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. Author manuscript; available in PMC: 2026 May 7.
Published in final edited form as: Sex Transm Dis. 2026 Apr 17;53(7):458–464. doi: 10.1097/OLQ.0000000000002330

Comparing Asymptomatic vs Symptomatic Bacterial Vaginosis: Clinical Characteristics, Vaginal Microbiota, and Lactic Acid Profiles

Xin He 1,*, Golsa M Yazdy 2,*, Carl Yeoman 3, Khalil G Ghanem 4, Daniel J Erchick 5, Alexis Taylor 4, Karen Griffin 4,±, Johanna Holm 6,7, Jacques Ravel 6,7, Rebecca M Brotman 6,8,#, Susan Tuddenham 4,#
PMCID: PMC13147338  NIHMSID: NIHMS2169768  PMID: 41996413

Abstract

Background

Irrespective of symptoms, bacterial vaginosis (BV) has been linked to increased risk of sexually transmitted infections (STI) and HIV, yet clinical guidelines recommend treatment only for symptomatic BV (sBV). We compared demographic, clinical, and vaginal microbiota factors between asymptomatic BV (aBV) and sBV.

Methods

Data from a previously conducted vaginitis cohort study were analyzed. Participants with Amsel-BV were classified as sBV if they reported vaginal symptoms and aBV if asymptomatic. Nugent scores were assigned. Cervicovaginal lavage fluid underwent 16S rRNA gene amplicon sequencing, broad-range qPCR, and lactic acid isomer quantitative assays. Wilcoxon rank-sum, Fisher’s exact, and Chi-squared tests compared factors between aBV and sBV. Taxonomic differences were evaluated using elastic net modeling and differential abundance testing.

Results

Among 70 participants with sBV and 270 with aBV, clinician-assessed abnormal discharge was more common in sBV (86%) than aBV (71%). In adjusted analyses, recent use of metronidazole or clindamycin, more sex partners, and education beyond high school were associated with increased odds for sBV, and injectable contraception with aBV. No consistent differences were observed between sBV versus aBV for other Amsel’s criteria, Nugent scores, or vaginal microbiota composition. D- and L-lactic acid levels were slightly higher in sBV than aBV.

Conclusions

Although some demographic, clinical, and behavioral factors differed between sBV and aBV, no consistent differences were found in vaginal microbiota composition; lactic acid isomer levels were low in both groups. Further research is needed to understand why some experience symptoms, to identify whether other biological factors differentiate aBV from sBV, and to evaluate their respective associations with adverse reproductive outcomes.

Keywords: Bacterial vaginosis, Vaginal Microbiome, Lactic Acid, Symptoms

SUMMARY

No consistent differences in vaginal microbiota composition were found between those with asymptomatic and symptomatic BV; lactic acid isomer levels were low in both groups.

INTRODUCTION

Bacterial vaginosis (BV) affects nearly 30% of reproductive-age North American women (1, 2) and is characterized by a vaginal microbiota with a low abundance of Lactobacillus spp. and predominance of facultative and strict anaerobic bacteria (3). While typical symptoms of BV are vaginal odor and discharge, most individuals with BV are asymptomatic (“asymptomatic BV” [aBV]-i.e. diagnosed via clinical Amsel’s criteria but do not endorse vaginal symptoms) (1, 4, 5). BV has been associated with an increased risk of acquiring various sexually transmitted infections (STI), including HIV, as well as the development of poor obstetric outcomes (e.g., preterm birth) (6–10). Yet, there is little existing data to distinguish the differential risk of adverse outcomes between women with asymptomatic BV (aBV) and symptomatic BV (sBV).

While several studies have found associations between BV (or the presence of specific vaginal BV-associated bacteria) and increased risk of incident HIV, most did not assess symptoms and none have evaluated whether those with aBV or sBV were more or less likely to acquire HIV(8, 11, 12). In one study which found associations with HIV, 86% of those with vaginal microbiomes suggestive of BV were asymptomatic; though clinical (Amsel’s) criteria were not used (8). One in-vitro study evaluating cervicovaginal mucus found high mobility of HIV virions in samples from patients with BV, regardless of symptoms, though these data were not correlated to clinical outcomes (13).

Investigators have also considered whether treating aBV might reduce risk of other STIs. A small pilot study of BV suppressive therapy in women with aBV indicated a reduction in Chlamydia trachomatis (CT) incidence (14). A secondary analysis of a clinical trial that administered monthly metronidazole to individuals with aBV showed a decrease in acquisition of STI (including CT, Neisseria gonorrhoeae [NG] and Mycoplasma genitalium) (15). These studies suggest possible benefits of treating aBV. However, data are conflicting. A 2016 prospective trial of home-based screening for aBV noted no statistically significant difference in NG/CT acquisition between those randomized to treatment versus no treatment (16). Importantly, current guidelines recommend testing and treatment only for women with sBV (17).

To date, research on aBV has been limited due to uncertainty regarding the risks associated with aBV and the relatively low efficacy of existing BV treatments. Approximately 50% of women with sBV who receive currently available therapies achieve a lasting cure (18, 19). However, novel, more effective therapies for BV are on the horizon, including intravaginal live biotherapeutics, (20–22) and concomitant male partner treatment (MPT) (23). As new and better treatments become available, it is critical to determine whether those with aBV, as well as sBV, should be treated to prevent adverse outcomes such as STI or HIV acquisition.

