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Journal of Central South University Medical Sciences logoLink to Journal of Central South University Medical Sciences
. 2023 Aug 28;48(8):1176–1184. [Article in Chinese] doi: 10.11817/j.issn.1672-7347.2023.230107

基于GWAS数据分析肠道菌群与脑出血的关系

Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data

LIN Dihui 1,2, LIU Xinpeng 1, LI Qi 2, QIN Jiabi 3, XIONG Zhendong 1, WU Xinrui 1,✉
Editor: 彭 敏宁
PMCID: PMC10930854  PMID: 37875357

Abstract

Objective

Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms.

Methods

Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data, we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then, a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants.

Results

MR analysis showed that Pasteurellales, Pasteurellaceae, and Haemophilus were negatively correlated with the risk of ICH, whileVerrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae, Akkermansia, Holdemanella, and LachnospiraceaeUCG010 were positively correlated with ICH. By applying the cFDR method, 3 pleiotropic loci (rs331083, rs4315115, and rs12553325) were found to be associated with both GM and ICH.

Conclusion

There is a causal association and pleiotropic variants between GM and ICH.

Keywords: gut microbiome, intracerebral hemorrhage, Mendelian randomization, conditional false discovery rate, genome wide association study

自发性脑出血(intracerebral hemorrhage，ICH)指非创伤性脑内血管破裂，导致血液在脑实质内聚集，属于脑卒中的常见亚型[1]。《全球疾病负担研究》显示：脑卒中是位列第2的死亡原因，在脑卒中各亚型中ICH病死率、致残率最高，超过80%的患者在治疗6个月后仍无自理能力，给社会和家庭带来了沉重的负担[2-3]。

肠道菌群(gut microbiome，GM)作为人类第2大基因组，与机体健康密切相关[4]。研究[5]表明：GM与中枢神经系统之间通过微生物群肠脑轴(microbiota- gut-brain axis，MGBA)进行双向沟通。GM及其代谢产物不仅通过动脉粥样硬化和血栓形成直接参与脑卒中的发生和发展，还可引发高血压、糖尿病等多种疾病间接影响脑卒中的发生[6]。动物实验[6]和病例-对照研究[7]结果显示：ICH患者存在GM的结构改变，Prevotella属的GM相对丰度下降。但关于MGBA的研究还存在以下问题：研究多集中在缺血性脑卒中这一亚型，关于GM与ICH机制的探索较少；研究仅关注GM的构成，GM改变与ICH发病因果关联的方向尚不清楚。

孟德尔随机化(Mendelian randomization，MR)分析是一种使用遗传变异作为工具变量来检测和量化暴露与结局之间因果关联的方法。这种方法既减少了环境因素引起的混杂[8]，又避免了逆向因果带来的偏倚[9-10]。一方面，人体肠道环境复杂、影响因素多，难以实现对混杂的充分测量和控制；另一方面，目前针对GM的研究多探讨相关性，无法推断因果。同时，基于GM的随机对照试验成本高，周期长，实施难度大。因此，MR分析尤其适用于GM。已有多项研究使用MR分析探讨GM与2型糖尿病[11]、慢性肾病[12]、阿尔兹海默病[13]等人类复杂疾病之间的关联。

基因多效性指一个基因同时影响多个疾病或性状的形成[14]，利用条件错误发现率(conditional false discovery rate，cFDR)法可以通过整合2个相关性状的全基因组关联分析(genome-wide association study，GWAS)数据集来识别多效性易感遗传变异，揭示复杂表型之间共同的生物机制和潜在的病理生理关系。该法的实用性和可靠性已被多项研究[15-17]验证。研究表明：宿主的遗传基因对GM有重要影响[18]，ICH的遗传度高达44%[19]，且两者可能存在基因多效性。基于GM和ICH之间潜在的因果关联、高基因遗传性和基因多效性，cFDR法结合基因功能注释对探讨GM和ICH之间的病因学和共病机制可能具有重大意义。

