CN116656846A - 口腔微生物作为微生物标记物在制备区分糖尿病患者的试剂盒中的应用 - Google Patents
口腔微生物作为微生物标记物在制备区分糖尿病患者的试剂盒中的应用 Download PDFInfo
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Abstract
本发明属于临床医学技术领域,具体涉及口腔微生物作为微生物标记物在制备区分糖尿病患者的试剂盒中的应用。本发明发现了T2DM患者不同疾病阶段口腔菌群组成结构的差异,并进行功能预测分析。本发明以口腔菌群为基础,构建T2DM的诊断模型,以期寻找诊断T2DM的新方法并进行早期筛查。本发明基于口腔菌群构建的诊断模型发现,口腔菌群标志物ASV76(Lactobacillales_unclassified)、ASV57(Streptococcus)、ASV90(Saccharimonadaceae_ TM7x)、ASV35(Rothia)、ASV126(Veillonella)可以从Pre‑DM组中特异性识别出HbA1C‑L组的患者。
Description
技术领域
本发明属于临床医学技术领域,具体涉及口腔微生物作为微生物标记物在制备区分糖尿病患者的试剂盒中的应用。
背景技术
糖尿病是一组以高血糖为特征的慢性代谢性疾病,由胰岛素抵抗和胰岛素分泌受损引起。有两种最常见的形式,即1型糖尿病和2型糖尿病(Type 2diabetes mellitus,T2DM)。糖尿病前期通常定义为血糖水平高于正常但低于糖尿病阈值,包括空腹血糖受损(Impaired fasting glucose,IFG)和糖耐量异常(Impaired glucose tolerance,IGT),是发展为糖尿病的高风险状态。在日常生活中,糖尿病前期患者很容易被忽略。每年大约5~5%的IGT患者发展为T2DM。血糖控制不佳会导致严重的并发症,如糖尿病肾病、糖尿病视网膜病变等,给人们生活质量和寿命带来很大影响。
近年来,研究发现,糖尿病与多种口腔疾病密切相关,包括口干症、龋齿、牙龈炎、牙周炎、味觉障碍和伤口愈合不良等,严重影响患者的生活质量。
口腔拥有仅次于肠道的第二大多样性微生物群,其中包含700多种细菌、真菌、病毒等。口腔菌群与宿主处于动态平衡,某些机会致病菌可能会破坏此平衡,产生致病性影响全身的健康。多种口腔菌群的相互作用有助于人体抵抗外界有害刺激的侵袭,但微生物菌群失衡会导致龋齿、牙周炎等多种口腔疾病。有研究发现,糖尿病可导致口腔菌群的多样性和数量发生改变,其中某些口腔菌群(如:hemolytic Streptococci、Staphylococcus、Prevotella、Leptotrichia)与血糖水平呈正相关,某些口腔菌群(如Proteobacteria、Bifidobacteria)与血糖水平呈负相关,然而,一些口腔菌群可能完全不受影响。综上,糖尿病可导致口腔菌群发生改变,但不同的菌群种类可能呈现不一样的变化趋势。
肠道是人体最大的免疫器官,肠道菌群在维持肠道微环境稳态、调节新陈代谢和免疫力等方面发挥着重要作用,也被称为“第二基因组”。肠道菌群失调是指菌群丰度、多样性、组成结构、定植部位发生改变,是导致糖尿病发病的重要危险因素。
肠道菌群失调可能是导致T2DM的重要危险因素。血糖升高可导致肠道菌群失调,使菌群结构及肠道代谢产物发生改变,同时破坏肠道屏障功能,促进肠道菌群及其代谢产物进入循环系统,从而降低胰岛素敏感性、影响葡萄糖代谢和打破免疫稳态,对多个器官造成损害。另一方面,一些菌群已被证明通过减少促炎因子的产生和维持肠道屏障的完整性来发挥保护作用,从而降低糖尿病的发病风险。因此,破坏肠道屏障功能和增加炎症反应可能是胰岛素抵抗和糖尿病的触发因素。
发明内容
本发明发现了T2DM患者不同疾病阶段口腔菌群和肠道菌群组成结构的差异,并进行功能预测分析。本发明以口腔菌群和肠道菌群为基础,构建T2DM的诊断模型,以期寻找诊断T2DM的新方法并进行早期筛查。
本发明采用以下技术方案:
本发明为病例对照临床研究,招募94名健康对照者(简称HC组)、55名糖尿病前期患者(简称pre-DM组)、63名HbA1C<8.0%的T2DM患者(简称HbA1C-L组)和65名HbA1C≥8.0%的T2DM患者(简称HbA1C-H组)。记录志愿者的临床数据、收集口腔及肠道标本,通过16S rRNA技术和PICRUSt菌群基因功能预测对四组人群口腔菌群和肠道菌群进行相关性分析,发现四组人群口腔菌群及肠道菌群组成和功能的差异。然后,分别利用口腔菌群及肠道菌群构建诊断模型,为疾病早期筛查和诊治提供新方法。最后,通过Venn图展示口腔菌群和肠道菌群组成相似性及重叠情况,探讨“口腔-肠道”轴的存在。
结果如下:
1.一般资料的比较
本发明共分析了277名受试者的样本和临床信息,其中54名健康对照者、55名糖尿病前期患者、52名HbA1C<8.0%和53名HbA1C≥8.0%的T2DM患者同时留取了口腔及肠道标本。临床数据分析发现,四组之间收缩压、HbA1C、空腹血糖、口服葡萄糖耐量试验后2小时的血糖值、C肽在0min、30min、60min、180min及胰岛素在30min、60min、180min的值均存在显著差异(P<0.05);而性别、年龄、体重指数、腰臀比、舒张压、C肽在120min和胰岛素在0min、120min的值、吸烟、饮酒情况及脱落牙齿数量、刷牙次数、刷牙时间不具有统计学意义(P>0.05)。
