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The Main Disruptors of the Vaginal Microbiome: A Research Analysis

The scientific literature is missing an extensive review of vaginal microbiome disruptors. So, we took it upon ourselves to close this knowledge gap and create one.

9 minutes

60 Citations

The vaginal microbiome (VMB) is the foundation of gynecological, urogenital, and reproductive health. A growing body of research demonstrates that the vaginal microbiome is easily disturbed by day-to-day activities, but an extensive review of VMB disruptors is missing in scientific literature. So, we took it upon ourselves to close this knowledge gap and create one. 

To do so, our R&D team analyzed the highest-resolution dataset ever collected on the vaginal microbiome by Dr. Jacques Ravel, a leading VMB researcher.9 Then, we conducted a highly comprehensive literature survey of existing scientific research. The result is the following reference paper—a detailed summary of primary and secondary disruptors to the vaginal microbiome.

Introduction

The vaginal microbiome (VMB) is the foundation of gynecological, urogenital, and reproductive health. However, this ecosystem of microbes is easily disrupted and can shift regularly, leaving the vagina susceptible to imbalances and discomforts. Optimal VMBs are typically characterized by low microbial diversity and a high abundance of Lactobacillus species.1 Lactobacillus-dominated VMBs are categorized into “community state types” (CSTs) defined by their taxonomic composition: Lactobacillus crispatus-dominated (CST I), L. gasseri-dominated (CST II), L. iners-dominated (CST III), and L. jensenii-dominated (CST V).2 CST IV VMBs are defined by low levels of lactobacilli and the dominance of diverse anaerobes.

Optimal L. crispatus-dominated CST I VMBs are associated with decreased risk of bacterial vaginosis (BV), vulvovaginal candidiasis, sexually transmitted infections (STIs), as well as favorable fertility outcomes and reduced risk of adverse pregnancy events such as preterm birth.3,4 CST IV VMBs are epidemiologically associated with an increased risk of adverse genitourinary and obstetric outcomes, including spontaneous preterm birth, low birth weight and sexually transmitted infections (STIs), pelvic inflammatory disease, urinary tract infections, cervical intraepithelial neoplasia, and infertility.1,4-8

Given the extensive evidence substantiating the connection between CST I and vaginal health, it is of substantial interest to assess the impact of daily factors and common disruptions on the composition and stability of the vaginal microbiome. Two complementary approaches were taken to address this issue. First, we analyzed the most comprehensive, highest-resolution dataset yet collected on the vaginal microbiome, which is uniquely well-suited to address the day-to-day impact of disruptors on the vaginal microbiome.9 In this cohort, 130 individuals collected vaginal samples over a 70-day period while also completing a detailed questionnaire on their day-to-day habits. Second, a comprehensive literature search was conducted to ascertain expert consensus on the impact of vaginal perturbations on the vaginal microbiome.

Key Findings

Analysis of the VMB cohort indicates that 90% of women have an unstable vaginal microbiome when stability is defined as being in an optimal microbiome state (CST I, dominated by Lactobacillus crispatus) for 90% of the time or more.9,10 This definition of vaginal microbiome stability is also reinforced by the finding that CST I microbiomes are the most stable and demonstrate the lowest probability (2%) of shifting to a dysbiotic, non-optimal CST-IV configuration of all Lactobacillus-dominated CSTs (compared with 5.6% for L. gasseri-dominated CST II and L. iners-dominated CST III). This aligns with the broader finding in the field that L. crispatus-dominated microbiomes tend to be more resistant to disruptions compared to other vaginal microbiome type .10

Impacts of Disturbances on Vaginal Microbiome Stability

Based on the analysis of the VMB cohort and a deep review of the literature, several key disruptors common in daily life were identified as being associated with vaginal microbiome instability. The primary factors most strongly associated with microbiome instability included vaginal intercourse, menstruation, condom use, sex toy use, oral sex, lubricant use, use of certain prescriptions, contraceptives, cleansers, and feminine hygiene products. In addition to these daily disruptors, the literature demonstrates that the postpartum period is associated with potential shifts away from an optimal CST I microbiome. Additional factors associated with vaginal microbiome changes in literature include exercise, stress, and diet. 

Disruptors of the Vaginal Microbiome 

Primary Disruptors of the Vaginal Microbiome: 

Sex

Menstruation

Condom use

Sex Toys

Oral Sex

Gels and Lubricants

Certain Contraceptives

Certain Prescriptions

Certain Cleansers

Feminine Hygiene Products

Past Pregnancy/ Postpartum

Primary disruptors have been linked/associated with imbalances in the vaginal microbiome and can include activities that are part of day-to-day life and/or cyclical hormonal variations. Primary disruptors include natural hormonal cycles, medications that alter hormonal cycles, and activities that involve the introduction of inter-/intravaginal products to the vagina. 

Sexual activity has been shown to directly impact the vaginal microbiome, increasing the prevalence of non-optimal vaginal species, such as L. iners and Gardnerella vaginalis, and reducing the concentration of L. crispatus.11,12 Sexual partners can also be a source for the re-introduction of non-optimal bacterial species. For example, vaginal dysbiosis-associated bacteria are detected in men who have vaginal sex.13 Notably, the penile microbiota is predictive for BV incidence in female sex partners, and the incidence of BV among women who have sex with women is high.14,15 Vaginal intercourse was strongly associated with changes from CST I to non-optimal CSTs in the VMB cohort (p < 0.001)). 

