Next Article in Journal
The Effects of Aromatherapy on Sleep Quality in Menopausal Women: A Systematic Review and Meta-Analysis
Previous Article in Journal
Breastfeeding and Intersectionality in the Deep South: Race, Class, Gender and Community Context in Coastal Mississippi
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Women
Volume 5
Issue 3
10.3390/women5030022
women-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Functional and Compositional Analysis of the Fecal and Vaginal Microbiota in Vestibulodynia: An Explorative Case–Control Study

by
Elisa Viciani
1,*,
Barbara Santacroce
1,
Antonella Padella
1,
Alena Velichevskaya
1,
Andrea Marcante
1,
Laura Di Rito
1,
Matteo Soverini
1,
Alessandra Graziottin
2,
Filippo Murina
3 and
Andrea Castagnetti
1
1
Wellmicro srl, 40138 Bologna, Italy
2
Centre of Gynaecology and Medical Sexology, San Raffaele Resnati Hospital, Via S. Croce 10/A, 20123 Milan, Italy
3
Lower Genital Tract Disease Unit, V. Buzzi Hospital—University of the Study of Milan, Via Castelvetro 24, 20124 Milan, Italy
*
Author to whom correspondence should be addressed.
Women 2025, 5(3), 22; https://doi.org/10.3390/women5030022
Submission received: 12 May 2025 / Revised: 6 June 2025 / Accepted: 10 June 2025 / Published: 20 June 2025
Browse Figures
Review Reports Versions Notes

Abstract

Vestibulodynia is vulvodynia localized to the vulvar vestibule and is a chronic disease defined as vulvar pain of at least three months’ duration, without a defined cause, that severely affects women’s health and quality of life with limited treatment options. We collected stool and vaginal samples from 30 women affected by vestibulodynia and 27 healthy women. Bacterial gut and vaginal microbiomes were characterized by amplicon sequencing, and compositional and functional differences between the control and the patient groups were assessed. No differences in vaginal or fecal alpha and beta diversity were found, but vaginal microbiota of patients was found to be associated with Lactobacillus iners. Moreover, the relative abundance of L. iners negatively correlated with the relative abundance of L. crispatus, and positive correlations between commensals and pathobionts were found in the vestibulodynia vaginal microbiota but not in the healthy controls. The bacterial functions and contributors were defined in the study groups for the fecal and vaginal microbiota. Our results portrayed the vaginal microbiome of patients with vestibulodynia as potentially not as efficient at living in an anaerobic environment as the healthy microbiome is and too inclined to acidify this environment, exposing it to the risk of developing other ailments.

1. Introduction

An idiopathic disease category into which up to 30% of women with vaginal symptoms, who are not given a diagnosis after standard diagnostic assessments, fall is vulvodynia [1]. Vulvodynia is characterized by vulvar pain of at least a 3-month duration with no apparent cause [2]. Despite many women worldwide experiencing this condition, the origin and underlying mechanisms of this disease are still poorly understood, which makes vulvodynia a complex condition without identifiable causes and limited treatment options. Most investigations concentrated on localized provoked vulvodynia at the vestibule, referred to as vestibulodynia (VBD), which constitutes the predominant subtype (about 80%) [2]. Dyspareunia is an important symptom of patients with VBD, along with chronic pelvic pain among menstruating women. The vulvar pain may range from mild to severe; it may be localized or generalized and can be described as a burning or stinging feeling that has a strong impact on sexual, physical, and everyday life activities, reducing women’s quality of life. During the last ten years, the role of the vaginal and fecal microbiome in VBD has been at the center of microbial investigations, which, until now, have led to no consensus regarding the microbiome characteristics in vestibulodynia [3,4,5,6,7]. However, since the microbiota can have a significant role in local immune-inflammatory responses, it may still be at the base of key pathogenic mechanisms that may lead to the progression of this disease. The objective of this study was to deepen the understanding of the associations between vaginal and fecal microbiome composition and functions with vestibulodynia establishment. From September to November 2023, we collected stool, vaginal samples, and patient records from 30 women affected by vulvodynia and 27 healthy women that represented the study control group. Bacterial gut and vaginal microbiome were characterized by amplicon sequencing on the MiSeq, Illumina’s integrated next-generation sequencing instrument. Downstream analyses were carried out to assess the presence of compositional and functional differences between the control and the patient groups. Using PICRUSt2, the functions and bacterial function contributors were defined in the control and vulvodynia groups for the fecal and vaginal microbiota. The results highlighted the fact that the vaginal microbiota in vulvodynia presents with a lesser anaerobic and a higher acidifying potential compared to the control vaginal microbiome.