2.口腔菌群组成的相关性分析
四组口腔菌群的α多样性无统计学差异。基于jaccard-binary距离的ADONIS分析发现,四组口腔菌群β多样性在整体上存在显著差异。其中,HC组与HbA1C-L组、HbA1C-H组存在显著差异(P<0.05),pre-DM组与HbA1C-L组、HbA1C-H组也显著不同(P<0.05),但HC组与pre-DM组及HbA1C-L组与HbA1C-H组无统计学差异(P>0.05)。在门水平上,Bacteroidota随着血糖水平的增加相对丰度逐渐降低;与HC组和pre-DM组相比,糖尿病组Actinobacteriota的相对丰度显著增加(P<0.05),而Patescibacteria显著降低(P<0.05)。在属水平上,Actinomyces、Veillonella、Fusobacterium、Saccharimonadaceae_TM7x四种菌群随着血糖水平的增加,菌群的相对丰度逐渐降低,而Aggregatibacter呈现增加的趋势;此外,Gemella、Solobacterium、Lachnoanaerobaculum、Selenomonas、Candidatus_Saccharimonas等菌属,随着血糖水平的增加呈现出先增加后降低的趋势,而Alloprevotella、Treponema等随着血糖水平的增加呈现出先降低后增加的趋势,即在Pre-DM组和或HbA1C-L组菌群含量降低,HbA1C-H组菌群含量呈现增加的趋势。
3.肠道菌群组成的相关性分析
与HC组相比,pre-DM组肠道菌群的α多样性存在差异(P<0.05),而HbA1C-L组、HbA1C-H组无差异(P>0.05)。基于jaccard-binary距离的ANONIS分析发现,四组肠道菌群的β多样性在整体上存在显著差异。与HC组相比,pre-DM、HbA1C-L、HbA1C-H均存在明显的分离趋势,pre-DM与HbA1C-L、HbA1C-H整体结构也有明显的分离趋势(P<0.05),但HbA1C-L与HbA1C-H无明显差异。在门水平上,与HC组相比,Pre-DM组Bacteroidota显著增加(P<0.05),在HbA1C-L组和HbA1C-H组,随着血糖水平的增加,相对丰度逐渐降低,而Verrucomicrobiota的相对丰度与血糖水平呈负相关。在属水平上,Akkermansia、Parasutterella随着血糖水平的增加,相对丰度逐渐降低;[Ruminococcus]_torques_group、Oscillospiraceae_NK4A214_group和Flavonifractor的相对丰度与血糖水平呈正相关;此外,Prevotella、Roseburia呈现先增加后降低的趋势,在Pre-DM组或HbA1C-L组菌群丰度增加,但在HbA1C-H组降低;而Ruminococcaceae_CAG-352、[Ruminococcus]_gnavus_group、Ruminococcaceae_uncultured、Ruminococcaceae_Incertae_Sedis呈现出先减少后增加的变化趋势。
4.口腔菌群和肠道菌群的功能预测分析
基于KEGG pathway数据库进行PICRUSt2功能预测,脂肪酸降解(Fatty_acid_degradation)、组氨酸代谢(Histidine_metabolism)在口腔和肠道中均富集,而叶酸生物合成(Folate_biosynthesis)通路均减少。此外,丙酮酸代谢(Pyruvate_metabolism)通路在口腔菌群功能预测中显著富集,甘油脂类代谢(Glycerolipid_metabolism)通路在肠道中显著富集。
5.以口腔菌群或肠道菌群为基础构建诊断模型
为了评估口腔和肠道菌群标志物对糖尿病的诊断价值,本发明分别按照不同组合构建随机森林分类器模型,并进行5倍交叉验证,找出能够最准确区分组间差异且含有最少ASV的组合。通过口腔菌群分类器模型(包括ASV76(Lactobacillales_unclassified)、ASV57(Streptococcus)、ASV90(Saccharimonadaceae_TM7x)、ASV35(Rothia)、ASV126(Veillonella))可以从pre-DM组中特异性识别出HbA1C-L组的患者,但HC组和pre-DM组无法识别;具体的,利用pre-DM患者或HbA1C-L患者口腔中ASV76(Lactobacillales_unclassified)、ASV57(Streptococcus)、ASV90(Saccharimonadaceae_TM7x)、ASV35(Rothia)、ASV126(Veillonella)来计算POD指数,其中POD指数较高且AUC>70%的患者为HbA1C-L患者。
