Background

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer’s disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility.

Objective

To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study.

Design

ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type).

Setting

Participants across various sites were involved in the ARMADA study for validating the NIHTB.

Participants

199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers).

Measurements

We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.

Results

The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 -0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 – 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.

Conclusion

Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

中文翻译：

---

使用美国国立卫生研究院工具箱 (NIHTB) 预测大脑淀粉样蛋白状态以评估神经和行为功能

背景

β 淀粉样蛋白 (Aβ) 斑块是阿尔茨海默病 (AD) 的神经病理学标志。随着抗淀粉样蛋白单克隆抗体进入市场，预测大脑淀粉样蛋白状态对于确定治疗资格至关重要。

客观的

在推进阿尔茨海默病和认知衰老 (ARMADA) 研究中的机器学习方法来预测大脑淀粉样蛋白状态。

设计

ARMADA 是一项多中心研究，针对不同认知能力水平（正常、轻度认知障碍、AD 型早期痴呆）的老年人实施了美国国立卫生研究院神经和行为功能评估工具箱 (NIHTB)。

环境

各个地点的参与者都参与了 ARMADA 研究，以验证 NIHTB。

参加者

199 名 ARMADA 参与者有 PET 或 CSF 信息（平均年龄 76.3 ± 7.7，51.3% 为女性，42.3% 接受过部分或完成大学教育，50.3% 接受过研究生教育，88.9% 为白人，33.2% 患有 AD 生物标志物阳性）。

测量

我们使用 NIHTB 的认知、情绪、运动、感觉评分和人口统计学来预测 PET 或 CSF 测量的淀粉样蛋白状态。我们应用 LASSO 和随机森林模型，并使用受试者工作曲线下面积 (AUROC) 来评估识别淀粉样蛋白阳性的能力。

结果

随机森林模型在保留测试集上的 AUROC 达到 0.74，特异性高于敏感性（AUROC 95% CI：0.73 -0.76，敏感性 0.50，特异性 0.88）；高于 LASSO 模型 (0.68 (95% CI:0.68 – 0.69))。随机森林模型中最重要的 10 个特征是：图片序列记忆、认知总复合、认知流体复合、列表排序工作记忆、噪声中的单词测试（听力）、模式比较处理速度、气味识别、2 -分钟步行耐力、4米步行步态速度、图片词汇。总体而言，我们的模型揭示了认知、运动和感觉领域测量与 AD 生物标志物相关的有效性。

结论

我们的结果支持使用 NIH 工具箱作为一种高效且可标准化的 AD 生物标志物测量方法，与阳性病例（即低敏感性）相比，它更能识别淀粉样蛋白阴性病例（即高特异性）。