2. Results

Women of Italian origin who suffered from vestibulodynia and were admitted to the Vittore Buzzi Hospital, Milan, Italy, from September to November 2023 were enrolled in this study together with healthy fertile asymptomatic women without any vulvovaginal conditions who attended cervical cancer screening programs. Screening, inclusion, and exclusion criteria are described in Figure 1.
The average age of the patients (N = 30) was 29.4 years, while the control group (N = 27) average age was 31.2 years. They had similar Body Mass Index (BMI), and a similar number of patients and controls used hormonal contraceptives. Thirteen patients suffered from recurrent vaginitis, while none of the healthy subjects had this disease (p-value < 0.01) (Table 1).
Hierarchical clustering analysis based on the Euclidean distance metric of vaginal microbiota profiles resulted in seven different CSTs according to the classification by France et al. [8]. CST I, Lactobacillus crispatus dominated, was the most frequent CST in both groups, followed by CST III, characterized by the dominance of L. iners, and CST II, defined by the bacterial dominance of L. gasseri. CST IV-B, characterized by the dominance of Gardnerella vaginalis, was present in one healthy subject, whilst CST IV-C3, dominated by Bifidobacterium, was present in one patient and two healthy subjects; CST L. johnsonii-dominated was only present in one patient, and CST V, characterized by the dominance of L. jensenii, was present only in three healthy subjects (Table 2 and Figure 2).
The alpha diversity of the vaginal and gut microbiota was analyzed in the patients with vestibulodynia (VBD) and in the controls. The observed species index, the Shannon–Wiener index [9], and the Inverse Simpson’s index [10,11] did not find any significant differences between the two conditions under study (Figure A1, panel a). Then, the beta diversity was investigated with the unweighted and weighted UniFrac [12] distance metric on a PCoA (principal coordinate analysis) graphic representation using PERMANOVA (permutational analysis of variance), but still no significant differences were found between the groups (Figure A1, panel b).
A microorganism that lives in close association with the human host without causing any harm or significant benefit is called a “commensal” bacterium, while a microorganism that normally resides in the host as a commensal but has the potential to induce disease under specific circumstances is referred to as a “pathobiont”. The fecal microbiota was tested with the LDA LEfSe (linear discriminant analysis effect size) [13] algorithm for the presence of bacterial signatures in health or disease. The patients had no distinctive signature, while the healthy controls resulted in being positively associated with Gemmiger qucibialis, an intestinal bacterial commensal (Figure 3). Successively, we assessed the presence of vaginal biomarkers in the VBD and control groups. This analysis found that L. iners was positively associated with VBD, where this microorganism was more abundant than in the controls (Figure 3). A negative association with Finegoldia magna was identified in the VBD group, though it was linked to three samples with high amounts of this bacterium in the controls in comparison to all the other healthy samples (Figure 3); thus, this latter observation was possibly driven by outliers and was disregarded.
Statistical correlations between bacterial species in the microbiota do not imply causation. Even so, correlation analysis is a valuable tool for identifying patterns and generating hypotheses regarding possible interactions in the microbiota; thus, they can be considered as a starting point for further investigations. In our study, Kendall’s Tau correlation analysis was applied to search for correlations between bacterial relative abundances inside the vaginal and fecal ecosystems. In the fecal ecosystem, the analysis found that Prevotella copri was strongly positively correlated with dissimilar commensal bacteria in the patients or in the controls; in fact, P. copri was positively correlated with Holdemanella biformis in the VBD gut microbiota, while it was positively correlated with Bifidobacterium adolescentis in the healthy group (Kendall’s Tau = 0.66, FDR-corrected p-value = 0.002, and Kendall’s Tau = 0.57, FDR-corrected p-value = 0.0033, respectively, Figure A2), but in both cases these correlations were between common human intestinal commensals. In the vaginal microbiota, a negative correlation was identified between L. iners and L. crispatus (Kendall’s Tau = −0.48, FDR-corrected p-value = 0.0005, see Figure A2) in the patients suffering from vestibulodynia, but not in the healthy controls. This correlation, when calculated on the CST I subjects only, grew stronger (Kendall’s Tau = −0.65, FDR-corrected p-value = 0.0049) and was still not present in the CST I subjects in the controls (Figure A3). In addition to this, a strong positive correlation was found between a genus commonly present in the healthy vaginal microbiota, Bifidobacterium leopoldii, and the potential pathobiont Megasphaera unassigned, belonging to a genus that can be found in bacterial vaginosis [14], in patients with VBD (Kendall’s Tau = 0.66, FDR-corrected p-value = 1.21 × 10−20, Figure A2). In the healthy vaginal microbiota instead, a positive strong correlation between vaginal pathobionts was detected between Streptococcus anginosus, known to be associated with aerobic vaginitis [15] and whose genus is found in CST IV [8], and Peptoniphilus unassigned, a genus found in the vaginal ecosystem, but also in vestibulodynia [6] (Kendall’s Tau = 0.71, FDR-corrected p-value = 6.07 × 10−13); nonetheless, no correlations between commensals and pathobionts were found in the healthy subjects. We searched for bacterial reservoirs shared between the vaginal and fecal environments, but we found no correlations between the relative abundances of the same bacterial species in the vaginal and the intestinal ecosystems (Figure A4).
The PICSTRUSt2 results were analyzed to compare fecal bacteria functional contribution in the two studied conditions. Amongst the intestinal functions with the highest summed normalized taxon function contribution in the patients, there was K00945, which was further investigated since its values were more than twenty times higher in the patients than in the healthy controls (Figure 4a); K00945 encodes the enzyme cytidine monophosphate kinase or cytidylate kinase (CMK), a member of the nucleoside monophosphate kinase family that is important in the RNA and DNA synthesis; the major bacterial contributor for this function in the patients was Ruminococcus bicirculans, while in the controls, the only contributor was Lachnospiraceae unassigned (Figure 4a). The function with the highest summed normalized taxon function contribution in the healthy subjects was K01265, which encodes the methionyl aminopeptidase (MetAP or MAP), an enzyme that removes the N-terminal from newly synthetized proteins and is involved in protein synthesis in bacterial cells. Of note, this bacterial function is more than halved in the patient’s fecal microbiota (Figure 4a). The bacterial contributor of this function in the patients and in the healthy subjects is Lachnospiraceae unassigned (Figure 4a). The PICSTRUSt2 results were also analyzed to compare vaginal bacteria’s functional contribution in the two studied conditions. The function with the highest summed normalized taxon function contribution in the VBD patients was K00016, which encodes the enzyme L-lactate dehydrogenase (L-LDH); these values were double the value of the summed normalized function contribution for K00016 in the healthy controls (Figure 4b). The major bacterial contributors for this function were L. crispatus and L. iners in VBD patients and L. crispatus, Bifidobacterium leopoldii, L. gasseri, L. iners, and G. vaginalis in the healthy subjects (Figure 4b). On the other hand, the VBD patients displayed a more than halved summed normalized taxon function contribution for K00244 (Figure 4b), which encodes the enzyme fumarate reductase flavoprotein subunit (FRDA) of the FRD enzyme (fumarate reductase) and is used by the bacteria to perform respiration in anaerobic conditions using fumarate as the final electron acceptor [16]. The main bacterial contributors to this function are L. crispatus in the VBD patients and L. crispatus, L. jensenii, and L. gasseri in the healthy controls (Figure 4b).

