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Front Immunol.2023; 14: 1234924.
在线发表于 2023 年 8 月 17 日。doi: 10.3389/fimmu.2023.1234924
Gut microbiota and sepsis: bidirectional Mendelian study and mediation analysis
肠道微生物群与败血症：双向孟德尔研究与中介分析
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Abstract 摘要
Background 背景介绍
There is a growing body of evidence that suggests a connection between the composition of gut microbiota and sepsis. However, more research is needed to better understand the causal relationship between the two. To gain a deeper insight into the association between gut microbiota, C-reactive protein (CRP), and sepsis, we conducted several Mendelian randomization (MR) analyses.
越来越多的证据表明，肠道微生物群的组成与败血症之间存在联系。然而，要更好地了解两者之间的因果关系还需要更多的研究。为了更深入地了解肠道微生物群、C反应蛋白（CRP）和败血症之间的关系，我们进行了多项孟德尔随机化（MR）分析。
Methods 方法
In this study, publicly available genome-wide association study (GWAS) summary statistics were examined to determine the correlation between gut microbiota and sepsis, including various sepsis subgroups (such as under 75, 28-day death, Critical Care Units (ICU), 28-day death in ICU). Initially, two-sample and reverse Mendelian randomization (MR) analyses were conducted to identify causality between gut microbiota and sepsis. Subsequently, multivariable and two-step MR analyses revealed that the relationship between microbiota and sepsis was mediated by CRP. The robustness of the findings was confirmed through several sensitivity analyses.
在这项研究中，我们研究了公开的全基因组关联研究（GWAS）汇总统计数据，以确定肠道微生物群与败血症之间的相关性，包括各种败血症亚组（如 75 岁以下、28 天死亡、重症监护病房（ICU）、在重症监护病房 28 天死亡）。首先，进行了双样本和反向孟德尔随机化（MR）分析，以确定肠道微生物群与败血症之间的因果关系。随后，多变量和两步 MR 分析显示，微生物群与脓毒症之间的关系是由 CRP 介导的。几项敏感性分析证实了这一结果的稳健性。
Findings 调查结果
In our study, we revealed positive correlations between 24 taxa and different sepsis outcomes, while 30 taxa demonstrated negative correlations with sepsis outcomes. Following the correction for multiple testing, we found that the Phylum Lentisphaerae (OR: 0.932, p = 2.64E-03), class Lentisphaeria, and order Victivallales (OR: 0.927, p = 1.42E-03) displayed a negative relationship with sepsis risk. In contrast, Phylum Tenericutes and class Mollicutes (OR: 1.274, p = 2.89E-03) were positively related to sepsis risk and death within 28 days. It is notable that Phylum Tenericutes and class Mollicutes (OR: 1.108, p = 1.72E-03) also indicated a positive relationship with sepsis risk in individuals under 75. From our analysis, it was shown that C-reactive protein (CRP) mediated 32.16% of the causal pathway from Phylum Tenericutes and class Mollicutes to sepsis for individuals under 75. Additionally, CRP was found to mediate 31.53% of the effect of the genus Gordonibacter on sepsis. Despite these findings, our reverse analysis did not indicate any influence of sepsis on the gut microbiota and CRP levels.
在我们的研究中，我们发现 24 个分类群与不同的败血症结果呈正相关，而 30 个分类群与败血症结果呈负相关。经多重检验校正后，我们发现扁桃纲（OR：0.932，p = 2.64E-03）、扁桃目（Lentisphaeria）和Victivallales目（OR：0.927，p = 1.42E-03）与败血症风险呈负相关。相反，真菌门和毛霉菌纲（OR：1.274，p = 2.89E-03）与败血症风险和 28 天内死亡呈正相关。值得注意的是，在 75 岁以下的人群中，真菌门和真菌纲（OR：1.108，p = 1.72E-03）与败血症风险也呈正相关。我们的分析表明，在 75 岁以下人群中，C 反应蛋白（CRP）介导了 32.16% 的从真菌门和真菌纲到败血症的因果关系。此外，研究还发现，CRP 在戈登氏菌属对败血症的影响中占 31.53%。尽管有这些发现，但我们的反向分析并未表明败血症对肠道微生物群和 CRP 水平有任何影响。
Conclusion 结论
The study showcased the connection between gut microbiota, CRP, and sepsis, which sheds new light on the potential role of CRP as a mediator in facilitating the impact of gut microbiota on sepsis.
这项研究揭示了肠道微生物群、CRP 和败血症之间的联系，从而揭示了 CRP 作为介质在促进肠道微生物群对败血症的影响方面的潜在作用。
关键词C反应蛋白 肠道微生物群 媒介 孟德尔随机化 败血症
1. Background 1.背景情况
Sepsis is a complex syndrome characterized by an unbalanced immune response to various infections (1), which can lead to malfunctioning of multiple organ systems such as the cardiopulmonary, renal, and digestive systems (2). According to epidemiological studies, sepsis rates of prevalence and mortality range from 25% to 30% in hospitals (3). Despite our growing understanding of the biological mechanisms behind sepsis, current treatments have proven ineffective in correcting the dysregulated immunity in patients (4), making it essential to develop targeted prevention and treatment strategies.
败血症是一种复杂的综合征，其特点是对各种感染的免疫反应失衡（1），可导致心肺、肾脏和消化系统等多个器官系统功能失调（2）。根据流行病学研究，败血症在医院的发病率和死亡率在 25% 至 30% 之间（3）。尽管我们对败血症背后的生物机制有了越来越多的了解，但目前的治疗方法已被证明无法有效纠正患者失调的免疫系统（4），因此制定有针对性的预防和治疗策略至关重要。
The gut microbiome has been found to contribute to the severity of sepsis and prognosis of treatment (5). Preclinical studies have shown that gut microbiota plays a pivotal role in the immune response to systemic inflammation and that disruption of this symbiosis increases susceptibility to sepsis (6). Additionally, the use of omic technologies to analyze the gut microbiota has confirmed the alteration of composition related to septic dysfunction across organs (7).
研究发现，肠道微生物群对败血症的严重程度和治疗预后有影响 ( 5)。临床前研究表明，肠道微生物群在全身炎症的免疫反应中起着关键作用，这种共生关系的破坏会增加脓毒症的易感性（6）。此外，使用 omic 技术分析肠道微生物群已证实，各器官中与败血症功能障碍有关的组成发生了改变 ( 7)。
Although probiotic supplementation has reported some positive effects (8–10), their efficacy and safety remain a subject of controversy (11, 12). Therefore, more research is necessary to identify the specificity and safety of probiotic supplements.
尽管益生菌补充剂具有一些积极作用（8- 10），但其功效和安全性仍存在争议（11，12）。因此，有必要开展更多研究，以确定益生菌补充剂的特异性和安全性。
In addition to being a biomarker of acute-phase inflammation, CRP has a role in defending against infections as it can bind to cells and some bacteria, triggering the complement system and helping to remove dead cells (13, 14). However, prospective studies have also revealed that elevated CRP levels correlate with a higher risk of infections in adults (15).
CRP 除了是急性期炎症的生物标志物外，还具有抵御感染的作用，因为它可以与细胞和某些细菌结合，触发补体系统并帮助清除死亡细胞 ( 13, 14)。然而，前瞻性研究也发现，CRP 水平升高与成人感染风险升高有关 ( 15)。
Mendelian randomization (MR) involves using genetic variants to construct instrumental variables of exposure and estimate the causal association between exposure and outcome (16). As the random distribution of alleles is not affected by common confounding factors, a causal relationship is generally considered to be reliable (17). However, in previous studies, we were unable to find any MR studies examining the relationship between gut microbiota, sepsis, and their association with CRP. Therefore, we conducted multiple MR analyses based on genome-wide association study (GWAS) summary statistics to evaluate the causal association among gut microbiota, CRP, and sepsis.
孟德尔随机法（MR）是利用基因变异构建暴露的工具变量，并估计暴露与结果之间的因果关系（16）。由于等位基因的随机分布不受常见混杂因素的影响，因果关系一般被认为是可靠的（17）。然而，在以往的研究中，我们未能找到任何 MR 研究来探讨肠道微生物群、败血症及其与 CRP 之间的关系。因此，我们根据全基因组关联研究（GWAS）的汇总统计进行了多重磁共振分析，以评估肠道微生物群、CRP 和败血症之间的因果关系。
2. Method 2.方法
2.1. Study design 2.1.研究设计
In this study, we conducted a two-sample and bidirectional Mendelian randomization (MR) to examine the causal relationship between gut microbiota and sepsis. We then used a two-step and multivariable MR approach to identify the mediation effect of CRP on the relationship between gut microbiota and sepsis. A summary of the study design is illustrated in
Figure 1
. Study used publicly available summary statistics for gut microbiota, C-reactive protein (CRP), and sepsis from previously published studies or consortiums. All of these studies were approved by their respective institutional review boards (IRBs), and hence, there was no need to re-apply for approval by the IRB.
