CN111334591A - 一种生物标记物的应用及其检测装置、试剂盒以及检测系统 - Google Patents

一种生物标记物的应用及其检测装置、试剂盒以及检测系统 Download PDF

Info

Publication number
CN111334591A
CN111334591A CN202010176650.7A CN202010176650A CN111334591A CN 111334591 A CN111334591 A CN 111334591A CN 202010176650 A CN202010176650 A CN 202010176650A CN 111334591 A CN111334591 A CN 111334591A
Authority
CN
China
Prior art keywords
flora
diabetes
risk score
proteobacteria
lactobacillales
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.)
Pending
Application number
CN202010176650.7A
Other languages
English (en)
Inventor
郑钜圣
陈裕民
苟望龙
蒋增良
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.)
Westlake University
Original Assignee
Westlake University
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 Westlake University filed Critical Westlake University
Priority to CN202010176650.7A priority Critical patent/CN111334591A/zh
Publication of CN111334591A publication Critical patent/CN111334591A/zh
Pending legal-status Critical Current

Links

Images

Classifications

    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING 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
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING 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
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING 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
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C12Q1/06Quantitative determination
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/04Endocrine or metabolic disorders
    • G01N2800/042Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/52Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Landscapes

  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Organic Chemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Genetics & Genomics (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Biochemistry (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Data Mining & Analysis (AREA)
  • Public Health (AREA)
  • Toxicology (AREA)
  • Epidemiology (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Primary Health Care (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

本公开提供一种生物标记物的应用、试剂盒、检测装置和检测系统,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α‑变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的一种或多种。本公开通过一组高效、可识别、可预测的生物标记物,能够用于评估健康水平或检测疾病,尤其能够针对2型糖尿病风险进行预测和治疗。

