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Background: Structural and functional changes of the choroid plexus (ChP) have been reported in Alzheimer's disease (AD). Nonetheless, the role of the ChP in the pathogenesis of AD remains largely unknown. We aim to unravel the relationship between ChP functioning and core AD pathogenesis using a unique proteomic approach in mice and humans. Method: We used an App knock‐in mouse model, AppNL‐G‐F, with amyloid pathology to study the association between AD brain pathology and protein changes in mouse ChP tissue and CSF, using liquid chromatography mass spectrometry. Mouse proteomes were investigated at 7 weeks old (n = 5) and at 40 weeks old (n = 5). Results were compared to human AD CSF proteomic data (n = 496; from EMIF‐AD MBD, Knight ADRC, and Maastricht BB‐ACL studies) to identify key proteins and pathways associated with ChP changes in AD. Result: Compared to wild‐type (WT) mice, AppNL‐G‐F mice aged 7 or 40 weeks old had significant differences in some protein levels in ChP tissue (Figure 1 and 2). At both ages, ChP tissue proteomic changes were related to pathways associated with epithelial cells, mitochondrion, protein modification, extracellular matrix (ECM) and lipids. Pathways related to lysosomal function, endocytosis, protein formation, actin, and complement were uniquely dysregulated at 7 weeks, while pathways associated with nervous system, immune system, protein degradation and vascular system were uniquely dysregulated at 40 weeks. Mouse CSF proteomics showed changes in similar pathways as seen in ChP tissue proteomics (Figure 3). Proteomic analyses were also performed in non‐demented human participants with abnormal amyloid but normal tau levels. Among proteins that were up‐regulated in CSF of those participants compared to controls, a significant number were predominantly expressed in the ChP (38 to 56% of the up‐regulated proteins). The dysregulated proteins with a predominant expression in the ChP in those individuals were related to similar pathways as seen in AppNL‐G‐F mice ChP tissue and CSF proteomics (Figure 3). Conclusion: ChP changes are associated with amyloid pathology. Key pathways involved in ChP dysfunction in AD are associated with the ECM, lysosomal function, lipids, protein processing, complement, vascular system and mitochondrion. The ChP could become a novel promising target for AD treatment. [ABSTRACT FROM AUTHOR]

Background: Published data have highlighted associations between Alzheimer's disease (AD) susceptibility loci and AD‐related brain changes. The amyloid imaging to prevent AD (AMYPAD) consortium is a European collaboration consisting of several parent cohorts, four of which had raw genotype array data available. We sought to integrate and harmonise the genetic data, calculate AD polygenic risk scores (PRS), and investigate their association with global amyloid deposition. Method: Raw genetic data (GRCh37) was available for 753 non‐demented participants, which underwent standard pre‐imputation quality control (QC) using PLINK. Imputation was performed using the Michigan Imputation server and HRC reference panel, and standard post‐imputation QC was done prior to the final merging of cohorts. PRSice was used for PRS calculations with Stage 1 summary statistics from Kunkle et al. (2019) as base file and European individuals from 1000 Genomes as reference for clumping. We calculated several builds of PRS at three p‐value thresholds for SNP inclusion: 5 × 10‐8, 1 × 10‐5, and 0.1. These included PRSall, PRSAPOEonly (Chr19:45‐48.8Mb), APOEε2+ε4 (the weighted sum of the two major APOE SNPs rs429358 and rs7412), PRSnoAPOE (all SNPs excluding Chr19:45‐48.8Mb), and a composite PRSnoAPOE+APOEε2+ε4. All were corrected for genetic principal components 1‐5 and standardised against 1000 Genomes. Cross‐sectional global amyloid burden (expressed in Centiloids) was available for everyone. Linear regression models determined if PRS were associated with amyloid deposition (age, sex, and education as covariates). Significance was based on an uncorrected p<0.05 divided by the number of SNP inclusion thresholds (N = 3). Result: Participants were 66.9±7.6 years, 58.6% female, and 207 (27%) considered amyloid positive (CL>21). In this preliminary analysis, we found that most PRS builds were significantly associated with amyloid at all SNP inclusion thresholds assessed (Table 1, representative plots Fig. 1A,1B), except for PRSnoAPOE (Table 1, representative plot Fig. 1C). Conclusion: We highlight the significant influence of APOE in determining AD genetic predisposition, and that AD PRS are associated with amyloid deposition. Our results show that AMYPAD is a suitable cohort for studying genetic factors driving amyloid deposition before clinical onset, which we aim to extend with the inclusion of more parent cohorts, and regional and longitudinal amyloid data. [ABSTRACT FROM AUTHOR]

