Abstract
Background:
The association between vaginal washing and HIV risk may be mediated by vaginal washing-associated changes in vaginal microbiota.
Methods:
Data from a cohort of HIV-negative US and Kenyan women enrolled in the Preventing Vaginal Infections trial were analyzed. Vaginal fluid samples and vaginal washing data were collected every two months for 12 months. Bacterial relative abundances were measured by broad-range 16S rRNA gene polymerase chain reaction with next generation sequencing. Generalized estimating equations were used to evaluate the association between vaginal washing and i) the Shannon Diversity Index (SDI); and ii) mean change in percent bacterial relative abundances, with application of a 10% false discovery rate (FDR).
Results:
Participants (N=111) contributed 93/630 (14.8%) vaginal washing visits. Mean SDI was 0.74 points higher (95%CI 0.35, 1.14; p<0.001) at washing visits among US participants (N=26). Vaginal washing was not associated with SDI in Kenyan participants (N=85). There were no associations between vaginal washing and vaginal bacterial relative abundances after applying the FDR.
Conclusions:
The discordant results in Kenyan versus US women suggests the link between vaginal washing and sub-optimal vaginal microbiota may be context specific. Vaginal microbial shifts may not fully explain the association between vaginal washing and HIV acquisition.
INTRODUCTION
Vaginal washing is a common practice that involves the use of water, soap, or commercial products to cleanse beyond the introitus.1 While women may perceive vaginal washing as a method to improve hygiene,1 this practice has been associated with increased risk of HIV acquisition.2 The mechanism linking vaginal washing to HIV remains unknown. One potential explanation is that vaginal washing leads to shifts in vaginal microbiota that predispose to HIV infection.2 The presence of sub-optimal vaginal microbiota, bacterial vaginosis (BV), and higher vaginal bacterial species diversity, have all been associated with higher HIV acquisition risk.2, 3
Vaginal washing has been associated with development of BV2 and higher concentrations of sub-optimal vaginal bacterial taxa4 in some studies, but not in others.5 To begin to understand how vaginal washing may influence vaginal microbiota, we recently performed a secondary analysis of data collected from a cohort of Kenyan and US females enrolled in the placebo arm of a trial of periodic presumptive treatment (PPT) to reduce vaginal infections.4, 6 In this cohort, vaginal washing was associated with a higher likelihood of detection of sub-optimal vaginal bacterial taxa, measured by taxon-directed quantitative polymerase chain-reaction (qPCR), in US participants only.4
One limitation of using taxon-directed qPCR to evaluate microbiota is that taxa must be pre-selected. The taxa evaluated in our prior study were chosen based on their association with BV in US women,7 and it is possible that different sub-optimal taxa are influenced by vaginal washing in Kenyan women compared to US women.4, 8 In contrast to taxon-directed qPCR, broad-range 16S rRNA gene PCR with next generation sequencing (NGS) allows for identification of multiple taxa in a microbial population, as well as evaluation of bacterial diversity. To further clarify the link between vaginal washing and sub-optimal vaginal microbiota, we used broad-range PCR with NGS to evaluate changes in vaginal bacterial species diversity and bacterial relative abundances at visits where vaginal washing was and was not reported in the same cohort of US and Kenyan participants.
MATERIALS AND METHODS
Study Population and Procedures
We performed a secondary analysis of data collected from 111/116 females participating in the placebo arm of the Preventing Vaginal Infections (PVI) trial, which was a randomized placebo-controlled trial to assess monthly intravaginal metronidazole plus miconazole for reducing BV and vulvovaginal candidiasis (VVC).6 In brief, a total of 234 participants aged 18-45 were enrolled in the US and Kenya from May 2011 to August 2012. Based on the interventions for the trial, only persons assigned female sex at birth and presenting with a vaginal infection at screening (BV, VVC, or Trichomonas vaginalis [TV]) were eligible. Women with symptomatic vaginal infections or TV at the screening visit were treated and instructed to return in 7-28 days if they wished to enroll in the study. At enrollment, participants were randomized to receive either monthly intravaginal metronidazole plus miconazole or placebo for a total of 12 months. Participants were excluded from the current analyses if they did not consent to storage and future use of specimens (N=1), exited the study early (N=1), or were lost to follow-up N=3). Thus, final study size (N=111) was based on the number of women enrolled in the placebo arm of the PVI trial who completed the study and consented to future use of specimens.
Vaginal samples were collected at enrollment and every two months thereafter for diagnosis of vaginal conditions (e.g., VVC, BV) and storage for future analyses. Data on vaginal washing practices, contraceptive use, date of the last menstrual period (LMP), and sexual practices were collected monthly during face-to-face interviews. Participants were asked two sequential questions to determine vaginal washing status. First, participants were asked “In the past month, did you douche or wash inside your vagina?” Those that answered “yes,” were asked “How far inside your vagina did you wash?” Participants could indicate that washing was external (e.g., performed at the “introitus only”, with substances used for washing passing “no deeper than a fingertip” beyond the introitus) or internal, “beyond the introitus.” Vaginal washing was defined as answering “yes” to the first question and “beyond the introitus” to the second question.” Women who met the definition for vaginal washing were asked to indicate all products used for vaginal washing in the past month (choices included water only, soap and water, vinegar and water, antiseptic, detergent, or the option to specify another substance), what they used to wash inside the vagina (finger, bathing flannel/cloth, douche applicator or other) and how frequently they performed vaginal washing per week (<1, 1-7, 8-14, >14).
Written informed consent for participation and storage and future use of specimens was obtained at enrollment. The trial was approved by the human subject’s research committees at the University of Washington (approval number 39507), the University of Alabama at Birmingham (approval number 100827002), and Kenyatta National Hospital (approval number P378/10/2010), and all procedures were in accordance with the revised Helsinki Declaration. The PVI trial was registered at ClinicalTrials.gov (NCT01230814; http://clinicaltrials.gov).
