期刊
CELLS
卷 10, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/cells10092430
关键词
heart failure; genetic factors; single nucleotide polymorphism; artificial intelligence
类别
资金
- Ministry of Health and Welfare (Smart Healthcare for Obesity Therapeutics) [PD-109-GP-02, MG-110-GP-03]
- Chang Gung Memorial Hospital [CRRPG2H0181-183, CORPG2H0041-0043, CMRPG2H000091-0093, CMRPG2K0141-142, CLRPG2L0051]
The study demonstrates that an AI-assisted identification of SNP signatures combined with clinical parameters is effective in identifying asymptomatic high-risk individuals predisposed to HF and predicting HF progression.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据