Objectives: The implementation of disease‐modifying treatments for Alzheimer's Disease (AD) will require cost‐effective diagnostic processes. As part of The Precision Medicine In AD consortium (PMI‐AD) project, the aim is to analyze the baseline costs of diagnosing early AD at memory clinics in Norway, Slovenia, and the Netherlands. Methods: The costs of cognitive testing and a clinical examination, apolipoprotein E, magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), positron emission tomography and blood‐based biomarkers (BBM), which are used in different combinations in the three countries, were analyzed. Standardized unit costs, adjusted for GDP per capita and based on Swedish conditions were applied. The costs were expressed in euros (€) as of 2019. A diagnostic set comprising clinical examination, cognitive testing, MRI and CSF was defined as the gold standard, with MRI mainly used as an exclusion filter. Results: Cost data were available for 994 persons in Norway, 169 in Slovenia and 1015 in the Netherlands. The mean diagnostic costs were 1478 (95% confidence interval 1433–1523) € in Norway, 851 (731–970) € in Slovenia and 1184 (1135–1232) € in the Netherlands. Norway had the highest unit costs but also the greatest use of tests. With a uniform diagnostic test set applied, the diagnostic costs were 1264 (1238–1291) €, in Norway, 843 (771–914) € in Slovenia and 1184 (1156–1213) € in the Netherlands. There were no major cost differences between the final set of diagnoses. Conclusions: The total costs for setting a diagnosis of AD varied somewhat in the three countries, depending on unit costs and use of tests. These costs are relatively low in comparison to the societal costs of AD. Key points: The introduction of blood‐based biomarkers for detecting Alzheimer's Disease (AD) can change the diagnostic process.Given the expected demands for treatment of cognitive disorders such as AD, the costs and capacity of the diagnostic work‐up are of great importance for care funders and planners.Our study incorporating diagnostic costs from three distinct European regions provides valuable inputs for cost‐effectiveness studies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We assessed whether co‐morbid small vessel disease (SVD) has clinical predictive value in preclinical or prodromal Alzheimer's disease. METHODS: In 1090 non‐demented participants (65.4 ± 10.7 years) SVD was assessed with magnetic resonance imaging and amyloid beta (Aβ) with lumbar puncture and/or positron emission tomography scan (mean follow‐up for cognitive function 3.1 ± 2.4 years). RESULTS: Thirty‐nine percent had neither Aβ nor SVD (A–V–), 21% had SVD only (A–V+), 23% Aβ only (A+V–), and 17% had both (A+V+). Pooled cohort linear mixed model analyses demonstrated that compared to A–V– (reference), A+V– had a faster rate of cognitive decline. Co‐morbid SVD (A+V+) did not further increase rate of decline. Cox regression showed that dementia risk was modestly increased in A–V+ (hazard ratio [95% confidence interval: 1.8 [1.0–3.2]) and most strongly in A+ groups. Also, mortality risk was increased in A+ groups. DISCUSSION: In non‐demented persons Aβ was predictive of cognitive decline, dementia, and mortality. SVD modestly predicts dementia in A–, but did not increase deleterious effects in A+. Highlights: Amyloid beta (Aβ; A) was predictive for cognitive decline, dementia, and mortality.Small vessel disease (SVD) had no additional deleterious effects in A+.SVD modestly predicted dementia in A–.Aβ should be assessed even when magnetic resonance imaging indicates vascular cognitive impairment. [ABSTRACT FROM AUTHOR]

The development of disease‐modifying therapies (DMTs) for Alzheimer's disease (AD) has progressed over the last decade, and the first‐ever therapies with potential to slow the progression of disease are approved in the United States. AD DMTs could provide life‐changing opportunities for people living with this disease, as well as for their caregivers. They could also ease some of the immense societal and economic burden of dementia. However, AD DMTs also come with major challenges due to the large unmet medical need, high prevalence of AD, new costs related to diagnosis, treatment and monitoring, and uncertainty in the therapies' actual clinical value. This perspective article discusses, from the broad perspective of various health systems and stakeholders, how we can overcome these challenges and improve society's readiness for AD DMTs. We propose that innovative payment models such as performance‐based payments, in combination with learning healthcare systems, could be the way forward to enable timely patient access to treatments, improve accuracy of cost‐effectiveness evaluations and overcome budgetary barriers. Other important considerations include the need for identification of key drivers of patient value, the relevance of different economic perspectives (i.e. healthcare vs. societal) and ethical questions in terms of treatment eligibility criteria. [ABSTRACT FROM AUTHOR]

