Gut microbiota and hypertensive disorders in pregnancy: evidence from the Mendelian randomization study

Results

A total of 7,121 SNPs associated with 119 bacterial genera were included for GM instruments. The characters of selected IVs were shown in Supplementary Table 1.

Forward MR analyses

HDP

Results at a significant threshold of P < 0.05 by using the inverse-variance weighted (IVW) method were shown in Figure 1. We found a causal association of increase in RuminococcaceaeUCG009 (OR = 1.18, 95%CI: 1.03-1.34, P = 0.015) and higher risk of HDP, while genetically increased in Bifidobacterium (OR = 0.81, 95%CI: 0.68-0.97, P = 0.022), Eubacterium (ruminantium group) (OR = 0.81, 95%CI: 0.69-0.96, P = 0.012), Intestinibacter (OR = 0.83, 95%CI: 0.72-0.96, P = 0.011), Parabacteroides (OR = 0.75, 95%CI: 0.57-0.99, P = 0.047), RuminococcaceaeUCG002 (OR = 0.84, 95%CI: 0.74-0.96, P = 0.011), Senegalimassilia (OR = 0.80, 95%CI: 0.65-0.98, P = 0.033), and Tyzzerella3 (OR = 0.87, 95% CI: 0.77-0.99, P = 0.039) were associated with protective effects on HDP. These causal associations, however, lost their significance when multiple comparisons were adjusted (q > 0.1). The F-statistics ranged from 142.45 to 242.46 among all the results above, excluding the weak IVs bias. Details of all the IVW results were shown in Supplementary Table 2.

Figure 1. The causal effect of gut microbial genera on RDP (GR, PE, and Eclampsia) identified at the nominal significance by using the IVW method (P < 0.05 / 0.01). Red represents the risk factors for RDP, blue represents the protective factors for RDP, and white represents no causal association. RDP, hypertensive disorders in pregnancy; GR, gestational hypertension; PE, pre-eclampsia; NS, No significant association.

GH

Using the IVW method, we found suggestive causal associations of increases in LachnospiraceaeUCG010 (OR = 1.29, 95%CI: 1.00-1.66, P = 0.049), Olsenella (OR = 1.15, 95% CI: 1.02-1.31, P = 0.028), RuminococcaceaeUCG009 (OR = 1.27, 95%CI: 1.08-1.49, P = 0.005), and Ruminococcus2 (OR = 1.25, 95%CI: 1.03-1.51, P = 0.022) and higher risk of GH, while genetically increased in Anaerotruncus (OR = 0.74, 95%CI: 0.55-0.99, P = 0.047), Bifidobacterium (OR = 0.80, 95%CI: 0.65-0.98, P = 0.030), and Intestinibacter (OR = 0.80, 95%CI: 0.67-0.97, P = 0.023) were related to protective effects on GH (Figure 1). However, causal associations lost their significance when multiple comparisons were adjusted. Details of all the IVW results were shown in Supplementary Table 3. The F-statistics ranged from 144.51 to 205.87 among all the results above. Additionally, causal associations between GM and GH risk were found in more than three MR methods (Table 1 and Figure 2), including IVW, Maximum Likelihood (MaxLik), Weighted Median (WM), MR-Egger regression, and MR robust adjusted profile score (MR.RAPS).

Table 1. MR analyses of gut microbiota on HDP subtypes by different methods.

