Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD.

专注于修饰 β-淀粉样蛋白 (Aβ) 的候选治疗药物的临床试验多次未能治疗阿尔茨海默病 (AD)，这表明 Aβ 可能不是治疗 AD 的最佳靶点。Aβ、tau 和神经退行性 (A/T/N) 生物标志物的评估已被提议用于对 AD 进行分类。然而，目前尚不清楚 A/T/N 框架的每个分支中的干扰是否在整个 AD 进展过程中都有同等作用。在这里，使用随机森林机器学习方法分析阿尔茨海默病神经影像学倡议数据集中的参与者，我们表明 A/T/N 生物标志物在预测 AD 发展方面表现出不同的重要性，Aβ 和 tau 的生物标志物升高可以更好地预测早期痴呆状态，和神经退行性变的生物标志物，尤其是葡萄糖低代谢，更好地预测后期痴呆状态。