Background: Tau tangles are one of the pathological hallmarks of Alzheimer's disease (AD) and can be quantified using PET. It remains unclear which molecular processes are related to tau aggregation. Protein levels in CSF can be used to study such underlying processes. This study aims to identify biological processes related to tau‐PET using unbiased CSF proteomics in participants along the AD continuum. Method: We included 89 participants with CSF proteomic and [18F]flortaucipir (tau)‐PET data available from the Amsterdam Dementia Cohort and the EMIF‐preclin AD study (N = 68 with abnormal CSF Aβ42 [64.7% cognitively impaired, 67.6% tau‐PET visual read positive] and N = 21 controls [normal cognition and normal CSF AD biomarkers]) (table 1). [18F]flortaucipir BPND was quantified in the temporal meta‐ROI (Braak I, Braak III, Braak IV). CSF proteomics was measured using untargeted LC‐MS/MS based on TMT labelling. We included proteins available in the whole sample (n = 1421 proteins). Protein concentrations were normalized to the control group. We determined the relationship between tau‐PET BPND (outcome) with protein levels (determinant) using linear regressions with sex and age as covariates, stratified for group. GO database was used for biological pathway enrichment analyses within the AD group for proteins thresholded at p<0.05 for positive and negative associations separately. Result: Higher CSF tau levels were associated with higher tau‐PET BPND (β = 0.092, p = 0.002). In total, 458 proteins were associated with tau‐PET BPND in AD. A higher tau‐PET BPND was related to higher levels of 140 proteins and lower levels of 318 proteins. Proteins that showed higher levels with increasing tau‐PET BPND were mainly involved in immune system processes (figure 1), while the proteins with lower levels were predominantly related to synaptic processes and cell signaling (figure 2). No significant associations were found within controls. Conclusion: Using CSF proteomics, we identified 458 proteins associated with tau‐PET in AD. Higher tau burden was associated with increased levels of proteins involved in immune activation suggesting a role of the immune system in tau accumulation. Furthermore, higher tau burden was associated with lower levels of proteins related to synaptic process hinting at a role for tau in synaptic degeneration. [ABSTRACT FROM AUTHOR]

Background: Previous results on amyloid‐PET imaging for predicting functional decline are conflicting. Given that the Centiloid approach (CL) may serve for inclusion in anti‐amyloid trials, we investigated the extent to which CL may predict functional decline in the AMYPAD‐Prognostic and Natural History Study (PNHS), including 1094 participants. Method: We analyzed, cross‐sectionally and longitudinally, the associations between CL and global and sum‐of‐boxes (SOB) Clinical Dementia Rating (CDR), and Instrumental Activity of Daily Living (IADL) scores. Mean follow‐up duration ranged from 2.7‐3.0 years (±1.1‐1.6) depending on the functional score. Longitudinal CDR‐SOB and IADL changes were modeled using linear mixed‐effects models with random intercepts and slopes, covarying age and sex, stratified by CDR global score. Analyses included comparisons between three CL groups (Aβ‐: CL<12; grey‐zone: CL = 12‐50; Aβ+: CL>50) and correlation between the baseline CL and the time for progression on the CDR global score. Result: The baseline CL was significantly higher in CDR = 0 (Mdn = 6.6) than in CDR = 0.5 participants (Mdn = 17.2; p<.001). Baseline CDR‐SOB was significantly higher in Aβ+ (M = 0.48) than in Aβ‐ (M = 0.12) and grey‐zone participants (M = 0.13, p‐values <.001). Baseline IADL was (marginally) significantly lower in Aβ+ (Mdn = 81.3) than in Aβ‐ (Mdn = 82.8, p =.02) and grey‐zone participants (Mdn = 83.5, p =.08, Table 1). Baseline CL significantly predicted subsequent changes in CDR‐SOB and IADL, both in CDR = 0 and CDR = 0.5 participants, but only in the CL>50 groups (βCL12‐50 = 0.03, p =.32,βCL>50 = 0.14, p<.001 for CDR‐SOB in CDR = 0 participants;βCL12‐50 = 0.19, p =.31,βCL>50 = 1.04, p =.01 for CDR‐SOB in CDR = 0.5 participants;βCL12‐50 = ‐0.002, p =.99, βCL>50 = ‐1.00, p =.08 for IADL in CDR = 0 participants; Fig.1). The participants who progressed from global CDR = 0 to CDR = 0.5 (N = 90) had a higher baseline CL (Mdn = 15.16) than stable participants (Mdn = 5.95, p =.002). However, the baseline CL was not associated with a shorter time of clinical progression (rs= ‐.02, p =.8; Fig.2). Conclusion: Cross‐sectionally, amyloid burden expressed in CL units was associated with functional outcomes. A baseline CL>50 predicted subsequent functional decline in CDR‐SOB and IADL scores in clinically normal and mildly clinically impaired participants. At lower CL values, no functional decline was observed during a three‐year follow‐up. Clinically normal individuals with increasing global CDR scores had higher baseline CL values, but these values were not associated with faster clinical progression. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐β (Aβ) PET is commonly used for studying the earliest phases of Alzheimer's disease (AD) in cognitively unimpaired (CU) individuals. In this group, the expected changes in Aβ pathology are small, which emphasizes the importance of selecting a method with the highest possible precision for measuring these changes. This study compared several methods for quantifying Aβ pathology longitudinally in mostly CU individuals from the AMYPAD‐PNHS cohort. Method: Participants were scanned with either [18F]flutemetamol (baseline N = 360, follow‐up N = 243) or [18F]florbetaben (N = 66 baseline, N = 49 follow‐up), according to a dual‐time window protocol(Table 1). A subset of participants had ≥2 timepoints available (N = 206, N = 43, respectively). SUVRs were calculated, and parametric modelling was performed using RPM, SRTM2, RLogan, MRTM0, MRTM and MRTM2 using PPET software to generate parametric BPND or DVR images for a global cortical region, all with cerebellar cortex as reference tissue. Annual percentage change (APC) was calculated and compared between methods using an ANOVA. To check for changes in Aβ pathology over time, a linear mixed effects model was used. Bland‐Altman analyses were used to explore agreement between SUVR, and the outcome parameter from the parametric methods. All analyses were run per tracer, separately for visual Aβ‐positive and Aβ‐negative individuals, with a p‐value threshold of p<0.05. Result: For both tracers, there were no differences in APC between methods. However, method‐dependent differences in interquartile range of the APC were observed(Figure 1,2). For [18F]flutemetamol, only the Aβ‐negative group showed a significant change in Aβ pathology for SUVR and SRTM2 (both ‐0.004/year). For [18F]florbetaben, only the Aβ‐positive group showed a significant change in Aβ pathology for all methods except MRTM2 (range: +0.020‐0.032/year). No significant bias was observed between APC in SUVR and the other methods for either tracer or Aβ‐group. Conclusion: There were no significant differences in APC between methods, despite method‐dependent variability. In the [18F]flutemetamol‐cohort, two methods showed a minimal decrease in Aβ pathology in the Aβ‐negative‐group, possibly related to measurement variability, which requires further investigation. In the [18F]florbetaben‐cohort, which included more participants with a very mild cognitive impairment, a significant increase in Aβ pathology was observed in the Aβ‐positive‐group, except for MRTM2. [ABSTRACT FROM AUTHOR]

