Biomarkers for idiopathic hyperaldosteronism and uses thereof

Abstract

The application belongs to the technical field of biological detection, and particularly relates to a biomarker for idiopathic hyperaldosteronism and application thereof. The biomarker for idiopathic hyperaldosteronism is human intestinal microorganisms, and the human intestinal microorganisms include one or more combinations of Desulfovibrionales, Peptostreptococcaceae, Ruminococcus or Prevotellaceae. A diagnostic model constructed based on the biomarker (two or more of Desulfovibrionales, Peptostreptococcus, Ruminococcus or Prevotella) provided by the application can be used for auxiliary diagnosis of idiopathic hyperaldosteronism, and has high accuracy, excellent sensitivity and good specificity.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological detection, and particularly relates to a biomarker for idiopathic hyperaldosteronism and application thereof. BACKGROUND

[0002] Primary aldosteronism (PA, referred to as primary aldosteronism) as the most common type of secondary hypertension, accounts for about 5% to 10% of all hypertensive patients, and its core pathological feature is the autonomous secretion of aldosterone caused by adrenal cortical lesions. Compared with primary hypertensive patients matched in age, gender and blood pressure level, the incidence and mortality of cardiovascular adverse events (including myocardial infarction, chronic heart failure, arrhythmia, stroke, etc.) and chronic kidney disease of PA patients are significantly increased, which poses a serious threat to the life and health of patients. In the clinical classification of PA, idiopathic hyperaldosteronism (IHA, referred to as idiopathic hyperaldosteronism) is the main subtype, and its etiology and treatment scheme have clear specificity: unilateral aldosterone tumor patients are recommended for surgical treatment, and IHA patients usually need long-term drug intervention through oral salt corticosteroid receptor antagonists (such as spironolactone); therefore, accurate diagnosis of IHA is the key prerequisite for individualized treatment of PA patients and reduction of disease risk.

[0003] The current clinical diagnosis process of IHA is extremely cumbersome, and needs to complete multiple links such as drug elution, screening test, diagnosis test, adrenal computer tomography (CT) and adrenal vein blood sampling (AVS) in turn. This process is not only complex and time-consuming, but also contains invasive operation steps such as AVS, which has the operation risks of bleeding, infection, etc., and the examination cost is high, resulting in that some patients delay diagnosis and treatment due to fear of risk or economic burden. From the clinical needs, the development of IHA diagnosis technology with non-invasive operation, simple process, high safety and controllable cost has important clinical value and practical significance for improving the efficiency of disease diagnosis, improving the patient's medical experience, and reducing the risk of cardiovascular and cerebrovascular diseases and death.

[0004] As a biochemical indicator that can mark the changes of body tissue and cell function, biomarkers are widely used in disease diagnosis, staging and efficacy evaluation. Among them, feces has become an ideal biomarker carrier due to its non-invasive collection, large sample size, and easy access. Existing studies (such as Front Endocrinol. 2021; 12: 667951) have found that the intestinal flora of PA patients is different from that of non-hypertensive control population, specifically manifested as a decrease in alpha diversity, a decrease in the abundance of short-chain fatty acid-producing bacteria (Prevotella, Blautia, etc.), an increase in the abundance of inflammation-related bacteria (Megasphaera, Sutterella, etc.), and 30 kinds of bacteria (including Blautia, Lactobacillus, etc.) can be used as diagnostic markers for PA, with an area under the ROC curve (AUC) of 0.8173. However, it should be noted that there are essential differences between IHA and PA in pathogenesis, and biomarkers reflecting the pathological characteristics of IHA should be clearly distinguished from the overall markers of PA. However, there is no report on intestinal flora biomarkers that can accurately reflect the specificity of IHA and use feces as a carrier in the prior art. SUMMARY

[0005] Based on this, the present application finds and verifies that the abundance of Desulfobacterales ( Desulfovibrionales ), Peptostreptococcaceae ( Peptostreptococcaceae ), Ruminococcus ( Ruminococcus ) and Prevotellaceae ( Prevotellaceae ) in the intestinal microorganisms of patients with idiopathic hyperaldosteronism is significantly different from that of healthy people, and the above four flora can be used as biomarkers for the diagnosis of idiopathic hyperaldosteronism, with excellent diagnostic accuracy.

