The effect of Akkermansia in patients with pregnancy induced hypertension

Abstract

Backgrounds

Intestinal dysbiosis in the second trimester is associated with pregnancy-induced hypertension (PIH) in the first trimester. However, the consequences and underlying mechanisms remain unclear.

Methods

In a follow-up cohort study, a nested case-control design was employed. Twenty healthy pregnant women in their second trimester were selected as controls, while nineteen patients with pregnancy-induced hypertension were included in the study. The 16 S rRNA sequencing was utilized to assess changes of gut microbiota patterns during early pregnancy. ELISA test was used to measure plasma inflammatory markers such as IL-1 beta, IL-6, TNF alpha, IL-8 and IL-10.

Results

The PIH group exhibited lower microbial α-diversity compared to the healthy group. Although no statistically significant difference was observed at the genus level (p = 0.05), at the phylum level, the PIH patients showed a reduced abundance of Verrucomicrobia and an increased abundance of Firmicutes (p = 0.011). Donis analysis revealed that the Gut Microbiome Health Index (GMHI) of the PIH group was significantly worse than that of the control group. Additionally, Akkermansia abundance was significantly lower in the PIH group compared to the control group. Furthermore, more pro-inflammatory cytokines, such as IL-18 and capase-1, were produced in PIH plasma compared to the control group.

Conclusion

The correlation analysis between gut microbiota and cytokines in PIH patients and controls revealed that Akkermansia was positively associated with IL-18 and capase-1 levels in PIH patients.

Introduction

Pregnancy-induced hypertension (PIH) occurs when systolic BP > 140 mm Hg and/or diastolic BP > 90 mm Hg, with or without proteinuria [1–4]. Approximately 10% of pregnant women have hypertension and its complications. It is a typical medical issue of pregnancy [5, 6]. After the birth of a child, the majority of patients with PIH experience a return to normal blood pressure. However, increasing data indicate that women with a history of PIH are more likely to develop future ischaemic heart disease, hypertension, or stroke [3, 7–10]. Furthermore, children of mothers with gestational hypertension may have higher blood pressure and a greater risk of various diseases compared to healthy mothers [11]. Pregnancy is now considered an important public health issue due to its increasing prevalence and serious health consequences it causes. However, the specific pathogenesis of PIH remains unclear.

The diverse and large microbial populations, known as gut bacteria, that reside in the digestive system have a significant impact on numerous human organs [12–14]. There is an association between changes in the microbial homeostasis of the gut and pregnancy [11]. In some studies, there is a significant reduction in the diversity of the gut microbiota during the third trimester of pregnancy [13, 15]. One of the main causes of high blood pressure and its consequences during pregnancy is the disturbance of the intestinal flora. A temporary specialised dysbiosis of the gut microbiota has also been observed in animal studies during the third trimester, when the body adapts to pregnancy and allows the healthy growth of the baby [16]. Furthermore, the mother’s blood pressure may be affected by intestinal dysbiosis, and the placenta may experience local inflammation if Fusobacterium enterica is transferred there. Additionally, butyrate-producing bacteria can lead to blood pressure imbalance and intestinal barrier dysfunction in cases of gestational hypertension [17–19]. Finding the link between intestinal microbiota and pregnancy-induced hypertension, as well as investigating the targeted management of intestinal microbiota for the treatment of gestational hypertension, is thus very important.

The fundamental mechanism of the relationship between the intestinal microbiota and gestational hypertension has not been studied, and the changes in the intestinal microbiota in women with gestational hypertension during the 2nd trimester remain unknown [20–22]. Microbiota dysbiosis has been reported in patients with hypertension during the third trimester of pregnancy. We used a case-control study to investigate the relationship between microorganisms and clinical outcomes in the second trimester of pregnancy and between microbes and clinical markers to elucidate the relationship between gut microbiota and pregnancy-induced hypertension.

Materials and methods

This is a single-centrel, controlled, observational study. The institutional review board (IRB) and the ethics committee of our hospital have both given their approval for this research. After being informed, each patient signed an informed consent form. In accordance with the CONSORT recommendations, these studies also adhered to the Declaration of Helsinki.

