Factors shaping vaginal microbiota long-term community dynamics in young adult women

Abstract The vaginal microbiota is categorised into five main community state types (CST) that are known to affect women’s health. Yet, there is a notable paucity of high-resolution follow-up studies lasting several months, which are required to interrogate the long-term dynamics and associations with demographic and behavioural covariates. Here, we present a high-resolution longitudinal cohort of 125 women followed for a median duration of 8.6 months, providing 11 samples per woman. Using a hierarchical Bayesian Markov model, we characterised the patterns of CST persistence and transition, simultaneously estimated the impact of 16 covariates and quantified individual variability among women. We showed that ‘optimal’ (CSTs I, II, and V) and ‘sub-optimal’ (CST III) communities are more stable in time than ‘non-optimal’ (CST IV) ones. Furthermore, we found that some covariates — most notably alcohol consumption — impacted the probability of shifting from one CST to another. We performed counterfactual simulations to confirm that alterations of key covariates, such as alcohol consumption, could shape the prevalence of different microbiota communities in the population. Finally, our analyses indicated that there is a relatively canalised pathway leading to the deterioration of vaginal microbiota communities, whereas the paths to recovery can be highly individualised among women. In addition to providing one of the first insights into vaginal microbiota dynamics over a year, our study showcases a novel application of a hierarchical Bayesian Markov model to clinical cohort data with many covariates. Our findings pave the way for an improved mechanistic understanding of microbial dynamics in the vaginal environment and the development of novel preventative and therapeutic strategies to improve vaginal health.