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# Vaginal microbiota stability over 18 months in young student women in France

Jeanne Tamarelle, Anne Thiébaut, Bertille de Barbeyrac, Cécile Bébéar, Antoine Bourret, Arnaud Fauconnier, Jacques Ravel, Elisabeth Delarocque-Astagneau

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## ► To cite this version:

Jeanne Tamarelle, Anne Thiébaut, Bertille de Barbeyrac, Cécile Bébéar, Antoine Bourret, et al. Vaginal microbiota stability over 18 months in young student women in France. European Journal of Clinical Microbiology and Infectious Diseases, 2024, 10.1007/s10096-024-04943-3. inserm-04717548

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https://inserm.hal.science/inserm-04717548v1

Submitted on 1 Oct 2024

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1 Vaginal microbiota stability over 18 months in young student women in France

2

3 AUTHORS

4 Jeanne Tamarelle, PhD, MPH (ORCID: 0000-0003-0956-0349)

5 This work was conducted while affiliated with: Epidemiology and Modelling of Antibiotic

6 Evasion Unit, Institut Pasteur, F-75475 Paris, France;

7 Current affiliation: University of Lausanne, Department of Fundamental Microbiology,

8 Lausanne, Switzerland

9

10 Anne C. M. Thiébaut, PhD, MPH (ORCID: 0000-0003-2651-2795)

11 Université Paris-Saclay, UVSQ, Inserm, CESP, High Dimensional Biostatistics for Drug

12 Safety and Genomics Team, F-94807 Villejuif, France

13

14 Bertille de Barbeyrac, PharmD, PhD (ORCID: 0000-0003-4906-2651)

15 Bordeaux University Hospital, Bacteriology department, French National Reference Center

16 for bacterial STIs, F-33000 Bordeaux, France

17

18 Cécile Bébéar, MD, PhD (ORCID: 0000-0001-6065-2332)

19 Bordeaux University Hospital, Bacteriology department, French National Reference Center

20 for bacterial STIs, F-33000 Bordeaux, France;

21 Univ. Bordeaux, CNRS, Microbiologie Fondamentale et Pathogénicité, UMR 5234, F-33000,

22 Bordeaux, France

23

24 Antoine Bourret, MD (ORCID: 0000-0002-9189-5713)

25 Université Paris Descartes, Sorbonne Paris Cité, Faculté de Médecine, Paris, France;

26 Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpital Universitaire Paris Centre

27 (HUPC), Service de Gynécologie, Obstétrique II, Paris, France.

28

29 Arnaud Fauconnier, MD, PhD (ORCID: 0000-0003-0724-6831)

30 Université Paris-Saclay, UVSQ, Unité de recherche 7285 « Risques cliniques et sécurité en

31 santé des femmes et en santé périnatale » (RISCQ), 78180 Montigny-le-Bretonneux, France;

32 Centre Hospitalier Intercommunal de Poissy-Saint-Germain-en-Laye, Service de gynécologie

33 & obstétrique, F-78300 Poissy, France

34

35 Jacques Ravel, PhD (ORCID: 0000-0002-0851-2233)

36 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD;
37 University of Maryland School of Medicine, Department of Microbiology and Immunology,
38 Baltimore, MD

39
40 Elisabeth Delarocque-Astagneau, MD, PhD, MPH (ORCID: 0000-0002-5129-9300)
41 Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective Evasion and
42 Pharmacoepidemiology Team, F-78180 Montigny-le-Bretonneux, France;
43 AP-HP, GHU Paris-Saclay University, Raymond Poincaré Hospital, Epidemiology and Public
44 Health Department, F-92380 Garches, France

45
46 The i-Predict study group*

47 *Study group team members are listed in the Acknowledgments.

48
49 CORRESPONDING AUTHOR

50 Anne Thiébaut: anne.thiebaut@inserm.fr

51

52 ACKNOWLEDGMENTS

53 The authors would like to thank Carla Balcon for her technical assistance in handling vaginal
54 samples and DNA extraction, as well as Mike Humphrys and the staff of Maryland Genomics,
55 a high-throughput genomics core facility at the Institute for Genome Sciences, University of
56 Maryland School of Medicine, for generating the 16S rRNA gene amplicon sequence data for
57 this study. The authors are grateful to all participants, practitioners, and study staff of the i-
58 Predict and i-Share study. The authors specifically acknowledge the coordination team of the
59 i-Share cohort: Prof. Christophe Tzourio, Clothilde Pollet and Edwige Perreira. The sponsor
60 of the i-Predict study was Assistance Publique – Hôpitaux de Paris (Délégation à la
61 Recherche Clinique et à l'Innovation). The authors specifically acknowledge the clinical
62 research assisting team from AP-HP: Nawal Derridj-Ait Younes (Project Manager),
63 Chamseddine Mediouni (Clinical Research Assistant), Abderraouf Hermez, Messaoud
64 Kaoula and Sylvie Azerad (Data Managers).

65

66 i-Predict study group membership:

67 Bénédicte Sabin, Prof. Didier Guillemot, Prof. Servaas Morré, Dr Isabelle Karsenti, Dr Marie-Françoise Claret-Vermeulen, Dr. Emmanuelle Mathiot-Vicaigne, Dr. Christian Régnier, Dr.
68
69 Philippe Aoussou, Dr. Raphaelle Badie-Perez, Dr. Karine Rebouillat-Savy, Dr Nadia Valin, Prof. Christophe Tzourio, Dr. Anne-Cécile Rahis, Prof. Philippe Judlin, Prof. Jean-Luc Brun,
70
71 Prof. André Bongain, Prof. Philippe Aegerter, Layidé Méaude-Roufai, Chanez Chemani, Prof.
72 Lamiae Grimaldi-Bensouda, Chamseddine Mediouni, Dr. Delphine Kersaudy-Rahib, Dr.
73 Nathalie Lydié.
74 **ABSTRACT**

75 *Purpose*. Non-optimal vaginal microbiota lacking lactobacilli and comprising a wide array of
76 anaerobic bacteria, typified by community state type (CST) IV, have been associated with
77 adverse gynecological and pregnancy outcomes. Here, we investigate the stability of the
78 vaginal microbiota sampled every 6 months over 18 months and how samples distantly
79 collected combined with exposures could provide insight on future microbiota compositional
80 changes.

81 *Methods*. Vaginal microbiota dynamics were analyzed in 241 female students aged 18-24
82 years and negative for *Chlamydia trachomatis* and *Neisseria gonorrhoeae*. The vaginal
83 microbiota was characterized using 16S rRNA gene amplicon sequencing and assigned to
84 CSTs. Vaginal microbiota longitudinal profiles were determined through hierarchical
85 clustering.

86 *Results*. At baseline, 11.2% of participants had a CST IV, 40.5% a CST I (*Lactobacillus crispatus*-dominated), and 38.1% a CST III (*Lactobacillus iners*-dominated). A total of 345 CST transitions were observed over the study period. Pain during sexual intercourse was associated with a higher probability of transition from CST III to CST IV, while self-reported yeast infection was associated with a higher probability of transition from CST IV to CST I. Over the study period, 32.0% participants displayed a stable CST trajectory. Composition of the vaginal microbiota of a single sample predicted with good accuracy the CST trajectory over the following 18 months.

94 *Conclusion*. Vaginal longitudinal CST patterns over 18 months could be clustered into three
95 main groups of trajectories. Performing molecular characterization at a single time point
96 could contribute to improved preventive care and optimization of young women's
97 reproductive and sexual health.

