tests, such as urinalysis, blood tests, electrocardiogram (ECG), or echocardiogram, to rule out the possibility of secondary hypertension. Generally, hypertension is diagnosed if a child's blood pressure consistently falls above the 95th percentile. Early detection and intervention are crucial for preventing long-term complications.

[0004] The gut microbiota is closely related to host metabolism, immune regulation, and the development of diseases. Gut microbiota dysbiosis is closely associated with obesity and hypertension. In particular, the role of the gut microbiota in the pathogenesis of cardiovascular diseases such as hypertension is receiving increasing attention. Existing studies have shown that beneficial bacteria are reduced and harmful bacteria that increase inflammation and immune responses are increased in children with hypertension. A meta-analysis on the effects of probiotics on blood pressure found that, compared with the control group, probiotic intake significantly reduced systolic blood pressure (SBP) by 2.18 mmHg and diastolic blood pressure (DBP) by 1.07 mmHg. Therefore, the risk of developing hypertension in adulthood may be reduced by targeting the gut microbiota of children to regulate blood pressure levels in early life.

[0005] Oral microbiota can be transferred to the gut via multiple pathways, including blood transmission and foodborne routes. Existing literature indicates that approximately 40% of the oral microbiota coexists in both the oral cavity and the gut, and of these, 59% exhibit ectopic transmission between the oral cavity and the gut. Oral-gut microbiota transmission can participate in disease pathogenesis through synergistic or cooperative mechanisms; conversely, it may also bring positive effects, such as improving gut health through probiotics. Research on oral microbiota as a health indicator is rapidly developing. However, currently, no studies have shown that oral microbiota can be used to diagnose hypertension. Summary of the Invention

[0006] To fill the gaps in existing technologies, this invention collects tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals. Metagenomic sequencing and statistical analysis of the sequencing data using bioinformatics are then performed to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, this invention can predict the likelihood of hypertension in obese children to the greatest extent possible. Specifically, this invention provides the following technical solution:

[0007] The first aspect of this invention provides a gut / oral flora biomarker for children with hypertension, wherein the flora biomarker is 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.

[0008] A second aspect of the present invention provides the application of the microbial biomarkers described in the first aspect of the present invention in the preparation of products for diagnosing hypertension in children.

[0009] In one embodiment, the product comprises reagents for detecting gut / oral flora markers.

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

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

[0012] 1. Nucleic acid sample separation unit, used to separate microbial nucleic acid samples from the test subject;

[0013] 2. A detection unit for performing relative abundance detection on isolated bacterial nucleic acid samples to obtain the abundance value results of the intestinal / oral biomarkers described in the first aspect of the present invention;

[0014] 3. The data processing unit imports the relative abundance values ​​of the acquired intestinal / oral biomarkers into the pediatric hypertension risk early warning system to obtain predicted values;

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

[0016] By comparing the relative abundance values ​​of gut / oral biomarkers obtained with predetermined threshold values, individuals can be identified as either hypertensive or healthy.

[0017] A 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 gut microbiota markers described in the first aspect of the present invention.

[0018] In one embodiment, the reagent includes primers, probes, antisense oligonucleotides, aptamers, or antibodies specific for detecting the microbial biomarkers. Furthermore, the product also includes reagents for extracting microbial genomic DNA, microbial proteins, and cell components.

[0019] The fifth aspect of the present invention provides the application of the microbial biomarkers described in the first aspect of the present invention in constructing a computational model for predicting hypertension in children.

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

[0021] This invention is the first to discover 16 microorganisms associated with childhood hypertension. Their abundance showed significant differences between children with hypertension and healthy individuals, as well as between children with hypertension and obese children. ROC curve analysis demonstrated high specificity and sensitivity as detection variables. Therefore, these 16 microorganisms can be used as detection biomarkers for early warning of childhood hypertension. Using these 16 microorganisms as detection biomarkers is completely non-invasive and highly accurate.

[0022] The 16 microbial biomarkers are:

[0023] Marker 1:

[0024] Streptococcus mitis: Streptococcus mitis is an important member of the VGS (Various Germs and Gastrointestinal Species), and is part of the normal microbiota of human skin, oropharynx, gastrointestinal tract, and female reproductive tract. While VGS is generally considered to have low pathogenicity potential in immunocompetent individuals, it can cause invasive diseases such as bloodstream infections, pneumonia, endocarditis, enteritis, and meningitis in immunocompromised patients or those with other risk factors. In the literature, pediatric cases of meningitis caused by S. mitis are primarily seen in patients with leukemia, lymphoma, or neutropenia, while meningitis and other serious illnesses caused by S. mitis are considered rare in healthy children. (PMID:37508318)

[0025] Marker 2:

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

[0027] Marker 3:

[0028] Fusobacterium varium: can be used in combination with other bacterial groups to differentiate diabetic nephropathy in 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 associated with 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 the saliva of children (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 group with poor oral hygiene. (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 culturable 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: The enrichment of Klebsiella pneumoniae is a direct contributor to the pathogenesis of elevated blood pressure and hypertension (PMID: 36259407).

