1. Introduction
Preterm birth, defined as delivery before 37 weeks of gestation, remains a leading cause of neonatal morbidity and mortality worldwide [
1,
2]. Despite advances in neonatal care, the rates of preterm births have not significantly decreased worldwide in recent years, highlighting the need for a deeper understanding of its etiology [
3,
4]. Emerging research has increasingly pointed to the role of the vaginal microbiota in maternal and neonatal health, suggesting a significant association between microbial dysbiosis and adverse pregnancy outcomes, including preterm birth [
5,
6]. The vaginal microbiome, a complex ecosystem of bacteria, varies widely among women and changes throughout pregnancy, influenced by factors such as genetics, environment, and lifestyle [
7,
8].
Recent studies have delineated a more detailed landscape of the vaginal microbiota, identifying specific bacterial genera and species that predominate in healthy pregnancies versus those associated with negative pregnancy outcomes [
9,
10].
Lactobacillus species, for example, are typically dominant in a healthy vaginal microbiome and are known for their protective roles, producing lactic acid and bacteriocins that inhibit the growth of pathogenic organisms [
11]. Conversely, an increase in microbial diversity and the presence of certain anaerobic bacteria like
Gardnerella and
Ureaplasma have been linked to inflammation, infection, and, subsequently, an increased risk of birth risks [
12,
13].
The immunological interactions between the vaginal microbiota and the host are crucial in maintaining pregnancy. A healthy microbiome contributes to the establishment of a tolerant immunological environment necessary for the successful continuation of pregnancy. However, dysbiosis can lead to an imbalance in pro-inflammatory and anti-inflammatory responses, potentially triggering pathways that lead to negative pregnancy outcomes [
14,
15]. Inflammation, particularly in the context of infection, is a well-recognized pathogenic pathway in preterm birth, and microbiota alterations are a key component in this process.
Technological advances in metagenomics and bioinformatics have allowed for more comprehensive and accurate profiling of the vaginal microbiome, providing insights into its structure, function, and dynamics during pregnancy [
16,
17]. These tools have facilitated large-scale studies that correlate specific microbiota profiles with pregnancy outcomes. However, there remains a gap in understanding the precise mechanisms by which these microbial communities influence the risk of preterm birth and how interventions can be tailored to modulate the microbiome to prevent negative outcomes.
This study aims to address these gaps by conducting a detailed analysis of the vaginal microbiota in a cohort of pregnant women and correlating these findings with preterm birth outcomes. The primary hypothesis is that specific variations in the vaginal microbiota are associated with an increased risk of preterm birth. Secondary hypotheses include the supposition that alterations in the microbial composition are linked to changes in the local immune environment, which in turn contribute to the risk of preterm delivery. The objectives of this study are to identify specific microbial species associated with preterm birth, understand the temporal dynamics of the vaginal microbiota throughout pregnancy, and explore the potential differences and microbial dysbiosis between pregnant women who gave birth preterm and full-term.
3. Results
A total of 89 pregnant women who gave birth preterm were included in the study, and 106 who gave birth full-term, respectively.
Table 1 presents the demographic and health characteristics of the study participants. The mean age of participants in the preterm group was 28.6 years, compared to the full-term group’s mean age of 29.1 years, although this difference was not statistically significant (
p = 0.502). The age categories further detailed that 69.7% of the preterm group and 74.5% of the full-term group were under 35 years of age, with the remaining participants being 35 years or older (
p = 0.449). The percentage of participants with a BMI of 30 kg/m
2 or greater was slightly higher in the preterm group (19.1%) compared to the full-term group (15.1%), but this difference was not statistically significant (
p = 0.457).
Lifestyle factors such as smoking and alcohol use during pregnancy were examined. In the preterm group, 13.5% reported smoking during pregnancy compared to 7.5% in the full-term group, and 5.6% of the preterm group reported alcohol use during pregnancy compared to 0.9% in the full-term group. Parity did not show a significant difference between the groups, with 59.6% primigravida and 40.4% multigravida in the preterm group compared to 59.4% and 40.6%, respectively, in the full-term group (p = 0.986). Medical history variables such as Urinary Tract Infections (UTIs), hypertension, diabetes, anemia, respiratory infections, and diarrheal illness during pregnancy were also compared. The preterm group had a higher percentage of UTIs (24.7% vs. 14.2%), but this difference was marginally not significant (p = 0.060). Hypertension, diabetes, anemia, respiratory infections, and diarrheal illness showed no statistically significant differences between the groups.