98 **ClinicalTrials.gov Identifier: NCT02904811. Registration date: September 19, 2016**
99

100

100 **KEYWORDS**

101 Vaginal microbiome, Community State Types, 16S rRNA gene amplicon sequencing,
102 trajectory, microbiota compositional profile

103

104 **WORD COUNT**

105 Manuscript: 4455/4500
106 Abstract: 250/250
107 INTRODUCTION

108 The vaginal microbiota plays a critical role in genital health. Epidemiological studies have 109 shown that vaginal microbiota dominated by species of *Lactobacillus* have been associated 110 with lower risk of sexually transmitted infections (STIs) and adverse pregnancy outcomes. 111 Conversely, vaginal microbiota lacking *Lactobacillus* spp. and comprising a diverse array of 112 strict and facultative anaerobes, commonly established in research settings using Gram 113 strain evaluation with Nugent score [1], are associated with increased risk of STIs, including 114 HIV [2-4], herpes simplex virus-2 [5, 6], human papillomavirus (HPV) [7, 8] and *Chlamydia* 115 trachomatis [8-11] infections, as well as adverse obstetric outcomes such as preterm birth 116 and low birth weight [12-14], among others. 117

118 Molecular characterization of the vaginal microbiota using marker gene amplicon 119 sequencing, such as the 16S rRNA gene, has afforded a high-resolution characterization of 120 the composition and structure of vaginal microbiota and the ecology of the vaginal 121 microenvironment. In reproductive age women, vaginal bacterial communities can be 122 clustered into five broad community state types (CSTs) [15, 16]. CST I, II, III and V are 123 dominated by one species of *Lactobacillus*, respectively *L. crispatus*, *L. gasseri*, *L. iners* and 124 *L. jensenii*, while CST IV lacks *Lactobacillus* spp. and consists either of a diverse array of 125 anaerobes, including *Gardnerella* spp., *Fannyhessea vaginae*, *Ca*. Lachnocurva vaginae or 126 *Prevotella* spp. [15] grouped together as CST IV-A and CST IV-B, or of aerobic pathobionts 127 grouped together as CST IV-C [17]. Previous works looking at CST IV on the whole, showed 128 that CST IV aligned well with bacterial vaginosis (BV) diagnosed with Nugent score (Nugent- 129 BV) and is also associated with adverse gynecological outcomes [4, 8, 18-22]. Hence, CST 130 IV is often referred to as molecular-BV [16]. Both Nugent-BV and molecular-BV can present 131 with or without symptoms and women can harbor CST IV vaginal microbiota for long periods 132 of time [23, 24]. Frequent sampling longitudinal studies have shown that some women can 133 experience frequent and rapid shifts in the composition of their vaginal microbiota while 134 others have highly stable vaginal microbiota [23]. The reasons for these differences in 135 stability or any predictors of community dynamics have eluded researchers in the field. 136 Interestingly, *L. iners*, which is predominant in CST III, stands out among other vaginal 137 lactobacilli as the most prevalent species in reproductive age women [15, 25-27]. However, 138 its inability to produce D-lactic acid [28, 29] makes it unable to provide optimal protection 139 against certain STIs, including HIV and *C. trachomatis* [30-32]. 140

141 Despite a large body of accumulated knowledge on associations between the composition of 142 the vaginal microbiota and adverse outcomes, translation into more effective clinical 143 diagnostics or therapies remains limited [33, 34]. Understanding the factors contributing to
the long-term stability of optimal vaginal microbiota could lead to novel interventions and
improved health outcomes. Previous work has characterized the temporal dynamics of the
vaginal microbiota over short periods of time, albeit with daily or bi-weekly samples, using the
Nugent score [35-40] or 16S rRNA gene amplicon sequencing [23, 41-43], the latter often in
small cohorts. Despite these studies, the factors associated with the dynamics of these
vaginal microbiota are still debated (i.e., factors triggering specific transitions or stability). In
addition, there is a lack of research on the long-term dynamics patterns and the drivers of
stability or instability. Establishing predictors of these dynamics patterns would afford the
development of innovative diagnostics and associated interventions (both behavioral and
therapeutic) to restore optimality in composition and stability of the vaginal microbiota.

In the present study, we explored the long-term dynamics patterns of vaginal microbiota in a
subcohort of 241 *C. trachomatis*-negative student women who participated in the i-Predict
(Prevention of Diseases Induced by *Chlamydia trachomatis*) trial in France [44], and who
were sampled every 6 months for 18 months. We aimed at investigating whether the
composition of the vaginal microbiota could predict dynamics patterns associated with
increased risk of adverse outcomes.

158

159 MATERIALS AND METHODS

160 *Study population and design*

161 This analysis is an ancillary study to the i-Predict (<u>Prevention of diseases induced by</u>
162 <u>Chlamydia trachomatis</u>) randomized controlled trial, which aimed at evaluating the efficacy of
163 <u>C. trachomatis</u> systematic and early screening in 18-24 years old female students on the
164 prevention of pelvic inflammatory disease incidence. The full protocol was described
165 elsewhere [44]. Briefly, the i-Predict trial enrolled women aged 18 to 24 years at seven study
166 sites in France (four in Paris, one in Versailles-Saint-Quentin-en-Yvelines (Paris suburb), one
167 in Bordeaux and one in Nice) between January 2017 and January 2020. Participants were
168 invited to participate at the University Medical Services by trained study staff. If they met the
169 inclusion criteria and agreed to participate, they were randomly assigned to one of two
groups: one group followed the current guidelines for <u>C. trachomatis</u> screening in young
170 women in France, that is they were tested for STI only if they sought test themselves at an
171 STI clinic or if a test was proposed by a healthcare professional during a visit; the other
172 group was screened for <u>C. trachomatis</u> every six months and received adequate treatment in
173 case of positivity. In both groups, participants provided self-collected samples at inclusion, 6,
174 12 and 18 months.
175
176

Vaginal microbiota characterization was carried out on a subset of samples from 316

177

participants of the i-Predict trial. For this study, only women with a complete follow-up and

178

persistently negative for *C. trachomatis* and *Neisseria gonorrhoeae* infections (established

179

after the observation study ended for those in the no routing testing arm and determined

180

through the Aptima Combo 2 CT/NG assay; Hologic) were included, which resulted in 241

181

participants and 964 samples available for analysis (SUPPL FIGURE 1).

182

## Data collection

183

As the i-Predict trial is nested within a nation-wide student cohort called i-Share (Internet-

184

based Students Health Research Enterprise, www.i-share.fr), demographic and

185

socioeconomic data were collected through the online self-administered i-Share enrollment

186

questionnaire and retrieved for this study, including age, education level, field of studies,

187

parents' education, financial situation, alcohol and tobacco consumption, and general health

188

perception. The i-Predict study included self-administered baseline and follow-up

189

questionnaires at each timepoint covering the period between two visits using the

190

Cleanweb® interface. The questionnaires included variables on antibiotic consumption,

191

contraception, gynecological history, hygiene practices and sexual practices.

192

## Vaginal microbiota characterization

193

Participants self-collected vaginal samples at 0, 6, 12 and 18 months using the commercially

194

available Aptima Multitest swab specimen collection kit (Hologic, San Diego, CA, USA). After

195

sampling, the kit was returned to the National Reference Center for STIs (Bordeaux, France)

196

in a pre-addressed, prepaid envelope. DNA extraction was done using the MagNA Pure 96

197

DNA and viral NA small-volume kit on the MagNA Pure 96 instrument (Roche Diagnostics).

198

The DNA was diluted to 10 ng/µL, and the V3-V4 regions of the 16S rRNA gene was

199

amplified and sequenced on an Illumina MiSeq instrument at Maryland Genomics, Institute

200

for Genome Sciences, University of Maryland School of Medicine, according to a previously

201

published and validated standard protocol using a unique dual indices system [45]. Negative

202

(extraction and PCR) and positive samples (ZymoBIOMICS Microbial Community DNA

203

Standard, ZYMO Research) were included. Sequence data processing, quality controls and

204

taxonomic assignment were performed using the bioinformatic pipeline developed by Holm et

205

al. [45] resulting in tables of bacterial phylotype sequence counts and relative abundances.

206

CSTs were assigned to each sample using VALENCIA, a nearest centroid-based

207

classification method [17]. CST IV-A, comprising of *Ca. Lachnocurva vaginae* [46] and

208

*Gardnerella* spp. among other anaerobes and CST IV-B, comprising of *G. vaginalis* and *F. vaginae* (formerly *Atopobium vaginae*) were grouped together as CST IV-AB.

209

210
211 Statistical analysis

212 All analyses were performed using R (version 4.1.2) [47]. Participants' characteristics were
213 described with means and proportions for the overall cohort and by baseline CST.

214 Transition probabilities between two CSTs at two consecutive time points (6 months apart)
215 and factors associated with specific CST transitions were assessed using multi-state
216 transition models implemented in the msm package [48]. Multi-state transition models
217 assume that the transition to a different CST only depends on the previous CST and not on
218 the overall longitudinal CST pattern of the participants. To increase convergence, the number
219 of CSTs was reduced to four by grouping CST II and V into CST II/V, as well as CST IV-AB
220 and IV-C (comprising of *Streptococcus* or *Corynebacterium* [16]) into CST IV. The variables
221 used in these models were assessed at baseline and follow-up questionnaires as listed in the
222 additional notes in the supplementary files. Time-varying variables at time *t* were used to
223 model CST transitions between time *t* − 1 and *t*, e.g., a variable referring to "the last 6
224 months" in the M12 questionnaire would be used as a covariate to model a CST transition
225 between M6 and M12.