因此，本研究通过对2个独立的GWAS数据集运用MR分析来探讨GM与ICH的因果关联，进一步运用cFDR法识别两者的多效性易感遗传变异，探索潜在的共同遗传机制，为ICH预防和早期干预提供线索。

1. 资料与方法

1.1. 数据来源

GM的汇总数据来自微生物基因组(Microbiome Genome，MiBioGen)联盟的一项研究，该研究包括24个队列，共招募了来自不同种族的18 340名研究对象[20]。项目组从人类粪便样本中提取DNA，利用16S rRNA基因测序，以Silva为参考数据库[21]，将人类GM注释到“属”的类别。ICH的汇总数据源于国际脑卒中遗传学会(International Stroke Genetics Consortium，ISGC)进行的一项包括6个队列的荟萃分析，研究对象共3 026人，其中1 545人为ICH病例组，1 481人为对照组[22]。该研究采用Affymetrix 6.0和Illumina HumanHap610-Quad进行基因分型，共鉴定出5 258 103个单核苷酸多态性(single nucleotide polymorphism，SNP)。以上2个汇总数据分别为迄今为止样本量最大的关于GM和ICH荟萃分析的GWAS数据集。

1.2. MR分析

1.2.1. 工具变量的选择

首先在GM(暴露)的GWAS数据库中选择与GM高度相关(P<1×10-5)的SNP[23]。然后根据连锁不平衡(linkage disequilibrium，LD)准则，保留r 2<0.001的SNP，确保工具变量之间相互独立。接着，剔除与结局(ICH)存在直接关联(P<5×10-8)的SNP。对于剔除的SNP，以千人基因组计划中欧洲人群为参考标准，寻找LD准则下r 2>0.8的替代SNP[24]。最后，剔除F<10的SNP，以排除弱工具变量造成的偏倚[25]。

1.2.2. 多效性检验

工具变量的基因多效性增大了MR分析中I型错误发生的概率[26]，若直接剔除可能导致弱工具变量的出现[27]。因此，本研究采用MR-Egger回归(MR-Egger regression)进行多效性检验，回归方程的截距 β0 表示除“工具变量-暴露-结局”以外其他通路产生的效应，用来评价多效性的大小[28]。若MR-Egger回归P>0.05，即截距 β0 与0的差异没有统计学意义，则工具变量不存在基因多效性。

1.2.3. 主分析

将挑选出的与GM相关的SNP工具变量信息和这些SNP在ICH GWAS数据库中的信息进行整合，得到该SNP在GM和ICH GWAS中的效应值(β值)、标准误(standard error，Se)以计算因果效应。本研究采用逆方差加权法(inverse variance weighted，IVW)作为分析因果关联的主分析方法。利用比值法，在截距为0的条件下，通过加权线性回归模型结合权重系数估计暴露对结局总的因果效应值[29]。当工具变量不存在多效性时，IVW法检验效能、精确度较高，可得到偏倚最小的效应估计值[30]。

1.2.4. 敏感性分析

在进行MR分析时，由于样本量不同、弱工具变量、遗传多效性等问题，会使结果产生一定误差，目前缺乏一个适用于所有研究的“金标准”。因此，本研究除使用IVW法外，还采用简单中位数法[31](simple median estimator，SME)、加权中位数法[31](weighted median estimator，WME)、最大似然估计法[32](maximum likelihood，MaxLik)进行敏感性分析，并与主分析所得的结果进行比较。不同的方法基于不同的原理和适用条件，当使用多种MR方法进行敏感性分析均获得一致的结果时，说明该因果效应具有稳健性[33]。

1.2.5. 双向MR分析

为判断因果关联方向，排除反向因果关联对结果的影响[34]，本研究进行了双向MR分析。反向MR分析以ICH为暴露因素，GM为结局因素评估两者之间的因果关系。反向MR分析的研究样本、方法、分析步骤均与正向MR分析相同。当正向MR分析有统计学意义而反向MR分析无统计学意义时，可进一步确证因果效应的方向。

