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Characterization of vaginal Lactobacillus species as a predictor of fertility among Iranian women with unexplained recurrent miscarriage and fertile women without miscarriage history using machine learning modeling

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A Correction to this article was published on 10 October 2023

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Abstract

Background

Lactobacillus spp. are the predominant bacteria of the vaginal tract, the alteration of which has been previously linked to miscarriage. Here, we investigated differences between selected vaginal Lactobacillus species of women with a history of recurrent miscarriages and fertile women without a history of miscarriage in Iran.

Methods and results

Vaginal swabs were taken from 29 fertile and 24 infertile women and quantitative real-time PCR (qPCR) assay was used to determine a selection of vaginal Lactobacillus species in both groups. The logistic regression (LR) model, Naive Bayes (NB) model, support vector machine model (SVM), and neural network model (NN) were developed to predict disease outcome by selected variables. LR analysis was used to construct a nomogram indicating predictions of the risk of miscarriage. The most abundant species among the patients were L. rhamnosus, L. ruminis, and L. acidophilus, while L. gasseri, L. vaginalis, L. fermentum, and L. iners were more abundant in healthy subjects. The distribution of L. ruminis, L. iners, and L. rhamnosus was higher in patients, while L. acidophilus, L. gasseri, and L. fermentum were highly distributed among healthy subjects. Higher AUC in predicting the disease outcome was observed for L. gasseri, L. rhamnosus, L. fermentum, and L. plantarum.

Conclusion

Our findings provide experimental evidence of vaginal Lactobacillus imbalance in infertile women and a suitable predictor for miscarriage based on the AUC algorithms. Further studies with larger sample size and using high-throughput technologies are needed to boost our understanding of the role of lactobacilli in miscarriage.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors wish to thank all members of the Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

This study was supported financially by a grant [Project No. 30133] from Institutional Review Committee for Human Subjects Research at Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Author information

Author notes

  1. Farkhondeh Pouresmaeili and Saeideh Alidoost have contributed equally to this work.

Authors and Affiliations

  1. Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Farkhondeh Pouresmaeili

  2. Medical Genetics Department, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Farkhondeh Pouresmaeili, Mahan Mohammadi, Iman Azari & Zahra Fazeli

  3. Department of Molecular Genetics, National Institute of Genetics Engineering and Biotechnology (VIGEB), Tehran, Iran

    Saeideh Alidoost

  4. Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Masoumeh Azimirad, Armitasadat Emami Meibodi & Abbas Yadegar

  5. Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mehdi Azizmohammad Looha

  6. Groupe de Recherche en Écologie Buccale, Faculté de Médecine Dentaire, Université Laval, Québec, QC, G1V 0A6, Canada

    Atieh Abedin-Do

  7. Axe Médecine Régénératrice, Centre de Recherche du CHU de Québec, Département de Chirurgie, Faculté de Médecine, Université Laval, Québec, QC, G1V 0A6, Canada

    Atieh Abedin-Do

  8. Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Hourieh Shamshiri

  9. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Abbas Yadegar

  10. Department of Midwifery, Kashan University of Medical Sciences, Kashan, Iran

    Tayebeh S. Hosseinpour

Authors
  1. Farkhondeh Pouresmaeili
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Contributions

FP, designed the study, and contributed to conceptualization and project administration, reviewed the literature and wrote the manuscript draft. SA, contributed to samples collection, transportation to the laboratory and DNA extraction. MA, participated in qPCR assays, data analysis, and writing the manuscript. MAL, performed the statistical analysis and contributed to drafting the manuscript. AEM contributed to drafting the manuscript and editing. AAD, contributed to methodology. HS, contributed to samples collection. MM and IZ, participated in the DNA extraction. ZF, performed the primer design. AY, supervised the project, contributed to data analysis, and critically revised the manuscript. TSH, contributed to the patient’s selection and sampling, and provided clinical consultations.

Corresponding authors

Correspondence to Abbas Yadegar or Tayebeh S. Hosseinpour.

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Conflict of interest

The authors declare that the research was conducted in the absence of any commercial, non-financial, or financial relationships that could be construed as a potential conflict of interest.

Ethics approval

The study was approved by the Institutional Ethics Review Committee of the Research Institute for Gastroenterology and Liver Diseases at Shahid Beheshti University of Medical Sciences (Project no. IR.SBMU.RIGLD.REC.1399.011), Tehran, Iran.

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Informed consent was obtained from all individual participants included in the study before sample collection.

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Pouresmaeili, F., Alidoost, S., Azimirad, M. et al. Characterization of vaginal Lactobacillus species as a predictor of fertility among Iranian women with unexplained recurrent miscarriage and fertile women without miscarriage history using machine learning modeling. Mol Biol Rep 50, 8785–8797 (2023). https://doi.org/10.1007/s11033-023-08745-2

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