Hofrenning, Ekaterina

Abstract INTRODUCTION We examined when Alzheimer's disease biomarkers become informative by identifying age‐related breakpoints with slope‐changing trajectories. METHODS In 2082 Mayo Clinic Study of Aging participants, we modeled plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid/tau positron emission tomography (PET), hippocampal volume (HVa), and global cognition. Generalized additive models described age trends; Davies' test and piecewise linear regression estimated breakpoints. A C2N subsample ( n = 462) provided mass‐spectrometry plasma markers (p‐tau181, p‐tau217, their ratios, Aβ42/40). RESULTS In the full cohort, Aβ42/40, HVa, and cognition declined with age; p‐tau181, NfL, GFAP, and amyloid/tau PET increased. We observed single breakpoints (years, 95% CI): GFAP 68.1 (63.5–71.8), NfL 70.7 (65.9–75.6), p‐tau181 67.2 (60.3–70.3), amyloid PET 62.3 (56.2–69.3), HVa 68.1 (63.1–71.9), cognition 59.8 (55.4–66.0); tau PET showed none. In the mass‐spectrometry subset, p‐tau217 and p‐tau181 broke at 72.6; their ratios and Aβ42/40 showed no breakpoints. DISCUSSION Breakpoints cluster near late midlife, suggesting windows for screening and monitoring.

AbstractINTRODUCTIONWe investigated the usefulness of plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) for capturing vascular cognitive impairment (VCI) in the context of amyloidosis.METHODSUsing two independent cohorts (n = 1810), we assessed the relationship of plasma NfL and GFAP with (1) vascular brain indices; (2) diagnostic states using the following definitions: vascular versus not (white matter hyperintensity/total intracranial volume ≥ 1.3%), and cognitively impaired (CI) versus cognitively unimpaired (CU) using Clinical Dementia Rating ([CDR] scale ≥ 0.5); and (3) their upstream predictors using structural equation models (SEMs).RESULTSPlasma NfL and GFAP were associated with vascular brain damage and differed across states (VCI > vascular CU > non‐vascular CI > non‐vascular CU). In a population‐based sample, these biomarkers distinguished vascular CU and VCI from non‐vascular CU groups with greater separation in amyloid negative participants. Pathway analyses showed NfL was primarily influenced by systemic/brain vascular health, whereas amyloid contributed to GFAP variability.DISCUSSIONPlasma biomarkers, particularly NfL, capture vascular brain changes and show promise for VCI identification.Highlights Plasma NfL and glial fibrillary acidic protein (GFAP) were associated with vascular brain indices. Plasma biomarkers differed across diagnostic states (VCI > vascular CU > non‐vascular CI > non‐vascular CU). Plasma markers discriminated vascular from non‐vascular states with greater separation in Aβ− participants. NfL was linked to vascular health, while amyloid influenced GFAP variability in the population‐based sample. Future longitudinal frameworks should consider systemic inflammation markers along with these plasma markers to better understand VCID‐related brain changes and cognitive decline.

Abstract Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment 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. Analysing 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, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. 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% versus 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 non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.