226 We then investigated the alternative and less restrictive hypothesis that the vaginal
227 microbiota composition at a particular time point depends on the overall longitudinal CST
228 pattern of the participant. Stability of the composition of the vaginal microbiota was evaluated
229 using the Bray-Curtis dissimilarity measure between consecutive samples implemented in
230 the vegan package [49]. The longitudinal CST pattern of each participant over the course of
231 the study was determined through hierarchical clustering of the optimal matching pairwise
232 distances, a method adapted to discrete data and implemented in the TraMineR package
233 [50]. The optimal number of groups corresponding to longitudinal CST pattern types in the
234 study was defined using a graphical method based on inertia (elbow method [51]). Variables
235 assessed for their association with longitudinal CST patterns were those from the baseline
236 questionnaires, and their statistical significance was evaluated using either the Pearson's
237 Chi-squared test, the Fisher's exact test or the Kruskal-Wallis rank sum test depending on
238 the type of variable.

239 Lastly, the ability to predict longitudinal CST patterns from the CST of one sample or two
240 samples was evaluated using multinomial logistic regression models implemented in the nnet
241 package [52], with the longitudinal CST pattern type as the outcome and one of the following
242 predictors: a CST taken at any timepoint, any pair of two CSTs at 6-, 12- or 18-month
243 intervals, and any combination of two CSTs (regardless of the time interval). The sensitivity,
244 specificity, positive predictive value (PPV) and negative predictive value (NPV) were
245 computed for each model.
246

All statistical tests were two-sided. P-values less than 0.05 were considered statistically significant.

247

248

### Ethical considerations

249

The i-Predict trial was approved by the Comité de Protection des Personnes (CPP) on June 2, 2016 (reference number P150950), the Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé (CCTIRS) on May 19, 2016 (reference number 16-327) and the Commission Nationale Informatique et Libertés (CNIL) on August 1, 2016 (reference number MMS/ABD/AR168237, decision number DR-2016-350). This trial was authorised by the Agence Nationale de Sécurité du Médicament et des Produits de Santé (ANSM) on May 30, 2016 (IDRCB and EudraCT reference number: 2016-A00491-50).

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258

## RESULTS

259

### Description of the cohort

260

The 241 female participants contributed 964 samples taken on average 184 days apart (standard deviation [sd] 25, interquartile range 21) and 32,740,915 16S rRNA amplicon sequences, that is on average 33,964 sequences per sample (sd 20,914, minimum 6,697, maximum 139,453). Baseline characteristics of the participants are presented in **TABLE 1** and **SUPPL TABLE 1**. Participants were on average 21 years old (min 18, max 24). Sixty-nine percent of female students were from Bordeaux, 27% from the Paris area (Paris and Versailles-Saint-Quentin-en-Yvelines) and 4% from Nice. Most of them had followed a general curriculum in high-school and were contemplating a graduate level degree. Eighty-three percent were satisfied with their financial resources and 67% had seen a gynecologist in the last 12 months. Fifty-three percent of participants were currently using oral contraception, while 74% declared always or often using condoms since sexual debut. In terms of sexual partners, 49% have had 4 or more male partners in their lifetime, and 9% have ever had a female partner in their lifetime.

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272

273

At baseline, 40.5% of participants had a CST I and 38.1% had a CST III, while only 11.2% had a CST IV vaginal microbiota (**TABLE 1**).

274

275

276
277 CST transitions and factors associated with CST transitions

278 All CST transitions at two consecutive samples are shown in **FIGURE 1A** and **B**, reproducing
279 a graphical representation used by Muñoz *et al.* [53]. Of 723 possible transitions, 345
280 (47.7%) corresponded to a change of CSTs. Participants remained for longer duration in
281 CST I (16 months) and in CST III (13 months) than in other CSTs, while they remained on
282 average 6 months in CST IV-AB. No specific average transition pattern was observed
283 regarding transitions from one CST to another (**FIGURE 1C**).

284 Both current use and use in the last 6 months of oral contraception were associated with a
decreased probability of transition from CST I to CST III. Conversely, tampon use and
285 sanitary pad use in the last 6 months were associated with a decreased probability of
286 transition from CST III to CST I. Pain during sexual intercourse in the last 6 months was
287 associated with a higher probability of transition from CST III to CST IV. Conversely, condom
288 use in the last 6 months and menstrual cup use during menses in the last 6 months were
289 associated with a higher probability of transition from CST IV to CST III, and sanitary pad use
290 in the last 6 months with a lower probability of such transition. Self-reported yeast infection in
291 the last 6 months was associated with an increased probability of transition from CST IV to
292 CST I. Lastly, some sexual practices showed a statistically significant association. For
293 instance, the number of male partners and particularly new male partners in the last 6
294 months, as well as having multiple concomitant partners in the last 6 months was associated
295 with an increased probability of transition from CST III to CST IV. Having had a new male
296 partner in last 6 months and the number of male partners in the last 6 months were also both
297 associated with an increased probability of transition from CST IV to CST III, and from CST
298 III to CST I (**FIGURE 2, SUPPL TABLE 2**).

300

301 *Factors associated with longitudinal CST patterns*

302 Longitudinal CST patterns from all participants were clustered in three groups (FIGURE 3).
303 Half the participants displayed longitudinal CST patterns comprising mostly CST I (47%, n =
304 114), labelled group 1, while 30% (n = 73) had longitudinal CST patterns comprising mostly
305 CST III, labelled group 3. Lastly, 22% (n = 54) of participants had longitudinal CST patterns
306 comprising mixtures of some *Lactobacillus*-dominated CSTs and mostly CST IV and were
307 labelled group 2. Group 1 and group 3 were more stable than group 2 with 32.5% and 39.7%
308 of women in these respective groups displaying the same CST at each time point versus
309 20.4% for group 2, although this was not statistically significant (p = 0.067). This was also
310 supported by lower means of Bray-Curtis dissimilarity between consecutive samples
311
312

(estimate of compositional stability) in group 3 than in group 1 (p = 0.013), which itself was
lower than in group 2 (p = 0.0004) (SUPPL FIGURE 2).

313
314
315
316
317

In terms of participants' baseline characteristics (TABLE 2), tampon use during menses was
significantly more common in women from group 3 (76% versus 49% in group 2 and 61% in
group 1, p = 0.008). Vaginal intercourse in the last 12 months and a higher number of lifetime
male partners were associated with group 1 and 3 compared to group 2 (p = 0.038 and 0.024
respectively).

318
319

CST as a predictor of longitudinal CST patterns

320
321
322
323
324
325
326
327

A good predictor would ideally have a high specificity for group 1 (optimal) and a high sensitivity for group 2 (unstable and non-optimal) in a clinical setting, to guarantee that group 2 longitudinal CST patterns are not missed. An alternative approach could be to focus on each trajectory and determine the best specificity/NPV combination for group 1, and the best sensitivity/PPV combination for group 2 and group 3. Using one CST at a single timepoint yielded a good specificity for group 1 (88%) and a good sensitivity for group 2 (88%, **TABLE 3**). Using two CSTs at different time intervals (SUPPL TABLE 3) did not sensibly improve performance compared with a single CST.

328
329

DISCUSSION

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337

In a cohort of 241 young female adults negative for *C. trachomatis* and *N. gonorrhoeae* infections who provided vaginal samples every 6 months for 18 months, we investigated CST transitions and longitudinal CST patterns. We did not find evidence of systematic transitions but highlighted three major types of longitudinal CST patterns: two stable longitudinal patterns dominated by CST I and CST III respectively; and one unstable longitudinal pattern comprising frequent transitions in and out of *Lactobacillus*-dominated CSTs and CST IV. The CST from one of the four samples was sufficient to predict with good accuracy longitudinal CST patterns over 18 months.

