[go: up one dir, main page]

WO2016049927A1 - Biomarqueurs pour les maladies liées à l'obésité - Google Patents

Biomarqueurs pour les maladies liées à l'obésité Download PDF

Info

Publication number
WO2016049927A1
WO2016049927A1 PCT/CN2014/088056 CN2014088056W WO2016049927A1 WO 2016049927 A1 WO2016049927 A1 WO 2016049927A1 CN 2014088056 W CN2014088056 W CN 2014088056W WO 2016049927 A1 WO2016049927 A1 WO 2016049927A1
Authority
WO
WIPO (PCT)
Prior art keywords
gene
sample
obesity
subject
markers
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2014/088056
Other languages
English (en)
Inventor
Qiang FENG
Dongya ZHANG
Longqing TANG
Jun Wang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BGI Shenzhen Co Ltd
Original Assignee
BGI Shenzhen Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BGI Shenzhen Co Ltd filed Critical BGI Shenzhen Co Ltd
Priority to CN201480082401.4A priority Critical patent/CN106795481B/zh
Priority to PCT/CN2014/088056 priority patent/WO2016049927A1/fr
Publication of WO2016049927A1 publication Critical patent/WO2016049927A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to biomarkers and methods for predicting the risk of a disease related to microbes, in particular obesity or related diseases.
  • Obesity which is prevalent in developed countries, has increased considerably worldwide (de Carvalho Pereira et al. , 2013) . It is reported that the prevalence of overweight and obesity combined rose by 27.5%for adults and 47.1%for children between 1980 and 2013 in the world. The number of overweight individuals increased from 857 million in 1980, to 2.1 billion in 2013, and of these, 671 million are affected by obesity. More than 50%of which live in ten countries, and USA has the largest number of obese individuals, followed by China (Ng et al. , 2014) .
  • BMI body mass index
  • Embodiments of the present disclosure seek to solve at least one of the problems existing in the prior art to at least some extent.
  • the present invention is based on the following findings by the inventors:
  • GWAS Metagenome-Wide Association Study
  • the inventors developed a disease classifier system based on the 54 gene markers that are defined as an optimal gene set by a minimum redundancy -maximum relevance (mRMR) feature selection method. For intuitive evaluation of the risk of obesity disease based on these 54 gut microbial gene markers, the inventors calculated a healthy index.
  • the inventors'data provide insight into the characteristics of the gut metagenome related to obesity risk, a paradigm for future studies of the pathophysiological role of the gut metagenome in other relevant disorders, and the potential usefulness for a gut-microbiota-based approach for assessment of individuals at risk of such disorders.
  • the markers of the present invention are more specific and sensitive as compared with conventional markers.
  • analysis of stool promises accuracy, safety, affordability, and patient compliance. And samples of stool are transportable.
  • the present invention relates to an in vitro method, which is comfortable and noninvasive, so people will participate in a given screening program more easily.
  • the markers of the present invention may also serve as tools for therapy monitoring in cancer patients to detect the response to therapy.
  • a biomarker set for predicting a disease related to microbiota in a subject consisting of:
  • the disease is obesity or related disease.
  • some disease related to the related to microbiota in a subject may be analyzed, for example obesity or related disease may be determined based on some sample from the subject , for example, some fecal sample may be used.
  • kits for determining the gene marker set described above comprising primers used for PCR amplification and designed according to the DNA sequecne as set forth in at least a partial sequence of SEQ ID NO: 1 to 54.
  • kits for determining the gene marker set described above comprising one or more probes designed according to the genes as set forth in SEQ ID NO: 1 to 54.
  • the risk of obesity or related disorder in a subject may be predicted by the following step:
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is at least 0.5834.
  • the risk of obesity or related disorder in a subject may be predicted by the following step:
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is at least 0.5834.
  • a method of diagnosing whether a subject has an abnormal condition related to microbiota or is at the risk of developing an abnormal condition related to microbiota comprising:
  • the risk of obesity or related disorder in a subject may be predicted by the following step:
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is at least 0.5834.
  • the abnormal condition related to microbiota is obesity or related disorder.
  • Fig. 1 The association analysis of Obese p-value distribution identified a disproportionate over-representation of strongly associated markers at lower P-values.
  • Example 1 Identifying biomarkers for evaluating obesity risk
  • Fecal samples from 158 Chinese subjects, including 78 obesity patients and 80 control subjects (training set) were collected by Rui Jin Hospital Shanghai Jiao Tong Univeristy School of Medicine in 2012. Obesity patients were age from 18 to 30 with BMI over 25. Subjects were asked to collect fresh feces samples at hospital. Collected samples were put in sterile tubes and stored at -80°Cimmediately until further analysis.
  • DNA library construction was performed following the manufacturer ⁇ s instruction (Illumina, insert size 350bp, read length 100bp) .
  • the inventors used the same workflow as described previously to perform cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturation, and hybridiza-tion of the sequencing primers.
  • the inventors constructed one paired-end (PE) library with insert size of 350 bp for each sample, followed by a high-throughput sequencing to obtain around 30 million PE reads of length 2x100bp. High-quality reads were obtained by filtering low-quality reads with ambiguous ⁇ N'bases, adapter contamination and human DNA contamination from the Illumina raw reads, and by trimming low-quality terminal bases of reads simultaneously.
  • PE paired-end
  • the inventors totally output about 5.9 Gb per sample of fecal micbiota sequencing data (high quality clean data) (Table 1) from 158 samples (78 cases and 80 controls) on Illumina HiSeq 2000 platform.
  • Table 1 Summary of metagenomic data. Fourth column reports results from Wilcoxon rank-sum tests.
  • the average reads mapping rate was shown on Table 1. This mapping rate was close to the samples in Li, J. et al. 2014, supra, which indicated that this mapping rate was sufficient for the further study.