Laboratory Procedures
Vaginal samples were stored and transported as previously described 4. Extraction of DNA was performed using the BiOstic Bacteremia DNA Isolation Kit (MoBio, now Qiagen, Carlsbad, CA, USA). Sham swabs were used as controls to monitor for contamination of PCR reagents, and assessment of PCR inhibitors was performed as previously described.9 Relative abundance data were generated by broad-range PCR targeting the V3-V4 region of the 16S rRNA gene. Next generation amplicon sequencing was performed using the Illumina MiSeq platform (San Diego, CA, USA).10 Raw data were analyzed using the DADA2 pipeline (Version 2.1.6.0),11 and resultant sequences were classified using a set of reference vaginal bacteria12 and the pplacer tool (v.1.1alpha19).13 Sequences are available in the NCBI Short Read Archive (PRJNA638104).14
Detection of Neisseria gonorrhoeae and Chlamydia trachomatis was performed using the Hologic Aptima Combo-2 assay (Hologic, San Diego California, USA). Herpes simplex virus type 2 (HSV-2) serostatus was measured by enzyme linked immunosorbent assay (ELISA) (HerpeSelect 2, Focus Diagnostics, Cypress California, USA), with positivity defined as optical density >2.1 in Kenyan participants and positive versus negative in US participants.15 BV was diagnosed by Gram stain using the method of Nugent and Hillier.16 Wet preparations with microscopy were performed to evaluate for the presence of vaginal yeast and TV.
Statistical Analysis
Baseline data on age, contraceptive use, exchange of sex for cash or in-kind payments, vaginal washing practices, and prevalence of N. gonorrhoeae, C. trachomatis, and HSV-2 infection are reported from the enrollment visit. Antibiotic treatment at the screening visit could have altered vaginal microbiota at the subsequent enrollment visit.8 Thus, clinical data collected at the baseline visit that may have been influenced by antibiotic treatment at the screening visit (e.g., Nugent score) were excluded, and data collected at the first follow-up visit are reported as the baseline for the present analyses. As with our earlier vaginal washing study in this dataset, analyses were performed for the full cohort and after stratification by country.4
For these analyses, the primary predictor was vaginal washing (defined under “study populations and procedures”) and the primary outcome was the Shannon Diversity Index (SDI) and the Chao 1 index. The SDI is a summary statistic generated based on the number of different bacterial taxa detected by high throughput sequencing (species richness) and the proportion of the entire population comprised by each species (species evenness).17 The Chao 1 index estimates the number of taxa (community richness) in the bacterial population.18 The SDI and Chao 1 index were calculated using sequence counts of individual bacterial taxa using the R microbiome package (version 3.3.2) at visits where vaginal washing was reported versus not reported. Results were compared using generalized estimating equations (GEE) with a Gaussian link, independent correlation structure, and robust standard errors. Adjusted analyses were performed for known or potential confounders of the association between vaginal washing and vaginal microbiota including country, age, condomless sex, HSV-2 serostatus, and menstrual cycle phase.4, 19-22 Consistent with our prior analyses, menstrual cycle phase was modeled as a categorical variable defined as 0-14 days since the start of the LMP (follicular phase), 15-28 days since the start of the LMP (luteal phase), >28 days since the start of the LMP, or no menstrual period for >3 months (amenorrheic).4, 19, 20 The β coefficients generated from unadjusted and adjusted analyses can be interpreted as the mean difference in the SDI or Chao 1 Index between visits with and without vaginal washing.
The association between vaginal washing (primary predictor) and bacterial relative abundances (secondary outcome) was analyzed. Taxa were selected for analyses using two methods. First, a Wald score was calculated using linear regression to screen for associations between bacterial relative abundances and vaginal washing. Taxa with a p-value cut-off of <0.1 by the Wald test and >1% prevalence were carried forward for further analyses. Second, a subset of bacterial taxa that were associated with a higher likelihood of detection by taxon directed qPCR at vaginal washing visits in a prior study were selected for analysis a priori: Fannyhessea (Atopobium) vaginae; Bacterial Vaginosis Associated Bacteria 1 (BVAB1); BVAB2; Gardnerella species; Mageeibacillus indolicus; Megasphaera lornae (formerly Megasphaera species type 1); Megasphaera hutchinsoni (formerly Megasphaera species type 2); Sneathia vaginalis (formerly Leptotrichia amnii); Sneathia sanguinegens; and Sneathia species.4 Because participants contributed data from multiple visits, associations between vaginal washing and bacterial relative abundances were assessed using GEE with a Gaussian link, independent correlation structure, and robust standard errors. A Benjamini-Hochberg false discovery rate (FDR) of 0.1 was applied to all bacterial taxa analyzed. Adjustment for potential confounding factors was performed using the variables described above. The β coefficients derived from these analyses represent the mean difference in bacterial relative abundance between visits exposed versus not exposed to vaginal washing.
A total of 20/111 (18.0%) participants missed 36/666 (5.4%) follow-up visits. Given the low frequency of missing visits, all participants in the placebo arm who completed the study and consented to storage and future use of samples were included in these analyses (N=111). All statistical analyses were performed using IBM SPSS Statistics (Version 26.0) and R (Version 3.3.2).
