Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure

Abstract

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual’s quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Electrical Remodelling in Cardiac Disease

“…Due to the multifactorial disease mechanisms also involving genetic factors, exuberant amounts of data accumulate that require machine learning techniques for meaningful conclusions. Here, Yang et al employ a combination of artificial intelligence-assisted identifications of single nucleotide polymorphisms and clinical parameters to identify asymptomatic high-risk subjects that are predisposed to heart failure [35].…”

Actin Up: An Overview of the Rac GEF Dock1/Dock180 and Its Role in Cytoskeleton Rearrangement

“…In addition to involvement in cancer and infectious disease, mutations, or changes in Dock180 activity are likely to be involved in additional developmental defects and pathological processes. Whole genome sequencing and GWAS studies have identified alterations in the Dock180 gene in various pathologies, but the involvement of Dock180 in these processes remains to be confirmed, and mechanisms of action have yet to be investigated [ 74 , 75 , 76 ].…”

Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine

“…The medical applications of AI are constantly evolving in research clinimetrics while taking over human-based decisions in systems' operations or the diagnosis and treatment of diseases. For example, electronic health record-derived databases processed through machine learning algorithms can facilitate patient care allocation and novel biomarkers research or support genomics to discover disease associations or contain microbial outbreaks (1)(2)(3).…”