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

Karin L. Meeker, Patrick H. Luckett, Nicolas R. Barthélemy, Diana A. Hobbs, Charles Chen, James Bollinger, Vitaliy Ovod, Shaney Flores, Sarah Keefe, Rachel L. Henson, Elizabeth M. Herries, Eric McDade, Jason J. Hassenstab, Chengjie Xiong, Carlos Cruchaga, Tammie L.S. Benzinger, David M. Holtzman, Suzanne E. Schindler, Randall J. Bateman, John C. MorrisBrian A. Gordon, Beau M. Ances

Research output: Contribution to journalArticlepeer-review


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/Aβ40lumi and Aβ42/Aβ40lumi 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/Aβ40lumi and t-tau/Aβ40lumi) 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/Aβ40lumi, p-tau181/Aβ40lumi, 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.

Original languageEnglish
JournalBrain Communications
Issue number2
StatePublished - 2024


  • Alzheimer’s disease
  • biomarkers
  • machine learning


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