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343

Our study aimed at filling the gaps on the dynamics of the vaginal microbiota of young French women. Thus, we believe that this study complements previous reports on the dynamics of the vaginal microbiota both by adding vaginal microbiota data from European populations poorly represented in the literature and through its long-term longitudinal design. Many studies have looked at the temporal dynamics of the vaginal microbiota in asymptomatic otherwise healthy women, with frequent sampling designs, but for rather short
344 study periods, i.e., from the length of a menstrual cycle up to 8 months [23, 24, 42, 54-60].
345 These studies have greatly improved our understanding of the vaginal microbiota short-term
dynamics and have examined the impact of specific factors on these short-term dynamics
346 (e.g., candidiasis infection, menstruation, antibiotics, sexual intercourse, hormonal changes).
347 However, over these short periods of time, most of the transitions observed in the vaginal
348 microbiota appeared short-lived [24] and might not be relevant to the clinical management of
349 vaginal health as frequent monitoring of the vaginal microbiota dynamics might not be
considered in clinical practice. This highlights the need to evaluate the long-term dynamics
350 patterns of the vaginal microbiota and the factors associated with unstable and/or CST IV-
dominated dynamics. This study is the first to generate data longitudinally on the vaginal
351 microbiota of young women in France, in a population typically difficult to capture as it is
352 composed of healthy 18-24-year-old student females who are also prone to high mobility
353 over a 2-year period. In our study, the proportion of women harboring a Lactobacillus-
dominated CSTs at baseline and belonging to group 1 (mostly CST I over time) and group 3
354 (mostly CST III over time) was higher than in previous studies studying longitudinal short or
355 long-term dynamics in healthy women from South Africa and North America [23, 53, 61],
356 which might be due to differences in sociodemographic and behavioral (e.g., hygiene
357 practices) characteristics.
358

361
362 In the present study, we investigated the dynamics of vaginal microbiota and associated
363 factors under two alternative hypotheses: that the composition of the vaginal microbiota at
364 one timepoint depended either on the previous timepoint or on the whole trajectory of the
365 vaginal microbiota of an individual. In our results, averaging CST transitions from one time
366 point to another, thus breaking up the concept of trajectory, did not suggest any systematic
367 pattern in transitions. Contrary to Muñoz et al. [53], in this population, we did not find
368 evidence that L. iners-dominated communities carried an inherent risk of transition to CST IV.
369 Instead, our analyses showed that CST III or CST IV microbiota can endure over long
370 periods of time, and do not appear to exist only as a transient non-optimal state, as Muñoz et
371 al. also previously suggested in a different population comprising South-African reproductive-
372 age women [53]. Vaginal microbiota harboring G. vaginalis (typified by CST IV) for long
373 periods of time have even been found in healthy premenarcheal girls [62]. However, health
374 outcomes have never been studied in women who consistently harbor these microbiota
375 profiles over a long period of time, often without the symptoms commonly associated with
376 BV, although ecological theory would predict that longer duration and higher frequency in a
377 CST IV would represent a window of opportunities for infection and an increased risk of
378 adverse outcomes.
379
A few factors were associated with CST transitions but not longitudinal CST patterns. Thus, reporting yeast infection in the last 6 months (e.g., in the 6-month period between two samples characterizing a transition or a non-transition) was associated with a higher probability of transition from CST IV to CST I, which is in line with previously reported association between vulvovaginal candidiasis, or carriage of *Candida* spp. and a *L. crispatus*-dominated vaginal microbiota [55, 63, 64]. However, in the analysis of factors associated with longitudinal CST patterns, group 1 and group 3 had slightly, but not statistically significantly, higher proportions of women who reported yeast infection (including candidiasis) in the last 12 months. Of note, yeast infection was self-reported by participants in our study and we cannot exclude misclassification. Similarly, use of oral contraceptives was associated with a lower probability of transition from CST I to CST III, which aligns with previous findings on the impact of oral contraceptives on the vaginal microbiota composition and stability [65, 66] in populations from Southern Africa, South-East Asia and North America, but we did not observe any association with longitudinal CST patterns in the present study.

Conversely, we did not find associations between antibiotic consumption and either CST transitions or longitudinal CST patterns, while previous works have shown an effect of antibiotics on the vaginal microbiome, but also other microbiomes, according to the types of antibiotic used, administration modes, doses and duration of treatment [67-70]. Regarding antibiotic treatments specifically targeting BV-associated bacteria, such as metronidazole or clindamycin, the disappointing results previously reported in terms of their ability to prevent the recurrence of BV and its symptoms [71, 72] and BV-associated bacteria [73] suggest that their effect might be in some instances short-lived, and thus not visible in our study design. This might as well be the case for antibiotics prescribed for other urogenital infections. Nevertheless, we should acknowledge the lack of statistical power to analyze the association between each family of antibiotics in our study and that grouping all antibiotics may have masked specific associations.

Finally, in terms of sexual exposures, having had male partners and particularly new male partners, as well as multiple concomitant partners in the last 6 months, were associated with an increased probability of transitioning from CST III to CST IV, and frequent condom use was associated with increased probability of transition from CST IV to CST III. But we also found unexpected directionality of the association between some sexual exposures and CST transitions or longitudinal CST patterns. In our transition model, having had 4 or more male partners in the last 6 months as well as having had new partner(s) in the last 6 months were also associated with a higher probability of transition from CST IV to CST III and from CST III to CST I. Having had 4 or more male partners in the last 6 months was also more common in women with a CST III-dominated longitudinal CST pattern (group 3) and a CST I dominated
longitudinal CST pattern (group 1). Although previous studies point to the impact of sexual
practices on the probability of undergoing vaginal microbiota composition transitions [23, 24],
the extent to which sexual practices have an impact on longitudinal CST patterns over longer
period requires further investigation and better understanding of the mechanism
underpinning the resilience of different vaginal microbiota compositions to such events. Our
results also relate to the debate on the extent to which vaginal microbiota bacteria are
sexually transmitted [74], with some studies suggesting sexual transmission of BV (e.g.,
rapid incidence of BV after first sex in Kenyan adolescent girls [75], evidence of strain-
sharing between sexual partners [76], an increased risk of BV acquisition between female
partners [77, 78], or a reduction of BV acquisition in women with circumcised partners [79]),
while others showed no change in the vaginal microbiota composition after sexual debut in
white American adolescent girls [80] and the presence of *G. vaginalis*, usually associated
with BV, in about a third of young American adolescents before the onset of menstruation
and any sexual activity [62]. Of note, the study by Carter *et al.* (2024) also showed strain-
sharing of *L. iners* between female partners, suggesting sexual transmission of this species
as well [76]. While our results support the idea of sexual exposures favoring transitions
between all types of CSTs and not particularly towards BV-associated microbiota
composition, two limitations must be pointed out: it is difficult to assess the temporal
relationship between sexual exposures and vaginal microbiota changes, and disentangling
the effect of sexual activity from other confounders is challenging.

This study has several limitations. The first limitation resides in its relatively homogenous
study population, as highlighted by sociodemographic characteristics and the large
proportion of participants displaying CST I and CST III. This hinders the representativity and
generalizability of our results to different populations and requires replication of this type of
analysis in different study settings. Although this study included a wide range of
epidemiological data, including sexual practices, gynecological history, contraception,
antibiotic consumption, among others, some important factors, such as BV testing or reliable
evaluation of symptoms were not implemented due to the home-sampling design. HPV
infection status was not available either, but we expect a very low prevalence of High Risk-
HPV 16 and 18 in this population [81]. Interestingly, HPV 16 has been shown to be
correlated to CST IV (as reviewed in [82]) and the frequency of CST IV in this cohort was low
compared to the frequency reported in other populations [15, 53, 61]. In addition, the
occurrence of certain exposures (such as antibiotics) or at-risk sexual practices was rare and
resulted in a limited statistical power to evaluate the associations between these exposures
and changes in CST profiles. By selecting only participants negative for *C. trachomatis* to
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451

characterize longitudinal CST patterns, we restricted ourselves in the possibility to evaluate
their association with clinical outcomes, which remains to explore further.

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It is critical to better understand the extent to which information generated by next generation
molecular characterization of the vaginal microbiota could inform clinical practice. The clinical
value of these assays could only be realized if the diagnostic significance is thoroughly
validated for specific clinical outcomes and associated with validated and implementable
interventions. As such, studies that would associate long-term dynamics patterns with
susceptibility to adverse health outcomes need to be performed. Such knowledge could be
used for women's gynecological and obstetric health management and that after puberty,
before or after sexual debut or before and after planned pregnancy. In addition, as we did not
identify specific exposures, practices associated with long term CST patterns, exploring other
factors such as human genetic, immunological or environmental factors, or exposure to a
certain type of vaginal microbiota at birth, among others, to explain longitudinal CST patterns
is desperately needed [83].

