Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease.

IF 7.6 1区医学 Q1 CLINICAL NEUROLOGY

Alzheimer's Research & Therapy Pub Date : 2025-05-31 DOI:10.1186/s13195-025-01765-z

Lorenzo Gaetano Amato, Michael Lassi, Alberto Arturo Vergani, Jacopo Carpaneto, Salvatore Mazzeo, Valentina Moschini, Rachele Burali, Giovanni Salvestrini, Carlo Fabbiani, Giulia Giacomucci, Giulia Galdo, Carmen Morinelli, Filippo Emiliani, Maenia Scarpino, Sonia Padiglioni, Benedetta Nacmias, Sandro Sorbi, Antonello Grippo, Valentina Bessi, Alberto Mazzoni

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引用次数: 0

Abstract

Background: The diagnosis of Alzheimer's disease (AD) in its preclinical stages, such as subjective cognitive decline (SCD), is crucial for a timely management of the condition. However, current early diagnostic methods are unsuitable for preclinical screenings due to limited availability and diagnostic reliability. Additionally, reliance on invasive and scarcely available methods exacerbates the underdiagnosis of AD in its preclinical forms.

Methods: We introduce an early diagnostic pipeline based on the Digital Alzheimer's Disease Diagnosis (DADD) digital twin model, which derives personalized AD biomarkers from non-invasive electroencephalographic (EEG) recordings. These biomarkers reconstruct patient-specific neurodegeneration, capturing synaptic and connectivity degeneration mechanisms. Digital biomarkers were used to predict cerebrospinal fluid (CSF) biomarker positivity for AD and clinical conversions at follow-up in 124 participants with varying degrees of cognitive decline, including a control group of 19 healthy subjects.

Results: Digital biomarkers derived from the DADD model: i) Robustly distinguished SCD from healthy participants, improving classification accuracy by 7% compared to standard EEG biomarkers; ii) Identified patients positive for CSF biomarkers of AD with 88% accuracy (significantly outperforming standard EEG biomarkers, which achieved 58% accuracy); iii) Predicted follow-up conversions to clinical cognitive decline with 87% accuracy (compared to 54% accuracy for standard EEG biomarkers).

Conclusions: The DADD model provided robust digital AD biomarkers with strong diagnostic and prognostic value for preclinical AD, enabling the prediction of CSF biomarkers and clinical conversions using only non-invasive EEG recordings. This is particularly important as preclinical patients, such as those with SCD, are often excluded from diagnostic procedures like lumbar puncture. Predicting CSF biomarkers by combining digital twins with non-invasive recordings could revolutionize AD diagnosis in its early stages, paving the way for the clinical application of digital twins in AD diagnostics.

Trial registration: Clinical Trial identifier: NCT05569083 (submitted 2022-08-24).

查看原文本刊更多论文

数字双胞胎和非侵入性录音使老年痴呆症的早期诊断成为可能。

背景：阿尔茨海默病（AD）在临床前阶段的诊断，如主观认知能力下降（SCD），对于及时治疗至关重要。然而，由于可用性和诊断可靠性有限，目前的早期诊断方法不适合临床前筛查。此外，依赖侵入性和难以获得的方法加剧了临床前形式的阿尔茨海默病的诊断不足。方法：我们引入了一个基于数字阿尔茨海默病诊断（DADD）数字孪生模型的早期诊断管道，该模型从无创脑电图（EEG）记录中提取个性化的阿尔茨海默病生物标志物。这些生物标志物重建患者特异性神经变性，捕获突触和连通性变性机制。数字生物标志物用于预测124名不同程度认知能力下降的参与者的脑脊液（CSF）生物标志物阳性和临床转化，其中包括19名健康受试者的对照组。结果：来自DADD模型的数字生物标志物：i)与标准脑电图生物标志物相比，可将SCD与健康参与者稳健区分，分类准确率提高7%；ii)识别出阿尔茨海默病脑脊液生物标志物阳性的患者，准确率为88%（显著优于标准脑电图生物标志物，准确率为58%）；iii)预测随访转化为临床认知衰退的准确率为87%（相比之下，标准脑电图生物标志物的准确率为54%）。结论：DADD模型提供了强大的数字AD生物标志物，对临床前AD具有很强的诊断和预后价值，仅使用无创脑电图记录就可以预测脑脊液生物标志物和临床转换。这一点尤其重要，因为临床前患者，如SCD患者，通常被排除在腰椎穿刺等诊断程序之外。结合数字双胞胎和无创记录预测脑脊液生物标志物，可以在早期阶段彻底改变AD的诊断，为数字双胞胎在AD诊断中的临床应用铺平道路。试验注册：临床试验标识符：NCT05569083（提交日期：2022-08-24）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.