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. 2022 Dec 16;13:1059281. doi: 10.3389/fmicb.2022.1059281

Causal associations between gut microbiota and adverse pregnancy outcomes: A two-sample Mendelian randomization study

Chuang Li 1,2, Caixia Liu 1,2, Na Li 1,2,*
PMCID: PMC9801412  PMID: 36590417

Abstract

Growing evidence indicates that gut microbiota could be closely associated with a variety of adverse pregnancy outcomes (APOs), but a causal link between gut microbiome and APOs has yet to be established. Therefore, in this study, we comprehensively investigated the relationship between gut microbiota and APOs to identify specific causal bacteria that may be associated with the development and occurrence of APOs by conducting a two-sample Mendelian randomization (MR) analysis. The microbiome genome-wide association study (GWAS) from the MiBioGen consortium was used as exposure data, and the GWAS for six common APOs was used as outcome data. Single-nucleotide polymorphisms (SNPs) that significantly correlated to exposure, data obtained from published GWAS, were selected as instrumental variables (IVs). We used the inverse variance-weighted (IVW) test as the main MR analysis to estimate the causal relationship. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were used to confirm the presence of horizontal pleiotropy and to exclude outlier SNPs. We performed Cochran's Q test to assess the heterogeneity among SNPs associated with each bacterium. The leave-one-out sensitivity analysis was used to evaluate whether the overall estimates were affected by a single SNP. Our analysis shows a causal association between specific gut microbiota and APOs. Our findings offer novel insights into the gut microbiota-mediated development mechanism of APOs.

Keywords: Mendelian randomization, instrumental variable, gut microbiota, adverse pregnancy outcomes, causal relationship

Introduction

Adverse pregnancy outcomes (APOs) include all pathological pregnancy and childbirth complications. Many public health and medical interventions have been implemented to reduce APOs, yet the incidence of APOs remains high. APOs are a leading cause of maternal, fetal, and neonatal deaths; severe maternal morbidity; and maternal intensive care unit admissions (Rana et al., 2019). APOs are widely recognized as major public health problems due to short- and long-term adverse impacts on mothers and infants.

The gut microbiota is a massive complex community of microbial species living in the digestive tract that not only participates in the digestion of food and the absorption of nutrients but also is involved in physiological regulation of the host through the production of hormonally active substances (Luo et al., 2022). Increasing evidence indicates that microbiome dysbiosis might play an important role in the pathogenesis of APOs (Qi et al., 2021). Zheng et al. (2020) found that dynamics in gut microbiota during the first half of pregnancy differed significantly between gestational diabetes mellitus (GDM) and normoglycemic pregnant women. Another case–control study showed that pregnant women with pre-eclampsia (PE) had a high abundance of Fusobacterium and Veillonella and a relatively low abundance of Faecalibacterium and Akkermansia compared with normotensive pregnant women (Chen C. et al., 2020; Chen X. et al., 2020). Yin et al. (2021) reported that women with preterm birth (PTB) showed a distinct gut microbiome dysbiosis compared with those who delivered at term. Opportunistic pathogens, particularly Porphyromonas, Streptococcus, Fusobacterium, and Veillonella, were enriched, while Coprococcus and Gemmiger were markedly depleted in the preterm group (Yin et al., 2021). However, whether these associations are causal has not been established because of potential biases including residual confounding factors and reverse causality.

Mendelian randomization (MR) is an approach integrating summary data of the genome-wide association study (GWAS) and resembles a randomized controlled trial by leveraging allelic randomization during meiosis and subsequent irreversible exposure to genotype at conception (Lawlor et al., 2008; Ellervik et al., 2019; Ference et al., 2019). Using genetic variants as instrumental variables (IVs), the MR design is typically less likely to be affected by residual confounding factors and reverse causation than conventional observational analysis, thereby strengthening the causal relationship between exposure and outcomes (Chen C. et al., 2020; Chen J. et al., 2020; Yuan and Larsson, 2022).