Background: Discordance between cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers of amyloid‐β (Aβ) occurs in up to 20% of individuals. Generally, Aβ discordance is defined using binary cut‐offs. Given the methodological limitations of this approach, we propose a continuous Aβ CSF/PET imbalance model. We investigated biological and methodological factors underlying discrepancy in these amyloid biomarkers and associations with longitudinal cognition. Method: We included 383 non‐demented subjects (Nunimpaired = 326, Nmildly‐impaired = 57) from the AMYPAD‐PNHS cohort. Z‐scored CSF Aβ42 and amyloid‐PET (Centiloids [CL]) units were fitted to a hyperbolic regression model by minimizing the Euclidian distance of the observed datapoints to the fitted line. Subject‐specific standardized residuals were derived as a measure of continuous imbalance (Zimbalance), with Zimbalance<0 reflecting greater Aβ abnormality in CSF relative to PET and Zimbalance>0 greater Aβ abnormality in PET relative to CSF (Figure‐1). Linear models adjusted for relevant covariates (Figure‐2) were run between Zimbalance and (1) methodological factors (CSF‐PET interval, CSF kit, PET tracer, and PET injected dose); (2) demographics (age, sex, education, APOE genotype) and CSF p‐tau biomarker; (3) vascular burden (white matter hyperintensities measured as Fazekas scores). Linear mixed models were used to investigate the predictive value of Zimbalance on longitudinal cognition in several domains. Result: Sample characteristics are shown in Table‐1. Methodological factors were not significantly associated with Zimbalance. Increased CSF p‐tau levels were significantly associated with a higher Zimbalance (i.e. greater Aβ abnormality in PET relative to CSF) (β = 0.06±0.03, p =.033) (Figure‐2). In addition, a lower Zimbalance (i.e. greater Aβ abnormality in CSF relative to PET) was associated with a higher Fazekas score (n = 189, β = ‐0.55±0.26, p =.038) and predictive of faster cognitive decline on a language categorical fluency test (β = ‐0.03±0.02, p =.038) (Figure‐2). Conclusion: We found that methodological factors did not contribute to a continuous model of Aβ CSF/PET imbalance, suggesting that this imbalance reflects distinct biological profiles. Indeed, continuous CSF/PET imbalance showed differential associations, with greater pathological levels of aggregated Aβ relative to soluble Aβ being related to increased CSF p‐tau levels and excess pathological levels of soluble Aβ relative to aggregated Aβ to increased white matter hyperintensities and steeper cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Currently, Alzheimer's disease (AD) diagnosis relies on biomarkers that are either expensive, invasive or time‐consuming. The retina is easily accessible and may be used as a patient‐friendly and cost‐effective diagnostic tool. Optina Diagnostics' Mydriatic Hyperspectral Retinal Camera (MHRC) may improve diagnostic abilities of the retina by using rich datasets and artificial intelligence. Here we aim to explore diagnostic possibilities of the MHRC by identifying image features associated with the cerebral amyloid status. Method: Six cognitively healthy participants with a negative amyloid‐PET scan and twenty‐five participants with a positive amyloid‐PET scan (clinical AD n = 4, preclinical AD n = 21, MMSE ≥17) were recruited from the EMIF‐AD PreclinAD Twin60++ study and Amsterdam Dementia Cohort (Table 1). Retinal imaging was performed using the MHRC that acquires 92 retinal images in an ∼1 second exposure, in steps of 5 nm increments across a spectral range of 450‐905 nm (visible and near‐infrared) on a 31° field‐of‐view (Figure 1). Spatial‐spectral features (n = 2304) were extracted from two or three hyperspectral cubes per participant using different combinations of anatomical masks, spectral regions and texture measures. Morphological features (n = 935) related to the blood vessels (diameter, tortuosity, density and fractal dimension) were also extracted from different retinal zones. Features were assessed with a Tukey's test for statistical significance to classify the cerebral amyloid status determined by amyloid‐PET scans. Features were considered significant if their p‐value was below 0.05 simultaneously for both our present cohort and an independent cohort of 499 subjects. Result: Explorative analysis identified thirty significant spatial‐spectral features (p‐value range 0.0033‐0.049) for the classification of the cerebral amyloid‐PET status. Examples of such features covering different spectral ranges and retinal anatomic regions are presented in Figure 2. In contrast, only three morphological features were identified (p‐value range 0.017‐0.049). Conclusion: Phenotypic features extracted from hyperspectral retinal images hold promise to discriminate between amyloid‐PET positive and negative individuals, beyond morphological features available with conventional retinal imaging. More data are collected from several centers to build a classifier of features with the aim to discriminate amyloid‐PET positive from negative subjects. This would yield a patient‐friendly and non‐invasive retinal biomarker for AD. [ABSTRACT FROM AUTHOR]