[0006] In order to achieve the above purpose, the present application can adopt the following technical solutions:

[0007] The present application provides a kind of idiopathic hyperaldosteronism biomarker, and the idiopathic hyperaldosteronism biomarker is human intestinal microorganism, and human intestinal microorganism includes two or more of Desulfobacterium ( Desulfovibrionales ), Peptostreptococcus ( Peptostreptococcaceae ), Ruminococcus ( Ruminococcus ) or Prevotella ( Prevotellaceae ).

[0008] Preferably, the above-mentioned idiopathic hyperaldosteronism biomarker is selected from any one of the following combinations:

[0009] (a) Desulfobacterium, Peptostreptococcus, Ruminococcus and Prevotella combination;

[0010] (b) Peptostreptococcus, Prevotella and Ruminococcus combination;

[0011] (c) Desulfovibrio, Prevotella, and Ruminococcus combination;

[0012] (d) Desulfovibrio, Peptostreptococcus, and Ruminococcus combination;

[0013] (e) Desulfovibrio, Peptostreptococcus, and Prevotella combination.

[0014] Preferably, the idiopathic hyperaldosteronism biomarker is a combination of Desulfovibrio and Ruminococcus.

[0015] Another aspect of the present application provides a use of a detection reagent of the idiopathic hyperaldosteronism biomarker in the present application in the preparation of an idiopathic hyperaldosteronism diagnostic reagent or kit.

[0016] Preferably, in the use, the detection reagent of the idiopathic hyperaldosteronism biomarker comprises a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

[0017] More preferably, in the use,

[0018] The PCR amplification sequencing-based detection reagent comprises a DNA extraction reagent and / or a PCR amplification primer;

[0019] The fluorescence quantitative qPCR-based detection reagent comprises one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescence reagent, or a fluorescence probe;

[0020] The metagenomic sequencing-based detection reagent comprises a pre-sequencing treatment reagent and / or a shotgun library construction reagent;

[0021] The metabolomics analysis-based detection reagent comprises one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard, and / or a liquid chromatography mobile phase.

[0022] Still another aspect of the present application provides an idiopathic hyperaldosteronism diagnostic reagent or kit, which comprises the detection reagent of the idiopathic hyperaldosteronism biomarker.

[0023] Preferably, in the idiopathic hyperaldosteronism diagnostic reagent or kit, the detection reagent of the idiopathic hyperaldosteronism biomarker comprises a PCR amplification sequencing-based detection reagent, a fluorescence quantitative qPCR-based detection reagent, a metagenomic sequencing-based detection reagent, or a metabolomics analysis-based detection reagent.

[0024] More preferably, in the idiopathic hyperaldosteronism diagnostic reagent or kit,

[0025] The detection reagent based on PCR amplification sequencing includes a DNA extraction reagent and / or a PCR amplification primer;

[0026] The detection reagent based on fluorescence quantitative qPCR includes one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescent reagent or a fluorescent probe;

[0027] The detection reagent based on metagenomic sequencing includes a pre-sequence treatment reagent and / or a shotgun library construction reagent;

[0028] The detection reagent based on metabolomics analysis includes one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard and / or a liquid chromatography mobile phase.

[0029] The beneficial effects of the present application include: the diagnostic model constructed based on the biomarkers provided by the present application (one or more combinations of Desulfuromonadales, Peptostreptococcaceae, Pediococcus or Prevotellaceae) can be used for the auxiliary diagnosis of idiopathic hyperaldosteronism, with high accuracy, excellent sensitivity and good specificity; especially the three combinations of Desulfuromonadales, Peptostreptococcaceae and Pediococcus as markers, through binary logistic regression and ROC analysis, the AUC value of the three combinations as markers can reach 0.926, the sensitivity can reach 100.0%, and the specificity can reach 77.8%. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 Shannon index of intestinal flora of IHA and HC subjects in Example 1;

[0031] Figure 2 Differences in intestinal flora at the genus level between the two groups of IHA and HC subjects in Example 1;

[0032] Figure 3 Statistical results of the relative abundance of four bacteria in Example 1;

[0033] Figure 4 ROC curve of the training set based on four biomarker combinations in Example 2;

[0034] Figure 5 ROC curve of the validation set based on four biomarker combinations in Example 2. DETAILED DESCRIPTION

[0035] The examples are provided to better illustrate the present application, but are not the only embodiments of the present application. Therefore, non-essential improvements and adjustments to the embodiments made by those skilled in the art based on the above description of the application still fall within the scope of the present application.