Exploring causal associations between gut microbiota and APOs not only deepens our understanding of pathogenesis derived from enteric bacteria but also facilitates the development of personalized treatments for pregnancy complications based on dietary or microbiome interventions (Di Simone et al., 2020). Elucidating the causal inference on the gut microbiome and its association with APOs is warranted. Thus, in this study, we conducted a two-sample MR analysis to evaluate the causal association between gut microbiota and six common APOs, namely, postpartum hemorrhage (PPH), abruptio placenta (AP), spontaneous abortion (SAB), premature rupture of membrane (PROM), gestational diabetes mellitus (GDM), and pre-eclampsia (PE) or eclampsia (Figure 1). To our knowledge, this is the first study assessing the causal role of gut microbiome in the development of pregnancy complications.

Figure 1.

Figure 1

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Study design of the present MR study on the associations of gut microbiota and adverse pregnancy complications. SNP, single-nucleotide polymorphism; IVW, inverse variance-weighted; MR, Mendelian randomization.

Materials and methods

Data sources

We obtained the GWAS dataset of human gut microbiome from the MiBioGen consortium (Kurilshikov et al., 2021). The MiBioGen study is the largest, multi-ethnic, and genome-wide analysis to investigate host –genetics– microbiome associations (Wang et al., 2018). This study coordinated 16S rRNA gene sequencing profiles and genotyping data of 18,340 participants from 24 population-based cohorts comprising 211 bacterial taxa (Kurilshikov et al., 2021), among which 15 microbial taxa without specific species name (unknown family or genus) were excluded, resulting in a total of 196 bacterial taxa being included in the present study.

GWAS summary data on PPH (3,670 cases and 98,626 controls), AP (294 cases and 104,247 controls), SAB (9,113 cases and 89,340 controls), PROM (3,011 cases and 104,247 controls), GDM (5,687 cases and 117,892 controls), and PE or eclampsia (3,903 cases and 114,735 controls) were obtained from results of the GWAS on the FinnGen consortium R5. Detailed data on the involved cohorts, genotypes, endpoint definition, and association test in the FinnGen consortium are available on the FinnGen webpage.

Instrumental variable selection

The accuracy of the conclusion on the causal effect of enteric microbiota on APOs was ensured by the following quality control steps used for the section of genetic predictors associated with microbiome features. We selected instrumental variables (IVs) with the locus-wide significance level (p < 1 × 10−5) to obtain a more comprehensive result (Sanna et al., 2019). The impact of linkage disequilibrium (LD) among included genetic variants on the results was avoided by excluding the SNPs using the PLINK clumping method (r2 > 0.001 and clump window <10,000 kb). We then extracted the corresponding data of the selected SNPs from the GWAS outcome data. When SNPs related to exposure were absent in the GWAS outcome data, the proxy SNPs with high LD (r2 > 0.80) would be selected. We removed palindromic SNPs to ensure the effects of SNPs on exposure correspond to the same allele as the effects of SNPs on the outcome during the harmonizing process.

Mendelian randomization analysis

MR analysis is a method used to estimate the magnitude of a causal effect of a phenotype on an outcome by using genetic variants such as IVs (Burgess et al., 2011; Davey Smith and Hemani, 2014). There are three important assumptions of MR analysis, which are depicted in Figure 2. The first assumption is that variants should be associated with exposure. The second assumption is that variants should not be associated with any confounders. The third assumption is that the association of genetic variants with outcomes should only be through exposure (Burgess and Thompson, 2015; Yuan S. et al., 2021).

Figure 2.

Figure 2

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Assumption of the Mendelian randomization study.