  • the inventors derived the gene profile (9.9Mb genes) from the mapping result using the same method as Li, J. et al. 2014, supra.
  • Taxonomic assignment of genes was performed using an in-house pipeline which had described in the published paper (Li, J. et al. 2014, supra) .
  • PERMANOVA permutational multivariate analysis of variance
  • the inventors performed the analysis using the method implemented in package ′′vegan′′ in R, and the permuted p-value was obtained by 10,000 times permutations.
  • the inventors also corrected for multiple testing using′′ p. adjust′′ in R with Benjamini-Hochberg method to get the q-value for each test.
  • PERMANOA identified three significant factors associated with gut microbe (based on gene profiles) (q ⁇ 0.05, Table 2) .
  • FDR false discovery rate
  • Receiver Operator Characteristic (ROC) analysis The inventors applied the ROC analysis to assess the performance of the obesity classification based on metagenomic markers. The inventors then used the “pROC” package in R to draw the ROC curve.
  • ROC Receiver Operator Characteristic
  • 237 MLG species based on the 396, 100 obesity associated maker genes profile.
  • the inventors used the 396, 100 gene markers to built the metagenomic linkage group (MLG) using the same method described in the published T2D paper (Qin et al. 2012, supra) . All the 396, 100 genes were annotated by aligning these genes to the 4, 653 reference genomes in IMG v400.
  • An MLG was assigned to a genome if more than 50%constitutive genes were annotated to that genome, otherwise it was termed as unclassified.
  • Total 237 MLG genomes with gene number > 100 were selected (P-value ⁇ 0.01) .
  • the inventors estimated the average abundance of the genes of the MLG species, after removing the 5%lowest and 5%highest abundant genes (Qin et al. 2012, supra) .
  • a random forest model (R. 2.14, randomForest4.6-7 package) (Liaw, Andy &Wiener, Matthew. Classification and Regression by randomForest, R News (2002) , Vol. 2/3 p. 18, incorporated herein by reference) was trained using the MLG abundance profile of the training cohort (158 samples) to select the optimal set of MLG markers. The model was tested on one or more testing sets and the prediction error was calculated.
  • RandomForest4.6-7 package package in R vision 2.14
  • input is a training dataset (namely relative abundance profiles of selected MLGs in training samples)
  • sample disease status sample disease status of training samples is a vectot, 1 for obesity, 0 for control
  • test set just the relative abundance profiles of selected MLGs in test set
  • the inventors used the randomForest function from randomForest package in R software to build the classification, and predict function was used to predict the test set.
  • Output is the prediction results (probability of illness; cutoff is 0.5 and if the probability of illness ⁇ 0.5, the subject is at risk of obesity)
  • MLG species marker identification To identify 237 MLG species makers, the inventors used “randomForest4.6-7 package” package in R vision 2.14 based on the 237 obesity associated MLG species. Firstly, the inventors sorted all the 237 MLG species by the importance given by the “randomForest” method (Liaw, Andy &Wiener, Matthew. Classification and Regression by randomForest, R News (2002) , Vol. 2/3 p. 18, incorporated herein by reference) . MLG marker sets were constructed by creating incremental subsets of the top ranked MLG species, starting from 1 MLG species and ending at all 237 MLG species. For each MLG makers set, the inventors calculated the false predication ratio in the 158 samples.
  • the 54 MLG species sets with lowest false prediction ratio were selected out as MLG species makers (Table 3-1) .
  • the inventors drew the ROC curve using the OOB (out of bag) prediction probability of illness from randomForest model based on the selected MLG species markers (Table 3-2) and the area under the ROC curve (AUC) was 0.9651 in the 158 samples (Fig. 2) .
  • AUC area under the ROC curve
  • TPR true positive rate
  • FPR false positive rate
  • Table 3-1 54 most discriminant MLGs (species markers) associated with obesity
  • mRMR minimum redundancy -maximum relevance
  • the inventors developed a disease classifier system based on the 54 gene markers that the inventors defined. For intuitive evaluation of the risk of disease based on these gut microbial gene markers, the inventors calculated a gut healthy index (obesity index).
  • the inventors defined and calculated the gut healthy index for each individual on the basis of the selected 54 gene markers as described above. For each individual sample, the gut healthy index of sample j that denoted by I j was calculated by the formula below:
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 54 selected gene markers),
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 54 selected gene markers),
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • the inventors computed a obesity index based on the relative abundance of these 54 gene markers, which clearly separated the obesity patient microbiomes from the control microbiomes (Table 6).
  • Classification of the 78 obesity patient microbiomes against the 80 control microbiomes using the obesity index exhibited an area under the receiver operating characteristic (ROC) curve of 0.9784 (Fig. 3).
  • ROC receiver operating characteristic
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 54 selected gene markers) ,
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 54 selected gene markers) ,
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • Table 7 shows the calculated index of each sample and Table 8 shows the relevant gene relative abundance of a representative sample DB68A.
  • TPR true positive rate
  • FPR false positive rate
  • Case means before operation samples
  • control means after operation 1 month and 3 month.
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 54 selected gene markers) ,
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 54 selected gene markers) ,
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • Table 10 shows the calculated index of each sample and Table 11 shows the relevant gene relative abundance of a representative sample DB62.
  • the error rate was 18.18% (4/22) , validating that the 54 gene markers can classify obesity individuals.
  • most of obesity patients (7/9) were diagnosed as obesity correctly.
  • TPR true positive rate
  • FPR false positive rate
  • the inventors have identified and validated 54 markers set by a minimum redundancy -maximum relevance (mRMR) feature selection method based on 54 obesity-associated gut microbes. And the inventors have built a gut healthy index to evaluate the risk of obesity disease based on these 54 gut microbial gene markers.
  • mRMR minimum redundancy -maximum relevance