464

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<caption>TABLE 1. Main sociodemographic characteristics and sexual history and practices in 241 participants, i-Predict study.</caption>

756

<table><thead><tr><th>Variables</th><th>N = 241<br>n</th><th>(%)</th></tr></thead><tbody><tr><td><strong>Age category (years)</strong></td><td></td><td></td></tr><tr><td>18-20</td><td>101</td><td>(42%)</td></tr><tr><td>21-25</td><td>140</td><td>(58%)</td></tr><tr><td><strong>Recruiting center</strong></td><td></td><td></td></tr><tr><td>Bordeaux</td><td>166</td><td>(69%)</td></tr><tr><td>Nice</td><td>10</td><td>(4.1%)</td></tr><tr><td>Paris</td><td>65</td><td>(27%)</td></tr><tr><td><strong>High-school type</strong></td><td></td><td></td></tr><tr><td>General</td><td>221</td><td>(96%)</td></tr><tr><td>Professional or equivalent</td><td>2</td><td>(0.9%)</td></tr><tr><td>Technical</td><td>8</td><td>(3.5%)</td></tr><tr><td><strong>Foreseen level of education</strong></td><td></td><td></td></tr><tr><td>Bachelor level</td><td>15</td><td>(6.5%)</td></tr><tr><td>Graduate level</td><td>192</td><td>(83%)</td></tr><tr><td>Other</td><td>24</td><td>(10%)</td></tr><tr><td><strong>Education field</strong></td><td></td><td></td></tr><tr><td>Sciences</td><td>32</td><td>(15%)</td></tr><tr><td>Health</td><td>96</td><td>(45%)</td></tr><tr><td>Human sciences</td><td>75</td><td>(35%)</td></tr><tr><td>Other</td><td>24</td><td>(10%)</td></tr><tr><td><strong>Family's socioeconomic level</strong></td><td></td><td></td></tr><tr><td>Comfortable</td><td>212</td><td>(92%)</td></tr><tr><td>Difficult</td><td>19</td><td>(8.2%)</td></tr><tr><td><strong>Housing type</strong></td><td></td><td></td></tr><tr><td>At parent's</td><td>65</td><td>(28%)</td></tr><tr><td>Residency/apartment</td><td>165</td><td>(72%)</td></tr><tr><td><strong>Self-perceived resources</strong></td><td></td><td></td></tr><tr><td>Satisfied</td><td>191</td><td>(83%)</td></tr><tr><td>Unsatisfied</td><td>39</td><td>(17%)</td></tr><tr><td><strong>Self-perceived overall health</strong></td><td></td><td></td></tr><tr><td>Bad</td><td>7</td><td>(3.1%)</td></tr><tr><td>Good</td><td>222</td><td>(97%)</td></tr><tr><td><strong>Smoking status</strong></td><td></td><td></td></tr><tr><td>Non-smoking</td><td>88</td><td>(39%)</td></tr><tr><td>Currently smoking</td><td>62</td><td>(27%)</td></tr><tr><td>Formerly smoking</td><td>78</td><td>(34%)</td></tr><tr><td><strong>Alcohol consumption frequency</strong></td><td></td><td></td></tr><tr><td>Less than once/week</td><td>185</td><td>(81%)</td></tr><tr><td>More than once/week</td><td>43</td><td>(19%)</td></tr><tr><td><strong>Ever taken an oral contraception</strong></td><td>198</td><td>(85%)</td></tr><tr><td><strong>Current use of oral contraception</strong></td><td>123</td><td>(53%)</td></tr><tr><td><strong>Lifetime number of male partners</strong></td><td></td><td></td></tr><tr><td>≤3</td><td>118</td><td>(51%)</td></tr><tr><td>≥4</td><td>112</td><td>(49%)</td></tr><tr><td><strong>Lifetime number of female partners</strong></td><td></td><td></td></tr><tr><td>0</td><td>207</td><td>(91%)</td></tr><tr><td>≥1</td><td>21</td><td>(9.2%)</td></tr><tr><td><strong>Lifetime condom use</strong></td><td></td><td></td></tr><tr><td>Sometimes/Never</td><td>59</td><td>(26%)</td></tr><tr><td>Often/Always</td><td>170</td><td>(74%)</td></tr><tr><td><strong>Ever douched</strong></td><td>27</td><td>(11%)</td></tr><tr><td><strong>Ever had unusual vaginal discharge</strong></td><td>108</td><td>(46%)</td></tr><tr><td><strong>Gynecologist consultation in last 12 months</strong></td><td>154</td><td>(67%)</td></tr><tr><td><strong>Reported yeast infection in last 12 months</strong></td><td>72</td><td>(31%)</td></tr><tr><td><strong>Reported urinary tract infection in last 12 months</strong></td><td>74</td><td>(32%)</td></tr><tr><td><strong>Reported STI diagnosis or symptoms in last 12 months</strong></td><td>12</td><td>(5.2%)</td></tr></tbody></table>

757
758 Table 1, continued

<table><thead><tr><th>Variables</th><th>N = 241<br>n</th><th>(%)</th></tr></thead><tbody><tr><td>Baseline community state type (CST)</td><td></td><td></td></tr><tr><td>I</td><td>96</td><td>(40%)</td></tr><tr><td>II</td><td>11</td><td>(4.6%)</td></tr><tr><td>III</td><td>91</td><td>(38%)</td></tr><tr><td>IV-AB</td><td>20</td><td>(8.3%)</td></tr><tr><td>IV-C</td><td>7</td><td>(2.9%)</td></tr><tr><td>V</td><td>16</td><td>(6.6%)</td></tr></tbody></table>