The two-sample IVW method was used as the main MR analysis to estimate the causal relationship of gut microbiota with APOs (Burgess et al., 2013). We performed sensitivity analysis in order to assess the significance of our results. We used the MR-Egger regression and MR-PRESSO to confirm the presence of horizontal pleiotropy (Bowden et al., 2015; Verbanck et al., 2018). Using MR-Egger regression, we can assess whether genetic instruments have pleiotropic effects on the outcome (Bowden et al., 2015). Compared with MR-PRESSO, the MR-Egger method has less precision and statistical power. The MR-PRESSO can detect horizonal pleiotropy and eliminate the effects of pleiotropy by conducting outlier removal (Verbanck et al., 2018). If horizontal pleiotropy was detected among the selected SNPs, the analyses were repeated after removing these pleiotropic SNPs. We performed Cochran's Q test to evaluate the heterogeneity among SNPs associated with each microbial taxon (Egger et al., 1997). The leave-one-out sensitivity analysis was used to assess the effects of a single SNP on the overall estimates. The strength of the selected IVs was assessed using F statistics, which determine if estimates of the causal associations are affected by weak instrument bias. We excluded weak IVs with F statistics <10 (Pierce et al., 2011). All MR analyses were performed in R (version 4.1.2) using R package TwoSampleMR (Hemani et al., 2018) and MRPRESSO (Verbanck et al., 2018).

Results

Selection of instrumental variables

After ensuring quality control, we selected 61 SNPs related to five bacterial taxa for PPH, 109 SNPs related to 10 bacterial taxa for AP, 33 SNPs related to four bacterial taxa for SAB, 81 SNPs related to seven bacterial taxa for PROM, 155 SNPs related to 14 bacterial taxa for GDM, 132 SNPs related to 11 bacterial taxa for PE or eclampsia, and 44 SNPs related to four bacterial taxa for PTB (Supplementary Table S1). The F statistics for IVs significantly associated with gut microbiota were all larger than 10, indicating that the estimates were less likely to suffer weak instrument bias.

Causal effects of gut microbiota on adverse pregnancy outcomes

PPH

The estimates of the IVW test indicated that the genetically predicted relative abundance of five genera, namely, Deltaproteobacteria, Butyricimonas, Marvinbryantia, Oscillibacter, and Peptococcus, was associated with an increased or reduced risk of PPH (Table 1; Figure 3) (Chen C. et al., 2020). Specifically, a higher genetically predicted abundance of Deltaproteobacteria was associated with a higher risk of PPH (OR: 1.26, 95% CI: 1.00–1.59, p = 0.047). A higher genetically predicted abundance of Butyricimonas was associated with a reduced risk of PPH (OR: 0.79, 95% CI: 0.63–0.98, p = 0.036). The genetically predicted abundance of Marvinbryantia was also associated with a reduced risk of PPH (OR: 0.76, 95% CI: 0.59–0.98, p = 0.033). The genetically predicted abundance of Oscillibacter was negatively related to the risk of PPH (OR: 0.80, 95% CI: 0.64–0.99, p = 0.042). The higher genetically predicted abundance of Peptococcus was also linked to a reduced risk of PPH (OR: 0.84, 95% CI: 0.72–0.97, p = 0.024).

Table 1.

Causal associations between gut microbiota and the risk of adverse pregnancy complications by using the IVW method.

