Gene-environment interactions and preterm birth predictors: A Bayesian network approach.

Area of Science:

• Perinatal epidemiology
• Human genetics
• Social determinants of health

Background:

• Preterm birth (PTB) is a leading cause of perinatal morbidity and mortality globally.
• Understanding gene-environment interactions is crucial for identifying PTB risk factors.
• Socioeconomic status and genetic predispositions are potential contributors to PTB.

Purpose of the Study:

• To investigate gene-environment interactions linked to spontaneous PTB or its predictors.
• To identify specific genetic variants and environmental exposures associated with PTB risk.

Main Methods:

• Retrospective case-control study involving 69 preterm and 61 term newborns.
• Collected parental sociodemographic, obstetric data, and newborn genetic variants.
• Employed Bayesian networks to model PTB predictors and logistic regression for interaction analysis.

Main Results:

• An interaction was identified between low neighborhood socioeconomic status and the PON1 rs2074351 (genotype GG) variant.
• This interaction significantly increased the risk of toxoplasmosis (OR 12.51, 95% CI: 1.71 - 91.36).
• Exploratory analysis suggests social disparities amplify PTB risk via exposures interacting with genetic traits.

Conclusions:

• Structural social disparities may influence PTB risk by increasing exposure frequency.
• Gene-environment interactions, like the one found with socioeconomic status and PON1, warrant further investigation.
• Larger studies are needed to validate these findings and confirm the role of social disparities in PTB.