RESULTS
A total of 116 participants enrolled in the placebo arm of the PVI trial, of which 111 (26 US participants and 85 Kenyan participants) contributed to these analyses. Participants contributed 630 follow-up visits (median 6 follow-up visits per participant, interquartile [IQR] range 6, 6) and reported vaginal washing beyond the introitus in the past month at 93 (14.8%) visits. Baseline characteristics are reported in Table 1. The median age of participants was 29 years (IQR 23-34), and the majority (107/111; 96.4%) reported Black race. At the baseline visit, only Kenyan participants (17/85, 15.3%) reported vaginal washing. Over the course of the study, 24/85 (28.2%) Kenyan participants contributed 77/481 (16.0%) vaginal washing visits. Substances used for vaginal washing reported by Kenyan participants at vaginal washing visits included water (39/77, 50.6%), soap and water (37/77, 48.1%), and antiseptic (1/77, 1.3%). Methods reported for washing at vaginal washing visits included using a finger (39/77, 50.6%), bathing flannel/cloth (37/377, 48.1%) and use of underpants (1/77, 1.3%). Kenyan participants indicated washing frequencies of two (17/77, 22.1%), three, (45/77, 58.4%) or four (15/77, 19.5%) times per week at visits where vaginal washing was reported. Although no US participants reported vaginal washing at baseline, 7/26 (26.9%) US participants contributed 16/149 (10.7%) vaginal washing visits over the study period. Substances used for vaginal washing among US participants included water only (1/16, 6.3%), soap and water (5/16, 31.3%), vinegar and water (2/16, 12.5%) and store-bought products (8/16, 50%). Most US participants used a douche applicator for washing (11/16, 68.8%), and the remainder reported using a bathing flannel/cloth (5/16, 31.3%). Reported frequency of vaginal washing among US participants was once per week (5/16, 31.3%), twice per week (10/16, 62.5%), and three times per week (1/16, 6.3%).
Table 1.
Baseline characteristics
| All participants (N=111) |
US (N=26) | Kenya (N=85) | |
|---|---|---|---|
| Age | |||
| 18-25 | 38 (34.2%) | 11 (42.3%) | 27 (31.8%) |
| 26-35 | 53 (47.7%) | 7 (26.9%) | 46 (54.1%) |
| 36-45 | 20 (18.0%) | 8 (30.8%) | 12 (14.1%) |
| Vaginal washing | |||
| Reports vaginal washing (yes/no) | 17 (15.3%) | 0 (0.0%)1 | 17 (20.0%) |
| Method of contraception | |||
| Oral contraceptive pills | 12 (10.8%) | 3 (11.5%) | 9 (10.6%) |
| Injectable | 25 (22.5%) | 3 (11.5%) | 22 (25.9%) |
| Implant | 10 (9.0%) | 1 (3.8%) | 9 (10.6%) |
| IUD | 10 (9.0%) | 2 (7.7%) | 8 (9.4%) |
| Tubal ligation | 5 (4.5%) | 5 (19.2%) | 0 (0%) |
| Other2 | 3 (2.7%) | 1 (3.8%) | 2 (2.4%) |
| Sexual history | |||
| Frequency of vaginal sex in the past week | 2 (1, 4) | 1 (0, 3.3) | 3 (1, 4) |
| Unprotected sex in the past week | 39 (35.1%) | 11 (42.3%) | 28 (32.9%) |
| No sex in the past week | 21 (18.9%) | 10 (38.5%) | 11 (12.9%) |
| Number of different sex partners in the past week | 1 (1, 3) | 1 (1, 1) | 1 (1, 3.75) |
| Exchange of money/goods for sex | 61 (51.0%) | 1 (1.6%) | 60 (70.6%) |
| Laboratory data | |||
| Gonorrhea | 0 | 0 | 0 |
| Chlamydia | 8 (7.2%) | 1 (3.8%) | 7 (8.2%) |
| HSV-2 | 70 (63.1%) | 13 (50.0%) | 57 (67.1%) |
| Vulvovaginal candidiasis | 36 (32.4%) | 9 (34.6%) | 27 (31.8%) |
| Trichomonas vaginalis | 2 (1.8%) | 0 (0.0%) | 2 (2.4%) |
| BV by Amsel’s criteria | 33 (29.7%) | 13 (50.0%) | 20 (23.5%) |
| Nugent score 0-3 | 50 (45.0%) | 12 (46.2%) | 38 (44.7%) |
| Nugent score 4-6 | 19 (17.1%) | 3 (11.5%) | 16 (18.8%) |
| Nugent score 7-10 | 42 (37.8%) | 11 (42.3%) | 31 (36.5%) |
Data are presented as N (%) or median (interquartile range). Abbreviations: HSV-2, herpes simplex virus 2; IUD, intrauterine device.
Although none of the US participants reported vaginal washing at the baseline visit for this study, 7/26 (26.9%) reported vaginal washing at 16 total visits.
Other included fertility awareness method, herbal pill, and withdrawal.
In the full cohort, vaginal washing was not associated with higher vaginal bacterial species diversity (measured by the SDI, adjusted β coefficient=0.16, 95% confidence interval [CI] −0.06, 0.38; p=0.15) or richness (measured by the Chao 1 index, adjusted β coefficient=0.17, 95% CI −1.34, 3.68; p=0.36) (Table 2). Similarly, no association was noted between vaginal washing and the SDI (adjusted β coefficient=0.08, 95% CI −0.18, 0.33; p=0.6) or Chao 1 index (adjusted β coefficient=0.64, 95% CI −2.28, 3.56; p=0.7) in Kenyan participants. In US participants, the SDI (adjusted β coefficient=0.74, 95% CI 0.35, 1.14; p<0.001) was significantly higher at visits where vaginal washing was reported. There was also a statistical trend toward an association between vaginal washing and a higher Chao 1 index (adjusted β coefficient=6.33, 95% CI −0.37, 13.03; p=0.06) among US participants.