在本研究中，我们采用双样本和双向孟德尔随机法（MR）研究了肠道微生物群与败血症之间的因果关系。然后，我们采用两步多变量 MR 方法来确定 CRP 对肠道微生物群与败血症之间关系的中介效应。研究设计概要见图 1。研究使用了从以前发表的研究或联盟中公开获得的有关肠道微生物群、C 反应蛋白 (CRP) 和败血症的汇总统计数据。所有这些研究都已获得各自机构审查委员会（IRB）的批准，因此无需再向 IRB 申请批准。
(A), Principles of Mendelian Randomization: I) Independence: The genetic variants utilized in the analysis are not associated with any confounders that could potentially influence the relationship between the exposure and the outcome. II) Relevance: The genetic variants selected as instrumental variables have a strong association with the exposure. III) Exclusion Restriction: The genetic variants influence the outcome solely through their effect on the exposure, and not through any alternative pathways; (B), Flowchart of Bidirectional Two-Sample Mendelian Randomization and mediation Analysis.
(A）、孟德尔随机化原则：I) 独立性：分析中使用的基因变异与任何可能影响暴露和结果之间关系的混杂因素无关。II) 相关性：被选为工具变量的基因变异与暴露有很强的相关性。III) 排除限制：B），双向双样本孟德尔随机化和中介分析流程图。
2.2. Data sources 2.2.数据来源
The gut microbiota data used in this study were sourced from the MiBioGen consortium (18). This consortium has curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals across 24 cohorts, which includes 14,306 European individuals from 18 cohorts. The consortium performed adjustments for age, sex, genetic principal components, technical covariates such as stool DNA isolation methods, 16S domain to reduce heterogeneity among the cohorts. However, the study did not account for other potential confounders like diet, medication use, and lifestyle factors, as this information was not consistently available for all cohorts (
Supplementary Table 1
).
本研究使用的肠道微生物群数据来自 MiBioGen 联盟（18）。该联盟对 24 个队列中 18,340 人的全基因组基因型和 16S 粪便微生物组数据进行了整理和分析，其中包括 18 个队列中的 14,306 名欧洲人。研究小组对年龄、性别、遗传主成分、技术协变量（如粪便 DNA 分离方法）和 16S 域进行了调整，以减少队列间的异质性。然而，该研究并未考虑其他潜在的混杂因素，如饮食、药物使用和生活方式因素，因为并非所有队列都能获得这些信息（补充表 1）。
C-reactive protein was derived from 1,000 individuals in the population-based KORA (Cooperative Health Research in the Region of Augsburg) study (19). The study used a highly multiplexed, aptamer-based, affinity proteomics platform (SOMAscan) to quantify levels of 1,124 proteins in blood plasma samples.
C 反应蛋白来自以人群为基础的 KORA（奥格斯堡地区合作健康研究）研究中的 1,000 人 ( 19)。该研究使用了一个高度复用的、基于适配体的亲和蛋白质组学平台（SOMAscan），对血浆样本中的 1,124 种蛋白质水平进行了量化。
The sepsis data and sepsis subgroups (under 75, 28-day death, Critical Care Units (ICU), 28-day death in ICU) was collected from the IEU Open GWAS with summary-level data obtained from the UK Biobank which included 11643,11568,1896,1380, and 347 sepsis cases and 474841,451301,484588, 429985, 431018 controls respectively. The study use Regenie v2.2.4 to analyze GWAS data, and adjusted for age, sex, chip, and the first 10 Principal Component Analysis (https://gwas.mrcieu.ac.uk/datasets/ieu-b-4980/).
脓毒症数据和脓毒症亚组（75岁以下、28天死亡、重症监护病房（ICU）、重症监护病房28天死亡）数据来自IEU Open GWAS，汇总级数据来自英国生物库，分别包括11643、11568、1896、1380和347例脓毒症病例和474841、451301、484588、429985、431018例对照。研究使用 Regenie v2.2.4 分析 GWAS 数据，并对年龄、性别、芯片和前 10 个主成分分析（https://gwas.mrcieu.ac.uk/datasets/ieu-b-4980/）进行了调整。
2.3. SNP selection 2.3.SNP 选择
We utilized MR analysis to investigate potential causal relationships between gut microbiota and sepsis, using genetic variants as instrumental variables (IVs). The validity of an MR analysis is contingent upon three key assumptions: (1) IVs are not associated with any confounding variables; (2) IVs are strongly associated with the exposure; and (3) IVs influence the outcome solely through the exposure (20).
我们将基因变异作为工具变量（IVs），利用 MR 分析来研究肠道微生物群与败血症之间的潜在因果关系。MR 分析的有效性取决于三个关键假设：（1）IVs 与任何混杂变量无关；（2）IVs 与暴露密切相关；（3）IVs 仅通过暴露影响结果 ( 20)。
Initially, we selected single nucleotide polymorphisms (SNPs) from the genome-wide association study (GWAS) summary data for exposures that exhibited a genome-wide significant association (p < 5×10−8) with the traits as IVs. In instances where the number of IVs was limited, we relaxed the significance threshold to 5×10−5 to prevent inaccurate results due to insufficient SNPs. The selection of other SNPs followed the same threshold. Subsequently, we employed linkage disequilibrium clumping to exclude certain undesirable SNPs (r2 < 0.01, window size > 10,000 kb) (21). Finally, we harmonized the exposure and outcome datasets and eliminated palindromic SNPs with allele frequencies close to 0.5. All the selected SNPS are placed in the
Supplementary Table 2.
最初，我们从全基因组关联研究（GWAS）的汇总数据中选择了与性状有全基因组显著关联（p < 5×10 −8 ）的单核苷酸多态性（SNPs）作为IV。在 IV 数量有限的情况下，我们将显著性阈值放宽到 5×10 −5 ，以防止因 SNP 不足而导致结果不准确。其他 SNP 的选择也遵循相同的阈值。随后，我们利用连锁不平衡聚类排除了某些不理想的 SNPs（r 2 < 0.01, window size > 10,000 kb） ( 21)。最后，我们统一了暴露数据集和结果数据集，并剔除了等位基因频率接近 0.5 的等位基因 SNP。所有筛选出的 SNP 均列于补充表 2 中。
We ensured the strength of genetic instruments for exposures by calculating the F statistic using the formula:F = (n - k − 1)/k×(R2/1− R2) (22), where R2 represents the cumulative explained variance in the selected SNPs, N is the sample size, and k is the number of SNPs in the analysis. An F statistic greater than 10 indicates sufficient strength to avoid the issue of weak instrument bias in the two-sample model (23).
我们通过计算 F 统计量来确保暴露的遗传工具强度，计算公式为：F = (n - k - 1)/k×(R 2 /1- R 2 ) ( 22)，其中 R 2 代表所选 SNPs 的累积解释方差，N 为样本量，k 为分析中的 SNPs 数量。F 统计量大于 10 表示有足够的强度来避免双样本模型中的弱工具偏差问题 ( 23)。
2.4. Statistical analysis
2.4.统计分析
We conducted bidirectional two-sample MR analyses to assess the relationship between gut microbiota and sepsis. Our primary analysis employed an inverse variance-weighted (IVW) meta-analysis approach, which is a robust method for MR analysis (17). We also performed secondary analyses using the weighted median (24), and MR-Egger regression approaches. We evaluated the potential impact of directional pleiotropy by testing the intercept value of the MR-Egger regression (25). We used Cochran’s Q test to assess heterogeneity (26). In cases of heterogeneity, we opted for a random-effects IVW for the primary analysis. At each feature level (phylum=9, class=15, order=19, family=30, and genus=117), according to previous reports (27), we used a multiple-testing significance threshold of p < 0.05/n (where n represents the effective number of independent bacterial taxa at the corresponding taxonomic level).
我们进行了双向双样本 MR 分析，以评估肠道微生物群与败血症之间的关系。我们的主要分析采用了反方差加权（IVW）荟萃分析方法，这是一种稳健的 MR 分析方法 ( 17)。我们还使用加权中位数 ( 24) 和 MR-Egger 回归法进行了二次分析。我们通过测试 MR-Egger 回归的截距值来评估方向性多效性的潜在影响 ( 25)。我们使用 Cochran's Q 检验来评估异质性（26）。在存在异质性的情况下，我们选择随机效应 IVW 进行主要分析。根据之前的报告（27），在每个特征水平（门=9、类=15、目=19、科=30、属=117），我们使用的多重检验显著性阈值为 p < 0.05/n（其中 n 代表相应分类水平上独立细菌类群的有效数量）。
In mediation terms, the total effect of an exposure on the outcome is estimated by univariable MR. Multivariable MR (MVMR) and two-step MR is used to decompose direct and indirect effects. The first step is to evaluate the effect of exposure on the mediator with univariable MR. The second step estimating the effect of the mediator on each outcome was carried out with MVMR. For this second step, MVMR has not been used in previous literature (28, 29), and a univariable MR has been proposed for calculating the mediator’s effect. However, in the case of MVMR, the mechanism of the mediator’s effect on the outcome can be ensured to be independent of the effect of the exposure (30). Furthermore, it exerts a direct effect on exposure. The indirect effect is estimated by multiplying the two-step (MR) estimates. Stepwise regression was used to select exposures and mediators with true effects (31).