1.3. cFDR分析

为进一步鉴定GM与ICH的多效性易感遗传变异，本研究使用cFDR法对与ICH有因果关联的GM物种和ICH性状进行分析。首先，将GM和ICH的GWAS数据合并，以HapMap3基因分型为参考，保留LD准则下r 2<0.02的SNP中最小等位基因频率(minor allele frequency，MAF)较大者[35]。然后，提取上一步筛选得到的SNP编码、染色体位置和在2组数据中各自的P值。接着计算2种性状(GM和ICH)各自的cFDR和联合条件错误发现率(conjunction cFDR，ccFDR)。cFDR是在传统单一性状错误发现率(false discovery rate，FDR)的基础上发展而来的，例如，当将ICH设置为主要性状，GM设置为条件性状，计算得到某一SNP的cFDRICH<0.05时，则该SNP被鉴定为可能与ICH相关。ccFDR值即cFDRICH和cFDRGM中较大者。当ccFDR<0.05时，则该SNP被鉴定为可能与GM和ICH均相关。计算步骤和公式详见Andreassen等[36]的研究。最后使用HaploReg(http://compbio.mit.edu/HaploReg)对鉴定出的SNP的临近基因等DNA特征进行注释并通过与欧洲生物信息学研究所网站(https://www.ebi.ac.uk/gwas)中GWAS报道过的SNP进行比较来定义新发现的SNP和基因。

上述分析均使用R4.2.2软件完成，其中MR分析使用R软件包TwoSampleMR，cFDR分析使用R软件包reshape。评价指标为比值比(odds ratio，OR)、95%置信区间(95% confidence interval，95% CI)、cFDR值和ccFDR值，双侧P<0.05为差异有统计学意义。

2. 结 果

根据本研究工具变量的筛选标准，GM数据集最终纳入8 269个SNP。这些工具变量来自不同的GM分类等级，包括9个门、16个纲、20个目、36个科、119个属。工具变量的基本信息见附表1(https://doi.org/10.11817/j.issn.1672-7347.2023.230107T1)。

表1.

GM与ICH因果关系的MR敏感性分析

Table 1 Sensitivity analyses of MR of GM and ICH

GM Classification level Maximum likelihood Weighted median
OR (95% CI) P OR (95% CI) P
Pasteurellaceae Family 0.471(0.292~0.760) <0.01 0.516(0.288~0.925) 0.03
Pasteurellales Order 0.423(0.240~0.745) <0.01 0.499(0.244~1.020) 0.06
Haemophilus Genus 0.475(0.247~0.914) 0.03 0.575(0.260~1.275) 0.17
Holdemanella Genus 2.164(1.144~4.094) 0.02 2.205(1.035~4.697) 0.04
Akkermansia Genus 1.884(1.018~3.489) 0.04 1.593(0.742~3.419) 0.23
Verrucomicrobiae Class 1.885(1.018~3.491) 0.04 1.594(0.708~3.588) 0.26
Verrucomicrobiaceae Family 1.885(1.018~3.491) 0.04 1.594(0.729~3.484) 0.24
Verrucomicrobiales Order 1.885(1.018~3.491) 0.04 1.594(0.730~3.483) 0.24
LachnospiraceaeUCG010 Genus 2.714(0.988~4.785) 0.05 2.052(0.780~5.396) 0.15
GM Simple median estimator MR-Egger intercept
OR (95% CI) P β (95% CI) P
Pasteurellaceae 0.500(0.266~0.941) 0.03 -0.648(-1.741~0.445) 0.28
Pasteurellales 0.496(0.252~0.974) 0.04 -0.350(-1.557~0.878) 0.61
Haemophilus 0.558(0.258~1.210) 0.14 -0.988(-2.688~0.712) 0.37
Holdemanella 2.172(1.039~4.541) 0.04 1.756(-0.370~3.882) 0.25
Akkermansia 1.644(0.733~3.689) 0.23 -0.112(-2.093~1.869) 0.92
Verrucomicrobiae 1.644(0.738~3.664) 0.22 -0.112(-2.091~1.868) 0.92
Verrucomicrobiaceae 1.644(0.736~3.674) 0.23 -0.112(-2.091~1.868) 0.92
Verrucomicrobiales 1.644(0.747~3.616) 0.22 -0.112(-0.291~1.868) 0.92
LachnospiraceaeUCG010 2.378(0.913~6.190) 0.08 0.963(-0.699~2.625) 0.37
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MR: Mendelian randomization; GM: Gut microbiome; ICH: intracerebral hemorrhage.