[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an overly literal sense unless expressly so defined herein. As used herein, it is also to be understood that the description of embodiments by terminology such as "including", "having", "with", "comprising" or "containing" does not exclude the presence of additional features, integers, steps, operations, elements, components, parts or combinations thereof. As used herein, " / " can be interpreted as "and / or" where appropriate.

[0037] In a first aspect, the embodiments of the present application provide a primary aldosteronism biomarker, the primary aldosteronism biomarker being human intestinal microorganisms, the human intestinal microorganisms including two or more of Desulfovibrio Desulfovibrionales , Peptostreptococcus Peptostreptococcaceae , Ruminococcus Ruminococcus or Prevotella Prevotellaceae .

[0038] It should be noted that the present application finds and verifies that the abundance of Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella in the intestinal microorganisms of primary aldosteronism patients is significantly different from that of healthy people, and two or more of the above four groups of bacteria can be used as biomarkers for the diagnosis of primary aldosteronism in different combinations.

[0039] In some specific examples, the above-mentioned primary aldosteronism biomarker is selected from any one of the following combinations:

[0040] (a) a combination of Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella; specifically, the AUC value of the combination of Desulfovibrio, Peptostreptococcus, Ruminococcus and Prevotella as a marker can reach 0.926, the sensitivity can reach 100.0%, and the specificity can reach 77.8%;

[0041] (b) a combination of Peptostreptococcus, Prevotella and Ruminococcus; specifically, the AUC value of the combination of Peptostreptococcus, Prevotella and Ruminococcus as a marker can reach 0.840;

[0042] (c) a combination of Desulfovibrio, Prevotella and Ruminococcus; specifically, the AUC value of the combination of Desulfovibrio, Prevotella and Ruminococcus as a marker can reach 0.901;

[0043] (d) a combination of Desulfovibrio, Peptostreptococcus and Ruminococcus; specifically, the AUC value of the combination of Desulfovibrio, Peptostreptococcus and Ruminococcus can reach 0.914;

[0044] (e) Desulfovibrio, Peptostreptococcus and Prevotella combination; in particular, the AUC value of the combination of Desulfovibrio, Peptostreptococcus and Prevotella can reach 0.790.

[0045] In a second aspect, the application provides a use of a detection reagent of the biomarker of idiopathic hyperaldosteronism in the application in the preparation of a diagnostic reagent or kit for idiopathic hyperaldosteronism.

[0046] It should be noted that the marker in the application has high accuracy in diagnosing idiopathic hyperaldosteronism, and the detection reagent of the marker can be prepared into a diagnostic reagent or kit for idiopathic hyperaldosteronism; the form of the diagnostic reagent or kit is known in the art.

[0047] In some specific examples, in the above application, the detection reagent of the biomarker of idiopathic hyperaldosteronism includes a detection reagent based on PCR amplification sequencing, a detection reagent based on fluorescence quantitative qPCR, a detection reagent based on metagenomic sequencing, or a detection reagent based on metabolomics analysis.

[0048] It should be noted that the detection reagent of the biomarker of idiopathic hyperaldosteronism in the application is a detection reagent of a known flora in the art, such as the reagents listed above.

[0049] In some specific examples, in the above application,

[0050] The detection reagent based on PCR amplification sequencing includes a DNA extraction reagent and / or a PCR amplification primer;

[0051] The detection reagent based on fluorescence quantitative qPCR includes one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescence reagent, or a fluorescence probe;

[0052] The detection reagent based on metagenomic sequencing includes a pre-sequence treatment reagent and / or a shotgun library construction reagent;

[0053] The detection reagent based on metabolomics analysis includes one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard, and / or a liquid chromatography mobile phase.

[0054] It should be noted that the detection reagent based on PCR amplification sequencing, the detection reagent based on fluorescence quantitative qPCR, the detection reagent based on metagenomic sequencing, or the detection reagent based on metabolomics analysis are known in the art, including but not limited to the reagents listed above.

[0055] In a third aspect, the embodiments of the present application provide a diagnostic reagent or kit for idiopathic hyperaldosteronism, which comprises the detection reagent for the biomarker of idiopathic hyperaldosteronism.