BV is diagnosed in clinical settings using Amsel’s criteria which requires at least three of the following four findings: a thin homogeneous vaginal discharge, a vaginal pH greater than 4.5, a positive “whiff” test upon adding KOH to vaginal secretions, and clue cells on microscopy (4, 6, 24). To complement these clinical diagnostic criteria, molecular studies have characterized the vaginal microbiota using 16S rRNA gene amplicon sequencing (8, 25–32) and shown that the vaginal microbiota of reproductive-aged participants can be classified into five distinct vaginal community state types (CSTs) and several subtypes based on bacterial composition and relative abundance. Four CSTs are dominated by Lactobacillus species (CST I, II, III, V). In contrast, most (though not all) women with Amsel-BV fall into CST IV, which is characterized by low relative abundance of Lactobacillus spp. and an increased abundance of BV-associated bacteria such as Fannyhessea, Prevotella, Gardnerella, Megasphaera, Sneathia, Eggerthella, Mobiluncus and Ca. Lachnocurva vaginae, a profile often referred to as “molecular-BV” (6). The increased risk of STI and HIV acquisition, as well as poor obstetric outcomes, observed with CST IV and BV-associated bacteria is mediated, at least in part, by decreased vaginal lactic acid levels and, thus, higher pH (33, 34). Lactic acid is produced in low amounts by vaginal epithelial cells and in high quantities by lactobacilli; epithelial cells can produce L-lactic acid, while most Lactobacillus species produce L- or D-lactic acid, or both. L. iners does not produce D-lactic acid. D-lactic acid, in particular, has been shown in vitro to inhibit the infectivity of STI pathogens, including HIV (35–37).

Studies that compare the vaginal microbiota and lactic acid isomers in those with self-reported symptoms (i.e., sBV) versus aBV are lacking, though prior research has related specific bacteria to signs (e.g., discharge and amine odor) as assessed by clinicians via Amsel’s criteria (38, 39). Determining whether the vaginal microbiota and lactic acid isomer levels vary between women with aBV and sBV will help clarify if these two factors confer a similar increased risk of adverse outcomes. If the vaginal microbiota and D-lactic acid levels are very similar or identical in those women with sBV and aBV, this would provide initial evidence that aBV is a condition deserving further attention. Our goal is to compare the vaginal microbiota, D- and L-lactic acid levels, and clinical, demographic, and behavioral characteristics between participants diagnosed with aBV or sBV.

METHODS

Parent Cohort:

This study was a secondary analysis of existing data from the National Institutes of Health Longitudinal Study of Vaginal Flora (LSVF). LSVF was a study of reproductive-aged women enrolled in Alabama who were assessed quarterly for 1 year between August 1999 and February 2002 (40–47). Inclusion criteria required participants to undergo a cervical examination; therefore, eligibility was limited to individuals with a cervix and excluded those with prior or anticipated hysterectomy. At each visit, participants underwent a pelvic examination and completed a survey on vulvovaginal symptoms, demographics, and behaviors. At enrollment, surveys asked about behaviors in the past 6 months, while at follow up visits, surveys asked about behaviors since the last interval (i.e., last study visit ~3 months ago). During the exam, the clinician inserted a speculum, either unlubricated or lubricated with water, into the vagina. If present, the quality and consistency of vaginal discharge were documented. Vaginal pH was measured, and a traditional wet mount was used to evaluate for clue cells by microscopy. The whiff test was performed according to Amsel’s criteria (4). Gram stains were performed on fixed vaginal smears obtained at each visit and categorized using the Nugent scoring method as low, intermediate, or high (0–3, 4–6, and 7–10, respectively), with scores of 7–10 indicating BV (48). Cervicovaginal lavages (CVL) were collected and stored for further testing at −80°C. Clinicians experienced in the diagnosis of vaginitis coded visits as aBV (meeting at least 3 of 4 Amsel’s criteria for BV without endorsing vaginal symptoms) or sBV (meeting at least 3 of 4 Amsel’s criteria while endorsing vaginal symptoms). All participants provided informed consent. The University of Alabama School of Medicine and the Jefferson County Department of Health institutional review boards approved the initial study, and our secondary analysis was approved by the institutional review board of Johns Hopkins (IRB00366819) and the University of Maryland Baltimore (HP-00100026).

A previous nested case-control study utilized archived CVL from a subset of LSVF participants to assess associations between the vaginal microenvironment (microbiome, metabolome, and soluble immune mediators) and incident sexually transmitted infections (STIs, including gonorrhea, chlamydia, and trichomoniasis) (49). CVLs collected from participants at the visit preceding the incident STI or from matched controls (matched on age, race, and visit number) who were also STI-free at the time of sampling were assayed. Our study analyzed the initial sample from the subset of participants in this nested case-control study who were diagnosed with aBV and sBV using Amsel’s criteria and had data on the vaginal microenvironment available (including D/L lactic acid and vaginal microbiota). We used this data to compare clinical, demographic, behavioral characteristics, and vaginal microbiota and lactic acid levels in cases of aBV versus sBV.

Vaginal Microbiota characterization:

The vaginal microbiota characterization protocols have been described in detail previously (50). Briefly, we performed amplification and sequencing of the V3-V4 region of the 16S rRNA gene, followed by bioinformatic analysis (51, 52). Sequencing was conducted using an Illumina HiSeq 2500 instrument (Illumina, Inc., San Diego, California) adapted to generate 300-base-pair paired-end reads, resulting in high-quality amplicon sequence data (52). The sequences were demultiplexed using the dual-barcode strategy, a mapping file linking barcode to samples, and split_libraries.py, a QIIME-dependent script (53). Further processing followed the DADA2 workflow for Big Data and DADA2 (v. 1.5.2) (https://benjjneb.github.io/dada2/bigdata.html) (54) and generated amplicon sequence variants (SV). After this stringent process, we retained an average of 62,354 sequences per sample.