Patients

Women meeting reference diagnostic criteria for PIH are eligible for inclusion in the study. These include sitting at arm and heart level, a single blood pressure measurement after five minutes of rest, and a blood pressure of > 140 mm Hg and/or a diastolic blood pressure of > 90 mm Hg [23]. Other requirements included a singleton pregnancy, meaning that the patient was pregnant from 14 weeks to the end of 27 weeks, and no history of medical or surgical complications such as diabetes, chronic hypertension, heart disease, chronic nephritis or systemic lupus erythematosus. Multiple pregnancies, prenatal abnormalities, persistent hypertension, autoimmune diseases, gingivitis and smoking are exclusion factors. Stool samples are collected between 31 and 41 weeks of pregnancy in addition to plasma samples.

Data collections

For the experimental and control groups, 20 cases were collected. However, only 39 formal samples were analyzed. This was because the quality control of one sample in the experimental group was inadequate when 16 S rRNA sequencing was performed. A total of 39 singleton pregnancies were included in the study. Twenty stool samples were collected from healthy control subjects, and 19 samples were collected from patients with PIH. The gut microbiota and cytokine levels were analyzed and matched with age and weight. Clinical data documented included age, mode of birth, weight, systolic and diastolic blood pressure, albumin, urine protein, gestational age, and fetal weight, as well as total protein.

Samples were collected for 16 S rRNA sequencing and quantitative PCR detection

Prior to collecting faecal samples in a faecal collection container, five millilitres of RNA stabilisation solution was pre-filled into each screw-cap catheter. Samples were collected in test tubes and stored at −80 °C. DNA was extracted from the samples. Absolute quantification of bacterial 16 S rRNA amplicon sequencing was performed by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Total microbial genomic DNA was extracted using the E.Z.N.A.^® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s instructions. The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Scientific Inc., USA). Twelve different spike-in sequences with four different concentrations (103, 104, 105, and 106 copies of internal standards) were added to the sample DNA pools. Spike-in sequences consisted of conserved regions identical to those of selected natural 16 S rRNA genes and artificial variable regions shared negligible identity nucleotide sequences with the public databases, which worked like internal standards and facilitated the absolute quantification across samples.

The V4 region of the bacterial 16 S rRNA gene was amplified using PCR with forward primer 515 F (5’-GTGCCAGCMGCCGCGGGTAA-3’) and reverse primer 806R (5’-GACTACHVGGTWTCTAAT-3’). For multiplexed sequencing, include sample-specific double-ended 6 bp barcoding on Illumina TruSeq adaptors [24]. The PCR component comprises 25 µL Phusion High Fidelity PCR Mastermix, 3 µL (10 μm) primers, 10 µL DNA template, 3 µL DMSO and 6 µL ddH2O. Denaturation at 98 °C for 15 s, annealing at 58 °C for 15 s, extension at 72 °C for 15 s and finally an extension at 72 °C for one minute were used for the first 25 cycles of the thermal cycle. The initial denaturation took 30 s. Agencourt AMPure XP beads (Beckman Coulter, Indianapolis, IN) and the PicoGreen dsDNA Assay Kit (Invitrogen, United States) were used for quantification of PCR amplicons. Following different quantification procedures, equivalent volumes of amplicons were pooled, and the Illumina NovaSeq6000 platform from GUHE Info Technology Co., Ltd. (Hangzhou, China) was used to perform double-ended 2 × 150 bp sequencing. In addition, TRIzol (Invitrogen, United States) was used to extract total RNA from approximately 50 mg of plasma tissue according to the manufacturer’s instructions. The GAPDH to 2-ΔΔCt comparison technique was used for the adjustment of relative gene expression levels. The Primer sequences of Akkermansia have been reported in previous studies [25, 26].‘FP: CAGCACGTGAAGGTGGGGAC, RP: CCTTGCGGTTGGCTTCAGAT’.

Sequence data analysis was mainly performed using the R package (v3.2.0) and QIIME (v1.9.0, 10.1038/nmeth.f.303 IF: 36.1 Q1 B1). Sequences were classified into operational taxa (OTUs, Vsearch v2.4.4) using the Usearch method if their distance similarity was 97% or greater. For each OTU, sample sequences aligned using PyNAST were examined based on sequence frequency. In order to examine the uniformity and richness of the OTUs across the samples, ranked abundance curves were constructed at the OTU level. Based on the known relative proportion of each OTU in the measured sequence, n reads (n less than the total number of reads measured) are extracted using the sparse curve to obtain the predicted value of each alpha diversity index. It can be used to compare species richness in samples with different amounts of sequencing data, as well as to indicate whether the amount of sequencing data in a sample is appropriate. Principal Coordinate Analysis (PCoA) and Non-Metric Multidimensional Scaling (NMDS) were used to represent structural changes in microbial communities in samples. The β diversity analysis was performed using the UniFrac distance measure [24, 27–30].