[0056] It should be noted that the detection reagent for the biomarker of idiopathic hyperaldosteronism can be prepared into a diagnostic reagent or kit for idiopathic hyperaldosteronism, and the preparation method is known in the art.

[0057] In some specific examples, the detection reagent for the biomarker of idiopathic hyperaldosteronism in the diagnostic reagent or kit for idiopathic hyperaldosteronism comprises a detection reagent based on PCR amplification sequencing, a detection reagent based on fluorescent quantitative qPCR, a detection reagent based on metagenomic sequencing, or a detection reagent based on metabolomics analysis.

[0058] In some specific examples, the detection reagent for the biomarker of idiopathic hyperaldosteronism in the diagnostic reagent or kit for idiopathic hyperaldosteronism comprises a detection reagent based on PCR amplification sequencing, a detection reagent based on fluorescent quantitative qPCR, a detection reagent based on metagenomic sequencing, or a detection reagent based on metabolomics analysis.

[0059] The detection reagent based on PCR amplification sequencing comprises a DNA extraction reagent and / or a PCR amplification primer;

[0060] The detection reagent based on fluorescent quantitative qPCR comprises one or more combinations of a DNA extraction reagent, a PCR amplification primer, a fluorescent reagent, or a fluorescent probe;

[0061] The detection reagent based on metagenomic sequencing comprises a pre-sequencing treatment reagent and / or a shotgun library construction reagent;

[0062] The detection reagent based on metabolomics analysis comprises one or more combinations of a T700 targeted metabolomics reagent, a Q500 full quantitative metabolomics reagent, an internal standard, and / or a liquid chromatography mobile phase.

[0063] In order to better understand the present application, the content of the present application will be further illustrated below in combination with specific examples, but the content of the present application is not limited to only the following examples.

[0064] Example 1

[0065] The embodiments of the present application provide a biomarker screening process for idiopathic hyperaldosteronism (Idiopathic Hyperaldosteronism, IHA, referred to as IHA for short).

[0066] (1) Mendelian randomization analysis

[0067] (1) Data source

[0068] In the following analysis, the data of gut microbiome were obtained from the Microbiome Genome (MiBioGen) consortium website (mibiogen.gcc.rug.nl / menu / main / home), which included 24 cohorts and recruited 18340 subjects from different ethnicities; DNA was extracted from human fecal samples, and 16S rRNA gene sequencing was used to annotate human gut microbiome to the genus level.

[0069] In the following analysis, the data of idiopathic hyperaldosteronism were obtained from the Chongqing Primary Aldosteronism Study (CONPASS) cohort; DNA was extracted from human blood, and whole genome sequencing was used to obtain single nucleotide polymorphisms (SNPs) related to primary hyperaldosteronism (especially idiopathic hyperaldosteronism).

[0070] (2) Two-sample Mendelian randomization analysis between gut microbiome and IHA-related SNPs

[0071] 1) Select SNPs highly associated with GM (gut microbiota) (P<1x10 -5 ) in the GWAS database of gut microbiome (exposure) (MiBioGen), and finally 14587 different SNPs were included; inverse variance weighted (IVW) method was used for analysis, and the results are shown in Table 1.

[0072] Table 1 IVW method to predict the causal association between gut microbiome and IHA

[0073]

[0074] Note: The specific meanings of the abbreviations and superscripts in the above table are as follows: SNP (Single Nucleotide Polymorphism): single nucleotide polymorphism; OR (Odds Ratio): odds ratio; CI (Confidence Interval): confidence interval.

[0075] From Table 1 above, it can be seen that Eubacterium (Eubacterium) ( Eubacterium nodatum group ), Peptostreptococcaceae (Peptostreptococcaceae) ( Peptostreptococcaceae ), Ruminococcus UCG003 (genus level, Ruminococcaceae UCG003 ), Ruminococcus 1 (Ruminococcus 1) ( Ruminococcus 1 ), Desulfovibrio (Desulfovibrio) ( Desulfovibrionales) had a negative causal relationship with IHA and were protective factors of IHA; while Ruminococcus UCG 014 (genus level, Ruminococcaceae UCG014 ) and Prevotellaceae (family level) Prevotellaceae had a positive causal relationship with IHA and were pathogenic factors of IHA.

