Race and Genetics in Congenital Heart Disease: Application of iPSCs, Omics, and Machine Learning Technologies

Abstract

Congenital heart disease (CHD) is a multifaceted cardiovascular anomaly that occurs when there are structural abnormalities in the heart before birth. Although various risk factors are known to influence the development of this disease, a full comprehension of the etiology and treatment for different patient populations remains elusive. For instance, racial minorities are disproportionally affected by this disease and typically have worse prognosis, possibly due to environmental and genetic disparities. Although research into CHD has highlighted a wide range of causal factors, the reasons for these differences seen in different patient populations are not fully known. Cardiovascular disease modeling using induced pluripotent stem cells (iPSCs) is a novel approach for investigating possible genetic variants in CHD that may be race specific, making it a valuable tool to help solve the mystery of higher incidence and mortality rates among minorities. Herein, we first review the prevalence, risk factors, and genetics of CHD and then discuss the use of iPSCs, omics, and machine learning technologies to investigate the etiology of CHD and its connection to racial disparities. We also explore the translational potential of iPSC-based disease modeling combined with genome editing and high throughput drug screening platforms.

Perfil das internações de crianças por malformações congênitas do aparelho circulatório no Brasil de 2010 a 2020

“…MINIS-TÉRIO DA SAÚDE, 2021; CAPPELLESSO; AGUIAR, 2017; CHOU; CHAKRADHAR; GHIMIRE, 2019;HOLMES, 2006). Contudo, não há estudos conclusivos sobre a diferença racial na distribuição dos defeitos cardíacos, embora alguns estudos confirmem que os negros, quando comparados com brancos, têm maior incidência e cerca de 50% mais chances de as MAC serem mais graves e complexas (KNOWLES et al, 2017;MULLEN et al, 2021).…”

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

“…However, a person's health or illness can be influence by a wide variety of factors. Beyond clinical information, these include the environmentome, microbiome, physiology, cell biology, proteome and metabolome, epigenome and transcriptome, and the genome ( 11 ). While genetic contributions as the cause of CHD have been the subject of many investigations, a growing body of literature has shown that copy number variations, mutations, and gene-environment interactions continue to play an active role later in the life of these patients, modifying their risk of adverse outcomes and response to interventions ( 12 ).…”

Human Induced Pluripotent Stem Cell as a Disease Modeling and Drug Development Platform—A Cardiac Perspective

“…One of the major causes of heart disease is various genetic mutations. However, different mutations in the same gene may have different mechanisms at the molecular level but lead to similar clinical outcomes of heart disease [ 177 , 178 ]. Advances in patient-specific hiPSC-derived-CMs research have provided a platform to effectively study patient- and disease-specific heart disease in vitro [ 179 , 180 , 181 ].…”