O&G open · 2025
Use of a Maternal Genetic Risk Score to Derive and Test a Genetically Customized Fetal Growth Curve.
СтатьяНазвание и аннотация: Перевод в очереди
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Joseph Merry, Pappas Lisa, Guerrero Rafael, Zeinomar Maryam, Mims Joseph R, Moore Barry, Workalemahu Tsegaselassie, Scifres Christina, Steller Jon G, Saade George, Silver Robert M, Blue Nathan R
Аннотация
OBJECTIVE: Our aim was to determine whether using a maternal genetic risk score for birth weight (GRSBW) to genetically customize a fetal growth curve would improve model fit and identification of perinatal morbidity.
METHODS: We performed a secondary analysis of a prospective cohort of individuals with singleton deliveries at or after 24 weeks of gestation, with ultrasound data and maternal genotypes. In random training (∼60%) and testing (∼40%) subgroups, we computed maternal GRSBW using 73 birth weight-associated single-nucleotide polymorphisms. In the training cohort, we performed log-linear repeated measures modeling of fetal growth using gestational age and fetal sex (standard model). We added GRSBW to the model (genetically customized model) to determine its effect on model fit using Akaike information criterion (AIC). In the testing cohort, we computed both standard and genetically customized birth weight percentiles and compared the area under the curve receiver operating characteristic curve (AUROC) to identify composite perinatal morbidity. Composite perinatal morbidity was defined as any of the following: perinatal death, neonatal intensive care unit stay longer than 2 days, mechanical ventilation, respiratory distress syndrome, neonatal sepsis, seizures, grade 3-4 intraventricular hemorrhage, or necrotizing enterocolitis. Birth weight classifications included small for gestational age (below the 10th percentile), appropriate for gestational age (10th-90th percentile), and large for gestational age (above the 90th percentile).
RESULTS: Of 9,020 eligible participants, 1,105 (12.3%) experienced the composite morbidity outcome. The GRSBW was associated with fetal weight in the model, and the genetically customized model had a lower AIC than the standard model (P=.04), reflecting significantly better fit. In the testing cohort, the genetically customized model changed the birth weight classification in only 1.1% of newborns. The genetically customized model did not improve identification of composite perinatal morbidity over the standard model (AUROC 0.56 for both, P=.9).
CONCLUSION: Maternal GRSBW improved the fit of a model for fetal growth but did not improve recognition of composite perinatal morbidity and mortality over the standard model.
METHODS: We performed a secondary analysis of a prospective cohort of individuals with singleton deliveries at or after 24 weeks of gestation, with ultrasound data and maternal genotypes. In random training (∼60%) and testing (∼40%) subgroups, we computed maternal GRSBW using 73 birth weight-associated single-nucleotide polymorphisms. In the training cohort, we performed log-linear repeated measures modeling of fetal growth using gestational age and fetal sex (standard model). We added GRSBW to the model (genetically customized model) to determine its effect on model fit using Akaike information criterion (AIC). In the testing cohort, we computed both standard and genetically customized birth weight percentiles and compared the area under the curve receiver operating characteristic curve (AUROC) to identify composite perinatal morbidity. Composite perinatal morbidity was defined as any of the following: perinatal death, neonatal intensive care unit stay longer than 2 days, mechanical ventilation, respiratory distress syndrome, neonatal sepsis, seizures, grade 3-4 intraventricular hemorrhage, or necrotizing enterocolitis. Birth weight classifications included small for gestational age (below the 10th percentile), appropriate for gestational age (10th-90th percentile), and large for gestational age (above the 90th percentile).
RESULTS: Of 9,020 eligible participants, 1,105 (12.3%) experienced the composite morbidity outcome. The GRSBW was associated with fetal weight in the model, and the genetically customized model had a lower AIC than the standard model (P=.04), reflecting significantly better fit. In the testing cohort, the genetically customized model changed the birth weight classification in only 1.1% of newborns. The genetically customized model did not improve identification of composite perinatal morbidity over the standard model (AUROC 0.56 for both, P=.9).
CONCLUSION: Maternal GRSBW improved the fit of a model for fetal growth but did not improve recognition of composite perinatal morbidity and mortality over the standard model.