Background: Plasma biomarkers are associated with cognitive performance and decline in Alzheimer's disease, making them promising for early detection. This study investigates their predictive value, combined with non-invasive measures, for cognitive decline in non-demented individuals. Methods: We developed a machine-learning approach incorporating plasma biomarkers (Aβ42/40, p-tau181, NfL), MRI, demographics, APOE4, and cognitive assessments. Various models were designed to predict decline rates across cognitive domains and assess their relevance in predicting dementia progression. Results: Cross-validated correlations between predicted and actual cognitive decline rates were 0.50 for memory, 0.49 for language, 0.42 for executive function, and 0.44 for visuospatial ability. MRI showed greater predictive importance than plasma biomarkers. Among plasma biomarkers, NfL and p-tau181 outperformed Aβ42/40. Conclusion: Plasma biomarkers, especially when combined with MRI, APOE4, and cognitive measures, have the potential to predict memory decline and assess conversion risk, even in cognitively unimpaired individuals.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis research has been supported by The Academy of Finland, grant 351849 under the frame of ERA PerMed (Pattern-Cog), grants 346934 (PRIMAL), and 358944 (Flagship of Advanced Mathematics for Sensing Imaging and Modeling) from the Research Council of Finland. And also a grant from Bundesministerium fur Bildung, Wissenschaft und Forschung, Grant/Award Number: ERAPERMED2021-127.
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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The ADNI study was approved by the Institutional Review Board (IRB) of each participating site and was conducted in accordance with Federal Regulations. For participants in ADNI protocols, written informed consent was obtained and the study was conducted in accordance with the Declaration of Helsinki.
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Data AvailabilityData used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). Details about data access are detailed there. The participant's RIDs are provided in Supplementary Materials
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