根据脑萎缩模式预测从主观认知能力下降到轻度认知障碍或痴呆症的进展。

IF 7.9 1区 医学 Q1 CLINICAL NEUROLOGY
Ondrej Lerch, Daniel Ferreira, Erik Stomrud, Danielle van Westen, Pontus Tideman, Sebastian Palmqvist, Niklas Mattsson-Carlgren, Jakub Hort, Oskar Hansson, Eric Westman
{"title":"根据脑萎缩模式预测从主观认知能力下降到轻度认知障碍或痴呆症的进展。","authors":"Ondrej Lerch, Daniel Ferreira, Erik Stomrud, Danielle van Westen, Pontus Tideman, Sebastian Palmqvist, Niklas Mattsson-Carlgren, Jakub Hort, Oskar Hansson, Eric Westman","doi":"10.1186/s13195-024-01517-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).</p><p><strong>Methods: </strong>We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk \"disease-like\" or low-risk \"CN-like\". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.</p><p><strong>Results: </strong>In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).</p><p><strong>Conclusion: </strong>When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.</p>","PeriodicalId":7516,"journal":{"name":"Alzheimer's Research & Therapy","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11225196/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.\",\"authors\":\"Ondrej Lerch, Daniel Ferreira, Erik Stomrud, Danielle van Westen, Pontus Tideman, Sebastian Palmqvist, Niklas Mattsson-Carlgren, Jakub Hort, Oskar Hansson, Eric Westman\",\"doi\":\"10.1186/s13195-024-01517-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).</p><p><strong>Methods: </strong>We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk \\\"disease-like\\\" or low-risk \\\"CN-like\\\". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.</p><p><strong>Results: </strong>In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).</p><p><strong>Conclusion: </strong>When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.</p>\",\"PeriodicalId\":7516,\"journal\":{\"name\":\"Alzheimer's Research & Therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11225196/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer's Research & Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13195-024-01517-5\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer's Research & Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13195-024-01517-5","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:阿尔茨海默病(AD)是一种进行性神经退行性疾病,其病理生理变化始于临床症状出现前数十年。利用结构性核磁共振成像和多变量数据分析分析脑萎缩模式,是识别主观认知能力下降(SCD)患者发展为阿兹海默病痴呆症风险较高的有效工具。通过对晚期 AD 和正常人进行分类而训练的模型所获得的萎缩模式,对于像 SCD 这样处于早期阶段的受试者来说可能并不理想。在这项研究中,我们比较了使用 "严重程度指数 "预测 SCD 进展的准确性,该指数是使用在 AD 痴呆症患者身上训练的标准分类模型和在β-淀粉样蛋白(Aβ)阳性的失忆性轻度认知障碍(aMCI)患者身上训练的新模型得出的:我们使用了瑞典 BioFINDER-1 研究队列中 504 名患者的结构 MRI 数据(认知正常 (CN),Aβ 阴性 = 220;SCD,Aβ 阳性和阴性 = 139;aMCI,Aβ 阳性 = 106;AD 痴呆 = 39)。我们应用多变量数据分析建立了两个预测模型,经过训练后可将 CN 患者与 Aβ 阳性 aMCI 或 AD 痴呆患者区分开来。模型应用于 SCD 患者,将其萎缩模式分为高风险的 "类疾病 "或低风险的 "CN 类"。利用8年的纵向数据对临床轨迹和模型的准确性进行了评估:在预测从 SCD 发展为 MCI 或痴呆症的过程中,基于痴呆症的标准模型达到了 100% 的特异性,但灵敏度仅为 10.6%,而基于 aMCI 的新模型则达到了 72.3% 的灵敏度和 60.9% 的特异性。与基于痴呆症的模型(AUC = 0.57)相比,基于 aMCI 的模型在预测 SCD 向 MCI 或痴呆症进展方面更具优势,其接收器操作特征曲线下面积(AUC = 0.72;P = 0.037)更高:结论:在使用结构性磁共振成像数据预测从SCD转为MCI或痴呆症时,与基于痴呆症的标准模型相比,基于萎缩程度较轻的个体(即aMCI)的预测模型可能具有更高的临床价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).

Methods: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.

Results: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).

Conclusion: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

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
Alzheimer's Research & Therapy
Alzheimer's Research & Therapy 医学-神经病学
CiteScore
13.10
自引率
3.30%
发文量
172
审稿时长
>12 weeks
期刊介绍: Alzheimer's Research & Therapy is an international peer-reviewed journal that focuses on translational research into Alzheimer's disease and other neurodegenerative diseases. It publishes open-access basic research, clinical trials, drug discovery and development studies, and epidemiologic studies. The journal also includes reviews, viewpoints, commentaries, debates, and reports. All articles published in Alzheimer's Research & Therapy are included in several reputable databases such as CAS, Current contents, DOAJ, Embase, Journal Citation Reports/Science Edition, MEDLINE, PubMed, PubMed Central, Science Citation Index Expanded (Web of Science) and Scopus.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信