[0076] 2) MR-Egger regression was used to test the pleiotropy of the instrumental variables, and the results are shown in Table 2. The results showed that the selected instrumental variables did not have pleiotropy (all P>0.05).

[0077] Table 2 Pleiotropy analysis of the causal relationship between intestinal flora and IHA

[0078]

[0079] (II) Metagenomic sequencing analysis

[0080] (1) Study subjects

[0081] A total of 60 subjects were recruited for this analysis, aged 32-71 years old, including 30 healthy individuals (Healthy Control, HC) and 30 individuals with idiopathic hyperaldosteronism (IHA). In addition, all patients who met the inclusion criteria of this study signed the informed consent form and agreed to participate in this clinical trial (approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Chongqing Medical University, ethics approval number: 2024-002-01). There was no statistically significant difference in age, gender, and BMI between the two groups. For detailed information of the subjects, please refer to Table 3.

[0082] Table 3 Information of subjects

[0083]

[0084] Note: The specific meanings of the abbreviations and superscripts in the above table are as follows: BMI (Body Mass Index): body mass index; ARR (Aldosterone Renin Ratio): aldosterone renin ratio; a Chi-square test; b Student T test.

[0085] (2) Collection and pretreatment of samples

[0086] Fresh fecal samples were collected from subjects and immediately placed in sterile tubes on ice; the fecal samples were aliquoted in the laboratory and stored at -80°C until subsequent sequencing; after all sample collection was complete, total DNA was extracted from the samples using the FastPure Stool DNA Isolation Kit; the DNA purity was detected using NanoDrop 2000, and the DNA concentration was detected using Synergy HTX. The DNA was broken into fragments of about 350 bp using an ultrasonic disruptor Covaris M220; the library was constructed using the NEXTFLEX Rapid DNA-Seq Kit.

[0087] (3) Bridge PCR and sequencing parameter settings

[0088] The constructed library was subjected to metagenomic sequencing using the Illumina NovaSeq™ X Plus sequencing platform, and the specific process was as follows:

[0089] 1) One end of the library molecules is complementary to the primer base, and after one round of amplification, the template information is fixed on the chip;

[0090] 2) The other end of the molecule fixed on the chip is randomly complementary to the other primer nearby and is also fixed, forming a "bridge";

[0091] 3) PCR amplification to produce DNA clusters;

[0092] 4) Linearization of DNA amplicons into single strands;

[0093] 5) Add modified DNA polymerase and dNTPs with 4 fluorescent labels, and only one base is synthesized each cycle;

[0094] 6) Scan the surface of the reaction plate with a laser to read the type of nucleotide polymerized on each template sequence in the first round of reaction;

[0095] 7) Chemically cut the "fluorescent group" and "termination group" to restore the 3' end stickiness and continue to polymerize the second nucleotide;

[0096] 8) Count the collected fluorescence signal results each round to obtain the sequence of the template DNA fragment.

[0097] (4) Data analysis

[0098] Whole genome sequencing was performed on the Illumina platform to obtain 150 bp long forward and reverse paired reads. The original data was pretreated using Trimmomatic v0.39 to obtain clean data; the specific processing steps were as follows:

[0099] 1) Remove low quality reads containing more than 35bp 'N' bases (default quality threshold < 15);

[0100] 2) Remove reads with a certain length (default length 10bp) of overlap with primers;

[0101] 3) Align reads in clean data to human genome reference database GRCh38 using Bowtie2.4.1 software (http: / / bowtie-bio.sourceforge.net / bowtie2 / index.shtml) and filter out reads from host; parameters as follows: --end-to-end, --more-sensitive, -I 200, -X 500;

[0102] 4) Use Megahit software (v1.2.9, https: / / github.com / voutcn / megahit) to de novo assemble high quality sequences after preprocessing for each sample, and output Contigs of at least 500bp or more for subsequent analysis;

[0103] 5) Contigs (≥500 bp) assembled for each sample are subjected to ORF prediction by MetaGeneMark software (v2.10, http: / / topaz.gatech.edu / GeneMark / ), and ORFs with length less than 100 nt are filtered from the prediction results using default parameters, and CD-HIT software (v4.8.1, http: / / www.bioinformatics.org / cd-hit) is used for de-redundancy to obtain a non-redundant gene set;

[0104] 6) Use featureCounts software (v2.0.1, http: / / subread.sourceforge.net) to align clean data of each sample to the non-redundant gene set, and calculate the number of reads aligned to each gene;

[0105] 7) Use FPKM standardization to obtain gene relative abundance information;

[0106] 8) Use MMseqs2 software (v13.45111, https: / / github.com / soedinglab / mmseqs2) to align non-redundant gene sequences to the NR database of NCBI (Version 2023.04, https: / / www.ncbi.nlm.nih.gov / );

[0107] 9) The final species annotation information of each gene was determined using the approximate 2bLCA algorithm.

