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. 2025 Jan 4;25:1. doi: 10.1186/s12866-024-03709-3

Microbiota analysis of perimenopausal women experiencing recurrent vaginitis in conjunction with urinary tract infection

Yingying Bi 1,#, Yuezhu Wang 2,#, Wu Li 1, Yuhang Chen 1, Jinlong Qin 1,✉, Huajun Zheng 2,✉
PMCID: PMC11699749  PMID: 39755613

Abstract

Background

Recurrent vaginitis in conjunction with urinary tract infection (RV/UTI) in perimenopausal women is a common clinical condition that impacts both doctors and patients. Its pathogenesis is not completely known, but the urogenital microbiota is thought to be involved. We compared the urogenital and gut microbiotas of perimenopausal women experiencing RV/UTI with those of age-matched controls to provide a new microbiological perspective and scheme for solving clinical problems.

Results

Fifty women of perimenopausal age who were diagnosed with RV/UTI and 50 age-matched healthy controls were enrolled. The urogenital and intestinal microbiota were analyzed via 16S ribosomal RNA gene sequencing by collecting samples from the mouth, anus, urine, cervix, and upper and lower vaginal ends. Among the microbiota of healthy perimenopausal women, the mouth had the highest richness, whereas the anus and mouth had the highest levels of diversity. Compared with those in healthy controls, in the microbiota of patients with RV/UTI, the evenness of the upper vaginal end, anus and cervix significantly increased, whereas the richness and diversity of the cervix significantly decreased. Lactobacillus accounted for 40.65% of the bacteria in the upper vaginal end and 39.85% of the bacteria in the lower vaginal end of healthy women of perimenopausal age, and there were no significant differences in Lactobacillus abundance among the patients with RV/UTI. The relative abundances of 54 genera and 97 species were significantly different between patients and healthy individuals, particularly in the cervix and urine. A total of 147 predicted pathways were significantly different between patients and healthy controls, with the microbiota of the anus exhibiting the greatest number of functional changes, followed by the urine microbiota. A random forest model composed of 16 genera in the lower vaginal end had the highest discriminatory power (AUC 81.48%) to predict RV/UTI.

Conclusions

Our study provides insight into the nature of the urogenital and intestinal microbiota in perimenopausal women, and reveals significant changes in the microbiota in patients with RV/UTI. This information will help characterize the relationship between the urogenital microbiota and RV/UTI, potentially aiding in the development of diagnostic and therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-024-03709-3.

Keywords: Recurrent vaginitis, Urinary tract infection, Urogenital microbiota, 16S rRNA gene sequencing

Background

Perimenopause is a special physiological period that generally occurs between 45 and 55 years of age. Due to the decrease in estrogen levels, vaginal mucosa atrophy, and glycogen reduction in epithelial cells, acidophilic Lactobacillus is no longer the dominant bacteria in perimenopausal women, and the vaginal microbiota is easily disrupted, resulting in vaginitis. Urinary tract infection (UTI) is a common bacterial infection in women, with a high incidence in perimenopausal women and a low threshold for recurrence [1].

In recent years, with the development of modern gene sequencing technology, the microbiota has received much attention. The microbiota is a key factor affecting different systems, such as immune function, gastrointestinal diseases, urogenital tract diseases and metabolic disorders [2, 3]. The female reproductive tract has a specific microbial composition that plays a crucial role in maintaining women’s health [4, 5]. Many studies have shown that the vaginal microbiota changes in patients with bacterial vaginitis (BV), the most prevalent vaginal infection worldwide [6, 7]. The interactions of vaginal microbes with Gardnerella species, a bacterium highly detectable in BV, might play a pivotal role in the shift from health to BV [7]. Indeed Joana Castro et al. revealed that some uropathogens, such as Escherichia coli and Enterococcus faecalis, can incorporate and enhance Gardnerella vaginalis biofilms [8]. Additionally, the urinary microbiota is thought to be associated with recurrent UTIs in menopausal women.

Perimenopausal female vaginitis has a high incidence and recurrence rates and is characterized by repeated attacks and difficult treatment [9, 10]. Clinically, some perimenopausal women suffer from recurrent vaginitis in conjunction with urinary tract infection (RV/UTI), and they have both vaginitis symptoms and urinary tract irritation symptoms, which seriously affect their physical and mental health and quality of life. Previous studies have provided limited information on the relationships between vaginitis and the vaginal microbiota, between vaginal tract infections and the urinary microbiota. However, these studies ignored a particular physiological period—perimenopause. We concentrated on patients who experienced RV/UTI during perimenopause, aiming to comprehensively study the microbiota status of these patients through 16S rRNA gene sequencing. Our primary goal was to analyze the variances and relationships of the microbiota across various regions of perimenopausal women and to identify microbial taxa that are significantly different between the case and control groups, thereby providing data for the development of novel preventative and therapeutic approaches based on the microbiota.