Table 2:
Comparison of species diversity and richness at visits with and without vaginal washing.
| Shannon Diversity Index | Washing visits mean (SD)1 |
Non-washing visits mean (SD)1 |
Unadjusted Coefficient (95% CI)2 |
p- value |
Adjusted Coefficient (95% CI)3 |
p- value |
|---|---|---|---|---|---|---|
| Full Cohort | 1.29 (0.77) | 1.11 (0.89) | 0.18 (−0.04, 0.39) | 0.10 | 0.16 (−0.06, 0.37) | 0.20 |
| Kenyan Participants | 1.21 (0.09) | 1.13 (0.04) | 0.08 (−0.18, 0.33) | 0.50 | 0.08 (−0.18, 0.33) | 0.60 |
| US Participants | 1.66 (0.18) | 1.05 (0.08) | 0.60 (0.23, 0.98) | 0.002 | 0.74 (0.35, 13.37) | <0.001 |
| Chao 1 Index | ||||||
| Full Cohort | 15.47 (10.09) | 13.97 (10.39) | 1.50 (−1.02, 4.03) | 0.20 | 1.13 (−1.36, 3.62) | 0.40 |
| Kenyan Participants | 14.86 (1.10) | 13.98 (0.52) | 0.87 (−1.99, 3.75) | 0.50 | 0.64 (−2.28, 3.56) | 0.70 |
| US Participants | 18.38 (3.00) | 13.93 (0.91) | 4.44 (−2.08, 10.97) | 0.20 | 6.33 (−0.37,13.03) | 0.06 |
Generalized estimating equations were used to compare species diversity (measured by the Shannon Diversity Index) and richness (measured by the Chao I Index) at visits with and without vaginal washing as described in the methods.
Data are presented as mean relative abundance (standard deviation).
Adjusted for country.
Adjusted for country, age, unprotected sex, HSV-2, and menstrual cycle phase (see methods).
Abbreviations: SD, standard deviation; CI, confidence interval.
Fifteen bacterial taxa met the pre-specified criteria for further analysis (Wald score p-value <0.1 and prevalence >1%), including: BVAB1; Corynebacterium aurimucosum/minutissimum/singulare; Gemella haemolysans/sanguinis; Lactobacillus salivarius; Lactobacillus animalis/murinus; Peptoniphilus lacrimalis; Peptostreptococcus anaerobius; Prevotella genogroup 4; Staphylococcus capitis/caprae/epidermis/hominis; Staphylococcus haemolyticus; Streptococcus agalactiae; Streptococcus mitis group; Streptococcus salivarius; and Sutterella species. No associations were identified between vaginal washing and bacterial relative abundances after application of a 10% FDR. In univariate analysis prior to application of the FDR, vaginal washing was associated with lower relative abundances of P. lacrimalis (unadjusted β coefficient= −0.23, 95% CI −0.44, −0.01; p=0.04) in Kenyan participants (Table 3). This effect was attenuated after adjustment for potential confounding factors (adjusted β coefficient= −0.18, 95% CI −0.39, 0.02; p=0.08).
Table 3:
Comparison of relative abundances of vaginal bacterial taxa selected by Wald score for analysis at visits with and without vaginal washing.
| Full Cohort (N=111) | Mean relative abundance (%)1 |
SD | Mean relative abundance (%)1 |
SD | Unadjusted Coefficient per 1-SD change (95% CI)2 |
p- value |
Adjusted Coefficient per 1-SD change (95% CI)3 |
p- value |
|---|---|---|---|---|---|---|---|---|
| Washing Visits (N=93) |
Non-washing visits (N=537) |
|||||||
| Corynebacterium aurimucosum/minutissimum/singulare | 0.03 (0.00, 1.69) | 0.19 | 0.00 (0.0, 1.26) | 0.08 | 0.18 (−0.19, 0.55) | 0.30 | 0.20 (−0.18, 0.57) | 0.30 |
| Gemella haemolysans/sanguinis | 0.54 (0.00, 25.55) | 3.05 | 0.02 (0.00, 3.52) | 0.21 | 0.43 (−0.23, 1.09) | 0.20 | 0.43 (−0.22, 1.09) | 0.20 |
| Lactobacillus salivarius | 0.04 (0.00, 2.80) | 0.30 | 0.00 (0.00, 0.59) | 0.03 | 0.30 (−0.23, 0.83) | 0.30 | 0.32 (−0.24, 0.88) | 0.30 |
| Lactobacillus animalis/murinus | 0.98 (0.00, 71.76) | 7.63 | 0.08 (0.00, 39.47) | 1.71 | 0.27 (−0.24, 0.77) | 0.30 | 0.28 (−0.23, 0.79) | 0.30 |