Exposure	Outcome	F-Stat	Inverse variance weighted			Maximum likelihood			Weighted median			MR.RAPS			MR Egger
			OR (95%CI)	P		OR (95%CI)	P		OR (95%CI)	P		OR (95%CI)	P		OR (95%CI)	P
Anaerotruncus	GH	166.34	0.74(0.55,0.99)	0.047		0.74(0.58,0.95)	0.019		0.86(0.61,1.23)	0.410		0.68(0.54,0.86)	0.001		0.54(0.23,1.31)	0.203
Bifidobacterium	GH	139.63	0.80(0.65,0.98)	0.030		0.80(0.66,0.97)	0.026		0.88(0.65,1.19)	0.401		0.79(0.66,0.95)	0.014		0.53(0.32,0.86)	0.027
Intestinibacter	GH	159.39	0.80 (0.67,0.97)	0.023		0.81(0.66,0.98)	0.027		0.76(0.59,0.97)	0.028		0.80(0.66,0.98)	0.031		0.62(0.34,1.14)	0.150
Lachnospiraceae UCG010	GH	144.51	1.29(1.00,1.66)	0.049		1.31(1.01,1.69)	0.046		1.28(0.90,1.83)	0.168		1.18(0.92,1.50)	0.188		1.15(0.53,2.51)	0.731
Olsenella	GH	205.87	1.15(1.02,1.31)	0.028		1.16(1.02,1.32)	0.028		1.20(1.01,1.42)	0.035		1.16(1.01,1.32)	0.038		1.05(0.70,1.58)	0.810
Ruminococcaceae UCG009	GH	177.26	1.27(1.08,1.49)	0.005		1.28(1.08,1.52)	0.005		1.22(0.97,1.54)	0.085		1.20(1.03,1.41)	0.023		1.70(0.88,3.29)	0.143
Ruminococcus2	GH	156.71	1.25(1.03,1.51)	0.022		1.25(1.03,1.52)	0.022		1.21(0.91,1.62)	0.190		1.26(1.03,1.54)	0.026		1.50(0.95,2.37)	0.106
Eubacterium (ruminantium group)	PE	157.34	0.86(0.75,0.99)	0.045		0.86(0.74,1.00)	0.049		0.90(0.74,1.10)	0.314		0.89(0.77,1.03)	0.118		1.24(0.77,2.01)	0.391
Eubacterium (ventriosum group)	PE	160.92	0.74(0.59,0.93)	0.011		0.75(0.59,0.94)	0.014		0.72(0.53,0.98)	0.034		0.75(0.59,0.94)	0.015		0.68(0.24,1.90)	0.473
Methanobrevibacter	PE	179.04	0.79(0.65,0.96)	0.019		0.79(0.65,0.96)	0.020		0.80(0.63,1.02)	0.070		0.79(0.66,0.95)	0.014		0.55(0.26,1.13)	0.180
Ruminococcaceae UCG002	PE	166.26	0.80(0.65,0.98)	0.029		0.80(0.66,0.97)	0.021		0.80(0.61,1.05)	0.105		0.85(0.71,1.02)	0.075		0.90(0.52,1.55)	0.702
Tyzzerella3	PE	147.81	0.80(0.68,0.93)	0.003		0.79(0.68,0.93)	0.004		0.77(0.63,0.95)	0.015		0.79(0.68,0.92)	0.003		0.73(0.29,1.80)	0.506
Dorea	Eclampsia	174.09	0.27(0.09,0.76)	0.014		0.26(0.09,0.75)	0.014		0.46(0.10,2.14)	0.323		0.16(0.06,0.46)	0.001		1.07(0.06,20.03)	0.963
Ruminococcaceae UCG010	Eclampsia	151.49	0.20(0.07,0.58)	0.003		0.20(0.07,0.60)	0.004		0.24(0.06,1.00)	0.050		0.30(0.11,0.83)	0.020		0.20(0.01,3.67)	0.340
GH, gestational hypertension; PE, pre-eclampsia; F-stat, F statistics to detect weak instrumental variable bias; MR.RAPS, Mendelian randomization robust adjusted profile score; OR, odds ratio; CI, confidence interval; P, P value.

Figure 2. Scatter plots for the causal relationship between gut microbiota and gestational hypertension.

PE

We found five suggestive causal effects of GM on PE (P < 0.05, q > 0.1; Figure 1). Specifically, Eubacterium(ruminantium group) (OR = 0.86, 95%CI: 0.75-0.99, P = 0.045), Eubacterium(ventriosum group) (OR = 0.74, 95%CI: 0.59-0.93, P = 0.011), Methanobrevibacter (OR = 0.79, 95%CI: 0.65-0.96, P = 0.019), RuminococcaceaeUCG002 (OR = 0.80, 0.65-0.98, P = 0.029), and Tyzzerella3 (OR = 0.80, 95%CI: 0.68-0.93, P = 0.003) were negatively associated with the risk of PE. Details of all the IVW results were shown in Supplementary Table 4. The F-statistics ranged from 147.81 to 179.04 among all the results above. Furthermore, causal associations between GM and PE risk were found in more than three MR methods (Table 1 and Figure 3).

