Modeling the temporal evolution of plasma and PET Alzheimer's disease biomarkers.

Background: Knowledge of the temporal evolution among plasma Alzheimer's biomarkers and PET will inform disease pathophysiology and appropriate implementation of plasma biomarkers (Therneau, NeuroImage 2021; Betthauser, Brain 2022). This study investigates the relative timing of progression of plasma p‐tau181, p‐tau217, GFAP, and amyloid PET on individual and population levels. Method: We included 957 participants from the Mayo Clinic Study of Aging with clinical diagnoses of cognitively unimpaired (n = 732), MCI (n = 205), and Alzheimer's clinical syndrome dementia (n = 20). An accelerated failure time (AFT) model was fit with amyloid (PiB) PET, plasma p‐tau217 (Meso Scale Discovery, Lilly), plasma p‐tau181, and GFAP (Simoa, Quanterix HD‐X) as quad‐variate endpoints, and sex, APOE genotype status, and education as covariates. The model output included a per subject adjustment or time‐shift for each biomarker indicating how much earlier or later it was estimated to progress relative to the average participant, the correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker's timing. Secondly, we estimated progression at a given age for selected cutpoints. Result: Temporal trajectories for each biomarker are shown in Figure 1. Individual‐level adjustments between plasma p‐tau217 and amyloid PET were highly correlated, R = 0.69(0.64‐0.74); how early or late an individual progressed on plasma p‐tau217 explained 48% of the variation in relative timing of amyloid PET progression. Associations of plasma p‐tau181 and GFAP with amyloid PET individual adjustments were moderate and low, respectively (Figure 2). Using the chosen cutpoints, the estimated order of biomarker progression was GFAP, amyloid PET, plasma p‐tau181, and plasma p‐tau217 (Figure 3). Conclusion: On the individual level, the strong association of the relative timing of progression of plasma p‐tau217 and amyloid PET supports that these processes are mechanistically related. The relatively lower associations of the timing of p‐tau181 and GFAP with amyloid PET likely reflect a lower biological association and greater measurement noise for p‐tau181. The individual adjustments, the primary output of this model, are independent of cutpoints. On the population level, relative timing of biomarker change is highly dependent on the cutpoints applied. This true for any analysis that employs cutpoints, regardless of the approach. [ABSTRACT FROM AUTHOR]