Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease.
Chi-Hsiao YehYi-Ju ChouTsung-Hsien TsaiPaul Wei-Che HsuChun-Hsien LiYun-Hsuan ChanShih-Feng TsaiSoh-Ching NgKuei-Mei ChouYu-Ching LinYu-Hsiang JuanTieh-Cheng FuChi-Chun LaiHuey-Kang SytwuTing-Fen TsaiPublished in: Biomedicines (2022)
An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.
Keyphrases
- antiplatelet therapy
- artificial intelligence
- acute coronary syndrome
- percutaneous coronary intervention
- cardiovascular events
- end stage renal disease
- genome wide
- newly diagnosed
- chronic kidney disease
- ejection fraction
- type diabetes
- machine learning
- big data
- deep learning
- peritoneal dialysis
- emergency department
- dna methylation
- patient reported outcomes
- skeletal muscle
- white matter
- functional connectivity
- left ventricular
- multiple sclerosis
- atrial fibrillation
- genetic diversity
- glycemic control