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Amato LG, Lassi M, Vergani AA, Carpaneto J, Mazzeo S, Moschini V, Burali R, Salvestrini G, Fabbiani C, Giacomucci G, Galdo G, Morinelli C, Emiliani F, Scarpino M, Padiglioni S, Nacmias B, Sorbi S, Grippo A, Bessi V, Mazzoni A. Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease. Alzheimers Res Ther 2025; 17:125. [PMID: 40450374 DOI: 10.1186/s13195-025-01765-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2025] [Accepted: 05/13/2025] [Indexed: 06/03/2025]
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).
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Affiliation(s)
- Lorenzo Gaetano Amato
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Piazza Martiri Della Libertà 33, 56127, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Michael Lassi
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Piazza Martiri Della Libertà 33, 56127, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Alberto Arturo Vergani
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Piazza Martiri Della Libertà 33, 56127, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Jacopo Carpaneto
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Piazza Martiri Della Libertà 33, 56127, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Salvatore Mazzeo
- Research and Innovation Center for Dementia-CRIDEM, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milan, Italy
- IRCCS Policlinico San Donato, Edmondo Malan 2, 20097, Milan, Italy
| | - Valentina Moschini
- Skeletal Muscles and Sensory Organs Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Rachele Burali
- IRCSS Fondazione Don Carlo Gnocchi, Via Di Scandicci 269, 50143, Florence, Italy
| | | | - Carlo Fabbiani
- IRCSS Fondazione Don Carlo Gnocchi, Via Di Scandicci 269, 50143, Florence, Italy
| | - Giulia Giacomucci
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Giulia Galdo
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Carmen Morinelli
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Emiliani
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Maenia Scarpino
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Sonia Padiglioni
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Benedetta Nacmias
- IRCSS Fondazione Don Carlo Gnocchi, Via Di Scandicci 269, 50143, Florence, Italy
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Sandro Sorbi
- IRCSS Fondazione Don Carlo Gnocchi, Via Di Scandicci 269, 50143, Florence, Italy
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Antonello Grippo
- Unit of Neurophysiology, Careggi University Hospital, Florence, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, Università Di Firenze, Largo Brambilla 3, 50134, Florence, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Piazza Martiri Della Libertà 33, 56127, Pisa, Italy.
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy.
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