LEfSe (10.1186/gb-2011-12-6-r60) analysis was used to identify the bacterial taxa that differed significantly between the two groups. Finally, only taxa with a logarithmic linear discriminant analysis (LDA) score greater than two were considered.

Cytokines assay

Plasma samples were collected and centrifuged at 3000 rpm for 5 min at 4 °C. And then ELISA test was used to measure the expression of IL-1 beta(Invitrogen, Cat:88–7261-88), IL-6(Invitrogen, Cat: 88–7066-88), TNF alpha(Invitrogen, Cat: 88–7346-88), IL-8(Invitrogen, Cat:88–8086-88) and IL-10(Invitrogen, Cat: 88–7106-88) according to the manufacturer’s instructions. Finally, the results was used to obtain the value of the absorbance at 450 nm [31].

Statistical analysis

For statistical descriptions, the median and the mean ± standard deviation were used. Non-parametric tests, ANOVA and t-tests were used for statistical inference. Two-tailed tests were used for all statistical analyses, and P < 0.05 indicated statistical significance. Scarcity analysis was used in the R program to assess the richness of species in the controls and cases according to the vegetarian packages. The Vegan package (version 2.6–6.1) in the R software (version 2.5) was used for the estimation of the alpha diversity index on the basis of the species profile. The ade4 software package (version 5.5) in R was used to perform principal coordinate analysis (PCoA). Non-metric multidimensional scaling (NMDS) analysis was performed using the vegan package in R software. The differential abundance of microorganisms and functional modules was tested using MaAsLin2 (version 1.2.0) [32]. In the study, only those features that were present in at least 10% of the individuals were taken into account. Correlation analysis was evaluated using the psych package of the R software (version 2.5) and Spearman’s correlation was used.

Results

General characteristics

Of the 956 individuals in the cohort, 815 survived the entire pregnancy and postpartum period. In the cohort, 89 people were diagnosed with PIH. Two PIH patients were excluded due to missing stool, blood or questionnaire data. Finally, 19 participants with PIH and 20 healthy controls were selected for this case-control study on the basis of matching parameters; Table 1 shows the relevant baseline characteristics. There were no significant differences between the two groups with regard to age, gestational age, ethnicity, employment, educational level and monthly income. As pervious study, Body weight, BMI, SBP, diastolic blood pressure (DBP), uric acids (UA), insulin (INS) and gamma glutamyl transferase (GGT) were higher in the PIH compared to the control group [10].

Table 1.

Baseline characteristic of patients

Variable	Control (20)	PE(19)	p-Value
Age (year)	30.59 ± 3.15	30.18 ± 4.05	0.124
Gestational age (week)	21.23 ± 2.13	21.92 ± 1.99	0.231
Race(Han)	19	19	0.667
Professionals	3	4	0.564
Company employee	10	9	0.876
Others	7	6	0.786
Junior college and below	9	10	0.873
Undergraduate and above	11	9	0.987
Income(below 10,000 yuan)	13	13	0.211
Income(above 10,000 yuan)	7	6	0.342
metaphase BMI (kg/m2)	23.63 ± 1.82	24.92 ± 2.11	0.022
metaphase SBP (mmHg)	102.93 ± 7.73	134.21 ± 8.99	0.011
metaphase DBP (mmHg)	73.21 ± 7.20	85.29 ± 6.03	0.002
HGB (g/L)	123.22 ± 9.23	124.18 ± 8.32	0.083
GLU (mmol/L)	4.22 ± 0.38	4.29 ± 0.48	0.112
ALB (g/L)	48.22 ± 2.93	48.91 ± 2.03	0.322
AST (U/L)	19.36 ± 4.42	21.99 ± 4.91	0.113
CREA (umol/L)	42.93 ± 8.65	45.14 ± 8.82	0.342
UA (umol/L)	199.01 ± 33.84	252.85 ± 44.11	0.011
UREA (mmol/L)	2.19 ± 0.23	2.45 ± 0.87	0.423
TG (mmol/L)	1.58 ± 0.52	1.66 ± 0.74	0.099
TCHOL (mmol/L)	4.93 ± 0.22	4.88 ± 0.35	0.113
HDLCH (mmol/L)	1.77 ± 0.34	1.89 ± 0.56	0.134
LDLCH (mmol/L)	2.93 ± 0.93	2.65 ± 0.42	0.324
hsCRP (mg/L)	3.87 ± 2.11	4.99 ± 2.88	0.139
INS (mU/L)	19.89 ± 11.23	19.78 ± 14.29	0.01
GGT (U/L)	13.88 ± 3.24	23.42 ± 9.25	0.004