Figure 3. Scatter plots for the causal association between gut microbiota and (A) pre-eclampsia or (B) eclampsia.

Eclampsia

We found suggestive causal effects of GM on Eclampsia in two microbial genera, including Dorea (OR = 0.27, 95%CI: 0.09-0.76, P = 0.014) and RuminococcaceaeUCG010 (OR = 0.20, 95%CI: 0.07-0.58, P = 0.003), which were negatively associated with the risk of eclampsia (Figure 1). Details of all the IVW results were shown in Supplementary Table 5. The F-statistics were 174.09 and 151.49, respectively, Furthermore, causal associations between GM and eclampsia risk were found in more than two of the MR methods (Table 1 and Figure 3).

Sensitivity analyses

Cochran’s Q statistics showed no significant heterogeneity in selected IVs (P > 0.05 in IVW and MR-Egger methods, Supplementary Table 6). Both the MR-Egger intercept and the MR-PRESSO global test confirmed there is no significant directional horizontal pleiotropy (P > 0.05, Supplementary Table 6). Additionally, the leave-one-out analysis revealed that there are no outlier IVs that would have a significant impact on the result if retained (Supplementary Figures 1–3).

Reverse MR analyses

We performed the reverse MR analysis to assess whether specific HDP subtypes causally affect gut microbiota to confirm the causal direction. However, all methods showed no causal relationship except for the genus Bifidobacterium (P > 0.05, Supplementary Table 7). The sensitivity analyses including Cochran’s Q test, MR-Egger regression intercept, MR-PRESSO global test, and the leave-one-out sensitivity analysis confirmed the robustness of the reverse MR results (Supplementary Table 8 and Supplementary Figures 4–6).

Multivariable MR analyses

MVMR analysis was performed to assess the causal effect of GM on GH, PE, and eclampsia, respectively after confounding factors were adjusted (BMI, alcohol drinking, smoking, and T2D). For the genus Intestinibacter, after adjusting for BMI (OR = 0.77, 95%CI: 0.67-0.88, P < 0.001), alcohol drinking (OR = 0.76, 95%CI: 0.67-0.85, P < 0.001), smoking (OR = 0.84, 95%CI: 0.70-0.99, P = 0.049), and T2D (OR = 0.81, 95%CI: 0.72-0.92, P = 0.001), Intestinibacter remained causally associated with GH risk (Figure 4). Detailed MVMR results of other suggestive association GM on HDP subtypes were shown in Table 2.

Figure 4. Forest plot of the causal effect of the genus Intestinibacter on gestational hypertension after adjusting for confounders.

Table 2. Multivariable MR analyses of gut microbiota on HDP subtypes after adjusting confounding factors.