IVW方法结果显示Pasteurellales、Pasteurellaceae在目和科的水平上与ICH有负向因果效应(OR=0.433，95% CI 0.255~0.737)；Haemophilus属GM的丰度每增加1个单位，其患ICH的风险降低51.8% (OR=0.482，95% CI 0.257~0.901)。Holdemanella属GM会导致ICH发病风险升高(OR=2.106，95% CI 1.049~4.229)，即GM丰度每增加1个单位，其患ICH的风险增加1.106倍；Verrucomicrobiae、Verrucomicrobiales、Verrucomicrobiaceae和Akkermansia在纲、目、科和属的水平上与ICH有正向因果效应(OR=1.866，95% CI 1.022~3.408)；LachnospiraceaeUCG010属GM的丰度增加是ICH发病的危险因素(OR=2.138，95% CI 1.009~4.530)。详细信息见图1。采用MR-Egger回归对工具变量的多效性进行检验，结果显示所选的工具变量不具有多效性(均P>0.05，表1)。敏感性分析结果显示用MaxLik、WME、SME法均得到了与IVW法一致的结果(表1)，提示在不同的MR方法下，GM与ICH的因果效应具有稳健性。

图1.

图1

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IVW法预测GM与ICH因果关联的森林图

Figure 1 Forest plot for the causal association between GM and ICH using IVW method

IVW: Inverse variance weighted; GM: Gut microbiome; ICH: Intracerebral hemorrhage; SNP: Single nucleotide polymorphism; OR: Odds ratio; CI: Confidence interval.

本研究以ICH为暴露因素，以正向MR分析中显示与ICH有因果关联的GM为结局进行反向MR分析。MR-Egger回归结果显示所选的工具变量不具有多效性(均P>0.05)。使用不同的MR方法均显示ICH不是各个分类级别GM的因(P>0.05，表2)，进一步确证了GM与ICH之间因果效应的方向。

表2.

反向MR分析及敏感性分析

Table 2 Sensitivity analyses of reverse MR analyses of ICH on GM

GM Inverse variance weighted Maximum likelihood Weighted median
OR (95% CI) P OR (95% CI) P OR (95% CI) P
Pasteurellaceae 1.018(0.980~1.058) 0.35 1.018(0.980~1.058) 0.35 1.022(0.975~1.071) 0.36
Pasteurellales 1.018(0.980~1.058) 0.35 1.018(0.980~1.058) 0.35 1.022(0.972~1.074) 0.39
Haemophilus 1.015(0.977~1.055) 0.45 1.015(0.976~1.055) 0.45 1.011(0.965~1.060) 0.64
Holdemanella 0.968(0.927~1.012) 0.16 0.968(0.925~1.012) 0.15 0.955(0.901~1.012) 0.12
Akkermansia 0.975(0.942~1.009) 0.15 0.974(0.941~1.009) 0.14 0.977(0.934~1.023) 0.32
Verrucomicrobiae 0.974(0.942~1.008) 0.14 0.974(0.940~1.008) 0.13 0.977(0.934~1.022) 0.31
Verrucomicrobiaceae 0.974(0.942~1.008) 0.14 0.974(0.940~1.008) 0.13 0.977(0.936~1.019) 0.28
Verrucomicrobiales 0.974(0.942~1.008) 0.14 0.974(0.940~1.008) 0.13 0.977(0.934~1.022) 0.31
LachnospiraceaeUCG010 0.995(0.963~1.027) 0.75 0.995(0.963~1.027) 0.75 0.993(0.955~1.033) 0.74
GM Simple median estimator MR-Egger intercept
OR (95% CI) P OR (95% CI) P
Pasteurellaceae 1.025(0.978~1.074) 0.30 0.017(-0.144~0.178) 0.84
Pasteurellales 1.025(0.978~1.075) 0.31 0.017(-0.144~0.178) 0.84
Haemophilus 1.012(0.963~1.064) 0.63 0.016(-0.148~0.179) 0.85
Holdemanella 0.956(0.901~1.014) 0.13 -0.035(-0.297~0.227) 0.80
Akkermansia 0.977(0.934~1.023) 0.32 -0.003(-0.158~0.152) 0.97
Verrucomicrobiae 0.977(0.934~1.023) 0.32 -0.005(-0.160~0.150) 0.95
Verrucomicrobiaceae 0.977(0.933~1.023) 0.32 -0.005(-0.160~0.150) 0.95
Verrucomicrobiales 0.977(0.933~1.023) 0.32 -0.005(-0.160~0.150) 0.95
LachnospiraceaeUCG010 0.993(0.956~1.030) 0.69 0.020(-0.122~0.162) 0.79
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MR: Mendelian randomization; GM: Gut microbiome; ICH: intracerebral hemorrhage.