| Peptoniphilus lacrimalis | 0.06 (0.00, 1.57) | 0.21 | 0.13 (0.00, 3.11) | 0.36 | −0.19 (−0.39, 0.01) | 0.10 | −0.18 (−0.37, 0.01 | 0.06 |
| Peptostreptococcus anaerobius | 0.44 (0.00, 14.74) | 1.85 | 0.23 (0.00, 8.39) | 0.94 | 0.19 (−0.18, 0.55) | 0.30 | 0.19 (−0.15, 0.52) | 0.30 |
| Prevotella genogroup 4 | 0.34 (0.00, 6.43) | 0.96 | 0.18 (0.00, 6.31) | 0.62 | 0.24 (−0.08, 0.56) | 0.14 | 0.22 (−0.07, 0.51) | 0.14 |
| Staphylococcus capitis/caprae/epidermis/hominis | 1.14 (0.00, 94.58) | 9.86 | 0.25 (0.00, 35.40) | 0.21 | 0.20 (−0.27, 0.67) | 0.40 | 0.20 (−0.27, 0.67) | 0.40 |
| Staphylococcus haemolyticus | 0.07 (0.00, 3.11) | 0.35 | 0.01, (0.00, 1.61) | 0.11 | 0.33 (−0.08, 0.73) | 0.12 | 0.33 (−0.07, 0.74) | 0.11 |
| Streptococcus agalactiae | 2.56 (0.00, 70.02) | 10.48 | 1.03 (0.00, 88.61) | 7.10 | 0.18 (−0.11, 0.47) | 0.20 | 0.21 (−0.08, 0.49) | 0.20 |
| Streptococcus mitis group | 3.53 (0.00, 91.43) | 14.97 | 0.50 (0.00, 90.84) | 0.53 | 0.40 (−0.09, 0.88) | 0.11 | 0.42 (−0.05, 0.89) | 0.08 |
| Streptococcus salivarius | 0.09 (0.00, 7.90) | 0.82 | 0.00 (0.00, 0.83) | 0.05 | 0.27 (−0.24, 0.77) | 0.30 | 0.28 (−0.24, 0.80) | 0.30 |
| Sutterella species | 0.02 (0.00, 1.85) | 0.19 | 0.01 (0.00, 0.70) | 0.05 | 0.21 (−0.28, 0.69) | 0.40 | 0.22 (−0.27, 0.70) | 0.40 |
| Kenyan Participants (N=85) | ||||||||
| Corynebacterium aurimucosum/minutissimum/singulare | 0.03 (0.00, 1.70) | 0.20 | 0.01 (0.00, 1.30) | 0.09 | 0.20 (−0.20, 0.59) | 0.30 | 0.20 (−0.20, 0.59) | 0.30 |
| Gemella haemolysans/sanguinis | 0.65 (0.00, 25.50) | 3.34 | 0.02 (0.00, 2.20) | 0.16 | 0.46 (−0.24, 1.17) | 0.20 | 0.45 (−0.23, 1.74) | 0.20 |
| Lactobacillus animalis/murinus | 1.18 (0.00, 71.80) | 8.40 | 0.10 (0.00, 39.50) | 1.98 | 0.28 (−0.25, 0.82) | 0.30 | 0.29 (−0.23, 0.80) | 0.30 |
| Lactobacillus salivarius | 0.05 (0.00, 7.90) | 0.91 | <0.01 (0.00, 0.80) | 0.05 | 0.34 (−0.24, 0.91) | 0.30 | 0.37 (−0.25, 0.99) | 0.20 |
| Peptoniphilus lacrimalis | 0.04 (0.00, 0.70) | 0.13 | 0.11 (0.00, 3.30) | 0.3 | −0.23 (−0.44, −0.01) | 0.04 | −0.18 (−0.39, 0.02) | 0.08 |
| Peptostreptococcus anaerobius | 0.40 (0.00, 14.70) | 1.76 | 0.27(0.00, 8.40) | 1.05 | 0.10 (−0.25, 0.46) | 0.60 | 0.13 (−0.22, 0.48) | 0.50 |
| Prevotella genogroup 4 | 0.34 (0.00, 6.40) | 1.02 | 0.14 (0.00, 4.00) | 0.50 | 0.32 (−0.10, 0.74) | 0.10 | 0.31 (−0.07, 0.69) | 0.10 |
| Staphylococcus capitis/caprae/epidermis/hominis | 1.38 (0.00, 94.60) | 10.84 | 0.33 (0.00, 35.40) | 2.43 | 0.22 (−0.29, 0.72) | 0.40 | 0.21 (−0.27, 0.68) | 0.40 |
| Staphylococcus haemolyticus | 0.09 (0.00, 3.10) | 0.39 | 0.02 (0.00, 1.60) | 0.12 | 0.35 (−0.08, 0.78) | 0.10 | 0.33 (−0.08, 0.75) | 0.10 |
| Streptococcus agalactiae | 3.09 (0.00, 70.00) | 11.47 | 1.37 (0.00, 88.60) | 8.18 | 0.20 (−0.11, 0.50) | 0.20 | 0.21 (−0.10, 0.51) | 0.20 |
| Streptococcus mitis group | 4.27 (0.00, 91.40) | 16.40 | 0.45 (0.00, 90.80) | 4.8 | 0.48 (−0.07, 1.03) | 0.09 | 0.48 (−0.05, 1.02) | 0.07 |
| Streptococcus salivarius | 0.05 (0.00, 2.80) | 0.32 | <0.01 (0.00, 0.30) | 0.02 | 0.34 (−0.24, 0.91) | 0.30 | 0.37 (−0.25, 0.99) | 0.30 |
| Sutterella species | <0.01 (0.00, 0.20) | 0.02 | 0.01 (0.00, 0.70) | 0.05 | −0.06 (−0.21, 0.09) | 0.40 | −0.06 (−0.22, 0.09) | 0.40 |
| US Participants (N=26) | ||||||||
| Corynebacterium aurimucosum/minutissimum/singulare | 0.00 (0.00, 0.00) | 0.00 | 0.00 (0.00, 0.00) | 0.00 | N/A4 | N/A | N/A | N/A |
| Gemella haemolysans/sanguinis | 0.02 (0.00, 0.30) | 0.07 | 0.04 (0.00, 3.50) | 0.32 | −0.07 (−0.17, 0.03) | 0.20 | 0.03 (−0.12, 0.19) | 0.70 |
| Lactobacillus animalis/murinus | 0.00 (0.00, 0.00) | 0.00 | 0.00 (0.00, 0.00) | 0.00 | N/A | N/A | N/A | N/A |