Adverse pregnancy complications (outcome) Bacterial taxa (exposure) N OR 95% CI P value
PPH Deltaproteobacteria 13 1.26 1.00–1.59 0.047
Butyricimonas 13 0.79 0.63–0.98 0.036
Marvinbryantia 10 0.76 0.59–0.98 0.033
Oscillibacter 13 0.80 0.64–0.99 0.042
Peptococcus 12 0.84 0.72–0.97 0.024
AP Acidaminococcaceae 7 0.30 0.12–0.74 0.008
Porphyromonadaceae 9 3.96 1.24–12.71 0.020
Victivallaceae 12 0.57 0.37–0.87 0.009
Butyricimonas 13 0.43 0.20–0.92 0.030
Eggerthella 10 1.78 1.00–3.17 0.049
Escherichia Shigella 10 2.73 1.11–6.74 0.029
Holdemanella 11 0.41 0.23–0.73 0.002
Howardella 9 1.75 1.10–2.80 0.019
Prevotella9 15 1.97 1.09–3.57 0.026
Roseburia 13 3.24 1.29–8.10 0.012
SAB Actinomyces 7 1.17 1.01–1.36 0.041
Lactococcus 9 0.90 0.81–1.00 0.041
Subdoligranulum 11 1.21 1.02–1.43 0.030
Veillonella 6 1.24 1.03–1.49 0.024
PROM Mollicutes 12 0.77 0.61–0.98 0.034
Collinsella 9 1.44 1.03–2.03 0.034
Intestinibacter 15 1.30 1.05–1.62 0.018
Marvinbryantia 10 0.74 0.55–0.98 0.034
Ruminococcaceae UCG003 12 0.73 0.57–0.95 0.017
Turicibacter 10 1.28 1.02–1.61 0.033
Tenericutes 12 0.77 0.61–0.98 0.034
GDM Betaproteobacteria 11 1.26 1.00–1.58 0.047
Mollicutes 12 0.81 0.67–0.97 0.022
Lactobacillaceae 8 1.19 1.02–1.39 0.032
Collinsella 9 1.32 1.01–1.74 0.044
Coprobacter 10 1.21 1.04–1.41 0.015
Lachnoclostridium 13 1.37 1.06–1.77 0.017
Lactobacillus 9 1.24 1.07–1.43 0.004
Methanobrevibacter 6 0.85 0.73–1.00 0.043
Olsenella 10 1.17 1.03–1.32 0.016
Oscillibacter 13 0.82 0.71–0.96 0.011
Prevotella9 15 1.16 1.01–1.34 0.036
Ruminococcus2 15 1.19 1.00–1.42 0.047
Euryarchaeota 12 0.87 0.78–0.97 0.013
Tenericutes 12 0.81 0.67–0.97 0.022
PE or eclampsia Enterobacteriaceae 7 0.67 0.49–0.93 0.015
Prevotellaceae 16 1.24 1.00–1.52 0.049
Enterorhabdus 6 0.75 0.58–0.97 0.027
Eubacterium ruminantium group 18 0.87 0.75–1.00 0.049
Eubacterium ventriosum group 15 0.73 0.59–0.91 0.006
Methanobrevibacter 6 0.80 0.67–0.97 0.021
Ruminococcaceae UCG002 22 0.77 0.63–0.93 0.008
Ruminococcus1 10 0.75 0.56–1.00 0.048
Tyzzerella3 13 0.83 0.72–0.97 0.016
Enterobacteriales 7 0.67 0.49–0.93 0.015
Euryarchaeota 12 0.81 0.72–0.92 0.001
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Figure 3.

Figure 3

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Causal associations of gut microbial genera on adverse pregnancy outcomes (APOs) identified at the nominal significance from the IVW method (P < 0.05/0.01). Red represents the risk bacterial traits for APOs, blue represents the protective bacterial traits for APOs, and white represents no causal bacterial traits for APOs. PPH, postpartum hemorrhage; AP, abruptio placentae; SAB, spontaneous abortion; PROM, premature rupture of membrane; GDM, gestational diabetes mellitus; PE, pre-eclampsia; NS, no significant association.

AP

The estimates of the IVW test suggested that the genetically predicted relative abundance of four genera, namely, Acidaminococcaceae (OR: 0.30, 95% CI: 0.12–0.74, p = 0.008), Victivallaceae (OR: 0.57, 95% CI: 0.37–0.87, p = 0.009), Butyricimonas (OR: 0.43, 95% CI: 0.20–0.92, p = 0.030), and Holdemanella (OR: 0.41, 95% CI: 0.23–0.73, p = 0.002), was negatively associated with the risk of AP. The genetically predicted relative abundance of a different five genera, namely, Porphyromonadaceae (OR: 3.96, 95% CI: 1.24–12.71, p = 0.020), Eggerthella (OR: 1.78, 95% CI: 1.00–3.17, p = 0.049), Escherichia Shigella (OR: 2.73, 95% CI: 1.11–6.74, p = 0.029), Howardella (OR: 1.75, 95% CI: 1.10–2.80, p = 0.019), Prevotella9 (OR: 1.97, 95% CI: 1.09–3.57, p = 0.026), and Roseburia (OR: 3.24, 95% CI: 1.29–8.10, p = 0.012), was positively associated with the risk of AP.

