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Environmental and genetic risk factors for preterm birth: interplays with stressful events during pregnancy

Pediatric Research (2025)Cite this article

Abstract

Background

Preterm birth (PTB) etiology remains poorly understood. Our aim was to investigate the relation of environmental factors and specific gene polymorphisms involved in PTB in the context of stressful life events during pregnancy.

Methods

Parental sociodemographic and obstetric data as well as genetic variants of 1263 preterm newborns were analyzed. Logistic regressions were used to identify shared environmental and genetic risk factors for PTB and stressful life events. A Lasso Ridge logistic regression with cross-validation was used to select the best predictors of maternal stress. Associations were evidenced through Bayesian networks.

Results

Starting from a great number of variables, our model was processed and reduced until it allowed to visualize only two environmental factors (alcohol intake and chronic hypertension) along with three SNPs rs66911171 (CR1), rs854552 (PON1), rs4966038 (IGF1R) and two interactions rs854552 x rs4966038 (PON1xIGFR1) and rs5742612 x rs1942386 (IGF1xPGR) related to PTB and maternal stress.

Conclusion

Machine learning techniques allow us to identify two environmental factors, three genetic markers, and two interactions related to PTB in the context of stressful life events. Findings of this exploratory study contribute to the understanding of the complex pathways relating maternal stress and PTB.

Impact

  • An analysis of environmental factors and preterm birth specific gene polymorphisms in the context of stressful life events during pregnancy is presented.

  • Alcohol intake and chronic hypertension along with SNPs of CR1, PON1, IGF1R and two interactions PON1xIGFR1 and IGF1xPGR are shown as related to preterm birth in the context of stressful life events.

  • This research could help in developing targeted interventions and preventive strategies for at-risk populations.

  • The study emphasizes the potential of machine learning to interpret biological and social interactions affecting health outcomes.

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Fig. 1: Bayesian network showing associations among genetic markers, environmental factors, and maternal stress.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the health care team at Maternidad Nuestra Señora de la Merced, Tucumán, Argentina, for their hard work and support and to Mariana Piola and Alejandra Mariona at ECLAMC who provided technical support.

Funding

The research program was supported by Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT-MINCyT), grant numbers PICT-2018-4275 (PI: López Camelo JS) and PICT-2018-4285 (PI: Lucas G. Gimenez), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), and INAGEMP [Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)] grant 465549/2014-4, Brazil.

Author information

Authors and Affiliations

  1. Dirección de Investigación, Centro de Educación Médica e Investigaciones Clínicas-Consejo Nacional de Investigaciones Científicas y Técnicas (CEMIC-CONICET), Ciudad Autónoma de Buenos Aires, Argentina

    Silvina L. Heisecke, Mercedes Negri Malbrán & Hugo Kupitzki

  2. Centro de Educación Médica e Investigaciones Clínicas-Consejo Nacional de Investigaciones Científicas y Técnicas (CEMIC-CONICET), sede San Isidro, Buenos Aires, Argentina

    María R. Santos

  3. Estudio Colaborativo Latino Americano de Malformaciones Congénitas (ECLAMC), Centro de Educación Médica e Investigaciones Clínicas-Consejo Nacional de Investigaciones Científicas y Técnicas (CEMIC-CONICET), Ciudad Autónoma de Buenos Aires, Argentina

    María R. Santos, Jorge S. López Camelo & Lucas G. Gimenez

  4. Comisión de Investigaciones Científicas Provincia Buenos Aires (CICPBA), La Plata, Argentina

    María R. Santos

  5. Departamento de Ginecología y Obstetricia, Unidad de Embarazo de Alto Riesgo, Centro de Educación Médica e Investigaciones Clínicas (CEMIC), Ciudad Autónoma de Buenos Aires, Argentina

    Mercedes Negri Malbrán & Gustavo Leguizamon

  6. Instituto Universitario, Centro de Educación Médica e Investigaciones Clínicas (CEMIC-IUC), Ciudad Autónoma de Buenos Aires, Argentina

    Hugo Kupitzki

  7. Centro de Investigaciones Cardiovasculares “Dr. Horacio E. Cingolani” (CCT-La Plata-CONICET), La Plata, Argentina

    Susana M. Mosca

  8. Laboratorio de Fisiología y Farmacología de la Reproducción, Centro de Estudios Farmacológicos y Botánicos (CEFYBO), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Argentina

    María L. Ribeiro

  9. Laboratorio de Epidemiología Genética, Centro de Educación Médica e Investigaciones Clínicas-Consejo Nacional de Investigaciones Científicas y Técnicas (CEMIC-CONICET), Ciudad Autónoma de Buenos Aires, Argentina

    Jorge S. López Camelo & Lucas G. Gimenez

  10. Instituto Nacional de Genética Médica Populacional (INAGEMP), Porto Alegre, Brasil

    Jorge S. López Camelo & Lucas G. Gimenez

Authors
  1. Silvina L. Heisecke
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  2. María R. Santos
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  3. Mercedes Negri Malbrán
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  4. Hugo Kupitzki
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  6. María L. Ribeiro
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  8. Jorge S. López Camelo
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  9. Lucas G. Gimenez
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Contributions

S.L.H., M.R.S., J.S.L.C., and L.G.G. made substantial contributions to design, acquisition of data, analysis and interpretation of data, drafting the article and approving the final manuscript as submitted. H.K., S.M.M., M.L.R., G.L., J.S.L.C., and L.G.G. made substantial contributions to design, acquisition of data, critical manuscript revision for important intellectual content, and approving of the final version as submitted. S.L.H., M.R.S., M.N.M., H.K., S.M.M., M.L.R., G.L., J.S.L.C., and L.G.G. made contributions to data analysis and interpretation, drafting and editing of the article, and approved the final manuscript as submitted.

Corresponding author

Correspondence to Lucas G. Gimenez.

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Heisecke, S.L., Santos, M.R., Malbrán, M.N. et al. Environmental and genetic risk factors for preterm birth: interplays with stressful events during pregnancy. Pediatr Res (2025). https://doi.org/10.1038/s41390-025-04047-4

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