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<table><caption>TABLE 2. Baseline characteristics of the participants according to longitudinal community state types (CST) trajectories (termed groups) of participants over the course of the study.</caption><thead><tr><th>Variables</th><th>Overall, N = 241<sup>†</sup></th><th>Group 1, N = 114<sup>†</sup></th><th>Group 2, N = 54<sup>†</sup></th><th>Group 3, N = 73<sup>†</sup></th><th>P<sup>2</sup></th></tr></thead><tbody><tr><td><strong>Age category (years)</strong></td><td></td><td></td><td></td><td></td><td>0.8</td></tr><tr><td>18-20</td><td>101 (42%)</td><td>45 (39%)</td><td>24 (44%)</td><td>32 (44%)</td><td></td></tr><tr><td>21-25</td><td>140 (58%)</td><td>69 (61%)</td><td>30 (56%)</td><td>41 (56%)</td><td></td></tr><tr><td><strong>Recruiting center</strong></td><td></td><td></td><td></td><td></td><td>0.7</td></tr><tr><td>Bordeaux</td><td>166 (69%)</td><td>80 (70%)</td><td>37 (69%)</td><td>49 (67%)</td><td></td></tr><tr><td>Nice</td><td>10 (4.1%)</td><td>4 (3.5%)</td><td>4 (7.4%)</td><td>2 (2.7%)</td><td></td></tr><tr><td>Paris</td><td>65 (27%)</td><td>30 (26%)</td><td>13 (24%)</td><td>22 (30%)</td><td></td></tr><tr><td><strong>Education field</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sciences</td><td>32 (15%)</td><td>13 (13%)</td><td>13 (26%)</td><td>6 (9.5%)</td><td>0.3</td></tr><tr><td>Health</td><td>96 (45%)</td><td>47 (47%)</td><td>17 (34%)</td><td>32 (51%)</td><td></td></tr><tr><td>Human sciences</td><td>75 (35%)</td><td>36 (36%)</td><td>18 (36%)</td><td>21 (33%)</td><td></td></tr><tr><td>Other</td><td>11 (5.1%)</td><td>5 (5.0%)</td><td>2 (4.0%)</td><td>4 (6.3%)</td><td></td></tr><tr><td><strong>First parent's education</strong></td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>High school degree</td><td>93 (41%)</td><td>49 (45%)</td><td>17 (35%)</td><td>27 (38%)</td><td></td></tr><tr><td>Higher education degree</td><td>136 (59%)</td><td>61 (55%)</td><td>31 (65%)</td><td>44 (62%)</td><td></td></tr><tr><td><strong>Second parent's education</strong></td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>High school degree</td><td>89 (41%)</td><td>48 (45%)</td><td>17 (38%)</td><td>24 (36%)</td><td></td></tr><tr><td>Higher education degree</td><td>128 (59%)</td><td>58 (55%)</td><td>28 (62%)</td><td>42 (64%)</td><td></td></tr><tr><td><strong>Family's socioeconomic level</strong></td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>Comfortable</td><td>212 (92%)</td><td>99 (90%)</td><td>48 (96%)</td><td>65 (92%)</td><td></td></tr><tr><td>Difficult</td><td>19 (8.2%)</td><td>11 (10%)</td><td>2 (4.0%)</td><td>6 (8.5%)</td><td></td></tr><tr><td><strong>Housing type</strong></td><td></td><td></td><td></td><td></td><td>0.043</td></tr><tr><td>At parent's</td><td>65 (28%)</td><td>33 (30%)</td><td>19 (39%)</td><td>13 (18%)</td><td></td></tr><tr><td>Residency/apartment</td><td>165 (72%)</td><td>77 (70%)</td><td>30 (61%)</td><td>58 (82%)</td><td></td></tr><tr><td><strong>Self-perceived resources</strong></td><td></td><td></td><td></td><td></td><td>&gt;0.9</td></tr><tr><td>Satisfying</td><td>191 (83%)</td><td>91 (83%)</td><td>41 (84%)</td><td>59 (83%)</td><td></td></tr><tr><td>Unsatisfying</td><td>39 (17%)</td><td>19 (17%)</td><td>8 (16%)</td><td>12 (17%)</td><td></td></tr><tr><td><strong>Self-perceived overall health</strong></td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>Bad</td><td>7 (3.1%)</td><td>6 (5.5%)</td><td>0 (0%)</td><td>1 (1.4%)</td><td></td></tr><tr><td>Good</td><td>222 (97%)</td><td>103 (94%)</td><td>49 (100%)</td><td>70 (99%)</td><td></td></tr><tr><td><strong>Regular sport practice</strong></td><td>122 (54%)</td><td>57 (53%)</td><td>28 (57%)</td><td>37 (52%)</td><td>0.8</td></tr><tr><td><strong>Gynecologist consultation in last 12 months</strong></td><td>154 (67%)</td><td>74 (68%)</td><td>33 (67%)</td><td>47 (66%)</td><td>&gt;0.9</td></tr><tr><td><strong>Smoking status</strong></td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>Non-smoking</td><td>88 (39%)</td><td>42 (39%)</td><td>21 (43%)</td><td>25 (35%)</td><td></td></tr><tr><td>Currently smoking</td><td>62 (27%)</td><td>33 (31%)</td><td>7 (14%)</td><td>22 (31%)</td><td></td></tr><tr><td>Formerly smoking</td><td>78 (34%)</td><td>33 (31%)</td><td>21 (43%)</td><td>24 (34%)</td><td></td></tr><tr><td><strong>Alcohol consumption frequency</strong></td><td></td><td></td><td></td><td></td><td>0.1</td></tr><tr><td>Less than once/week</td><td>185 (81%)</td><td>90 (83%)</td><td>43 (88%)</td><td>52 (73%)</td><td></td></tr><tr><td>More than once/week</td><td>43 (19%)</td><td>18 (17%)</td><td>6 (12%)</td><td>19 (27%)</td><td></td></tr><tr><td><strong>Cannabis consumption in lifetime</strong></td><td>127 (56%)</td><td>61 (57%)</td><td>24 (50%)</td><td>42 (60%)</td><td>0.6</td></tr><tr><td><strong>Specific food diet</strong></td><td>31 (14%)</td><td>13 (12%)</td><td>5 (10%)</td><td>13 (18%)</td><td>0.4</td></tr><tr><td><strong>Age at first menstruation (years)</strong></td><td></td><td></td><td></td><td></td><td>0.11</td></tr><tr><td>≤12</td><td>126 (54%)</td><td>67 (60%)</td><td>22 (42%)</td><td>37 (54%)</td><td></td></tr><tr><td>≥13</td><td>106 (46%)</td><td>45 (40%)</td><td>30 (58%)</td><td>31 (46%)</td><td></td></tr><tr><td><strong>Menstruation frequency</strong></td><td></td><td></td><td></td><td></td><td>0.6</td></tr><tr><td>Every month</td><td>175 (96%)</td><td>84 (98%)</td><td>39 (95%)</td><td>52 (95%)</td><td></td></tr><tr><td>Less than every month</td><td>7 (3.8%)</td><td>2 (2.3%)</td><td>2 (4.9%)</td><td>3 (5.5%)</td><td></td></tr><tr><td><strong>Life frequency of vaginal discharge/itching</strong></td><td></td><td></td><td></td><td></td><td>0.7</td></tr><tr><td>Never/less than once a year</td><td>126 (54%)</td><td>56 (50%)</td><td>30 (57%)</td><td>40 (56%)</td><td></td></tr><tr><td>More than once a year</td><td>109 (46%)</td><td>55 (50%)</td><td>23 (43%)</td><td>31 (44%)</td><td></td></tr><tr><td><strong>Age at first intercourse (years)</strong></td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>≤16</td><td>106 (47%)</td><td>50 (47%)</td><td>19 (37%)</td><td>37 (54%)</td><td></td></tr><tr><td>≥17</td><td>120 (53%)</td><td>56 (53%)</td><td>32 (63%)</td><td>32 (46%)</td><td></td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>Overall, N = 241<sup>1</sup></th><th>Group 1, N = 114<sup>1</sup></th><th>Group 2, N = 54<sup>1</sup></th><th>Group 3, N = 73<sup>1</sup></th><th>P<sup>2</sup></th></tr></thead><tbody><tr><td>Lifetime number of male partners</td><td></td><td></td><td></td><td></td><td>0.024</td></tr><tr><td>≤3</td><td>118 (51%)</td><td>55 (50%)</td><td>35 (66%)</td><td>28 (41%)</td><td></td></tr><tr><td>≥4</td><td>112 (49%)</td><td>54 (50%)</td><td>18 (34%)</td><td>40 (59%)</td><td></td></tr><tr><td>Lifetime number of female partners</td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>0</td><td>207 (91%)</td><td>98 (91%)</td><td>50 (94%)</td><td>59 (88%)</td><td></td></tr><tr><td>≥1</td><td>21 (9.2%)</td><td>10 (9.3%)</td><td>3 (5.7%)</td><td>8 (12%)</td><td></td></tr><tr><td>Lifetime condom use</td><td></td><td></td><td></td><td></td><td>0.7</td></tr><tr><td>Sometimes/Never</td><td>59 (26%)</td><td>27 (25%)</td><td>12 (23%)</td><td>20 (29%)</td><td></td></tr><tr><td>Often/Always</td><td>170 (74%)</td><td>81 (75%)</td><td>41 (77%)</td><td>48 (71%)</td><td></td></tr><tr><td>Lifetime oral contraception frequency</td><td></td><td></td><td></td><td></td><td>0.6</td></tr><tr><td>More than 3 months in a row</td><td>185 (81%)</td><td>92 (84%)</td><td>40 (77%)</td><td>53 (78%)</td><td></td></tr><tr><td>Less than 3 months in a row</td><td>8 (3.5%)</td><td>2 (1.8%)</td><td>3 (5.8%)</td><td>3 (4.4%)</td><td></td></tr><tr><td>Never</td><td>36 (16%)</td><td>15 (14%)</td><td>9 (17%)</td><td>12 (18%)</td><td></td></tr><tr><td>Lifetime implant frequency</td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>More than 3 months in a row</td><td>23 (10%)</td><td>13 (12%)</td><td>5 (9.8%)</td><td>5 (7.5%)</td><td></td></tr><tr><td>Less than 3 months in a row</td><td>6 (2.6%)</td><td>2 (1.8%)</td><td>3 (5.9%)</td><td>1 (1.5%)</td><td></td></tr><tr><td>Never</td><td>198 (87%)</td><td>94 (86%)</td><td>43 (84%)</td><td>61 (91%)</td><td></td></tr><tr><td>Lifetime Intra-Uterine Device frequency</td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>More than 3 months in a row</td><td>29 (13%)</td><td>13 (12%)</td><td>7 (14%)</td><td>9 (13%)</td><td></td></tr><tr><td>Less than 3 months in a row</td><td>7 (3.1%)</td><td>3 (2.8%)</td><td>0 (0%)</td><td>4 (6.0%)</td><td></td></tr><tr><td>Never</td><td>190 (84%)</td><td>93 (85%)</td><td>43 (86%)</td><td>54 (81%)</td><td></td></tr><tr><td>Frequency of condom use over lifetime</td><td></td><td></td><td></td><td></td><td>0.9</td></tr><tr><td>Regularly</td><td>146 (63%)</td><td>69 (63%)</td><td>35 (66%)</td><td>42 (62%)</td><td></td></tr><tr><td>Sometimes</td><td>80 (35%)</td><td>39 (36%)</td><td>17 (32%)</td><td>24 (35%)</td><td></td></tr><tr><td>Never</td><td>4 (1.7%)</td><td>1 (0.9%)</td><td>1 (1.9%)</td><td>2 (2.9%)</td><td></td></tr><tr><td>Tampon use during menses</td><td>149 (63%)</td><td>69 (61%)</td><td>26 (49%)</td><td>54 (76%)</td><td>0.008</td></tr><tr><td>Sanitary pad use during menses</td><td>166 (70%)</td><td>77 (68%)</td><td>37 (70%)</td><td>52 (73%)</td><td>0.8</td></tr><tr><td>Menstrual cup during menses</td><td>67 (28%)</td><td>33 (29%)</td><td>14 (26%)</td><td>20 (28%)</td><td>>0.9</td></tr><tr><td>Ever douched</td><td>27 (11%)</td><td>12 (11%)</td><td>5 (9.4%)</td><td>10 (14%)</td><td>0.7</td></tr><tr><td>Current use of oral contraception</td><td>123 (53%)</td><td>63 (57%)</td><td>25 (48%)</td><td>35 (49%)</td><td>0.5</td></tr><tr><td>Ever had unusual vaginal discharge</td><td>108 (46%)</td><td>52 (46%)</td><td>17 (33%)</td><td>39 (55%)</td><td>0.05</td></tr><tr><td>Ever bled outside menses</td><td>96 (41%)</td><td>48 (43%)</td><td>19 (36%)</td><td>29 (41%)</td><td>0.7</td></tr><tr><td>Ever had pain during sexual intercourse</td><td>136 (58%)</td><td>73 (65%)</td><td>26 (50%)</td><td>37 (52%)</td><td>0.093</td></tr><tr><td>Ever had unusual pelvic pain</td><td>55 (23%)</td><td>29 (26%)</td><td>11 (21%)</td><td>15 (21%)</td><td>0.7</td></tr><tr><td>Reported yeast infection in last 12 months</td><td>72 (31%)</td><td>34 (31%)</td><td>13 (25%)</td><td>25 (36%)</td><td>0.4</td></tr><tr><td>Reported urinary tract infection in last 12 months</td><td>74 (32%)</td><td>37 (34%)</td><td>14 (27%)</td><td>23 (32%)</td><td>0.7</td></tr><tr><td>Reported STI diagnosis or symptoms in last 12 months</td><td>12 (5.2%)</td><td>9 (8.1%)</td><td>1 (1.9%)</td><td>2 (3.0%)</td><td>0.2</td></tr><tr><td>Antibiotic use in last 12 months</td><td>103 (44%)</td><td>52 (47%)</td><td>21 (40%)</td><td>30 (42%)</td><td>0.7</td></tr><tr><td>Antibiotic use in last month</td><td>19 (8.2%)</td><td>9 (8.3%)</td><td>6 (12%)</td><td>4 (5.6%)</td><td>0.5</td></tr><tr><td>Number of male partners in last 12 months</td><td></td><td></td><td></td><td></td><td>0.092</td></tr><tr><td>≤1</td><td>135 (59%)</td><td>63 (59%)</td><td>37 (71%)</td><td>35 (51%)</td><td></td></tr><tr><td>≥2</td><td>92 (41%)</td><td>44 (41%)</td><td>15 (29%)</td><td>33 (49%)</td><td></td></tr><tr><td>Number of female partners in last 12 months</td><td></td><td></td><td></td><td></td><td>0.4</td></tr><tr><td>0</td><td>218 (96%)</td><td>104 (98%)</td><td>49 (94%)</td><td>65 (96%)</td><td></td></tr><tr><td>≥1</td><td>8 (3.5%)</td><td>2 (1.9%)</td><td>3 (5.8%)</td><td>3 (4.4%)</td><td></td></tr><tr><td>Number of new male partners in last 12 months</td><td></td><td></td><td></td><td></td><td>0.3</td></tr><tr><td>≤1</td><td>166 (73%)</td><td>77 (72%)</td><td>42 (81%)</td><td>47 (69%)</td><td></td></tr><tr><td>≥2</td><td>61 (27%)</td><td>30 (28%)</td><td>10 (19%)</td><td>21 (31%)</td><td></td></tr><tr><td>Number of new female partners in last 12 months</td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>0</td><td>221 (98%)</td><td>104 (99%)</td><td>52 (100%)</td><td>65 (96%)</td><td></td></tr><tr><td>≥1</td><td>4 (1.8%)</td><td>1 (1.0%)</td><td>0 (0%)</td><td>3 (4.4%)</td><td></td></tr><tr><td>Had multiple concomitant partners in last 12 months</td><td>43 (19%)</td><td>16 (15%)</td><td>9 (17%)</td><td>18 (26%)</td><td>0.2</td></tr><tr><td>Condom use in last 12 months</td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>Sometimes/Never</td><td>110 (48%)</td><td>59 (55%)</td><td>23 (44%)</td><td>28 (41%)</td><td></td></tr><tr><td>Often/Always</td><td>117 (52%)</td><td>48 (45%)</td><td>29 (56%)</td><td>40 (59%)</td><td></td></tr></tbody></table>
<table><thead><tr><th>Variables</th><th>Overall, N = 241<sup>1</sup></th><th>Group 1, N = 114<sup>1</sup></th><th>Group 2, N = 54<sup>1</sup></th><th>Group 3, N = 73<sup>1</sup></th><th>P<sup>2</sup></th></tr></thead><tbody><tr><td><strong>Vaginal/Anal intercourse during menses in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.4</td></tr><tr><td>Rarely/Never</td><td>217 (96%)</td><td>104 (97%)</td><td>47 (92%)</td><td>66 (97%)</td><td></td></tr><tr><td>Sometimes/Often</td><td>9 (4.0%)</td><td>3 (2.8%)</td><td>4 (7.8%)</td><td>2 (2.9%)</td><td></td></tr><tr><td><strong>Gave oral sex in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>Never</td><td>17 (7.5%)</td><td>10 (9.4%)</td><td>5 (9.6%)</td><td>2 (2.9%)</td><td></td></tr><tr><td>Rarely/Sometimes</td><td>85 (38%)</td><td>37 (35%)</td><td>21 (40%)</td><td>27 (40%)</td><td></td></tr><tr><td>Often</td><td>124 (55%)</td><td>59 (56%)</td><td>26 (50%)</td><td>39 (57%)</td><td></td></tr><tr><td><strong>Received oral sex in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.4</td></tr><tr><td>Never</td><td>18 (8.0%)</td><td>6 (5.7%)</td><td>5 (9.6%)</td><td>7 (10%)</td><td></td></tr><tr><td>Rarely/Sometimes</td><td>113 (50%)</td><td>55 (52%)</td><td>29 (56%)</td><td>29 (43%)</td><td></td></tr><tr><td>Often</td><td>95 (42%)</td><td>45 (42%)</td><td>18 (35%)</td><td>32 (47%)</td><td></td></tr><tr><td><strong>Sex against sex intercourse in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.2</td></tr><tr><td>Never</td><td>16 (7.1%)</td><td>11 (10%)</td><td>3 (5.8%)</td><td>2 (2.9%)</td><td></td></tr><tr><td>Rarely/Sometimes</td><td>65 (29%)</td><td>25 (24%)</td><td>20 (38%)</td><td>20 (29%)</td><td></td></tr><tr><td>Often</td><td>145 (64%)</td><td>70 (66%)</td><td>29 (56%)</td><td>46 (68%)</td><td></td></tr><tr><td><strong>Vaginal intercourse in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.038</td></tr><tr><td>Never</td><td>4 (1.8%)</td><td>3 (2.8%)</td><td>1 (1.9%)</td><td>0 (0%)</td><td></td></tr><tr><td>Rarely/Sometimes</td><td>29 (13%)</td><td>8 (7.5%)</td><td>12 (23%)</td><td>9 (13%)</td><td></td></tr><tr><td>Often</td><td>193 (85%)</td><td>95 (90%)</td><td>39 (75%)</td><td>59 (87%)</td><td></td></tr><tr><td><strong>Anal intercourse in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>&gt;0.9</td></tr><tr><td>Never</td><td>156 (69%)</td><td>74 (70%)</td><td>36 (69%)</td><td>46 (68%)</td><td></td></tr><tr><td>Rarely/Sometimes/Often</td><td>70 (31%)</td><td>32 (30%)</td><td>16 (31%)</td><td>22 (32%)</td><td></td></tr><tr><td><strong>Sexual intercourse after important alcohol consumption in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.059</td></tr><tr><td>Never</td><td>102 (45%)</td><td>48 (45%)</td><td>29 (56%)</td><td>25 (37%)</td><td></td></tr><tr><td>Rarely/Sometimes</td><td>114 (50%)</td><td>50 (47%)</td><td>23 (44%)</td><td>41 (60%)</td><td></td></tr><tr><td>Often</td><td>10 (4.4%)</td><td>8 (7.5%)</td><td>0 (0%)</td><td>2 (2.9%)</td><td></td></tr><tr><td><strong>Sexual intercourse after drug use in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>&gt;0.9</td></tr><tr><td>Never</td><td>216 (96%)</td><td>101 (95%)</td><td>50 (96%)</td><td>65 (96%)</td><td></td></tr><tr><td>Rarely/Sometimes/Often</td><td>10 (4.4%)</td><td>5 (4.7%)</td><td>2 (3.8%)</td><td>3 (4.4%)</td><td></td></tr><tr><td><strong>Sextoy use in last 12 months</strong></td><td></td><td></td><td></td><td></td><td>0.5</td></tr><tr><td>Never</td><td>175 (77%)</td><td>81 (76%)</td><td>38 (73%)</td><td>56 (82%)</td><td></td></tr><tr><td>Rarely/Sometimes/Often</td><td>51 (23%)</td><td>25 (24%)</td><td>14 (27%)</td><td>12 (18%)</td><td></td></tr><tr><td><strong>Number of sexual intercourses with male partners in last month</strong></td><td></td><td></td><td></td><td></td><td>0.9</td></tr><tr><td>≤1</td><td>69 (32%)</td><td>34 (32%)</td><td>16 (33%)</td><td>19 (29%)</td><td></td></tr><tr><td>≥2</td><td>150 (68%)</td><td>71 (68%)</td><td>33 (67%)</td><td>46 (71%)</td><td></td></tr><tr><td><strong>Number of sexual intercourses with female partners in last month</strong></td><td></td><td></td><td></td><td></td><td>0.8</td></tr><tr><td>0</td><td>205 (98%)</td><td>97 (97%)</td><td>47 (100%)</td><td>61 (98%)</td><td></td></tr><tr><td>≥1</td><td>4 (1.9%)</td><td>3 (3.0%)</td><td>0 (0%)</td><td>1 (1.6%)</td><td></td></tr><tr><td><strong>Unusual vaginal discharge in last week</strong></td><td>17 (7.3%)</td><td>9 (8.1%)</td><td>3 (5.9%)</td><td>5 (7.0%)</td><td>&gt;0.9</td></tr></tbody></table>