| Lactobacillus salivarius | 0.00 (0.00, 0.00) | 0.00 | <0.01 (0.00, 0.50) | 0.05 | N/A | N/A | N/A | N/A |
| Peptoniphilus lacrimalis | 0.15 (0.00, 0.20) | 0.41 | 0.18 (0.00, 2.50) | 0.42 | −0.09 (−0.62, 0.45) | 0.80 | 0.12 (−0.49, 0.74) | 0.70 |
| Peptostreptococcus anaerobius | 0.68 (0.00, 9.10) | 2.28 | 0.09 (0.00, 4.70) | 0.47 | 0.67 (−0.72, 2.05) | 0.30 | 1.01 (−0.49, 2.52) | 0.20 |
| Prevotella genogroup 4 | 0.32 (0.00, 2.20) | 0.66 | 0.30 (0.00, 6.30) | 0.87 | 0.01 (−0.30, 0.33) | 0.90 | 0.04 (−0.44, .51) | 0.90 |
| Staphylococcus capitis/caprae/epidermis/hominis | 0.00 (0.00, 0.00) | 0.00 | <0.01 (0.00, 0.40) | 0.05 | N/A | N/A | N/A | N/A |
| Staphylococcus haemolyticus | 0.00 (0.00, 0.00) | 0.00 | <0.01 (0.0, 0.2) | 0.02 | N/A | N/A | N/A | N/A |
| Streptococcus agalactiae | 0.02 (0.00, 0.30) | 0.08 | 0.04 (0.00, 2.10) | 0.24 | −0.09 (−0.31, 0.13) | 0.40 | −0.05 (−0.38, 0.28) | 0.80 |
| Streptococcus mitis group | 0.01 (0.00, 0.100) | 0.03 | 0.65 (0.00, 75.40) | 6.54 | −0.11 (−0.28, 0.07) | 0.20 | −0.02 (−0.07, 0.02) | 0.3 |
| Streptococcus salivarius | 0.01 (0.00, 0.20) | 0.06 | <0.01 (0.00, 0.40) | 0.04 | 0.21 (−0.52, 0.94) | 0.60 | −0.14 (−1.33, 1.04) | 0.80 |
| Sutterella species | 0.12 (0.00, 1.80) | 0.46 | 0.01 (0.00, 0.60) | 0.06 | 0.69 (−0.79, 2.17) | 0.40 | 0.94 (−0.74, 2.61) | 0.30 |
Taxa were selected for analysis based on a Wald score with a p-value of <0.1 and prevalence >1% (see methods). Results that were statistically significant at alpha=0.05 prior to application of a 10% false discovery rate (FDR) are indicated with bold font. After application of a 10% FDR, none of the results remained statistically significant.
Data are presented as percent mean relative abundance (range).
Adjusted for country.
Adjusted for country, age, unprotected sex, HSV-2, and menstrual cycle phase (see methods).
Models marked with N/A did not converge.
Abbreviations: SD, standard deviation; CI, confidence interval; N/A, not applicable.
Next, the associations between vaginal washing and vaginal bacterial taxa selected for analyses a priori were analyzed. There were no associations between vaginal washing and higher relative abundances of the pre-selected taxa after adjustment for a 10% FDR in either US or Kenyan participants. Prior to application of the FDR, some associations between vaginal washing and higher or lower abundance of different taxa were observed in US women (Table 4).
Table 4:
Comparison of relative abundances of vaginal bacterial taxa selected for analysis a priori at visits with and without vaginal washing.
| Full Cohort | Mean relative abundance (%)1 |
SD | Mean relative abundance (%)1 |
SD | Unadjusted Coefficient per 1-SD change (95% CI)2 |
p- value |
Adjusted Coefficient per 1-SD change (95% CI)3 |
p- value |
|---|---|---|---|---|---|---|---|---|
| Washing visits (N=93) |
Non-washing visits (N=537) |
|||||||
| Fannyhessea vaginae | 5.63 (0.00, 33.80) | 7.36 | 4.84 (0.00, 65.30) | 8.58 | 0.09 (−0.14, 0.33) | 0.40 | 0.07 (−0.17, 0.31) | 0.50 |
| BVAB1 | 3.06 (0.00, 57.05) | 10.84 | 6.18 (0.00, 75.80) | 16.93 | −0.19 (−0.42, 0.03) | 0.09 | −0.21 (−0.44, 0.02) | 0.08 |
| BVAB2 | 1.00 (0.00, 30.10) | 3.44 | 0.85 (0.00, 16.95) | 2.06 | 0.06 (−0.28, 0.40) | 0.70 | 0.09 (−0.27, 0.45) | 0.60 |
| Gardnerella species | 20.22 (0.00, 75.30) | 20.85 | 17.23 (0.00, 99.90) | 21.44 | 0.14 (−0.12, 0.40) | 0.30 | 0.02 (−0.03, 0.08) | 0.40 |
| Mageeibacillus indolicus | 0.15 (0.00, 5.45) | 0.70 | 0.26 (0.00, 21.64) | 1.26 | −0.09 (−0.29, 0.12) | 0.40 | −0.07 (−0.26, 0.12) | 0.50 |
| Megasphaera lornae | 1.92 (0.00, 15.90) | 3.95 | 1.46 (0.00, 27.40) | 3.41 | 0.13 (−0.27, 0.53) | 0.50 | 0.11 (−0.25, 0.47) | 0.60 |
