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}
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).
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.