肠道菌群分类器模型(包括ASV82(Lachnoclostridium)、ASV11(Bacteroides)、ASV33(Megamonas))对Pre-DM组的患者具有巨大的诊断潜力。具体的,利用正常人或者Pre-DM患者肠道中的ASV82(Lachnoclostridium)、ASV11(Bacteroides)、ASV33(Megamonas)来计算POD指数,其中POD指数较高且AUC>70%的患者为Pre-DM患者。
6.通过Venn图探讨“口腔-肠道”轴的存在
最后,本发明通过Venn图展示口腔菌群和肠道菌群组成相似性及重叠情况。与HC组相比,在Pre-DM组和糖尿病组的肠道菌群中,口腔与肠道共有菌占比明显增加。结果表明,随着血糖水平的增加口腔和肠道菌群更相似,口腔来源菌群可能更容易在肠道定植。
本发明的有益效果为:
1、本发明发现,与正常对照组相比,随着血糖水平的增加,糖尿病前期和糖尿病患者的口腔菌群及肠道菌群发生改变,且与糖尿病疾病进展相关。
2、本发明基于口腔菌群及肠道菌群构建的诊断模型发现,口腔菌群标志物可以从Pre-DM组中特异性识别出HbA1C-L组的患者,肠道菌群标志物对Pre-DM组患者具有巨大的诊断潜力。这种无创性的操作方法可以早期筛查和诊断糖尿病,并对糖尿病患者的血糖水平的预后作出指向。
3、本发明通过Venn图展示发现,随着血糖水平的增加,肠道中口腔与肠道菌群共有菌占比明显增加,表明口腔和肠道微生物群更相似,口腔来源菌群可能更容易在肠道中定植。
附图说明
图1为四组口腔菌群Alpha、Beta多样性比较。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组。
图2为四组口腔菌群组成分析注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组*P<0.05,**P<0.01,***P<0.001。
图3为口腔菌群与临床指标相关性。注:OGTT-0min:OGTT前空腹血糖值;GTT-120min:OGTT后120分钟时血糖值;c-peptide:C肽值;insulin:胰岛素值*P<0.05,**P<0.01,***P<0.001。
图4为四组口腔菌群功能预测分析。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组*P<0.05,**P<0.01,***P<0.001。
图5口腔菌群标志物在糖尿病患者中的诊断潜力。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组。
图6为四组肠道菌群Alpha、Beta多样性比较。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组。
图7为四组肠道菌群组成分析。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组*P<0.05,**P<0.01,***P<0.001。
图8为肠道菌群与临床数据相关性。注:OGTT-0min:OGTT前空腹血糖值;GTT-120min:OGTT后120分钟时血糖值;c-peptide:C肽值;insulin:胰岛素值*P<0.05,**P<0.01,***P<0.001。
图9为四组肠道菌群功能预测分析。注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组*P<0.05,**P<0.01,***P<0.001。
图10为肠道菌群标志物在糖尿病前期患者中的诊断潜力。
图11为口腔与肠道菌群的Venn图。注:F:粪便标本;S:唾液标本。
具体实施方式
下面通过具体实施方式对本发明进行更加详细的说明,以便于对本发明技术方案的理解,但并不用于对本发明保护范围的限制。
实施例1
1研究对象
1.1研究对象
研究对象招募:选取2021年6月至2022年11月就诊于河南省人民医院内分泌科、体检中心、口腔科的未用药的T2DM患者、糖尿病前期患者及健康志愿者,严格按照纳入和排出标准挑选符合要求的受试者,招募HbA1C≥8.0%的T2DM患者65例(简称HbA1C-H组),HbA1C<8.0%的T2DM患者63例(简称HbA1C-L组),糖尿病前期患者55例(简称pre-DM组),健康受试者94例(简称HC组)。所有受试者均签署书面知情同意书。该研究得到伦理委员会批准,伦理批号:(2023)伦审第(13)号。
1.2纳入及排除标准
1.2.1纳入标准
T2DM患者组:(1)年龄25~70岁;(2)以1999年WHO标准诊断的T2DM;(3)6.5%≤HbA1C≤13%。
糖尿病前期组:(1)IFG:空腹血糖6.1mmol/L~7.0mmol/L,糖负荷后2小时血糖<7.8mmol/L。(2)IGT:空腹血糖<7.0mmol/L和糖负荷后2小时血糖7.8~11.1mmol/L。
健康对照组:与T2DM患者在年龄、籍贯及饮食习惯相匹配的血糖正常者。
1.2.2排除标准
(1)1型糖尿病、妊娠期糖尿病、其他特殊类型糖尿病患者;
(2)患有口腔疾病的患者或过去一年进行过口腔治疗者;
(3)纳入前3个月内接受过抗生素、益生菌、漱口水或任何其他可能影响口腔、肠道微生物群的药物治疗的患者;
(4)严重消化道症状(如:持续性呕吐、便秘、腹泻)或有消化系统手术者;