Sexual behaviors can also shape the vaginal microbiome. Use of condoms is inversely associated with L. iners-dominated CST-III, and associated with L. crispatus colonization.16,17 Frequent use of sex toys (defined as >10 toy-vaginal acts in the past 3 months) can also cause disruption by increasing the colonization of vaginal pathogens such as G. vaginalis, and reducing the abundance of Lactobacillus spp.18 Oral sex with a female partner is associated with an increased risk of BV in a dose-dependent response with increasing number of episodes.19 Use of lubricant products containing chlorhexidine gluconate or nonoxynol-9 inhibit Lactobacillus spp. growth, while lubricants that do not directly impact bacterial growth can still substantially decrease attachment of beneficial L. crispatus to the vaginal epithelium.20 Mean relative abundance of L. crispatus decreased for those who used lubricants, especially those that were CST-III L. iners-dominated prior to exposure.21 These findings were substantiated in the VMB cohort, where condom use, oral sex, sex toy use, and lubricant use were all significantly associated with changes in CST (p < 0.05).

Hormonal cycles can also drive vaginal microbiome change, where even individuals with optimal CSTs can transition to less protective states during menses and Lactobacillus spp. decrease in abundance.10,22 Menstruation was strongly associated with changes in CST state over short timescales in the VMB cohort (p<0.001). Postpartum hormonal changes drive shifts in the vaginal microbiome with an increase in mixed species and a decrease in Lactobacillus spp. observed postpartum versus in the third trimester.23 A survey of American women showed that 77% of postpartum vaginal communities contain a mix of non-optimal species, including G. vaginalis and Prevotella, and low proportions of Lactobacillus spp.24 Overall, studies show that postpartum vaginal microbiome samples have reduced levels of lactobacilli, a complex mixed species community, and increased microbial diversity.25,26 

Vaginal products, including certain contraceptives, prescriptions, and cleansers can disrupt the vaginal microbiome. A stable vaginal microbiome is promoted by oral contraceptives, contraceptive injections (Depot Medroxyprogesterone),  hormonal-IUDs (Levonorgestrel), and NuvaRing implant while a higher prevalence of BV is observed in women using non-hormonal copper IUDs.27-36 A decrease in the prevalence of lactobacilli is observed with spermicide use.37,38 Prescriptions, such as antibiotics, can completely reorganize the vaginal microbiome, even after one day of initiating therapy, and is the standard-of-care to treat BV infections (CDC).39 Cleansers may do more harm than good, as vaginal washing has been associated with an increased risk of BV and HIV acquisition.40-45

Menstrual products and feminine hygiene practices can also result in vaginal microbiome disruption. Menstrual cups have been positively associated with the presence of  L. crispatus, while menstrual pads were negatively associated with L. crispatus (and positively associated with Prevotella, especially when used in the past 48 hrs.). In addition, pubic shaving is associated with lower levels of non-optimal species.46 While the effect of douching on the vaginal microbiome depended on the product used, in general, douching increases the amount of non-optimal bacteria and increases the risk of BV.44,47-50

Secondary Disruptors of the Vaginal Microbiome: 

Diet

Stress

Exercise 

Swimming

Secondary disruptors have also been linked to disruptions of the vaginal microbiome, and include more systemic influences, such as exercise, diet, and stress.

Exercise has been observed to trend with diversity of the vaginal microbiome; for low to moderate intensity, the average Shannon Index (a measure of species diversity) increased with exercise time.51 While many non-scientific resources online link swimming with disruption of the vaginal microbiome, there is limited data substantiating the link. Relevant data includes the detection of Candida spp. in swimming pool facilities, a possible connection between tight-fitting/poorly ventilated undergarments and vulvovaginal candidiasis (VVC), and studies demonstrating that the skin microbiome is impacted by swimming.52-57 

In terms of diet, higher consumption of low-fat dairy is associated with increased likelihood of an optimal vaginal microbiome state.58 In a recent citizen-science project (ISALA), sugary beverages and meat were linked with lower levels of L. crispatus, and a high frequency of vegetable consumption in the past 24 hrs. was associated with higher levels of L. crispatus.46

Stress has also been linked to vaginal microbiome changes, with increased perceived stress associated with greater odds of BV incidence.59 In a separate study, a 5-unit increase in the Cohen’s Perceived Stress Scale was associated with greater risks of transitioning from the L. iners-dominated community state type (26% higher) to BV or maintaining BV from baseline. The inverse was also true—women with baseline BV reporting a 5-unit stress increase were less likely to transition to microbiota dominated by L. crispatus, L. gasseri, or L. jensenii.60

Methods: VMB Cohort Analysis

We analyzed high-resolution vaginal microbiome data collected for 130 individuals over a 70-day period, to determine overall microbiome stability and the impacts of lifestyle disturbances tracked in a cohort questionnaire. 

Vaginal microbiome stability was assessed using the proportion of non-menstrual timepoints each subject spent in community state type I (CST I). The stability score was defined as the number of non-menstrual timepoints in CST I divided by the total number of non-menstrual timepoints for each subject. We found that subjects who spent less than 90% of their time in CST I (L. crispatus dominated microbiomes) represented 90% of the total population in the cohort. As a secondary measure of microbiome stability, we also assessed the overall probability of transitioning between CSTs. We found that CST I, which is roughly defined by a dominance of L. crispatus, is the most stable, with the lowest probability of shifting to any other state (2%). 

This dataset includes detailed questionnaire data tracking the occurrence of common disruptors that can be used to directly investigate the relationship between disruptors, and the number of CST changes observed (a measure of microbiome instability). To do so, a sliding window approach was used. For each subject, whenever a transition between CSTs occurred, the number of reports of each tracked event (e.g., sexual behaviors, feminine hygiene practices, menstruation, etc.) within a window of 7 timepoints preceding the transition was summed. The total event count within all windows within a participant was then directly correlated with the number of transitions per subject, allowing for a ranking of the association between various factors and instability. 

Citations

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