The gut Microbiome diversity alteration

Gut microbiota data from 16 S rRNA gene sequencing generated sparse curves. The curve was flat in our sample size, which means that almost all of the microbial diversity in the individuals was represented in the sequencing data (Fig. 1A). The species differences were not evident in the hierarchical clustering when beta diversity was compared to the overall community structure (Fig. 1B). A statistically significant difference between the two groups was found using a principal component analysis (Fig. 1C). The results indicate that there are no significant differences in the alpha diversity between the two groups of communities. On the other hand, NMDS and PCoA results showed a statistically significant difference in beta diversity between PIH and control.

Fig. 1.

Results of intestinal microbiota analysis. A Shannon curves. B hierarchical clustering free on OUT level. C Principal cause analysis. D neutral community model. E Rank of distance of Akk. F PLS-DA on phylum level. G typing analysis on level. H Gut microbiota health index. I microbiotal dysbiosis index. J Venn plot. K percent of community abundance on phylum level

Microbial taxa alteration

At the phylum level, PIH patients had higher Firmicutes richness (p = 0.011) and lower Verrucomicrobia richness (p = 0.011). The neutral community model describing the repeatability model has an R2 result of 0.7494. This is closer to the neutral model (Fig. 1D). Three, four, four, six, seven and ten different taxa were found at the phylum, class, order, family, genus and species level respectively when comparing the PIH and control groups. In terms of the overall trend, the abundance of Firmicutes and Bacteroidota bacteria increased significantly. However, the abundance of Verrucomincrobiota bacteria decreased significantly. Among them, Firmicutes is an important dominant bacteria. A total of 1100 bacterial species were found to be prevalent in both the control group and the PIH group, according to the Venn plot data (Fig. 1E). In addition, a significant statistical difference was found between the control group and the PIH group using a partial least squares discriminant analysis (Fig. 1F). Microbiota typing of the samples revealed that the primary differential type of bacteria was the abundance of Akkermansia (Fig. 1G). The gut microbiome health index (GMHI) of the PIH group was significantly worse than that of the control group. The gut microbiota was disturbed, as shown by the gut dysbiosis index (GDI) of the PIH group, which was significantly higher than that of the control group (Fig. 1H). According to the Adonis analysis, the abundance of Akkermansia in the PIH group was significantly lower than in the control group (Fig. 1J).

The heat map diagram shows the microbial community map of the dominant gut microbiota (Fig. 2A), while the results of the community histogram showed the changes in the microbiota between the PIH group and the control group (Fig. 1K). Circos plots show species-sample correspondences. For example, Fig. 2B shows the variation of Akkermansia in each sample group. The association between Akkermansia and other microbiota was demonstrated by Lefse multi-level species differential discriminant score, association analysis and model prediction (Fig. 2C, D).

Fig. 2.

Results of intestinal microbiota analysis in patients with PE. A Community heatmap analysis of genus level. B Circos plot. C network analysis. D cladogram plot. E relative analysis between gut microbiota and inflammation factors. F KEGG pathway analysis. G COG function analysis.H. Variations in composition of phenotypes. I relative abunacne between control and PE group. J MicroPITA. K Wilcoxon rank-sum test for control and PE group

Pro-inflammatory cytokine levels in patients with PIH

To determine the relationship between the microbiome composition and the severity of inflammation, we assessed the levels of pro-inflammatory cytokines in the PIN group and healthy control plasma.

While the expression of IL-10 was lower in the PIH group, the production of pro-inflammatory cytokines (IL-6, IL-1β, IL-18 and TNF-α) was higher in the plasma of the PIH group than in the control group. Similarly, plasma production of inflammatory cytokines (NLRP3, ASC, caspase-1, IL-1β and IL-18) was higher in the PIH group compared to healthy controls (Table 2).