Materials and methods

Study design and sample collection

In this study, samples were collected from 50 patients admitted to the Department of Obstetrics and Gynecology of Shanghai Fourth People’s Hospital from August 2022 to August 2023, and 50 healthy women were collected from the physical examination center as controls. The enrolled patients were 45 to 55 years old and met the following inclusion criteria: (1) were perimenopausal, (2) patients were diagnosed as BV by Amsel criteria and had obvious symptoms (abnormal vaginal discharge, odor, itching or burning sensation), patients visited the hospital for vaginitis symptoms more than 2 times within six months or 3 times within twelve months, and (3) simultaneously with symptomatic UTI (frequent urination, urgent urination, painful urination) or physical signs. The exclusion criteria were (1) use of antibiotics, vaginal suppositories or hormonal products within one month prior to the hospital visit; (2) systemic disease (diabetes, gastrointestinal disease, malignancy) or history of total hysterectomy or vaginal surgery and urinary tract surgery; and (3) use of oral prebiotics, probiotics or any other similar drugs.

Healthy controls were matched with patients for age and body mass index (BMI). A total of 50 perimenopausal patients with RV/UTI and 50 healthy controls were included in the study. To understand the composition characteristics of the vaginal microbiota, urinary microbiota and intestinal microbiota of patients and explore the potential correlations between them, swabs were collected from the mouth, anus, urine, cervix, and upper and lower vaginal ends.

All enrolled populations were sampled prior to receiving medication, ruling out possible effects of drug use on the microbiota. All of the women underwent catheter sampling, which provided confidence in the characteristics of the urinary microbiota.The demographic information and clinical characteristics of the enrolled subjects are listed in Table 1.

Table 1.

Clinical characteristics of participants in this study

Demographics Case
(n = 50)		 Con
(n = 50)		 p-
Value
Age/years 48.5(45–55) 49(45–55) 0.167
BMI/(kg/m2) 22.85(19.08–28.98) 22.84(18.20-26.73) 0.788
E2/(pmol/L) 115.9(43.6–366) 102.45(21.6-356.7) 0.983
FSH/(mIU/mL) 24.21(3.04–83.34) 28.22(2.88-112.82) 0.476
LH/(mIU/mL) 12.17(0.83–58.04) 14.46(2.04–55.1) 0.595
Number of pregnancies 3(0–5) 3(1–5) 0.891
Number of deliveries 1(0–2) 1(1–2) 0.687
Candida positive/(%) 0 0 -
Trichomonad positive/(%) 0 0 -
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Genomic DNA extraction, PCR amplification and 16S rRNA gene sequencing

Bacterial genomic DNA was extracted from each sample with a MagicPure® Buccal Swab Genomic DNA Kit (TransGen) following the default operation process of the protocol, which is suitable for isolating genomic DNA from buccal swabs (cotton swabs or nylon flocking swabs). The V3-4 region of the 16S rRNA gene was amplified with the primers 338 F and 806R [11] via TransStart Fastpfu DNA Polymerase (TransGen). The amplicons were purified with an AxyPrep DNA Gel Extraction Kit (Axygen) and pooled equivalent after assessment by spectrophotometry (QuantiFluor-ST, Promega). Sequencing of 16S rRNA gene amplicons was performed on an Illumina NextSeq2000 instrument with 2 × 300 cycles.

Bioinformatics and statistical analysis

To analyze the microbiota data, raw paired FASTQ files were treated using QIIME 2, and amplicon sequence variants (ASVs) were generated using the DADA2 plugin with default parameters [12]. The minimum sample size was the criterion for data normalization. Community richness, evenness and diversity analyses (Shannon, Simpsonenven, ACE, Chao and Good’s coverage) were performed using Mothur (version 1.47.0) [13]. Student’s t-test (with 95% confidence intervals) was used to determine whether the means of the evaluation indices were significantly different, and a p value < 0.05 was considered to indicate statistical significance. Taxonomy was assigned using the RDP classifier (version 2.14, August 2023) [14] with the default parameter (80% threshold) based on the Ribosomal Database Project [15], which incorporates the updated taxonomy information derived from publicly available scientific literature and public sequence repositories, and GenBank is one of primarily source. The species was identified using BLASTN with the SILVA database (version 138.2) and the HOMD database (version 15.23), and the best hit was chosen with an identity score > 97% and an alignment score > 97%. LEfSe [16] used the Kruskal‒Wallis test to detect differentially abundant taxa (p < 0.05) between two groups and estimate the linear discriminant analysis effect size (LDA score > 2). The analysis of similarities (ANOSIM) method was used to determine differences between microbial communities based on the jclass distance matrix, which was subsequently analyzed by principal coordinate analysis (PCoA). PICRUSt2 [17] was used to predict microbial functions based on MetaCyc pathway annotation [18]. The differences in microbial functions were determined using STAMP with default parameters [19]. A nonparametric Spearman rank correlation algorithm was used to calculate the coefficient relationships between species, genera and potential functions using the R package (p < 0.001), and the parameters were set as a coefficient > 0.35 or -0.35, which was considered to represent modest to high correlations.

