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. 2024 Feb;20(2):1225-1238.
doi: 10.1002/alz.13539. Epub 2023 Nov 14.

Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET

Petrice M Cogswell  1 , Emily S Lundt  2 , Terry M Therneau  2 , Heather J Wiste  2 , Jonathan Graff-Radford  3 , Alicia Algeciras-Schimnich  4 , Val J Lowe  1 , Michelle M Mielke  5 , Christopher G Schwarz  1 , Matthew L Senjem  1   6 , Jeffrey L Gunter  1 , David S Knopman  3 , Prashanthi Vemuri  1 , Ronald C Petersen  2   3 , Clifford R Jack Jr  1
Affiliations

Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET

Petrice M Cogswell et al. Alzheimers Dement. 2024 Feb.

Abstract

Introduction: The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co-evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers.

Methods: We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p-tau217, p-tau181, and glial fibrillary acidic protein (GFAP) as endpoints.

Results: Individual timing of plasma p-tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p-tau, and tau PET temporal meta-regions of interest when applying cut points based on young, cognitively unimpaired participants.

Discussion: Plasma p-tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p-tau and Aβ PET was the strongest.

Highlights: Plasma p-tau progression was more strongly associated with Aβ than tau PET. Progression on plasma p-tau was associated with Aβ PET and GFAP progression. P-tau181 and p-tau217 become abnormal after Aβ PET and before tau PET. GFAP became abnormal first, before plasma p-tau and Aβ PET.

Keywords: Alzheimer's disease; amyloid beta PET; plasma p-tau; tau PET; temporal modeling.

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Conflict of interest statement

P.M.C, E.S.L., T.M.T., H.J.W., and J.L.G. have no disclosures. J.G.R. serves on a data safety monitoring board for Strokenet and receives research support from the NIH. A.A.‐S. has participated in advisory boards for Roche Diagnostics, Fujirebio Diagnostics and Siemens Healthineers. C.G.S. receives research support from the NIH. M.L.S. holds stock in medical‐related companies, unrelated to the current work: Align Technology, Inc. He has also owned stock in these medical‐related companies within the past 3 years, unrelated to the current work: LHC Group, Inc. V.J.L. is a consultant for AVID Radiopharmaceuticals, Eisai Co. Inc., Bayer Schering Pharma, GE Healthcare, Piramal Life Sciences, and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, and NIH (NIA, NCI). M.M.M. has served on scientific advisory boards and/or has consulted for Biogen, LabCorp, Lilly, Merck, Siemens Healthineers, and Sunbird Bio and receives grant support from the NIH and Department of Defense. D.S.K. served on a data safety monitoring board for the DIAN study. He serves on a data safety monitoring board for a tau therapeutic for Biogen but receives no personal compensation. He was a site investigator in the Biogen aducanumab trials. He is an investigator in a clinical trial sponsored by Lilly Pharmaceuticals and the University of Southern California. He serves as a consultant for Samus Therapeutics, Third Rock, Roche, and Alzeca Biosciences but receives no personal compensation. He receives research support from the NIH. P.V. received speaker fees from Miller Medical Communications, Inc. and receives research support from the NIH. R.C.P. serves as a consultant for Roche Inc., Genetech, Inc., Nestle, Inc., Eli Llly and Co., and Eisai, Inc. He serves on the data safety monitoring board for Genentech, Inc. and receives royalties from Oxford University Press, UpToDate, and MedScape. He receives research support from the NIH and the Alzheimer's Association. C.R.J. receives no personal compensation from any commercial entity. He receives research support from the NIH and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic. Author disclosures are available in the supporting information.

Figures

FIGURE 1
FIGURE 1
Relationships of PET and plasma biomarkers with age from an AFT model for a subset of participants. Individual trajectories of Aβ PET SUVR, tau PET SUVR, p‐tau217 (pg/ml), p‐tau181 (pg/ml), and GFAP (pg/ml) versus age (A, C, E, G, I) and adjusted age (B, D, F, H, J). The adjusted age is the participant's estimated age with respect to the biomarker of interest based on both the covariate and random effects. The red curves indicate a hypothetical common curve; we assumed all individuals followed this trajectory of biomarker progression with the curve shifted left or right based on the random effects and covariate effects.
FIGURE 2
FIGURE 2
Relationships of individual adjustments between Aβ PET (meta‐ROI), tau PET (temporal meta‐ROI), p‐tau217 (MSD, Lilly), p‐tau181 (Quanterix, Simoa), and GFAP (Quanterix, Simoa). An 80% ellipse is included with a perfect circle indicating no relationship between adjustments. The percent variation explained (square of correlation×100) between individual‐level adjustments is given in the upper left. The x‐ and y‐axes are flipped for the individual adjustments. A higher positive value or earlier onset relative to the population mean is shown to the left of the x‐axis and bottom of the y‐axis. Each dot represents one participant, and the number of participants included in each comparison varies by data availability: Aβ versus tau PET, n = 866; Aβ versus p‐tau217, n = 955; Aβ versus p‐tau181, n = 1405; Aβ versus GFAP, n = 1408; tau PET versus p‐tau217, n = 548; tau PET versus p‐tau181, n = 863; p‐tau217 versus p‐tau181, n = 953; p‐tau217 versus GFAP, n = 955; p‐tau181 versus GFAP, n = 1405.
FIGURE 3
FIGURE 3
Estimated covariate effects with 95% credible interval. As in Figure 2, the x‐axis is flipped; a higher positive value or earlier onset relative to the population mean is shown to the left. For example, referral (to the ADRC) is associated with much earlier/younger onset of biomarker progression compared with the overall sample average.
FIGURE 4
FIGURE 4
By year of age, predicted proportion of MCSA participants having abnormal biomarker levels using (A, C) 2SD and (B, D) 3SD above the mean of young normal (YN) participants, cognitively unimpaired participants aged 30 to 59 years, as cut points for the primary model (A, B) and model using the tau PET Braak regions in place of the temporal meta‐ROI (C, D).
FIGURE 5
FIGURE 5
Effect of GFAP on temporal association of Aβ PET with plasma p‐tau and tau PET. Box plots of plasma p‐tau181 (A), plasma p‐tau217 (B), and tau PET (C) individual adjustments by combinations of early (individual adjustment > 0) versus late (individual adjustments < 0) progression of Aβ PET and plasma GFAP. These groups correspond to quadrants in plots of the associations of individual adjustments (Figure 2).
See this image and copyright information in PMC

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