(5)肝功能异常或中、重度肾功能损伤的患者;
(6)患有严重器质性疾病,如癌症、心脑血管疾病及造血系统疾病等;
(7)患有肺结核和艾滋病等传染病;
(8)患有消化道溃疡、尿路感染、其他内分泌代谢性疾病(如:甲状腺功能亢进、多囊卵巢综合症等),且过去3个月内接受药物治疗者;
2 研究方法
2.1 临床数据收集
对于所有研究志愿者,禁食至少8小时,进行口服葡萄糖耐量试验(Oral glucosetolerance test,OGTT),分别收集OGTT前的空腹血糖(简称OGTT-0min)、OGTT后120min血糖值(OGTT-120min)、五个时间点的C肽和胰岛素数值、HbA1C,同时完善病例报告表,包括性别、年龄、体重指数(Body mass index,BMI)、腰臀比(Waist-hip ratio,WHR)、收缩压(Systolic blood pressure,SBP)、舒张压(Diastolic blood pressure,DBP)、心率、吸烟饮酒情况、牙齿脱落数量、每天刷牙次数、刷牙时间、既往病史和用药史等。
2.2样本收集
2.21粪便标本采集
收集粪便标本的当天清晨,受试者在取样前应严格洗手并进行必要的清洁措施,然后使用无菌勺采集适量的新鲜粪便放入无菌粪便冻存盒中,采集时应避免粪便样品被尿液污染,取样完毕后,立即放入-80℃冰箱中进行冷冻保存。
2.22口腔样本采集
受试者在唾液标本采集前一天晚餐后禁食,睡前刷牙,采集标本当天清晨未刷牙和进食的情况下留取标本。在采样前30分钟,患者用15mL无菌蒸馏水清洗口腔,共3次,在此30分钟内不进食、饮水、吸烟等,漱口后采集唾液。在取样前进行严格洗手及其他必要的清洁措施,通过让唾液积聚在口腔底部,然后每60秒向样本管中吐入唾液,收集至少2mL未受刺激的唾液,取样完毕后,立即放入-80℃冰箱中进行冷冻保存。
2.3DNA提取及测序
将收集好的标本送至上海慕柏生物医学科技有限公司进行菌群测序分析。测序数据通过拼接及过滤得到高质量的序列,在97%的相似性水平下将其聚类为用于物种分类的操作分类单元(Operational taxonomic units,OTU),根据SILVA数据库(SSU138)对每个OTU的代表序列进行物种注释,进而通过分类学分析以得到样本中的物种组成信息。采用Alpha多样性分析口腔、肠道菌群的丰富度和多样性。采用基于jaccard-binary距离的主坐标分析(PCoA)来比较四组间的群落结构差异性,菌群组成结构差异小的趋于聚集在一起,而差异较大的样本则相对较远。采用R软件分别从门和属水平绘制物种组成的柱状图,并在四组间进一步分析菌群相对含量的差异,采用Kruskal-Wallis秩和检验,p<0.05表示差异显著。最后,通过PICRUSt2分析对菌群功能基因和代谢通路进行预测,并通过秩和检验分析挑选出存在组间差异的代谢通路。
2.4构建诊断模型
2.4.1口腔菌群的诊断模型
首先,在pre-DM组和HbA1C-L组中随机挑选2/3受试者(具体为38名pre-DM组的受试者和38名HbA1C-L组的受试者)作为发现队列,用秩和检验挑选出四组间差异显著的ASV,按照不同组合构建随机森林分类器模型,并进行5倍交叉验证,找出能够最准确区分组间差异且含最少的ASV组合,再进行ROC分析,以模型的敏感性为纵坐标代表真阳性率,(1-特异性)为横坐标代表假阳性率,绘制ROC曲线。ROC曲线越靠近左上角,模型的准确性就越高,可通过ROC曲线下的面积(AUC)来对模型的诊断效能进行评估。然后,用剩余的1/3pre-DM组和HbA1C-L组受试者(具体为17名pre-DM组的受试者和19名HbA1C-L组的受试者)作为验证队列,验证所获得的诊断模型对pre-DM组和HbA1C-L组患者的区分能力。最后,用验证队列中的pre-DM和全部的HC患者(具体为17名pre-DM组的受试者和54名HC组的受试者)来进一步验证该模型对糖尿病早期的诊断效能。
2.4.2肠道菌群的诊断模型
首先,在HC组和pre-DM组中随机挑选2/3受试者(具体为64名HC组的受试者和38名pre-DM组的受试者)作为发现队列,用秩和检验挑选出四组间差异显著的ASV,按照不同组合构建随机森林分类器模型,并进行5倍交叉验证,找出能够最准确区分组间差异的最少的ASV组合,再进行ROC分析,通过ROC曲线下的面积(AUC)来对模型的诊断效能进行评估。然后,用剩余的1/3HC组和pre-DM组受试者(具体为30名HC组的受试者和17名pre-DM组的受试者)作为验证队列,验证所获得的诊断模型对HC组和pre-DM患者的区分能力。
2.5绘制Venn图
在属的水平,分别统计肠道菌群和口腔菌群中共有和独有的菌群数目,并通过Venn图展示两部位中菌群组成相似性及重叠情况。用不同颜色的圆圈表示肠道菌群和口腔菌群,中间重叠的部分为两部位共有的细菌的数目。
2.6统计检验
对于正态分布数据或非正态分布数据,连续变量分别表示为平均值±SD或具有四分位间距的中位数。分类变量以百分比表示。所有统计分析,包括单因素方差分析、Kruskal-Wallis检验及卡方检验,通过Bonferroni校正调整显著差异,均使用SPSS 25.0版进行,双侧P<0.05表示有统计学意义。
