Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success
Abstract Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In this pilot study, we compared vaginal mic...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Biofilms and Microbiomes |
| Online Access: | https://doi.org/10.1038/s41522-025-00732-8 |
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| author | Ofri Bar Stylianos Vagios Omer Barkai Joseph Elshirbini Irene Souter Jiawu Xu Kaitlyn James Charles Bormann Makiko Mitsunami Jorge E. Chavarro Philipp Foessleitner Douglas S. Kwon Moran Yassour Caroline Mitchell |
| author_facet | Ofri Bar Stylianos Vagios Omer Barkai Joseph Elshirbini Irene Souter Jiawu Xu Kaitlyn James Charles Bormann Makiko Mitsunami Jorge E. Chavarro Philipp Foessleitner Douglas S. Kwon Moran Yassour Caroline Mitchell |
| author_sort | Ofri Bar |
| collection | DOAJ |
| description | Abstract Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In this pilot study, we compared vaginal microbiota composition and immune marker concentrations between patients with unexplained or male factor infertility (MFI), as a control. We applied a supervised machine learning algorithm that integrated microbiome and inflammation data to predict pregnancy outcomes. Twenty-eight participants provided vaginal swabs at three IVF cycle time points; 18 achieved pregnancy. Pregnant participants had lower microbial diversity and inflammation. Among them, MFI cases had higher diversity but lower inflammation than those with unexplained infertility. Our model showed the highest prediction accuracy at time point 2 of the IVF cycle. These findings suggest that vaginal microbiota and inflammation jointly impact fertility and can inform predictive tools in reproductive medicine. |
| format | Article |
| id | doaj-art-e2be7ce2dc3d48e19089be47e845d664 |
| institution | OA Journals |
| issn | 2055-5008 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Biofilms and Microbiomes |
| spelling | doaj-art-e2be7ce2dc3d48e19089be47e845d6642025-08-20T02:31:03ZengNature Portfolionpj Biofilms and Microbiomes2055-50082025-06-0111111010.1038/s41522-025-00732-8Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF successOfri Bar0Stylianos Vagios1Omer Barkai2Joseph Elshirbini3Irene Souter4Jiawu Xu5Kaitlyn James6Charles Bormann7Makiko Mitsunami8Jorge E. Chavarro9Philipp Foessleitner10Douglas S. Kwon11Moran Yassour12Caroline Mitchell13Department of Obstetrics and Gynecology, Massachusetts General HospitalDepartment of Obstetrics & Gynecology, Tufts University Medical CenterHarvard Medical SchoolRagon Institute of MGH, MIT, and Harvard, Massachusetts General HospitalDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Harvard Medical School, Massachusetts General Hospital Fertility CenterRagon Institute of MGH, MIT, and Harvard, Massachusetts General HospitalDepartment of Obstetrics and Gynecology, Massachusetts General HospitalDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Harvard Medical School, Massachusetts General Hospital Fertility CenterDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Harvard Medical School, Massachusetts General Hospital Fertility CenterDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Harvard Medical School, Massachusetts General Hospital Fertility CenterDepartment of Obstetrics and Gynecology, Massachusetts General HospitalHarvard Medical SchoolDepartment of Microbiology and Molecular Genetics, Faculty of Medicine, Hebrew University of JerusalemDepartment of Obstetrics and Gynecology, Massachusetts General HospitalAbstract Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In this pilot study, we compared vaginal microbiota composition and immune marker concentrations between patients with unexplained or male factor infertility (MFI), as a control. We applied a supervised machine learning algorithm that integrated microbiome and inflammation data to predict pregnancy outcomes. Twenty-eight participants provided vaginal swabs at three IVF cycle time points; 18 achieved pregnancy. Pregnant participants had lower microbial diversity and inflammation. Among them, MFI cases had higher diversity but lower inflammation than those with unexplained infertility. Our model showed the highest prediction accuracy at time point 2 of the IVF cycle. These findings suggest that vaginal microbiota and inflammation jointly impact fertility and can inform predictive tools in reproductive medicine.https://doi.org/10.1038/s41522-025-00732-8 |
| spellingShingle | Ofri Bar Stylianos Vagios Omer Barkai Joseph Elshirbini Irene Souter Jiawu Xu Kaitlyn James Charles Bormann Makiko Mitsunami Jorge E. Chavarro Philipp Foessleitner Douglas S. Kwon Moran Yassour Caroline Mitchell Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success npj Biofilms and Microbiomes |
| title | Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success |
| title_full | Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success |
| title_fullStr | Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success |
| title_full_unstemmed | Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success |
| title_short | Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success |
| title_sort | harnessing vaginal inflammation and microbiome a machine learning model for predicting ivf success |
| url | https://doi.org/10.1038/s41522-025-00732-8 |
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