就中介作用而言，暴露对结果的总效应是通过单变量 MR 估算的。多变量 MR（MVMR）和两步式 MR 用于分解直接效应和间接效应。第一步是用单变量 MR 评估暴露对中介效应的影响。第二步是用 MVMR 估计中介因子对每个结果的影响。在第二步中，以前的文献（28、29）没有使用 MVMR，而是建议使用单变量 MR 计算中介效应。然而，在 MVMR 的情况下，可以确保中介效应对结果的影响机制独立于暴露效应（30）。此外，它还对暴露产生直接影响。间接效应通过乘以两步（MR）估计值来估算。逐步回归法用于选择具有真实效应的暴露因子和中介因子 ( 31)。
3. Result 3.结果
3.1. Two-Sample and bidirectional MR analysis of gut microbiota and sepsis, sepsis subgroups risk
3.1.肠道微生物群与败血症、败血症亚组风险的双样本和双向 MR 分析
Four MR approaches were utilized to investigate the association between gut microbiota and sepsis (
Figure 2
and
Supplementary Table 3
). Positive associations were observed for the genera Actinomyces, Enterorhabdus, Gordonibacter, and Ruminococcaceae UCG014, and the families Coriobacteriaceae and Prevotellaceae, with various outcomes. For example, the genus Actinomyces was associated with an increased likelihood of critical care units (OR = 1.21, p = 2.58E-02) and 28-day death in critical care units (OR = 1.46, p = 2.58E-02). The genus Fusicatenibacter demonstrated a strong positive association with 28-day death in critical care units(OR = 1.49, p = 3.90E-02). In contrast, several taxa showed negative associations with sepsis outcomes. For instance, the genera Anaerotruncus, Coprococcus1, Coprococcus2, Dialister, Dorea, Eubacterium ventriosum group, Eubacterium xylanophilum group, Faecalibacterium, Intestinimonas, Lachnospiraceae UCG001, Lachnospiraceae UCG004, and Peptococcus, and the family Enterobacteriaceae, all demonstrated negative associations with various outcomes.
利用四种磁共振方法研究了肠道微生物群与败血症之间的关系（图 2 和补充表 3）。观察到放线菌属、肠杆菌属、戈登杆菌属和反刍球菌属 UCG014 以及 Coriobacteriaceae 和 Prevotellaceae 科与各种结果呈正相关。例如，放线菌属与重症监护病房（OR = 1.21，p = 2.58E-02）和重症监护病房 28 天死亡（OR = 1.46，p = 2.58E-02）的可能性增加有关。Fusicatenibacter 属与危重症监护病房 28 天死亡有很强的正相关性（OR = 1.49，p = 3.90E-02）。相比之下，一些类群与败血症结果呈负相关。例如，Anaerotruncus属、Coprococcus1属、Coprococcus2属、Dialister属、Dorea属、Eubacterium ventriosum群、Eubacterium xylanophilum群、Faecalibacterium属、Intestinimonas属、Lachnospiraceae UCG001属、Lachnospiraceae UCG004属和Peptococcus属以及肠杆菌科均与各种结果呈负相关。
Each cell in the heatmap corresponds to a specific micrbiota taxa-sepsis pair. The color of the cell indicates the OR value associated with that pair, with a color scale used to differentiate between positive, negative, and zero beta values.
热图中的每个单元格都对应一个特定的微生物群分类群-败血症配对。单元格的颜色表示与该配对相关的 OR 值，用色标来区分正、负和零贝塔值。
Notably, the genus Erysipelotrichaceae UCG003 displayed a particularly strong positive association with 28-day death in ICU (OR = 4.97, p = 2.43E-02), suggesting a potential role in severe sepsis outcomes. The genus Eubacterium xylanophilum group showed negative associations with both 28-day death (OR = 0.78, p = 3.26E-03) and sepsis (OR = 0.92, p = 1.68E-02), suggesting a protective role.
值得注意的是，赤霉菌属 UCG003 与重症监护室 28 天死亡有特别强的正相关性（OR = 4.97，p = 2.43E-02），这表明赤霉菌属在严重脓毒症结果中的潜在作用。木兰嗜酸乳杆菌属组与 28 天死亡（OR = 0.78，p = 3.26E-03）和败血症（OR = 0.92，p = 1.68E-02）均呈负相关，表明其具有保护作用。
Multiple-testing correction was taken into account by setting significance thresholds as follows: phylum p = 5.56×10-3 (0.05/9), class p = 3.13×10-3 (0.05/16), order p = 2.63 × 10-3 (0.05/19), family p = 1.67 × 10-3 (0.05/30), genus p = 4.27 × 10-4 (0.05/117). As the SNPs within a class might overlap with those in a related phylum and other subcategories, the MR results would remain similar if a class was considered a subcategory of a phylum or another subcategory.
考虑到多重检验校正，设定显著性阈值如下：门 p = 5.56×10 -3 (0.05/9)(0.05/9)，类 p = 3.13×10 -3 (0.05/16)(0.05/16），目 p = 2.63×10 -3 （0.05/19(0.05/19), 科 p = 1.67×10 -3 .(0.05/30), 属 p = 4.27 × 10 -4 .(0.05/117).由于类内的 SNPs 可能与相关门和其他亚类的 SNPs 重叠，因此，如果将类视为门或其他亚类的一个亚类，则 MR 结果将保持相似。
Based on the results of IVW fixed-effects analyses
Table 1
, phylum Lentisphaerae (OR = 0.932, 95% CI = 0.89-0.98, p = 2.64E-03), class Lentisphaeria and order Victivallales (OR = 0.927, 95% CI = 0.88-0.97, p = 1.42E-03) were negatively associated with the risk of Sepsis. Conversely, phylum Tenericutes and class Mollicutes (OR = 1.274, 95% CI = 1.09-1.49, p = 2.89E-03) were positively correlated with the risk of Sepsis (28 day death). Interestingly, Phylum Tenericutes and class Mollicutes (OR = 1.108, 95% CI = 1.04-1.18, p = 1.72E-03) were positively correlated with the risk of Sepsis (under 75 years) as well. No effect of sepsis on gut microbiota was found in the reverse analysis (
Supplementary Table 4
).
根据表 1 IVW 固定效应分析的结果，扁平苔藓门（OR = 0.932，95% CI = 0.89-0.98，p = 2.64E-03）、扁平苔藓纲和 Victivallales 目（OR = 0.927，95% CI = 0.88-0.97，p = 1.42E-03）与败血症风险呈负相关。相反，真菌门和毛霉菌纲（OR = 1.274，95% CI = 1.09-1.49，p = 2.89E-03）与败血症（28 天死亡）风险呈正相关。有趣的是，担子菌门和毛霉菌类（OR = 1.108，95% CI = 1.04-1.18，p = 1.72E-03）与败血症（75 岁以下）风险也呈正相关。在反向分析中没有发现败血症对肠道微生物群的影响（补充表 4）。
Table 1 表 1
MR result of gut microbiota on sepsis.