3. Discussion

Vestibulodynia (VBD) remains a challenging disease whose origin and molecular mechanisms are still unknown, which makes it a painful and multifaceted idiopathic condition with limited treatment options.
We first observed that patients with VBD had no association with intestinal bacterial opportunistic pathogens. These results disagree with another study on VBD, where patients had a significant increase in Escherichia coli [17]. The microbiological methods used in this study, though, were not NGS-based, making their microbiological assumptions not comparable with our study. Regarding the vaginal microbiota, we found that L. iners was positively associated with VBD. This is in line with the study by Jayaram et al. [4], where they found an increased prevalence of Streptococcus and L. iners in women with VBD. However, in most studies on VBD, either no major differences between the vaginal bacterial composition of women with vestibulodynia and that of controls [3,5,7] or no microbiological consensus regarding bacterial signatures were found. As an example, in the study published by Awad-Igbaria et al. in 2022 [18], the Ochrobactrum genus and Pseudomonadaceae family were identified as indicators for VBD, while in a contemporary study by Panzarella et al., Bifidobacterium longum, the bacterial genus Sneathia, and the bacterial family Leptotrichiaceae were found to be negatively correlated with VBD [6]. In a previous study from Murina et al., in 2020, Lactobacillus, Gardnerella, and Atopobium were indicated as the dominant genera and Lactobacillus gasseri as the dominant species in VBD [19]. This led us to consider other aspects. In this regard, for the first time, we detected a negative correlation between the relative abundances of L. iners and L. crispatus in the vaginal microbiota of patients with VBD, which was not found in healthy individuals. This means that the higher L. iners is present in VBD, the lower L. crispatus is likely to be present, and vice versa. Moreover, we found a positive correlation between Bifidobacterium leopoldii and a potential pathobiont, Megasphaera unassigned, that can be found in bacterial vaginosis [14]. Instead, no positive correlations between commensals and pathobionts were found in the healthy controls.
Regarding the functional aspects, in the gut microbiota, the function coding for the cytidine monophosphate kinase, important for RNA and DNA synthesis, was twenty times higher in the patients than in the healthy controls. The cytidine monophosphate kinase is a crucial enzyme in the pyrimidine nucleotide salvage pathway, which is pivotal in rapidly dividing cells, such as activated immune cells, in the intestinal mucosa, but also in the bacterial cells; a higher bacterial functional contribution for this enzyme could be an indication of a higher bacterial growth in the patients’ intestine coupled with a higher scavenging of nucleotide precursors from the gut lumen. We hypothesize that this behavior could indirectly alter the intestinal immune and mucosal functionality in the long term. In the healthy subjects, the methionyl aminopeptidase, which is involved in protein synthesis in bacterial cells, had more than double the contributions found in VBD. This enzyme can be depleted in individuals who recently developed Ulcerative Colitis [20]. K01265 is a fundamental enzyme in bacterial metabolism since it is actively involved in the protein maturation process. A higher level of this enzyme in the control group could be associated with a sound metabolic activity in the healthy intestinal microbiota.
Subsequently, in the vaginal microbiota, the function coding for the L-lactate dehydrogenase (L-LDH) was doubled in VBD. In line with this, L. crispatus and L. iners are known to be able to synthetize L-LDH [21,22], and L. crispatus-dominated CST I and L. iners-dominated CST III represented the 86.7% of the VBD patients (56.7% added to 30%, respectively), while they represented the 63.0% of the healthy controls (44.4% added to 18.5%, respectively, see Table 1). Physiological concentrations of lactate in the vagina protect the epithelial barrier and increase its integrity [21], although a higher production of L-lactate over D-lactate increases the risk of having a higher level of MMP-8 (matrix metalloproteinase) [23]. This enzyme can alter the integrity of the cervix since it degrades the extracellular matrix and can help bacteria cross the endocervix [24]. Moreover, the vaginal environment exposed to a highly acidic pH (<3.8) is prone to several ailments like cytolytic vaginosis, which has also been found in patients suffering from vestibulodynia [25]; in this regard, our findings could help explain how the two conditions may be linked. In addition to this, the observation that the VBD patients display a more than halved functional potential for FRDA of the fumarate reductase, used to perform anaerobic respiration consuming fumarate as the final electron acceptor [16], suggests that in VBD, the vaginal microbiota might possess a lower capacity to use alternative respiration pathways compared to the healthy controls. Considering that the vaginal ecosystem should be microaerophilic [26] or anaerobic with 0.5–1.8% of oxygen [27], this might indicate that the VBD microbiota might be less keen towards strict anaerobic respiration.
Our study has some limitations; the first one is the modest sample size of the investigation, which was designed as a single-center pilot study; another limitation is the fact that using the tool PICRUSt2, which bases its analysis on predicted gene content, our functional analysis is a functional inference, and might not reflect actual metabolic activity, but the metabolic potential of the microbiome.
These findings describe the vaginal microbiome in vestibulodynia as potentially not as efficient at living in a stringent anaerobic atmosphere as the healthy microbiome is and potentially too inclined to acidify this environment, exposing it to the risk of developing other ailments. We believe that our findings could be of clinical use for the diagnosis of this condition, its management, and the development of new effective treatments.

4. Materials and Methods

4.1. Study Population

The study included women of Italian origin who suffered from vestibulodynia and were admitted to the Vittore Buzzi Hospital, Milan, Italy, from September to November 2023. Samples were coded and anonymized. The consent form for participation was distributed to all participants involved in the study and signed. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Ethical Board (IEB) with approval number n. 225/23 (Protocol n. SDSM-2023-01.1). Screening procedures and inclusion and exclusion criteria are described in detail in Figure 1. A control cohort was selected from healthy, fertile, asymptomatic women without any vulvovaginal conditions who attended cervical cancer screening programs. The average age of the patients was 29.4 years, while the control group’s average age was 31.2 years.

4.2. Sample Collection and Methodology

Fecal samples were collected from 26 controls and 29 patients, while vaginal samples were collected from all enrolled patients (N = 27 and N = 30, respectively) and analyzed at the Wellmicro S.r.l. laboratory (Bologna, Italy). Fecal sampling occurred following spontaneous evacuation, as per standard procedure in clinical practice. Both vaginal and fecal samples were collected using the Copan eNat ® System sampling device (Copan Italia s.p.a., Brescia, Italy).

4.3. DNA Extraction and Purification

Total microbial DNA was extracted from fecal samples using the DNeasy 96 PowerSoil Pro QIAcube HT Kit on the QIAcube HT instrument (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. A bead-beating step with Lysing Matrix E (MP Biomedicals, Irvine, CA, USA) was performed on a FastPrep24 bead-beater (MP Biomedicals, Irvine, CA, USA) at 6.0 movements per second for 40 s. Negative controls were PCR-grade water (no sample). DNA was quantified using the Qubit™ 4 Fluorometer (Fisher Scientific Italia, Segrate, Italy) following Illumina amplicon sequencing Sample Preparation Guide (https://emea.support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf, accessed on 8 February 2024).