[0108] Through the above metagenomic test analysis, a total of 5 phylum-level species, 111 door-level species, 150 class-level species, 282 order-level species, 555 family-level species, 1651 genus-level species, and 7521 species-level species were identified; among them, the Shannon index of the HC group was higher than that of the IHA group (P < 0.05). Figure 1 The results showed that the intestinal flora alpha diversity level of the IHA patients was significantly lower than that of the HC group.

[0109] In addition, the Linear Discriminant Analysis (LDA) effect size (LEfSe) method was used to identify the different abundance of bacteria between the two groups. At the genus level, the relative abundance of 26 bacteria was significantly different between the two groups (P < 0.05); at the species level, the relative abundance of 117 bacteria was significantly different between the two groups. Figure 2

[0110] (Three) Screening of biomarkers capable of identifying idiopathic hyperaldosteronism

[0111] In order to study the intestinal flora biomarkers related to the diagnosis of idiopathic hyperaldosteronism, further evaluation was made on the different levels of flora found by Mendelian randomization analysis and metagenomic sequencing analysis, as follows: Through metagenomic sequencing research, 4 of the 7 bacteria found by Mendelian randomization in the previous two samples were detected (see Table 4 below), which were Desulfovibrionales (up-regulated in IHA (P < 0.05), Ruminococcus (down-regulated in IHA (P < 0.05), and Streptococcaceae (P < 0.05) and Prevotellaceae (P < 0.05) with no difference between the two groups, which can be used as biomarkers for idiopathic hyperaldosteronism (although Streptococcaceae and Prevotellaceae have no difference in metagenomic analysis, but have difference in Mendelian randomization analysis, so they can be used as markers). Desulfovibrionales Ruminococcus Peptostreptococcaceae Prevotellaceae Figure 3

[0112] Table 4 Basic information of 4 kinds of bacteria

[0113]

[0114] In addition, the conditions of Desulfovibrio, Streptococcus, Ruminococcus, and Prevotella are shown in Table 5 below.

[0115] Table 5 Functional description of biomarkers ​​​​​​

[0116]

[0117] Example 2

[0118] (1) Four biomarker combinations

[0119] The embodiments of the present application provide a process for constructing a diagnostic model based on the above-mentioned biomarkers of idiopathic hyperaldosteronism and verification, which are as follows:

[0120] (1) After completing the above-mentioned marker screening, 60 subjects (30 healthy people (HC) and 30 people with idiopathic hyperaldosteronism (IHA) are recruited, and the requirements and procedures for the subjects are the same as those for the subjects and procedures for recruiting in the above-mentioned metagenomic sequencing analysis;

[0121] (2) The 60 subjects are randomly divided into a training set and a validation set in a ratio of 7:3, wherein the training set includes 21 IHA and 21 HC, and the validation set includes 9 IHA and 9 HC;

[0122] (3) Based on the four bacteria, a diagnostic model is established using binary logistic regression (the diagnostic variable (independent variable) is the relative abundance of the four biomarkers, and the dependent variable is the IHA and the healthy person HC, i.e. the binary variable of whether the IHA is present), and a receiver operating characteristic (ROC) analysis is performed to quantify the diagnostic performance of the biomarker combination containing the four bacteria; wherein the binary logistic regression and the ROC analysis are both performed using the conventional statistical software SPSS21.0 (IBM Corp., Armonk, NY, USA) of the prior art; the ROC curve is used to evaluate the goodness of the classification and detection results, and is a very important and common statistical analysis method, which is a coordinate graph composed of the false positive rate (1-specificity) as the horizontal axis and the true positive rate (sensitivity) as the vertical axis. Different results obtained by the test sample at different judgment standards (threshold) are drawn into a curve; the area under the curve (AUC) is used to represent the accuracy, and the higher the AUC value, the higher the accuracy.

