KR20220018368A - Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof - Google Patents

Abstract

The present application relates to a composition for predicting obesity through evaluation of the level of an intestinal microorganism colony and a use thereof. Provided are a composition for predicting the risk of obesity, a kit for predicting the risk of obesity, a method for providing information for predicting the risk of obesity, and a method for screening a substance for treating obesity.

Description

Composition for predicting obesity by evaluating the level of intestinal microorganism colony, and use thereof

It relates to a composition for predicting obesity through evaluation of the level of the intestinal microbial community and its use.

Since the World Health Organization defined obesity as a 'disease requiring long-term treatment', obesity has been recognized as one of the most important diseases to overcome. Obesity is a phenomenon in which extra energy is accumulated in the form of body fat due to low energy consumption compared to chronically ingested nutrients, and the obesity is caused by various factors including complex genetic factors and environmental factors. Specifically, irregular eating habits, excessive food intake, lack of exercise, endocrine system diseases, genetic factors, mental factors, and overdose of drugs are pointed out as realistic causes of obesity. Currently, the diagnosis of obesity is made through body mass index (BMI) measurement method, bioelectrical resistance measurement method, waist circumference measurement method, and visceral fat measurement method using abdominal fat CT scan.

On the other hand, recent studies on obesity pay attention to the microbial flora (microciota) present in the human intestine, and report that the microbial flora affects energy metabolism in the body and is related to obesity. The most well-known metagenome-based obesity risk assessment method is to analyze the composition ratio of Firmicutes and Bacteroidetes in the stool, and Firmicutes/Bacteroidetes ratio (F/B ratio) is The higher the score, the higher the risk of obesity. However, recently, discrepancies between the F/B ratio and the obesity phenotype have been frequently reported due to pathological conditions, host genetic characteristics, and differences in the 16S rRNA target region of the microbial base sequence.

In addition, the study on the correlation between the F/B ratio and BMI was mainly conducted on the group with a Western-style diet. Accordingly, the above correlation may not be applied as it is for the group with other dietary habits. Therefore, there is an urgent need to develop a new obesity risk prediction technology that can replace the conventional technology having these limitations (Korean Patent Registration No. 10-1992789).

One aspect is Romboutsia genus, Sutterella genus, and Prevotella genus A composition for predicting obesity risk including an agent capable of detecting one or more microorganisms selected from the group consisting of genus will provide

Another aspect is to provide a kit for predicting the risk of obesity using microorganisms in the feces comprising the composition for predicting obesity.

Another aspect includes measuring the level of amplification products for one or more microorganisms selected from the group consisting of the genus Lombustia, the genus Certerella, and the genus Prevotella in the stool sample isolated from the subject. It is to provide a method of providing information for prediction.

Another aspect comprises the steps of treating a test substance to one or more microorganisms selected from the group consisting of Lombustia, Serterella, and Prevotella in a biological sample isolated from an individual; measuring the level of microorganisms in the biological sample; And it is to provide a method for screening a substance for treating obesity, comprising the step of comparing the level of the microorganism with a control that is not treated with the test substance.

One aspect is Romboutsia genus, Sutterella genus, and Prevotella genus A composition for predicting obesity risk including an agent capable of detecting one or more microorganisms selected from the group consisting of genus to provide.

As used herein, the term “obesity” refers to a phenomenon in which adipose tissue is excessively accumulated in the body, and the remaining calories are converted into fat and accumulated in the subcutaneous tissue or the abdominal mesentery membrane in the body. As a conventional technique for predicting the risk of obesity, there is a method of analyzing the composition ratio between Firmicutes and Bacteroidetes , but these are predicted according to genetic characteristics and differences in dietary habits by ethnicity. It has a limitation that the accuracy may be low. Under this technical background, the present inventors confirmed that the risk of obesity can be more accurately predicted by detecting specific microorganisms at the genus level in a stool sample and quantitatively evaluating their levels.