[0035] Marker 7:

[0036] Ruminococcus gnavus: Ruminococcus abundance was significantly higher in hypertensive women (PMID: 37073724). Intestinal abundance was significantly lower in stroke patients compared to the hypertensive group.

[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 Bacteroidetes phylum, represented by Alistipes finegoldii, are prominent anaerobic Gram-negative residents of the gut microbiome. A key characteristic of childhood IBD is reduced abundance of Alistipes finegoldii (PMID: 30102706); it is also a potential broad-spectrum target in obese individuals (PMID: 36564713).

[0041] Marker 10:

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

[0043] Marker 11:

[0044] Oscillibacter valericigenes: The Oscillibacter valericigenes in the T2DM group were higher than those in the healthy group (PMID: 36428566).

[0045] Marker 12:

[0046] Bicirculans: Low-carbohydrate, high-fat, weight-loss diets can induce changes in the human gut microbiota: an increase in 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 fecal abundance in patients with periodontitis (PMID: 38528960).

[0051] Marker 15:

[0052] intestinimonas butyriciproducens: A bacterium that produces SCFAs. This bacterium increases the upregulation of SCFAs-glucagon-like peptide-1 (GLP-1) and peptide tyrosine (PYY) to alleviate the symptoms of type 2 diabetes (PMID: 32663709).

[0053] Marker 16: [Probiotics]

[0054] Akkermansia muciniphila: Present in the human gut microbiota from infancy and gradually increasing in adulthood. The potential impact of A. muciniphila abundance on major cardiovascular diseases has been investigated (PMID: 36153618), and Akkermansia muciniphila abundance is negatively correlated with overweight, obesity, untreated type 2 diabetes, or hypertension (PMID: 31263284). Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This refers to the α-diversity results in Example 1 of the present invention;

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

[0058] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0059] Example 1: Screening of tongue flora biomarkers associated with childhood hypertension and establishment of an early warning model.

[0060] (1) Study subjects: Tongue coating samples were collected from 100 obese children with normal blood pressure (CO) and 100 obese children with hypertension (HTN). Clinical statistics 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, following the instructions.

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

[0067] PCR amplification and product purification: Specific primers with barcodes or fusion primers with misaligned bases were synthesized according to the specified sequencing regions. PCR was performed using TransGen AP221-02: TransStart Fastpfu DNA Polymerase. All samples were processed under standard experimental conditions, with three replicates per sample. PCR products from the same sample were mixed and detected by 2% agarose gel electrophoresis. PCR products were recovered by gel excision using the AxyPrep DNA Gel Recovery Kit (AXYGEN), followed by Tris-HC1 elution; detection was then performed by 2% agarose gel electrophoresis.

[0068] Library construction: DNA samples that passed the initial screening were randomly fragmented into segments approximately 350 bp in length using an ultrasonic disruptor. The fragments underwent end repair, tailing, adapter ligation, purification, and PCR amplification to construct the entire library. After library construction, the size of each insert fragment in the library was measured using an Agilent 2100 scanner. If the size met expectations, the effective concentration of the library was accurately quantified using q-PCR. When the library passed the initial screening and the effective concentration was >3 nmol / L, sequencing was performed on an Illumina PE150 platform.

[0069] Metagenomic sequencing and Metagenome assembly: Readfq (V8) was used to perform quality control and host filtering on the raw data, removing low-quality fragments exceeding 40 bp (with a quality threshold ≤38), fragments with N-base lengths ≥10 bp, and fragments overlapping with adapters by more than 15 bp. This ensured the acquisition of high-quality fragments for subsequent analysis, guaranteeing the accuracy and reliability of the information analysis results. Next, the quality-controlled valid data underwent Metagenome assembly, including assembly analysis of valid data using SOAPdenovo (V2.04) software and mixed assembly of unused fragments from each sample using 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 for fragments with a combined assembly length ≥500 bp. Redundancy was removed from the prediction results using CD-HIT software. Bowtie2 software was used to align the valid data from each sample to the initial gene catalog, calculating the number of aligned fragments for each gene in each sample. Only genes with ≥2 aligned fragments were considered relevant to the sample. Based on the number of aligned fragments and gene length, the abundance information of each gene in each sample was calculated.

[0071] Species annotation and functional database annotation: Species annotation was performed using DIAMOND software, aligning each gene with bacterial, fungal, archaea, and viral sequences extracted from the NCBI NR database. For each sequence alignment, sequences with an evalue ≤ the lowest evalue × 10 were selected, and the lowest common ancestor (LCA) algorithm was used to determine the species annotation information for that sequence. Functional data annotation was performed using DIAMOND software, aligning each gene with the eggNOG functional database. For each sequence alignment, the best match was selected for subsequent functional analysis.