Taxonomy was assigned to each ASV using speciateIT (version 1.0) (55). For each sample, read counts for ASVs assigned to the same taxonomic classification were combined to calculate the total abundance for that taxon. Any taxon detected in negative controls was subtracted from the corresponding taxon in the study samples to account for potential contamination. If the same taxon appeared in more than one negative control, the highest abundance observed across controls was used for subtraction. Taxa with study-wide abundance was lower than 10−6 were removed. Lastly, samples with fewer than 1,000 reads were excluded. Vaginal community state types (CST) were assigned to each sample using VALENCIA (56).

Vaginal Microbiota: Estimates of bacterial taxa absolute abundance.

The total number of 16S rRNA gene copies in each sample was measured using the TaqMan® Bact-Quant assay targeting the V3–V4 regions of the gene (57). The total number of 16S rRNA gene copies was expressed as copies per ml of CVL, representing an estimate of a sample’s bacterial load.

Lactic acid:

D- and L-lactic acid concentrations were measured using a commercially available enzymatic assay kit (R-Biopharm, Darmstadt, Germany), following the manufacturer’s protocol on undiluted cervicovaginal lavage (CVL) specimens. Each sample was run in duplicate, using 50 μL for D-lactic acid to accommodate lower expected concentrations and 25 μL for L-lactic acid. Reactions were performed in 96-well, flat-bottom, transparent plates (Costar 9017; Corning, Corning, NY), and absorbance was read at 340 nm on a SpectraMax M5 plate reader using SoftMax Pro 5.3 software (Molecular Devices, San Jose, CA). Standard curves were generated for each plate using D-lactic acid concentrations ranging from 0.0133 to 1.776 mM and L-lactic acid concentrations from 0.0266 to 3.552 mM (50 μL input volumes). Measurements falling outside the assay range were assigned to the nearest boundary value (upper or lower standard, as appropriate).

Statistical analysis:

To compare demographic, behavioral, and clinical factors between participants with sBV and aBV, Wilcoxon rank-sum tests were used for continuous variables (due to violations of the normality assumption), and Chi-square tests, Fisher’s exact tests or Freeman-Halton tests were used for categorical variables. Similar bivariate analyses were conducted to examine the differences between the sBV and aBV groups in Amsel’s criteria, Nugent scores, frequencies of vaginal CST, and D- and L-lactic acid levels. A multivariable logistic regression model was then used to identify factors associated with aBV or sBV status.

To identify individual taxa that may differ between sBV and aBV, we applied elastic-net logistic regression and the linear discriminant analysis effect size (LEfSe) method (58). In addition, partial least squares discriminant analysis (PLS-DA) was performed to visualize the separation between the sBV and aBV groups. Finally, differential abundances of taxa present in at least 10% of all sBV and aBV samples were assessed using taxon count data scaled with the “poscounts” option in estimateSizeFactors, which calculates a modified geometric mean by taking the n-th root of the product of the non-zero counts (as per the DESeq2 package)(59). A mixed effects logistic regression model was fit using the R packages limma and voom, and differential abundance was assessed using empirical Bayes moderation via eBayes. P-values were adjusted for multiple testing following the Benjamini-Hochberg procedure (60). Unadjusted log-fold changes in abundances were reported, and significance was defined as adjusted p < 0.05. Analyses were conducted using Stata v18 (Stata Corp, College Station, TX) and R. The LEfSe analysis was performed using the Metabiome Portal at George Mason University (http://mbac.gmu.edu:8080/).

RESULTS

A total of 270 participants with aBV and 70 with sBV were included in the analysis. We confirmed that none of the 270 participants with aBV reported vulvovaginal symptoms across several self-reported variables. Additionally, all individuals, whether classified as aBV or sBV, were confirmed to have met at least three of the four Amsel criteria.

Demographic, behavioral, and clinical factors in participants with sBV and aBV:

Participants included in this analysis were, on average, relatively young (mean age: 24.5 [sBV], 25.0 [aBV]) and most self-identified as Black (95.7% sBV, 93.3% aBV). A higher proportion of those participants with sBV had more than a high school education and reported having two or more sexual partners in the last 3 to 6 months compared to those with aBV (Table 1). These were principally male partners; only 2 female partners in the sBV group and 2 female partners in the aBV group were noted. Additionally, participants with aBV exhibited a lower median frequency of vaginal sex per week than those with sBV. Those with aBV were more likely to use injectable contraceptives and were less likely to have received treatment with metronidazole or clindamycin in the preceding 3–6 months (Table 1). Generally, personal care practices—including douching; use of towelettes, sprays, or vaginal suppositories; and frequency of bathing and showering—did not differ significantly between those with sBV and aBV. There were minor differences in towelette usage when categorized, but sample sizes were small, and no differences were observed when analyzed as “ever” vs. “never” (Suppl. Table 1). In multivariable modeling using logistic regression, injectable contraceptive use was associated with higher adjusted odds of aBV compared to sBV (aOR: 2.4, 95% CI: 1.05–5.32) (Suppl. Table 2). Additionally, the use of metronidazole or clindamycin in the prior interval (i.e., prior six months at enrollment, and last three months at follow-up visits), having more sexual partners in the past six months, and attaining an education level beyond high school were all associated with higher adjusted odds of sBV (Suppl. Table 2).