肠道微生物群对败血症的 MR 结果。
| Exposure 接触 | Methods 方法 | Number of SNPs SNPs 数量 | OR | 95%CI | p val | Cochran’s 科克兰 Q statistic (p val) Q 统计量（p 值） | Egger 埃格 intercept(p val) 截取（p 值） | F |
|---|---|---|---|---|---|---|---|---|
| Sepsis(Outcome) 败血症（结果） | ||||||||
| phylum 门类 Lentisphaerae 扁桃科 | IVW-FE | 41 | 0.932 | 0.89-0.98 | 2.64E-03 | 0.31 | 0.979 | 19.67 |
| IVW-RE | 0.932 | 0.89-0.98 | 4.11E-03 | |||||
| MR Egger 埃格先生 | 0.93 | 0.78-1.11 | 0.429 | |||||
| WM | 0.969 | 0.91-1.04 | 0.347 | |||||
| class Lentisphaeria 类 order Victivallales 订购 Victivallales | IVW-FE | 40 | 0.927 | 0.88-0.97 | 1.42E-03 | 0.663 | 0.982 | 19.56 |
| IVW-RE | 0.927 | 0.88-0.97 | 1.42E-03 | |||||
| MR Egger 埃格先生 | 0.929 | 0.78-1.1 | 0.406 | |||||
| WM | 0.958 | 0.89-1.03 | 0.234 | |||||
| Sepsis (28 day death)(Outcome) 败血症（28 天死亡）（结果） | ||||||||
| phylum Tenericutes 植物门 class Mollicutes 多孔菌类 | IVW-FE | 46 | 1.274 | 1.09-1.49 | 2.89E-03 | 0.691 | 0.489 | 19.45 |
| IVW-RE | 1.274 | 1.09-1.49 | 2.89E-03 | |||||
| MR Egger 埃格先生 | 1.099 | 0.7-1.71 | 0.68 | |||||
| WM | 1.288 | 1.02-1.63 | 0.034 | |||||
| Sepsis(under 75)(Outcome) 败血症（75 岁以下）（结果） | ||||||||
| phylum Tenericutes 植物门 class Mollicutes 多孔菌类 | IVW-FE | 46 | 1.108 | 1.04-1.18 | 1.72E-03 | 0.204 | 0.27 | 19.5 |
| IVW-RE | 1.108 | 1.03-1.19 | 3.73E-03 | |||||
| MR Egger 埃格先生 | 1 | 0.83-1.21 | 0.999 | |||||
| WM | 1.105 | 1-1.22 | 0.047 | |||||
IVW-FE, Inverse variance weighted-Fixed model; IVW-RE, Inverse variance weighted-Random model; WM, weight median; F is the value of F statistics to examine the weak instrument bias; Significant p-values were bold after multiple-testing correction [phylum p = 5.56×10-3 (0.05/9), class p = 3.13×10-3 (0.05/16), order p = 2.63×10-3 (0.05/19), family p = 1.67×10-3 (0.05/30), genus p = 4.27×10-4 (0.05/117)].
IVW-FE，逆方差加权-固定模型；IVW-RE，逆方差加权-随机模型；WM，权重中位数；F 为考察弱工具偏差的 F 统计量值；多重检验校正后，显著的 p 值为粗体[门类 p = 5.56×10 -3 (0 05/9), 类 p = 3.13×10 -3 (0 05/9)] 。(0.05/9)，类 p = 3.13×10 -3(0.05/16), 目 p = 2.63×10 -3 .(0.05/19), 科 p = 1.67×10 -3 .(0.05/30), 属 p = 4.27×10 -4 ].(0.05/117)].
For additional confirmation of the robustness of the results, several sensitivity tests were conducted (
Supplementary Table 4
). Most of the results were consistent in sensitivity analyses, though with wider confidence intervals. In addition, all results of Cochran’s Q test were above 0.05, signifying that there was no significant heterogeneity. The MR-PRESSO analysis also corroborated this, demonstrating no outlier of SNPs. Moreover, the MR-Egger intercept test and the global test p-values both revealed no statistically significant results, suggesting no presence of horizontal pleiotropy.
为了进一步确认结果的稳健性，还进行了几项敏感性测试（补充表 4）。大多数结果在敏感性分析中都是一致的，只是置信区间更宽。此外，Cochran's Q 检验的所有结果均高于 0.05，表明不存在显著的异质性。MR-PRESSO 分析也证实了这一点，表明没有异常 SNPs。此外，MR-Egger 截距检验和全局检验的 p 值均未显示出具有统计学意义的结果，表明不存在水平多效性。
3.2. Gut microbiota and C-reactive protein level
3.2.肠道微生物群与 C 反应蛋白水平
Similarly, we conducted two-sample analyses to examine the relationship between gut microbiota and C-reactive protein (CRP). The IVW fixed-effects analyses
Table 2
showed that family Coriobacteriaceae, order Coriobacteriales, class Coriobacteriia had a negative correlation with CRP levels (Beta = -0.502 p = 0.046), However, Phylum Tenericutes, class Mollicutes, genus Dialister, genus Gordonibacter had a positive correlation with CRP levels, and WM analysis also obtained similar causal estimates.
同样，我们对肠道微生物群与 C 反应蛋白（CRP）之间的关系进行了双样本分析。表 2 的 IVW 固定效应分析表明，科罗杆菌科、科罗杆菌目、科罗杆菌属与 CRP 水平呈负相关（Beta = -0.502 p = 0.046），然而，担子菌门、毛霉菌纲、 Dialister 属、Gordonibacter 属与 CRP 水平呈正相关，WM 分析也得到了类似的因果关系估计值。
Table 2 表 2
MR result of gut microbiota on CRP.
肠道微生物群对 CRP 的影响。
| Exposure 接触 | Methods 方法 | Number of SNPs SNPs 数量 | Beta | Se | Pval | Cochran’s Q statistic (P-value) Cochran's Q 统计量（P 值） | Egger intercept(P-value) 埃格截距（P 值） | F |
|---|---|---|---|---|---|---|---|---|
| CRP(Outcome) CRP（成果） | ||||||||
| family Coriobacteriaceae 科 | IVW-FE | 12 | -0.502 | 0.252 | 4.59E-02 | 0.642 | 0.582 | 19.388 |
| class Coriobacteriia Coriobacteria | IVW-RE | 12 | -0.502 | 0.252 | 4.59E-02 | 0.642 | 0.582 | 19.388 |
| order Coriobacteriales 珊瑚目 | MR Egger 埃格先生 | 12 | -1.304 | 1.430 | 3.83E-01 | 0.642 | 0.582 | 19.388 |
| WM | 12 | -0.492 | 0.335 | 1.42E-01 | 0.642 | 0.582 | 19.388 | |
| class Mollicutes 多孔菌类 | IVW-FE | 6 | 0.736 | 0.292 | 1.17E-02 | 0.878 | 0.760 | 18.688 |
| phylum Tenericutes 植物门 | IVW-RE | 6 | 0.736 | 0.292 | 1.17E-02 | 0.878 | 0.760 | 18.688 |
| MR Egger 埃格先生 | 6 | 0.408 | 1.043 | 7.16E-01 | 0.878 | 0.760 | 18.688 | |
| WM | 6 | 0.819 | 0.370 | 2.68E-02 | 0.878 | 0.760 | 18.688 | |
| genus Dialister Dialister 属 | IVW-FE | 9 | 0.585 | 0.238 | 1.41E-02 | 0.267 | 0.463 | 18.297 |
| IVW-RE | 9 | 0.585 | 0.266 | 2.80E-02 | 0.267 | 0.463 | 18.297 | |
| MR Egger 埃格先生 | 9 | 1.523 | 1.238 | 2.58E-01 | 0.267 | 0.463 | 18.297 | |
| WM | 9 | 0.410 | 0.335 | 2.22E-01 | 0.267 | 0.463 | 18.297 | |
| genus Gordonibacter 高登氏菌属 | IVW-FE | 9 | 0.293 | 0.136 | 3.10E-02 | 0.927 | 0.822 | 18.667 |
| IVW-RE | 9 | 0.293 | 0.136 | 3.10E-02 | 0.927 | 0.822 | 18.667 | |
| MR Egger 埃格先生 | 9 | 0.162 | 0.574 | 7.86E-01 | 0.927 | 0.822 | 18.667 | |
| WM | 9 | 0.188 | 0.178 | 2.89E-01 | 0.927 | 0.822 | 18.667 | |
IVW-FE, Inverse variance weighted-fixed model; IVW-RE, Inverse variance weighted-random model; WM, weight median; CRP, C reactive protein; F is the value of F statistics to examine the weak instrument bias.
IVW-FE，逆方差加权-固定模型；IVW-RE，逆方差加权-随机模型；WM，体重中位数；CRP，C 反应蛋白；F 为 F 统计值，用于检验弱工具偏差。
Bold means that the p-value is less than 0.05.
粗体表示 p 值小于 0.05。
A series of sensitivity analyses, including WM, Cochran’s Q test, MR-Egger regression, intercept test were conducted
Table 2
. These results were consistent in sensitivity analyses, though some with wider confidence intervals. Additionally, all p values from both the Cochran’s Q test and the MR-Egger intercept test were greater than 0.05, indicating the absence of heterogeneity and horizontal pleiotropy. The reverse analysis did not find any effect of CRP on gut microbiota (
Supplementary Table 5
).
表 2 进行了一系列敏感性分析，包括 WM、Cochran's Q 检验、MR-Egger 回归和截距检验。这些结果在敏感性分析中是一致的，尽管有些结果的置信区间更宽。此外，Cochran's Q 检验和 MR-Egger 截距检验的所有 p 值均大于 0.05，表明不存在异质性和水平多向性。反向分析没有发现 CRP 对肠道微生物群有任何影响（补充表 5）。
3.3. C-reactive protein level and sepsis, sepsis subgroups
3.3.C 反应蛋白水平与败血症，败血症亚组
Initially, we conducted two-sample MR analyses (
Table 3
) to examine the effect of C-reactive protein levels on sepsis and its subgroups. The table presents the results, and the IVW fixed-effects analyses showed a positive correlation between CRP levels and Sepsis and Sepsis (under 75). No effect of sepsis on CRP was found in the reverse analysis (
Supplementary Table 6
). Furthermore, a series of sensitivity analyses validated the robustness of the findings.