As used herein, the term "prediction of risk" is to evaluate whether obesity is likely to occur, and to delay or prevent the onset of the onset or prevent the onset of the disease through special and appropriate management of individuals at high risk of obesity, or to select the most appropriate treatment method By selecting it can be used clinically to make treatment decisions. In addition, the presence or characteristics of a pathological condition that may be caused in the future may be predicted through the risk prediction. For example, the term is "predicting the likelihood of obesity", "diagnosing the state of obesity", that is, diagnosing the degree of obesity, judging the risk prognosis of obesity-related diseases, diagnosis for determining the dosage of a drug for preventing or treating obesity , can be used interchangeably with diagnosis to determine treatment options for obesity progression. The obesity-related disease is a metabolic disorder associated with or caused by obesity, for example, metabolic syndrome, insulin deficiency or insulin-resistance related disorder, diabetes (eg, type 2 diabetes), glucose intolerance, dystrophy. Immune system dysfunction associated with lipid metabolism, atherosclerosis, hypertension, cardiac pathology, stroke, nonalcoholic fatty liver disease, hyperglycemia, fatty liver, dyslipidemia, overweight and obesity, cardiovascular disease, high cholesterol, triglyceride elevation, asthma, sleep apnea, osteoarthritis, neurodegeneration, gallbladder disease, syndrome X, inflammatory and immune diseases, atherogenic dyslipidemia, cancer, and the like.

In one embodiment, as a result of confirming the overall colony of intestinal microbes between the obese group and the normal weight group, among a total of 32 genera detected by 70% or more in both groups, Romboutsia , Sutterella ) , Prevotella microorganisms showed a significant difference between the two groups with a p-value of less than 0.05, and showed a positive correlation with the risk of obesity. Therefore, an agent capable of detecting the microorganism can be used to predict the risk of obesity. The microorganisms to be detected of the composition, that is, the genus Lombustia, the genus Sterterella, and the genus Prevotella, are all intestinal microorganisms, showing a higher detection frequency in the intestine of the obese group compared to the normal weight group, specifically in the stool sample. Bar, by measuring the level in these samples, it is possible to predict the risk of obesity. On the other hand, the subspecies of the Rombustia genus include, for example, Romboutsia hominis , Romboutsia ilealis , Romboutsia lituseburensis , and the like. , The subspecies of the genus Sutterella include, for example, Sutterella timonensis, Sutterella wadsworthensis , Sutterella massiliensis, etc., and the Prevotella genus Subspecies include, for example, Prevotella brevis , Prevotella buccalis , Prevotella disiens , and the like.

In another example, as a result of confirming the overall colony of intestinal microbes between the obese group and the normal weight group for Westerners, among a total of 35 genera detected by 70% or more in both groups, Lachnoclostridium genus , Subdoligranulum , Intestinibacter , Agathobacter , showed a significant difference between the two groups with a p-value of 0.05 or less, indicating a significant difference between the two groups, and the risk and amount of obesity showed a correlation of Therefore, an agent capable of detecting the microorganism can also be used to predict the risk of obesity. On the other hand, as a subspecies of the genus Lachinoclostridium, for example, Lachnoclostridium edouardi ( Lachnoclostridium edouardi ), Lachnoclostridium pacaense ( Lachnoclostridium pacaense ) and the like, and the subspecies of the subdoli granulum genus Species include, for example, Subdoligranulum variabile , and the like, and the subspecies of the Intestinibacter genus include, for example, Intestinibacter bartlettii and the like, and the Subspecies of the genus Agatobacter include, for example, Agathobacter rectalis and Agathobacter ruminis .

As an agent capable of detecting the microorganism, specific organic biomolecules such as proteins, nucleic acids, lipids, glycolipids, glycoproteins or sugars (monosaccharides, disaccharides, oligosaccharides, etc.) A primer, a probe, an antisense oligonucleotide, an aptamer, an antibody, and the like that can be physically detected may be used.

In one embodiment, the agent capable of detecting the microorganism may be a primer capable of detecting the microorganism. Preferably, the primer specifically detects the genomic sequence (eg, 16S rRNA) of the microorganisms and does not specifically bind to the genomic sequence of other microorganisms.

