European Journal of Cardiovascular Medicine

The human microbiome, comprising trillions of microorganisms residing in various body niches, plays a pivotal role in regulating host immunity, metabolism, and endocrine functions. During pregnancy, maternal microbiota undergo significant compositional and functional changes, which are increasingly recognized as key contributors to maternal and fetal health outcomes (1). The dynamic interplay between maternal microbial communities and physiological adaptations during gestation may influence the risk of complications such as preeclampsia, gestational diabetes mellitus (GDM), preterm birth, and intrauterine growth restriction (IUGR) (2,3).

Among the different microbial niches, the vaginal microbiome has been most extensively studied in relation to pregnancy. A Lactobacillus-dominant vaginal flora is typically associated with healthy term pregnancies, while dysbiosis marked by an overgrowth of Gardnerella, Atopobium, and Prevotella species has been linked with an elevated risk of preterm labor and chorioamnionitis (4). Similarly, alterations in the gut microbiome, especially in terms of microbial diversity and the Firmicutes/Bacteroidetes ratio, have been implicated in metabolic dysregulation, potentially contributing to the development of GDM and excessive gestational weight gain (5).

Recent advancements in high-throughput sequencing technologies have enabled detailed characterization of microbial profiles across various trimesters, revealing trimester-specific shifts in microbial composition (6). Despite this progress, there remains a gap in understanding the temporal dynamics of the maternal microbiome and its association with distinct pregnancy outcomes. Furthermore, most existing studies are cross-sectional, limiting causal inference.

This longitudinal study was designed to investigate how changes in the maternal microbiome, particularly in the vaginal, gut, and oral niches, correlate with pregnancy outcomes over time. By profiling microbial communities at three distinct gestational stages, this study aims to identify microbial biomarkers that may serve as predictive indicators for adverse obstetric outcomes.

Study Design and Population
A total of 120 pregnant women aged between 20 and 35 years with singleton pregnancies and gestational age less than 12 weeks at enrollment were recruited from the antenatal outpatient department. Participants with chronic systemic diseases, antibiotic usage within the past 30 days, or known immunocompromised conditions were excluded from the study.

Sample Collection and Timepoints
Biological samples were collected from each participant during all three trimesters: first trimester (<12 weeks), second trimester (20–24 weeks), and third trimester (32–36 weeks). At each timepoint, vaginal swabs, stool samples, and oral rinse specimens were obtained following aseptic techniques. Vaginal swabs were collected using sterile specula and Dacron swabs, while fecal samples were self-collected by participants using provided sterile containers. Oral rinse samples were collected after participants swished with sterile saline for 30 seconds.

DNA Extraction and Microbial Sequencing
Microbial DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Germany) as per manufacturer’s protocol. DNA quality and concentration were verified using a NanoDrop spectrophotometer. Amplification of the 16S rRNA gene V3–V4 regions was carried out using universal primers. Amplicons were sequenced on the Illumina MiSeq platform. Sequence reads were processed using QIIME2 pipeline. Taxonomic assignment was performed using the SILVA reference database.

Data Analysis
Alpha diversity (Shannon index, Chao1) and beta diversity (Bray-Curtis dissimilarity) metrics were calculated to assess microbial diversity. Differences in microbial composition across trimesters and outcome groups were evaluated using PERMANOVA and ANOVA where appropriate. Associations between microbial taxa and pregnancy outcomes such as gestational diabetes, preterm birth, preeclampsia, and birth weight were assessed using multivariate logistic regression models adjusted for maternal age, BMI, and parity. Statistical significance was set at p<0.05.

Outcome Measures
Primary outcomes included gestational age at delivery, incidence of gestational diabetes mellitus, preeclampsia, and birth weight of the neonate. Secondary outcomes included mode of delivery and Apgar scores. Pregnancy outcomes were retrieved from hospital delivery records following completion of gestation.

Out of the 120 women initially enrolled, 112 completed the study protocol and had complete data across all three trimesters. The mean maternal age was 27.3 ± 3.4 years, and the average gestational age at delivery was 38.1 ± 1.6 weeks.

Microbial Diversity Trends

Analysis of alpha diversity revealed a significant reduction in vaginal microbiota diversity in participants who developed preeclampsia. In the second trimester, the Shannon diversity index for the preeclampsia group (n=14) was markedly lower (2.1 ± 0.4) than in normotensive participants (3.5 ± 0.6; p<0.001). Gut microbial diversity, assessed via the Chao1 index, declined significantly in women diagnosed with GDM during the third trimester (145.2 ± 12.7 vs 172.4 ± 15.3 in non-GDM; p=0.02) (Table 1).

Table 1: Alpha Diversity Metrics Across Pregnancy Outcomes

(Table 1 shows significant variation in alpha diversity of vaginal and gut microbiota across pregnancy outcomes.)