Longitudinal CST trajectories were determined through hierarchical clustering of the optimal matching pairwise distances. Group 1 is comprised of participants that commonly have a CST I over time, group 2 is comprised of participants that have rather unstable trajectories or trajectories with CST IV (diverse), and group 3 is comprised of participants that commonly have a CST III over time. The proportion of missing values for each question was always below 20% except for menstruation frequency where there was 24% of missing values.
776 ^2 Pearson's Chi-squared test or Fisher's exact test in case of small counts

777
<table><caption>TABLE 3. Sensitivity, specificity, and predictive values of randomly picking one community state type (CST) for predicting the trajectory of participants for group 1 versus all (2+3), group 2 versus all (1+3), and group 3 versus all (1+2).</caption><thead><tr><th></th><th>Sensitivity</th><th>Specificity</th><th>Positive Predictive Value</th><th>Negative Predictive Value</th></tr></thead><tbody><tr><td>Group 1 versus all</td><td>0.73</td><td>0.88</td><td>0.85</td><td>0.79</td></tr><tr><td>Group 2 versus all</td><td>0.88</td><td>0.58</td><td>0.9</td><td>0.54</td></tr><tr><td>Group 3 versus all</td><td>0.81</td><td>0.77</td><td>0.87</td><td>0.68</td></tr></tbody></table>

Longitudinal CST trajectories were determined through hierarchical clustering of the optimal matching pairwise distances. Group 1 corresponded to women with a CST I most of the time during the study, group 3 to women with a CST III most of the time and group 2 to women with other trajectories.
787 **Fig. 1.** Principal coordinates analysis over 964 vaginal samples, using Euclidean
distance. a) All samples, b) All consecutive samples from the same participants linked
with an arrow, c) Median of the arrows corresponding to consecutive samples from
the same participants. Additional notes on the figure are available in the supplementary
files.
788
789
790
791

792

793

794 **Fig. 2.** a) Number of observed transitions in i-Predict participants, b) Factors
795 significantly associated with specific transitions between consecutive samples (6-
796 month apart) in univariate analysis. Tested category versus reference category for all
797 variables presented here were "Yes" versus "No" except for number of male partners in last 6
798 months for which it was "two or more" versus "one or less". Additional notes on the figure are
799 available in the supplementary files.
800

800

801

802 **Fig. 3.** Community State Types trajectories over the course of the study, among 241 C.
803 trachomatis-negative participants from the i-Predict trial. Longitudinal CST trajectories
804 were determined through hierarchical clustering of the optimal matching pairwise distances.
805 Group 1 corresponded to women with a CST I most of the time during the study, group 3 to
806 women with a CST III most of the time and group 2 to women with other trajectories.
807 Additional notes on the figure are available in the supplementary files.
808

808

809

810
811 STATEMENTS AND DECLARATIONS

812 FUNDING

813 The i-Predict trial was funded by a grant from *Programme Hospitalier de Recherche Clinique*

814 National PHRC-N-2015 (Ministère de la Santé) under reference number PHRC-15-0063.

815 Preliminary work for this trial was financed by 2013 *Institut National du Cancer - Institut de*

816 *Recherche en Santé Publique* (INCa-IReSP) call for projects "*Recherche Prévention*

817 *Primaire*" (reference number A15012LS). Jeanne Tamarelle was funded by the Région Ile-

818 de-France during her doctoral training when this work started. This study was supported in

819 part by the National Institute of Allergy and Infectious Diseases of the National Institutes of

820 Health under award number R21AI130627.

821

822 COMPETING INTERESTS

823 Jacques Ravel is the cofounder of LUCA Biologics, a biotechnology company focusing on

824 translating microbiome research into live biotherapeutics drugs for women's health. All other

825 authors declare that they have no competing interests.

826

827 AUTHORS' CONTRIBUTIONS

828 Anne Thiébaut, Elisabeth Delarocque-Astagneau, Bertille de Barbeyrac and Cécile Bébéar

829 were major contributors in the conception and carrying out of the i-Predict trial. Antoine

830 Bourret and Arnaud Fauconnier contributed to the carrying-out of the i-Predict trial. Jacques

831 Ravel, Elisabeth Delarocque-Astagneau, Anne Thiébaut and Jeanne Tamarelle were major

832 contributors to the conception of this ancillary study, interpretation of the results and writing

833 of the present manuscript. Jacques Ravel carried out the microbiome analyses. Jeanne

834 Tamarelle and Anne Thiébaut performed data curation and data analysis. Elisabeth

835 Delarocque-Astagneau supervised the data analysis. All authors read the final version of the

836 manuscript.

837

838 AVAILABILITY OF DATA AND MATERIALS

839 In accordance with the sponsor (AP-HP) Data Sharing Policy, data are available upon

840 reasonable request. The procedures carried out with the French data privacy authority (CNIL,

841 *Commission nationale de l'informatique et des libertés*) and the European regulation (GDPR)

842 do not provide for the transmission of the database, nor do the information and consent

843 documents signed by the patients. Consultation by the editorial board or interested

844 researchers of individual participant data that underlie the results reported in the article after

845 deidentification may nevertheless be considered, subject to prior determination of the terms

846 and conditions of such consultation and in respect for compliance with the applicable

847 regulations. For all inquiries, please contact the following: [drc-secretariat-promotion@aphp.fr](mailto:drc-secretariat-promotion@aphp.fr)

848

849 ETHICS APPROVAL AND CONSENT TO PARTICIPATE

850 The study was performed in accordance with the ethical standards as laid down in the 1964

851 Declaration of Helsinki. The i-Predict trial was approved by the *Comité de Protection des*

852 *Personnes* (CPP), the *Comité Consultatif sur le Traitement de l'Information en matière de*
853 Recherche dans le domaine de la Santé (CCTIRS) and the Commission Nationale

854 Informatique et Libertés (CNIL). Informed consent was obtained from all individual

855 participants included in the study.