SAB

A higher genetically predicted abundance of Actinomyces (OR: 1.17, 95% CI: 1.01–1.36, p = 0.041), Subdoligranulum (OR: 1.21, 95% CI: 1.02–1.43, p = 0.030), and Veillonella (OR: 1.24, 95% CI: 1.03–1.49, p = 0.024) was significantly associated with the risk of SAB. There was a tendency toward a protective effect of increasing genetically predicted abundance of Lactococcus on the risk of SAB (OR: 0.90, 95% CI: 0.81–1.00, p = 0.041).

PROM

The genetically predicted relative abundance of three genera, namely, Collinsella (OR: 1.44, 95% CI: 1.03–2.03, p = 0.034), Intestinibacter (OR: 1.30, 95% CI: 1.05–1.62, p = 0.018), and Turicibacter (OR: 1.28, 95% CI: 1.02–1.61, p = 0.033), was associated with an increased risk of PROM. By contrast, there was a tendency toward a protective effect of increasing genetically predicted abundance of Mollicutes (OR: 0.77, 95% CI: 0.61–0.98, p = 0.034), Marvinbryantia (OR: 0.74, 95% CI: 0.55–0.98, p = 0.034), Ruminococcaceae UCG003 (OR: 0.73, 95% CI: 0.57–0.95, p = 0.017), and Tenericutes (OR: 0.77, 95% CI: 0.61–0.98, p = 0.034) on the risk of PROM.

GDM

The estimates of the IVW test suggested that the genetically predicted relative abundance of five genera, namely, Mollicutes (OR: 0.81, 95% CI: 0.67–0.97, p = 0.022), Methanobrevibacter (OR: 0.85, 95% CI: 0.73–1.00, p = 0.043), Oscillibacter (OR: 0.82, 95% CI: 0.71–0.96, p = 0.011), Euryarchaeota (OR: 0.87, 95% CI: 0.78–0.97, p = 0.013), and Tenericutes (OR: 0.81, 95% CI: 0.67–0.97, p = 0.022), was in negatively associated with the risk of GDM, whereas the genetically predicted relative abundance of nine other genera, namely, Betaproteobacteria (OR: 1.26, 95% CI: 1.00–1.58, p = 0.047), Lactobacillaceae (OR: 1.19, 95% CI: 1.02–1.39, p = 0.032), Collinsella (OR: 1.32, 95% CI: 1.01–1.74, p = 0.044), Coprobacter (OR: 1.21, 95% CI: 1.04–1.41, p = 0.015), Lachnoclostridium (OR: 1.37, 95% CI: 1.06–1.77, p = 0.017), Lactobacillus (OR: 1.24, 95% CI: 1.07–1.43, p = 0.004), Olsenella (OR: 1.17, 95% CI: 1.03–1.32, p = 0.016), Prevotella9 (OR: 1.16, 95% CI: 1.01–1.34, p = 0.036), and Ruminococcus2 (OR: 1.19, 95% CI: 1.00–1.42, p = 0.047), was positively associated with the risk of GDM.