Description

一种生物标记物的应用及其检测装置、试剂盒以及检测系统
技术领域
本公开涉及生物检测领域,具体涉及一种生物标记物在评估健康水平或检测疾病中,尤其是在2型糖尿病风险预测和治疗中的应用以及所述生物标记物的试剂盒、检测装置和检测系统。
背景技术
糖尿病是世界上发病率增长最快的疾病,根据病因学证据糖尿病可分为4大类,即1型糖尿病、2型糖尿病(T2D)、妊娠期糖尿病(GDM)及其它特殊类型糖尿病,其中,1型糖尿病、2型糖尿病和GDM是临床常见类型。2型糖尿病的病因和发病机制目前还不明确,其显著的病理生理学特征为胰岛素调控葡萄糖代谢能力的下降(胰岛素抵抗),伴随胰岛β细胞功能缺陷所导致的胰岛素分泌减少(或相对减少)。
2型糖尿病是个体遗传因素及后天生活方式等环境因素共同作用的结果,作为人体“内部环境”的重要组成部分,数万亿微生物栖息在人体肠道中,它们重约1.5千克,被视为人体的另一重要“器官”行使诸多功能,例如将不可消化的膳食纤维分解为短链脂肪酸,合成氨基酸和维生素,以及产生神经递质和激素等。最近的研究表明,肠道微生物群与各种人类疾病的发展密切相关,包括肥胖,癌症,自身免疫,心血管疾病(CVDs),代谢综合征,胰岛素抵抗和2型糖尿病等。
肠道微生物组对2型糖尿病发病机制和临床表现的潜在影响,加上其可塑性,使得肠道微生物有可能成为诊断和治疗2型糖尿病的重要潜在靶点或生物标记物。目前,市面上和学术界均未发现可用于有效甄别和预测糖尿病风险的靶点或生物标记物,更未见可用于糖尿病风险预测和干预治疗的检测系统,特别是以肠道微生物为特征的检测系统。
发明内容
本公开的目的是提供一种生物标记物在评估健康水平或检测疾病中,尤其是在2型糖尿病风险预测和治疗中的应用以及所述生物标记物的试剂盒、检测装置和检测系统。
本公开的第一方面,本公开提供一种微生物标记物在评估健康水平或检测疾病中的应用,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的一种或多种。
在一些实施例中,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的5种。
在一些实施例中,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、罗斯氏菌属(roseburia)。
在一些实施例中,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的8种。
在一些实施例中,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的10种。
在一些实施例中,所述生物标记物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的13种。
在一些实施例中,适合本公开所述可检测疾病的作为生物标记物的微生物的来源可以有多种,包括体内微生物、排泄物中的微生物、体表微生物。所述体内微生物,可以是消化系统微生物,例如食道、胃部、肠内微生物;所述排泄物微生物可以为大便微生物;所述体表微生物可以为皮肤表层附着的微生物等。
优选的,本公开所述的微生物选择消化系统微生物,更优选的本公开的微生物为肠道微生物。
另一方面,本公开还提供一种生物标记物在2型糖尿病风险预测和治疗中的应用。
再一方面,本公开还提供一种可检测用于评估健康水平或检测疾病或者预测和治疗2型糖尿病的生物标记物的检测装置。
在一些实施例中所述检测装置可以是试剂盒、检测试纸、检测组合物等。优选地,所述检测装置是试剂盒或检测试纸。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的一种或多种。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的5种。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物包括可检测乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、罗斯氏菌属(roseburia)。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的8种。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的10种。
在一些实施例中,所述检测装置用于检测生物标记物,所述生物标记物为微生物,所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的13种。
在一些实施例中,本公开还涉及一种可检测用于评估健康水平或检测疾病的生物标记物的检测方法。
所述检测可用于检测上述微生物,所述检测方法可以是宏基因组测序、16SrDNA测序或qPCR检测中的任一种或至少两种的组合。
另一方面,本公开还涉及一种可检测上述生物标记物的检测系统,所述检测系统还包括对所述生物标记物的菌群特性进行测量;
在一些实施例中,所述检测系统还包括对肠道微生物的菌群丰度、α-多样性指标进行测量。所述α-多样性指标包括Observed OTU number,Shannon index,Simpson index,Chao1 index,Goods coverage index。
在一些实施例中,所述检测包括对微生物来源个体的粪便进行16S Rrna进行检测。
本公开的有益效果:
本公开通过一组高效、可识别、可预测的生物标记物,能够用于评估健康水平或检测疾病,尤其能够针对2型糖尿病风险进行预测和治疗;本公开明确与2型糖尿病高度相关的菌群;本公开中涉及的生物标记物可检测2型糖尿病和评估罹患2型糖尿病的风险;科学全面的考虑2型糖尿病潜在患者的各项主要指标,可以非介入式的快捷、便利的检测个人的2型糖尿病患病风险。
附图说明
在不一定按比例绘制的附图中,相同的附图标记可以在不同的视图中描述相似的部件。具有字母后缀或不同字母后缀的相同附图标记可以表示相似部件的不同实例。附图大体上通过举例而不是限制的方式示出各种实施例,并且与说明书以及权利要求书一起用于对所公开的实施例进行说明。在适当的时候,在所有附图中使用相同的附图标记指代同一或相似的部分。这样的实施例是例证性的,而并非旨在作为本装置或方法的穷尽或排他实施例。
图1为本公开13种生物标记物评分的AUC分析结果;
