Predicting brain amyloid load with digital and blood-based biomarkers
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Le résumé fourni par la source
BACKGROUND: With the recent approval of anti-β-amyloid (Aβ) treatment for Alzheimer's disease (AD), a demand has emerged for scalable, convenient and accurate estimations of brain Aβ burden for the detection of AD that would enable timely, accurate and reliable diagnosis in one's primary care physician's (PCPs) office as called for recently by World Health Organization (WHO). METHODS: MemTrax, a 2-minute online memory test, was selected as the digital biomarker of cognitive impairment, and blood-based biomarkers (BBMs) including Aβ42, Aβ40, P-tau181, GFAP and NfL were used to estimate AD-related metrics in different groups of elderly individuals (n = 349) for comparison with Aβ PET scans of brain Aβ burden. The correlations between MemTrax, MoCA, BBMs and brain Aβ burden, expressed in centiloid (CL) values, were analyzed for predicting CL value alone or in combinations using machine-learning (ML). RESULTS: Both MemTrax and the MoCA were able to differentiate Aβ status similarly. Integration of MemTrax and BBMs using ML, however, significantly improved the AUCs (over the same with MoCA) for differentiating Aβ status. MemTrax and p-Tau181/Aβ42 composite showed the strongest relationship with CL value among other BBMs. Most importantly, regression analyses of MemTrax and p-Tau181/Aβ42 aptly predicted CL values. CONCLUSION: The combination of MemTrax and BBMs provides an accurate, convenient, non-invasive, cost-effective and scalable way to estimate Aβ load, which provides an opportunity for mass screening and timely and accurate diagnosis of AD. Our findings could also facilitate more effective AD clinical management in the PCPs office worldwide for more equitable access to current standard of care.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Predicting brain amyloid load with digital and blood-based biomarkers
- Date Crossref
- 05/07/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Sun Yat-sen University Department of Neurology pays non établi dans la noticeUniversité ou école supérieure
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Shanghai FRP Research Institute (China) pays non établi dans la noticeEntreprise
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Kunming University pays non établi dans la noticeUniversité ou école supérieure
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Kunming Medical University Center for Clinical Pharmacy pays non établi dans la noticeUniversité ou école supérieure
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First Affiliated Hospital of Kunming Medical University pays non établi dans la noticeÉtablissement de santé
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University of Hartford Department of Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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VA Palo Alto Health Care System pays non établi dans la noticeÉtablissement de santé
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School of Medicine Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases pays non établi dans la noticeUniversité ou école supérieure
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Washington Institute of Clinical Research Center for Alzheimer's Research pays non établi dans la noticeStructure de recherche
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MemTrax pays non établi dans la noticeInstitution
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Kunming Escher Technology Co. Ltd pays non établi dans la noticeEntreprise
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Stanford University Department of Psychiatry & Behavioral Sciences pays non établi dans la noticeUniversité ou école supérieure
Department of Neurology — Sun Yat-sen University, Shanghai FRP Research Institute (China) et Kunming University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.