首先，我们进行了双样本 MR 分析（表 3），以研究 C 反应蛋白水平对败血症及其亚组的影响。表中列出了结果，IVW 固定效应分析显示 CRP 水平与败血症和败血症（75 岁以下）呈正相关。反向分析中未发现败血症对 CRP 的影响（补充表 6 ）。此外，一系列敏感性分析验证了研究结果的稳健性。
Table 3 表 3
MR result of CRP on sepsis.
CRP 对败血症的 MR 结果。
| Exposure 接触 | Methods 方法 | Number of SNPs SNPs 数量 | OR | 95%CI | Pval | Cochran's Q statistic (P-value) Cochran's Q 统计量（P 值） | Egger intercept(P-value) 埃格截距（P 值） | F |
|---|---|---|---|---|---|---|---|---|
| Sepsis(Outcome) 败血症（结果） | ||||||||
| CRP | IVW-FE | 21 | 1.046 | 1.01-1.08 | 0.006 | 0.608 | 0.497 | 29.45 |
| IVW-RE | 21 | 1.046 | 1.01-1.08 | 0.006 | ||||
| MR Egger 埃格先生 | 21 | 1.077 | 0.99-1.18 | 0.114 | ||||
| WM | 21 | 1.036 | 0.99-1.09 | 0.135 | ||||
| Sepsis (28 day death)(Outcome) 败血症（28 天死亡）（结果） | ||||||||
| CRP | IVW-FE | 21 | 1.042 | 0.96-1.13 | 0.305 | 0.448 | 0.108 | 29.45 |
| IVW-RE | 21 | 1.042 | 0.96-1.13 | 0.307 | ||||
| MR Egger 埃格先生 | 21 | 1.238 | 1-1.53 | 0.067 | ||||
| WM | 21 | 1.073 | 0.96-1.2 | 0.215 | ||||
| Sepsis (28 day death in Critical Care Units)(Outcome) 败血症（重症监护室 28 天死亡）（结果） | ||||||||
| CRP | IVW-FE | 21 | 1.0823696 | 0.9 | 1.302 | 0.414 | 0.027 | 29.45 |
| IVW-RE | 21 | 1.0823696 | 0.9 | 1.307 | ||||
| MR Egger 埃格先生 | 21 | 1.9199409 | 1.2 | 3.176 | ||||
| WM | 21 | 1.137025 | 0.9 | 1.485 | ||||
| Sepsis (under 75)(Outcome) 败血症（75 岁以下）（结果） | ||||||||
| CRP | IVW-FE | 21 | 1.046 | 1.01-1.08 | 0.005 | 0.729 | 0.597 | 29.45 |
| IVW-RE | 21 | 1.046 | 1.01-1.08 | 0.005 | ||||
| MR Egger 埃格先生 | 21 | 1.069 | 0.98-1.17 | 0.142 | ||||
| WM | 21 | 1.054 | 1.01-1.1 | 0.02 | ||||
IVW-FE, Inverse variance weighted-fixed model; IVW-RE, Inverse variance weighted-random model; WM, weight median; CRP, C reactive protein; F is the value of F statistics to examine the weak instrument bias.
IVW-FE，逆方差加权-固定模型；IVW-RE，逆方差加权-随机模型；WM，体重中位数；CRP，C 反应蛋白；F 为 F 统计值，用于检验弱工具偏差。
Bold means that the p-value is less than 0.05.
粗体表示 p 值小于 0.05。
Secondly, we utilized MVMR (as shown in
Table 4
) to assess the independent impact of CRP on sepsis, which was independence of gut microbiota. The results indicate a significant positive association between CRP levels and a higher risk of sepsis as well as sepsis under 75 years old.
其次，我们利用 MVMR（如表 4 所示）来评估 CRP 对败血症的独立影响，这与肠道微生物群无关。结果表明，CRP 水平与较高的败血症风险以及 75 岁以下的败血症之间存在明显的正相关。
Table 4 表 4
MVMR result of gut microbiota and CRP on sepsis.
肠道微生物群和 CRP 对败血症的 MVMR 结果。
| Exposure 接触 | Number of SNPs SNPs 数量 | OR | 95%CI | pval |
|---|---|---|---|---|
| Sepsis (under 75)(Outcome) 败血症（75 岁以下）（结果） | ||||
| phylum Tenericutes/ class Mollicutes 真菌门/真菌纲 | 23 | 1.1 | 0.93-1.3 | 0.26 |
| CRP | 23 | 1.05 | 1.01-1.08 | 0.011 |
| Sepsis(Outcome) 败血症（结果） | ||||
| genus Gordonibacter 高登氏菌属 | 25 | 0.98 | 0.9-1.07 | 0.692 |
| CRP | 25 | 1.05 | 1.01-1.08 | 0.011 |
MVMR, Multivariable Mendelian randomization; IVW-FE, Inverse Variance Weighted-Fixed model; IVW-RE, Inverse Variance Weighted-Random model; CRP, C reactive protein; WM, Weight Median; Significant P-values were bold.
MVMR，多变量孟德尔随机化；IVW-FE，逆方差加权固定模型；IVW-RE，逆方差加权随机模型；CRP，C 反应蛋白；WM，体重中位数；显著的 P 值用粗体表示。
As shown in
Table 5
, the mediation analysis revealed that CRP plays a significant role (32.02% mediation effect) in the causal pathway from Phylum Tenericutes and class Mollicutes to sepsis (in individuals under 75 years old). And CRP mediate 31.53% effect of genus Gordonibacter on sepsis.
如表 5 所示，中介分析表明，在从担子菌门和毛霉菌纲到败血症（75 岁以下人群）的因果关系中，CRP 起着重要作用（中介效应为 32.02%）。而 CRP 在戈登氏菌属对败血症的影响中起了 31.53% 的中介作用。
Table 5 表 5
Two-step Mendelian randomization.
两步孟德尔随机法。
| Exposure 接触 | Mediation 调解 | Total effect (Beta) 总效应 (Beta) | A (Beta) A（β） | B (Beta) B（贝塔） | Indirect effect (Beta) 间接效应 (Beta) | Mediation effect/ Total effect 调解效果/总效果 |
|---|---|---|---|---|---|---|
| Sepsis (under 75)(Outcome) 败血症（75 岁以下）（结果） | ||||||
| phylum tenericutes/ class Mollicutes 真菌门/真菌纲 | CRP | 0.102 | 0.736 | 0.044 | 0.033 | 32.02% |
| Sepsis(Outcome) 败血症（结果） | ||||||
| genus Gordonibacter 高登氏菌属 | CRP | 0.045 | 0.293 | 0.045 | 1.32% | 31.53% |
A, the effect of Exposure on Mediation; B, the effect of Mediation on Outcome is independent of the effect of the exposure.
A，暴露对中介的影响；B，中介对结果的影响与暴露的影响无关。
4. Discussion 4.讨论
Over the past decade, numerous studies have confirmed the diverse biological functions of gut microbes, including aiding in food digestion, hormone production, and enhancing the immune system, among others (32–34). In this study, we collected data from the largest GWAS to date on gut microbiota and sepsis, and evaluated the causal relationship between all gut microbiota taxa and sepsis. We found that 24 taxa were positively associated with various sepsis outcomes, 30 taxa were negatively associated with sepsis outcomes. In total, we identified 37 unique taxa, including 23 at the genus level, 5 at the family level, 3 at the order level, 4 at the class level, and 2 at the phylum level. After multiple-testing correction, phylum Lentisphaerae, class Lentisphaeria, and order Victivallales were still associated with a reduced risk of sepsis, while Phylum Tenericutes and class Mollicutes were linked to an increased risk of sepsis (particularly in individuals under 75 years old) and 28-day mortality. Notably, we did not observe any significant association between sepsis and these gut microbiota. Taken together, our findings provide valuable insights into the role of gut microbiota in sepsis treatment, including reducing the risk of sepsis, minimizing mortality, and improving sepsis prognosis.
