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, in forecasting cognitive decline in individuals without dementia. We developed a multimodal machine-learning approach incorporating plasma biomarkers (Amyloidβ42/40 (Aβ42/40), p-tau181, NfL), MRI, demographics, APOE4, and cognitive assessments to predict the rate of cognitive decline. Various models were designed to predict decline rates across cognitive domains (memory, executive function, language, and visuospatial abilities) and assess their relevance in predicting dementia progression. 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. Predicting cognitive decline and progression to MCI/dementia was most accurate in the memory domain, where plasma biomarkers (Aβ42/40, p-tau181, NfL) added significant value to predictive models, likely due to their AD-specific nature. Plasma biomarkers contributed less to predictions in other cognitive domains. The results indicate that plasma biomarkers, particularly when combined with MRI, demographics, APOE4, and cognitive measures, have significant potential for predicting memory decline and assessing the risk of dementia progression, even in cognitively unimpaired individuals.
Keywords: Alzheimer’s disease; Amyloid beta; Conversion prediction; Machine learning; Mild cognitive impairment; Neurofilament light; P-tau 181.
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