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Published on: June 13, 2021
Environmental and genetic risk factors for preterm birth: interplays with stressful events during pregnancy
Silvina L Heisecke1, María R Santos2,3,4, Mercedes Negri Malbrán1,5
1Dirección de Investigación, Centro de Educación Médica e Investigaciones Clínicas-Consejo Nacional de Investigaciones Científicas y Técnicas (CEMIC-CONICET), Ciudad Autónoma de Buenos Aires, Argentina.
Insights
Investigating preterm birth (PTB) causes revealed that maternal stress is linked to alcohol intake, chronic hypertension, and specific gene variations. Machine learning identified key environmental and genetic factors contributing to PTB risk.
Area of Science:
- Genetics and Environmental Health
- Reproductive Medicine
- Computational Biology
Background:
- The etiology of preterm birth (PTB) is complex and not fully understood.
- Investigating the interplay between environmental exposures and genetic predispositions is crucial for understanding PTB.
- Maternal stress during pregnancy is a potential contributing factor to PTB.
Purpose of the Study:
- To explore the relationship between environmental factors, specific gene polymorphisms, and PTB.
- To examine these relationships within the context of stressful life events during pregnancy.
- To identify shared environmental and genetic risk factors for PTB and maternal stress.
Main Methods:
- Analysis of sociodemographic, obstetric, and genetic data from 1263 preterm newborns.
- Utilized logistic regression and Lasso Ridge logistic regression for risk factor identification and predictor selection.
- Employed Bayesian networks to evidence associations between variables.
Main Results:
- Identified alcohol intake and chronic hypertension as significant environmental factors associated with PTB and maternal stress.
- Pinpointed three single nucleotide polymorphisms (SNPs): rs66911171 (CR1), rs854552 (PON1), and rs4966038 (IGF1R) as related to PTB.
- Revealed two significant gene-gene interactions: rs854552 x rs4966038 (PON1xIGFR1) and rs5742612 x rs1942386 (IGF1xPGR) in the context of maternal stress and PTB.
Conclusions:
- Machine learning effectively identified key environmental factors, genetic markers, and their interactions related to PTB in the context of maternal stress.
- These findings enhance the understanding of the complex pathways linking maternal stress and PTB.
- The study highlights the potential for targeted interventions and preventive strategies for at-risk populations, leveraging machine learning insights.
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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