Table 2.

Pro-inflammatory cytokine levels in patients with PE

Variable	Control (20)	PE(19)	p-Value
IL-6	102.23 ± 12.35	133.28 ± 23.85	0.032
IL-10	121.05 ± 35.83	78.34 ± 19.24	0.032
IL-18	23,12 ± 9.32	52.33 ± 3.99	0.011
IL-1b	52.02 ± 9.82	88.24 ± 13.92	0.023
TNF-α	177.38 ± 32.39	189.23 ± 33.11	0.041
NLRP3	8.00 ± 1.21	13.42 ± 2.22	0.013
ASC	18.25 ± 2.29	83.23 ± 13.00	0.043
Caspase-1	19.33 ± 8.22	25.21 ± 6.46	0.001

In order to learn more about the relationship between inflammation and gut microbiota, we performed a correlation study, where we labelled cytokines in branching plots between the plasma, the plasma and the gut microbiome. We then selected relevant cytokines and gut microbiota for correlation analysis (p < 0.05). Akkermansia was positively associated with the levels of IL-18 and capase-1 in the plasma tissues, according to the correlation study between the gut microbiota and the cytokines between the PIH group and the control group.

Functional prediction analysis

To investigate the possible role and mechanism of the gut microbiota in developing PIH, the transcriptomic information of the samples was compared to KO and GO standard libraries. After a preliminary screening of various functions based on “p < 0.01”, relevant functions were selected and graphically displayed in a heat map following a literature review. The RNA processing and modification, chromatin structure and dynamics, energery production and conversion, and cell cycle control, cell division, chromosome partitioning were illustrated by the functional abundance data from KEGG and COG (Fig. 2E, G). KEGG functional annotation of 16 S RNA gene sequences was performed using Tax4 Fun after converting the 16 S taxonomic lineage in the Silva database to the prokaryotic taxonomic lineage in the KEGG database. The results indicate that the metabolic, genetic information processing, environmentral information processing, cellular processes, human diseases and organismal systems were changes significant (Fig. 2F to H). Finally, we used the q-PCR assay to confirm the abundance of Akkermansia, and the results demonstrated that the PIH group was significantly lower than the control group (Fig. 2K). BugBase analysis and microPITA were able to perform phenotypic prediction to show changes in intestinal microbiota (Fig. 2I, J).

Discussion

The mother is significantly affected by high blood pressure and it is clear that both the mother and the unborn child suffer from high blood pressure during pregnancy. The intestinal flora plays an important role in the development of gestational hypertension [20, 33, 34]. Although several ideas have been developed to explain the pathophysiology of PIH, such as placental implantation defects, vascularisation or metabolic variables, the process is not fully understood [2, 35]. The gut microbiota is essential for metabolic and systemic immunology [35]. Numerous diseases are closely linked to changes in the gut microbiota, and these changes could be the focus of therapeutic treatments [35]. But the link between PIH and gut microbiome disturbances isn’t well understood. Here, we describe a remarkable perturbation in individuals with gestational hypertension. There was a significant decrease in the abundance of Akkermansia which is only one main species in that genus.