The white blood cell count showed a statistically significant difference between the two groups. The median WBC count was higher in the preterm group (10.2 × 103/mm3) compared to the full-term group (7.6 × 103/mm3) with a p-value of 0.009, suggesting a possible association between elevated WBC counts and the incidence of preterm births. Neutrophil counts were also significantly different between the groups. Preterm births were associated with a higher median neutrophil count (7.2 × 103/mm3) compared to full-term births (5.1 × 103/mm3) with a p-value of less than 0.001.
However, no statistically significant differences were observed in the lymphocyte counts, platelet (PLT) counts, red blood cell (RBC) counts, hemoglobin levels, C-Reactive Protein (CRP) levels, creatinine, or urea between the preterm and full-term groups. The
p-values for these tests ranged from 0.109 to 0.753, indicating no strong evidence of difference in these parameters between the two groups, as presented in
Table 2.
The gestational weight of the newborns was significantly different between the two groups (p < 0.001). In the preterm birth group, fewer babies weighed over 2500 g (52.8%) compared to the full-term birth group (84.9%). Conversely, higher percentages of lower-weight categories were observed in the preterm group: 3.4% weighed 500–999 g, 9.0% weighed 1000–1499 g, and 34.8% weighed 1500–2499 g. In contrast, in the full-term group, only 0.9% weighed 1000–1499 g, and 14.2% weighed 1500–2499 g. This significant difference in weight distribution underscores the association between lower birth weight and preterm deliveries.
Regarding gestational age, all preterm births occurred before 37 weeks, with 7.9% being early preterm (<28 weeks), 21.3% moderate preterm (28–32 weeks), and 70.8% later preterm (32–36 weeks). In contrast, the full-term births included early-term (17.0%), full-term (69.8%), and post-term (13.2%) babies, highlighting the clear demarcation in gestational age between the preterm and full-term groups.
The type of birth also showed a significant difference (
p < 0.001) between the two groups. The preterm group had a higher percentage of cesarean deliveries (65.2%) compared to the full-term group (18.9%). Vaginal deliveries were more common in the full-term group (73.6%) than in the preterm group (28.1%). Assisted deliveries were relatively low in both groups but were slightly more common in the full-term group (7.5%) compared to the preterm group (6.7%), as seen in
Table 3.
The pH test results indicated a higher median pH value for the preterm group (5.6) compared to the full-term group (4.4), with a highly significant p-value of less than 0.001. The overall assessment based on the Nugent Score, which categorizes the vaginal microbiota as normal, intermediate, or showing bacterial vaginosis, showed significant differences between the two groups (p = 0.001). In the preterm group, 39.3% had normal microbiota compared to 64.2% in the full-term group, indicating a higher prevalence of normal microbiota in full-term births. Intermediate microbiota was found in 31.5% of the preterm group and 23.6% of the full-term group. Notably, a higher percentage of the preterm group (29.2%) had bacterial vaginosis compared to the full-term group (12.3%), suggesting that bacterial vaginosis is more prevalent among women who deliver preterm.
In examining particular microbiota types, coccoid microbiota was more common in the preterm group (19.1%) compared to the full-term group (8.5%), with a
p-value of 0.029, indicating a possible association with preterm birth. Staphoid vaginitis was also more prevalent in the preterm group (13.5% vs. 6.6%); however, the difference was not statistically significant (
p = 0.106). Fungal infection, indicated by the presence of fungi, was significantly more common in the preterm group (23.6% vs. 9.4%,
p = 0.007). The combination of bacterial vaginosis and fungi was also more frequent among the preterm group (10.1% vs. 3.8%), although this result was marginally not significant (
p = 0.077), as presented in
Table 4 and
Figure 1.
