动脉样硬化心血管疾病中的多种病
Liv Tybjærg Nordestgaard1, Brooke N Wolford2, David de Gonzalo-Calvo3
1Department of Clinical Biochemistry, Copenhagen University Hospital - Herlev and Gentofte, Borgmester Ib Juuls Vej 1, 2730, Herlev, Denmark; Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, BS8 2BN, United Kingdom.
Atherosclerosis
|July 15, 2025
概括
多基因组数据,包括基因组学和蛋白质组学,显示出预测动脉样硬化心血管疾病 (ASCVD) 的前景. 将这些数据与机器学习 (ML) 和人工智能 (AI) 集成,可以推进ASCVD的精准医学.
科学领域:
- 生物医学科学 生物医学科学
- 心血管研究研究心血管研究
- 基因组学和生物信息学
背景情况:
- 动脉样性心血管疾病 (ASCVD) 是全球主要的死亡原因.
- 技术进步使得在ASCVD诊断和治疗中可以使用多种不同数据集 (基因组学,表观基因组学,转录组学,蛋白质组学,代谢组学).
- 欧米克斯数据提供了对ASCVD病原体的更深入的理解.
研究的目的:
- 审查ASCVD中关于OMIC数据应用的当前文献.
- 探索多原子数据与机器学习 (ML) 和人工智能 (AI) 的集成,用于ASCVD管理.
- 讨论这些综合方法在推进精准医学方面的潜力.
主要方法:
- 在ASCVD中对OMIC数据的系统文献综述.
- 分析整合OMIC数据与ML/AI方法的研究.
- 综合了关于不同欧米层的预测和诊断能力的发现.
主要成果:
- 基因组学为ASCVD风险预测提供多基因风险评分 (PRS).
- 表观遗传学强调了动脉样硬化中DNA甲基化变化.
- 转录组学识别了涉及动脉样硬化进展的微RNA (miRNA).
- 蛋白质组学和脂组学提供独立的预测价值,优于传统的临床模型.
- 与ML/AI结合的多原子数据显示了开发临床模型的潜力.
结论:
- 需要大量的努力将OMIC数据和技术转化为临床实践.
- 开发经过临床批准的AI/ML算法对于解释大型数据集至关重要.
- 这些算法将为ASCVD提供准确的精准医学方法.
- 与AI/ML的多原子数据集成有望为个性化的ASCVD预防和治疗提供希望.
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