Comprehensive assessment of the genetic characteristics of small for gestational age newborns in NICU: from diagnosis of genetic disorders to prediction of prognosis

Abstract

Abstract Background In China, ~1,072,100 small for gestational age (SGA) births occur annually. These SGA newborns are a high-risk population of developmental delay. Our study aimed to evaluate the genetic profile of SGA newborns in the newborn intensive care unit (NICU) and establish a prognosis prediction model by combining clinical and genetic factors. Methods A cohort of 723 SGA and 1317 appropriate for gestational age (AGA) newborns were recruited between June 2018 and June 2020. Clinical exome sequencing was performed for each newborn. The gene-based rare-variant collapsing analyses and the gene burden test were applied to identify the risk genes for SGA and SGA with poor prognosis. The Gradient Boosting Machine framework was used to generate two models to predict the prognosis of SGA. The performance of two models were validated with an independent cohort of 115 SGA newborns without genetic diagnosis from July 2020 to April 2022. All newborns in this study were recruited through the China Neonatal Genomes Project (CNGP) and were hospitalized in NICU, Children’s Hospital of Fudan University, Shanghai, China. Results Among the 723 SGA newborns, 88(12.2%) received genetic diagnosis, including 42(47.7%) with monogenic diseases and 46(52.3%) with chromosomal abnormalities. SGA with genetic diagnosis showed higher rates in severe SGA(54.5% vs. 41.9%, P=0.0025) than SGA without genetic diagnosis. SGA with chromosomal abnormalities showed higher incidences of physical and neurodevelopmental delay compared to those with monogenic diseases (45.7% vs. 19.0%, P=0.012). We filtered out 3 genes (ITGB4, TXNRD2, RRM2B) as potential causative genes for SGA and 1 gene (ADIPOQ) as potential causative gene for SGA with poor prognosis. The model integrating clinical and genetic factors demonstrated a higher area under the receiver operating characteristic curve (AUC) over the model based solely on clinical factors in both the SGA-model generation dataset (AUC=0.9[95% confidence interval 0.84–0.96] vs. AUC=0.74 [0.64–0.84]; P=0.00196) and the independent SGA-validation dataset (AUC=0.76 [0.6–0.93] vs. AUC=0.53[0.29–0.76]; P=0.0117). Conclusion SGA newborns in NICU presented with roughly equal proportions of monogenic and chromosomal abnormalities. Chromosomal disorders were associated with poorer prognosis. The rare-variant collapsing analyses studies have the ability to identify potential causative factors associated with growth and development. The SGA prognosis prediction model integrating genetic and clinical factors outperformed that relying solely on clinical factors. The application of genetic sequencing in hospitalized SGA newborns may improve early genetic diagnosis and prognosis prediction.