The community state types (CSTs) of vaginal microbiota were determined referring Ravel et al. [20], with four CSTs separately dominated by Lactobacillus crispatus (CST-I), Lactobacillus gasseri (CST-II), Lactobacillus iners (CST-III), and Lactobacillus jensenii (CST-V), whereas CST-IV has low proportion of Lactobacillus.

Prediction model construction

A random forest (RF) model was used to identify specific microbial markers that distinguished ASDs from NTs with the “randomForest” and “e1071” packages in R 4.2.2. All the samples were divided into an 80% training set and a 20% validation set. An internal tenfold cross-validation method was used for hyperparameter selection in the training set, and model evaluation was performed on the validation set. The “pROC” and “caret” packages were used to calculate the area under the curve (AUC) and plot the receiver operating characteristic (ROC) curves to visualize the diagnostic capability. An independent external testing cohort, comprising vaginal microbiota of 80 aerobic vaginitis patients and 160 healthy individuals in China, was selected to assess the generalization ability of the prediction model. The raw data was downloaded from NCBI (Accession: PRJNA511717; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA511717/).

Results

Characteristics of the microbiota of healthy women during perimenopause

A total of 287 samples were collected from six sites, including the mouth (50), anus (50), urine (41), cervix (48), upper (49) and lower (49) vaginal ends, of healthy women. The bacterial populations at different sites in the healthy controls presented variable richness, evenness and diversity (Fig. 1). The bacterial richness at the mouth (average Chao 251.8) was significantly greater than those at the anus (average Chao 210.6), in the urine (average Chao 148.7) and at the other three sites (average Chao 66.0-83.2). However, for evenness, as measured by the Simpsoneven calculator, the mouth and urine samples had lower evenness than did the other sites (p < 0.05). The levels of bacterial diversity at the anus and mouth were greater than those at the other sites according to the Shannon index (p < 0.05, average 3.40). The levels of bacterial diversity at the cervix and upper vaginal end were lower than that at the other sites (p < 0.05, average 1.49). The phylogenetic tree revealed that the microbiota of the cervix and upper vagina were highly similar (ANOSIM, R=-0.011), and that the microbiota of the mouth was more divergent from those of the other sites (average R = 0.890) (Fig. 2).

Fig. 1.

Fig. 1

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Box and whisker plots for microbial diversity variability. A and B show the richness indices (ASVs and Chao indices), C shows the evenness index (Simpsoneven index), and D shows the diversity index (Shannon index) of the bacterial communities in recurrent vaginitis patients with urinary tract infection (Case, n = 50) and healthy controls (Con, n = 50), respectively

Fig. 2.

Fig. 2

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Phylogenetic tree of the microbiota at six sites. The R value is the average value of ANOSIM, and blue and red font indicate healthy controls and patients, respectively

Microbiota composition analysis revealed a total of 41 phyla, 82 classes, 131 orders, 285 families, 792 genera and 1,889 species in the samples from healthy women. At the phylum level, Bacillota (Firmicutes, 58.46%), Actinomycetota (Actinobacteria, 14.14%), Bacteroidota (Bacteroidetes, 12.79%) and Pseudomonadota (Proteobacteria, 10.24%) were the dominant phyla in the normal samples. Among the 792 identified genera, 19 were defined as major genera (with a relative abundance > 3% at least one site), including Lactobacillus, Streptococcus, Gardnerella, Prevotella, Escherichia/Shigella, Finegoldia, and Anaerococcus (Supplementary Table S1). There were significant differences in the microbiota compositions among the six sites (Fig. 3). The most abundant genera were Finegoldia (average 13.18%) at the anus, Streptococcus (average 38.69%) at the mouth, Lactobacillus at the cervix (average 39.84%), and Lactobacillus in the urine (average 32.31%) and at the upper vaginal end (average 40.65%) and lower vaginal end (average 39.85%). A total of 29 species were identified as major species (Supplementary Table S2, relative abundance > 2% at least one site); Streptococcus oralis (average 22.01%) was the most abundant species in the mouth, and Finegoldia magna (average 13.16%) was the most abundant species in the anus; and Lactobacillus iners (9.14-20.24%) and Lactobacillus crispatus (16.04-17.71%) were the two most abundant species in the four urogenital sites.

Fig. 3.

Fig. 3

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Microbiota composition of six sites at the phylum (A) and genus (B) levels. A. The five abundance phyla in six sites; B. 25 kinds of abundance genera in six sites

Depending on the proportion of Lactobacillus spp in vagina, 16.3% of healthy perimenopausal women were classified to CST-I, 20.4% to CST-III, and 59.2% to CST-IV.

Characteristics of the microbiota of women who experienced RV/UTI

A total of 287 samples were collected from six sites, including the mouth (48), anus (49), urine (46), cervix (46), upper (49) and lower (49) vaginal ends of patients with RV/UTI. The alpha diversity and phylogenetic tree of patient samples were similar to those of normal samples at the same site (Figs. 1 and 2).