通过cFDR法，本研究识别出3个与GM和ICH均相关的多效性位点，它们分别是rs331083、rs4315115和rs12553325。这些SNP映射在3个不同的染色体上，分别对应FBN2和RP11基因(表3)。

表3.

GM与ICH的3个多效性易感遗传变异

Table 3 Three pleiotropic variants of GM and ICH

SNP GM Chromosome

SNP

location

 Effector allele Adjacent gene

Annotation

area

 cFDRGM cFDRICH ccFDR
rs331083 Pasteurellaceae 5 128 431 410 T FBN2 Intragenes 0.019 031 0.026 733 0.026 733
rs4315115 Haemophilus 11 114 927 419 C ― Intergenes 0.035 759 0.033 684 0.035 759
rs12553325 LachnospiraceaeUCG010 9 89 657 585 C RP11 Intragenes 0.014 363 0.038 620 0.038 620
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GM: Gut microbiome; ICH: intracerebral hemorrhage; SNP: Single nucleotide polymorphism; cFDRGM: Conditional false discovery rate of GM; cFDRICH: Conditional false discovery rate of ICH; ccFDR: Conjunction conditional false discovery rate.

3. 讨 论

本研究通过MR分析，在纲、目、科、属的水平上找到了与ICH存在因果关联的GM，结果显示LachnospiraceaeUCG010属的GM与ICH发病存在正向因果关联。LachnospiraceaeUCG010即毛螺菌UCG010，属于厚壁菌门的梭状菌群，是一类专性厌氧菌。Zhang等[37]对164份粪便样本进行16S rRNA测序，发现Lachnospiraceae科GM的相对丰度与收缩压呈正相关。而大量基于人群队列研究和病例对照研究[38-39]的证据皆显示高血压是ICH发病最重要的单一危险因素。Kostic等[40]研究发现Lachnospiraceae菌可通过损害糖代谢加剧机体炎症，促进1型糖尿病的发展。Sarwar等[41]对102项前瞻性队列研究进行荟萃分析，指出糖尿病会增大ICH的发病风险，其相对危险度为1.6。GM所含的脂多糖可通过激活血管内皮细胞，引起多种细胞因子的合成和释放，诱导动脉内出现淀粉样沉积[42]。当β-淀粉样蛋白聚集在脑皮质血管时，会导致血液渗出，引发ICH[43]。脑叶区域50%的ICH与淀粉样血管病变有关[44]。本研究与以往研究结果皆提示LachnospiraceaeUCG010属GM可能促进ICH的发病。

本研究中MR分析的结果显示Akkermansia属GM(属于Verrucomicrobia纲、Verrucomicrobiales目、Verrucomicrobiaceae科)是ICH的危险因素。一项针对高血压性脑出血患者的队列研究[45]表明：高血压性脑出血患者Verrucomicrobia纲的菌群构成改变，其中Akkermansia属GM的相对丰度显著增加。小鼠实验[46]的结果显示：Akkermansia属GM可分泌一种 84 kD(1 D=1 u)的蛋白质P9，纯化的蛋白质P9可以诱导胰高血糖素样肽-1分泌和棕色脂肪组织产热。而脂质代谢产物[47]和体内血糖水平[48]均与ICH的发生和不良结局显著相关。由此推断，Akkermansia属GM可能通过上述机制影响ICH的发生。