图2为本公开10种生物标记物评分的AUC分析结果;
图3为本公开8种生物标记物评分的AUC分析结果;
图4为本公开8种生物标记物评分的AUC分析结果;
图5为本公开5种生物标记物评分的AUC分析结果。
具体实施方式
为使本领域技术人员更好的理解本公开的技术方案,下面结合对本公开作详细说明。本公开使用的所有术语(包括技术术语或者科学术语)与本公开所属领域的普通技术人员理解的含义相同,除非另外特别定义。还应当理解,在诸如通用字典中定义的术语应当被解释为具有与它们在相关技术的上下文中的含义相一致的含义,而不应用理想化或极度形式化的意义来解释,除非这里明确地这样定义。
基于广州营养与健康队列1814名(糖尿病289名)志愿者的饮食、运动、身体测量、肠道菌群特征等训练机器学习模型,并基于发现的菌群靶点构建糖尿病菌群风险评分体系。
纳入训练模型的变量按属性具体可分以下几类:
人口统计学特征:主要包括年龄、性别、教育程度、收入、婚姻状态。人口统计学信息源于面对面问卷调查,其中教育程度划分为三个层次:初中及以下、高中或中专、本科及以上,收入主要按人均每月收入划分为四个等级:≤500,501-1500,1501-3000,>3000,婚姻状态分为:已婚及其它两个类别;
生活方式:主要包括饮食、运动、吸烟及饮酒状况等,通过面对面问卷调查获取。其中饮食信息通过过去一年79种常见食物的饮食频率问卷获取,运动信息通过过去一个月19种不同类型体力活动运动时长问卷获取。
可纳入的志愿者生活方式相关的因素有:吸烟、饮酒、喝茶状况(是或否);根据饮食频率问卷条目分别计算得到的每日水果、蔬菜、红肉及加工肉类、酸奶及鱼的摄入量(克);结合“中国食物成分表”计算得到的志愿者平均每天的能量摄入(千卡);结合不同运动的身体活动强度计算志愿者每天的身体活动强度总量(MET);
身体测量指标主要包括:身高、体重、腰围、臀围、颈围、收缩压、舒张压、空腹血清血糖、甘油三酯(TG)、血清总胆固醇(TC)、高密度脂蛋白(HDL)、低密度脂蛋白(LDL)等,其中身高、体重、腰围、臀围、颈围、收缩压及舒张压由专业的护士测量得到,其它血液相关指标数据由专业公司测量得到;
肠道菌群特征:通过对16S rRNA V4区域进行测序,然后将测序获得的Paired-end(PE)reads拼接成一条序列,对目标序列进行质控过滤,过滤后的序列与参考数据库作比对,去除嵌合体序列得到最终得优化序列。基于优化序列进行OTU聚类分析和物种分类注释,依OTU聚类结果进行多样性分析,整个分析流程通过QIIME软件(QuantitativeInsights into Microbial Ecology)完成,拟将最终得到的菌群相对丰度、α-多样性指标(Observed OTU number,Shannon index,Simpson index,Chao1 index,Goods coverageindex)及β-多样性指标(Unweight UniFrac distances及Weight UniFrac distances)作为菌群特征。
可以通过数学方法对菌群构建模型,例如采用机器学习模型进行建模分析,得到2型糖尿病相关的13个菌群特征,根据找到的疾病的菌群特征,例如2型糖尿的病菌群特征构建菌群风险评分(MRS)模型,其中一种可行的计算
Figure BDA0002411052710000071
公式如下:
这里,i表示需计算菌群风险评分的个体编号,j表示与2型糖尿病相关的菌种编号,分别指代上文提到的13种不同菌种,n为纳入计算的总的变量数,n=13;依次对乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)编号为1,2,3,4,5…13。Sij为第i个个体第j个菌种标记物的风险评分,不同菌种风险评分计算公式如下:
Figure BDA0002411052710000081
Figure BDA0002411052710000082
Figure BDA0002411052710000083
Figure BDA0002411052710000084
Figure BDA0002411052710000085
Figure BDA0002411052710000086
Figure BDA0002411052710000087
Figure BDA0002411052710000088
Figure BDA0002411052710000089
Figure BDA00024110527100000810
Figure BDA00024110527100000811
Figure BDA00024110527100000812
Figure BDA00024110527100000813
根据评分体系,采用不同的标记物对不同人员进行采样并验证。基于AUC分析结果,如图1所示,若:MRSi≥7.5则判定为2型糖尿病,否则为正常。
实施例1
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0,0.00192,0.00041,0.00324,0.0328,0.00114,0.0172,0.000182,0.0117,0.0131,0.00041,0.0109,0.000958。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例2
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0,0.00275,0.00055,0.00712,0.0378,0.00301,0.00686,0.000647,0.0196,0.0281,0.00055,0.0134,0.0012。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例3
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0000332,0.00149,0.000299,0.00395,0.0407,0.00126,0.0115,0.000465,0.0169,0.0011,0.000299,0.00953,0.00176。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例4