4.4. Determination of Bacterial Profiles by Amplicon Sequencing

V3 to V4 region of the 16S rRNA gene was amplified for bacterial classification using the primer set S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 [28]. Indexed libraries were prepared by limited-cycle PCR using Nextera technology (Illumina, San Diego, CA, USA) and further cleaned up with VAHTS DNA Clean Beads (Vazyme, Red Maple Hi-tech Industry Park, Nanjing, China). Libraries were pooled at equimolar concentrations (4 nM), denatured, and diluted to 5 pM before loading onto the MiSeq (Illumina, San Diego, CA, USA). Sequencing on the MiSeq platform was performed by using a 2 × 300 bp paired-end protocol.

4.5. Data Processing and Analysis

Sequenced reads were analyzed using QIIME2 [29] (version 2020.6). The DADA2 [30] (Divisive Amplicon Denoising Algorithm 2) plugin was used to remove noise and chimeras and to generate ASVs (Amplicon Sequence Variants). Quality filtering and clustering were performed using VSEARCH [31] version 2020.6.0. High-quality reads were classified taxonomically using the Greengenes2 [32], reference database version 2022.10. The taxonomic classification of Gardnerella ASVs was further confirmed by aligning ASVs’ sequences to NCBI Nucleotide Collection (nr/nt) against the Gardnerella database (tax id: 2701) using the online Nucleotide BLAST program (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&BLAST_SPEC=GeoBlast&PAGE_TYPE=BlastSearch, accessed on 8 January 2024). The data were imported into R [33] (version 4.2.2) on Rstudio (version 2022.07.2 Build 576), where downstream analysis was performed using R packages phyloseq [34], rbiom [35], ggplot2 [36], tidyverse [37], tidyr [38], ape [39], ggpubr [40] and dplyr [41]. Environmental microbial contaminants were excluded from the present analysis by filtering out ASVs that were specifically present in the negative controls (no sample) using the decontam [42] R package at 5% stringency. Data were normalized by rarefaction without replacement. The minimum sample sequence depth was at 5167 reads for the vaginal samples, and 11,566 reads for the fecal samples. The differences in alpha diversity were evaluated using ANOVA and Tukey’s HSD (honestly significant difference) tests for normally distributed data or Wilcoxon–Mann–Whitney with Holm–Bonferroni correction method for non-normally distributed data. Beta diversity was measured by calculating the weighted or unweighted UniFrac [12] distance metric. Principal coordinate analysis (PCoA) was applied on the distance matrices to generate bi-dimensional plots in R. Dispersion of the PCoA clusters was compared using the betadisper function in R vegan [43] package. The permutational analysis of variance (PERMANOVA) test, calculated using the function adonis2 in the vegan package, was performed to determine whether there was a significant separation between different sample groups. Linear discriminant analysis (LDA) effect size (LEfSe) algorithm [13], a tool that is hosted on the Galaxy web application at http://mbac.gmu.edu:8080/, accessed on 10 January 2024, was used to discover bacterial taxa associated with each condition. The differences in abundance were regarded as significant when the logarithmic LDA score was higher than 2. Hierarchical clustering was performed using the R package pheatmap [44] with Euclidean distance metric and method “complete”. Kendall’s Tau correlation analysis was executed using the cor function of the R package stats [45] and only bacterial species that had an RA% > 0.5% and that were present in at least 10% of the samples were computed in the analysis. Sequencing data used in this study were deposited in the Sequence Read Archive (SRA) repository with the PRJNA1195896 project number.

4.6. Functional Prediction of the Microbiota

The functional composition of the vaginal and fecal microbiota was inferred using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) [46] (version 2020.6). The normalized taxon function bacterial contributions were annotated with the Kyoto Encyclopedia of Genes and Genomes (KEGG); then, the normalized taxon contribution for each function was summed across the samples of the same study group to obtain the total contribution for each bacterial function on the ecosystem.

4.7. Statistical Analysis

The Permutational multivariate analysis of variance (PERMANOVA, 999 permutations) was used to test the difference among groups of microbial beta diversity. Categorical variables are presented as counts and percentages, and continuous variables as median, minimum, and maximum values. For group comparisons, the Shapiro–Wilk’s test or the Kolmogorov–Smirnov Test of Normality was used to test data for normality assumptions. Fisher’s exact test was used to analyze categorical variables, the Mann–Whitney U test was used on non-normally distributed continuous data, and the T-Test was performed on normally distributed continuous data.

Author Contributions

Conceptualization, E.V., A.G., F.M. and A.C.; Data curation, E.V.; Formal analysis, E.V.; Investigation, E.V., A.G., F.M. and A.C.; Methodology, E.V., B.S., A.P., A.V., A.M., L.D.R. and M.S.; Project administration, A.C.; Resources, F.M. and A.C.; Software, E.V. and M.S.; Supervision, A.C.; Validation, E.V., M.S., F.M. and A.C.; Visualization, E.V.; Writing—original draft, E.V.; Writing—review and editing, E.V., F.M. and A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Ethical Board (IEB) with approval number n. 225/23 (Protocol n. SDSM-2023-01.1).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original data presented in the study are openly available in Sequence Read Archive (SRA) with the PRJNA1195896 project number.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CSTCommunity State Type
VBDVestibulodynia
PCoAPrincipal coordinate analysis
LDA LEfSeLinear discriminant analysis effect size
NGSNext-generation sequencing
DNADeoxyribonucleic Acid
RNARibonucleic Acid
L-LDHL-lactate dehydrogenase
PCRPolymerase Chain Reaction
FRDAFumarate reductase flavoprotein subunit