The effect of gestational hypertension on the composition of the gut microbiota

Firstly, we found that the faecal microbial diversity of the PIH and control groups was comparable. This is also in line with previous research [36]. The establishment and development of the early gut microbiome is closely related to host growth and immune development [37]. The gut microbiome of neonates varies widely between individuals, and the establishment of the microbiome is influenced by internal characteristics of the host and external factors [38]. As the host moves from infancy to early childhood, from adolescence to old age, and even during pregnancy, the composition of the gut microbiota changes [39, 40]. Alpha diversity increases throughout the first few years of life [41, 42]. Preterm infants have been reported to have a lower diversity of gut microbiota and a higher proportion of Enterobacteriaceae and Staphylococci compared to the gut microbiota of term infants [43], and the abundance of Enterobacteriaceae in particular is strongly associated with increased intestinal inflammation [44]. Furthermore, the abundance of Bacteroidetes and Proteobacteria increased in the PIH group throughout the study. The abundance of Firmicutes decreased [45, 46]. Bacteroidetes, the largest Gram-negative phylum in the human gastrointestinal tract, are thought to have an important role in the maintenance of complex homeostasis and overall health, which is safeguarded by the gut microbiota [47]. According to Wu et al., trend analysis revealed significant differences in the relative frequency of Xanthomonas, Polycyclovorans, and Phenylobacterium in the control group and Thermomonas, Xanthomonas, and Phenylobacterium during pregnancy in the hypertensive group. The intestinal microbiota of gestational hypertension patients will undergo significant changes, mainly realized that there is a clear correlation between methanobrevibacter and blood pressure [48]. It is essential for the development of immunological and systemic diseases, such as neurological disorders and the metabolic syndrome [22]. Pregnant women with gestational hypertension may have changes in their gut flora, according to our research. The ‘cardiovascular gut relationship’ has also been linked to this process [49]. An increase in harmful bacteria, hydrogen sulphide and lipopolysaccharides, a decrease in beneficial bacteria and short-chain fatty acids, a decrease in intestinal tight junction proteins and an increase in intestinal permeability are all consequences of hypertension-induced gut microbiota imbalance and intestinal barrier dysfunction [50]. Gut microbiota dysbiosis has been directly implicated in the onset and progression of hypertension [51]. Through a number of processes, including altering host microbiome-related gene pathways induced by gut dysbiosis, the gut microbiota may control blood pressure. Microbiota-derived metabolites can activate multiple downstream signalling pathways via G protein-coupled receptors or direct immune cell activation. These metabolites can be either beneficial (such as short-chain fatty acids and indole-3-lactic acid) or toxic (such as trimethylamine N-oxide) [52]. Furthermore, disturbances associated with intestinal epithelial barrier dysregulation disrupt gut mechanical transduction and trigger systemic inflammation [53]. These changes trigger systems such as the immune system, the autonomic nervous system and the renin-angiotensin-aldosterone system, which are often associated with the management of blood pressure [54].

Possible mechanisms of intestinal microbiota regulating gestational hypertension

The gut microbiota can regulate blood pressure in a variety of ways [51]. When the intestinal barrier function is normal, the intestinal permeability is low, which can effectively inhibit the leakage of intestinal pathogens and enterotoxins into the body, reduce the inflammatory damage to the intestinal blood vessels, and thus maintain normal blood pressure [55]. At the same time, the levels of intestinal pathogens and enterotoxin LPS are elevated, both of which enter the blood circulation, triggering chronic inflammatory responses and impairment of vascular endothelial function, thereby promoting hypertension by reducing vasodilators and enhancing vasoconstriction factors [56, 57]. Some metabolites of the gut microbiota (SCFAs, BAs, H2S) can lower blood pressure by dilating peripheral blood vessels, maintaining vascular endothelial function, improving insulin sensitivity, lowering blood lipids, reducing inflammatory responses, lowering heart rate, inhibiting the sympathetic nervous system, and protecting renal function, while other metabolites (TMAO, LPS) can raise blood pressure by constricting blood vessels, increasing inflammatory responses, impairing vascular endothelial function [58]. The intestinal microbiota plays an important role in maintaining blood pressure stability, and the structural changes of the intestinal microbiota are closely related to the occurrence of hypertension [59]. An imbalance in the gut microbiota is often manifested by a decrease in probiotics and an increase in harmful bacteria, which promotes inflammation and leads to aberrant expression of tight junction proteins, closed band-1 (ZO-1), and occlusion in the intestinal mucosa, thereby increasing intestinal permeability and impairing intestinal barrier function [60, 61]. In terms of the influence of gut microbiota and gestational hypertension, in addition to the inflammatory factors in this study, diet and genetic background also play an important role [12].

The exact and specific reasons for the decline in microbial richness and diversity in developed countries are unknown. However, in addition to general improvements in living and hygiene, the use of antibiotics to combat infectious diseases is also suspected to be a contributing factor [62]. For example, the use of antibiotics before, during, or in early childhood may alter the composition of the gut microbiota in infants and children, and these practices occur simultaneously with an increase in the incidence of early-onset obesity [63, 64]. In adults, short-term treatment of young healthy individuals with broad-spectrum antibiotics results in the long-term consumption of some commensal and commensal bacteria [65]. However, a causal relationship between antibiotic use, disruption of the gut microbiota, and metabolic dysregulation has not been shown [66]. Accumulating data suggest that the microbiota may affect host metabolic phenotypes through the production of metabolites. These bioactive microbial metabolites, sensitive fingerprints of microbial function, can act as inter-kingdom signaling messengers via penetration into host blood circulation and tissues [67]. In terms of microbiota composition, we found that Firmicutes and Bacteroidetes were the two most important phyla, and the abundance of Firmicutes increased significantly, which was also consistent with the results of previous studies [21]. There was no significant difference in the abundance of Firmicutes and Bacteroidetes in the second trimester of pregnancy between PIH patients and healthy pregnant women, but contrary differences were observed in the third trimeste [68]. This suggests that the gut microbiome is constantly changing during disease and pregnancy, and that there may be opposite patterns, depending on the timing of sampling and the study population.

