Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease.

Autor:	Meeker KL; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Luckett PH; Department of Neurosurgery, Washington University in St Louis, St Louis, MO 63110, USA., Barthélemy NR; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Hobbs DA; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA., Chen C; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA., Bollinger J; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Ovod V; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Flores S; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA., Keefe S; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA., Henson RL; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Herries EM; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., McDade E; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Hassenstab JJ; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Xiong C; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA.; Division of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA., Cruchaga C; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA.; Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA., Benzinger TLS; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Holtzman DM; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Schindler SE; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Bateman RJ; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA., Morris JC; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Gordon BA; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA., Ances BM; Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA.; Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA.; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA.
Jazyk:	angličtina
Zdroj:	Brain communications [Brain Commun] 2024 Mar 15; Vol. 6 (2), pp. fcae081. Date of Electronic Publication: 2024 Mar 15 (Print Publication: 2024).
DOI:	10.1093/braincomms/fcae081
Abstrakt:	Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/A β 40 lumi and A β 42/A β 40 lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/A β 40 lumi and t-tau/A β 40 lumi ) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/A β 40 lumi , p-tau181/A β 40 lumi , CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials. Competing Interests: C2N Diagnostics was co-founded by Drs Randall Bateman and David Holtzman, who are faculty members at Washington University. The PrecivityAD test was developed in the laboratory of Dr Randall Bateman at Washington University and licensed to C2N Diagnostics. Washington University has a financial interest in the PrecivityAD test. D.M.H. consults for C2N Diagnostics, Denali, Genentech, Cajal Neurosciences and Alector. R.J.B. consults for C2N Diagnostics, Eli Lilly and Co., Roche, Janssen, Avid and Eisai. E.M. has received royalty payments for an educational program supported by Eli Lilly and as a member of a scientific advisory board for Eli Lilly. Washington University, with R.J.B., E.M. and N.R.B. as co-inventors, has submitted the US nonprovisional patent application ‘Cerebrospinal fluid (CSF) tau rate of phosphorylation measurement to define stages of Alzheimer’s disease and monitor brain kinases/phosphatases activity’. S.E.S. has analysed data provided by C2N Diagnostics to Washington University and has served on a Scientific Advisory Board for Eisai. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)
Databáze:	MEDLINE
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