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0000811,0.003,0.000487,0.00162,0.0179,0.000243,0.0115,0.000122,0.0155,0.118,0.000487,0.0103,0.00105。
依上述计算公式得到的该个体2型糖尿病风险评分为1,实际结果为正常。
实施例5
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0000399,0.00164,0.000479,0.00395,0.0271,0.003,0.00551,0.00016,0.0229,0.000519,0.000479,0.0123,0.000479。
依上述计算公式得到的该个体2型糖尿病风险评分为2,实际结果为正常。
实施例6
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.000356,0.000966,0.000305,0.00686,0.0134,0.00935,0.0387,0.000102,0.0118,0.000966,0.000305,0.0182,0.00569。
依上述计算公式得到的该个体2型糖尿病风险评分为3,实际结果为正常。
实施例7
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.000608,0.0032,0.00333,0.0402,0.0334,0,0.0454,0,0.0183,0.00154,0.00333,0.0126,0.0114。
依上述计算公式得到的该个体2型糖尿病风险评分为4,实际结果为正常。
实施例8
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0,0.000214,0.0000427,0.000256,0.0303,0.0189,0.00483,0.0000427,0.0111,0.0435,0.0000427,0.0227,0.000385。
依上述计算公式得到的该个体2型糖尿病风险评分为5,实际结果为正常。
实施例9
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0000914,0.00016,0.00149,0.0123,0.00633,0.0000457,0.00229,0.0000229,0.0239,0.0795,0.00149,0.00818,0.0315。
依上述计算公式得到的该个体2型糖尿病风险评分为6,实际结果为正常。
实施例10
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0,0.00471,0,0.00386,0.00145,0.00015,0.0365,0.000251,0.00847,0.00376,0,0.00761,0.00296。
依上述计算公式得到的该个体2型糖尿病风险评分为7,实际结果为正常。
实施例11
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.000163,0.000261,0,0.00124,0.00297,0.000327,0.0202,0.000947,0.0257,0.00062,0,0.008,0.000881。
依上述计算公式得到的该个体2型糖尿病风险评分为8,实际结果为2型糖尿病。
实施例12
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.000113,0.00138,0.000113,0.00107,0.000423,0.0000282,0.0078,0,0.00837,0.000113,0.000113,0.00741,0.00459。
依上述计算公式得到的该个体2型糖尿病风险评分为9,实际结果为2型糖尿病。
实施例13
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0,0.0000998,0,0,0.0026,0,0.0118,0.0000998,0.0000998,0.0000998,0,0,0.143。
依上述计算公式得到的该个体2型糖尿病风险评分为10,实际结果为2型糖尿病。
实施例14
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.00191,0.00436,0.0000272,0.000218,0.00945,0,0.032,0,0.0308,0,0.0000272,0.0121,0。
依上述计算公式得到的该个体2型糖尿病风险评分为11,实际结果为2型糖尿病。
实施例15
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.00526,0.000175,0,0.0202,0.00311,0.0000251,0.0642,0.009,0.0002,0,0.00536,0.0197。
依上述计算公式得到的该个体2型糖尿病风险评分为12,实际结果为2型糖尿病。
实施例16
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0501 0,0,0.000528,0,0,0.0622,0,0.00695,0,0,0.00195,0.0133。
依上述计算公式得到的该个体2型糖尿病风险评分为13,实际结果为2型糖尿病。
实施例17
13种计算菌群风险评分的菌群标记物相对丰度及多样性依次为:
0.0109,0.000262,0.0000291,0.00282,0.00146,0,0.0227,0,0.00533,0.0000291,0.0000291,0.0023,0.000233。
依上述计算公式得到的该个体2型糖尿病风险评分为13,实际结果为2型糖尿病。
以下的实施例是10种计算菌群风险评分的生物标记物,例如选用以下组合:
f__lactobacillaceae,c__alphaproteobacteria f__mogibacteriaceae,g__clostridiaceaeother,c__deltaproteobacteria,g__butyrivibrio,o__lactobacillales,f__comamonadaceae,g__roseburia,g__megamonas的应用实例,其中,如果MRSi≥5.5则判定为2型糖尿病,否则为正常,如图2所示。