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Organic Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Engineering & Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Biotechnology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Biochemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

L'invention concerne des biomarqueurs et des méthodes permettant de prédire le risque d'une maladie liée aux microbes, en particulier l'obésité ou les maladies liées à l'obésité.
PCT/CN2014/088056 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité Ceased WO2016049927A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201480082401.4A CN106795481B (zh) 2014-09-30 2014-09-30 用于肥胖症相关疾病的生物标记物
PCT/CN2014/088056 WO2016049927A1 (fr) 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2014/088056 WO2016049927A1 (fr) 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité

Publications (1)

Publication Number Publication Date
WO2016049927A1 true WO2016049927A1 (fr) 2016-04-07

Family

ID=55629350

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2014/088056 Ceased WO2016049927A1 (fr) 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité

Country Status (2)

Country Link
CN (1) CN106795481B (fr)
WO (1) WO2016049927A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7079320B2 (ja) * 2017-08-29 2022-06-01 ビージーアイ シェンチェン 脂質代謝関連疾患を予防及び／又は治療するための組成物の調製におけるＡｌｉｓｔｉｐｅｓ ｓｈａｈｉｉの適用
US12251407B2 (en) * 2018-12-07 2025-03-18 Bgi Shenzhen Use of Megamonas funiformis in preventing and/or treating metabolic diseases