Exposure	Outcome	BMI			Alcohol drinking			Smoking			T2D
		OR (95%CI)	P		OR (95%CI)	P		OR (95%CI)	P		OR (95%CI)	P
Anaerotruncus	GH	0.71(0.43,1.18)	0.187		0.76(0.52,1.11)	0.159		0.70(0.50,0.98)	0.035		0.91(0.71,1.15)	0.419
Bifidobacterium	GH	0.79(0.68,0.93)	0.004		0.76(0.63,0.91)	0.004		0.74(0.62,0.89)	0.001		0.77(0.54,1.08)	0.128
Intestinibacter	GH	0.77(0.67,0.88)	<0.001		0.76(0.67,0.85)	<0.001		0.84(0.70,0.99)	0.049		0.81(0.72,0.92)	0.001
Lachnospiraceae UCG010	GH	1.59(1.21,2.08)	0.001		1.15(0.84,1.56)	0.388		1.33(1.10,1.59)	0.003		1.13(1.06,1.19)	0.025
Olsenella	GH	1.14(0.93,1.42)	0.215		1.13(1.00,1.28)	0.049		1.12(1.01,1.25)	0.041		1.17(1.06,1.289)	0.003
Ruminococcaceae UCG009	GH	1.40(1.11,1.77)	0.005		1.28(1.04,1.57)	0.020		1.49(1.28,1.74)	<0.001		1.12(1.06,1.19)	<0.001
Ruminococcus2	GH	1.25(1.04,1.51)	0.021		1.18(0.94,1.47)	0.154		1.29(1.09,1.54)	0.004		1.24(0.84,1.84)	0.285
Eubacterium (ruminantium group)	PE	0.85(0.71,1.02)	0.082		0.89(0.77,1.03)	0.119		0.87(0.74,1.02)	0.085		0.81(0.65,1.02)	0.070
Eubacterium (ventriosum group)	PE	0.79(0.64,0.98)	0.026		0.73(0.63,0.85)	<0.001		0.76(0.63,0.93)	0.006		0.82(0.71,0.95)	0.007
Methanobrevibacter	PE	0.77(0.59,1.02)	0.067		0.77(0.66,0.91)	0.002		0.65(0.50,0.86)	0.002		0.76(0.64,0.90)	0.001
RuminococcaceaeUCG002	PE	1.13(0.87,1.48)	0.360		0.79(0.63,0.98)	0.034		0.80(0.65,0.98)	0.003		0.67(0.50,0.90)	0.008
Tyzzerella3	PE	0.76(0.69,0.83)	<0.001		0.88(0.76,1.02)	0.089		0.76(0.65,0.90)	0.001		0.87(0.21,1.20)	0.394
Dorea	Eclampsia	0.53(0.25,1.14)	0.106		0.30(0.12,0.72)	0.007		0.29(0.11,0.74)	0.010		0.22(0.06,0.82)	0.024
RuminococcaceaeUCG010	Eclampsia	0.01(0.00,0.01)	<0.001		0.31(0.09,1.03)	0.057		0.40(0.17,0.90)	0.027		20.61(1.71,247.96)	0.017
GH, gestational hypertension; PE, pre-eclampsia; F-stat, F statistics to detect weak instrumental variable bias; OR, odds ratio; CI, confidence interval; P, P value; BMI, body mass index; T2D, type 2 diabetes.

Materials and Methods

Data sources

GWAS summary statistics for GM were obtained from the Microbiome Genome (MiBioGen) Consortium which consisted of 24 multiple ancestry cohorts including 18,340 participants [43]. After extracting DNA from fecal samples, data were generated by 16S rRNA gene sequencing in the Illumina platform. Setting SILVA as the reference, all the data were annotated to genus and higher levels to profile the microbial composition [44].

According to the pathogenesis, biochemical index, and degree of organ damage, HDP could be divided into five subtypes including gestational hypertension (GH), pre-eclampsia (PE), eclampsia and so on [45]. In this study, firstly we tested the whole HDP group and then primarily focused on GH, PE, and eclampsia patients because they are major or most serious HDP subtypes. GWAS summary statistics for HDP were extracted from the FinnGen Consortium and updated in 2023 [46]. Briefly, the study for HDP included 123,579 female subjects (7,686 cases and 115,893 controls) covering a total of 16,379,784 SNPs. The genetic association datasets consisted of 118,990 pregnant women (4,255 cases and 114,735 controls) with GH, 118,291 pregnant women (3,556 cases and 114,735 controls) with PE, and 115,025 pregnant women (290 cases and 114,735 controls) with eclampsia, respectively. Association analysis was conducted with sex, age, genotyping batch, and 10 principal components corrected as covariates. Detailed information on exposure and outcome GWAS datasets were summarized in Supplementary Table 9.

