基于免疫和临床生物化学参数预测心血管衰老的多式计算方法.
Madina Suleimenova1, Kuat Abzaliyev2, Ainur Manapova3
1Department of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Diagnostics (Basel, Switzerland)
|August 14, 2025
概括
这项研究开发了一种使用机器学习的新型模型,通过分析临床,免疫和生化标志物来预测心血管疾病风险. 这些发现有助于早期检测和针对心血管衰老的个性化预防策略.
科学领域:
- 心脏病学 心脏病学
- 免疫学 免疫学 免疫学
- 生物化学 生化学
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 风险预测通过整合临床,免疫学和生化标志物来增强.
- 分析了广泛的生物标志物,包括免疫细胞 (例如CD14,CD16),细胞因子 (例如IL-10),炎症标志物 (例如CRP) 和器官功能指数 (例如GFR,NT-proBNP).
- 还包括了诸如动脉高血压,糖尿病和生活方式选择等临床因素.
研究的目的:
- 开发一种针对心血管疾病 (CVD) 风险的创新预测模型.
- 允许早期检测心血管疾病及其并发症的倾向.
- 创建针对心血管疾病预防和管理的个性化建议.
主要方法:
- 利用数学建模和机器学习技术进行风险预测.
- 包括52名65岁及以上的患者.
- 采用了诸如Runge-Kutta,Adams-Bashforth和向后导向的欧勒等数值方法,以准确地建模生物标志物动态.
主要成果:
- HLA-DR (50%),CD14 (41%) 和CD16 (38%) 显示出与衰老过程最强烈的关联.
- 身体质量指数 (BMI) 与胎盘生长因子 (PGF) 相关 (37%).
- 淋巴膜过率 (GFR) 与体力活动 (47%) 有积极的关联,而超氧化失调酶 (SOD) 活性则显示出负相关性 (48%),表明抗氧化剂防御减少.
结论:
- 这项研究提高了心血管风险预测的准确性.
- 结果为预防和管理心血管疾病提供了个性化的建议.
- 综合方法强调了多种生物标志物在评估心血管健康方面的重要性.
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