Background: The relationship between cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease (AD), APOE‐ε4 and longitudinal cortical amyloid deposition in preclinical AD is relevant for AD trial design but remains unclear. Thus, we aimed to investigate longitudinal cortical amyloid accumulation on PET as predicted by baseline CSF AD biomarker profiles and APOE‐ε4 carriership in cognitively unimpaired individuals. Method: We selected 330 cognitively unimpaired individuals from 6 cohorts of the AMYPAD study. All had available baseline data on CSF aß1‐42 (A), p‐tau181 (T), APOE‐ε4 carriership (yes/no), and baseline and follow‐up amyloid PET data. To evaluate different AD stages, individuals were classified as A‐T‐, A‐T+, A+T‐, or A+T+ based on CSF biomarker abnormality using center‐specific cut‐offs. We used a continuous outcome measure of global cortical amyloid deposition on PET expressed in centiloids. Analyses were performed in the total sample and stratified for APOE‐ε4, using generalized linear mixed models with random intercepts and slopes, adjusted for age and sex. Result: 58% were female, 49% APOE‐ε4 carriers, and mean age was 64+‐6.1 years, with an average follow‐up of 3 years. 170 individuals were A‐T‐, 70 A‐T+, 59 A+T‐, and 31 A+T+. Baseline cortical amyloid deposition was different between all groups, with A+T+ having the highest deposition, followed by A+T‐, A‐T+ and A‐T‐ (Figure 1, Table 1). Longitudinally, all groups showed increased amyloid deposition, except A‐T‐. Moreover, A+ groups showed greater increases in longitudinal amyloid deposition than A‐ groups. In APOE‐ε4 carriers, all AT groups showed increased longitudinal amyloid deposition, while in non‐carriers, A‐T+ and A‐T‐ did not (Figure 2, Table 1). Only in APOE‐ε4 carriers, A+T+ showed greater increases in longitudinal amyloid deposition than A+T‐, with A+T‐ showing a similar longitudinal deposition as A‐T+. Conclusion: APOE‐ε4 impacts the association between CSF AD biomarkers and longitudinal amyloid PET deposition in cognitively unimpaired persons. Amyloid‐negative APOE‐ε4 carriers could be at an early AD stage, as they showed increased cortical amyloid longitudinally, independent of baseline p‐tau status. In amyloid‐positive persons, p‐tau abnormality related to higher baseline cortical amyloid, but only in APOE‐ε4 carriers to steeper increases in cortical amyloid longitudinally. This has important implications for AD trial design. [ABSTRACT FROM AUTHOR]

Background: Synapse pathology has been linked to excitotoxicity and glutamate receptor dysregulation (such as AMPAR) in Alzheimer´s Disease (AD) progression. Of the core cerebrospinal fluid (CSF) AD biomarkers for amyloid, tau pathology and neurodegeneration (A/T/N), T/N status is proposed to reflect levels of neuronal plasticity, but fluid biomarkers for precision interventions in subgroups with increased excitotoxicity are lacking. Herein, we assess group differences of 19 CSF synapse markers including the AMPAR‐modulating NPTX, in both cognitively normal (CN) and Mild Cognitive Impairment (MCI) with either A+/T‐/N‐ and A+/T or N+ (A+/T/N+) as compared to CN A‐/T‐/N‐ and assess relationships with verbal memory recall and hippocampal volume. Methods: A/T/N was determined using the CSF Aβ42/40 ratio, p‐tau181 and total‐tau in predementia AD cases (n = 194) and CN participants with normal AD biomarkers (n = 145) in the Dementia Disease Initiation cohort. CSF synapse marker concentrations were determined using mass‐spectrometry (table 1) and ELISA for GAP‐43. Adjusted for age and sex, we compared marker concentrations between CN cases (A+/T‐/N‐ n = 20; A+/T/N+ n = 40) and MCI cases (A+/T‐/N‐ n = 25;A+/T/N+ n = 109) with CN A‐/T‐/N‐ (n = 145) as the reference group. Associations to verbal memory recall and brain imaging were performed in the total sample and respective A/T/N groups adjusted for covariates. Results p<.01 were considered significant. Results: Top markers based on between‐group results are reported (Table 1 & figure 1‐2 for all markers). Compared to CN A‐/T‐/N‐, 14‐3‐3 ζ/δ concentrations were higher in all A+/T/N+ groups (p<.001), but unaltered in A+/T‐/N‐ groups. In A+/T‐/N‐, NPTX2 concentrations were lower in MCI (p<.001) but not in CN. In A+/T/N+, NPTX2 was higher in CN (p<.001) but not in MCI. 14‐3‐3 ζ/δ was associated with worse memory in the total sample (p<.001) but not in the A+ groups. Lower NPTX2 was associated with worse memory in the total sample (p<.001) and in both A+ groups (p<.01; p<001). Neither 14‐3‐3 ζ/δ nor NPTX2 were significantly associated with hippocampal volume (figure 2). Conclusion: Overall, increased 14‐3‐3 ζ/δ was most closely associated with worse memory recall. Lower NPTX2 was linked to poorer cognitive status within A+ groups, suggesting links to AMPAR dysregulation and excitotoxicity. [ABSTRACT FROM AUTHOR]

