The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study
Abstract Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-i...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/131488 |
| _version_ | 1826213543743062016 |
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| author | Perrotta, Allison R Borrelli, Giuliano M Martins, Carlo O Kallas, Esper G Sanabani, Sabri S Griffith, Linda G Alm, Eric J Abrao, Mauricio S |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Perrotta, Allison R Borrelli, Giuliano M Martins, Carlo O Kallas, Esper G Sanabani, Sabri S Griffith, Linda G Alm, Eric J Abrao, Mauricio S |
| author_sort | Perrotta, Allison R |
| collection | MIT |
| description | Abstract
Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-invasive diagnostic tool for endometriosis. Overall, 59 women were included, n = 35 with endometriosis and n = 24 controls. Rectal and vaginal samples were collected in two different periods of the menstrual cycle from all subjects. Gut and vaginal microbiomes from patients with different rASRM (revised American Society for Reproductive Medicine) endometriosis stages and controls were analyzed. Illumina sequencing libraries were constructed using a two-step 16S rRNA gene PCR amplicon approach. Correlations of 16S rRNA gene amplicon data with clinical metadata were conducted using a random forest-based machine-learning classification analysis. Distribution of vaginal CSTs (community state types) significantly differed between follicular and menstrual phases of the menstrual cycle (p = 0.021, Fisher’s exact test). Vaginal and rectal microbiome profiles and their association to severity of endometriosis (according to rASRM stages) were evaluated. Classification models built with machine-learning methods on the microbiota composition during follicular and menstrual phases of the cycle were built, and it was possible to accurately predict rASRM stages 1–2 verses rASRM stages 3–4 endometriosis. The feature contributing the most to this prediction was an OTU (operational taxonomic unit) from the genus Anaerococcus. Gut and vaginal microbiomes of women with endometriosis have been investigated. Our findings suggest for the first time that vaginal microbiome may predict stage of disease when endometriosis is present. |
| first_indexed | 2024-09-23T15:50:58Z |
| format | Article |
| id | mit-1721.1/131488 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:50:58Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1314882023-02-22T20:16:47Z The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study Perrotta, Allison R Borrelli, Giuliano M Martins, Carlo O Kallas, Esper G Sanabani, Sabri S Griffith, Linda G Alm, Eric J Abrao, Mauricio S Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Center for Gynepathology Research Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics Abstract Endometriosis remains a challenge to understand and to diagnose. This is an observational cross-sectional pilot study to characterize the gut and vaginal microbiome profiles among endometriosis patients and control subjects without the disease and to explore their potential use as a less-invasive diagnostic tool for endometriosis. Overall, 59 women were included, n = 35 with endometriosis and n = 24 controls. Rectal and vaginal samples were collected in two different periods of the menstrual cycle from all subjects. Gut and vaginal microbiomes from patients with different rASRM (revised American Society for Reproductive Medicine) endometriosis stages and controls were analyzed. Illumina sequencing libraries were constructed using a two-step 16S rRNA gene PCR amplicon approach. Correlations of 16S rRNA gene amplicon data with clinical metadata were conducted using a random forest-based machine-learning classification analysis. Distribution of vaginal CSTs (community state types) significantly differed between follicular and menstrual phases of the menstrual cycle (p = 0.021, Fisher’s exact test). Vaginal and rectal microbiome profiles and their association to severity of endometriosis (according to rASRM stages) were evaluated. Classification models built with machine-learning methods on the microbiota composition during follicular and menstrual phases of the cycle were built, and it was possible to accurately predict rASRM stages 1–2 verses rASRM stages 3–4 endometriosis. The feature contributing the most to this prediction was an OTU (operational taxonomic unit) from the genus Anaerococcus. Gut and vaginal microbiomes of women with endometriosis have been investigated. Our findings suggest for the first time that vaginal microbiome may predict stage of disease when endometriosis is present. 2021-09-20T17:17:17Z 2021-09-20T17:17:17Z 2020-01-06 2020-09-24T21:20:47Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131488 en https://doi.org/10.1007/s43032-019-00113-5 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ Society for Reproductive Investigation application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Perrotta, Allison R Borrelli, Giuliano M Martins, Carlo O Kallas, Esper G Sanabani, Sabri S Griffith, Linda G Alm, Eric J Abrao, Mauricio S The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title | The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title_full | The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title_fullStr | The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title_full_unstemmed | The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title_short | The Vaginal Microbiome as a Tool to Predict rASRM Stage of Disease in Endometriosis: a Pilot Study |
| title_sort | vaginal microbiome as a tool to predict rasrm stage of disease in endometriosis a pilot study |
| url | https://hdl.handle.net/1721.1/131488 |
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