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101886132A (zh) * 2009-07-15 2010-11-17 北京百迈客生物科技有限公司 基于测序和bsa技术的性状相关的分子标记筛选方法
CN101921748A (zh) * 2010-06-30 2010-12-22 深圳华大基因科技有限公司 用于高通量检测人类乳头瘤病毒的dna分子标签
WO2014019271A1 (fr) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarqueurs pour le diabète et utilisations correspondantes

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050239706A1 (en) * 2003-10-31 2005-10-27 Washington University In St. Louis Modulation of fiaf and the gastrointestinal microbiota as a means to control energy storage in a subject
DK2291543T3 (en) * 2008-05-16 2015-06-01 Interleukin Genetics Inc GENETIC MARKERS FOR WEIGHT CONTROL AND methods of use thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101886132A (zh) * 2009-07-15 2010-11-17 北京百迈客生物科技有限公司 基于测序和bsa技术的性状相关的分子标记筛选方法
CN101921748A (zh) * 2010-06-30 2010-12-22 深圳华大基因科技有限公司 用于高通量检测人类乳头瘤病毒的dna分子标签
WO2014019271A1 (fr) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarqueurs pour le diabète et utilisations correspondantes

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KOETH, R.A. ET AL.: "Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis.", NATURE MEDICINE, 7 April 2013 (2013-04-07), pages 576 - 585 *
QIN, JUNJIE ET AL.: "A metagenome-wide association study of gut microbiota in type 2 diabetes.", NATURE, vol. 490, 4 October 2012 (2012-10-04), pages 55 - 60, XP055111695, DOI: doi:10.1038/nature11450 *
WANG, ZENENG ET AL.: "Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease.", NATURE, vol. 472, 7 April 2011 (2011-04-07), pages 57 - 63, XP055120871, DOI: doi:10.1038/nature09922 *

Also Published As

Publication number Publication date
CN106795481B (zh) 2021-05-04
CN106795481A (zh) 2017-05-31

Similar Documents

Publication Publication Date Title
WO2016049932A1 (fr) Biomarqueurs pour maladies liées à l'obésité
US20190367995A1 (en) Biomarkers for colorectal cancer
CN105132518B (zh) 大肠癌标志物及其应用
CN110904213B (zh) 一种基于肠道菌群的溃疡性结肠炎生物标志物及其应用
CN105473738A (zh) 结直肠癌生物标志物
CN107075453B (zh) 冠状动脉疾病的生物标记物
WO2020244017A1 (fr) Combinaison de biomarqueurs de la schizophrénie à base de flore intestinale, applications associées et procédé de balayage motu associé
WO2016112488A1 (fr) Biomarqueurs de maladies liées au cancer colorectal
CN105473739A (zh) 结直肠癌生物标志物
JP2019511922A (ja) 早産転帰に対する早期リスク評価のための方法及びシステム
CN111676291B (zh) 一种用于肺癌患病风险评估的miRNA标志物
CN109072306A (zh) 分离的核酸及应用
WO2016049927A1 (fr) Biomarqueurs pour les maladies liées à l'obésité
CN114231633A (zh) 用于肺癌诊断的试剂盒、装置及方法
CN112384634B (zh) 骨质疏松生物标志物及其用途
CN109715828B (zh) 用于子宫内膜异位症检测的生物标志物组合及其应用
CN119662826A (zh) 一种基于肠道菌群的胰腺癌生物标志物及其应用
CN115881229B (zh) 基于肠道微生物信息的过敏预测模型构建方法
CN109072278A (zh) 分离的核酸及应用
CN110527721A (zh) 一种陈旧性结核病标志物及其应用
WO2016049917A1 (fr) Biomarqueurs pour les maladies liées à l'obésité
TWI485252B (zh) 一種以糞便中細胞基因套組偵測大腸直腸癌可能性的方法
CN118961905A (zh) 颅咽管瘤血清代谢物谱、肠道菌群谱及其应用
HK1240266A1 (en) Biomarkers for obesity related diseases
CN119360942A (zh) 一种溃疡性结肠炎肠道微生物标志物及其应用与溃疡性结肠炎检测模型

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14903455

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14903455

Country of ref document: EP

Kind code of ref document: A1