3 结果
3.1 一般临床资料比较
本发明分析了491例样本(所有口腔和肠道标本)和相关临床信息,包括健康对照组94例,糖尿病前期组55例,HbA1C<8.0%的糖尿病患者63例,HbA1C≥8.0%的糖尿病患者65例。其中,54名健康对照者、55名糖尿病前期患者、52名HbA1C<8.0%和53名HbA1C≥8.0%的T2DM患者同时留取了口腔及肠道标本。四组之间HbA1C、OGTT-0min、OGTT-120min、c-peptide-0min、c-peptide-30min、c-peptide-60min、c-peptide-180min、insulin-30min、insulin-60min、insulin-180min、SBP、心率存在显著差异(P<0.05),而性别、年龄、BMI、WHR、DBP、c-peptide-120min、insulin-0min、insulin-120min、吸烟饮酒情况及牙齿脱落数量、刷牙次数、刷牙时间均不具有统计学意义(P>0.05)。具体数据统计结果见表1。
表1.参与者特征和临床参数
注:HC:健康对照组;pre-DM:糖尿病前期组;HbA1C-L:HbA1C<8.0%的糖尿病组;HbA1C-H:HbA1C≥8.0%的糖尿病组;OGTT-0min:OGTT前空腹血糖值;OGTT-120min:OGTT后120分钟时血糖值;c-peptide:C肽值;insulin:胰岛素值;
a:HC组与Pre-DM组相比,P<0.05;b:HC组与Pre-DM组相比,P<0.01;
c:HC组与HbA1C-L组相比,P<0.05;d:HC组与HbA1C-L组相比,P<0.01;
e:HC组与HbA1C-H组相比,P<0.05;f:HC组与HbA1C-H组相比,P<0.01;
g:Pre-DM组与HbA1C-L组相比,P<0.05;h:Pre-DM组与HbA1C-L组相比,P<0.01;
i:Pre-DM组与HbA1C-H组相比,P<0.05;j:Pre-DM组与HbA1C-H组相比,P<0.01;
k:HbA1C-L组与HbA1C-H组相比,P<0.05;l:HbA1C-L组与HbA1C-H组相比,P<0.01。
3.2口腔菌群组成的相关性分析
3.2.1.口腔菌群多样性
通过Ace指数、Chao指数、Shannon指数和simpson指数评估四组间α多样性均没有显著变化(图1A-D)。此外,基于jaccard-binary距离的PCoA分析发现,四组的口腔菌群有聚集到图中不同位置的趋势(图1E);其中,HC组与HbA1C-L组及HbA1C-H组存在显著差异(P<0.05),pre-DM组与HbA1C-L组及HbA1C-H组也显著不同(P<0.05),但HC组与pre-DM组、HbA1C-L组与HbA1C-H组无统计学差异(P>0.05)。这些结果表明,与健康受试者相比,糖尿病前期菌群多样性未发生明显变化,到达糖尿病期菌群才表现显著变化,且到达糖尿病后菌群趋于稳定。
3.2.2门水平上口腔菌群物种组成分析
发明人进一步分析了口腔菌群的分类组成和变化,四组细菌群落在门水平上的平均组成和相对丰度如图2A所示,四组均以Firmicutes(38.56%vs41.48%vs35.51%vs37.96%)、Proteobacteria(25.47%vs 25.38%vs 31.94%vs 30.33)、Bacteroidota(24.04%vs 21.59%vs 19.42%vs 19.17%)、Actinobacteriota(3.63%vs3.62%vs6.05%vs 5.99%)、Fusobacteriota(5.27%vs 4.95%vs 4.76%vs 4.28%)、Patescibacteria(2.32%vs 2.33%vs 1.62%vs 1.32%)为优势菌门,大约占菌群总数量的99.25%。其中Bacteroidota(24.04%vs 21.59%vs 19.42%vs 19.17%)随着血糖水平的增加相对丰度逐渐降低(P<0.05);而与HC组和pre-DM组相比,糖尿病组Actinobacteriota(3.63%vs 3.62%vs 6.05%vs 5.99%)的相对丰度显著增加(P<0.05),而Patescibacteria(2.32%vs 2.33%vs 1.62%vs 1.32%)显著降低(P<0.05)(图2C)。
3.2.3属水平上口腔菌群物种组成分析
