Work

Combined use of plasma p‐tau217, NfL, and GFAP predicts domain‐specific cognitive decline in cognitively unimpaired and MCI individuals

Year: 2025
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Accurate identification of individuals at risk for cognitive decline is critical for treatment planning and trial enrichment strategies. We evaluated the combined utility of plasma phosphorylated tau at threonine 217 (p-tau217), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in predicting domain-specific cognitive decline. METHODS: Participants (n = 523; 40.9% cognitively unimpaired [CU]; 59.1% mild cognitive impairment [MCI]) were from the Massachusetts Alzheimer's Disease Research Center. Cognition was assessed using the National Alzheimer's Coordinating Center Uniform Data Set. Participants were classified as high(+)/low(-) for each biomarker using Gaussian mixture models. RESULTS: Among all participants, high p-tau217 alone [p-tau217(+)NfL(-)GFAP(-)] was associated with a steeper decline in episodic/semantic memory and processing speed compared to the all-low group (p ≤ 0.02). With the addition of high GFAP [p-tau217(+)NfL(-)GFAP(+)], steeper decline extended to most cognitive domains, including global cognition and executive function, compared to the all-low group. In CU, faster decline in global cognition and executive function was seen when all biomarkers were elevated ([p-tau217(+)NfL(+)GFAP(+)]; p ≤ 0.04). DISCUSSION: Combined plasma biomarkers predict decline in cognitive domains vulnerable to early disease. HIGHLIGHTS: High phosphorylated tau at threonine 217 (p-tau217) alone was associated with declines in semantic/episodic memory, whereas its combination with elevated glial fibrillary acidic protein (GFAP) predicted declines in a wider range of cognitive domains. Elevated neurofilament light chain (NfL) amplifies the cognitive decline already driven by p-tau217 and GFAP. In cognitively unimpaired individuals, subtle domain-specific cognitive declines can be detected when both core and non-core Alzheimer's disease biomarkers are used. Our finding highlights the importance of focusing on vulnerable cognitive domains during early disease where global cognition may appear stable but specific impairments can be masked within composite scores.
Language: English
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $4,200
Awards: Alzheimer's Disease Research Center, G1859882300, G2174163889, Web-enabled social interaction to delay cognitive decline among seniors with MCI: Phase I, 1Florida Alzheimer's Disease Research Center, G348743638, Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers, Vanderbilt Alzheimer's Disease Research Center, Conversational engagement as a means to delay onset AD: Phase II, G9000208313, Boston University Alzheimer's Disease Research Center, Yale Alzheimer Disease Research Center, UCSD Shiley-Marcos Alzheimer's Disease Research Center P30, Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease, Michigan Alzheimer's Disease Research Center, University of Kansas Alzheimer's Disease Research Center (KU ADRC), Alzheimer Disease Research Center, Alzheimer's Disease Research Center, Alzheimer's Disease Research Center, UC Davis Alzheimer's Disease Research Center, Johns Hopkins Alzheimer's Disease Research Center, Indiana Alzheimer's Disease Research Center, Cleveland Alzheimers Disease Research Center, New Approaches to Dementia Heterogeneity, Stanford Alzheimer's Disease Research Center, Mount Sinai Alzheimer's Disease Research Center, Mayo Alzheimer's Disease Research Center, University of Washington Alzheimer's Disease Research Center, New Mexico Alzheimer's Disease Research Center, Duke/UNC Alzheimer's Disease Research Center, Wisconsin Alzheimer's Disease Research Center, Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers, Alzheimer's Disease Research Centers, Profiling and forecasting individual unique progression in ADRD using machine learning, UAB Alzheimer's Disease Research Center, National Alzheimer's Coordinating Center, University of Kentucky Alzheimer's Disease Research Center, NORTHWESTERN Alzheimer's Disease Research Center, Penn Alzheimer's Disease Research Center (ADRC), Alzheimer's Disease Research Center, South Texas Alzheimer's Disease Center, Goizueta Alzheimer's Disease Research Center, The Alzheimer's Disease Research Center at the University of California, Irvine, Rush Alzheimer's Disease Research Center
OpenAlex ID: W4416827424
FWCI: 7.36
Cites: 28
Cited by: 12
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