在过去十年中，大量研究证实了肠道微生物的多种生物功能，包括帮助食物消化、产生激素和增强免疫系统等（32- 34）。在本研究中，我们收集了迄今为止最大的有关肠道微生物群和败血症的 GWAS 数据，并评估了所有肠道微生物群分类群与败血症之间的因果关系。我们发现，24 个分类群与各种败血症结果呈正相关，30 个分类群与败血症结果呈负相关。我们总共发现了 37 个独特的分类群，其中属级 23 个，科级 5 个，目级 3 个，类级 4 个，门级 2 个。经过多重检验校正后，扁平苔藓门、扁平苔藓纲和Victivallales目仍与败血症风险降低有关，而Tenericutes门和Mollicutes纲则与败血症风险升高（尤其是75岁以下人群）和28天死亡率升高有关。值得注意的是，我们没有观察到败血症与这些肠道微生物群之间有任何明显的关联。总之，我们的研究结果为肠道微生物群在败血症治疗中的作用提供了宝贵的见解，包括降低败血症风险、最大限度地降低死亡率和改善败血症预后。
Tenericutes and Mollicutes are primarily associated with infections in pregnant women and newborns. Several studies have shown that mycoplasma infections can cause puerperal sepsis (35), and in newborns, these infections are linked to an increased risk of bronchopulmonary dysplasia, early-onset neonatal sepsis, and meningitis (36, 37). In contrast, Lentisphaerae (phylum), Lentisphaeria (class), and Victivallales (order) are relatively under-studied bacterial groups. However, recent research suggests that these microbial communities are closely associated with immune regulation. Lentisphaerae, for instance, has been found to be more abundant in cases of inflammatory bowel diseases (38), while its abundance is reduced in patients with rosacea (39). Furthermore, in patients diagnosed with post-traumatic stress disorder, Lentisphaerae has been associated with a decrease in symptom severity scores (40). genus Gordonibacter is primarily found to be excessively increased in patients with Crohn’s disease and Rheumatoid Arthritis (RA), which indicates its close relationship with immunity and inflammation (41). This also indirectly confirms its association with the increase in CRP.
特纳菌属和毛霉菌属主要与孕妇和新生儿感染有关。一些研究表明，支原体感染可导致产褥败血症（35），而在新生儿中，这些感染与支气管肺发育不良、早发新生儿败血症和脑膜炎的风险增加有关（36，37）。相比之下，对扁桃目（门）、扁桃属（类）和Victivallales（目）细菌群的研究相对较少。不过，最近的研究表明，这些微生物群落与免疫调节密切相关。例如，研究发现在炎症性肠病（38）的病例中，旬藻菌的数量较多；而在酒糟鼻患者（39）中，旬藻菌的数量则有所减少。此外，在被诊断患有创伤后应激障碍的患者中，Lentisphaerae 与症状严重程度评分的降低有关（40）。Gordonibacter 属主要在克罗恩病和类风湿性关节炎（RA）患者中过度增加，这表明它与免疫和炎症密切相关（41）。这也间接证实了它与 CRP 增高的关系。
Previous MR analyses have suggested that gut microbiota and their metabolites can impact Systemic Lupus Erythematosus, inflammatory bowel diseases, and blood metabolites (42–44). These findings emphasize the significance of these bacterial groups in regulating inflammation in the human body. Their presence and abundance in various disease conditions imply a potential role in modulating immune responses and contributing to the development or resolution of inflammation-related disorders.
之前的磁共振分析表明，肠道微生物群及其代谢物会影响系统性红斑狼疮、炎症性肠病和血液代谢物（42- 44）。这些发现强调了这些细菌群在调节人体炎症中的重要作用。它们在各种疾病中的存在和丰富程度意味着它们在调节免疫反应和促进炎症相关疾病的发展或缓解方面可能发挥作用。
Recently, a study employed regression analysis to investigate the potential impact of the interaction between gut microbiota and CRP using individual level genotype data from UK Biobank (45). Nonetheless, due to the insufficient research on the relationship between gut microbiota and serum inflammation, we examined the effect of CRP, an inflammation protein linked to a higher risk of infections in adults (15), in the association between gut microbiota and sepsis. Our findings indicate that Phylum Tenericutes and class Mollicutes are strongly associated with increasing levels of C-reactive protein. Previous studies have shown elevated levels of CRP in patients with mycoplasma infection. Taken together with our results, this implies that CRP might not only work as a biomarker for mycoplasma infection but also play a role in mediating the pathogenic mechanisms of mycoplasma. These results establish the role of certain gut microbiota in systemic inflammation and immune response.
最近，一项研究利用英国生物库（UK Biobank）的个体水平基因型数据，采用回归分析法研究了肠道微生物群与 CRP 之间相互作用的潜在影响（45）。然而，由于对肠道微生物群与血清炎症之间关系的研究不足，我们研究了 CRP（一种与成人感染风险较高有关的炎症蛋白）对肠道微生物群与败血症之间关系的影响（15）。我们的研究结果表明，真菌门和毛霉菌门与 C 反应蛋白水平的升高密切相关。以前的研究表明，支原体感染患者的 CRP 水平升高。结合我们的研究结果，这意味着 CRP 可能不仅是支原体感染的生物标志物，还可能在支原体的致病机制中起介导作用。这些结果证实了某些肠道微生物群在全身炎症和免疫反应中的作用。
Current research on the effects of serum substances on sepsis has primarily focused on lipid and iron metabolism (46, 47). Several cross-sectional studies have demonstrated that elevated CRP levels are linked to increased morbidity and mortality in sepsis (48–50). In our examination of the relationship between CRP and sepsis, we found that CRP is associated with a higher incidence of sepsis and sepsis-related deaths among those under 75 years of age. Reverse analysis revealed no effect on CRP. Meanwhile, mediation analysis found that CRP mediates 32% of the effects of Phylum Tenericutes and class Mollicutes on sepsis (under 75 years). Based we used multivariate MR, the effect of CRP on sepsis were independent of the effect of the exposure (17). Our Mendelian randomization study on the relationship between our microbiota and the risk of developing and dying from sepsis will help us understand how changes in the gut microbiome lead to immune dysregulation in sepsis, which in turn can aid in improving sepsis management.
目前有关血清物质对脓毒症影响的研究主要集中在脂质和铁代谢方面 ( 46, 47)。多项横断面研究表明，CRP 水平升高与败血症发病率和死亡率升高有关（48-50）。在研究 CRP 与败血症之间的关系时，我们发现 CRP 与 75 岁以下人群中败血症发病率和败血症相关死亡的增加有关。反向分析显示 CRP 没有影响。同时，中介分析发现，CRP 对脓毒症（75 岁以下）有 32% 的中介作用。基于我们使用的多变量 MR，CRP 对败血症的影响与暴露的影响无关（17）。我们关于微生物群与脓毒症发病和死亡风险之间关系的孟德尔随机化研究将有助于我们了解肠道微生物群的变化如何导致脓毒症的免疫失调，进而有助于改善脓毒症的管理。
Firstly, our study used multiple sensitive analysis, thereby bolstering the reliability of our findings. The consistency between the most of the WM and MR-Egger methods with those from the IVW method attests to the robustness of our results. Despite the presence of wide confidence intervals in some results, the overarching pattern of associations remained consistent. Secondly, we implemented the MR-PRESSO technique to identify and exclude potential outliers that could introduce bias into our findings, enhancing the reliability of our results. Thirdly, our study was instrumental in spotlighting certain genera that showed a more significant association with sepsis compared to other microbial classes. Even though these associations didn’t retain their statistical significance after multiple testing adjustment, they still constitute crucial preliminary observations and may be indicative of underlying biological phenomena. Fourthly, through the use of PhenomeScan, we found that no SNPs from the microbiota, CRP and sepsis were associated with infections, malignant diseases, or antibiotic use. This suggests that the observed links among the microbiota, CRP, and sepsis were unlikely to be confounded by the genetic predispositions that are typically represented by SNPs. Lastly, given that both the exposure and outcome populations were of European descent, the potential for bias resulting from population stratification was minimized.
首先，我们的研究采用了多重敏感分析，从而增强了研究结果的可靠性。WM法和MR-Egger法得出的结果与IVW法得出的结果一致，这证明我们的结果是可靠的。尽管某些结果的置信区间较宽，但总体关联模式保持一致。其次，我们采用了 MR-PRESSO 技术来识别和排除可能给研究结果带来偏差的潜在异常值，从而提高了研究结果的可靠性。第三，与其他微生物类别相比，我们的研究有助于发现某些菌属与败血症的关系更为显著。尽管经过多重检验调整后，这些关联并没有保持其统计学意义，但它们仍然是重要的初步观察结果，并可能表明潜在的生物学现象。第四，通过使用 PhenomeScan，我们发现微生物群、CRP 和败血症中没有 SNP 与感染、恶性疾病或抗生素使用相关。这表明，所观察到的微生物群、CRP 和败血症之间的联系不太可能受到 SNPs 所代表的遗传倾向的干扰。最后，由于暴露人群和结果人群都是欧洲后裔，因此人口分层造成偏差的可能性降到了最低。
However, there are several limitations to our study. Firstly, a limited amount of non-European population data on gut microbiota was obtained, which may have biased our findings. Secondly, we were unable to discern any non-linear correlations among mcirbiota, CRP and sepsis, such as U-shaped, J shaped patterns. Thirdly, the number of loci related to CRP is relatively small compared to those associated with sepsis and gut microbiota. Fourthly, our Mendelian randomization study was unable to access individual-level data, which posed a constraint on the depth of our analysis. For instance, we were unable to perform a hierarchical analysis, specifically in the case of sepsis. Ideally, we would have liked to divide the sepsis data into two groups according to the Sepsis-2 and Sepsis-3 guidelines, which could provide insights into the differences between these two classifications. However, due to the unavailability of the required individual-level data, we were unable to conduct such an analysis.
