Vaginal Microbiome, Inflammatory Mediators, Metabolome, and Dysmenorrhea Symptom-based Phenotypes

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PROJECT SUMMARY Dysmenorrhea is a prevalent pain condition and a risk factor for developing other chronic pain conditions. Dysmenorrhea experiences vary significantly among individual women. Some women are symptom-free, and those with symptoms fall into three dysmenorrhea symptom phenotypes: mild localized pain, severe localized pain, and multiple severe symptoms. The mechanisms underlying this individual variation remain incompletely understood, creating barriers for expanding and personalizing dysmenorrhea treatment options. Given the role of the vaginal microbiota in inflammation and female reproductive health and the proof-of-concept data linking vaginal microbiota and dysmenorrhea phenotypes, the study of vaginal microbiota and their function is a promising avenue to understand individual differences in dysmenorrhea. Our central hypothesis is that vaginal microbiota contributes to dysmenorrhea symptoms by modulating the host inflammatory response in the genital tract. The purpose of this proposed, prospective, longitudinal study is to examine relationships among vaginal microbiome, inflammatory mediators, sex hormones, metabolome, and dysmenorrhea phenotypes. Racially diverse female participants (aged 14-39) will be recruited into four groups: three dysmenorrhea phenotype groups and one symptom-free group. These individuals will provide vaginal samples off- and on-menses, blood samples, and questionnaire data. The specific aims of the study are to (1) differentiate vaginal microbial taxa, genes, and pathways associated with dysmenorrhea phenotypes using shotgun metagenomic and qPCR methods; (2) differentiate inflammatory mediators and metabolites associated with dysmenorrhea phenotypes using immunoassays of cytokines and mass spectrometry-based metabolomics; and (3) identify taxonomic drivers of functional shifts in vaginal metabolome associated with severe dysmenorrhea symptoms. Network- based systems biology and predictive modeling approaches will be used to integrate phenotypic, demographic, behavioral, metagenomic, cytokine, metabolomic, and hormonal data. This multi-omics approach will provide rich information on the function of vaginal microbiota and metabolites to uncover mechanisms underlying individual differences in dysmenorrhea. The expected impact of this research is to (1) suggest new avenues for treating dysmenorrhea through modifying the vaginal microbiota or metabolites (e.g., using drugs, probiotics, and/or behavioral interventions), (2) reveal vaginal microbiota and/or metabolites as biomarkers, and (3) generate a rich resource with large microbial metagenomic sequencing, metabolomic profiling, and detailed phenotype, hormonal, and behavioral data to study mechanisms of dysmenorrhea and menstrual health. In the long term, this work has the potential to lead to additional dysmenorrhea treatment options with the ultimate goals of reducing pain and improving women's quality of life.