Table 1.

Demographic, Behavioral and Clinical Factors in Participants with sBV and aBV

sBV (n=70) aBV (n=270) P-value
Age1 24.5 [20 – 29] 25.0 [20 – 30] 0.89
Black race2 67 (95.7%) 252 (93.3%) 0.59
BMI1 29.6 [25 – 34] 29.4 [24 – 35] 0.81
Currently menstruating (Yes)2 0 (0%) 1 (0.4%) >0.99
High school education3 High school or less 47 (68.1%) 216 (80%) 0.03
More than high school 22 (31.9%) 54 (20%)
Income (monthly)3 <$500 15 (22.1%) 67 (28.3%) 0.07
$500–$800 19 (27.9%) 81 (36.3%)
$800–$3,000 30 (44.1%) 86 (36.7%)
>$3,000 4 (5.9%) 3 (1.3%)
Behaviors in the last 3–6 months*
 Number of sex partners 3 0 3 (4.3%) 44 (16.3%) 0.01
1 48 (68.6%) 184 (68.2%)
≥ 2 19 (27.1%) 42 (15.6%)
 Frequency of vaginal sex per week1 2 [1 – 3] 1 [0.3 – 3] 0.05
 Cigarette use (Yes)3 18 (25.7%) 93 (34.4%) 0.17
 Number of cigarettes/cigars per day1 0 [0 – 2] 0 [0 – 5] 0.43
 How often do you drink alcohol3 Every day 2 (2.9%) 6 (2.2%) 0.57
Several times a week 10 (14.3%) 41 (15.2%)
About once a week 5 (7.1%) 32 (11.8%)
Several times a month 3 (4.4%) 11 (4.1%)
Once a month or less 21 (30%) 55 (20.4%)
Never 29 (41.4%) 125 (46.3%)
Hormonal Contraception (current use) #:
 Any hormonal contraception use (i.e., pills, implant, injectable, morning after pill) (Yes)3,4 24 (34.8%) 133 (49.3%) 0.03
 Oral contraceptive pill use (Yes)3 13 (18.8%) 55 (20.4%) 0.78
 Implant use (Yes)2 1 (1.4%) 5 (1.9%) >0.99
 Injectable use (Yes)3 10 (14.3%) 73 (27%) 0.03
Metronidazole or clindamycin treatment in last interval or on last visit2 12 (17.1%) 8 (3%) <0.001
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1

Wilcoxon rank-sum test

2

Fisher’s exact test

3

Chi-square test

4

No participants used the morning after pill

*

Survey questions at visit 1 referred to the prior 6 months, other follow-up visits referred to 3 months prior.

Median and lower and upper quartiles are presented for values in brackets [ ].

#

Only 1 participant reported IUD use; whether this was hormonal or non-hormonal was not asked.

Amsel’s criteria and Nugent Score in Participants with sBV and aBV

When comparing the clinical (Amsel’s) criteria between the two groups, clinician-identified homogeneous discharge was reported in a slightly higher proportion of those with sBV (86% vs 71%, p=0.01); however, the other three criteria, including pH, clue cells, and the whiff test did not differ between groups. The median Nugent score was not statistically different between groups; most of those with sBV and aBV had a Nugent score of 7–10 (Supp. Table 3).

Vaginal Microbiota: Community State Types in Participants with sBV and aBV

Each participant’s vaginal microbiota was assigned to a CST (Figure 1, Table 2). CST III (n=59) was dominated by L. iners. CST III-A (n=15) represented vaginal microbiota with a higher relative abundance of L. iners, while CST III-B (n=44) included samples dominated by L. iners but at a lower relative abundance than in CST III-A (56). CST IV (n=281) comprised samples with a low relative abundance of lactobacilli. CST IV-A (n=134) had a high relative abundance of Candidatus Lachnocurva vaginae (formerly known as BVAB1) and a moderate relative abundance of G. vaginalis. In contrast, CST IV-B (n=144) had a high relative abundance of G. vaginalis and a low relative abundance of Ca. L. vaginae. Both IV-A and IV-B exhibited moderate relative abundances of Fannyhessea vaginae. Samples assigned to CST IV-C (n=3) displayed a low relative abundance of Lactobacillus spp., G. vaginalis, F. vaginae, and Ca. L. vaginae and were characterized by a diverse array of facultative and strictly anaerobic bacteria. In this sample, CST IV-C1 (n=3) was dominated by Streptococcus (56). The majority of participants were assigned to CST IV, with a few in the L. iners-dominated CST III. No samples were dominated by L. crispatus, L. jensenii, or L. gasseri. There were no significant differences between those with sBV and aBV at either the CST or sub-CST level (Table 2).

Figure 1. Heatmap of Relative Abundances of Top 25 Taxa, by aBV and sBV Status and Community State Type (CST).

Figure 1.

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The “CST” color bar indicates which community state types were present among individuals with sBV and aBV.

Below this, taxon abundance for key bacterial species is indicated.

Table 2.