As used herein, the term “primer” refers to 7 to 50 nucleic acid sequences capable of forming a base pair complementary to the template strand and functioning as a starting point for template strand copying. Primers are usually synthesized but can also be used on naturally occurring nucleic acids. The sequence of the primer does not necessarily have to be exactly the same as the sequence of the template, but only if it is sufficiently complementary to hybridize with the template. Additional features that do not change the basic properties of the primer may be incorporated. Examples of additional features that may be incorporated include, but are not limited to, methylation, encapsulation, substitution of one or more nucleic acids with homologs, and modifications between nucleic acids. As used herein, the term "16s rRNA" is an rRNA constituting the 30S subunit of prokaryotic ribosomes, and while most of the nucleotide sequences are fairly conserved, high nucleotide sequence diversity appears in some sections. In particular, since there is little diversity among homogeneous species while diversity appears among other species, prokaryotes can be usefully identified by comparing the sequences of 16S rRNA.

In one embodiment, the primer may be used to amplify the 16S rRNA sequence conserved in the microorganism, and the presence of the microorganism may be detected through whether a desired product is produced as a result of the sequence amplification. A sequence amplification method using a primer may use various methods known in the art. For example, polymerase chain reaction (PCR), reverse transcription-polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hot start PCR, nested PCR, Booster PCR, real-time PCR, differential display PCR (DD-PCR), rapid amplification of cDNA ends (RACE), inverse polymerase chain reaction , vectorette PCR, TAIL-PCR (thermal asymmetric interlaced PCR), ligase chain reaction, repair chain reaction, transcription-mediated amplification, self-maintaining sequence cloning, selective amplification of the target sequence can be used, However, the present invention is not limited thereto.

In addition, the agent capable of detecting the microorganism may be an antibody, and the microorganism may be detected using an immunological method based on an antigen-antibody reaction. Analysis methods for this include Western blot, ELISA (enzyme linked immunosorbent asay), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, and rocket immunoelectrolysis. Electrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter (FACS), protein chip, etc., but are not limited thereto.

In addition, molecular and immunological methods widely used in the art may be used to detect the microorganism according to an embodiment.

When the risk of obesity is predicted using the composition for predicting the risk of obesity according to an aspect, it can be determined whether the diet currently maintained induces obesity, so that a person with a normal weight can adjust an appropriate diet so as not to become obese. It can effectively prevent obesity.

Another aspect provides a kit for predicting obesity risk comprising the composition for predicting obesity.

Since the kit for predicting the risk of obesity includes or uses the composition for predicting obesity as described above, descriptions of common content between the two will be omitted.

In one embodiment, the kit includes a detection agent such as a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody for detecting the microorganisms, as well as one or more other component compositions suitable for the analysis method, a solution, or devices may be included.

Specifically, the kit may be an RT-PCR kit, a microarray chip kit, or a protein chip kit. In one embodiment, the kit comprising a primer specific to the microorganism may include essential elements for performing an amplification reaction such as PCR, for example, the kit for PCR includes a test tube or other suitable container; Reaction buffers, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase reverse transcriptase, DNase, RNAse inhibitors, DEPC-water, sterile water, and the like may be included. In another embodiment, the kit may be applied to a biological sample isolated from a subject, for example, the sample may be a fecal or fecal sample. The kit includes a collection device for sampling for collection of a sample, and the collection device may be, for example, a brush, an absorbent pad, a cotton swab, a dropper, a swab, a syringe, or the like.

Another aspect includes measuring the level of amplification products for one or more microorganisms selected from the group consisting of the genus Lombustia, the genus Certerella, and the genus Prevotella in the stool sample isolated from the subject. It is to provide a method of providing information for prediction.

Since the information providing method for predicting the risk of obesity includes or uses the above-described composition for predicting obesity as it is, descriptions of common content between the two will be omitted.

The method further comprises measuring the level of amplification products for one or more microorganisms selected from the group consisting of Lachinoclostridium genus, Subdoligranulum genus, Intestinibacter genus, and Agatobacter genus. may be doing

In one embodiment, the method may be to predict that the risk of obesity is high when the amount of the amplification product for the microorganism in the sample is greater than the amplification product of the microorganism in the normal control sample.

The individual may be a vertebrate, a mammal, an amphibian, a reptile, a bird, etc., may be a mammal, for example, a human ( Homo sapiens ). According to one embodiment, one or more microorganisms selected from the group consisting of the genus Lombustia, the genus Certerella, and the genus Prevotella may be detected from a sample obtained from an Asian, and the genus Lachinoclostridium, One or more microorganisms selected from the group consisting of subdoligranulum genus, Intestinibacter genus, and Agatobacter genus may be detected from samples obtained from Westerners.