The inflammatory factors in the regulation of intestinal microbiota and gestational hypertension

Change of the microbial composition causes inflammation [69], while low-grade intestinal inflammation disrupts the gut microbiota. Fetal mortality, mitochondrial dysfunction and chronic inflammation have all been associated with PIH [70]. According to our findings, the serum had higher levels of pro-inflammatory cytokines (IL-6, IL-1β, IL-18 and TNF-α) and lower levels of anti-inflammatory cytokines (IL-10). Previous research has suggested that pro-inflammatory cytokines may be a predictor of prognosis in PIH [71]. It was curious that we found that the group with PIH had a higher level of NLRP3 expression. The main explanation for this was that the activation of NLRP3 is crucial for the maturation of pro-inflammatory cytokines [72]. It has been implicated in the development of type II diabetes, hypertension, inflammation of the female reproductive tract and other inflammatory diseases, and is considered to be an important part of the body’s natural immune system [73, 74]. Many endogenous and exogenous stimuli activate NLRP3. NLRP3 then self-oligomerises to recruit the adaptins caspase-1 and ASC to form mature inflammasomes. The latter, by cleaving the inactivated pro-inflammatory cytokine precursors pro-IL-1β and pro-IL-18 into mature IL-1β and IL-18, promotes the maturation and secretion of IL-1β and IL-18, resulting in inflammation [75].

The role of Akkermansia in gestational hypertension

There is growing interest in the health-promoting properties of commensal bacteria living in Akkermansia. Treatment with Akkermansia in rats reduces intestinal permeability, steatosis, insulin resistance, glucose intolerance and obesity [76]. Metformin promotes glucose tolerance and glucose metabolism by secreting glucose-regulating peptides and producing short-chain fatty acids (SCFAs) in humans and mice, whereas Akkermansia enhances the beneficial effects of drugs by producing CD4 T cells, particularly the anti-cancer drug cisplatin in lung cancer mice and anti-PDI therapies [77]. Akkermansia, which is more prevalent in the colon, breaks down more mucus, which damages the mucosal barrier and allows leakage of the inflammatory signal LPS, triggering the inflammatory response [78]. According to Ganesh and colleagues, the degree of inflammation may influence the differential effects of Akkermansia on intestinal ecology [79]. Finally, they showed that Akkermansia is present in Salmonella enterica and that muciniphilus mice infected with murine typhus had increased mRNA levels of IFN-γ, IP-10, TNF-α, IL-2, IL-17 and IL-6 in cecal and colonic tissues compared to muciniphilus control mice, thereby exacerbating the intestinal inflammatory response [80–82]. We then screened for microbial markers using LEfSe. The PIH group showed a significant reduction in Akkermansia.

The role of other factors in gut microbiota and gestational hypertension

Different regions and living environments may affect the gut microbiota. Gut microbiota (GM) dynamics during pregnancy vary among different populations and are affected by many factors, such as living environments and diet. A meta-analysis of 29 studies showed a positive correlation between BMI and PIH in previous pregnancies from different countries [83]. Deborah et al. found that higher mean BP levels and PIH in urban Ghana than in rural Ghana. BMI was independently related to high BP. Left unchecked, the increasing prevalence of overweight and obesity in Ghana will exacerbate PIH levels in Ghana [84]. Wu et al. also found that MR approach to detect the causal relationships between GM and specific HDP subtypes. Our findings may promote the prevention and treatment of HDP targeted on GM and provide valuable insights to understand the mechanism of HDP in different subtypes from the perspective of GM [34]. Short-term and long-term gut microbiome perturbations by antibiotic exposure were detectable but substantially smaller than those associated with breastfeeding and infant age [85]. During P1, P2, and P3 of pregnancy, the α diversity index (Shannon index) gradually increased. In contrast, other reports have found no significant changes in GMO diversity during pregnancy [86, 87]. Since GM structure can be regulated by host and environmental factors, it is important to explore the role of different populations and living environments in pregnancy GM dynamics, and then apply individualized GM-targeted interventions to improve health during pregnancy [86, 88].