The vaginal microbiota: community state type (CST) in participants with sBV and aBV

sBV aBV P-value1
CST2 III 11 (15.7%) 48 (17.8%) 0.47
IV-A 32 (45.7%) 102 (37.8%)
IV-B 26 (37.1%) 118 (43.7%)
IV-C 1 (1.4%) 2 (0.7%)
SubCST2 III-A 1 (1.4%) 14 (5.2%) 0.40
III-B 10 (14.3%) 34 (12.6%)
IV-A 32 (45.7%) 102 (37.8%)
IV-B 26 (37.1%) 118 (43.7%)
IV-C1 1 (1.4%) 2 (0.7%)
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1

Freeman-Halton test

2

CST III was dominated by L. iners, CST IV was characterized by low abundance of lactobacilli. See text for detailed description of sub-CSTs

Vaginal Microbiota: Individual bacterial taxa relative and absolute abundance in participants with sBV and aBV

We first performed a LEfSe analysis to assess differences in taxa relative abundances between the aBV and sBV groups. This analysis identified a few taxa that distinguished the two groups (Suppl. Table 4). However, several of these taxa, including L. crispatus, were present in only a small subset of samples, resulting in a limited number of non-zero observations. Additionally, the PLS-DA plot did not show clear visual separation between the groups on individual taxa (Suppl. Figure 1). On further investigations, histograms graphing relative frequency of the taxa identified by LEfSe suggested that the observed differences were primarily driven by a few outliers (Suppl Figures 2 A–G).

To further investigate potential differences, we applied two additional analytical approaches: elastic net logistic regression and limma-voom analysis. Elastic net logistic regression did not identify any taxa that significantly differentiated aBV and sBV based on taxa relative abundances. Similarly, limma-voom analysis, performed on scaled taxon counts, revealed no significant differences between sBV and aBV (Suppl. Figure 3A) or within CSTs (Suppl. Figure 3B). Finally, analyses using a 16S rRNA gene qPCR-derived estimate of absolute abundance also showed no significant difference between the two groups (Suppl. Table 5). Across all methods, no taxa consistently distinguished aBV from sBV.

D- and L-lactic acid levels

D (-) and L (+) lactic acid levels were statistically significantly higher in those with sBV than aBV, whether measured as median or as the proportion in the highest quartile. However, levels of both lactic acid isomers were quite low, as expected in the setting of BV. The ratio of D/L was similar between groups (p=0.36) (Table 3).

Table 3.

D and L isomers of lactic acid by sBV and aBV status

sBV
median [Q1-Q3]		 aBV P-value
D-lactic acid1 mM 0.06 [0.03 – 0.12] 0.03 [0.01 – 0.09] <0.01
L-lactic acid1 mM 0.20 [0.11 – 0.47] 0.16 [0.09 – 0.30] 0.02
D/L ratio1 0.31 [0.15 – 0.45] 0.24 [0.13 – 0.50] 0.36
D-lactic acid quartiles2 0.0133 – 0.0133 12 (17.1%) 90 (33.3%) <0.01
0.0135 – 0.0381 10 (14.3%) 58 (21.5%)
0.0381 – 0.0903 26 (37.1%) 59 (21.9%)
0.0910 – 0.9185 22 (31.4%) 63 (23.3%)
L-lactic acid quartiles2 0.0266 – 0.0953 13 (18.6%) 72 (26.7%) 0.01
0.0953 – 0.1671 17 (24.3%) 68 (25.2%)
0.1677 – 0.3246 12 (17.1%) 73 (27%)
0.3248 – 3.2472 28 (40%) 57 (21.1%)
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1

Wilcoxon rank-sum test

2

Chi-square test

DISCUSSION

No consistent differences were found in vaginal microbiota composition (based on 16S rRNA gene sequencing, qPCR-derived estimates of bacterial load, and Nugent scoring) between those with aBV and sBV; lactic acid isomer levels were low in both groups. There were also no consistent differences in Nugent scoring (microscopy) or by bacterial compositional profiles generated using 16S rRNA gene amplicon sequencing. Similarly, 16S rRNA gene qPCR-derived estimates of bacterial load were comparable between groups. The only differences between individuals with sBV and aBV were related to a few demographic, clinical, and behavioral factors.

One hypothesis posits that some women may mask or diminish their perception of BV symptoms by using personal hygiene products, or alternatively that demographic or behavioral factors influence symptom perception. However, we found no significant differences between aBV and sBV groups in reports of douching, showering, bathing, or application of sprays, powders, or vaginal suppositories. In multivariable analysis, women with higher levels of education were more likely to have sBV and to report greater numbers of sexual partners, though the reasons for the relationship between these factors and symptom reporting or perception are unclear. One possible explanation is that exposure to semen, which has an alkaline pH and contains amines, may transiently alter the vaginal microenvironment and contribute to odor perception. It is possible that there was a greater tendency to seek treatment in those with sBV, which might account for recent use of metronidazole or clindamycin being more frequently reported among women with sBV. Participants with aBV were more likely to be using hormonal contraception, particularly injectable contraceptives, which may point to a biological effect, but the mechanism behind this association remains unclear.

We sought to determine if there were any biologic or physiologic differences between aBV and sBV beyond patient-recognized symptoms. Clinicians more frequently reported a homogenous discharge and a greater quantity of discharge in women with sBV, suggesting a possible measurable difference. However, although vaginal odor is a common complaint among patients with BV, clinician-assessed whiff test positivity, which detects the characteristic fishy or amine odor of BV, was similarly high (>90%) in both groups, with no statistically significant difference. Likewise, there were no noted differences in clinician-assessed clue cells or vaginal pH.

D-lactic acid levels, which have been associated with protection against STIs (36, 61), were slightly higher in women with sBV compared to those with aBV. This raises the possibility that lower D-lactic acid levels in aBV may contribute to increased susceptibility to STI/HIV acquisition. However, levels of both D (-) and L (+) lactic acid were low in both groups when compared to individuals without BV in this study and in previous literature (see below) (62).

