Abstract
Background: Dysmenorrhea is highly prevalent; it places women at risk for other chronic pain conditions. There is a high degree of individual variability in menstrual pain severity, the number of painful sites, and co-occurring gastrointestinal symptoms. Distinct dysmenorrhea symptom-based phenotypes were previously identified, but the biological underpinnings of these phenotypes are less known. One underexplored contributor is the vaginal microbiome. The vaginal microbiota differs significantly among reproductive-age women and may modulate as well as amplify reproductive tract inflammation, which may contribute to dysmenorrhea symptoms.
Objectives: The objective of this study was to examine associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome compositions on- and off-menses.
Methods: We conducted a prospective, longitudinal, pilot study of 20 women (aged 15-24 years) grouped into three dysmenorrhea symptom-based phenotypes: "mild localized pain," "severe localized pain," and "severe multiple pain and gastrointestinal symptoms." Over one menstrual cycle, participants provided vaginal swabs when they were on- and off-menses. We assayed the vaginal microbiome using 16S rRNA gene sequencing. Permutational multivariate analysis of variance tests were used to compare microbiome compositions across phenotypes, with heat maps generated to visualize the relative abundance of bacterial taxa.
Results: The vaginal microbiome compositions (n = 40) were different across the three phenotypes. After separating the on-menses (n = 20) and off-menses (n = 20) specimens, the statistically significant difference was seen on-menses, but not off-menses. Compared to the "mild localized pain" phenotype, participants in the "multiple severe symptoms" phenotype had a lower lactobacilli level and a higher abundance of Prevotella, Atopobium, and Gardnerella when on-menses. We also observed trends of differences across phenotypes in vaginal microbiome change from off- to on-menses.
Discussion: The study provides proof-of-concept data to support larger studies on associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome that might lead to new intervention targets and/or biomarkers for dysmenorrhea. This line of research has the potential to inform precision dysmenorrhea treatment that can improve women's quality of life.