Appendix A

Women 05 00022 g0a1
Figure A1. (a) Violin plots with box-and-whisker plots showing the comparison of alpha diversity measures between patients with vestibulodynia (N = 30 in vaginal samples, N = 29 in fecal samples, in blue) and control subjects (N = 27 in vaginal samples, N = 26 in fecal samples, in lime green). Observed = observed species; Phylogenetic_Diversity = Phylogenetic Diversity Whole Tree; Shannon = Shannon–Wiener index; InvSimpson = Inverse Simpson’s index. The median, first, third quartile, and outliers are shown; (b) principal coordinate analysis (PCoA) on unweighted and weighted UniFrac distance metric calculated on patients with vestibulodynia (N = 30 in vaginal samples, N = 29 in fecal samples, blue dots) and healthy controls (N = 27 in vaginal samples, N = 26 in fecal samples, lime green dots). Each sample is represented by a dot. Vaginal microbiota: the horizontal axes (Axis 2 and Axis 1, respectively) explained 12.6% and 74.4% of the variation in the unweighted and weighted UniFrac PCoA, respectively, while the vertical axes (Axis 3 and Axis 2, respectively) explained 6.5% and 13.9% of the variation observed on the unweighted and weighted UniFrac PCoA, respectively. Fecal microbiota: the horizontal axes (Axis 2 and Axis 1, respectively) explained 8.6% and 30.3% of the variation in the unweighted and weighted UniFrac PCoA, respectively. The vertical axes (Axis 3 and Axis 2, respectively) explained 4.4% and 15.5% of the variation observed on the unweighted and weighted UniFrac PCoA, respectively. Dashed blue ellipses for vestibulodynia patient data, or lime green ellipses for healthy control data, were calculated on the cluster of the sample data using the function ‘stat_ellipse’ and assuming a multivariate t-distribution; PERMANOVA on weighted and unweighted UniFrac of both ecosystems: Pr(>F) = n. s. (not significant).
Figure A1. (a) Violin plots with box-and-whisker plots showing the comparison of alpha diversity measures between patients with vestibulodynia (N = 30 in vaginal samples, N = 29 in fecal samples, in blue) and control subjects (N = 27 in vaginal samples, N = 26 in fecal samples, in lime green). Observed = observed species; Phylogenetic_Diversity = Phylogenetic Diversity Whole Tree; Shannon = Shannon–Wiener index; InvSimpson = Inverse Simpson’s index. The median, first, third quartile, and outliers are shown; (b) principal coordinate analysis (PCoA) on unweighted and weighted UniFrac distance metric calculated on patients with vestibulodynia (N = 30 in vaginal samples, N = 29 in fecal samples, blue dots) and healthy controls (N = 27 in vaginal samples, N = 26 in fecal samples, lime green dots). Each sample is represented by a dot. Vaginal microbiota: the horizontal axes (Axis 2 and Axis 1, respectively) explained 12.6% and 74.4% of the variation in the unweighted and weighted UniFrac PCoA, respectively, while the vertical axes (Axis 3 and Axis 2, respectively) explained 6.5% and 13.9% of the variation observed on the unweighted and weighted UniFrac PCoA, respectively. Fecal microbiota: the horizontal axes (Axis 2 and Axis 1, respectively) explained 8.6% and 30.3% of the variation in the unweighted and weighted UniFrac PCoA, respectively. The vertical axes (Axis 3 and Axis 2, respectively) explained 4.4% and 15.5% of the variation observed on the unweighted and weighted UniFrac PCoA, respectively. Dashed blue ellipses for vestibulodynia patient data, or lime green ellipses for healthy control data, were calculated on the cluster of the sample data using the function ‘stat_ellipse’ and assuming a multivariate t-distribution; PERMANOVA on weighted and unweighted UniFrac of both ecosystems: Pr(>F) = n. s. (not significant).
Women 05 00022 g0a1
Women 05 00022 g0a2
Figure A2. Heatmaps of the Kendall’s Tau correlations between the relative abundances of bacterial species in the vaginal (left side of the picture) or fecal (right side of the picture) microbiota. The heatmap value color legends are displayed on the right of each figure. The data of the patients with vestibulodynia (VBD) are shown on the top panel, while the data from the healthy controls are displayed on the bottom panel. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%.
Figure A2. Heatmaps of the Kendall’s Tau correlations between the relative abundances of bacterial species in the vaginal (left side of the picture) or fecal (right side of the picture) microbiota. The heatmap value color legends are displayed on the right of each figure. The data of the patients with vestibulodynia (VBD) are shown on the top panel, while the data from the healthy controls are displayed on the bottom panel. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%.
Women 05 00022 g0a2
Women 05 00022 g0a3
Figure A3. Heatmap of the Kendall’s Tau correlations between the relative abundances of bacterial species in the vagina in the patients with vestibulodynia belonging to CST I. The heatmap value color legends are displayed on the right of each figure. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%.
Figure A3. Heatmap of the Kendall’s Tau correlations between the relative abundances of bacterial species in the vagina in the patients with vestibulodynia belonging to CST I. The heatmap value color legends are displayed on the right of each figure. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%.
Women 05 00022 g0a3
Women 05 00022 g0a4
Figure A4. Heatmaps of the Kendall’s Tau correlations between the relative abundances of the bacterial species shared between the vaginal or fecal microbiota of the patients with vestibulodynia (VBD) or in the control subjects. The heatmap value color legends are displayed on the right of each figure. The data of the patients with vestibulodynia (VBD) are shown on the top panel, while the data from the healthy controls are displayed on the bottom panel. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%. The fecal taxa names have a “.fec” suffix, while the vaginal taxa names have a “.vag” suffix.
Figure A4. Heatmaps of the Kendall’s Tau correlations between the relative abundances of the bacterial species shared between the vaginal or fecal microbiota of the patients with vestibulodynia (VBD) or in the control subjects. The heatmap value color legends are displayed on the right of each figure. The data of the patients with vestibulodynia (VBD) are shown on the top panel, while the data from the healthy controls are displayed on the bottom panel. Species taxa shown were present in at least 10% of the samples and at an average RA% > 0.5%. The fecal taxa names have a “.fec” suffix, while the vaginal taxa names have a “.vag” suffix.
Women 05 00022 g0a4