实施例18
10种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0000522,0.00146,0.000261,0.0047,0.0119,0.0011,0.00261,0.000209,0.0292,0.00104。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例19
10种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0000318,0.00264,0.00035,0.00229,0.0168,0.000255,0.0151,0.00105,0.0153,0.0821。
依上述计算公式得到的该个体2型糖尿病风险评分为1,实际结果为正常。
实施例20
10种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0000406,0.00112,0.000122,0.00101,0.0107,0.00268,0.00897,0.0015,0.00736,0.0551。
依上述计算公式得到的该个体2型糖尿病风险评分为3,实际结果为正常。
实施例21
10种计算菌群风险评分的菌群标记物相对丰度依次为:
0.00151,0.000438,0.00374,0.121,0.0991,0.0033,0.0272,0,0.00446,0。
依上述计算公式得到的该个体2型糖尿病风险评分为6,实际结果为糖尿病。
实施例22
10种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0109,0.00026,0.0000291,0.00282,0.00146,0,0.0227,0,0.00533,0.0000291。
依上述计算公式得到的该个体2型糖尿病风险评分为10,实际结果为糖尿病。
以下的实施例是8种计算菌群风险评分的生物标记物,例如选用以下组合:
f__lactobacillaceae,c__alphaproteobacteria f__mogibacteriaceae,g__clostridiaceaeother,c__deltaproteobacteria,g__butyrivibrio,o__lactobacillales,f__comamonadaceae的应用实例,其中,如果MRSi≥3.5则判定为2型糖尿病,否则为正常,如图3所示。
实施例23
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.0035,0.0000667,0.00968,0.0226,0.0002,0.0116,0.0000667。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例24
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.00275,0.00055,0.00712,0.0378,0.00301,0.00686,0.000647。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例25
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.00275,0.00055,0.00712,0.0378,0.00301,0.00686,0.000647。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例26
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.000162,0.00158,0.000405,0.0112,0.0476,0.0015,0.0678,0.0000811。
依上述计算公式得到的该个体2型糖尿病风险评分为2,实际结果为正常。
实施例27
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0000806,0.00343,0,0.00471,0.00391,0,0.047,0.000121。
依上述计算公式得到的该个体2型糖尿病风险评分为4,实际结果为糖尿病。
实施例28
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.000151,0.00173,0,0.000603,0.00234,0.0000377,0.00449,0。
依上述计算公式得到的该个体2型糖尿病风险评分为6,实际结果为糖尿病。
实施例29
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0209,0.000388,0,0.000259,0.00611,0.0000432,0.0295,0。
依上述计算公式得到的该个体2型糖尿病风险评分为8,实际结果为糖尿病。
以下的实施例是8种计算菌群风险评分的生物标记物,例如选用以下组合:
f__lactobacillaceae,f__mogibacteriaceae,g__clostridiaceaeother,c__deltaproteobacteria,o__lactobacillales,g__roseburia,g__mogibacteriaceaeother,g__dorea的应用实例,若MRSi≥4.5则判定为2型糖尿病,否则为正常,如图4所示。
实施例30
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.000602,0.00481,0.0197,0.00481,0.0148,0.000602,0.0121。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例31
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.00638,0.000106,0.00574,0.0177,0.0717,0.0294,0.000106,0.0138。