Another aspect comprises the steps of treating a test substance to one or more microorganisms selected from the group consisting of Lombustia, Serterella, and Prevotella in a biological sample isolated from an individual; measuring the level of microorganisms in the biological sample; And it provides a method for screening an obesity treatment substance, comprising the step of comparing the level of the microorganism with the control group not treated with the test substance.

Since the method for screening the obesity treatment material includes or uses the above-described composition for predicting obesity as it is, descriptions of common content between the two will be omitted.

The method may further include the step of treating the test substance with one or more microorganisms selected from the group consisting of Lachinoclostridium genus, Subdolli granulum genus, Intestinibacter genus, and Agatobacter genus have.

In one embodiment, the method compares the two by measuring the level of the microorganism in the sample or the degree of colonization in the sample in the presence and absence of the test substance for the prevention or treatment of obesity, respectively, and then, when the test substance is present, If the level or the degree of colonization is further reduced, the test substance may be selected as a preventive or therapeutic agent for obesity.

The test substance may include various low molecular weight compounds, high molecular weight compounds, nucleic acid molecules (eg, DNA, RNA, PNA, etc.), proteins, sugars and lipids that can be used for the prevention or treatment of obesity.

According to the composition according to one aspect, there is an effect of predicting the risk of obesity with a small amount of feces. In addition, the gut microbiome according to an aspect is significantly related to obesity, and can be used as a target for developing new drugs for preventing or treating obesity, as well as increasing the accuracy of diagnosis compared to conventional studies conducted on intestinal microbes. .

1 shows the microbial diversity in fecal samples of an obese group and a normal-weight group by principal component analysis.
Figure 2 shows the difference in the composition of microorganisms in the stool samples of the obese group and the normal weight group at the phylum level.
Figure 3 shows the difference in the composition of microorganisms in the stool samples of the obese group and the normal weight group at the class level.
4 is a bar graph showing the microbial genus having a significant difference in abundance among microorganisms in the feces samples of the obese group and the normal weight group.
5A is a boxplot showing the ratio of Lombustia genus in stool samples of the obese group and the normal weight group.
Figure 5b is a boxplot showing the ratio of the genus Certerella in the stool samples of the obese group and the normal weight group.
5c is a boxplot showing the ratio of Prevotella genus in the stool samples of the obese group and the normal weight group.
6a is a boxplot showing the ratio of Lachinoclostridium genus in the fecal samples of Westerners of the obese group and the normal weight group.
6b is a boxplot showing the ratio of subdoligranulum genus in the western feces samples of the obese group and the normal weight group.
Figure 6c is a box plot showing the ratio of Intestinibacter genus in the fecal samples of Westerners of the obese group and the normal weight group.
Figure 6d is a boxplot showing the ratio of Agatobacter genus in the fecal samples of Westerners of the obese group and the normal weight group.

Hereinafter, preferred examples are presented to help the understanding of the present invention. However, the following examples are only provided for easier understanding of the present invention, and the contents of the present invention are not limited by the following examples.

[Example]

Example 1. Test subject and sample collection

Stool samples were collected from 675 Koreans who did not have a serious disease. Among them, 13 people who were underweight, 158 people who were overweight, and 7 people with missing body measurements were excluded from this experiment. 214 adults with a BMI of 25 or more were set as the obese group, and 283 adults with a BMI (18.5~23) in the normal BMI range were set as the control group (normal weight group). The BMI of the two groups showed a significant difference of P-value < 0.0001, and the average age of the obese group and the normal weight group was 48 years old, and there was no significant difference. The categories of obesity were classified based on the WHO Asia Pacific region criteria. After feces were collected using a sterile cotton swab, they were stored at room temperature in a state of mixing with a preservative solution.

[Table 1]

After moving the stored sample to the laboratory, genomic DNA was extracted from the sample using a stool DNA preparation kit, and the amount of DNA was quantified using Nanodrop. The extracted DNA was PCR amplified using 515F/806R primer targeting the V4 region of 16S rRNA of bacteria, and the nucleotide sequence data of the amplified product was sequenced using Illumina's MiSeq.

Meanwhile, the barcode fusion primer sequences used for amplification are shown in Table 1 below. A library was prepared by a dual indexing method, and the 8bp index sequence used was denoted by X in the nucleotide sequence of Table 1.

