ESPE Abstracts (2019) 92 FC12.4

Integration of Transcriptomic and Epigenomic Data in Childhood Identifies a Subset of Individuals Born Small for Gestational Age (SGA) with "catch-up" Growth Who Become Pre-Hypertensive in Early Adulthood

Terence Garner, Philip Murray, Robert Sellers, Andrew Whatmore, Peter Clayton, Adam Stevens


University of Manchester, Manchester, United Kingdom


Background: Children born SGA are known to develop cardiometabolic conditions in adulthood1. Nothing is known about the relationship of the transcriptome (gene expression) and epigenome (DNA methylation) to birth size and the future development of cardiometabolic disease.

Aim: To identify, I) differences and functional links between epigenome age-7years, transcriptome age-9years associated and birth size in a normal population; II) links between the transcriptome and epigenome in childhood and adult cardiometabolic risk.

Study Design: Normal children (n=6487) from the Avon Longitudinal Study of Parents and Children were assigned to groups based on birth size using bodyweight and gestation and divided into groups using population level 10th and 90th centiles. Adverse cardiometabolic risk at age-17years was defined by the National Heart Lung and Blood Institute criteria of prehypertension using systolic and diastolic blood pressure2. Blood epigenome and transcriptome were available from 980 children age-7years and 947 children age-9years respectively. Hypernetworks were used to integrate differentially expressed genes (DEGs) and methylated points (CpGs), identifying functional links. Random Forest, a machine learning approach, was used to determine the predictive value of 'omic data presented as the area under the curve of the receiver operating characteristic (AUC).

Results: Unsupervised analysis identified significant differences between birthweight groups in the whole transcriptome and epigenome (p-value range, 6.6X10-16<P<1.0x10-2). Pre-hypertensive participants at age-17years were distinguished from normotensive participants and 'omic differences were defined (232DEGs; 2.0x10-6<P<1.0x10-2 & 830CpGs; 1.1x10-8<P<1.0x10-2). The pre-hypertensive group was enriched for children born small who caught up by age-7years (155/611 unhealthy/healthy compared to 1979/12746 in the other groups; 1.6-fold, P<1x10-5). This group had a greater height velocity during their catch-up period than the normotensive participants (1.2-fold, P=0.027). Hypernetwork integration of 'omic data identified a functional relationship between DEGs at age-9years and CpGs at age-7years (55DEGs, 520CpGs). Identified CpGs grouped into 5 chromosomal regions (corrected-P<6.7x10-5) including the genes SLC44A2 and PON1 with known associations to lipid metabolism. Genetic associations are known between PON1 and SGA2. Random forest analysis was able to accurately predict the presence of pre-hypertension aged 17 from the early life 'omic data (epigenome age-7years AUC:0.982 and transcriptome age-9years AUC:0.973).

Conclusions: We have identified an integrated 'omic signature in childhood which defines a subset of individuals, born small who catch up by age-7years and are at increased risk of later cardiometabolic disease.

1 Barker etal. (1988) BMJ 297(6641):134-135.

2 Chobanian etal. (2003) JAMA 289(19):2560-2571.

3 Infante-Rivard (2010) Am.J.Epidemiol 171(9):999-1006.

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