依上述计算公式得到的该个体2型糖尿病风险评分为2,实际结果为正常。
实施例32
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.0000277,0.000388,0.0109,0.00155,0.0281,0.0000277,0.00677。
依上述计算公式得到的该个体2型糖尿病风险评分为4,实际结果为正常。
实施例33
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.000196,0,0.000147,0.00845,0.00489,0.0132,0,0.0124。
依上述计算公式得到的该个体2型糖尿病风险评分为5,实际结果为糖尿病。
实施例34
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0,0.000164,0.00652,0.00296,0.00564,0,0.00411。
依上述计算公式得到的该个体2型糖尿病风险评分为6,实际结果为糖尿病。
实施例35
8种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0109,0.0000291,0.00282,0.00146,0.0227,0.00533,0.0000291,0.0023。
依上述计算公式得到的该个体2型糖尿病风险评分为8,实际结果为糖尿病。
以下的实施例是5种计算菌群风险评分的生物标记物,例如选用以下组合:
f__lactobacillaceae,c__alphaproteobacteria,f__mogibacteriaceae,g__clostridiaceaeother,c__deltaproteobacteria的应用实例,若MRSi≥2.5则判定为2型糖尿病,否则为正常,如图5所示。
实施例36
5种计算菌群风险评分的菌群标记物相对丰度依次为:
0.000054,0.0154,0.000054,0.0047,0.0615。
依上述计算公式得到的该个体2型糖尿病风险评分为0,实际结果为正常。
实施例37
5种计算菌群风险评分的菌群标记物相对丰度依次为:
0,0.00163,0.000381,0.0044,0.0225。
依上述计算公式得到的该个体2型糖尿病风险评分为1,实际结果为正常。
实施例38
5种计算菌群风险评分的菌群标记物相对丰度依次为:
0.0000306,0.00165,0,0.000733,0.00938。
依上述计算公式得到的该个体2型糖尿病风险评分为3,实际结果为糖尿病。
实施例39
5种计算菌群风险评分的菌群标记物相对丰度依次为:
0.00191,0.00436,0.0000272,0.000218,0.00945。
依上述计算公式得到的该个体2型糖尿病风险评分为4,实际结果为糖尿病。
实施例40
5种计算菌群风险评分的菌群标记物相对丰度依次为:
0.000281,0.000402,0,0.00213,0.00771。
依上述计算公式得到的该个体2型糖尿病风险评分为5,实际结果为糖尿病。
实施例41
一种可检测本公开用于评估健康水平或检测疾病的生物标记物的试剂盒,所述试剂盒可以包括固相载体、结合物、阴性对照品和阳性对照品、参考标准品、结合物及标本的稀释液、洗涤液、反应终止液等。
实施例42
一种可检测本公开用于评估健康水平或检测疾病的生物标记物的检测装置,所述的检测装置可以是试剂盒、检测试纸、检测组合物等。所述的检测装置包括可以包括检测试剂承载装置,检测装置、检测结果输出装置。所述检测结果输出装置,可以是显示装置、打印装置、二维码扫描装置或发送装置(例如,通过电子邮件)。
实施例43
一种可检测本公开用于评估健康水平或检测疾病的生物标记物的检测系统,所述检测系统包括对特定微生物的菌群特性进行测量;还包括对肠道微生物的菌群丰度、α-多样性指标进行测量。所述α-多样性指标包括Observed OTU number,Shannon index,Simpson index,Chao1 index,Goods coverage index和/或对微生物来源个体的粪便进行16S Rrna进行检测。所述检测系统包括对选自lactobacillaceae、alphaproteobacteria、mogibacteriaceae、clostridiaceaeother、deltaproteobacteria、butyrivibrio、lactobacillales、Comamonadaceae、roseburia、megamonas、mogibacteria、ceaeother、dorea、dispar中的一种或多种菌群的肠道微生物进行测量并打分的步骤。
本领域技术人员应当理解,上述所列出的一系列的实施例仅仅是针对本公开的可行性实施方式的具体说明,它们并非用以限制本公开的保护范围,凡未脱离本公开创新所作的等效实施方式或变更均应包含在本公开的保护范围。
此外,尽管已经在本文中描述了示例性实施例,其范围包括任何和所有基于本公开的具有等同元件、修改、省略、组合(例如,各种实施例交叉的方案)、改编或改变的实施例。权利要求书中的元件将被基于权利要求中采用的语言宽泛地解释,并不限于在本说明书中或本申请的实施期间所描述的示例,其示例将被解释为非排他性的。因此,本说明书和示例旨在仅被认为是示例,真正的范围和精神由以下权利要求以及其等同物的全部范围所指示。
以上描述旨在是说明性的而不是限制性的。例如,上述示例(或其一个或更多方案)可以彼此组合使用。例如本领域普通技术人员在阅读上述描述时可以使用其它实施例。另外,在上述具体实施方式中,各种特征可以被分组在一起以简单化本公开。这不应解释为一种不要求保护的公开的特征对于任一权利要求是必要的意图。相反,本公开的主题可以少于特定的公开的实施例的全部特征。从而,以下权利要求书作为示例或实施例在此并入具体实施方式中,其中每个权利要求独立地作为单独的实施例,并且考虑这些实施例可以以各种组合或排列彼此组合。本公开的范围应参照所附权利要求以及这些权利要求赋权的等同形式的全部范围来确定。
以上实施例仅为本公开的示例性实施例,不用于限制本公开,本公开的保护范围由权利要求书限定。本领域技术人员可以在本公开的实质和保护范围内,对本公开做出各种修改或等同替换,这种修改或等同替换也应视为落在本公开的保护范围内。