[Table 2]

Thereafter, unpaired single reads were removed from the sequenced sequence data, and only high quality reads were filtered in consideration of the quality score. Each read pair was merged into one read, and Operational Taxonomy Unit (OTU) clustering was performed according to the sequence similarity using sklearn as the sequence clustering algorithm. Then, using SILVA 132 as the 16S rRNA taxonomy database, bacteria having a sequence similarity of 99% or more were assigned to each OTU and taxonomic analysis was performed. QIIME2 was used as a tool for the overall analysis, and the microbial community diversity index of the obese group and the normal weight group was confirmed, and the microbial distribution at the phylum, genus, and genus levels was analyzed.

Example 2. Analysis of flora in stool samples

(2.1) Diversity indicators and principal component analysis results

To understand the overall microbial pattern present in the samples of the obese group and the normal weight group, first, the diversity of the microbial community was compared, and the results are shown in Table 3 below. Here, Faith_pd is phylogenetic diversity, Observed_OTUs is the number of observed Operational Taxonomic Units (OTUs), and Shannon index is species diversity.

[Table 3]

As shown in Table 3, as a result of analyzing the diversity index of the microbial community, it was confirmed that the normal weight group was significantly higher than the obese group in all indicators.

Next, Emperor Principal Coordinates Analysis (PCoA), a principal component analysis, was performed using QIIME2.

Figure 1 shows the microbial diversity in the stool samples of the obese group and the normal weight group by the main component analysis, and as a result, it was confirmed that the obese group and the normal weight group cannot be clearly distinguished from the above analysis result alone.

(2.2) Analysis of microbial composition at the Phylum and Class level

Figure 2 shows the difference in the composition of microorganisms in the stool samples of the obese group and the normal weight group at the phylum level, and Figure 3 shows the difference in the composition of the microorganisms in the stool samples of the obese group and the normal weight group at the class level. will be.

As a result of checking the distribution ratio of microorganisms detected by more than 1% for each sample, there was no significant difference in the abundance of microorganisms between each group. On the other hand, as a result of confirming the distribution of microorganisms at the river level, there was a significant difference in the composition of intestinal microorganisms between the two groups. Specifically, as a result of confirming the distribution ratio of the microbial class detected by more than 1% for each sample, both groups Bacteroidia , Clostridia , Gammaproteobacteria , Negativicocus ( Negativicutes ) , were abundantly present in the order of Actinobacteria class . However, the abundance between the two groups for Bacteroidia, Clostridia, and Actinobacteria classes showed a significant difference.

Example 3. Correlation analysis between intestinal microbial distribution and obesity in Asians

In order to more accurately identify the association between the fecal flora and obesity, the intestinal microflora was analyzed at the genus level. Specifically, from among 578 genera of microorganisms detected in the feces of a total of 497 people including the normal weight group and the obese group, the genera having a detection frequency of 70% or more was selected and shown in Table 4.

[Table 4]

As shown in Table 4, a total of 32 genera detected 70% or more in both groups, and among them, Alistipes , Oscillibacter , and Lombus were found between the normal weight group and the obese group. Romboutsia , Sutterella , Odoribacter , Ruminiclostridium , Prevotella , Bifidobacterium microorganisms are p-value It was confirmed that this 0.05 or less indicates a significant difference.

Figure 4 is a bar graph showing the microbial genus (genus) showing a significant difference in the abundance between the groups among microorganisms in the stool samples of the obese group and the normal weight group. As shown in Figure 4, in the case of the genus Prevotella , the abundance was the highest among the eight genera of microorganisms, and the abundance was 15.03% in the normal weight group and 18.64% in the obese group.

5A to 5C are box plots showing the difference in the relative abundance of microorganisms in stool samples between the obese group and the normal weight group. 5a to 5c, in the case of microorganisms of the genus Romboutsia, the genus Sutterella, and the genus Prevotella, it was found that they were relatively abundant in the obese group compared to the normal weight group. could check That is, it was found that the microorganism genus is an intestinal harmful bacteria.