PE or eclampsia

The genetically predicted abundance of Prevotellaceae (OR: 1.24, 95% CI: 1.00–1.52, p = 0.049) was associated with an increased risk of PE or eclampsia. By contrast, there was a tendency toward a protective effect of increasing genetically predicted abundance of Enterobacteriaceae (OR: 0.67, 95% CI: 0.49–0.93, p = 0.015), Enterorhabdus (OR: 0.75, 95% CI: 0.58–0.97, p = 0.027), Eubacterium ruminantium (OR: 0.87, 95% CI: 0.75–1.00, p = 0.049), Eubacterium ventriosum (OR: 0.73, 95% CI: 0.59–0.91, p = 0.006), Ruminococcaceae UCG002 (OR: 0.77, 95% CI: 0.63–0.93, p = 0.008), Ruminococcus1 (OR: 0.75, 95% CI: 0.56–1.00, p = 0.048), Tyzzerella3 (OR: 0.83, 95% CI: 0.72–0.97, p = 0.016), Enterobacterales (OR: 0.67, 95% CI: 0.49–0.93, p = 0.015), and Euryarchaeota (OR: 0.81, 95% CI: 0.72–0.92, p = 0.001) on PE or eclampsia.

Sensitivity analysis

We did not find pleiotropic effects among the selected SNPs using the MR-Egger and MR-PROSSO global test (p > 0.05) (Supplementary Tables S2, S3). No evidence of heterogeneity was observed between the selected IVs and APOs (Supplementary Table S4). In addition, we found no causal relationships between identified bacterial taxa and APOs that were driven by any single SNP (Supplementary Table S5).

Discussion

In the present study, we found causal relationships between genetically predicted abundance of specific bacterial taxa and six common pregnancy complications (PPH, AP, SAB, PROM, GDM, and PE or eclampsia) using large-scale GWAS summary data.

Considerable interest in the potential molecular mechanisms of the gut microbiome in the pathogenesis of PPH was evidenced by an abundance of prior studies. The gut microbiome composition was found to be involved in the pathogenesis of PPH by regulating inflammation, which are closely associated with development of PPH (Farhana et al., 2015; Yang et al., 2021). We successfully identified that Butyricimonas, Marvinbryantia, Oscillibacter, and Peptococcus were negatively associated with the risk of PPH. Butyricimonas, Marvinbryantia, and Oscillibacter can produce butyrate, which participates in regulating gut permeability and induces the inflammatory response (Jang et al., 2020; Chen B. D. et al., 2021; Chen Z. et al., 2021; Liu et al., 2022). Prior studies have reported that the abundance of Peptococcus increases when intestinal inflammation occurs, indicating its pro-inflammatory effects (Sun et al., 2022). The exact mechanism of Peptococcus on the pathogenesis of PPH warrants verification studies. This study also suggested that increased abundance of Deltaproteobacteria was causally related to a higher risk of PPH. Deltaproteobacteria is a common pathogenic bacterium, which initiates inflammation by activating the Notch signaling pathway and induces the expression of pro-inflammatory factors including pro-IL-1β and SOCS3 (Jin et al., 2016; Singh et al., 2021).

Emerging evidence has shown the role of perturbations in mitochondrial biogenesis, oxidative phosphorylation, and oxidative stress pathways in the pathogenesis of AP (Workalemahu et al., 2018). The relationship between gut microbiota dysbiosis and AP remains unclear. This study found that the abundance of Butyricimonas and Holdemanella reduced the risk of AP. The association may be mediated by the short-chain fatty acid butyrate as Butyricimonas and Holdemanella are involved in its production (Zagato et al., 2020; Jing et al., 2021). Butyrate has a key role in maintaining mucosal integrity, reducing inflammation via macrophage function, and regulating oxidative stress (Hu et al., 2020; Jing et al., 2021). We also showed that abundance of Eggerthella and Escherichia Shigella was positively associated with the risk of AP. Eggerthella is a pro-inflammatory genus, which has been associated with the depletion of butyrate (Nikolova et al., 2021). Escherichia Shigella can also mediate inflammation and oxidative stress by regulating the lipopolysaccharide/TLR4/NF-kB signaling pathway (Peng et al., 2020). This study demonstrated that Roseburia and Porphyromonadaceae, butyrate-producing bacteria, were positively related to the risk of AP (Wang et al., 2022). It can suggest that a single microbial taxon may not affect the development of AP, and the overall role of gut microbiota may have a more important role in the onset of AP. We showed that some novel gut bacteria including Acidaminococcaceae, Victivallaceae, Howardella, and Prevotella9 were closely associated with the pathogenesis of AP, but the exact mechanism of these gut microbiome on the development of AP warrants additional investigations.