[0123] The ROC curves in the training set and the validation set are shown in Figure 4 and Figure 5 The area under the ROC curve (AUC) value, 95% confidence interval (CI), sensitivity and specificity are shown in Table 6 below.

[0124] Table 6 AUC value, 95% confidence interval (CI), sensitivity and specificity in the training set and the validation set

[0125]

[0126] It can be seen from Table 6 that the accuracy, sensitivity and specificity of the diagnostic model constructed by using binary logistic regression based on the biomarkers provided by the present application are relatively high.

[0127] (II) combination of Streptococcus, Prevotella and Ruminococcus

[0128] Streptococcus, Prevotella and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.840. The above results show that after excluding Desulfovibrio, the AUC value of the diagnostic model decreases.

[0129] (III) combination of Desulfovibrio, Prevotella and Ruminococcus

[0130] Desulfovibrio, Prevotella and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.901. The above results show that after excluding Streptococcus, the AUC value of the diagnostic model decreases.

[0131] (IV) combination of Desulfovibrio, Streptococcus and Ruminococcus

[0132] Desulfovibrio, Streptococcus and Ruminococcus were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The results showed that the AUC value of the diagnostic model was 0.914. After excluding Prevotella, the AUC value of the diagnostic model decreases.

[0133] (V) combination of Desulfovibrio, Streptococcus and Prevotella

[0134] Desulfovibrio, Streptococcus and Prevotella were selected as biomarkers for diagnosing idiopathic hyperaldosteronism, and binary logistic regression and ROC analysis were performed according to the above method. The AUC value of the diagnostic model was 0.790. After excluding Ruminococcus, the AUC value of the diagnostic model decreases.

[0135] Finally, it should be pointed out that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the present application, and they should be covered by the scope of the claims of the present application.

Claims

1. Biomarkers for idiopathic hyperaldosteronism, characterized in that, The biomarkers for idiopathic aldosteronism are human gut microbiota, and the biomarkers for idiopathic aldosteronism are selected from any of the following combinations: (a) Desulfovibrioles Desulfovibrionales Peptostreptococci Peptostreptococcaceae Rumenococcus Ruminococcus and Prevotellaceae Prevotellaceae combination; (b) Peptostreptococci Peptostreptococcaceae Prevotaceae Prevotellaceae and Rumenococcus Ruminococcus combination; (c) Desulfovibrioles Desulfovibrionales Prevotaceae Prevotellaceae and Rumenococcus Ruminococcus combination; (d) Desulfovibrioles Desulfovibrionales Peptostreptococci Peptostreptococcaceae and Rumenococcus Ruminococcus combination; (e) Desulfovibrioles Desulfovibrionales Peptostreptococci Peptostreptococcaceae and Prevotellaceae Prevotellaceae combination.

2. The use of the detection reagent for the biomarker of idiopathic aldosteronism according to claim 1 in the preparation of diagnostic reagents or kits for idiopathic aldosteronism.

3. The application according to claim 2, characterized in that, Biomarker detection reagents for idiopathic aldosteronism include PCR-based amplification and sequencing reagents or fluorescence quantitative qPCR-based reagents.

4. The application according to claim 3, characterized in that, PCR-based sequencing detection reagents include DNA extraction reagents and / or PCR amplification primers; Detection reagents based on quantitative real-time qPCR include one or more combinations of DNA extraction reagents, PCR amplification primers, fluorescent reagents, or fluorescent probes.

5. A diagnostic reagent or kit for idiopathic aldosteronism, characterized in that, The reagent for detecting biomarkers of idiopathic aldosteronism as described in claim 1.

6. The diagnostic reagent or kit for idiopathic aldosteronism according to claim 5, characterized in that, Biomarker detection reagents for idiopathic aldosteronism include PCR-based amplification and sequencing reagents or fluorescence quantitative qPCR-based reagents.

7. The diagnostic reagent or kit for idiopathic aldosteronism according to claim 6, characterized in that, PCR-based sequencing detection reagents include DNA extraction reagents and / or PCR amplification primers; Detection reagents based on quantitative real-time qPCR include one or more combinations of DNA extraction reagents, PCR amplification primers, fluorescent reagents, or fluorescent probes.

Images