| Megasphaera hutchinsoni | 0.48 (0.00, 5.40) | 1.24 | 0.80 (0.00, 21.50) | 2.63 | −0.13 (−0.34, 1.48) | 0.20 | −0.15 (−0.37, 0.08) | 0.20 |
| Sneathia vaginalis | 1.81 (0.00, 17.90) | 3.87 | 2.37 (0.00, 37.30) | 5.59 | −0.10 (−0.30, 0.09) | 0.30 | −0.11 (−0.30, 0.08) | 0.30 |
| Sneathia sanguinegens | 1.15 (0.00, 20.90) | 2.92 | 0.95 (0.00, 34.40) | 2.63 | 0.08 (−0.17, 0.32) | 0.60 | 0.06 (−0.18, 0.31) | 0.60 |
| Sneathia species | 0.28 (0.00, 8.48) | 1.20 | 0.44 (0.00, 19.10) | 1.80 | −0.09 (−0.31, 0.13) | 0.40 | −0.06 (−0.25, 0.13) | 0.50 |
| Kenyan Participants | ||||||||
| Fannyhessea vaginae | 5.89 (0.00, 33.80) | 7.85 | 5.31 (0.00, 65.30) | 9.24 | 0.06 (−0.20, 0.32) | 0.60 | 0.07 (−0.19, 0.33) | 0.60 |
| BVAB1 | 2.11 (0.00, 57.10) | 9.65 | 3.67 (0.00, 75.80) | 13.23 | −0.12 (−0.35, 0.11) | 0.30 | −0.18 (−0.43, 0.07) | 0.20 |
| BVAB2 | 0.86 (0.00, 30.10) | 3.55 | 0.88 (0.00, 17.00) | 2.23 | −0.01 (−0.38, 0.37) | 1.00 | 0.06 (−0.33, 0.46) | 0.80 |
| Gardnerella species | 19.41 (0.00, 75.30) | 21.05 | 18.07 (0.00, 97.70) | 21.40 | 0.06 (−0.24, 0.36) | 0.20 | 0.01 (−0.28, 0.30) | 0.90 |
| Mageeibacillus indolicus | 0.17 (0.00, 5.40) | 0.77 | 0.22 (0.00, 21.60) | 1.28 | −0.04 (−0.28, 0.20) | 0.70 | −0.06 (−0.27, 0.16) | 0.60 |
| Megasphaera lornae | 1.08 (0.00, 12.80) | 2.82 | 1.34 (0.00, 27.40) | 3.46 | −0.08 (−0.39, 0.22) | 0.60 | −0.10 (−0.38, 0.17) | 0.50 |
| Megasphaera hutchinsoni | 0.47 (0.00, 5.40) | 1.27 | 0.90 (0.00, 21.50) | 2.90 | −0.16 (−0.39, 0.08) | 0.20 | −0.19 (−0.46, 0.07) | 0.20 |
| Sneathia vaginalis | 1.62 (0.00, 16.40) | 3.65 | 2.62 (0.00, 37.30) | 5.63 | −0.18 (−0.38, 0.03) | 0.09 | −0.15 (−0.35, 0.06) | 0.20 |
| Sneathia sanguinegens | 1.14 (0.00, 20.90) | 3.05 | 1.08 (0.00, 34.40) | 2.87 | 0.02 (−0.24, 0.29) | 0.90 | 0.04 (−0.23, 0.30) | 0.80 |
| Sneathia species | 0.25 (0.00, 8.50) | 1.28 | 0.52 (0.00, 19.10) | 1.96 | −0.15 (−0.40, 0.10) | 0.20 | −0.13 (−0.35, 0.09) | 0.20 |
| US Participants | ||||||||
| Fannyhessea vaginae | 4.43 (0.00, 17.50) | 4.36 | 3.46 (0.00, 36.00) | 6.05 | 0.17 (−0.25, 0.58) | 0.40 | 0.24 (−0.36, 0.84) | 0.40 |
| BVAB1 | 7.58 (0.00, 46.90) | 14.87 | 13.70 (0.00, 73.00) | 23.41 | −0.27 (−0.81, 0.27) | 0.30 | −0.47 (−1.15, 0.20) | 0.20 |
| BVAB2 | 1.65 (0.00, 11.90) | 2.90 | 0.78 (0.00, 7.00) | 1.41 | 0.53 (−0.03, 1.09) | 0.06 | 0.48 (0.05, 0.90) | 0.03 |
| Gardnerella species | 24.06 (0.40, 60.80) | 20.08 | 14.69 (0.00, 99.90) | 21.43 | 0.44 (0.01, 0.86) | 0.04 | 0.06 (−0.07, 0.18) | 0.40 |
| Mageeibacillus indolicus | 0.07 (0.00, 0.30) | 0.11 | 0.35 (0.00, 9.00) | 1.17 | −0.26 (−0.51, −0.01) | 0.04 | −0.11 (−0.35, 0.14) | 0.40 |
| Megasphaera lornae | 5.91 (0.00, 15.90) | 5.84 | 1.82 (0.00, 14.60) | 3.27 | 1.07 (0.05, 2.09) | 0.04 | 0.77 (−0.04, 1.57) | 0.06 |
| Megasphaera hutchinsoni | 0.50 (0.00, 3.40) | 1.13 | 0.52 (0.00, 10.60) | 1.55 | −0.01 (−0.43, 0.41) | 1.00 | 0.32 (−0.03, 0.66) | 0.07 |
| Sneathia vaginalis | 2.70 (0.00, 17.90) | 4.82 | 1.63 (0.00, 27.80) | 4.39 | 0.24 (−0.26, 0.74) | 0.30 | 0.27 (−0.15, 0.70) | 0.20 |
| Sneathia sanguinegens | 1.17 (0.00, 7.40) | 2.26 | 0.57 (0.00, 10.70) | 1.54 | 0.37 (−0.26, 1.00) | 0.30 | 0.24 (−0.52, 0.99) | 0.50 |
| Sneathia species | 0.45 (0.00, 1.70) | 0.71 | 0.20 (0.00, 12.80) | 1.20 | 0.22 (−0.22, 0.67) | 0.30 | 0.29 (−0.01, 0.59) | 0.06 |
Results that were statistically significant at alpha=0.05 prior to application of a 10% false discovery rate (FDR) are indicated with bold font. After application of a 10% FDR, none of the results remained statistically significant.