Microbiota composition analysis revealed a total of 35 phyla, 78 classes, 134 orders, 289 families, 829 genera and 2,067 species in the samples of patients with RV/UTI. The most abundant features (phylum, genus and species) of the patient samples were similar to those of the normal samples. At the phylum level, Bacillota (Firmicutes, 59.23%), Actinomycetota (Actinobacteria, 18.63%), Bacteroidota (Bacteroidetes, 9.61%) and Pseudomonadota (Proteobacteria, 8.96%) were the dominant phyla in the patient samples. Among the 829 identified genera, 22 genera were defined as major genera (those with a relative abundance > 3% at least one site), including Lactobacillus, Streptococcus, Gardnerella, Corynebacterium and Bifidobacterium (Fig. 3, Supplementary Table S1). The most abundant genera were Finegoldia (average 9.79%) at the anus; Streptococcus (average 44.01%) at the mouth; and Lactobacillus at the cervix (average 44.42%), in the urine (average 30.90%), upper vaginal end (average 44.26%) and lower vaginal end (average 42.29%).A total of 29 species were identified as major species in the patient samples (Supplementary Table S2, relative abundance > 2% at least one site); S. oralis (average 25.94%) was the most abundant species in the mouth, and F. magna (average 9.78%) was the most abundant species in the anus; L. iners (15.3-25.36%) and L. crispatus (12.35-16.78%) were the two most abundant species in the four urogenital sites. According to the proportion of Lactobacillus spp in vagina, 12.2% of patients were classified to CST-I, 28.6% to CST-III, and 57.1% to CST-IV.

Comparison of bacterial composition between patients with RV/UTI and healthy women

Compared with those of healthy controls, the levels of evenness of the upper vaginal end (p = 0.036), anus (p = 0.014) and cervix (p = 0.014) significantly increased in the microbiota of patient samples. In the patient samples, the levels of microbiota richness of the anus (p = 0.003) and cervix (p = 0.005) significantly decreased, and only the diversity of the cervix microbiota (p = 0.018) significantly decreased.

PCoA revealed significant differences (ANOSIM) in the microbiota structures at four sites, namely, the anus (p = 0.037), cervix (p = 0.019), urine (p = 0.002) and lower vaginal end (p = 0.037) (Fig. 4).

Fig. 4.

Fig. 4

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PCoA diagram of each site showing the overall microbial diversity between patients and healthy controls. The analysis of similarities (ANOSIM) was calculated based on the jclass distance matrix, anus (p = 0.037), cervix (p = 0.019), mouth (p = 0.07), urine (p = 0.002), upper vaginal ends (p = 0.078) and low vaginal ends (p = 0.037)

A comparison of the microbiota composition at the same sites between patient and normal samples via LEfSe revealed that a total of 151 taxa, including 54 genera and 97 species, were significantly different (Supplementary Table S3). At the genus level (Fig. 5, LDA > 2, p < 0.05), the microbiota of the cervix had more differentially abundant genera (24 genera) than did those of the other sites, and the microbiota of the urine had a greater proportion (13.6%) of changes than did those of the other sites. Among the 54 genera with significant differences, 13 were major genera (> 3% at least one site), but Lactobacillus and Streptococcus were not significantly different. The major genus Finegoldia was significantly decreased (p < 0.05) in the cervices of patients with RV/UTI. The abundances of three genera, including the major genus Corynebacterium, which significantly differed at the cervix, increased at all patient sites. A total of 10 genera were decreased at all patient sites, including four major genera, Anaerococcus, Hoylesella, Peptoniphilus and Porphyromonas, which were significantly different between the cervix and the upper vaginal end. At the anus, the abundance of the genus Escherichia/Shigella significantly increased in the patients. At the mouth, the abundance of the genus Achromobacter significantly increased in the patients.

Fig. 5.

Fig. 5

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Biomarkers of different sites at the genus level. LEfSe analysis revealed different genera between Control and Case, including 11 genera (anus), 24 genera (cervix), 11 genera (mouth), 17 genera (urine), 13 genera (upper vaginal ends) and five genera (lower vaginal ends), respectively

Among the 97 different species, 19 were major species (> 2% at least one site), but the major species S. oralis, L. iners and L. crispatus were not significantly different. A total of 15 different species were decreased at all sites in the patient samples, including two major species, Peptoniphilus porci and Prevotella timonensis, which were significantly different at the upper vaginal end. The abundance of F. magna in the cervix significantly decreased (p < 0.05) in the patient samples (Fig. 6). The abundance of the species Corynebacterium pyruviciproducens significantly increased (0.83%) at the anus in patient samples, and that of the species Dialister propionicifaciens significantly increased at both the cervix (0.20%) and the upper vaginal end (0.04%) in patient samples. The major species Streptococcus toyakuensis significantly increased (1.67%) at the mouth in patient samples, the species Nocardia coeliaca significantly increased in at the urine (0.36%) and at the mouth (0.08%) in patient samples, and the major species Anaerococcus prevotii significantly decreased (1.93%) at the lower vaginal end in patient samples. In addition, some species, such as A. prevotii and Haemophilus parainfluenzae, significantly changed at multiple sites (Supplementary Table S3). A. prevotii was decreased in the cervix and upper and lower vaginal ends of patient samples, whereas H. parainfluenzae was increased in the anus and decreased in the urine of patient samples.