Our study has several limitations. It was conducted in a single geographic location (Birmingham, Alabama), which may limit the generalizability of our findings to other populations. The study was conducted from 1999–2002; it is possible that personal care practices may have shifted in this time though it is unlikely that the biological entity of BV has changed significantly. Finally, we did not examine lactic acid levels in individuals without BV in the parent cohort. However, levels in both aBV and sBV were low (e.g., median D-lactic acid 0.06 mM [sBV], 0.03 mM [aBV]) compared to prior reports in non-BV with L. crispatus-dominated microbiomes (e.g., median D-lactic acid 0.32 mM in one study (62) and 1.13 mM in LSVF participants with CST I profiles (49)). A strength of our study is the use of data from LSVF, which followed a cohort of women with detailed assessments of symptoms, clinical exam findings, demographics, and behaviors.

While we examined differences in clinical assessments, vaginal microbiota, and lactic acid isomer levels, future studies should investigate variations in vaginal soluble immune mediators and metabolites between aBV and sBV, as these factors may influence susceptibility to STIs or HIV and adverse obstetric outcomes. Metagenomic and metatranscriptomic analysis may also reveal more nuanced differences between sBV and aBV and shed light on functional variations in microbial activity. Further research to clarify biological differences between sBV and aBV may provide insight into symptom development and guide more effective treatment strategies.

Our findings suggest that aBV and sBV are similar based on the vaginal microbiota profiles and lactic acid isomer levels. Further research is needed to understand whether aBV may confer a comparable risk of STI/HIV acquisition and adverse obstetric outcomes. As new and more effective therapies for sBV are developed, it remains important to continue investigating aBV and to consider if women with aBV would benefit from treatment.

Supplementary Material

Supplementary Material

Funding:

Research reported in this publication was supported by NIAID under award number R21AI168984, R01-AI116799, R01-AI167629, and K23AI193080. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Since this manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH), it is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH

Disclosure of Interests:

  • ST and KGG receive royalties from UpToDate.

  • RMB, KGG, JR and ST participate in research supported by in-kind donation of STI test kits by Hologic.

  • RMB’s research is also supported in part by Hologic.

  • JR is co-founder of LUCA Biologics, a biotechnology company focusing on translating microbiome research into live biotherapeutics drugs for women’s health.

XH, GMY, CY, DJE, AT, KG, and JH declare no relevant financial or non-financial interests to disclose