不过，我们的研究也存在一些局限性。首先，我们获得的非欧洲人群肠道微生物群数据数量有限，这可能会使我们的研究结果产生偏差。其次，我们无法发现微生物群、CRP 和败血症之间的任何非线性相关性，如 U 形、J 形模式。第三，与脓毒症和肠道微生物群相关的基因位点相比，与 CRP 相关的基因位点数量相对较少。第四，我们的孟德尔随机化研究无法获取个体水平的数据，这对我们的分析深度造成了限制。例如，我们无法进行分层分析，特别是在败血症的情况下。理想情况下，我们希望根据败血症-2（Sepsis-2）和败血症-3（Sepsis-3）指南将败血症数据分为两组，从而深入了解这两种分类之间的差异。然而，由于无法获得所需的个人层面数据，我们无法进行这样的分析。
5. Conclusion 5.结论
In conclusion, our bi-directional Mendelian randomization analysis has clearly indicated a causal relationship between the 37 unique gut microbiota taxa and increased risk of sepsis, whereas the reverse causality hypothesis did not hold. Importantly, our findings suggest that C-reactive protein (CRP) acts as a mediator of the impact of the gut microbiota on sepsis. For a more nuanced understanding of the observed association between the gut microbiota and sepsis, future research should focus on potential mechanistic pathways, while also attempting to adjust for potential confounders such as diet, lifestyle, and medication, provided these data are available. Furthermore, an analysis of sepsis as a heterogeneous condition, acknowledging its multi-stages and variations as defined by the sepsis-3 criteria, would be beneficial, throught acquire individual-level data in future. Our work constitutes a significant stride in deciphering the relationship between gut microbiota and sepsis, however, more experimental and clinical studies are warranted to verify and extend our findings. It is our hope that our study acts as a catalyst for further exploration in this field, and thereby contribute to the ceaseless enhancement of patient care in intensive care units.
总之，我们的双向孟德尔随机分析清楚地表明了 37 个独特的肠道微生物群分类群与败血症风险增加之间的因果关系，而反向因果关系假设并不成立。重要的是，我们的研究结果表明，C反应蛋白（CRP）是肠道微生物群对败血症影响的介导因素。为了更细致地了解所观察到的肠道微生物群与脓毒症之间的关系，未来的研究应侧重于潜在的机理途径，同时尝试调整潜在的混杂因素，如饮食、生活方式和药物（如果有这些数据的话）。此外，将脓毒症作为一种异质性疾病进行分析，承认脓毒症-3 标准所定义的脓毒症的多阶段性和变异性，这将是有益的，并将在未来获得个体层面的数据。我们的研究在解读肠道微生物群与脓毒症之间的关系方面迈出了重要一步，但还需要更多的实验和临床研究来验证和扩展我们的发现。我们希望我们的研究能成为该领域进一步探索的催化剂，从而为不断提高重症监护病房的病人护理水平做出贡献。
Data availability statement
数据可用性声明
The original contributions presented in the study are included in the article/
Supplementary Material
. Further inquiries can be directed to the corresponding author.
该研究的原创性贡献载于文章/补充材料中。如需进一步咨询，请联系通讯作者。
Author contributions 作者供稿
ZZ, LC, and DN designed the study and collected and collated the data. DN analyzed the data, LC and ZZ drafted the paper. All authors contributed to the article and approved the submitted version.
ZZ、LC 和 DN 设计了这项研究，并收集和整理了数据。DN 分析了数据，LC 和 ZZ 起草了论文。所有作者均对文章有贡献，并批准了提交的版本。
Acknowledgments 致谢
We appreciate all the volunteers who participated in this study. We are grateful to the MiBioGen consortium and Open GWAS for providing GWAS summary statistics.
我们感谢所有参与本研究的志愿者。我们感谢 MiBioGen 联盟和 Open GWAS 提供的 GWAS 统计摘要。
Conflict of interest 利益冲突
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
作者声明，本研究在进行过程中不存在任何可能被视为潜在利益冲突的商业或经济关系。
Publisher’s note 出版商说明
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
本文中表述的所有主张仅代表作者本人，并不一定代表其附属机构的主张，也不代表出版商、编辑和审稿人的主张。本文可能评估的任何产品，或其制造商可能提出的任何主张，均未得到出版商的保证或认可。
Supplementary material 补充材料
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1234924/full#supplementary-material
点击此处查看附加数据文件。 (229K, xlsx)
References 参考资料
1.Reinhart K, Daniels R, Kissoon N, MaChado FR, Schachter RD, Finfer S. Recognizing sepsis as a global health priority - A WHO resolution.N Engl J Med (2017) 377(5):414-7. doi: 10.1056/NEJMp1707170 [ PubMed] [ CrossRef] [ Google Scholar].
2.Lelubre C, Vincent JL.败血症器官衰竭的机制与治疗。Nat Rev Nephrol.(2018) 14(7):417-27. doi: 10.1038/s41581-018-0005-7 [ PubMed] [ CrossRef] [ Google Scholar].
3.Cohen J, Vincent JL, Adhikari NK, Machado FR, Angus DC, Calandra T, et al.败血症：未来研究路线图。doi: 10.1016/S1473-3099(15)70112-X [ PubMed] [ CrossRef] [ Google Scholar].
4.Gotts JE, Matthay MA.败血症：病理生理学和临床管理。BMJ (2016) 353:i1585. doi: 10.1136/bmj.i1585 [ PubMed] [ CrossRef] [ Google Scholar].
6.Haak BW, Prescott HC, Wiersinga WJ.肠道微生物群在预防和治疗败血症中的治疗潜力。Front Immunol (2018) 9:2042. doi: 10.3389/fimmu.2018.02042 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
7.Liu Z, Li N, Fang H, Chen X, Guo Y, Gong S, et al.肠道菌群失调与患者败血症相关。FASEB J (2019) 33(11):12299-310. Doi: 10.1096/fj.201900398RR [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
8.Vulevic J, Juric A, Walton GE, Claus SP, Tzortzis G, Toward RE, et al.半乳寡糖混合物（B-GOS）对老年人肠道微生物群、免疫参数和代谢组学的影响。Doi: 10.1017/S0007114515001889 [ PubMed] [ CrossRef] [ Google Scholar].
9.Panigrahi P、Parida S、Nanda NC、Satpathy R、Pradhan L、Chandel DS 等。预防印度农村婴儿败血症的随机益生菌试验。自然(2017) 548(7668):407-12. doi: 10.1038/nature23480 [ PubMed] [ CrossRef] [ Google Scholar].
10.Shimizu K, Yamada T, Ogura H, Mohri T, Kiguchi T, Fujimi S, et al.Synbiotics modulate gut microbiota and reduce enteritis and ventilator-associated pneumonia in patients with sepsis: a randomized controlled trial.Doi: 10.1186/s13054-018-2167-x [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
11.Barraud D, Blard C, Hein F, Marçon O, Cravoisy A, Nace L, et al.重症患者使用益生菌：双盲、随机、安慰剂对照试验》。Doi: 10.1007/s00134-010-1927-0 [ PubMed] [ CrossRef] [ Google Scholar].
12.Besselink MG, van Santvoort HC, Renooij W, de Smet MB, Boermeester MA, Fischer K, et al.急性胰腺炎特定益生菌成分随机试验中的肠道屏障功能障碍。Doi: 10.1097/SLA.0b013e3181bce5bd [ PubMed] [ CrossRef] [ Google Scholar].
13.Pepys MB, Hirschfield GM.C反应蛋白：重要更新。J Clin Invest.(DOI: 10.1172/JCI18921 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
14.Peisajovich A, Marnell L, Mold C, Du Clos TW.先天免疫与炎症界面上的 C 反应蛋白。Doi: 10.1586/1744666X.4.3.379 [ PubMed] [ CrossRef] [ Google Scholar].
15.Zacho J, Benfield T, Tybjærg-Hansen A, Nordestgaard BG.基线C反应蛋白浓度升高与感染风险增加有关：来自2个大型丹麦人群队列的结果。Doi: 10.1373/clinchem.2015.249680 [ PubMed] [ CrossRef] [ Google Scholar].
