Genes, exposures, and interactions on preterm birth risk: an exploratory study in an Argentine population

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

“…In this way, it could be understood that the interaction between the PON1 rs2074351 variant and the low NSES context presented a higher risk of toxoplasmosis than their individual effects. It also suggests that structural social disparities, in addition to their direct and indirect effects on PTB risk ( e. g. access to healthcare services) ( Elias et al, 2022 a ), might influence PTB risk by increasing the frequency of exposures that potentiate the risk associated with individual characteristics, such as genetic traits.…”

“…Exclusion criteria were medically induced PTB, neonates with congenital anomalies, multiple gestation, and maternal age under 16 years. This study is part of an international collaborative project aimed at elucidating factors associated with PTB ( Krupitzki et al, 2013 ; Gimenez et al, 2016 ; Gimenez et al ., 2017 ; Elias et al, 2021 ; Elias et al ., 2022a ; Elias et al ., 2022b ).…”

“…Only the exposures and newborns’ genetic variants that presented the highest PTB predictive power found in a previous study ( Elias et al, 2022b ), which was conducted with the same data of the present study, were included. The exposures were maternal individual characteristics [few prenatal visits (<5), sexual activity during the last month of pregnancy, maternal blood ABO group A, gestation number, toxoplasmosis [determined from the IgG serological test performed during routine screening ( Dirección Nacional de Maternidad e Infancia, 2010 )], body mass index (BMI) at the beginning of pregnancy (calculated from height and self-reported weight at beginning of pregnancy), maternal age and anemia], residential context characteristics [NSES estimated on the proportion of neighborhood households without Unsatisfied Basic Needs (UBN), described in Appendix S1 ], and newborn genetic variants [rs4845397 ( KCNN3 ), rs11680670 ( COL4A3 ), rs12621551 ( COL4A3 ), rs73993878 ( COL4A3 ), rs2074351 ( PON1 ), rs8073146 ( CRHR1 )] ( Elias et al ., 2022b ). Variables that could have a moderating or confounding effect on the analyzed interactions were included in a sensitivity analysis (maternal schooling, self-reported ancestry, urinary tract infections, vaginal discharge, tobacco smoking, newborn sex, living in large urban conglomerate, and address accuracy).…”

“…We also used BN to analyse the association of sociodemographic and obstetric characteristics with PTB ( Elias et al ., 2022a ). In another study, we carried out newborn DNA candidate genes sequencing and identified characteristics with the highest PTB predictive power; they included maternal sociodemographic and biological characteristics, neighbourhood socioeconomic status (NSES), and newborn genetic variants in KCNN3 , COL4A3 , PON1 , and CRHR1 genes ( Elias et al ., 2022b ). In the present study, we created a BN with exposures and genetic variants previously related with PTB to identify GxE interactions associated with PTB or its predictors.…”