Fig. 6.

Fig. 6

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Biomarkers at different sites at the species level (differences > 0.1%). The other significantly different species were listed in Supplementary Table S3. There were 31 species (anus), 30 species (cervix), 18 species (mouth), 23 species (urine), 25 species (upper vaginal ends) and 11 species (lower vaginal ends), respectively

Correlations between bacterial composition and predicted functional potential changes

We used PICRUST2 to predict the potential functions of the microbiota, and 447 pathways (a minimal set of pathways) were predicted, 147 of which were significantly different between patients and healthy controls (Supplementary Table S4). Compared with those at other sites, the microbiota of the anus presented the greatest number of functional changes (66 pathways), followed by those of the urine (48), cervix (33), upper vaginal end (17), mouth (13) and lower vaginal end (6). At different sites, the tendency of functional change was different. At the anus, most changed pathways (19) belonged to “Amino Acid Biosynthesis”. For the cervix, upper vaginal end and mouth sites, the most abundant pathways (8, 7 and 6) belonged to “Cofactor, Carrier, and Vitamin Biosynthesis”. In urine, most abundant pathways (16) belonged to “Aromatic Compound Degradation”. In the healthy controls, there was comparable functional enrichment in the cervix and the upper vaginal end, with ten pathways identified at both sites (Supplementary Table S4, listed in Fig. 7). In the cervix in patients, a singular pathway (formaldehyde oxidation I, RUMP-PWY) was notably enriched, which also represented the sole pathway enriched in the upper vaginal end. We calculated the correlations between different species and changed functions (Supplementary Table S5), and a total of 123 pathways were related to 59 species (p < 0.001). Among the 59 species, three major species, Streptococcus ilei, Achromobacter deleyi and Escherichia fergusonii, were enriched in the patients and were positively related to 30 pathways. Both S. ilei and E. fergusonii were significantly increased at the anus. There were no species related to RUMP-PWY in the cervix or upper vaginal end. There were 11 species enriched in patients (differences > 0.1%, from Fig. 6), four of which had no related pathways, including D. propionicifaciens, S. toyakuensis, Bacteroides ovatus and Bacteroides fragilis. C. pyruviciproducens (enriched in patients) was positively related to the TCA cycle IV (2-oxoglutarate decarboxylase) (P105-PWY). N. coeliaca was positively related to 17 pathways in the urine and mouth. The species E. fergusonii, Enterocloster clostridioformis and Thomasclavelia ramosa were enriched at the anus, and were positively related to six pathways (BIOTIN-BIOSYNTHESIS-PWY, FUC-RHAMCAT-PWY, PWY-6519, PWY-6629, PWY-7013 and PWY-7315). Among the 59 identified species, 13 major species were enriched in healthy controls and were related to 55 pathways. At the anus, the major species P. porci was related to 16 pathways whose expression levels changed (14 negative and two positive). At the cervix, the major species Prevotella bivia was positively related to two altered pathways. In the urine, the major species Paenarthrobacter nitroguajacolicus, Novosphingobium capsulatum and Cupriavidus campinensis were positively related to 26 pathways. In the upper vaginal tract, the major species P. timonensis was positively related to 12 pathways. At the cervix, 10 species were positively correlated with P381-PWY (adenosylcobalamin biosynthesis II) (Fig. 7A), and 10 species were increased in healthy controls. In both the cervix and upper vaginal end of healthy controls, seven out of ten enriched pathways were positively correlated with 11 species that were significantly abundant at both sites (Supplementary Table S5), three pathways were related to different species present at each site, and one pathway (PWY-6892) had no related species (Fig. 7B). In addition, the major species P. bivia, P. porci and F. magna were related to those pathways in the upper vaginal end or cervix, respectively.

Fig. 7.

Fig. 7

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The correlations of species and pathways in healthy control. (A) In cervix, the 10 species related to P381-PWY, including Acinetobacter johnsonii, Acinetobacter oryzae, F. magna, Herbaspirillum huttiense, Hoylesella buccalis, Lawsonella clevelandensis, Peptoniphilus duerdenii, P. timonensis, Pseudomonas paralactis and Stenotrophomonas geniculata. (B) In cervix and upper vaginal end, nine pathways were enriched in two sites, and correlated with 14 species. The pathways were CODH-PWY, P108-PWY, P162-PWY, PWY-5509, PWY-5676, PWY-6269, PWY-7332, RIBOSYN2-PWY, THISYN-PWY. 14 species were A. prevotii, Fenollaria massiliensis, F magna, P porci, P. bivia, P. timonensis, A. oryzae, Anaerococcus lactolyticus, Anaerococcus obesiensis, H. buccalis, L. clevelandensis, P. duerdenii, Prevotella disiens and Varibaculum cambriense

Construction of the prediction model

To test whether potential diagnostic biomarkers can be used to predict recurrent vaginitis in conjunction with urinary tract infection, we developed a prediction model using a random forest (RF) model at the genus level.

