Associations Between Dysmenorrhea Symptom-Based Phenotypes and Vaginal Microbiome Article Swipe
Chen X. Chen
,
Janet S. Carpenter
,
Xiang Gao
,
Evelyn Toh
,
Qunfeng Dong
,
David E. Nelson
,
Caroline M. Mitchell
,
J. Dennis Fortenberry
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1097/nnr.0000000000000510
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1097/nnr.0000000000000510
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/nnr.0000000000000510
- OA Status
- green
- Cited By
- 9
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3141062453
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3141062453Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1097/nnr.0000000000000510Digital Object Identifier
- Title
-
Associations Between Dysmenorrhea Symptom-Based Phenotypes and Vaginal MicrobiomeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-05Full publication date if available
- Authors
-
Chen X. Chen, Janet S. Carpenter, Xiang Gao, Evelyn Toh, Qunfeng Dong, David E. Nelson, Caroline M. Mitchell, J. Dennis FortenberryList of authors in order
- Landing page
-
https://doi.org/10.1097/nnr.0000000000000510Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/8222084Direct OA link when available
- Concepts
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Microbiome, Medicine, Phenotype, Menstrual cycle, Physiology, Internal medicine, Bioinformatics, Biology, Gene, Hormone, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 2, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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