[0072] Diversity analysis: Alpha diversity analysis of single samples reflects the richness and diversity of the microbial community. QIIME is used to calculate the beta diversity distance matrix, and the nonmetric multidimensional scaling (NMDS) analysis and plotting are performed using the R language vegan package.

[0073] Differential microbiota analysis: Differential microbiota were obtained by LEfSe differential discriminant analysis (screening criteria: P<0.05, LDA>3).

[0074] (3) Results

[0075] Species diversity analysis showed significant differences in both α-diversity and β-diversity.

[0076] Alpha diversity results: The alpha diversity of gut microbiota was lower in children with hypertension than in children with normal blood pressure. Figure 1 ).

[0077] β-diversity results: There were differences in β-diversity between children with hypertension and children with normal blood pressure. Figure 2 ).

[0078] The results of species difference analysis revealed that 16 differentially expressed bacterial groups showed significant differences between the two groups (Table 2).

[0079] Table 216 Differentially Species

[0080]

[0081]

[0082] (4) ROC analysis was used to verify the accuracy of the screened biomarkers and the early warning binary classification model.

[0083] The R software performs specificity and sensitivity calculations and plots ROC curves. Internally, it first calculates the threshold for the actual measured value, then calculates the corresponding number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Specificity (true negative rate) = TN / (TN+FP), and sensitivity (true positive rate) = TP / (TP+FN). The ROC curve is constructed by subtracting specificity and sensitivity from 1. The integral of the ROC curve is the AUC. 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 that indicator.

[0084] Receiver operating characteristic (ROC) curve analysis was performed using the relative abundance values ​​of single or multiple microbial biomarkers to determine the cutoff value. The results (Table 3) show that when 16 microorganisms 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 316 differentially expressed bacteria for early warning of hypertension in children.

[0086]

[0087] Example 2: Validation of the effectiveness of 16 biomarkers in the diagnosis of hypertension in children

[0088] To further validate the effectiveness of the early warning binary classification model, we collected tongue coating samples from 100 children with hypertension and 100 children with normal blood pressure. We detected the expression levels of 16 bacteria in the samples and randomly selected 50 samples from children with hypertension and 50 samples from children with normal blood pressure as the validation set. We plotted ROC curves based on the abundance of each strain in the samples and calculated the area under the curve (AUC) to evaluate the classification effect and diagnostic value of the 16 differentially expressed bacteria on childhood hypertension. The results are shown in Table 4.

[0089] As can be seen from the data in Table 4, the results of the validation set and the aforementioned sample set data have strong consistency, indicating that the model has good prediction performance.

[0090] Table 4

[0091]

[0092] In addition, we compared the model diagnosis with the clinical expert diagnosis of the remaining 50 children with hypertension and 50 children with normal blood pressure. The comparison showed that the model's diagnostic accuracy 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 modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A biomarker for oral flora in children with hypertension, characterized in that, The flora markers are Streptococcusmitis, Fusobacterium mortiferum, Fusobacterium varium, Veillonella dispar, Veillonella atypica, Klebsiella pneumoniae, Ruminococcus gnavus, Alistipesonderdonkii, Alistipes finegoldii, Alistipes shahii, Oscillibactervalericigenes, Ruminococcus bicirculans, Ruminococcus champanellensis, Ruthenibacterium lactatiformans, Intestinimonas butyriciproducens and Akkermansia muciniphila.

2. The application of the microbial biomarker as described in claim 1 in the preparation of products for diagnosing childhood hypertension.

3. The application of the reagent for specifically detecting the oral microbiota marker of childhood hypertension as described in claim 1 in the preparation of products for diagnosing childhood hypertension.

4. The application as described in claim 3, characterized in that, The reagents are primers, probes, antisense oligonucleotides, aptamers, or antibodies that are specific to the bacterial community markers.

5. A system for predicting hypertension in children, comprising: (1) Nucleic acid sample separation unit, used to separate microbial nucleic acid samples from the test object; (2) A detection unit, used to perform relative abundance detection on isolated microbial nucleic acid samples to obtain the abundance value of the oral microbial biomarker as described in claim 1; (3) A data processing unit, used to import the relative abundance values ​​of the oral microbiota markers as described in claim 1 into a childhood hypertension risk warning system to obtain predicted values; (4) Result determination unit, used to compare the predicted value obtained by the data processing unit with the set diagnostic value; The individual was determined to be either hypertensive or healthy by comparing the obtained relative abundance value with a predetermined threshold value.

6. A product for diagnosing hypertension in children, characterized in that, The product is a reagent for specifically detecting the abundance of oral microbiota markers as described in claim 1.

7. The product as described in claim 6, characterized in that, The reagents include primers, probes, antisense oligonucleotides, aptamers, or antibodies that specifically detect the oral microbiota markers.

8. The product as described in claim 7, characterized in that, The product also includes reagents for extracting microbial genomic DNA and microbial proteins.

9. The application of the microbial biomarker as described in claim 1 in constructing a computational model for predicting hypertension in children.

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