GM was characterized in PIH patients, and the abundance of Fusobacterium and Veronia was found to be increased, while the abundance of Ackermania and Faecalibacterium was decreased; Colonization of this malnourished GM sample induced typical preeclampsia characteristics in mice [36]. Jin et al. reported that oral inoculation with Akkermansia muciniphila (Am) ameliorated PE symptoms through its metabolites (short-chain fatty acids) in a murine model of PE. However, the mechanisms underlying important host–microbiota interactions in PE pathogenesis remain poorly understood. Moreover, whether targeted microbial modulation can serve as a potential drug‐development approach for PE remains unclear [20]. Akkermansia is involved in the proliferation of intestinal cells and interferes with metabolites produced by a high-fat diet, neutralizing their effects on human health [25]. This is similar to our findings. We further analyzed the changes in the intestinal microbiota and inflammatory cytokines of PIH. Akkermansia muciniphila, propionate, or butyrate significantly alleviate symptoms in rats with preeclampsia. Akkermansia muciniphila as marker features in the healthy-state microbiota, which showed a strong positive correlation with inflammation [89]. A. muciniphila supplementation attenuates T1D development in mice by modulating the tolerogenic immune response and is a promising new therapeutic tool for this autoimmune disease [90].Mechanistically, they significantly promote autophagy and M2 polarization of macrophages in the placental bed, thereby inhibiting inflammation [20]. Imbalances in gut microbiota composition are linked to hypertension, host metabolic abnormalities, systemic inflammation, and other conditions. LPS level was negatively associated with Akkermansia in PIH [91]. In our study, we also found that the Akkermansia was positively associated with IL-18 and capase-1 levels in PIH patients.

Limitations

There were some limitations in our study. The small sample size, which may have reduced the statistical power of the results, was the main limitation of our study. A small sample size may lead to the inability to detect some small but meaningful differences and the impact on the universality of the research conclusions. Furthermore, there was no investigation of the relationship between the gut microbiota and PIH and inflammatory factors and beneficial microbial metabolites. The mechanisms of the gut microbiota in PIH require further investigation. Finally, in our study, we analyzed the relationship between inflammatory factors in the intestinal microbiota and gestational hypertension, but there are many factors that affect the intestinal microbiota, including mental attitude, race, genetics, diet and other factors will significantly affect the composition of the intestinal microbiota. Therefore, the impact of these potential factors needs to be further clarified in future research.

Conclusion

The results of this study indicated that Akkermansia may be used as potential targets for pregnancy-induced hypertension which was positively associated with IL-18 and capase-1 levels. This first to investigate the relationship between changes in the gut microbiota and gestational hypertension in the second trimester of pregnancy, indicate that both inflammatory markers and the gut microbiota undergo significant changes during this period. However, considering the small sample size and many confounding factors in this study, the relationship between gut microbiota and gestational hypertension needs to be further analyzed in future studies.

Authors' contributions

CFY, YJY, MQ, DJ, JH Data curation: CFY, YJY, MQ, DJ, JH Formal analysis: CFY, YJY, MQ, DJ, JH Funding acquisition: CFY, YJY, MQ, DJ, JH Investigation: CFY, YJY, MQ, DJ, JH Methodology: CFY, YJY, MQ, DJ, JH Project administration: CFY, YJY, MQ, DJ, JH Resources: CFY, YJY, MQ, DJ, JH Software: CFY, YJY, MQ, DJ, JH Supervision: CFY, YJY, MQ, DJ, JH Validation: CFY, YJY, MQ, DJ, JH Visualization: DJ, JH Writing – original draft: DJ, JH Writing – review & editing: DJ, JH.

Funding

This research was funded by Changzhou Municipal Health Commission Young Talent Science and Technology Project, Project No. QN202218.

Data availability

The datasets generated and/or analyzed during the current study are included in manuscript.

Declarations

Ethics approval and consent to participate

The institutional review board (IRB) and the ethics committee of our hospital (The Third Affiliated Hospital of Soochow University) have both given their approval for this research. After being informed, each patient signed an informed consent form. In accordance with the CONSORT recommendations, these studies also adhered to the Declaration of Helsinki.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.