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

Saturday, April 29, 2023

3:30 PM – 6:00 PM ET

Poster Number: 183
Publication Number: 183.213

Hui Xiao, Children's Hospital of Fudan University, Shanghai, Shanghai, China (People's Republic); Huiyao chen, Children's Hospital of Fudan University, Shanghai, Shanghai, China (People's Republic); Xiang Chen, Children's Hospital of Fudan University, Shanghai, Shanghai, China (People's Republic); Yulan Lu, Children‘s Hospital of Fudan university, shanghai, Shanghai, China (People's Republic); Bingbing Wu, children's hospital of Fudan University, shanghai, Shanghai, China (People's Republic); Huijun Wang, Children's Hospital of Fudan U, Shanghai, Shanghai, China (People's Republic); Yun Cao, Children's Hospital of Fudan University, Shanghai, PCR, Shanghai, Shanghai, China (People's Republic); Liyuan Hu, Fudan University, shanghai, Shanghai, China (People's Republic); xinran dong, fudan university, shanghai, Shanghai, China (People's Republic); Lin Yang, Children's Hospital of Fudan University, Shanghai, Shanghai, China (People's Republic); Wenhao Zhou, Children's Hospital of Fudan University, Shanghai, Shanghai, China (People's Republic)

Presenting Author(s)

LY

Lin Yang, Ph.D

Children's Hospital of Fudan University
Shanghai, Shanghai, China (People's Republic)

First Author(s)

HX

Hui Xiao (she/her/hers)

NA
Children's Hospital of Fudan University
Shanghai, Shanghai, China (People's Republic)

Background:

The annual number of small for gestational age (SGA) births is ~1,072,100 in China, and SGA survivors remain at a high risk of adverse prognosis. It is crucial to identify disease disorders and predict prognosis for the SGA population.

Objective:

We aimed to describe the genetic characteristics and design a prognosis prediction model of SGA newborns.

Design/Methods:

This retrospective cohort study enrolled 723 SGA newborns from the China Neonatal Genomes Project (CNGP) between June 2018 and June 2020. Clinical exome sequencing was administered to each newborn. All SGA newborns were followed up over 2 years to document any documented any physical growth delay, neurodevelopmental delay, or death, either of which was classified as a poor prognosis. The gene-based collapsing analyses and the gene burden analysis 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.

Results:

Of the 723 SGA newborns (439 [60.7%] males; mean [SD] age, 36.5 [3.3] weeks; mean [SD] birth weight, 2002.1 [626.8] g), 88 (12.2%) had genetic diagnosis, including 42 (47.7%) with monogenic diseases and 46 (52.3%) with chromosomal abnormalities. SGA newborns with genetic diagnosis presented higher rates in the proportion of severe SGA (birth weight less than the third percentile), and in the proportion of poor prognosis during follow-up than SGA without genetic diagnosis. SGA with chromosomal abnormalities showed higher proportions in physical growth and in neurodevelopmental delay than those with monogenic diseases (45.7% vs. 19.0%, P = 0.012). Risk genes for SGA and SGA with poor prognosis were enriched in development-related pathways. The SGA prognosis prediction model combining clinical and genetic factors achieved a higher area under the receiver operating characteristic curve (AUROC=0.82, 95% confidence interval [CI] 0.73-0.92) than the model using only clinical factors (AUROC=0.71, 95%CI 0.61-0.81; P =0.032).

Conclusion(s):

Genetic characteristics of SGA newborns included monogenic diseases and chromosomal abnormalities in approximately equal proportions, SGA newborns with chromosomal disorders had worse prognosis. The SGA prognosis prediction model combining genetic and clinical factors performed better. The application of next-generation sequencing in SGA newborns is helpful for early genetic diagnosis and clinical prognosis prediction.