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.

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 Aβ1–42/Aβ1–40 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 Aβ1–42/Aβ1–40 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 Aβ1–42/Aβ1–40 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 Aβ1–42/Aβ1–40 correlated similarly with amyloid-PET for both platforms (Spearman ρ = − 0.32, p <  0.0001), yet correlations with CSF Aβ1–42/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 Aβ1–42 and Aβ1–40 measured by SIMOA consistently underestimating those measured by ELISA. Conclusions ELISA and SIMOA demonstrated equivalent performances in detecting cerebral amyloidosis through plasma Aβ1–42/Aβ1–40, 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).

Background: Reduced cerebrospinal fluid (CSF) concentration of amyloid-β1-42 (Aβ1-42) reflects the presence of amyloidopathy in brains of subjects with Alzheimer’s disease (AD).Objective: To qualify the use of Aβ1-42/Aβ1-40 for improvement of standard operating procedures (SOP) for measurement of CSF Aβ with a focus on CSF collection, storage, and analysis.Methods: Euroimmun ELISAs for CSF Aβ isoforms were used to set up a SOP with respect to recipient properties (low binding, polypropylene), volume of tubes, freeze/thaw cycles, addition of detergents (Triton X-100, Tween-20) in collection or storage tubes or during CSF analysis. Data were analyzed with linear repeated measures and mixed effects models.Results: Optimization of CSF analysis included a pre-wash of recipients (e.g., tubes, 96-well plates) before sample analysis. Using the Aβ1-42/Aβ1-40 ratio, in contrast to Aβ1-42, eliminated effects of tube type, additional freeze/thaw cycles, or effect of CSF volumes for polypropylene storage tubes. ‘Low binding’ tubes reduced the loss of Aβ when aliquoting CSF or in function of additional freeze/thaw cycles. Addition of detergent in CSF collection tubes resulted in an almost complete absence of variation in function of collection procedures, but affected the concentration of Aβ isoforms in the immunoassay.Conclusion: The ratio of Aβ1-42/Aβ1-40 is a more robust biomarker than Aβ1-42 toward (pre-) analytical interfering factors. Further, ‘low binding’ recipients and addition of detergent in collection tubes are able to remove effects of SOP-related confounding factors. Integration of the Aβ1-42/Aβ1-40 ratio and ‘low-binding tubes’ into guidance criteria may speed up worldwide standardization of CSF biomarker analysis.

Introduction Blood‐based assays to measure brain amyloid beta (Aβ) deposition are an attractive alternative to the cerebrospinal fluid (CSF)–based assays currently used in clinical settings. In this study, we examined different blood‐based assays to measure Aβ and how they compare among centers and assays. Methods Aliquots from 81 plasma samples were distributed to 10 participating centers. Seven immunological assays and four mass‐spectrometric methods were used to measure plasma Aβ concentrations. Results Correlations were weak for Aβ42 while Aβ40 correlations were stronger. The ratio Aβ42/Aβ40 did not improve the correlations and showed weak correlations. Discussion The poor correlations for Aβ42 in plasma might have several potential explanations, such as the high levels of plasma proteins (compared to CSF), sensitivity to pre‐analytical sample handling and specificity, and cross‐reactivity of different antibodies. Different methods might also measure different pools of plasma Aβ42. We, however, hypothesize that greater correlations might be seen in future studies because many of the methods have been refined during completion of this study.

BackgroundThe pathophysiology of neurodegeneration is complex. Its diagnosis requires an early identification of sequential changes in several hallmarks in the brains of affected subjects. The presence of brain pathology can be visualized in the cerebrospinal fluid (CSF) by protein profiling. It is clear that the field of Alzheimer’s disease (AD) will benefit from an integration of algorithms including CSF concentrations of individual proteins, especially as an aid in clinical decision-making or to improve patient enrolment in clinical trials. The protein profiling approach requires standard operating procedures for collection and storage of CSF which must be easy to integrate into a routine clinical lab environment. Our study provides recommendations for analysis of neurogranin trunc P75, α-synuclein, and tau, in combination with the ratio of β-amyloid Aβ(1–42)/Aβ(1–40).MethodsProtocols for CSF collection were compared with CSF derived from subjects with normal pressure hydrocephalus (n = 19). Variables included recipient type (collection, storage), tube volume, and addition of detergents at the time of collection. CSF biomarker analysis was performed with enzyme-linked immunosorbent assays (ELISAs). Data were analyzed with linear repeated measures and mixed effects models.ResultsAdsorption to recipients is lower for neurogranin trunc P75, α-synuclein, and tau (<10%), as compared to Aβ(1–42). For neurogranin trunc P75 and total tau, there is still an effect on analyte concentrations as a function of the tube volume. Protocol-related differences for Aβ(1–42) can be normalized at the (pre-)analytical level using the ratio Aβ(1–42)/Aβ(1–40), but not by using the ratio Aβ(1–42)/tau. The addition of detergent at the time of collection eliminates differences due to adsorption.ConclusionsOur study recommends the use of low protein binding tubes for quantification in CSF (without additives) of all relevant CSF biomarkers. Pre-analytical factors have less effect on α-synuclein, neurogranin trunc P75, and total tau, as compared to Aβ(1–42). The ratio of Aβ(1–42)/Aβ(1–40), but not Aβ(1–42)/tau, can be used to adjust for pre-analytical differences in analyte concentrations. Our study does not recommend the inclusion of detergents at the time of collection of CSF. The present results provide an experimental basis for new recommendations for parallel analysis of several proteins using one protocol for collection and storage of CSF.Electronic supplementary materialThe online version of this article (doi:10.1186/s13195-017-0265-7) contains supplementary material, which is available to authorized users.