利用美国国家卫生研究院用于评估神经和行为功能的工具箱(NIHTB)预测脑淀粉样蛋白状态

Y. Cheng, E. Ho, S. Weintraub, D. Rentz, R. Gershon, Sudeshna Das, Hiroko H. Dodge
{"title":"利用美国国家卫生研究院用于评估神经和行为功能的工具箱(NIHTB)预测脑淀粉样蛋白状态","authors":"Y. Cheng, E. Ho, S. Weintraub, D. Rentz, R. Gershon, Sudeshna Das, Hiroko H. Dodge","doi":"10.14283/jpad.2024.77","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Objective</h3><p>To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study.</p><h3 data-test=\"abstract-sub-heading\">Design</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Setting</h3><p>Participants across various sites were involved in the ARMADA study for validating the NIHTB.</p><h3 data-test=\"abstract-sub-heading\">Participants</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Measurements</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>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).</p>","PeriodicalId":22711,"journal":{"name":"The Journal of Prevention of Alzheimer's Disease","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function\",\"authors\":\"Y. Cheng, E. Ho, S. Weintraub, D. Rentz, R. Gershon, Sudeshna Das, Hiroko H. Dodge\",\"doi\":\"10.14283/jpad.2024.77\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Background</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Objective</h3><p>To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study.</p><h3 data-test=\\\"abstract-sub-heading\\\">Design</h3><p>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).</p><h3 data-test=\\\"abstract-sub-heading\\\">Setting</h3><p>Participants across various sites were involved in the ARMADA study for validating the NIHTB.</p><h3 data-test=\\\"abstract-sub-heading\\\">Participants</h3><p>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).</p><h3 data-test=\\\"abstract-sub-heading\\\">Measurements</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>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).</p>\",\"PeriodicalId\":22711,\"journal\":{\"name\":\"The Journal of Prevention of Alzheimer's Disease\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Prevention of Alzheimer's Disease\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14283/jpad.2024.77\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Prevention of Alzheimer's Disease","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14283/jpad.2024.77","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0

摘要

背景淀粉样蛋白-β(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,预测脑淀粉样蛋白状态对于确定治疗资格至关重要。目的在阿尔茨海默病和认知老化的可靠测量研究(ARMADA)中利用机器学习方法预测脑淀粉样蛋白状态。设计ARMADA是一项多站点研究,在不同认知能力水平(正常、轻度认知障碍、AD型早期痴呆)的老年人中实施美国国立卫生研究院神经和行为功能评估工具箱(NIHTB)。参与者199名ARMADA参与者拥有PET或CSF信息(平均年龄76.3 ± 7.7岁,51.3%为女性,42.3%受过一些或完整的大学教育,50.3%受过研究生教育,88.9%为白人,33.2%具有阳性AD生物标记物)。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。结果随机森林模型的AUROC为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))。随机森林模型中重要性最高的 10 个特征是:图片序列记忆、认知总综合、认知流体综合、列表排序工作记忆、噪声词测试(听力)、模式比较处理速度、气味识别、2 分钟步行耐力、4 米步行步态速度和图片词汇量。总之,我们的模型揭示了认知、运动和感觉领域的测量结果在与注意力缺失症生物标志物相关联方面的有效性。结论我们的研究结果支持将美国国立卫生研究院工具箱作为一种高效、可标准化的注意力缺失症生物标志物测量方法来使用,它在识别淀粉样蛋白阴性病例(即特异性高)方面优于阳性病例(即灵敏度低)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function

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).

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
The Journal of Prevention of Alzheimer's Disease
The Journal of Prevention of Alzheimer's Disease Medicine-Psychiatry and Mental Health
CiteScore
9.20
自引率
0.00%
发文量
0
期刊介绍: The JPAD Journal of Prevention of Alzheimer’Disease will publish reviews, original research articles and short reports to improve our knowledge in the field of Alzheimer prevention including: neurosciences, biomarkers, imaging, epidemiology, public health, physical cognitive exercise, nutrition, risk and protective factors, drug development, trials design, and heath economic outcomes.JPAD will publish also the meeting abstracts from Clinical Trial on Alzheimer Disease (CTAD) and will be distributed both in paper and online version worldwide.We hope that JPAD with your contribution will play a role in the development of Alzheimer prevention.
期刊最新文献
Association between Cognitive Reserve Indicator and Chronic Disease-Free Survival: A Large Community-Based Longitudinal Study Phase 1 Studies of the Anti-Tau Monoclonal Antibody JNJ-63733657 in Healthy Participants and Participants with Alzheimer’s Disease Roles of TREM2 in the Pathological Mechanism and the Therapeutic Strategies of Alzheimer’s Disease Development and Validation the Mobile Toolbox (MTB) Spelling Test Correlates of Subjective Cognitive Decline in Black American Men
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1