Elisabeth H. Thijssen

Abstract Background Blood-based biomarkers for Alzheimer’s disease (AD) might facilitate identification of participants for clinical trials targeting amyloid beta (Abeta) accumulation, and aid in AD diagnostics. We examined the potential of plasma markers Abeta (1-42/1-40) , glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) to identify cerebral amyloidosis and/or disease severity. Methods We included individuals with a positive ( n = 176: 63 ± 7 years, 87 (49%) females) or negative ( n = 76: 61 ± 9 years, 27 (36%) females) amyloid PET status, with syndrome diagnosis subjective cognitive decline (18 PET+, 25 PET−), mild cognitive impairment (26 PET+, 24 PET−), or AD-dementia (132 PET+). Plasma Abeta (1-42/1-40) , GFAP, and NfL were measured by Simoa. We applied two-way ANOVA adjusted for age and sex to investigate the associations of the plasma markers with amyloid PET status and syndrome diagnosis; logistic regression analysis with Wald’s backward selection to identify an optimal panel that identifies amyloid PET positivity; age, sex, and education-adjusted linear regression analysis to investigate associations between the plasma markers and neuropsychological test performance; and Spearman’s correlation analysis to investigate associations between the plasma markers and medial temporal lobe atrophy (MTA). Results Abeta (1-42/1-40) and GFAP independently associated with amyloid PET status ( p = 0.009 and p < 0.001 respectively), and GFAP and NfL independently associated with syndrome diagnosis ( p = 0.001 and p = 0.048 respectively). The optimal panel identifying a positive amyloid status included Abeta (1-42/1-40) and GFAP, alongside age and APOE (AUC = 88% (95% CI 83–93%), 82% sensitivity, 86% specificity), while excluding NfL and sex. GFAP and NfL robustly associated with cognitive performance on global cognition and all major cognitive domains (GFAP: range standardized β (sβ) = − 0.40 to − 0.26; NfL: range sβ = − 0.35 to − 0.18; all: p < 0.002), whereas Abeta (1-42/1-40) associated with global cognition, memory, attention, and executive functioning (range sβ = 0.22 – 0.11; all: p < 0.05) but not language. GFAP and NfL showed moderate positive correlations with MTA (both: Spearman’s rho> 0.33, p < 0.001). Abeta (1-42/1-40) showed a moderate negative correlation with MTA (Spearman’s rho = − 0.24, p = 0.001). Discussion and conclusions Combination of plasma Abeta (1-42/1-40) and GFAP provides a valuable tool for the identification of amyloid PET status. Furthermore, plasma GFAP and NfL associate with various disease severity measures suggesting potential for disease monitoring.

Abstract Background Blood-based amyloid biomarkers may provide a non-invasive, cost-effective and scalable manner for detecting cerebral amyloidosis in early disease stages. Methods In this prospective cross-sectional study, we quantified plasma Aβ 1–42 /Aβ 1–40 ratios with both routinely available ELISAs and novel SIMOA Amyblood assays, and provided a head-to-head comparison of their performances to detect cerebral amyloidosis in a nondemented elderly cohort ( n = 199). Participants were stratified according to amyloid-PET status, and the performance of plasma Aβ 1–42 /Aβ 1–40 to detect cerebral amyloidosis was assessed using receiver operating characteristic analysis. We additionally investigated the correlations of plasma Aβ ratios with amyloid-PET and CSF Alzheimer’s disease biomarkers, as well as platform agreement using Passing-Bablok regression and Bland-Altman analysis for both Aβ isoforms. Results ELISA and SIMOA plasma Aβ 1–42 /Aβ 1–40 detected cerebral amyloidosis with identical accuracy (ELISA: area under curve (AUC) 0.78, 95% CI 0.72–0.84; SIMOA: AUC 0.79, 95% CI 0.73–0.85), and both increased the performance of a basic demographic model including only age and APOE-ε4 genotype ( p ≤ 0.02). ELISA and SIMOA had positive predictive values of respectively 41% and 36% in cognitively normal elderly and negative predictive values all exceeding 88%. Plasma Aβ 1–42 /Aβ 1–40 correlated similarly with amyloid-PET for both platforms (Spearman ρ = − 0.32, p < 0.0001), yet correlations with CSF Aβ 1–42 /t-tau were stronger for ELISA ( ρ = 0.41, p = 0.002) than for SIMOA ( ρ = 0.29, p = 0.03). Plasma Aβ levels demonstrated poor agreement between ELISA and SIMOA with concentrations of both Aβ 1–42 and Aβ 1–40 measured by SIMOA consistently underestimating those measured by ELISA. Conclusions ELISA and SIMOA demonstrated equivalent performances in detecting cerebral amyloidosis through plasma Aβ 1–42 /Aβ 1–40 , both with high negative predictive values, making them equally suitable non-invasive prescreening tools for clinical trials by reducing the number of necessary PET scans for clinical trial recruitment. Trial registration EudraCT 2009-014475-45 (registered on 23 Sept 2009) and EudraCT 2013-004671-12 (registered on 20 May 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-004671-12/BE ).

Abstract Introduction We explored what combination of blood‐based biomarkers (amyloid beta [Aβ] 1‐42/1‐40 , phosphorylated tau [p‐tau]181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP]) differentiates Alzheimer's disease (AD) dementia, frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Methods We measured the biomarkers with Simoa in two separate cohorts (n = 160 and n = 152). In one cohort, Aβ 1‐42/1‐40 was also measured with mass spectrometry (MS). We assessed the differential diagnostic value of the markers, by logistic regression with Wald's backward selection. Results MS and Simoa Aβ 1‐42/1‐40 similarly differentiated AD from controls. The Simoa panel that optimally differentiated AD from FTD consisted of NfL and p‐tau181 (area under the curve [AUC] = 0.94; cohort 1) or NfL, GFAP, and p‐tau181 (AUC = 0.90; cohort 2). For AD from DLB, the panel consisted of NfL, p‐tau181, and GFAP (AUC = 0.88; cohort 1), and only p‐tau181 (AUC = 0.81; cohort 2). Discussion A combination of plasma p‐tau181, NfL, and GFAP, but not Aβ 1‐42/1‐40 , might be useful to discriminate AD, FTD, and DLB.