Background: Diabetes and blood glucose markers have previously been linked to cognitive decline. However, findings on the relation of diabetes with underlying Alzheimer's disease (AD) biomarkers are inconclusive. We aimed to explore whether diabetes and blood glucose markers are associated with AD biomarkers in cerebrospinal fluid (CSF) by performing a meta‐analysis of existing studies. Method: A systematic search in PubMed and Embase with abstract and full‐text screening resulted in the inclusion of 13 studies with 5,052 participants in our meta‐analysis. We included studies that provided results on the association between a diabetes measure and an AD biomarker in CSF (Table 1). Diabetes measures included diabetes diagnosis or glucose measures, i.e. fasting blood glucose, HbA1c, or insulin resistance index. AD biomarkers included CSF amyloid‐beta42, p‐tau and/or t‐tau. Where needed, results were converted to partial or biserial correlation coefficients. We performed a random effects meta‐analysis on the correlation coefficients per CSF biomarker, using the DerSimonian and Laird procedure and Knapp‐Hartung method. Meta‐regression was used to test potential moderating effects of age, sex, setting (memory clinic or population), % dementia cases, correlation type (Pearson, partial, or biserial), diabetes measure (diabetes diagnosis or glucose marker), MMSE score, and APOEe4 carriership (yes/no). All analyses were performed using the metafor package in R. Result: Our meta‐analysis showed small but significant associations of diabetes measures with increased (more normal) levels of amyloid‐beta42 (r = 0.08, p = 0.04, Figure 1) and increased (more abnormal) levels of p‐tau (r = 0.09, p = 0.04, Figure 2) and t‐tau (r = 0.10, p = 0.04, graph not shown as comparable to p‐tau). Heterogeneity was high for all outcomes (I2>60%, p<0.05 for Q‐test, wide prediction intervals). Meta‐regression analyses showed that heterogeneity in CSF amyloid could partly be explained by setting, as the association between diabetes measures and amyloid was only present in memory clinic studies as compared to population studies (difference estimate = 0.14, p = 0.03). Similarly, studies with more dementia cases showed a stronger association with amyloid (slope estimate = 0.0029, p = 0.001). No other moderating effects were found. Conclusion: Our findings suggest that diabetes measures are associated with tau‐related neurodegeneration that is independent from amyloid. This is valuable for improving diagnostics and treatment of patients with diabetes and AD. [ABSTRACT FROM AUTHOR]

Background: The role of cardiovascular risk factors and cerebral small vessel disease (CSVD) on the sequences of Alzheimer's Disease (AD) pathological events remains to be determined. Method: We included 1592 non‐demented participants from EPAD‐LCS (Table‐1). Linear models were used to study associations between the Framingham score (FRS) and CSF‐Aβ1‐42; CSVD indices (visual assessment for perivascular spaces [PVS] in the basal ganglia [BG] and centrum‐semiovale [CS], periventricular and deep white matter hyperintensities [WMHs], presence of lobar or deep cerebral microbleeds [CMBs], and lacunes, and quantification of global and lobar WMH volumes) and Aβ1‐42 and p‐Tau181, including an interaction between CSVD indices and Aβ1‐42 for the latter. Hippocampal volumes (HCV) were quantified using LEAP. Structural equation models (SEM) were used to assess the association between clinical variables as described in Figure‐1. Models were corrected for age, sex, site and multiple comparisons. Result: Higher FRS scores were associated with lower CSF Aβ1‐42 (β = ‐0.18; p<0.001) and higher P‐tau181 (β = 0.21; p<0.001). Aβ1‐42 was negatively associated with all CSVD markers (all p<0.001; Figure‐2). We found stronger association between Aβ1‐42 and P‐tau181 in participants with higher Fazekas deep and periventricular (p‐interaction<0.005) scores, and higher regional WMH (all p‐interactions<0.005) volumes. Using SEM, the CSVD‐burden latent factor fully mediated the association between FRS and Aβ1‐42 (indirect effect: β = ‐0.03; p<0.001). Aβ1‐42 did not significantly predict the CSVD‐burden latent factor. We observed a significant direct effect of Aβ1‐42 on P‐tau181 levels (β = ‐0.14; p<0.001) and HCV (β = 0.06; p<0.05), and of P‐tau181 levels onto HCV (β = ‐0.05; p<0.05). A significant indirect, but not direct, effect of the latent factor on both P‐tau181 (indirect effect: β = 0.36; p < 0.05) and HCV (indirect effect: β = ‐0.24; p < 0.05) through Aβ1‐42 was observed. Conclusion: Expression of cerebrovascular pathology fully mediates the effects of vascular risk factors on amyloid and accelerates manifestation of downstream biomarkers in the preclinical phases of AD, stressing the importance of vascular‐protective treatments. [ABSTRACT FROM AUTHOR]

