Introduction: Plasma glial fibrillary acidic protein (GFAP) may be associated with amyloid burden, neurodegeneration, and stroke but its specificity for Alzheimer's disease (AD) in the general population is unclear. We examined associations of plasma GFAP with amyloid and tau positron emission tomography (PET), cortical thickness, white matter hyperintensities (WMH), and cerebral microbleeds (CMBs). Methods: The study included 200 individuals from the Mayo Clinic Study of Aging who underwent amyloid and tau PET and magnetic resonance imaging and had plasma GFAP concurrently assayed; multiple linear regression and hurdle model analyses were used to investigate associations controlling for age and sex. Results: GFAP was associated with amyloid and tau PET in multivariable models. After adjusting for amyloid, the association with tau PET was no longer significant. GFAP was associated with cortical thickness, WMH, and lobar CMBs only among those who were amyloid‐positive. Discussion: This cross‐sectional analysis demonstrates the utility of GFAP as a plasma biomarker for AD‐related pathologies.

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Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrollment, and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analyzing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, Aβ42/40, GFAP, or NfL. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% vs. 68%; p = 0.001). A machine learning approach incorporating plasma biomarkers, demographics, and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set), and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity which could noninvasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Background: The National Institute on Aging-Alzheimer’s Association Research Framework proposes defining Alzheimer’s disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear. Objective: To determine whether cerebrospinal fluid (CSF)-neurodegenerative biomarkers (N) map differentially to Alzheimer’s disease pathology measured by Aβ 42 (an indicator of amyloidosis, [A]), p-tau (an indicator of tau deposition, [T]), and MRI vascular pathology indicators (measured by white-matter integrity, infarcts, and microbleeds [V]). Methods: Participants were from Mayo Clinic Study of Aging (MCSA) with CSF measures of NfL, Ng, t-tau, Aβ 42, and p-tau and available MRI brain imaging. Linear models assessed associations between CSF neurodegeneration (N) markers, amyloid markers (A), tau (T), and vascular pathology (V). Results: Participants (n = 408) had a mean age of 69.2±10.7; male, 217 (53.2%); cognitively unimpaired, 359 (88%). All three neurodegeneration biomarkers correlated with age (p < 0.001 for NfL and t-tau, p = 0.018 for Ng). Men had higher CSF-NfL levels; women had higher Ng (p < 0.001). NfL and t-tau levels correlated with infarcts (p = 0.009, p = 0.034 respectively); no biomarkers correlated with white-matter integrity. N biomarkers correlated with p-tau levels (T, p < 0.001). Higher Aβ 42 levels associated with higher N-biomarker levels but only among cognitively unimpaired (A, p < 0.001). Conclusion: The influence of vascular pathology in the general population on CSF (N) biomarkers is modest, with greater influence of infarcts than white-matter disruption. Neurodegeneration markers more closely correlated with tau than amyloid markers.