Example 4. Correlation analysis between intestinal microbial distribution and obesity in Westerners

(4.1) Test subject and sample collection

In the same manner as in the above example using public metagenomic data for British subjects, microorganisms showing a significant difference between the obese group and the normal weight group at the genus level were identified. Among the published data of European Bioinformatics Institute (EBI), metagenomic data ERP006339 was used for comparative analysis. Experimental data were produced by the 16S rRNA amplicon sequencing method targeting the V4 region in the same manner as in Example 1, and Illumina's MiSeq equipment was used as a sequencing platform.

Excluding 18 cases, 7 cases of underweight, and 95 cases of overweight among a total of 525 samples, 276 adults with a BMI of 25 or more were set as the obese group, and 129 subjects with a BMI normal range (18.5~23) were set as the control group. (Normal weight group) was set for analysis. Information on the obese group and the normal weight group is shown in Table 5 below.

[Table 5]

(4.2) Analysis results

Among the microbial genera detected in the feces of a total of 405 people including the normal weight group and the obese group, genera having a detection frequency of 70% or more was selected and shown in Table 6.

[Table 6]

As shown in Table 6, there were a total of 35 genera detected by about 70% or more in both groups, among which, between the normal weight group and the obese group, Lachnoclostridium genus, Subdoligranulum ) It was confirmed that microorganisms of the genus, Intestinibacter genus, and Agathobacter genus exhibit a significant difference with a p-value of 0.05 or less.

6A to 6D are box plots showing the difference in the relative abundance of microorganisms in stool samples between the obese group and the normal weight group. As shown in Figures 6a to 6d, in the case of Lachinoclostridium genus, Subdoligranulum genus, Intestinibacter genus, Agatobacter genus microorganisms, relatively abundant in the obese group compared to the normal weight group. could confirm that That is, it was found that the microorganism genus is an intestinal harmful bacteria.

The description of the present invention described above is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

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Claims (14)

Romboutsia genus, Sutterella genus, and Prevotella genus A composition for predicting obesity risk comprising an agent capable of detecting one or more microorganisms selected from the group consisting of. The method according to claim 1, wherein the composition is Lachnoclostridium genus, Subdoligranulum genus, Intestinibacter genus, and Agatobacter 1 selected from the group consisting of genus A composition for predicting obesity risk further comprising an agent capable of detecting more than one species of microorganism. The composition for predicting obesity risk according to claim 1, wherein the agent capable of detecting the microorganism is a primer, probe, antisense oligonucleotide, aptamer or antibody specific to the microorganism. The composition for predicting obesity risk according to claim 3, wherein the primer is a primer capable of amplifying the 16s rRNA of the microorganism. The composition of claim 1, wherein the composition is applied to a stool sample. A kit for predicting obesity risk comprising the composition for predicting obesity risk of claim 1 or 2. The kit for predicting obesity risk according to claim 6, wherein the kit is an RT-PCR kit, a microarray chip kit, or a protein chip kit. For predicting the risk of obesity, comprising measuring the level of amplification products for one or more microorganisms selected from the group consisting of the genus Lombustia, the genus Certerella, and the genus Prevotella in the stool sample isolated from the subject How to provide information. The method according to claim 8, wherein the step of measuring the level of the amplification product for one or more microorganisms selected from the group consisting of Lachinoclostridium genus, Subdoligranulum genus, Intestinibacter genus, and Agatobacter genus is added. Including, an information providing method for predicting the risk of obesity. The method according to claim 8, wherein the step of measuring the level of the amplification product further comprises calculating a relative ratio of the total microorganisms in the stool sample. The method for providing information for predicting the risk of obesity according to claim 8, wherein when the level of the amplification product for the microorganism is higher than that of the normal control group, the risk of obesity is predicted to be high. treating a test substance with one or more microorganisms selected from the group consisting of Lombustia, Certerella, and Prevotella in a biological sample isolated from an individual;
measuring the level of microorganisms in the biological sample; and
A method of screening for a substance for treating obesity, comprising comparing the level of the microorganism with a control group not treated with the test substance. The method according to claim 12, wherein the test substance is treated with one or more microorganisms selected from the group consisting of Lachinoclostridium, Subdoligranulum, Intestinibacter, and Agatobacter in the biological sample isolated from the subject. A method of screening for an anti-obesity substance, further comprising the step of: The method of claim 12 , wherein the biological sample is a stool sample.

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