Background: Gait is a complex everyday activity that depends upon supraspinal activity and a host of cognitive functions such as attention and executive functions. As cognition declines in neurodegenerative diseases, the interaction and competition for neuronal resources during motor‐cognitive dual‐tasking (e.g., walking while talking) might be a sensitive measure of subtle functional impairments in early Alzheimer's disease (AD). Here, we aim to identify gait deficits due to neuronal competition across the AD spectrum. Method: This investigation is part of the ongoing Remote Assessment of Disease and Relapse – Alzheimer's Disease (RADAR‐AD) study. We attached three inertial measurement units (accelerometer and gyroscope) to both feet and one hip to assess dual task effects (DTE) assessing gait performance with/without concurrent serial subtraction‐by‐1 task in four groups: 1) amyloid negative healthy controls (HC, N = 59); and 2) amyloid positive preclinical AD (PreAD, N = 30); 3) prodromal AD (ProAD, N = 51); and 4) mild‐to‐moderate AD dementia (MildAD, N = 44) (Table 1). We furthermore investigated associations of DTE with observer‐reported cognition. Result: Group comparisons showed that dual‐tasking induced lower cadence and increased stance, which were significantly different between HC and ProAD. Several DTE measures of variability differed significantly between PreAD and MildAD, with variability in the path length separating best between PreAD and ProAD (Table 2, Figure 1). DTE measures were associated with observer‐rated divided attention only in the MildAD group. Conclusion: Neuronal competition as assessed with motor‐cognitive dual‐tasking, specifically the DTE variability, might reflect functional deficits already in early AD, and could be a valuable additional measure to detect early impairments not captured by cognitive or motor tests alone. Future studies should implement an adaptive cognitive load to improve sensitivity/specificity in early AD stages and investigate the use of sensor technologies in predicting and monitoring changes in gait and fall prevention in later stages of the disease. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cerebrospinal fluid (CSF) biomarkers for specific cellular disease processes are lacking for tauopathies. In this translational study we aimed to identify CSF biomarkers reflecting early tau pathology‐associated unfolded protein response (UPR) activation. METHODS: We employed mass spectrometry proteomics and targeted immunoanalysis in a combination of biomarker discovery in primary mouse neurons in vitro and validation in patient CSF from two independent large multicentre cohorts (EMIF‐AD MBD, n = 310; PRIDE, n = 771). RESULTS: First, we identify members of the protein disulfide isomerase (PDI) family in the neuronal UPR‐activated secretome and validate secretion upon tau aggregation in vitro. Next, we demonstrate that PDIA1 and PDIA3 levels correlate with total‐ and phosphorylated‐tau levels in CSF. PDIA1 levels are increased in CSF from AD patients compared to controls and patients with tau‐unrelated frontotemporal and Lewy body dementia (LBD). Highlights: Neuronal unfolded protein response (UPR) activation induces the secretion of protein disulfide isomerases (PDIs) in vitro.PDIA1 is secreted upon tau aggregation in neurons in vitro.PDIA1 and PDIA3 levels correlate with total and phosphorylated tau levels in CSF.PDIA1 levels are increased in CSF from Alzheimer's disease (AD) patients compared to controls.PDIA1 levels are not increased in CSF from tau‐unrelated frontotemporal dementia (FTD) and Lewy body dementia (LBD) patients. [ABSTRACT FROM AUTHOR]

