小密度低密度脂蛋白和微量元素在冠状动脉疾病中的诊断价值
Na Zhang1, Yue Xu2, Hao Liang1
1Affiliated Hospital of Chengde Medical University, Department of Cardiology, Hebei Sheng, Chengde Prefecture, China.
Annales de biologie clinique
|March 28, 2025
概括
冠状动脉疾病 (CAD) 的早期诊断至关重要. 这项研究确定了特定的低密度脂蛋白分片和微量元素作为使用机器学习模型检测CAD的潜在生物标志物.
科学领域:
- 心血管医学 心血管医学
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 冠状动脉疾病 (CAD) 是全球主要的死亡原因.
- 显著比例的CAD患者经历了动脉样硬化症的长期无症状阶段,需要早期诊断策略.
- 识别用于早期CAD检测的新生物标志物是一个关键的未满足的临床需求.
研究的目的:
- 为了研究血清低密度脂蛋白 (LDL) 分数和微量元素作为早期冠状动脉疾病 (CAD) 检测生物标志物的潜力.
- 评估各种机器学习算法在基于这些生物标志物的CAD诊断模型构建中的有效性.
- 为了确定特定的LDL分数和微量元素,以区分CAD患者与非CAD对照.
主要方法:
- 一项观察性病例对照研究,涉及489名CAD患者和75名对照.
- 使用Quantimetrix Lipoprint LDL系统量化LDL亚分数 (LDLC1-7) 的数量.
- 通过感应合等离子体质谱法测量18种微量元素.
- 开发和比较六种机器学习算法 (逻辑回归,K邻居,高斯NB,随机森林,决策树,XGBoost) 用于CAD诊断.
主要成果:
- 在CAD患者和对照人群之间,在特定的LDL分片 (LDLC-1,LDLC-3,LDLC-4,LDLC-5) 和微量元素 (,,,,,,) 的血清水平上观察到显著差异.
- 确定LDLC-3,LDLC-4和是CAD的危险因素,而LDLC-1,,,和显示有保护作用.
- 在评估的机器学习模型中,XGBoost算法展示了最高的诊断性能.
结论:
- 特定的LDL分数和微量元素作为具有和没有CAD的个体之间的显著差异化生物标志物.
- 机器学习算法,特别是XGBoost,可以有效地整合这些生物标志物,以提高CAD诊断能力.
- 这些发现表明,开发冠状动脉疾病的非侵入性早期诊断工具是一个有希望的途径.
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