16.Greenland S. An introduction to instrumental variables for epidemiologists.Int J Epidemiol.(2018) 47(1):358. doi: 10.1093/ije/dyx275 [ PubMed] [ CrossRef] [ Google Scholar].
17.Burgess S, Butterworth A, Thompson SG.使用汇总数据进行多基因变异的孟德尔随机分析。Genet Epidemiol.(doi: 10.1002/gepi.21758 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
18.Kurilshikov A, Medina-Gomez C, Bacigalupe R, Radjabzadeh D, Wang J, Demirkan A, et al.大规模关联分析确定了影响人类肠道微生物组组成的宿主因素。Doi: 10.1038/s41588-020-00763-1 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
19.Suhre K、Arnold M、Bhagwat AM、Cotton RJ、Engelke R、Raffler J 等。通过人类血浆蛋白质组将遗传风险与疾病终点联系起来。Nat Commun (2017) 8:14357. doi: 10.1038/ncomms14357 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
20.Sanderson E、Glymour MM、Holmes MV、Kang H、Morrison J、Munafò MR 等。孟德尔随机化。Nat Rev Methods Primers.(2022) 2:6. doi: 10.1038/s43586-021-00092-5 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
21.Ganapathiraju MK, Subramanian S, Chaparala S, Karunakaran KB.人类基因组中的 DNA 重码及其在 1000 个基因组中的变异参考目录。Doi: 10.1038/s41439-020-00127-5 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
22.Pierce BL, Ahsan H, Vanderweele TJ.使用多种遗传变异的孟德尔随机化研究的功率和工具强度要求。Int J Epidemiol.(Doi: 10.1093/ije/dyq151 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
23.Burgess S, Thompson SG, CRP CHD 遗传学协作组 .避免孟德尔随机化研究中弱工具造成的偏差。Int J Epidemiol.(2011) 40(3):755-64doi: 10.1093/ije/dyr036 [ PubMed] [ CrossRef] [ Google Scholar].
24.Bowden J, Davey Smith G, Haycock PC, Burgess S. 使用加权中位数估计器对某些无效工具的泯灭随机化进行一致估计。Genet Epidemiol.(doi: 10.1002/gepi.21965 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
25.Burgess S, Thompson SG.使用 MR-Egger 方法解释孟德尔随机化的结果。Eur J Epidemiol.(2017) 32(5):377-89. doi: 10.1007/s10654-017-0255-x [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
26.Kulinskaya E, Dollinger MB.基于Cochran's Q统计量的几率比同质性精确检验。BMC Med Res Methodol (2015) 15:49. doi: 10.1186/s12874-015-0034-x [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
27.Ni JJ, Xu Q, Yan SS, Han BX, Zhang H, Wei XT, et al.肠道微生物群与精神障碍：一项双样本泯灭随机研究。Doi: 10.3389/fmicb.2021.737197 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
28.Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease.Int J Epidemiol.(doi: 10.1093/ije/dyr233 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
29.Burgess S, Daniel RM, Butterworth AS, Thompson SG, EPIC-InterAct Consortium .网络孟德尔随机化：使用遗传变异作为工具变量来研究因果途径中的中介作用。Int J Epidemiol.(doi: 10.1093/ije/dyu176 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
30.Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, et al.用于中介分析的孟德尔随机化：当前方法与实施挑战》。Eur J Epidemiol.(DOI: 10.1007/s10654-021-00757-1 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
31.Baron RM, Kenny DA.J Pers Soc Psychol (1986) 51(6:1173-82.Doi: 10.1037//0022-3514.51.6.1173 [ PubMed] [ CrossRef] [ Google Scholar].
32.Holscher HD.膳食纤维和益生元与胃肠道微生物群。Gut Microbes (2017) 8(2):172-84. doi: 10.1080/19490976.2017.1290756 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
33.Chung H、Pamp SJ、Hill JA、Surana NK、Edelman SM、Troy EB 等。肠道免疫成熟取决于宿主特异性微生物群的定植。细胞》（Cell.(DOI: 10.1016/j.cell.2012.04.037 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
34.Li X, Watanabe K, Kimura I. Gut microbiota dysbiosis drives and implies novel therapeutic strategies for diabetes mellitus and related metabolic diseases.Front Immunol (2017) 8:1882. doi: 10.3389/fimmu.2017.01882 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
36.Stol K, Jans J, Ott de Bruin L, Unger W, van Rossum A. Perinatal infections with ureaplasma.Pediatr Infect Dis J (2021) 40(5S):S26-30. doi: 10.1097/INF.0000000000002859 [ PubMed] [ CrossRef] [ Google Scholar].
37.Glaser K, Gradzka-Luczewska A, Szymankiewicz-Breborowicz M, Kawczynska-Leda N, Henrich B, Waaga-Gasser AM, et al.围产期尿解支原体暴露与早产儿晚期败血症和不平衡炎症风险增加有关，并可能加重肺损伤。Front Cell Infect Microbiol (2019) 9:68. doi: 10.3389/fcimb.2019.00068 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
38.Fan HN, Zhu P, Lu YM, Guo JH, Zhang J, Qu GQ, et al.回肠末端炎症期间粘膜微生物组的轻度变化。Microb Pathog (2020) 142:104104. doi: 10.1016/j.micpath.2020.104104 [ PubMed] [ CrossRef] [ Google Scholar].
39.Chen YJ, Lee WH, Ho HJ, Tseng CH, Wu CY.红斑痤疮患者与匹配对照组相比粪便微生物谱的改变。Doi: 10.1016/j.jfma.2020.04.034 [ PubMed] [ CrossRef] [ Google Scholar].
40.Hemmings SMJ, Malan-Müller S, van den Heuvel LL, Demmitt BA, Stanislawski MA, Smith DG, et al.创伤后应激障碍和创伤暴露对照组的微生物组：一项探索性研究。Psychosom Med (2017) 79(8):936-46. doi: 10.1097/PSY.0000000000000512 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
41.Salem F、Kindt N、Marchesi JR、Netter P、Lopez A、Kokten T 等。慢性风湿病和炎症性肠病的肠道微生物组：异同。United Eur Gastroenterol J (2019) 7(8):1008-32. doi: 10.1177/2050640619867555 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
42.Zhuang Z, Li N, Wang J, Yang R, Wang W, Liu Z, et al.GWAS相关细菌及其代谢产物似乎与炎症性肠病的发生有因果关系。doi: 10.1038/s41430-022-01074-w [ PubMed] [ CrossRef] [ Google Scholar].
43.Xiang K, Wang P, Xu Z, Hu YQ, He YS, Chen Y, et al.肠道微生物组对系统性红斑狼疮的因果效应：一项双样本泯灭随机研究。Doi: 10.3389/fimmu.2021.667097 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
44.Liu X, Tong X, Zou Y, Lin X, Zhao H, Tia L, et al.孟德尔随机分析支持血液代谢物与肠道微生物组之间的因果关系。Doi: 10.1038/s41588-021-00968-y [ PubMed] [ CrossRef] [ Google Scholar].
45.Chen Y, Meng P, Cheng S, Jia Y, Wen Y, Yang X, et al.评估 C 反应蛋白与肠道微生物群对焦虑和抑郁风险的交互作用。Mol Brain.(DOI: 10.1186/s13041-021-00843-1 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
46.Ponsford MJ、Gkatzionis A、Walker VM、Grant AJ、Wootton RE、Moore LSP 等。心脏代谢特征、败血症和严重 COVID-19：一项孟德尔随机化调查。Circulation.(DOI: 10.1161/CIRCULATIONAHA.120.050753 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
47.Mohus RM、Flatby H、Liyanarachi KV、DeWan AT、Solligård E、Damås JK 等。铁状态与败血症和严重 COVID-19 的风险：双样本孟德尔随机研究。Doi: 10.1038/s41598-022-20679-6 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
48.Hu J, Qin X. 早产新生儿早发败血症的细菌谱和风险因素。Doi: 10.15537/smj.2021.42.12.20210430 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
49.Sano H, Kobayashi R, Iguchi A, Suzuki D, Kishimoto K, Yasuda K, et al.患有血液病和恶性疾病的儿童和青少年败血症相关死亡的风险因素。J Microbiol Immunol Infect (2017) 50(2):232-8. doi: 10.1016/j.jmii.2015.04.002 [ PubMed] [ CrossRef] [ Google Scholar].
50.Lenz M、Maiberger T、Armbrust L、Kiwit A、Von der Wense A、Reinshagen K 等。cfDNA 和 DNases：早产新生儿败血症的新生物标志物--一项试点研究。细胞。(DOI: 10.3390/cells11020192 [ PMC free article] [ PubMed] [ CrossRef] [ Google Scholar].
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