In the anus, when an RF model that included 64 genera was used, the model achieved the lowest error rate, resulting in an AUC value of 58.33%. Similarly, in the mouth, the lowest error rate was observed when the RF classification model included eight characteristic genera, yielding an AUC of 58.89%. In the upper vaginal tract, the model that included eight genera attained the lowest error rate, with an AUC of 67.86%. For the cervix, the optimal model included four characteristic genera, leading to a minimal error rate and an AUC of 70.37%. In the urine samples, the combination of 16 characteristic genera in the RF model resulted in the lowest error rate and an AUC of 79.19%. Among these samples, the lower vaginal end exhibited the highest discriminatory power between patients and controls, with the model including 16 genera (Achromobacter, Acinetobacter, Bifidobacterium, Corynebacterium, Cupriavidus, Finegoldia, Lactobacillus, Peptoniphilus, Porphyromonas, Prevotella, Rhodococcus, Staphylococcus, Stenotrophomonas, Streptococcus, Ureaplasma, and Herbaspirillum) achieving the lowest error rate and a remarkable AUC of 81.48% (Fig. 8). To evaluate the generalization ability of the prediction model at the lower vaginal end, an external test dataset comprising vaginal microbiota from 80 patients with vaginitis and 160 healthy controls in China was utilized. The analysis showed an AUC value of 58.94%.

Fig. 8.

Fig. 8

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ROC curves for the classification of RV/UTI and healthy controls using RF based on genus

Discussion

The vaginal microbiota undergoes continuous changes throughout a woman’s life, significantly influencing her quality of life from infancy to post-menopausal stages [21]. Lactobacillus species dominate vaginal bacterial communities and produce lactic acid and bacteriocins that inhibit dysbiosis-associated microbes and work to maintain homeostasis and reduce the risk of various adverse health outcomes [22–24]. Notable shifts occur in both vaginal and urinary microbiota during the transition from premenopausal to postmenopausal stages [25]. In premenopausal women, Lactobacillus spp. dominate the vaginal microbiota, comprising 83% of the community; however, their prevalence decreases significantly to 54% in postmenopausal women [26]. Similarly, the urinary microbiota also changes with menopause, as postmenopausal women exhibit significantly higher alpha diversity compared to premenopausal women. The relative abundance of Lactobacillus in the urinary microbiota drops from 77.8% in premenopausal women to 42.0% in postmenopausal women, accompanied by an increased presence of genera such as Gardnerella and Prevotella [27]. In this study, we observed that in healthy perimenopausal women, the relative abundance of Lactobacillus ranged from 39.85 to 40.65% in the vaginal microbiota and was 32.31% in the urinary microbiota. In contrast, women who experienced RV/UTI showed a relative abundance of Lactobacillus ranging from 42.29 to 44.26% in the vaginal microbiota and 30.90% in the urinary microbiota.

Women lacking vaginal lactobacilli were found to be at increased risk for BV and UTI [28]. BV is an inflammation of the vaginal mucosa characterized by abnormal vaginal discharge, odor, itching or burning sensations. UTI is a common infectious disease in clinical practice, and its symptoms include bladder irritation, such as frequent, urgent, and painful urination. Previous studies have shown some relevance between BV and UTI, as some uropathogens can participate in biofilm formation in BV [8]. In our study, patients had symptoms of both BV and UTI simultaneously, and this coinfection had a negative impact on women’s quality of life, with some women expressing anxiety, shame, and hygiene concerns, particularly those who experienced recurrent symptoms.

The structure of the vaginal microbiota is influenced by gonadal hormones, with significant differences revealed between the menstrual and follicular stages of the menstrual cycle [29]. However, the level of estrogen increases and decreases unevenly during perimenopause, making it difficult to collect samples with a uniform level of estrogen. In Harrisham Kaur’s study, relative stable alpha diversity of the vaginal microbiota was shown from premenopause to perimenopause [29]; therefore, we collected samples at the subjects’ baseline visit.

In previous studies, vaginal microbiota sampling was usually performed at the site of the posterior fornix of the vaginal wall. In our study, we collected more detailed samples from the cervix and upper and lower vaginal ends. The phylogenetic tree revealed that the microbiotas of the cervix and upper vaginal end were highly similar, but they were different from those of the lower vaginal end. This difference was also evident in the lower levels of bacterial diversity found at the cervix and upper vaginal end compared to the lower vaginal end. Anatomically, the cervix is connected to the upper end of the vagina, suggesting that a similar environment might contribute to the unique microbiota that differs from that of the lower vaginal end.

