生物标志物 生物标志物
Amir Glik1,2,3,4, Omry Arbiv5,6, Keshet Prado4,7
1ALZAI Health Corporation, Toronto, ON, Canada.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
常规血液检查可以预测阿尔茨海默氏症的痴呆风险. 分析血液工作的机器学习模型可以识别早期干预和药物获取风险的个体.
科学领域:
- 神经学 神经学
- 生物医学数据科学 生物医学数据科学
- 老年学是指老年学的学科.
背景情况:
- 预测阿尔茨海默病 (AD) 痴呆风险对于早期干预至关重要,通过血管风险因素管理,有可能预防30%的病例.
- 新兴的AD疾病修饰药物在临床早期最有效,需要在认知健康的个体中早期检测工具.
- 需要一个具有成本效益的,可扩展的查工具来识别高风险的认知健康 (CH) 个体,以改善早期治疗的机会.
研究的目的:
- 开发一种用于评估阿尔茨海默病痴呆风险的预测工具,使用常规血液检测数据.
- 评估在大量人群中利用常规血液计数和化学面板用于早期AD风险查的可行性.
主要方法:
- 分析了来自克拉利特医疗保健服务的381,754名认知健康受试者 (45岁以上) 的社区队列.
- 机器学习 (ML) 模型使用历史血液检查数据 (1-10年) 和预测地平线 (1-10年) 进行训练.
- 用准确性,AUC,精度,回忆,F1得分和其他统计指标来评估模型性能.
主要成果:
- 模型在各种历史时期和预测时间范围内显示出0.73左右的一致准确性.
- 曲线下的面积 (AUC) 在2年的历史和3年的预测期达到0.84.
- 精度各不相同 (0.19-0.28),而回忆率仍然很高 (0.73-0.82),表明识别有风险的个体的潜力.
结论:
- 常规的血液计数和化学含有有价值的信息来预测未来的阿尔茨海默病痴呆风险.
- 机器学习和人工智能可以将常规血液检查转化为AD痴呆风险评估的有效查工具.
- 这种方法提供了一种可扩展和低成本的方法来识别那些可能从早期AD干预中受益的人.
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