在属水平上,Streptococcus(25.45%vs 27.46%vs 23.73%vs26.49%)、Neisseria(16.61%vs 15.99%vs 23.98%vs 21.92%)、prevotella(10.37%vs 8.71%vs 8.73%vs 7.78%)、Porphyromonas(8.50%vs 9.21%vs 7.37%vs 7.56%)、Haemophilus(6.99%vs 7.21%vs 5.11%vs 5.73%)、Veillonella(4.95%vs 4.22%vs3.93%vs 3.70%)六种优势菌属约占72.93%(图2B)。进一步分析发现,其中Actinomyces(0.83%vs 0.81%vs 0.61%vs 0.51%)、Veillonella(4.95%vs 4.22%vs3.93%vs 3.70%)、Fusobacterium(4.32%vs 3.84%vs 3.58%vs 3.21%)、Saccharimonadaceae_TM7x(0.77%vs 0.66%vs 0.38%vs 0.35%)四种菌群随着血糖水平的增加,菌群的相对丰度逐渐降低;而Aggregatibacter(0.46%vs 0.54%vs1.02%vs0.94%)呈现增加的趋势;此外,Gemella(2.25%vs 2.76%vs 2.01%vs1.76%)、Solobacterium(0.28%vs 0.31%vs 0.28%vs 0.27%)、Lachnoanaerobaculum(0.26%vs0.27%vs 0.20%vs 0.18%)、Selenomonas(0.16%vs 0.19%vs 0.15%vs0.15%)、Candidatus_Saccharimonas(0.15%vs 0.18%vs 0.10%vs 0.08%)等菌属,随着血糖水平的增加,呈现出先增加后降低的趋势,即与HC组相比,Pre-DM组菌群相对丰度增多,在HbA1C-L组和HbA1C-H组,随着血糖水平的增加,相对丰度降低;而Alloprevotella(2.25%vs 1.49%vs 1.26%vs 1.80%)、Treponema(0.35%vs 0.34%vs 0.33%vs 0.50%)等随着血糖水平的增加呈现出先降低后增加的趋势,即在Pre-DM组和或HbA1C-L组菌群相对丰度降低,HbA1C-H组菌群相对丰度呈现增多的趋势(图2D)。
3.2.4口腔菌群与临床数据相关性分析
在HC组获得24个C肽和胰岛素释放试验数值;Pre-DM组获得23个C肽和24个胰岛素释放试验数值;HbA1C-L组获得22个C肽和21个胰岛素释放数值;HbA1C-H组获得33个C肽和16个胰岛素释放数值。所有患者均记录了HbA1C、OGTT-0min、OGTT-120min。进一步分析口腔菌群与血糖指标的相关性发现,Aggregatibacter、Treponema与HbA1C、OGTT-0min、OGTT-120min呈正相关,而Fusobacterium、Actinomyces、Lachnoanaerobaculum与HbA1C、OGTT-0min、OGTT-120min呈负相关(图3A);且Lachnoanaerobaculum与c-peptide-30min、c-peptide-60min、c-peptide-120min、insulin-60min显著正相关(P<0.05),Fusobacterium与c-peptide-60min呈显著正相关(P<0.05)(图3B)。以上结果表明,Aggregatibacter、Treponema与糖尿病的发生发展密切相关,Fusobacterium、Actinomyces、Lachnoanaerobaculum可能有利于降低血糖。
3.2.5口腔菌群功能预测分析
为了阐明HC组、Pre-DM组、HbA1C-L组和HbA1C-H组之间口腔菌群的功能和代谢变化,基于KEGG pathway数据库进行PICRUSt2功能预测,再通过秩和检验,挑选出在四组口腔菌群中存在显著差异的代谢通路(L3水平),随血糖水平增加脂肪酸降解(Fatty_acid_degradation)、组氨酸代谢(Histidine_metabolism)、丙酮酸代谢(Pyruvate_metabolism)通路富集,而叶酸生物合成(Folate_biosynthesis)通路减少(图4)。
3.2.6基于口腔菌群标志物的糖尿病诊断潜力
通过上述对口腔菌群分析发现,与健康对照组相比,糖尿病前期菌群未发生明显变化,到达糖尿病期菌群才表现显著差异,且到达糖尿病后菌群趋于稳定。为了评估口腔菌群标志物对糖尿病的诊断价值,发明人在38个Pre-DM组的患者和38个HbA1C-L组的患者之间构建了随机森林分类器模型。首先,用秩和检验挑选出的四组间差异显著的ASV,按照不同组合构建随机森林分类器模型,并进行5倍交叉验证,找出能够最准确区分组间差异且最少的ASV组合,最终选取能够准确识别两组差异的5个ASV作为最优标记集(图5A、B),具体包括ASV76(Lactobacillales_unclassified)、ASV57(Streptococcus)、ASV90(Saccharimonadaceae_TM7x)、ASV35(Rothia)、ASV126(Veillonella)。然后使用5个ASV集计算两组的POD指数,HbA1C-L的POD指数显著高于Pre-DM(图5C),其AUC为81.72%(95% CI72.06%-91.37%,p<0.0001)(图5D)。此外,使用17个Pre-DM组的患者和19个HbA1C-L组的患者验证菌群标志物对糖尿病的诊断效能,得到相似的结果,HbA1C-L组的POD指数显著高于Pre-DM组(图5E),其AUC为72.45%(95% CI 55.57%-89.33%,p=0.0212)(图5F)。最后,使用54个HC组的患者和17个Pre-DM组的患者再次验证菌群标志物对糖尿病的诊断效能,HC组的POD指数高于Pre-DM组(图5G),但两组之间的AUC值为56.43%(95% CI40.56%-72.30%)(p=0.4305)(图5H)。这些数据表明,口腔微生物标志物在一定程度上可以从糖尿病前期中特异性识别HbA1C-L组的患者,但是正常对照组和糖尿病前期是无法识别的。