REFERENCES

  • 1.Koumans EH, Sternberg M, Bruce C, McQuillan G, Kendrick J, Sutton M, et al. The prevalence of bacterial vaginosis in the United States, 2001–2004; associations with symptoms, sexual behaviors, and reproductive health. Sex TransmDis. 2007;34(11):864–9. [DOI] [PubMed] [Google Scholar]
  • 2.Peebles K, Velloza J, Balkus JE, McClelland RS, Barnabas RV. High Global Burden and Costs of Bacterial Vaginosis: A Systematic Review and Meta-Analysis. Sex Transm Dis. 2019;46(5):304–11. [DOI] [PubMed] [Google Scholar]
  • 3.Martin DH, Marrazzo JM. The Vaginal Microbiome: Current Understanding and Future Directions. J Infect Dis. 2016;214 Suppl 1:S36–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Amsel R, Totten PA, Spiegel CA, Chen KC, Eschenbach D, Holmes KK. Nonspecific vaginitis. Diagnostic criteria and microbial and epidemiologic associations. Am J Med. 1983;74(1):14–22. [DOI] [PubMed] [Google Scholar]
  • 5.Muzny CA, Schwebke JR. Asymptomatic Bacterial Vaginosis: To Treat or Not to Treat? Curr Infect Dis Rep. 2020;22(12). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.McKinnon LR, Achilles SL, Bradshaw CS, Burgener A, Crucitti T, Fredricks DN, et al. The Evolving Facets of Bacterial Vaginosis: Implications for HIV Transmission. AIDS Res Hum Retroviruses. 2019;35(3):219–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Atashili J, Poole C, Ndumbe PM, Adimora AA, Smith JS. Bacterial vaginosis and HIV acquisition: a meta-analysis of published studies. AIDS (London, England). 2008;22(12):1493–501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Gosmann C, Anahtar MN, Handley SA, Farcasanu M, Abu-Ali G, Bowman BA, et al. Lactobacillus-Deficient Cervicovaginal Bacterial Communities Are Associated with Increased HIV Acquisition in Young South African Women. Immunity. 2017;46(1):29–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hillier SL, Nugent RP, Eschenbach DA, Krohn MA, Gibbs RS, Martin DH, et al. Association between bacterial vaginosis and preterm delivery of a low-birth-weight infant. The Vaginal Infections and Prematurity Study Group. N Engl J Med. 1995;333(26):1737–42. [DOI] [PubMed] [Google Scholar]
  • 10.Tabatabaei N, Eren AM, Barreiro LB, Yotova V, Dumaine A, Allard C, et al. Vaginal microbiome in early pregnancy and subsequent risk of spontaneous preterm birth: a case-control study. BJOG. 2019;126(3):349–58. [DOI] [PubMed] [Google Scholar]
  • 11.McClelland RS, Lingappa JR, Srinivasan S, Kinuthia J, John-Stewart GC, Jaoko W, et al. Evaluation of the association between the concentrations of key vaginal bacteria and the increased risk of HIV acquisition in African women from five cohorts: a nested case-control study. Lancet Infect Dis. 2018;18(5):554–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Srinivasan S, Richardson BA, Wallis JM, Fiedler TL, Strenk SM, Hoffman NG, et al. Vaginal Bacteria and Proinflammatory Host Immune Mediators as Biomarkers of Human Immunodeficiency Virus Acquisition Risk Among African Women. J Infect Dis. 2024;230(6):1444–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Hoang T, Toler E, DeLong K, Mafunda NA, Bloom SM, Zierden HC, et al. The cervicovaginal mucus barrier to HIV-1 is diminished in bacterial vaginosis. PLoS Pathog. 2020;16(1):e1008236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Schwebke JR, Desmond R. A randomized trial of metronidazole in asymptomatic bacterial vaginosis to prevent the acquisition of sexually transmitted diseases. Am J Obstet Gynecol. 2007;196(6):517 e1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Balkus JE, Manhart LE, Lee J, Anzala O, Kimani J, Schwebke J, et al. Periodic Presumptive Treatment for Vaginal Infections May Reduce the Incidence of Sexually Transmitted Bacterial Infections. J Infect Dis. 2016;213(12):1932–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Schwebke JR, Lee JY, Lensing S, Philip SS, Wiesenfeld HC, Sena AC, et al. Home Screening for Bacterial Vaginosis to Prevent Sexually Transmitted Diseases. Clin Infect Dis. 2016;62(5):531–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Workowski KA. Centers for Disease Control and Prevention Sexually Transmitted Diseases Treatment Guidelines. Clin Infect Dis. 2015;61 Suppl 8:S759–62. [DOI] [PubMed] [Google Scholar]
  • 18.Bradshaw CS, Morton AN, Hocking J, Garland SM, Morris MB, Moss LM, et al. High recurrence rates of bacterial vaginosis over the course of 12 months after oral metronidazole therapy and factors associated with recurrence. J Infect Dis. 2006;193(11):1478–86. [DOI] [PubMed] [Google Scholar]
  • 19.Sobel JD, Ferris D, Schwebke J, Nyirjesy P, Wiesenfeld HC, Peipert J, et al. Suppressive antibacterial therapy with 0.75% metronidazole vaginal gel to prevent recurrent bacterial vaginosis. Am J Obstet Gynecol. 2006;194(5):1283–9. [DOI] [PubMed] [Google Scholar]
  • 20.Cohen CR, Wierzbicki MR, French AL, Morris S, Newmann S, Reno H, et al. Randomized Trial of Lactin-V to Prevent Recurrence of Bacterial Vaginosis. N Engl J Med. 2020;382(20):1906–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ravel J, Simmons S, Jaswa EG, Gottfried S, Greene M, Kellogg-Spadt S, et al. Impact of a multi-strain L. crispatus-based vaginal synbiotic on the vaginal microbiome: a randomized placebo-controlled trial. NPJ Biofilms Microbiomes. 2025;11(1):158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Potloane D, Symul L, Ngcapu S, Lewis L, France M, Vermeren L, et al. VIBRANT: A phase 1 randomized trial of multi-strain vaginal L. crispatus live biotherapeutic products in people with bacterial vaginosis. Cell Host Microbe. 2026. [DOI] [PubMed] [Google Scholar]
  • 23.Vodstrcil LA, Plummer EL, Fairley CK, Hocking JS, Law MG, Petoumenos K, et al. Male-Partner Treatment to Prevent Recurrence of Bacterial Vaginosis. N Engl J Med. 2025;392(10):947–57. [DOI] [PubMed] [Google Scholar]
  • 24.Sha BE, Chen HY, Wang QJ, Zariffard MR, Cohen MH, Spear GT. Utility of Amsel criteria, Nugent score, and quantitative PCR for Gardnerella vaginalis, Mycoplasma hominis, and Lactobacillus spp. for diagnosis of bacterial vaginosis in human immunodeficiency virus-infected women. J Clin Microbiol. 2005;43(9):4607–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, McCulle SL, et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4680–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ravel J, Brotman RM, Gajer P, Ma B, Nandy M, Fadrosh DW, et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome. 2013;1(1):29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Gajer P, Brotman RM, Bai G, Sakamoto J, Schutte UM, Zhong X, et al. Temporal dynamics of the human vaginal microbiota. Sci Transl Med. 2012;4(132):132ra52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Fettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH, Parikh HI, et al. The vaginal microbiome and preterm birth. Nat Med. 2019;25(6):1012–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Brooks JP, Buck GA, Chen G, Diao L, Edwards DJ, Fettweis JM, et al. Changes in vaginal community state types reflect major shifts in the microbiome. Microb Ecol Health Dis. 2017;28(1):1303265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Brotman RM, Shardell MD, Gajer P, Fadrosh D, Chang K, Silver MI, et al. Association between the vaginal microbiota, menopause status, and signs of vulvovaginal atrophy. Menopause. 