公共卫生 公共卫生
Liwei Ma1,2, Yihan Wang1,2, Chenyin Chu1,2
1University of Melbourne, Parkville, VIC, Australia.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
血液学和生物化学标记可以帮助预测阿尔茨海默病 (AD) 生物标记物. 机器学习模型在检测临床前阿尔茨海默病方面表现有前途,有助于早期干预策略.
科学领域:
- 神经学 神经学
- 生物化学 生物化学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 临床前阿尔茨海默病 (AD) 的特点是,在临床症状出现之前,阿尔茨海默病生物标志物在15-20年内默默积累.
- 临床前阿尔茨海默病的早期发现和监测对于及时干预和治疗发展至关重要.
研究的目的:
- 开发和验证在临床前阶段对阿尔茨海默病关键生物标志物的预测模型.
- 为了研究常规血液学和血清生物化学标记与AD病理学之间的关联.
- 使用机器学习增强大脑粉样蛋白-PET SUVR和血pTau-217水平的预测.
主要方法:
- 以A4研究 (2014年2月 - 2023年6月) 的数据为依据的回顾性队列研究.
- 对420名患有临床前阿尔茨海默病的参与者的分析,检查了血液学/血清生物化学标记.
- 应用多变量线性回归和机器学习模型来预测AD生物标志物 (粉胺-PET SUVR,血pTau-217) 在4.5年内.
主要成果:
- 单细胞数量的增加与大脑粉样蛋白-PET SUVR 的增加相关.
- 较高的平均体积 (MCV) 与血pTau-217.7升高有关.
- 血液学和血清标志物,包括AST-to-ALT比率和肌素,改善了血pTau-217的预测模型,但单细胞计数并不是粉样蛋白-PET SUVR的强有力的预测指标.
结论:
- 机器学习模型显示出预测阿尔茨海默病生物标志物的巨大潜力.
- 常规的血液标记物可以有助于早期检测和监测临床前AD.
- 需要进一步的研究来评估这些预测模型的临床和研究适用性.
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