References

  1. De Seta, F.; Lonnee-Hoffmann, R.; Campisciano, G.; Comar, M.; Verstraelen, H.; Vieira-Baptista, P.; Ventolini, G.; Lev-Sagie, A. The Vaginal Microbiome: III. The Vaginal Microbiome in Various Urogenital Disorders. J. Low. Genit. Tract Disease 2022, 26, 85. [Google Scholar] [CrossRef] [PubMed]
  2. Bornstein, J.; Goldstein, A.T.; Stockdale, C.K.; Bergeron, S.; Pukall, C.; Zolnoun, D.; Coady, D.; International Society for the Study of Vulvovaginal Disease (ISSVD). 2015 ISSVD, ISSWSH, and IPPS Consensus Terminology and Classification of Persistent Vulvar Pain and Vulvodynia. J. Sex. Med. 2016, 13, 607–612. [Google Scholar] [CrossRef] [PubMed]
  3. Mitchell, C.M.; Watson, L.; Mitchell, A.J.; Hyrien, O.; Bergerat, A.; Valint, D.J.; Pascale, A.; Hoffman, N.; Srinivasan, S.; Fredricks, D.N. Vaginal Microbiota and Mucosal Immune Markers in Women With Vulvovaginal Discomfort. Sex. Transm. Dis. 2020, 47, 269. [Google Scholar] [CrossRef] [PubMed]
  4. Jayaram, A.; Witkin, S.S.; Zhou, X.; Brown, C.J.; Rey, G.E.; Linhares, I.M.; Ledger, W.J.; Forney, L.J. The Bacterial Microbiome in Paired Vaginal and Vestibular Samples from Women with Vulvar Vestibulitis Syndrome. Pathog. Dis. 2014, 72, 161–166. [Google Scholar] [CrossRef]
  5. Bedford, L.; Parker, S.E.; Davis, E.; Salzman, E.; Hillier, S.L.; Foxman, B.; Harlow, B.L. Characteristics of the Vaginal Microbiome in Women with and without Clinically Confirmed Vulvodynia. Am. J. Obstet. Gynecol. 2020, 223, 406.e1–406.e16. [Google Scholar] [CrossRef]
  6. Panzarella, D.A.; Peresleni, T.; Collier, J.L.; Kocis, C.; Baker, D.A. Vestibulodynia and the Vaginal Microbiome: A Case-Control Study. J. Sex. Med. 2022, 19, 1451–1462. [Google Scholar] [CrossRef]
  7. Sacinti, K.G.; Razeghian, H.; Awad-Igbaria, Y.; Lima-Silva, J.; Palzur, E.; Vieira-Baptista, P.; Verstraelen, H.; Bornstein, J. Is Vulvodynia Associated With an Altered Vaginal Microbiota?: A Systematic Review. J. Low. Genit. Tract. Dis. 2024, 28, 64–72. [Google Scholar] [CrossRef]
  8. France, M.T.; Ma, B.; Gajer, P.; Brown, S.; Humphrys, M.S.; Holm, J.B.; Waetjen, L.E.; Brotman, R.M.; Ravel, J. VALENCIA: A Nearest Centroid Classification Method for Vaginal Microbial Communities Based on Composition. Microbiome 2020, 8, 166. [Google Scholar] [CrossRef]
  9. Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef]
  10. Somerfield, P.J.; Clarke, K.R.; Warwick, R.M. Simpson Index. In Encyclopedia of Ecology; Jørgensen, S.E., Fath, B.D., Eds.; Academic Press: Oxford, UK, 2008; pp. 3252–3255. ISBN 978-0-08-045405-4. [Google Scholar]
  11. Simpson, E.H. Measurement of Diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
  12. Lozupone, C.; Lladser, M.E.; Knights, D.; Stombaugh, J.; Knight, R. UniFrac: An Effective Distance Metric for Microbial Community Comparison. ISME J. 2011, 5, 169–172. [Google Scholar] [CrossRef]
  13. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery and Explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [PubMed]
  14. Lev-Sagie, A.; De Seta, F.; Verstraelen, H.; Ventolini, G.; Lonnee-Hoffmann, R.; Vieira-Baptista, P. The Vaginal Microbiome: II. Vaginal Dysbiotic Conditions. J. Low. Genit. Tract Dis. 2022, 26, 79. [Google Scholar] [CrossRef]
  15. Tao, Z.; Zhang, L.; Zhang, Q.; Lv, T.; Chen, R.; Wang, L.; Huang, Z.; Hu, L.; Liao, Q. The Pathogenesis Of Streptococcus Anginosus In Aerobic Vaginitis. IDR 2019, 12, 3745–3754. [Google Scholar] [CrossRef] [PubMed]
  16. Van Hellemond, J.J.; Tielens, A.G.M. Expression and Functional Properties of Fumarate Reductase. Biochem. J. 1994, 304, 321–331. [Google Scholar] [CrossRef] [PubMed]
  17. Coda, L.; Cassis, P.; Angioletti, S.; Angeloni, C.; Piloni, S.; Testa, C. Evaluation of Gut Microbiota in Patients With Vulvovestibular Syndrome. J. Clin. Med. Res. 2021, 13, 101–106. [Google Scholar] [CrossRef] [PubMed]
  18. Awad-Igbaria, Y.; Palzur, E.; Nasser, M.; Vieira-Baptista, P.; Bornstein, J. Changes in the Vaginal Microbiota of Women With Secondary Localized Provoked Vulvodynia. J. Low. Genit. Tract Dis. 2022, 26, 339. [Google Scholar] [CrossRef]
  19. Murina, F.; Caimi, C.; Di Pierro, F.; Di Francesco, S.; Cetin, I. Features of the Vaginal and Vestibular Microbioma in Patients With Vestibulodynia: A Case-Control Study. J. Low. Genit. Tract Dis. 2020, 24, 290–294. [Google Scholar] [CrossRef]
  20. Galipeau, H.J.; Caminero, A.; Turpin, W.; Bermudez-Brito, M.; Santiago, A.; Libertucci, J.; Constante, M.; Raygoza Garay, J.A.; Rueda, G.; Armstrong, S.; et al. Novel Fecal Biomarkers That Precede Clinical Diagnosis of Ulcerative Colitis. Gastroenterology 2021, 160, 1532–1545. [Google Scholar] [CrossRef]
  21. Delgado-Diaz, D.J.; Jesaveluk, B.; Hayward, J.A.; Tyssen, D.; Alisoltani, A.; Potgieter, M.; Bell, L.; Ross, E.; Iranzadeh, A.; Allali, I.; et al. Lactic Acid from Vaginal Microbiota Enhances Cervicovaginal Epithelial Barrier Integrity by Promoting Tight Junction Protein Expression. Microbiome 2022, 10, 141. [Google Scholar] [CrossRef]
  22. Lauer, E.; Kandler, O. Lactobacillus Gasseri Sp. Nov., a New Species of the Subgenus Thermobacterium. Zentralblatt Für Bakteriol.: I. Abt. Orig. C Allg. Angew. Und Okol. Mikrobiol. 1980, 1, 75–78. [Google Scholar] [CrossRef]
  23. Witkin, S.S.; Mendes-Soares, H.; Linhares, I.M.; Jayaram, A.; Ledger, W.J.; Forney, L.J. Influence of Vaginal Bacteria and D- and L-Lactic Acid Isomers on Vaginal Extracellular Matrix Metalloproteinase Inducer: Implications for Protection against Upper Genital Tract Infections. mBio 2013, 4, e00460-13. [Google Scholar] [CrossRef] [PubMed]
  24. Rahkonen, L.; Rutanen, E.-M.; Unkila-Kallio, L.; Nuutila, M.; Nieminen, P.; Sorsa, T.; Paavonen, J. Factors Affecting Matrix Metalloproteinase-8 Levels in the Vaginal and Cervical Fluids in the First and Second Trimester of Pregnancy. Hum. Reprod. 2009, 24, 2693–2702. [Google Scholar] [CrossRef]
  25. Vieira-Baptista, P.; Bornstein, J. Candidiasis, Bacterial Vaginosis, Trichomoniasis and Other Vaginal Conditions Affecting the Vulva. In Vulvar Disease: Breaking the Myths; Bornstein, J., Ed.; Springer International Publishing: Cham, Switzerland, 2019; pp. 167–205. ISBN 978-3-319-61621-6. [Google Scholar]
  26. Rashad, A.L.; Toffler, W.L.; Wolf, N.; Thornburg, K.; Kirk, E.P.; Ellis, G.; Whitehead, W.E. Vaginal PO2 in Healthy Women and in Women Infected with Trichomonas Vaginalis: Potential Implications for Metronidazole Therapy. Am. J. Obstet. Gynecol. 1992, 166, 620–624. [Google Scholar] [CrossRef]
  27. O’Hanlon, D.E.; Moench, T.R.; Cone, R.A. Vaginal pH and Microbicidal Lactic Acid When Lactobacilli Dominate the Microbiota. PLoS ONE 2013, 8, e80074. [Google Scholar] [CrossRef] [PubMed]
  28. Klindworth, A.; Pruesse, E.; Schweer, T.; Peplies, J.; Quast, C.; Horn, M.; Glöckner, F.O. Evaluation of General 16S Ribosomal RNA Gene PCR Primers for Classical and Next-Generation Sequencing-Based Diversity Studies. Nucl. Acids Res. 2013, 41, e1. [Google Scholar] [CrossRef] [PubMed]
  29. Hall, M.; Beiko, R.G. 16S rRNA Gene Analysis with QIIME2. In Microbiome Analysis: Methods and Protocols; Beiko, R.G., Hsiao, W., Parkinson, J., Eds.; Springer: New York, NY, USA, 2018; pp. 113–129. ISBN 978-1-4939-8728-3. [Google Scholar]
  30. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High Resolution Sample Inference from Illumina Amplicon Data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef]
  31. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A Versatile Open Source Tool for Metagenomics. PeerJ. 2016, 4, e2584. [Google Scholar] [CrossRef]
  32. McDonald, D.; Jiang, Y.; Balaban, M.; Cantrell, K.; Zhu, Q.; Gonzalez, A.; Morton, J.T.; Nicolaou, G.; Parks, D.H.; Karst, S.M.; et al. Greengenes2 Unifies Microbial Data in a Single Reference Tree. Nat. Biotechnol. 2024, 42, 715–718. [Google Scholar] [CrossRef]
  33. R: The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 28 April 2025).
  34. McMurdie, P.J.; Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef]
  35. Rbiom: Read/Write, Analyze, and Visualize “BIOM” Data. Available online: https://cmmr.r-universe.dev/rbiom (accessed on 29 April 2025).
  36. Wickham, H.; Chang, W.; Henry, L.; Pedersen, T.L.; Takahashi, K.; Wilke, C.; Woo, K.; Yutani, H.; Dunnington, D.; van den Brand, T.; et al. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics 2025. Available online: https://cran.r-project.org/web/packages/ggplot2/index.html (accessed on 6 June 2025).