There is evidence suggesting that microbial species may translocate from the gut to the vagina [30, 31]. As the oral cavity is the uppermost part of the digestive tract, we collected samples from the oral cavity. But analysis results showed that there is no significant difference of oral microbiota between patients and healthy controls. For anus microbiota, we found the evenness significantly increased and the richness significantly decreased in patients. The PCoA diagrams revealed significant differences in the microbiota composition at four sites, namely, the anus, urine, cervix and lower vaginal end (p < 0.05). When the microbiota composition at the same sites were compared between patient and normal samples, at the genus level, the microbiota of the cervix had more genera (24 genera), and the microbiota of the urine had a greater proportion (13.6%) of changes than those at other sites.Vaginal bacterial communities in reproductive-age Asian women are predominantly composed of Lactobacillus species, categorized into CST-I, II, III, and V; and only 19.8% of these women belong to CST-IV, a group characterized by a low abundance of Lactobacillus, higher vaginal pH (5.3 ± 0.6), and higher Nugent scores, as reported in a previous study [20]. However, in perimenopausal women from this study, CST-IV accounted for 59.2% of the healthy control group and 57.1% of patients with RV/UTI. The substantial decrease in Lactobacillus promotes the proliferation of pathogenic bacteria in the vaginal environment, elevating the risk of vaginitis. This condition may subsequently affect the urethra, as previous studies have shown that vaginal bacteria can contribute to recurrent UTIs [32].

Some gynecological diseases are characterized by the presence of potential pathogenic microbes, whereas others are characterized by the depletion of health-associated bacteria. To date, information on the urinary and genital microbiota of healthy females is available mostly at the genus level. Our study revealed significant changes in the relative abundance of 13 major genera. The abundance of the major genus Finegoldia significantly decreased (p < 0.05) in the cervix, whereas that of Corynebacterium significantly increased. The abundances of the genera Anaerococcus, Hoylesella, Peptoniphilus and Porphyromonas were significantly decreased in the cervix and upper vaginal end. The abundance of Escherichia/Shigella significantly increased in the anus.

Among the 97 different species, H. parainfluenzae increased at the anus site (0.05% in the control group vs. 0.09% in the patient group) but decreased at the urine site (0.05% in the control group vs. 0.01% in the patient group). H. parainfluenzae is a gram-negative rod that causes several severe infections, such as respiratory tract infections, septic arthritis, pseudomembranous colitis, and endocarditis [33–36]. F. magna is a gram-positive anaerobic commensal bacterium and an important opportunistic pathogen [37]. The abundances of F. magna and A. prevotii are increased after probiotic supplementation in BV-positive women [38], and A. prevotii is also enriched in the absence of active recurrent urinary tract infection [39]. The abundances of F. magna and V. cambriense are lower in highly inflamed samples from patients with BV than in those from healthy controls [40]. Another report revealed that V. cambriense is a causative agent of abscess formation in soft tissues [41]. In our study, F. magna had a greater abundance in the anus samples from healthy controls and was significantly decreased (p < 0.05) in the cervix samples in the patient groups, and V. cambriense and A. prevotii were decreased in the cervix and upper vaginal end in patient samples. In the cervix and upper vaginal end, our analysis revealed that the most distinct pathways were significantly enriched in healthy controls (Supplementary Table S4), suggesting that these enriched pathways play crucial roles in maintaining the health of both sites. Certain species, including F. magna, A. prevotii, and V. cambriense, presented positive correlations with these enriched pathways (Fig. 7B) at both locations, indicating that their presence and abundance contribute to the preservation of cervical and upper vaginal health.

S. ilei is present in most human populations and at various body sites but is especially abundant in the oral cavity; it is also a pathogen that is ubiquitous in the human microbiota [42]. In our study, the relative abundance of S. ilei showed no significant difference in oral samples between patients and healthy controls but was significantly higher in anal samples from the patient group and was positively related to 20 altered pathways (Supplementary Table S5). S. ilei might be transported from the mouth, and its high abundance might be related to pathogenicity. D. propionicifaciens is known as a BV-associated bacterium, and a higher abundance of D. propionicifaciens is associated with a greater risk of vaginal yeast detection [43].

In our study, four species, C. pyruviciproducens (anus), D. propionicifaciens (cervix and upper vaginal end), S. toyakuensis (mouth) and N. coeliaca (mouth and urine), which were strongly increased in patient samples, might be potential biomarkers for the detection of RV/UTI.