本研究MR分析的结果显示Haemophilus属GM与ICH存在负向因果关联。Haemophilus即嗜血杆菌属，是一类革兰氏阴性苛养菌。Jang等[49]研究发现：相较于久坐的健康成人，运动员体内Haemophilus属GM的相对丰度明显增加。另一项随机对照试验[50]结果也显示：在接受地中海饮食(限制能量摄入)和增加体育锻炼的人群中，研究对象的体重指数、腰围与体内Haemophilus属GM的相对丰度呈负相关。研究[51]表明规律锻炼是ICH的保护因素。Harshfield等[52]对生活方式和脑卒中亚型进行MR分析也发现腰臀比与ICH具有负向因果关联。但膳食因素与ICH是否相关并未得出一致的结论，也不清楚是何种成分导致了膳食与ICH的关联[53-54]。因此，Haemophilus属GM与ICH之间的因果关联和作用机制仍需要基于人群的前瞻性研究和实验予以确证。

ICH的基因遗传度高达44%[19]。但迄今仅鉴定出8个与ICH及相关性状有关的基因和31个基因位点[55]。本研究通过MR分析找到与ICH存在因果关联的GM之后，又通过cFDR法鉴定出新的多效性遗传位点，扩大了对ICH总遗传变异的解释效果，且有利于阐明GM与ICH间潜在的共同遗传机制。通过cFDR法首次鉴定出FBN2和RP11这2个基因与GM和ICH均相关。rs12553325是RP11基因的内含子变体。RP11-728F11.4可作用于FXYD6蛋白并诱导细胞内胆固醇的积累和促炎因子的增加[56]。小鼠实验[57]表明血浆胆固醇水平和肝胆固醇的合成与GM相对丰度密切相关。胆汁酸(胆固醇在肝肠循环中分解的产物)可能通过干扰RNA二级结构、导致DNA损伤和促进蛋白质错误折叠来破坏大分子稳定性，从而影响肠道微生物的存活和定植[58]。另一项队列研究[59]结果也显示Lachnospiraceae属GM与人体内微小高密度脂蛋白颗粒的浓度呈正相关。一项荟萃分析[60]表明人体内高密度脂蛋白胆固醇水平升高会增大ICH的发病风险。Holmes等[61]对脑卒中患者进行的巢式病例对照研究也得到了相同的结果。以上证据均提示，rs12553325(代表RP11基因)可能通过胆固醇代谢及其产物同时影响GM和ICH，在两者的发病机制中发挥重要作用。

本研究的优势：1)研究基于对目前最新、样本量最大的GWAS数据进行二次挖掘，无需额外的实验成本，结果兼具经济性和可靠性；2)使用MR分析探索GM和ICH的关联，可以避免反向因果和未知混杂造成的偏倚，且不同的MR方法得到一致的因果效应，表明结果的稳健性；3)采用cFDR法鉴定出新的影响GM和ICH的多效性遗传位点，有利于探讨两者共同的遗传机制。本研究也存在一定的缺陷：1)目前公开发表的GM的GWAS是基于16S rRNA的测序，只能精确到“属”的分类级别，同时由于GWAS参与者大多是欧洲裔，可能影响结果的外推。未来可寻找人群范围更广泛、测序更精准的宏基因组测序GWAS(可精确到“种”分类级别)进行分析。2)研究结果提示GM与ICH关联的证据链较为曲折，缺乏两者直接作用的证据。这可能归因于目前关于GM与ICH的研究较少。本研究探索GM和ICH的因果关联及两者间潜在的共同生物学机制，研究结果可为后续的人群研究提供线索，也可通过功能学实验和精细定位方法进一步确证。

综上所述，本研究利用2个独立的GWAS数据集，探讨了GM和ICH之间的因果关系，鉴定出与GM和ICH相关的新的多效性遗传位点。这将有利于阐明GM与ICH的因果关联，从GM方面为ICH的病因及机制研究补充证据，也为ICH的预防和治疗开辟了新的方向。

附录.