3.3肠道菌群组成的相关性分析
3.3.1肠道菌群多样性变化
通过Ace指数、Chao指数、Shannon指数和simpson指数评估肠道菌群α多样性,结果显示,与HC组相比,Pre-DM组α多样性显著降低(P<0.05),而HC组与HbA1C-L组、HbA1C-H组的α多样性无统计学意义。与Pre-DM组相比,HbA1C-L组、HbA1C-H组多样性显著增加(P<0.05),但HbA1C-L组与HbA1C-H组的α多样性无统计学意义(图6A-D)。基于jaccard-binary距离的PCoA分析,发现四组的肠道菌群有聚集到图中不同位置的趋势,呈现一定的分开趋势,其中HC组与pre-DM组就已经存在显著差异(P<0.05),而HbA1C-L组与HbA1C-H组无统计学差异(图6E)。结果表明,四组肠道菌群整体上发生显著变化。
3.3.2门水平上肠道菌群物种组成分析
Firmicutes(56.27%vs 46.94%vs 68.28%vs 64.72%)、Bacteroidota(27.20%vs42.97%vs 21.60%vs 20.33%)、Proteobacteria(11.63%vs 5.76%vs5.71%vs9.77%)、Actinobacteriota(3.17%vs 2.19%vs 3.71%vs 4.31%)在四组中平均占98.64%(图7A)。与HC组相比,Pre-DM组Bacteroidota显著增加(P<0.05),而在HbA1C-L组和HbA1C-H组,随着血糖水平的增加,相对丰度逐渐降低(图7C)。Verrucomicrobiota的相对丰度与血糖水平呈负相关(图7C)。
3.3.3属水平上肠道菌群物种组成分析
在属水平,四组菌群均以Bacteroides(14.24%vs 26.52%vs 10.32%vs11.29)、Prevotella(10.39%vs 14.76%vs 8.97%vs 6.86%)、Subdoligranulum(6.15%vs 4.07%vs 10.35%vs 11.16%)、Faecalibacterium(7.40%vs 6.19%vs 10.69%vs6.87%)、Escherichia-Shigella(6.10%vs 2.84%vs 3.50%vs 6.52%)、Blautia(3.52%vs 1.92%vs 4.32%vs 4.11%)为优势菌(图7B)。其中,Akkermansia(1.50%vs0.84%vs 0.46%vs 0.44%)、Parasutterella(0.56%vs 0.50%vs 0.29%vs 0.10%)随着血糖水平的增加,相对丰度逐渐降低;[Ruminococcus]_torques_group(0.60%vs1.01%vs 1.16%vs 1.90%)、Oscillospiraceae_NK4A214_group(0.37%vs 0.83%vs0.91%vs 0.92%)、Flavonifractor(0.06%vs 0.07%vs 0.12vs 0.16%)的相对丰度与血糖水平呈正相关;此外,Prevotella(10.39%vs 14.76%vs 8.97%vs 6.86%)、Roseburia(1.73%vs 2.11%vs 2.53%vs 1.49%)呈现先增加后降低的趋势,在Pre-DM组或HbA1C-L组菌群丰度增加,在HbA1C-H组降低;而Ruminococcaceae_CAG-352(0.99%vs0.31%vs 0.80%vs 1.06%)、[Ruminococcus]_gnavus_group(0.50%vs 0.49%vs0.36%vs 0.60%)、Ruminococcaceae_uncultured(0.20%vs 0.12%vs 0.17%vs0.26%)、Ruminococcaceae_Incertae_Sedis(0.11%vs 0.07%vs 0.17%vs 0.19%)呈现出先减少后增加的变化趋势(图7D)。
3.3.4肠道菌群与临床指标相关性分析
[Ruminococcus]_torques_group、Ruminococcaceae_Incertae_Sedis、Flavonifractor与HbA1C、OGTT-0min、OGTT-120min显著正相关,且具有统计学意义(P<0.05);Parasutterella与OGTT-0min显著负相关(P<0.05),与HbA1C、OGTT-120min呈负相关,但无统计学差异(图8A)。Prevotella与insulin-30min正相关;Akkermansia与c-peptide-180min显著正相关;Ruminococcaceae_Incertae_Sedis、Flavonifractor与insulin-30min、insulin-60min显著负相关(图8B)。以上结果与菌群随血糖变化趋势存在一致性,即[Ruminococcus]_torques_group、Ruminococcaceae_Incertae_Sedis、Flavonifractor与糖尿病密切相关,而Parasutterella、Akkermansia、Prevotella有利于降低血糖。