The presence of Candida spp. was significantly higher in the preterm group (24.7%) compared to the full-term group (9.4%), with a p-value of 0.004. Similarly, the prevalence of Gardnerella vaginalis was considerably higher in the preterm group (25.8%) than in the full-term group (12.3%), with a p-value of 0.015. A significant reduction in Lactobacillus spp. was noted among the preterm group (73.0%) compared to the full-term group (87.7%), with a p-value of 0.009. Mycoplasma hominis was also significantly more prevalent in the preterm group (16.9%) compared to the full-term group (5.7%), with a p-value of 0.012. Ureaplasma urealyticum was found more frequently in the preterm group (14.6%) than in the full-term group (3.8%), with a significant p-value of 0.007.
Non-significant findings, including the presence of
Actinomyces spp.,
Bacillus spp.,
Corynebacterium spp.,
Enterococcus spp.,
Escherichia coli,
Haemophilus influenzae,
Klebsiella spp.,
Peptostreptococcus anaerobius,
Staphylococcus aureus,
Staphylococcus epidermidis,
Staphylococcus haemolyticus,
Streptococcus agalactiae,
Streptococcus anginosus,
Streptococcus mitis, and
Streptococcus salivarius, did not show a strong correlation with preterm or full-term births, as presented in
Table 5.
A significant negative correlation was found between gestational age and white blood cell (WBC) count (rho = −0.307 *), indicating that as gestational age decreases, indicative of preterm birth, WBC counts tend to increase. Vaginal pH showed a strong negative correlation with gestational age (rho = −0.452 *), suggesting that lower gestational ages are associated with higher vaginal pH levels.
Lactobacillus spp. had a positive correlation with gestational age (rho = 0.406 *) and a strong negative correlation with vaginal pH (rho = −0.559 *). Candida spp. showed a positive correlation with vaginal pH (rho = 0.308 *), implying that yeast infections are associated with higher pH levels, which could contribute to adverse pregnancy outcomes. Additionally, there was a negative correlation between Candida spp. and Lactobacillus spp. (rho = −0.256 *), highlighting the competitive relationship between these microorganisms.
Mycoplasma hominis exhibited negative correlations with gestational age (rho = −0.217 *) and Lactobacillus spp. (rho = −0.312 *) and positive correlations with vaginal pH (rho = 0.357 *) and Ureaplasma urealyticum (rho = 0.504 *), suggesting its association with conditions leading to preterm birth. Ureaplasma urealyticum correlated negatively with gestational age (rho = −0.259 *) and Lactobacillus spp. (rho = −0.359 *) and positively with vaginal pH (rho = 0.418 *), indicating its role in the microbial imbalance associated with preterm birth.
The Nugent Score, which is used to diagnose bacterial vaginosis, showed the strongest negative correlation with gestational age (rho = −0.551 *), indicating that higher scores (and thus more severe bacterial vaginosis) are strongly associated with preterm births. It also had a substantial positive correlation with vaginal pH (rho = 0.672 *) and a negative correlation with
Lactobacillus spp. (rho = −0.601 *), reinforcing the link between bacterial vaginosis, pH imbalance, and decreased beneficial bacteria, as presented in
Table 6 and
Figure 2.
The presence of Candida spp. in vaginal cultures was found to increase the odds of preterm birth by 84% (OR = 1.84, p = 0.018), while the presence of Gardenerella vaginalis more than doubled the risk (OR = 2.29, p = 0.003). Mycoplasma hominis and Ureaplasma urealyticum were also identified as significant contributors to the likelihood of preterm delivery, with odds ratios of 1.97 (p = 0.007) and 2.43 (p = 0.001), respectively, indicating a near doubling of risk with their presence. Notably, an increased vaginal pH greater than 4.5 was associated with a 68% increase in preterm birth risk (OR = 1.68, p = 0.014), and a high Nugent Score (7–10), indicative of bacterial vaginosis, was strongly linked to preterm birth with an odds ratio of 2.83 (p < 0.001).
Conversely, a reduction in
Lactobacillus spp., typically considered protective against pathogenic invasion and imbalance, was associated with a significantly decreased risk of preterm birth (OR = 0.46,
p = 0.001), underscoring its importance in vaginal health. The combination of fungi presence with abnormal microbiota further increased the risk of preterm delivery (OR = 2.03,
p = 0.008), suggesting a complex interplay of microbial factors contributing to adverse pregnancy outcomes, as presented in
Table 7 and
Figure 3.