Data are presented as percent mean relative abundance (range).
Adjusted for country.
Adjusted for country, age, unprotected sex, HSV-2 serostatus, and menstrual cycle phase (see methods).
Abbreviations: SD, standard deviation; CI, confidence interval; BVAB1, Bacterial vaginosis associated bacterium 1; BVAB2, Bacterial vaginosis associated bacterium 2
DISCUSSION
In this exploratory analysis, broad-range 16S rRNA gene PCR with NGS was used to assess relationships between vaginal washing and vaginal microbiota in a cohort of US and Kenyan women. Vaginal washing was associated with higher bacterial species diversity among US women, but not Kenyan women. No associations were noted between vaginal washing and bacterial relative abundances when a 10% FDR was applied.
Although vaginal washing has been associated with BV in some studies,2 this association is not universal and is likely context specific.5, 21, 23 Furthermore, the association between vaginal washing and BV is often attenuated after adjusting for potential confounders (e.g., unprotected sex).21 Additional factors that may influence the observed association between vaginal washing and sub-optimal microbiota include stigma around reporting of vaginal washing (which may lead to underreporting), motivations for vaginal washing, substances used, and frequency.2, 21, 24-26 Specifically, components found in commercial vaginal washing products (e.g., povidone-iodine) may cause greater disruption of vaginal microbiota than other substances (e.g., saline or water).24 In this study, half of the vaginal washing events in US women included commercial douching products, whereas no commercial product use was reported by Kenyan women. This difference may partially explain the discordance in species diversity between the two groups.
Vaginal washing has consistently been associated with increased risk of HIV acquisition.2, 27 In this study, vaginal washing was not associated with meaningful differences in the relative abundance of individual bacterial taxa in Kenyan or US women. These data suggest that the mechanism by which vaginal washing increases HIV risk may be mediated by factors other than the vaginal microbiota. For example, vaginal washing may cause cervicovaginal inflammation, which has been linked with HIV acquisition risk.28 Vaginal washing could also lead to mucosal barrier disruption, which has been shown to facilitate transport of HIV virions through cervicovaginal mucus.29 Additionally, these data are consistent with prior studies demonstrating associations between vaginal washing and HIV acquisition risk that were independent of BV.27
This study had several strengths. Retention was high, reducing the risk of bias from loss to follow-up. Additionally, complete data were available to allow for adjustment of several potential confounding factors including frequency of condomless sex and diagnosis of STIs. Finally, data on vaginal washing were collected using detailed questions to ensure that only visits at which women reported washing beyond the introitus were categorized as washing visits. This point is important, as incorrect classification of washing versus non-washing visits would have potentially attenuated any observed associations in the data.
This study also had several limitations. First, this secondary analysis of data from an existing clinical trial dataset was exploratory, so the findings should be interpreted as hypothesis generating. Second, there were only 16 vaginal washing visits recorded among 7/26 women in the US cohort, so this subset analysis may not have been sufficiently powered to detect small associations. Third, many factors influence the reliability of relative abundance data, including the selection of broad range PCR primers and the quality controls used in the analysis pipeline.30 Fourth, this study did not assess motivations for vaginal washing.
Among US women, vaginal washing was associated with higher vaginal bacterial diversity. However, no associations were found between vaginal washing and differences in vaginal bacterial relative abundances after application of an FDR in US or Kenya women. It is important to consider that the mechanism linking vaginal washing and HIV acquisition could be related to factors other than disruption of the vaginal microbiota. Further studies characterizing how intravaginal practices alter the mucosal environment of the female reproductive tract could help to explain the relationship between vaginal washing and HIV susceptibility.
ACKNOWLEDGEMENTS:
We would like to thank the trial participants for their time, effort, and commitment to the study. Additionally, we would like to thank the staff at each clinical site, Mombasa County, and Coast General Teaching and Referral Hospital for the use of their clinical and laboratory facilities, respectively.
FUNDING
This work was supported by the National Institute of Allergy and Infectious Diseases (contract HHSN266200400073C [to the Preventing Vaginal Infections trial], through the Sexually Transmitted Infections Clinical Trials Group and R01-AI099106]; the University of Washington Center for AIDS Research [P30-AI27757 and AI027757]; and the National Institute of Child Health and Human Development of the National Institutes of Health [K23 HD100221].
Footnotes
CONFLICTS OF INTEREST
RSM receives research funding, paid to the University of Washington, from Hologic Corporation. BAR receives honoraria from Gilead for serving on a data safety monitoring board. DNF and TLF receive a royalty from BD. SS has received speaking honoraria from Lupin Pharmaceuticals. JS consults for Hologic, Talis and PhagoMed. All other authors reported no conflicts of interest.
DATA SHARING STATEMENT
This study was conducted with approval from the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee (KNH-UON ERC), which requires that we release data from Kenyan studies (including de identified data) only after they have provided their written approval for additional analyses. As such, data for this study will be available from the authors upon request, with written approval for the proposed analysis from the KNH/UON ERC. Their application forms and guidelines can be accessed at http://erc.uonbi.ac.ke/. To request these data, please contact Sarah Holte at sholte@fredhutch.org.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study was conducted with approval from the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee (KNH-UON ERC), which requires that we release data from Kenyan studies (including de identified data) only after they have provided their written approval for additional analyses. As such, data for this study will be available from the authors upon request, with written approval for the proposed analysis from the KNH/UON ERC. Their application forms and guidelines can be accessed at http://erc.uonbi.ac.ke/. To request these data, please contact Sarah Holte at sholte@fredhutch.org.