3.3.5肠道菌群功能预测分析
基于KEGG pathway数据库及秩和检验分析进行功能预测(L3水平)。随血糖水平的增加组氨酸代谢(Histidine_metabolism)、脂肪酸降解(Fatty_acid_degradation)、甘油脂类(Glycerolipid_metabolism)代谢通路富集。而叶酸生物合成(Folate_biosynthesis)代谢通路减少(图9)。
3.3.6基于肠道菌群标志物的糖尿病诊断潜力
通过对肠道菌群分析发现,肠道菌群在糖尿病前期就已经发生显著变化,糖尿病期菌群趋于稳定。为了评估肠道菌群标志物对糖尿病前期的诊断价值,发明人使用64个HC组的患者和38个Pre-DM组的患者构建了随机森林分类器模型。首先,选取能够准确识别两组差异的3个ASV作为最优标记集(图10A、B),具体为ASV82(Lachnoclostridium)、ASV11(Bacteroides)、ASV33(Megamonas);然后使用3个ASV集计算两组的POD指数,Pre-DM组的POD指数显著高于HC组(图10C),其AUC为81.89%(95% CI 73.38%-90.39%,p<0.0001)(图10D)。同时,使用30个HC组的患者和17个Pre-DM组的患者验证菌群标志物对Pre-DM的诊断效能,同样,Pre-DM组的POD指数显著高于HC组(图10E),两组之间的AUC值为76.37%(95% CI 61.35%-91.39%,p=0.0077)(图10F)。这些数据表明,该分类器对糖尿病前期患者具有巨大的诊断潜力。
3.4口腔与肠道菌群的Venn图
在属的水平,分别统计粪便菌群和唾液菌群中共有和独有的细菌数目,并通过Venn图展示两部位中细菌组成相似性及重叠情况。在HC组,共有77个属同时出现在口腔和肠道菌群中,大约占正常受试者肠道菌群的34.07%(图11A)。在Pre-DM组,口腔和肠道共有菌群为70个,占糖尿病前期肠道菌群的40%(图11B)。在糖尿病组,共有97个属同时出现在口腔和肠道菌群中,占糖尿病肠道菌群的42.36%(图11C)。综上所述,与HC组相比,在Pre-DM组和糖尿病组口腔与肠道共有菌占比明显增加,表明糖尿病期及糖尿病患者的口腔和肠道微生物群更相似,口腔来源菌群可能更容易在肠道定植。
以上所述之实施例,只是本发明的较佳实施例而已,并非限制本发明的实施范围,故凡依本发明专利范围所述的构造、特征及原理所做的等效变化或修饰,均应包括于本发明申请专利范围内。
Claims (5)
1.口腔微生物作为微生物标记物在制备区分糖尿病患者的试剂盒中的应用,其特征在于,所述口腔微生物包括:ASV76 (Lactobacillales_unclassified)、ASV57(Streptococcus)、ASV90 (Saccharimonadaceae_TM7x)、ASV35 (Rothia)、ASV126(Veillonella),所述试剂盒用于区分糖尿病前期患者和HbA1C-L患者,HbA1C-L患者的HbA1C<8.0%。
2.根据权利要求1所述的应用,其特征在于,利用糖尿病前期患者或者HbA1C-L患者口腔中的ASV76 (Lactobacillales_unclassified)、ASV57 (Streptococcus)、ASV90(Saccharimonadaceae_TM7x)、ASV35 (Rothia)、ASV126 (Veillonella)来计算POD指数,其中POD指数较高且AUC>70%的患者为HbA1C-L患者。
3.检测口腔微生物的试剂在制备区分糖尿病患者的试剂盒中的应用,其特征在于,所述口腔微生物包括:ASV76 (Lactobacillales_unclassified)、ASV57 (Streptococcus)、ASV90 (Saccharimonadaceae_TM7x)、ASV35 (Rothia)、ASV126 (Veillonella),所述试剂盒用于区分糖尿病前期患者和HbA1C-L患者,HbA1C-L患者的HbA1C<8.0%。
4.一种区分糖尿病患者的试剂盒,其特征在于,所述试剂盒中包含用于检测ASV76(Lactobacillales_unclassified)、ASV57 (Streptococcus)、ASV90(Saccharimonadaceae_TM7x)、ASV35 (Rothia)、ASV126 (Veillonella)的试剂,所述试剂盒用于区分糖尿病前期患者和HbA1C-L患者,HbA1C-L患者的HbA1C<8.0%。
5.根据权利要求4所述的一种区分糖尿病患者的试剂盒,其特征在于,所述试剂为检测人体口腔中微生物标记物丰度的试剂。
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