Instrumental variables

To satisfy the three key assumptions of MR analysis (Figure 5), five steps were applied to select the optimal IVs: 1) SNPs under a locus-wide significance threshold of P < 1e-05 were obtained as potential IVs related to exposure [14]. 2) PLINK clumping method (r² < 0.001, clump window < 10,000 kb) was performed to ensure the IVs were independent [47]. 3) SNPs with minor allele frequency < 0.01 and palindromic SNPs were excluded. 4) The proxy SNPs (r² > 0.8) were selected based on European population data in the 1000 Genome project after removing the SNPs closely related to the outcome phenotype (P < 5e-08) [48]. 5) SNPs with F-statistics < 10 were eliminated to avoid weak IV bias [49].

Figure 5. Schematic representation of the MR analysis. The three assumptions of MR are as follows: (1) Instrumental variables must be associated with gut microbiota, (2) Instrumental variables must not be associated with confounders; and (3) Instrumental variables must influence hypertensive disorders in pregnancy only through gut microbiota, not through other pathways.

Statistical analyses

We used the inverse-variance weighted (IVW) method as the primary MR analysis to detect the causal associations between exposure (GM) and outcomes (HDP, GH, PE, and eclampsia). The IVW method calculates the total causal effect by using the weighted linear regression model combined with the weight coefficient, under the condition that the intercept is zero [50]. IVW results were corrected for multiple comparisons applying the q-value procedure (q < 0.1), while P < 0.05 but q > 0.1 was considered to have a suggestive association [51].

Several MR methods including Maximum Likelihood (MaxLik), Weighted Median (WM), MR-Egger regression, and MR robust adjusted profile score (MR.RAPS) were also conducted to test the robustness of our study. MaxLik estimates the parameter values that have the greatest likelihood of leading to a particular outcome by using the known sample. Its standard error would be lower than IVW when heterogeneity and horizontal pleiotropy do not exist [52]. WM improves the power of causal effect detection based on the assumption that up to 50% of IVs are valid [53]. MR-Egger regression method could identify and correct pleiotropy, but the estimation accuracy will be very low unless using a larger sample size [54]. MR.RAPS applies robust estimates to correct for systematic and idiosyncratic pleiotropy, the results of which are unbiased even though weak IVs exist [55].

Cochran’s IVW Q statistics and leave-one-out analysis were used to identify potential heterogeneous IVs. MR-Egger intercept and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test were conducted to test whether directional horizontal pleiotropy is driving the results of MR analyses [56, 57].

Reverse MR analysis was used to confirm the causal direction. The methods were similar to those of forward MR except for setting exposures as HDP subtypes and outcome as GM. Finally, we conducted multivariable MR (MVMR) analyses considering the possible confounders which may affect the outcome. The confounders including BMI (IEU number: ukb-b-19953), alcohol drinking (IEU number: ukb-b-5779), smoking (IEU number: ieu-b-4877), and T2D (IEU number: ebi-a-GCST006867).

The flowchart of this study was shown in Figure 6. All MR analyses were performed by the packages “TwoSampleMR”, “MRPRESSO”, and “qvalue” in R software.

Figure 6. Flowchart of this study. GWAS, genome-wide association studies; HDP, hypertensive disorders in pregnancy; GH, gestational hypertension; PE, pre-eclampsia; MR, Mendelian randomization; IVW, inverse-variance weighted; MaxLik, maximum likelihood; WM, weighted median; MR.RAPS, MR robust adjusted profile score; MR-PRESSO, MR Pleiotropy RESidual Sum and Outlier; BMI, body mass index; T2D, type 2 diabetes.

Consent for publication

All the authors endorsed the publication of the manuscript.

Author Contributions

XW conceived the study, obtained the data. XW and QL composed the article. QL and DL performed the statistical analysis. JC and HH prepared the figures and tables. HT conceived the study and reviewed the article. All authors approved the submitted version.

Conflicts of Interest

The authors declared there are no conflicts of interest exist.

Ethical Statement

No ethical statement is required because the study was based on GWAS publicly available data..

Funding

This work was supported by the National Natural Science Foundation of China (82360665), the Key Research and Development Program of Hunan Province (2018SK2061), the Natural Science Foundation of Hunan Province (2022JJ40343), the Scientific Research Project of Education Department of Hunan Province (21B0513), the Scientific Research Project of Hunan Provincial Health Commission (202212053368).

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