2014;21(5):450–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Brotman RM, Shardell MD, Gajer P, Tracy JK, Zenilman JM, Ravel J, et al. Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection. J Infect Dis. 2014;210(11):1723–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Elovitz MA, Gajer P, Riis V, Brown AG, Humphrys MS, Holm JB, et al. Cervicovaginal microbiota and local immune response modulate the risk of spontaneous preterm delivery. Nat Commun. 2019;10(1):1305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Armstrong E, Kaul R. Beyond bacterial vaginosis: vaginal lactobacilli and HIV risk. Microbiome. 2021;9(1):239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Tuddenham S, Ravel J, Marrazzo JM. Protection and Risk: Male and Female Genital Microbiota and Sexually Transmitted Infections. J Infect Dis. 2021;223(12 Suppl 2):S222–S35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zalambani C, Rizzardi N, Marziali G, Foschi C, Morselli S, Djusse ME, et al. Role of D(-)-Lactic Acid in Prevention of Chlamydia trachomatis Infection in an In Vitro Model of HeLa Cells. Pathogens. 2023;12(7). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Edwards VL, Smith SB, McComb EJ, Tamarelle J, Ma B, Humphrys MS, et al. The Cervicovaginal Microbiota-Host Interaction Modulates Chlamydia trachomatis Infection. mBio. 2019;10(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Tyssen D, Wang YY, Hayward JA, Agius PA, DeLong K, Aldunate M, et al. Anti-HIV-1 Activity of Lactic Acid in Human Cervicovaginal Fluid. mSphere. 2018;3(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Carter KA, Balkus JE, Anzala O, Kimani J, Hoffman NG, Fiedler TL, et al. Associations Between Vaginal Bacteria and Bacterial Vaginosis Signs and Symptoms: A Comparative Study of Kenyan and American Women. Front Cell Infect Microbiol. 2022;12:801770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Srinivasan S, Hoffman NG, Morgan MT, Matsen FA, Fiedler TL, Hall RW, et al. Bacterial communities in women with bacterial vaginosis: high resolution phylogenetic analyses reveal relationships of microbiota to clinical criteria. PLoS One. 2012;7(6):e37818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Brotman RM, Klebanoff MA, Nansel TR, Andrews WW, Schwebke JR, Zhang J, et al. A longitudinal study of vaginal douching and bacterial vaginosis--a marginal structural modeling analysis. AmJEpidemiol. 2008;168(2):188–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Brotman RM, Klebanoff MA, Nansel TR, Yu KF, Andrews WW, Zhang J, et al. Bacterial vaginosis assessed by gram stain and diminished colonization resistance to incident gonococcal, chlamydial, and trichomonal genital infection. J Infect Dis. 2010;202(12):1907–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Klebanoff MA, Andrews WW, Zhang J, Brotman RM, Nansel TR, Yu KF, et al. Race of Male Sex Partners and Occurrence of Bacterial Vaginosis. Sex TransmDis. 2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Klebanoff MA, Nansel TR, Brotman RM, Zhang J, Yu KF, Schwebke JR, et al. Personal hygienic behaviors and bacterial vaginosis. Sex Transm Dis. 2010;37(2):94–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Klebanoff MA, Schwebke JR, Zhang J, Nansel TR, Yu KF, Andrews WW. Vulvovaginal symptoms in women with bacterial vaginosis. Obstetrics and gynecology. 2004;104(2):267–72. [DOI] [PubMed] [Google Scholar]
  • 45.Nansel TR, Riggs MA, Yu KF, Andrews WW, Schwebke JR, Klebanoff MA. The association of psychosocial stress and bacterial vaginosis in a longitudinal cohort. AmJObstetGynecol. 2006;194(2):381–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Neggers YH, Nansel TR, Andrews WW, Schwebke JR, Yu KF, Goldenberg RL, et al. Dietary intake of selected nutrients affects bacterial vaginosis in women. JNutr. 2007;137(9):2128–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Riggs M, Klebanoff M, Nansel T, Zhang J, Schwebke J, Andrews W. Longitudinal association between hormonal contraceptives and bacterial vaginosis in women of reproductive age. Sex TransmDis. 2007;34(12):954–9. [PubMed] [Google Scholar]
  • 48.Nugent RP, Krohn MA, Hillier SL. Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation. J Clin Microbiol. 1991;29(2):297–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Brotman RM H J, Borgogna JL, Robinson CK, Klebanoff MA, Ghanem KG, Yeoman CJ, Ravel J, Shardell MD, editor The vaginal microenvironment prior to incident STI.. The International Society for Sexually Transmitted Disease Research, 23rd Biennial Congress; 2019. July 14–17; Vancouver, Canada. [Google Scholar]
  • 50.Turpin R, Slopen N, Borgogna JC, Yeoman CJ, He X, Miller RS, et al. Perceived Stress and Molecular Bacterial Vaginosis in the National Institutes of Health Longitudinal Study of Vaginal Flora. Am J Epidemiol. 2021;190(11):2374–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome. 2014;2(1):6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, et al. Ultrahigh-Throughput Multiplexing and Sequencing of >500-Base-Pair Amplicon Regions on the Illumina HiSeq 2500 Platform. mSystems. 2019;4(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Estaki M, Jiang L, Bokulich NA, McDonald D, Gonzalez A, Kosciolek T, et al. QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data. Curr Protoc Bioinformatics. 2020;70(1):e100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Holm JB, Gajer P, Ravel J. SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota. BMC Bioinformatics. 2024;25(1):313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.France MT, Ma B, Gajer P, Brown S, Humphrys MS, Holm JB, et al. VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition. Microbiome. 2020;8(1):166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Liu CM, Aziz M, Kachur S, Hsueh PR, Huang YT, Keim P, et al. BactQuant: an enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 2012;12:56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 2018;57(1):289–300. [Google Scholar]
  • 61.Nunn KL, Wang YY, Harit D, Humphrys MS, Ma B, Cone R, et al. Enhanced Trapping of HIV-1 by Human Cervicovaginal Mucus Is Associated with Lactobacillus crispatus-Dominant Microbiota. mBio. 2015;6(5):e01084–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Witkin SS, Mendes-Soares H, Linhares IM, Jayaram A, Ledger WJ, Forney LJ. 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(4). [DOI] [PMC free article] [PubMed] [Google Scholar]

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