We discovered that the abundance of Actinomyces, Subdoligranulum, and Veillonella was associated with an increased risk of SAB, while a high abundance of Lactococcus was related to a lower risk of SAB. Immunologic dysfunction and thrombophilia are known key factors in the pathogenesis of SAB (Liu et al., 2021a). A previous study showed that the relative abundance of Actinomyces and Subdoligranulum is closely related to multiple autoimmune diseases, including systemic lupus erythematosus and chronic spontaneous urticaria, suggesting that these bacteria play an important role in the host immune response (Chen B. D. et al., 2021; Liu et al., 2021). The protective effect of Lactococcus on SAB was likely due to lactic acid bacteria reducing autoimmunity and recovering immune homeostasis (Xu et al., 2020). Increased thrombophilia is one of the clinical signs of SAB (de Jong et al., 2014). Veillonella may influence the host trimethylamine N-oxide (TMAO) level by mediating carnitine production and metabolism, which contribute to platelet hyperreactivity and thrombosis (Yang et al., 2019). The exact the mechanism of gut microbiota leading to SAB remains poorly understood and warrants additional studies.

Although exact mechanisms underlying the occurrence of PROM are not fully elucidated, oxidative stress and inflammation have been reported to play an important role (Liu et al., 2021b). Our study revealed that the genetically predicted abundance of Turicibacter was positively correlated to the risk of PROM. Animal studies have reported that the abundance of Turicibacter was negatively associated with propionic acid and butyric acid, which can inhibit the inflammatory response (Wan et al., 2021). We have found that an increased abundance of Marvinbryantia and Ruminococcaceae, which are known to produce short-chain fatty acid, was negatively associated with the risk of PROM (Chen B. D. et al., 2021; Chen Z. et al., 2021).

Previous studies on GDM or post-GDM women with impaired glucose tolerance discovered a broad range of gut microbiota dysbiosis, which was associated with Collinsella (Crusell et al., 2018), Olsenella (Crusell et al., 2018), Prevotella9 (Hasain et al., 2020), and Ruminococcus2 (Crusell et al., 2018). On the other hand, beneficial butyrate-producing bacteria including Methanobrevibacter (Kuang et al., 2017) and Oscillibacter (Crusell et al., 2018) were depleted. A prior study also indicated that individuals with diminished insulin sensitivity had a lower abundance of Tenericutes, suggesting it might be associated with the occurrence and development of GDM (Yuan X. et al., 2021). Larsen et al. found that the relative abundance of Betaproteobacteria was higher in the type 2 diabetes group and positively correlated with the plasma glucose level (Larsen et al., 2010). These results were consistent with the present study. The mechanisms through which these bacteria exert beneficial or detrimental effects on the pathogenesis of GDM deserve to be studied.

Our study also showed that the abundance of Eubacterium ruminantium, Eubacterium ventriosum, Ruminococcaceae UCG002, Ruminococcus1, and Enterobacterales was negatively linked to the risk of PE or eclampsia. Prior studies indicated that chronic peripheral and placental inflammation were critical to the onset of PE (Black and Horowitz, 2018). Eubacterium, Ruminococcaceae, and Enterobacterales are involved in the production of short-chain fatty acids, such that their depletion is causally linked to inflammation (Tran et al., 2019; Zhuang et al., 2020). Wang et al. (2019) showed that fecal microbiota showed a significant reduction of Ruminococcus in the PE group, which was important in maintaining gut homeostasis. This study also demonstrated that some bacteria such as Prevotellaceae, Enterobacteriaceae, Enterorhabdus, and Euryarchaeota were involved in the pathogenesis of PE. Future studies are warranted to elucidate the functional significance of these bacteria in the onset of PE.