附表1.

工具变量的基本信息

Attached Table 1 Basic information of instrumental variables

分类级别 GM种类(SNP个数)
门(Phylum) Actinobacteria(336)、Bacteroidetes(133)、Cyanobacteria(71)、Euryarchaeota(53)、Firmicutes(65)、Lentisphaerae(19)、Proteobacteria(94)、Tenericutes(96)、Verrucomicrobia(70)
纲(Class) Actinobacteria(724)、Alphaproteobacteria(17)、Bacilli(71)、Bacteroidia(132)、Betaproteobacteria(66)、Clostridia(35)、Coriobacteriia(32)、Deltaproteobacteria(32)、Erysipelotrichia(66)、Gammaproteobacteria(72)、Lentisphaeria(20)、Melainabacteria(61)、Methanobacteria(64)、Mollicutes(96)、Negativicutes(35)、Verrucomicrobiae(59)
目(Order) Actinomycetales(28)、Bacillales(63)、Bacteroidales(132)、Bifidobacteriales(639)、Burkholderiales(97)、Clostridiales(36)、Coriobacteriales(32)、Desulfovibrionales(24)、Enterobacteriales(18)、Erysipelotrichales(66)、Gastranaerophilales(59)、Lactobacillales(76)、Methanobacteriales(64)、MollicutesRF9(41)、NB1n(55)、Pasteurellales(30)、Rhodospirillales(68)、Selenomonadales(35)、Verrucomicrobiales(59)、Victivallales(20)
科(Family) Acidaminococcaceae(16)、Actinomycetaceae(28)、Alcaligenaceae(142)、Bacteroidaceae(24)、BacteroidalesS24.7group(44)、Bifidobacteriaceae(639)、Christensenellaceae(19)、Clostridiaceae1(25)、ClostridialesvadinBB60group(85)、Coriobacteriaceae(32)、Defluviitaleaceae(28)、Desulfovibrionaceae(23)、Enterobacteriaceae(18)、Erysipelotrichaceae(66)、FamilyXI(32)、FamilyXIII(68)、Lachnospiraceae(53)、Lactobacillaceae(27)、Methanobacteriaceae(64)、Oxalobacteraceae(83)、Pasteurellaceae(30)、Peptococcaceae(21)、Peptostreptococcaceae(84)、Porphyromonadaceae(53)、Prevotellaceae(53)、Rhodospirillaceae(82)、Rikenellaceae(118)、Ruminococcaceae(24)、Streptococcaceae(265)、Unknownfamily(20)、Veillonellaceae(43)、Verrucomicrobiaceae(58)、Victivallaceae(61)
属(Genus) Actinomyces(12)、Adlercreutzia(42)、Aetinomyces(2)、Akkermansia(63)、Alistipes(82)、Allisonella(111)、Alloprevotella(13)、Anaerofilum(47)、Anaerostipes(158)、Anaerotruncus(104)、Bacteroides(24)、Barnesiella(27)、Bifidobacterium(626)、Bilophila(35)、Blautia(40)、Butyricicoccus(18)、Butyricimonas(64)、Butyrivibrio(155)、CandidatusSoleaferrea(56)、Catenibacterium(11)、ChristensenellaceaeR.7group(63)、Clostridiuminnocuumgroup(29)、Clostridiumsensustricto1(18)、Collinsella(25)、Coprobacter(34)、Coprococcus1(33)、Coprococcus2(34)、Coprococcus3(27)、DefluviitaleaceaeUCG011(19)、Desulfovibrio(22)、Dialister(32)、Dorea(29)、Eggerthella(53)、Eisenbergiella(36)、Enterorhabdus(38)、Erysipelatoclostridium(49)、ErysipelotrichaceaeUCG003(46)、Escherichia(22)、Eubacteriumbrachygroup(44)、Eubacteriumcoprostanoligenesgroup(68)、ActinEubacteriumeligensgroup (18)、Eubacteriumfissicatenagroup(23)、Eubacteriumhalliigroup(34)、Eubacteriumnodatumgroup(43)、Eubacteriumoxidoreducensgroup(24)、Eubacteriumrectalegroup(27)、Eubacteriumruminantiumgroup(62)、Eubacteriumventriosumgroup(50)、Eubacteriumxylanophilumgroup(30)、Faecalibacterium(23)、FamilyXIIIAD3011group(64)、FamilyXIIIUCG001(35)、Flavonifractor(26)、Fusicatenibacter(45)、Gordonibacter(27)、Haemophilus(21)、Holdemanella(25)、Holdemania(44)、Howardella(26)、Hungatella(64)、Intestinibacter(84)、Intestinimonas(62)、Lachnoclostridium(108)、Lachnospira(27)、LachnospiraceaeFCS020group(93)、LachnospiraceaeNC2004group(26)、LachnospiraceaeND3007group(18)、LachnospiraceaeNK4A136group(57)、LachnospiraceaeUCG001(75)、LachnospiraceaeUCG004(42)、LachnospiraceaeUCG008(97)、LachnospiraceaeUCG010(30)、Lactobacillus(28)、Lactococcus(82)、Lntestinibacter(7)、Lntestinimonas(4)、Marvinbryantia(57)、Methanobrevibacter(26)、Odoribacter(51)、Olsenella(37)、Oscillibacter(75)、Oscillospira(18)、Oxalobacter(91)、Parabacteroides(16)、Paraprevotella(74)、Parasutterella(164)、Peptococcus(241)、Phascolarctobacterium(26)、Prevotella7(27)、Prevotella9(55)、RikenellaceaeRC9gutgroup(43)、Romboutsia(45)、Roseburia(39)、Ruminiclostridium5(74)、Ruminiclostridium6(58)、Ruminiclostridium9(199)、RuminococcaceaeNK4A214group(26)、RuminococcaceaeUCG002(71)、RuminococcaceaeUCG003(101)、RuminococcaceaeUCG004(128)、RuminococcaceaeUCG005(37)、RuminococcaceaeUCG009(19)、RuminococcaceaeUCG010(20)、RuminococcaceaeUCG011(195)、RuminococcaceaeUCG013(32)、RuminococcaceaeUCG014(55)、Ruminococcus1(79)、Ruminococcus2(40)、Ruminococcusgauvreauiigroup(40)、Ruminococcusgnavusgroup(77)、Ruminococcustorquesgroup(112)、Sellimonas(80)、Senegalimassilia(29)、Slackia(25)、Streptococcus(212)、Subdoligranulum(63)、Sutterella(37)、Terrisporobacter(12)、Turicibacter(66)、Tyzzerella3(56)、Unknowngenus(57)、Veillonella(34)、Victivallis(42)
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GM：肠道菌群；SNP：单核苷酸多态性。

基金资助

国家自然科学基金(82073653)；湖南省自然科学基金(2022JJ10087，2022JJ40343)；湖南省教育厅科学研究项目(21B0513)；湖南省卫生健康委员会科研计划项目(202212053368)；吉首大学校级科研项目(Jdx22035)。

This work was supported by the National Natural Science Foundation (82073653), the Natural Science Foundation of Hunan Province (2022JJ10087, 2022JJ40343), the Scientific Research Project of Education Department of Hunan Province (21B0513), the Scientific Research Project of Hunan Provincial Health Commission (202212053368), and the Scientific Research Project of Jishou University (Jdx22035), China.

利益冲突声明

作者声称无任何利益冲突。

作者贡献

林迪慧 数据分析，论文撰写与修改；刘新鹏 数据采集，论文撰写与修改；黎祺 数据分析，论文指导；秦家碧 论文指导；熊震东 论文撰写；吴欣锐 研究设计，论文指导及修改。所有作者阅读并同意最终的文本。

原文网址

http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/2023081176.pdf

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