Improving the accuracy of fetal growth restriction diagnosis by integrating fetal genetic growth potential: a proof of concept study.

BACKGROUND

Fetal growth restriction (FGR) is a common obstetric complication where a fetus fails to reach its genetically determined growth potential. Current diagnostic methods rely on population-based fetal biometric percentiles, which struggle to distinguish pathological FGR from healthy small-for-gestational-age (SGA) infants and may miss cases of growth restriction in fetuses with a high genetic growth potential who fall above conventional SGA thresholds. This study aimed to develop a novel diagnostic framework for FGR by incorporating individualized genetic potential modeling to improve precision in identification and risk stratification.

METHODS

Using data from the Shanghai Birth Cohort (1,806 mother-infant pairs), fetal growth potential was calculated using a polygenic risk score for birth weight derived from fetal genome-wide association studies. A new metric, “FGR_degree,” was developed as the difference between standardized genetic growth potential and actual birth weight Z-score. Fetuses were stratified into risk categories using a dual-threshold approach: the top 10% of FGR_degree values combined with SGA criteria (birth weight < 10th percentile). The high-risk “trueFGR” group included fetuses meeting both criteria. Maternal characteristics, pregnancy complications, neonatal outcomes, and neurodevelopmental outcomes at six months were analyzed using regression models.

RESULTS

Among 1,806 mother-child pairs, 181 newborns were classified into the top 10% FGR_degree group, including 46 trueFGR cases. Compared to SGA infants, trueFGR infants exhibited consistent growth impairments: lower birth weight (2,599.7 g vs. 2,720.9 g, P = 0.02), reduced GROW centiles (4.8% vs. 5.7%, P < 0.001), and disproportionate body proportionality. NICU admission rates were higher in trueFGR (17.4% vs. 12.5%, P = 0.59). Neurodevelopmental outcomes showed a dose-response relationship between FGR_degree and 6-month deficits, with trueFGR conferring the highest adjusted risk for gross motor delay (OR 5.04, 95% CI [2.17, 11.69] vs. 3.51, 95% CI [1.85, 6.67] in SGA). Sensitivity analyses confirmed enhanced risk prediction by combining Top10_FGR_degree with SGA.

CONCLUSIONS

Integrating fetal genomic data with birth weight enabled the development of a novel FGR severity metric. Combining FGR_degree with SGA classifications improved risk stratification, identifying high-risk fetuses more precisely. TrueFGR was associated with maternal complications, adverse outcomes, and neurodevelopmental delays at six months. Future research should explore non-invasive fetal DNA profiling and multi-ethnic validation.