Claims (8)

1.一种生物标记物在评估健康水平或检测疾病中的应用,所述生物标记物为微生物,其特征在于所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的一种或多种。
2.一种如权利要求1所述的生物标记物在评估健康水平或检测疾病中的应用,所述生物标记物为微生物,其特征在于所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的5种。
3.一种如权利要求1所述的生物标记物在评估健康水平或检测疾病中的应用,所述生物标记物为微生物,其特征在于所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、罗斯氏菌属(roseburia)。
4.一种如权利要求1所述的生物标记物在评估健康水平或检测疾病中的应用,所述生物标记物为微生物,其特征在于所述微生物选自乳杆菌科(lactobacillaceae)、α-变形菌纲(alphaproteobacteria)、艰难杆菌科(mogibacteriaceae)、其它梭菌属(clostridiaceaeother)、变形菌纲(deltaproteobacteria)、丁酸弧菌属(butyrivibrio)、乳杆菌目(lactobacillales)、丛毛单胞菌科(comamonadaceae)、罗斯氏菌属(roseburia)、巨单胞菌属(megamonas)、其它艰难杆菌属(mogibacteriaceaeother)、多尔氏菌属(dorea)、殊异韦荣菌(dispar)中的8种。
5.权利要求1-4中任一项中的生物标记物在2型糖尿病风险预测和治疗中的应用。
6.一种试剂盒,其特征在于可检测权利要求1-5中任一项中的所述生物标记物。
7.一种检测装置,其特征在于可检测权利要求1-5中任一项中的所述生物标记物。
8.一种检测系统,其特征在于可检测权利要求1-5中任一项中的所述生物标记物以及测量所述生物标记物的菌群特性。
CN202010176650.7A 2020-03-13 2020-03-13 一种生物标记物的应用及其检测装置、试剂盒以及检测系统 Pending CN111334591A (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010176650.7A CN111334591A (zh) 2020-03-13 2020-03-13 一种生物标记物的应用及其检测装置、试剂盒以及检测系统

