Biology subjects

Symul, L.

Publications and source records attributed to Symul, L..

3 recordsLinked to original sources

bioRxiv2022

Longitudinal dynamics of the human vaginal ecosystem across the reproductive cycle

The vaginal ecosystem is closely tied to human health and reproductive outcomes. However, its dynamics in the wake of childbirth remain poorly characterized. Here, we profiled the vaginal microbiota and cytokine milieu of subjects sampled throughout pregnancy (two cohorts; n = 196 pregnancies) and, in a subset, for one year postpartum (one cohort; n = 72 pregnancies). Delivery was associated with a vaginal pro-inflammatory cytokine response and the depletion of dominant taxa - typically, Lactobacillus species. By contrast, neither the progression of gestation nor the approach of labor strongly altered the vaginal ecosystem. At ~9.5 months postpartum (the latest timepoint at which cytokines were analyzed), elevated inflammation was associated with vaginal bacterial communities that had remained perturbed (i.e., highly diverse) from the time of delivery. Using time-to-event analysis, we found that the one-year postpartum probability of transitioning to Lactobacillus dominance was 49.4% (95% confidence interval (CI) [33.6%, 61.5%]; n = 58 at-risk cases, 86.2% of whom experienced this state prior to delivery). As diversity and inflammation declined postpartum, dominance by L. crispatus, the quintessential health-associated state, failed to recover: its prevalence before, immediately after, and one year after delivery was 41%, 4%, and 9%, respectively. Over the same period, states quasi-dominated by non-Lactobacillus species grew more common. Prompted by these findings, we revisited our pre-delivery data, discovering that a history of prior live birth was associated with a lower odds of L. crispatus dominance in pregnant subjects (odds ratio (OR) 0.14; 95% CI [0.06, 0.32]; P < 0.001) - an outcome modestly tempered by a longer (>18-month) interpregnancy interval. Our results suggest that reproductive history and childbirth in particular remodel the vaginal ecosystem and that the timing and degree of recovery from delivery may help determine the subsequent health of the woman and of future pregnancies.

microbiology↗

bioRxiv2021

Sub-communities of the vaginal ecosystem in pregnant and non-pregnant women.

Diverse and non-Lactobacillus-dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and the acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches are commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving substructures and revealing transitions between potential sub-communities. Here, we address this need with an approach based on mixed membership topic models, using longitudinal data from cohorts of pregnant and non-pregnant study participants. We identify several non-Lactobacillus-dominated sub-communities common to both cohorts and independent of reproductive status. In non-pregnant individuals, we find that the menstrual cycle modulates transitions between and within sub-communities. In addition, a specific non-Lactobacillus-dominated sub-community, which was associated with preterm delivery in pregnant participants, was also more common during menses, a time of elevated vaginal inflammation in non-pregnant participants. Overall, our analyses based on mixed membership models reveal substructures of vaginal ecosystems which may have important clinical and biological associations.

microbiology↗

bioRxiv2018

Assessment of Menstrual Health Status and Evolution through Mobile Apps for Fertility Awareness

For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individuals reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, we were interested in exploring the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. We also investigated whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to womens health overall, which has historically been severely understudied.

epidemiology↗