Association Between Microbial Composition and Pregnancy Complications

Relative abundance analyses indicated that increased proportions of Gardnerella vaginalis and Prevotella spp. in the vaginal microbiota were significantly associated with preterm birth (n=11). Women who delivered preterm had a mean Gardnerella abundance of 24.3% versus 8.7% in term deliveries (p=0.004). Similarly, Prevotella spp. abundance was 19.1% in preterm versus 5.6% in term births (p=0.01) (Table 2).

Table 2: Vaginal Microbial Composition in Preterm vs Term Births

(Table 2 illustrates the association between specific vaginal bacteria and risk of preterm delivery.)

Gut Microbiome and Gestational Diabetes

A notable decrease in the Firmicutes/Bacteroidetes ratio was observed in women diagnosed with GDM. The mean ratio was 1.9 ± 0.5 in the GDM group compared to 3.3 ± 0.7 in the non-GDM group (p=0.003), suggesting a microbial dysbiosis pattern associated with impaired glucose regulation (Table 3).

Table 3: Gut Microbiota Characteristics in GDM vs Non-GDM

(Table 3 highlights alterations in gut microbial composition in participants with GDM.)

No significant differences were detected in oral microbiota profiles between any outcome groups.

These findings suggest that specific shifts in vaginal and gut microbiota composition and diversity across gestation are associated with adverse pregnancy outcomes such as preeclampsia, preterm birth, and GDM.

This longitudinal study demonstrates that alterations in the maternal microbiome, particularly in the vaginal and gut niches, are significantly associated with various adverse pregnancy outcomes, including preeclampsia, preterm birth, and gestational diabetes mellitus (GDM). Our findings are consistent with previous reports suggesting that microbiome composition and diversity shift throughout gestation and can influence maternal and neonatal health trajectories (1,2).

A key observation in this study was the significantly reduced vaginal microbial diversity in women who developed preeclampsia during pregnancy. This finding aligns with prior evidence indicating that decreased diversity and dominance of pathogenic anaerobes such as Gardnerella vaginalis may compromise the mucosal barrier and induce inflammatory responses, thereby contributing to hypertensive disorders of pregnancy (3,4). Lactobacillus-deficient vaginal environments have also been implicated in poor pregnancy outcomes, including miscarriage and late preterm labor, due to the overgrowth of bacterial vaginosis-associated taxa (5,6).

The association of elevated Gardnerella and Prevotella abundance with preterm birth in our cohort supports previously published metagenomic studies (7). These bacteria are known to produce pro-inflammatory metabolites and proteolytic enzymes that may weaken the cervical and fetal membranes, promoting early labor (8,9). This microbial pattern, often linked to dysbiosis, represents a promising biomarker for identifying women at risk for spontaneous preterm birth in early and mid-pregnancy (10).

In relation to metabolic outcomes, we observed that women who developed GDM had significantly lower gut microbial diversity and a decreased Firmicutes/Bacteroidetes ratio. These findings are in line with studies showing that a reduction in beneficial Firmicutes (such as Faecalibacterium prausnitzii) and an increase in pro-inflammatory Bacteroidetes taxa are common features of gut dysbiosis associated with insulin resistance (11,12). The role of the gut microbiota in regulating host metabolism, energy extraction, and systemic inflammation suggests that it may serve both as a marker and mediator of glucose intolerance in pregnancy (13).

Interestingly, oral microbiome patterns remained largely unchanged across outcome groups in our study, which contrasts with some reports that have linked periodontal pathogens to adverse pregnancy outcomes through hematogenous spread and systemic inflammation (14). The discrepancy may be due to sampling timepoints, microbial load, or host immune responses that were not captured in this study.

The longitudinal design of our study allowed for tracking temporal changes in microbial communities, strengthening the causal inference between microbiome shifts and clinical outcomes. Moreover, the use of high-throughput 16S rRNA sequencing provided a comprehensive overview of microbial dynamics across niches and trimesters (15).

However, several limitations must be acknowledged. First, the sample size for some outcome subgroups (e.g., preterm birth, preeclampsia) was relatively small, potentially limiting statistical power. Second, while 16S rRNA sequencing offers broad taxonomic resolution, it lacks the precision of metagenomic or metabolomic approaches in identifying microbial function. Third, host factors such as diet, genetics, and lifestyle, which may influence microbiome composition, were not extensively controlled in this study.

In conclusion, this study supports the hypothesis that the maternal microbiome plays a vital role in determining pregnancy outcomes. The identification of specific microbial signatures associated with preeclampsia, preterm birth, and GDM suggests potential for developing predictive and preventive strategies based on microbiome modulation. Future research should focus on integrating microbial, immunological, and metabolic data in larger, multi-ethnic cohorts to develop personalized interventions aimed at improving maternal-fetal health.

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