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010176650.7A CN111334591A (zh) 2020-03-13 2020-03-13 一种生物标记物的应用及其检测装置、试剂盒以及检测系统

Publications (1)

Publication Number Publication Date
CN111334591A true CN111334591A (zh) 2020-06-26

Family

ID=71178237

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010176650.7A Pending CN111334591A (zh) 2020-03-13 2020-03-13 一种生物标记物的应用及其检测装置、试剂盒以及检测系统

Country Status (1)

Country Link
CN (1) CN111334591A (zh)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102743420A (zh) * 2012-06-06 2012-10-24 上海交通大学 改善肠道菌群结构的方法及应用
WO2014019271A1 (en) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarkers for diabetes and usages thereof
CN104540962A (zh) * 2012-08-01 2015-04-22 深圳华大基因研究院 糖尿病生物标志物及其应用
US20180122510A1 (en) * 2014-10-21 2018-05-03 uBiome, Inc. Method and system for characterizing diet-related conditions
CN109797190A (zh) * 2019-03-11 2019-05-24 上海宝藤生物医药科技股份有限公司 一种用于评估ii型糖尿病风险的微生物标志物及其应用

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102743420A (zh) * 2012-06-06 2012-10-24 上海交通大学 改善肠道菌群结构的方法及应用
WO2014019271A1 (en) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarkers for diabetes and usages thereof
CN104540962A (zh) * 2012-08-01 2015-04-22 深圳华大基因研究院 糖尿病生物标志物及其应用
US20150211053A1 (en) * 2012-08-01 2015-07-30 Bgi-Shenzhen Biomarkers for diabetes and usages thereof
US20180122510A1 (en) * 2014-10-21 2018-05-03 uBiome, Inc. Method and system for characterizing diet-related conditions
CN109797190A (zh) * 2019-03-11 2019-05-24 上海宝藤生物医药科技股份有限公司 一种用于评估ii型糖尿病风险的微生物标志物及其应用

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HALAWA MR.等: "The Gut Microbiome, Lactobacillus acidophilus; Relation with Type 2 Diabetes Mellitus", 《CURR DIABETES REV》 *
马苏娴等: "2型糖尿病患者肠道菌群变化及意义", 《山东医药》 *

Similar Documents

Publication Publication Date Title
Wolters et al. Dietary fat, the gut microbiota, and metabolic health–A systematic review conducted within the MyNewGut project
Niland et al. Health benefits and adverse effects of a gluten-free diet in non–celiac disease patients
Cherian et al. Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) diet slows cognitive decline after stroke
Salas-Salvadó et al. Prevention of diabetes with Mediterranean diets: a subgroup analysis of a randomized trial
Whitaker et al. Depressive symptoms are associated with dietary intake but not physical activity among overweight and obese women from disadvantaged neighborhoods
Herman et al. Dietary habits of 2-to 9-year-old American children are associated with gut microbiome composition
JP2012165716A (ja) 腸内常在菌解析情報を基にした食事支援システム
Booth et al. Methods of the NSW schools physical activity and nutrition survey (SPANS)
Robare et al. The “10 keys” to healthy aging: 24-month follow-up results from an innovative community-based prevention program
Wutthi-In et al. Gut microbiota profiles of treated metabolic syndrome patients and their relationship with metabolic health
Nicklas et al. Differing statistical approaches affect the relation between egg consumption, adiposity, and cardiovascular risk factors in adults
Mager et al. Diet patterns in an ethnically diverse pediatric population with celiac disease and chronic gastrointestinal complaints
Valido et al. Systematic review of the effects of oat intake on gastrointestinal health
Amadieu et al. Dietary fiber deficiency as a component of malnutrition associated with psychological alterations in alcohol use disorder
Petridi et al. The impact of ultra-processed foods on obesity and cardiometabolic comorbidities in children and adolescents: a systematic review
Shin et al. Association between different types of plant-based diet and dyslipidaemia in Korean adults
Hassan et al. Effect of weight loss program using prebiotics and probiotics on body composition, physique, and metabolic products: longitudinal intervention study
CN111334591A (zh) 一种生物标记物的应用及其检测装置、试剂盒以及检测系统
Byrne et al. A study protocol for a randomised crossover study evaluating the effect of diets differing in carbohydrate quality on ileal content and appetite regulation in healthy humans
Hasan et al. A relationship between knowledge, attitude, and practice about balanced nutrition guidelines and metabolic syndrome among central obese teachers in Makassar
Mohr et al. A systematic scoping review of study methodology for randomized controlled trials investigating probiotics in athletic and physically active populations
Agbozo et al. Lifestyle habits and perceived wellbeing of adults presenting with metabolic syndrome at a diabetic clinic in Ghana: A case-control study
Näslund-Koch et al. Adherence to general national dietary guidelines and risk of psoriasis: results from a general population study of 105,332 individuals
Salami et al. Prevalence and Determinants of Childhood Obesity among Primary School Pupils in Edo State: A Cross-Sectional Study
Mahmudiono et al. Dietary and Physical Activity Modifications Intervention for Older People

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination