开发和评估用于预测无痴呆症人群大脑淀粉样蛋白阳性率的算法。

IF 7.9 1区 医学 Q1 CLINICAL NEUROLOGY Alzheimer's Research & Therapy Pub Date : 2024-10-11 DOI:10.1186/s13195-024-01595-5
Lisa Le Scouarnec, Vincent Bouteloup, Pieter J van der Veere, Wiesje M van der Flier, Charlotte E Teunissen, Inge M W Verberk, Vincent Planche, Geneviève Chêne, Carole Dufouil
{"title":"开发和评估用于预测无痴呆症人群大脑淀粉样蛋白阳性率的算法。","authors":"Lisa Le Scouarnec, Vincent Bouteloup, Pieter J van der Veere, Wiesje M van der Flier, Charlotte E Teunissen, Inge M W Verberk, Vincent Planche, Geneviève Chêne, Carole Dufouil","doi":"10.1186/s13195-024-01595-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.</p><p><strong>Methods: </strong>Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.</p><p><strong>Results: </strong>The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.</p><p><strong>Conclusion: </strong>A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.</p>","PeriodicalId":7516,"journal":{"name":"Alzheimer's Research & Therapy","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468062/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia.\",\"authors\":\"Lisa Le Scouarnec, Vincent Bouteloup, Pieter J van der Veere, Wiesje M van der Flier, Charlotte E Teunissen, Inge M W Verberk, Vincent Planche, Geneviève Chêne, Carole Dufouil\",\"doi\":\"10.1186/s13195-024-01595-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.</p><p><strong>Methods: </strong>Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.</p><p><strong>Results: </strong>The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.</p><p><strong>Conclusion: </strong>A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.</p>\",\"PeriodicalId\":7516,\"journal\":{\"name\":\"Alzheimer's Research & Therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468062/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer's Research & Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13195-024-01595-5\",\"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/s13195-024-01595-5","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:淀粉样蛋白-β(Aβ)肽在大脑中的积聚是阿尔茨海默病(AD)的标志,在症状出现前数年就会出现。目前量化体内淀粉样蛋白负荷的方法涉及侵入性或昂贵的程序,因此限制了可及性。早期检测非痴呆个体的淀粉样蛋白阳性对帮助早期诊断AD和在早期阶段启动抗淀粉样蛋白免疫疗法至关重要。本研究旨在利用日常收集的临床数据,开发并验证识别非痴呆患者脑淀粉样蛋白阳性的预测模型:方法:利用MEMENTO队列中853名非痴呆患者的数据开发了淀粉样蛋白阳性预测模型。在研究过程中,淀粉样蛋白水平可能会通过正电子发射断层扫描或脑脊液分析反复测量。淀粉样蛋白阳性的概率采用混合效应逻辑回归建模。预测因素包括人口统计学信息、认知评估、海马体萎缩和脑叶微出血的视觉脑磁共振成像评估、AD相关血液生物标记物(Aβ42/40和P-tau181)以及载脂蛋白E4状态。利用阿姆斯特丹痴呆队列的数据对模型进行了内部交叉验证和外部验证。此外,还对符合lecanemab适当使用建议(AUR)主要标准的子样本进行了性能评估:最有效的模型包含了人口统计学数据、认知评估、载脂蛋白E状态和AD相关血液生物标记物,在MEMENTO样本中的AUC为0.82 [95%CI 0.81-0.82],在外部验证样本中的AUC为0.90 [95%CI 0.86-0.94]。该模型明显优于仅基于人口统计学和认知数据的参考模型,在 MEMENTO 中的 AUC 差值为 0.10 [95%CI 0.10-0.11]。一个没有载脂蛋白E基因型的类似模型也取得了相当的判别性能。磁共振成像标记物并没有提高模型的性能。利卡单抗子样本的AUR表现相当:综合人口统计学、认知和血液生物标记物数据的预测模型为帮助识别非痴呆患者的淀粉样蛋白状态提供了一种很有前景的方法。载脂蛋白E基因型和脑部磁共振成像数据并不是强大鉴别能力的必要条件,这表明在评估希望接受治疗的淀粉样蛋白阳性患者接受免疫疗法的风险收益比时,可以推迟载脂蛋白E基因型的检测。将该模型融入临床实践可减少腰椎穿刺或正电子发射计算机断层扫描检查确认淀粉样蛋白状态的需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia.

Background: The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.

Methods: Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.

Results: The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.

Conclusion: A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

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.
期刊最新文献
Facial aging, cognitive impairment, and dementia risk. Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review. Sex-specific risk factors and clinical dementia outcomes for white matter hyperintensities in a large South Korean cohort. Perivascular space enlargement accelerates in ageing and Alzheimer's disease pathology: evidence from a three-year longitudinal multicentre study. Assessing the metabolism of the olfactory circuit by use of 18F-FDG PET-CT imaging in patients suspected of suffering from Alzheimer's disease or frontotemporal dementia.
×
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