  37. Wickham, H. RStudio Tidyverse: Easily Install and Load the “Tidyverse” 2023. Available online: https://cran.r-project.org/web/packages/tidyverse/index.html (accessed on 6 June 2025).
  38. Wickham, H.; Vaughan, D.; Girlich, M.; Ushey, K.; Software, P. PBC Tidyr: Tidy Messy Data 2024. Available online: https://cran.r-project.org/web/packages/tidyr/index.html (accessed on 6 June 2025).
  39. Paradis, E.; Blomberg, S.; Bolker, B.; Brown, J.; Claramunt, S.; Claude, J.; Cuong, H.S.; Desper, R.; Didier, G.; Durand, B.; et al. Ape: Analyses of Phylogenetics and Evolution 2024. Available online: https://cran.r-project.org/web/packages/ape/index.html (accessed on 6 June 2025).
  40. Ggplot2 Based Publication Ready Plots. Available online: https://rpkgs.datanovia.com/ggpubr/ (accessed on 29 April 2025).
  41. Wickham, H.; François, R.; Henry, L.; Müller, K.; Vaughan, D.; Software, P. PBC Dplyr: A Grammar of Data Manipulation 2023. Available online: https://cran.r-project.org/web/packages/dplyr/index.html (accessed on 6 June 2025).
  42. Davis, N.M.; Proctor, D.M.; Holmes, S.P.; Relman, D.A.; Callahan, B.J. Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data. Microbiome 2018, 6, 226. [Google Scholar] [CrossRef] [PubMed]
  43. Oksanen, J.; Simpson, G.L.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’Hara, R.B.; Solymos, P.; Stevens, M.H.H.; Szoecs, E.; et al. Vegan: Community Ecology Package 2025. Available online: https://cran.r-project.org/web/packages/vegan/index.html (accessed on 6 June 2025).
  44. Kolde, R. Pheatmap: Pretty Heatmaps 2019. Available online: https://cran.r-project.org/web/packages/pheatmap/index.html (accessed on 6 June 2025).
  45. R: The R Stats Package. Available online: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html (accessed on 29 April 2025).
  46. Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for Prediction of Metagenome Functions. Nat. Biotechnol 2020, 38, 685–688. [Google Scholar] [CrossRef] [PubMed]
Women 05 00022 g001
Figure 1. Flow diagram of patient recruiting process with screening, inclusion, and exclusion criteria. VBD = vestibulodynia.
Figure 1. Flow diagram of patient recruiting process with screening, inclusion, and exclusion criteria. VBD = vestibulodynia.
Women 05 00022 g001
Women 05 00022 g002
Figure 2. Hierarchical clustering based on Euclidean distance metric calculated on vaginal microbiota relative abundances (RA%) from patients with vestibulodynia (N = 30, in blue) and healthy subjects (N = 27, in lime green) displayed as a heatmap. The CSTs are displayed below the heatmap, while the bacterial species are listed on the right of the picture.
Figure 2. Hierarchical clustering based on Euclidean distance metric calculated on vaginal microbiota relative abundances (RA%) from patients with vestibulodynia (N = 30, in blue) and healthy subjects (N = 27, in lime green) displayed as a heatmap. The CSTs are displayed below the heatmap, while the bacterial species are listed on the right of the picture.
Women 05 00022 g002
Women 05 00022 g003
Figure 3. LDA LEfSe barplot displaying the different associations of bacterial genera between patients with vestibulodynia (blue) and healthy controls (lime green) (LDA score > 2.0) in the vaginal and fecal microbiota samples. Box-and-whisker plots with data points show the relative abundances (RA%) of the bacterial biomarkers found in the two groups. The median, first, and third quartiles are shown. Mann–Whitney U Test result of the group comparison is shown.
Figure 3. LDA LEfSe barplot displaying the different associations of bacterial genera between patients with vestibulodynia (blue) and healthy controls (lime green) (LDA score > 2.0) in the vaginal and fecal microbiota samples. Box-and-whisker plots with data points show the relative abundances (RA%) of the bacterial biomarkers found in the two groups. The median, first, and third quartiles are shown. Mann–Whitney U Test result of the group comparison is shown.
Women 05 00022 g003
Women 05 00022 g004
Figure 4. Summed normalized taxon functions in VBD patients and healthy controls fecal and vaginal microbiota: (a) This figure summarizes the top ten fecal bacterial functions in the control group (bars in green) and in the patients with VBD (bars in blue) with a focus on the most distinctive functions between patients and controls; the species contributing to K00945 and K01265 in patients or healthy subjects are shown in the last panel. (b) This figure summarizes the vaginal bacterial functions in the control group (bars in green) and in the patients with VBD (bars in blue) with a focus on the most distinctive functions between patients and controls; the species contributing to K00016 and K00244 in patients or healthy subjects are shown in the last panel at the bottom of the picture.
Figure 4. Summed normalized taxon functions in VBD patients and healthy controls fecal and vaginal microbiota: (a) This figure summarizes the top ten fecal bacterial functions in the control group (bars in green) and in the patients with VBD (bars in blue) with a focus on the most distinctive functions between patients and controls; the species contributing to K00945 and K01265 in patients or healthy subjects are shown in the last panel. (b) This figure summarizes the vaginal bacterial functions in the control group (bars in green) and in the patients with VBD (bars in blue) with a focus on the most distinctive functions between patients and controls; the species contributing to K00016 and K00244 in patients or healthy subjects are shown in the last panel at the bottom of the picture.
Women 05 00022 g004
Table 1. Overview of the anamnestic and clinical data of the two groups analyzed in the present study. NA: Not Available data; SD: standard deviation. p-value was calculated using Fisher’s exact test.
Table 1. Overview of the anamnestic and clinical data of the two groups analyzed in the present study. NA: Not Available data; SD: standard deviation. p-value was calculated using Fisher’s exact test.
Anamnestic and Clinical DataControl Group (N = 27)Patients with VBD (N = 30)p-Value
Age (average years ± SD)31.2 ± 5.029.4 ± 5.5
BMI (average ± SD)20.5 ± 1.519.8 ± 1.9
Recurring vaginitis013<0.01
Contraceptive use2120
Disease duration:
3–12 months
24–48 months
60–120 months
NA
NA
NA
5
13
2
Table 2. Clinical characteristics and frequency of Community State Types (CSTs) in vestibulodynia patients and healthy controls. N, % = number of subjects and percentage above the total subjects of the group. Fisher’s exact test was used on categorical data; the Mann–Whitney U two-tailed test was used for non-normally distributed continuous data; the two-tailed T-Test was used for normally distributed continuous data; data distribution was estimated with the Kolmogorov–Smirnov Test of Normality; p-value was calculated using the Fisher’s exact test.
Table 2. Clinical characteristics and frequency of Community State Types (CSTs) in vestibulodynia patients and healthy controls. N, % = number of subjects and percentage above the total subjects of the group. Fisher’s exact test was used on categorical data; the Mann–Whitney U two-tailed test was used for non-normally distributed continuous data; the two-tailed T-Test was used for normally distributed continuous data; data distribution was estimated with the Kolmogorov–Smirnov Test of Normality; p-value was calculated using the Fisher’s exact test.
CSTsControl Group (N, %)Patients with VBD (N, %)p-Value
CST I12 (44.4)17 (56.7)0.4309
CST II4 (14.8)2 (6.7)0.4077
CST III5 (18.5)9 (30.0)0.3686
CST IV-B1 (3.7)0 (0.0)0.4737
CST IV-C32 (7.4)1 (3.3)0.5986
CST johnsonii-dominated0 (0.0)1 (3.3)1.0000
CST V3 (11.1)0 (0.0)0.1000
CST I + CST III17 (63.0)26 (86.7)0.0631
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Viciani, E.; Santacroce, B.; Padella, A.; Velichevskaya, A.; Marcante, A.; Di Rito, L.; Soverini, M.; Graziottin, A.; Murina, F.; Castagnetti, A. Functional and Compositional Analysis of the Fecal and Vaginal Microbiota in Vestibulodynia: An Explorative Case–Control Study. Women 2025, 5, 22. https://doi.org/10.3390/women5030022

