The effects of congenital heart disease on brain development in individuals with 22q11.2 deletion syndrome
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Citation
Abstract
BACKGROUND: 22q11.2 deletion syndrome (22q11.2 DS) is a chromosomal microdeletion disorder, occurring in 1:4000 births, and often associated with neurologic and cardiologic manifestations. This autosomal dominant disorder is caused by a heterozygous deletion which occurs de novo in approximately 90% of cases. People with 22q11.2 DS can have variable penetrance and phenotypic heterogeneity, and most individuals with 22q11.2 DS require specialty care throughout their lives. Due to the high prevalence of neuropsychiatric disorders, many studies have assessed how brain development is altered by deletion of the 22q11.2 region. However, most studies agree that the haploinsufficiency of genes in the 22q11.2 region alone is insufficient to explain the broad spectrum of phenotypes and varying levels of severities observed. Despite this consensus however, there are limited studies of human development that explore the correlations between non-neurological diagnoses and brain development in people with 22q11.2 DS. Specifically, the association between congenital heart disease (CHD) and structural brain development has been infrequently studied in 22q11.2 DS, despite CHD being present in ~75% of people with 22q11.2 DS and the main cause of mortality in this population.
OBJECTIVE: The objective of this study is to investigate correlations between clinical diagnoses and structural brain development in people with 22q11.2 DS. We aim to use analysis of brain magnetic resonance imaging (MRI) data to identify potential correlations between non neurological phenotypes such as CHD and brain development in people with 22q11.2 DS.
MATERIAL AND METHODS: Individuals with 22q11.2 DS who had obtained a clinical brain MRI were identified via electronic medical record query. Confirmation of 22q11.2 DS was obtained via review of genetic testing, and MRI data availability was confirmed within radiology records. Retrospective chart review was conducted to extract clinical history of CHD and other diagnoses. Brain MRI data was quantitatively assessed with an age prediction deep learning algorithm. The difference between the true chronological age of the individual and estimated brain age (by the algorithm) was calculated and labeled as “brain age gap” (BAG). The distributions of the BAG values for individuals with CHD and those without CHD were compared via Mann-Whitney U Test. A correlation matrix was then run to calculate Pearson coefficients between each of the individual phenotypic variables collected, as well as between the individual phenotypic variables and BAG.
RESULTS, DISCUSSION, AND/OR KEY LEARNING: We identified a significant difference in the median values and distribution sizes of brain age gap among people with 22q11.2 DS, depending on the presence or absence of CHD. This pilot experiment highlights a large potential role for CHD in brain health among people with 22q11.2 DS. We did not find any significant correlation between the specific subtype of CHD and age prediction values. We also did not find any strong correlations between the non-neurological phenotypes themselves.
Description
2024
License
Attribution 4.0 International