A machine learning (ML) model can utilize microbiota data to predict disease presence [44], with the Random Forest (RF) model being one of the most commonly employed ML models. Ideally, pre-disease samples, rather than active disease samples, are the preferred choice for developing predictive models. However, active disease samples can still offer valuable insights when compared to those from healthy populations, as certain markers observed during active disease may also be present at subclinical stages. In this study, we developed RF prediction models using microbiota data of patients with RV/UTI to aid in the diagnosis of RV/UTI. The model exhibited the highest discriminatory power with an AUC of 81.48% at the lower vaginal end. But due to diverse population and characteristics of the input data, external validation usually produced lower AUC [45]. In this study, as we cannot obtain external microbiota data of BV/UTI from the public database, so the external validation was performed using the vaginal microbiota of patients with aerobic vaginitis (AV), which resulted in a lower AUC of 58.94%. This discrepancy may reflect the distinct vaginal microbiota profiles between patients with BV/UTI and those with aerobic vaginitis. Indeed AV is specifically marked by the dominance of aerobic or facultative anaerobic bacteria, while bacterial vaginitis is associated with anaerobic bacteria, such as Gardnerella [46, 47]. In the patients with RV/UTI, the genus Acinetobacter and Stenotrophomonas were significantly reduced in both urine and lower vaginal end. This also hinted the possibility by using urine microbiota to aid in the diagnosis of RV/UTI. The RF model’s high AUC of 79.19% for urine samples further supports this hypothesis.Several limitations exist in our study. First, only 50 patients and 50 matched healthy controls were enrolled. This was primarily due to our strict patient recruitment criteria and the challenge of ensuring subject compliance with multiple specimen collections. To more comprehensively capture the microbiota characteristics of perimenopausal women with RV/UTI, we plan to expand our sample size and adopt a multi-center research design to increase the breadth and depth of our data collection. Second, we did not study the changes in the microbiota after treatment. The absence of treatment outcome data makes it difficult to assess the potential prognostic value of the observed microbial changes, limiting the clinical applicability of the findings. In future studies, we will follow up on the treatment effects and microbiota changes in patients and conduct correlation analysis. Third, the shortage of microbiota data from patients with RV/UTI limited the external validation of the RF model based on the microbiota of lower vaginal end or urine. Finally, we did not measure hormone levels. Because hormone levels during perimenopause are unstable, the vaginal microbiota of different subjects might be affected. Therefore, the hormone levels need to be controlled in future studies.

Conclusions

Our study offers insight into the nature of the urogenital and intestinal microbiota in healthy perimenopausal women and the significant changes in patients with RV/UTI. Approximately 60% of perimenopausal women had their vaginal microbiota classified as CST-IV, characterized by a low abundance of Lactobacillus. This might promote the proliferation of pathogenic bacteria in the urogenital tract. Compared with healthy controls, patients had increased evenness (upper vaginal end, anus and cervix) and decreased richness (anus and cervix) and diversity (cervix) at certain sites. Significant differences in 54 genera and 97 species were detected between patients and healthy samples, particularly in the cervix and urine. A total of 147 pathways predicted by PICRUST2 were significantly different between patients and healthy controls, indicating species-related functional changes. A random forest model was developed to predict RV/UTI, with lower vaginal samples showing the highest discriminatory power (AUC 81.48%). This study provides insights into the microbiota of perimenopausal women and its associations with health and disease, potentially aiding in diagnostic and therapeutic strategy development.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (13.9KB, xlsx)
Supplementary Material 2 (15.8KB, xlsx)
Supplementary Material 3 (35.5KB, xlsx)
Supplementary Material 4 (21KB, xlsx)
Supplementary Material 5 (41.5KB, xlsx)

Acknowledgements

The authors have the greatest gratitude for all of the participants in the study. We also thank those experimenters who performed the DNA extraction and 16S rRNA gene sequencing.

Author contributions

YC and WL collected samples.YB and YW collected and analyzed the data, and wrote the manuscript.YB and YW contributed equally to the work as co-frst authors. JQ and HZ conceptualized the fgures and tables and revised the manuscript. JQ and HZ are the corresponding authors.All authors reviewed the manuscript.

Funding

This work was supported by Shanghai Hongkou District Public Health Key Support Discipline Construction Project(HKGWFC 202401) and Shanghai Hongkou District Science and Technology Commission(2022-31).

Data availability

Some of the datasets used and/or analysed during the current study are available in the supplementary information documents.The sequence data have been submitted to the GenBank Sequence Read Archive (accession number PRJNA1143599).

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Shanghai Fourth People’s Hospital (SYLL2023124). All specimens were collected after obtaining written informed consent from participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

YingyingBi and Yuezhu Wang contributed equally to this work as co-first authors.

Contributor Information

Jinlong Qin, Email: jinqinlong@yeah.net.

Huajun Zheng, Email: zhenghj@chgc.sh.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (13.9KB, xlsx)
Supplementary Material 2 (15.8KB, xlsx)
Supplementary Material 3 (35.5KB, xlsx)
Supplementary Material 4 (21KB, xlsx)
Supplementary Material 5 (41.5KB, xlsx)

Data Availability Statement

Some of the datasets used and/or analysed during the current study are available in the supplementary information documents.The sequence data have been submitted to the GenBank Sequence Read Archive (accession number PRJNA1143599).

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