AMA Style

Viciani E, Santacroce B, Padella A, Velichevskaya A, Marcante A, Di Rito L, Soverini M, Graziottin A, Murina F, Castagnetti A. Functional and Compositional Analysis of the Fecal and Vaginal Microbiota in Vestibulodynia: An Explorative Case–Control Study. Women. 2025; 5(3):22. https://doi.org/10.3390/women5030022

Chicago/Turabian Style

Viciani, Elisa, Barbara Santacroce, Antonella Padella, Alena Velichevskaya, Andrea Marcante, Laura Di Rito, Matteo Soverini, Alessandra Graziottin, Filippo Murina, and Andrea Castagnetti. 2025. "Functional and Compositional Analysis of the Fecal and Vaginal Microbiota in Vestibulodynia: An Explorative Case–Control Study" Women 5, no. 3: 22. https://doi.org/10.3390/women5030022

APA Style

Viciani, E., Santacroce, B., Padella, A., Velichevskaya, A., Marcante, A., Di Rito, L., Soverini, M., Graziottin, A., Murina, F., & Castagnetti, A. (2025). Functional and Compositional Analysis of the Fecal and Vaginal Microbiota in Vestibulodynia: An Explorative Case–Control Study. Women, 5(3), 22. https://doi.org/10.3390/women5030022

Article Metrics

Back to TopTop