The advantages of this study as follows: First, the MR design diminishes the residual confounding and reverse causality and thereby improves the causal inference in relationships of gut microbiota and APOs. Second, we comprehensively studied up to six common APOs. Third, the identified causal associations in our MR analysis may provide candidate microbial taxa for subsequent functional studies and help in the development of new approaches for the prevention and treatment of pregnancy complications by targeting specific gut bacteria.

The limitations of our study must also be acknowledged. First, SNPs obtained based on the genome-wide statistical significance threshold (5 × 10−8) were too limited for further study; therefore, we included the SNPs that met the locus-wide significance level (1 × 10−5) in this study. Second, while the GWAS summary data in this study were obtained mostly from European populations, only a small amount of the gut microbiome data was taken from other races, which may partially bias our findings. Third, we cannot exclude the possible diet–gene or gene–environment interactions on outcomes, which may influence the observed results.

In conclusion, this MR study comprehensively explores causal effects of gut microbiota on APOs for the first time. Our findings provide new insights into the prevention, course of disease, and treatment of APOs by targeting specific bacteria taxa. Further studies are warranted to identify the exact mechanism of enteric microbiota–pregnancy complication associations.

Data availability statement

Summary data used for this study can be accessed through the following links: microbiota, https://mibiogen.gcc.rug.nl/; post-partum hemorrhage, https://r5.finngen.fi/pheno/O15_POSTPART_HEAMORRH; abruptio placenta, https://r5.finngen.fi/pheno/O15_PLAC_PREMAT_SEPAR; spontaneous abortion, https://r5.finngen.fi/pheno/O15_ABORT_SPONTAN; premature rupture of membrane, https://r5.finngen.fi/pheno/O15_MEMBR_PREMAT_RUPT; gestational diabetes mellitus, https://r5.finngen.fi/pheno/GEST_DIABETES; pre-eclampsia or eclampsia, https://r5.finngen.fi/pheno/O15_PRE_OR_ECLAMPSIA.

Ethics statement

This study was approved by Ethics Committee of Shengjing Hospital of China Medical University.

Author contributions

CLi and CLiu designed the research. CLi collected and analyzed the data and drafted the manuscript. CLiu and NL supervised the study. CLi, CLiu, and NL were involved in writing the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We thank the FinnGen consortium and MiBioGen consortium for providing GWAS summary statistics data for our analysis.

Funding Statement

This work was supported by the National Key Research and Development Program of China (2021YFC2701503).

Abbreviations

MR, Mendelian randomization; APO, Adverse pregnancy outcome; GWAS, Genome-wide association study; IV, Instrumental variable; SNP, Single-nucleotide polymorphism; IVW, Inverse variance-weighted; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; GDM, Gestational diabetes mellitus; PE, Pre-eclampsia; PTB, Preterm birth; PPH, Postpartum hemorrhage; AP, Abruptio placenta; SAB, Spontaneous abortion; PROM, Premature rupture of membrane; LD, Linkage disequilibrium.

Conflict of interest

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.

Publisher's note

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2022.1059281/full#supplementary-material

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (139.7KB, xlsx)

Data Availability Statement

Summary data used for this study can be accessed through the following links: microbiota, https://mibiogen.gcc.rug.nl/; post-partum hemorrhage, https://r5.finngen.fi/pheno/O15_POSTPART_HEAMORRH; abruptio placenta, https://r5.finngen.fi/pheno/O15_PLAC_PREMAT_SEPAR; spontaneous abortion, https://r5.finngen.fi/pheno/O15_ABORT_SPONTAN; premature rupture of membrane, https://r5.finngen.fi/pheno/O15_MEMBR_PREMAT_RUPT; gestational diabetes mellitus, https://r5.finngen.fi/pheno/GEST_DIABETES; pre-eclampsia or eclampsia, https://r5.finngen.fi/pheno/O15_PRE_OR_ECLAMPSIA.

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