Background: As cerebrovascular and Alzheimer's proteinopathy have previously shown to affect cerebral blood flow (CBF) as well as cognition, CBF could be a potential early hemodynamic biomarker of cognitive decline. Here, we investigated to what extent cardiovascular risk factors and amyloid burden affect CBF in an elderly cognitively unimpaired (CU) population. Method: We included 153 CU participants (minimal MMSE = 28) from the EMIF‐AD PreclinAD Twin60++ cohort (Table 1), who underwent [18F]flutemetamol PET and arterial spin labeling (ASL) MRI. Amyloid‐PET scans were visually assessed as negative or positive, upon which participants were grouped based on their longitudinal changes in amyloid positivity (visual read groups). Cortical amyloid burden was quantified with the Centiloid method globally and for 4 early amyloid accumulation regions of interest (ROIs). ASL scans were processed and quantified with ExploreASL for total gray matter (GM), and for vascular territories overlapping with the amyloid ROIs (Figure 1). Longitudinal analysis including baseline Centiloid values and yearly CBF change rates (Delta CBF) was performed for 98 participants with longitudinal imaging data available (4.23 years ± 0.43 follow‐up time). Associations between CBF and amyloid — with and without the interaction of vascular risk factors (i.e., Framingham score) — were assessed using generalized estimating equations (GEEs), both for baseline and rates of change measurements. Models were adjusted for age, sex, and twin dependency. Result: While no association between amyloid burden and CBF was observed across the cohort, in participants with a high Framingham vascular risk score, higher amyloid was associated with increased CBF, for most ROIs (Table 2, Figure 2). Additionally, precuneus amyloid burden was predictive of CBF change in the corresponding vascular territory (Figure 3). Visual reading shows that subjects with high amyloid burden at baseline had a higher increase of CBF at follow‐up (Stable AB+, Figure 4). Conclusion: We found that the combination of cardiovascular risk and amyloid burden in AD signature regions was associated with increased CBF in CU individuals, which may reflect a vascular or inflammatory compensatory response to early Alzheimer pathology. Future studies may help understanding how these mechanisms affect cognition. [ABSTRACT FROM AUTHOR]

Background: Previous consensus initiatives on outcomes in Alzheimer's disease (AD) have focused on clinical trials, may not be applicable to real‐world settings, and did not necessarily consider a comprehensive list or prioritize outcomes. The aim of this initiative was to achieve consensus among experts on priority outcomes and outcome measures for use in clinical practice when caring for patients with symptomatic AD. Method: The Consensus Group consisted of 20 international voting members and a non‐voting Chair representing diverse specialities relevant to AD. A Delphi approach was used to generate shortlists of the most important outcomes and measures, and to develop consensus statements/recommendations (Figure 1). Initial lists of outcomes/measures were obtained from the ROADMAP project (roadmap‐alzheimer.org), and panel members were given the opportunity to suggest additional ones. The threshold for consensus was predefined as ≥70% voting "agree" or "strongly agree". Shortlists of outcomes and measures were ranked by priority, their relative importance for mild and moderate disease was indicated, and assessment frequency was recommended. The modified GRADE criteria were used to rate the strength of recommendations and quality of evidence. Result: Strong consensus was readily reached on 37 statements/recommendations, grouped by 'General Considerations' (9) and nine domains (28) (Table 1), as well as on priority shortlists of outcomes and measures across the nine domains; an example of a priority shortlist for the domain 'Cognitive abilities' is shown in Table 2. The level of evidence for most items was low; therefore, most statements are based on panelists' expert opinion. Key recommendations include: patient quality of life (QoL) outcome assessments should measure what the person performs well in addition to deficits, and take into account the values of patients and/or caregivers; there is a need for a more comprehensive measure to assess language and communication; 'functional ability & dependency' should include an assessment of motor function. Conclusion: This Delphi consensus highlights outcomes of highest importance in AD from a professional perspective and includes recommendations regarding the most appropriate measures for use in clinical practice. Further studies are needed to raise the strength of the recommendations and to explore patient/caregiver views. [ABSTRACT FROM AUTHOR]

Introduction: We aim to provide guidance on outcomes and measures for use in patients with Alzheimer's clinical syndrome. Methods: A consensus group of 20 voting members nominated by 10 professional societies, and a non‐voting chair, used a Delphi approach and modified GRADE criteria. Results: Consensus was reached on priority outcomes (n = 66), measures (n = 49) and statements (n = 37) across nine domains. A number of outcomes and measurement instruments were ranked for: Cognitive abilities; Functional abilities/dependency; Behavioural and neuropsychiatric symptoms; Patient quality of life (QoL); Caregiver QoL; Healthcare and treatment‐related outcomes; Medical investigations; Disease‐related life events; and Global outcomes. Discussion: This work provides indications on the domains and ideal pertinent measurement instruments that clinicians may wish to use to follow patients with cognitive impairment. More work is needed to develop instruments that are more feasible in the context of the constraints of clinical routine. [ABSTRACT FROM AUTHOR]

Background: While the association between tau deposition on PET and brain atrophy on MRI is well documented in the literature, the relation between amyloid deposition and neurodegeneration in the earliest stages of Alzheimer's disease (AD) is less studied. We investigated this relationship in a large cohort of non‐demented individuals. Method: We included 1355 participants from the AMYPAD Prognostic & Natural History study (PNHS) with available MRI and amyloid‐PET. Among those, 753 had longitudinal MRI, with a mean follow‐up time of 3.47 years (SD = 1.55). The FreeSurfer 7.0.1 longitudinal pipeline was used to measure gray matter (GM) thickness and volumes in 40 regions (Reuter et al., 2012; Desikan et al., 2006). From PET scans, cortical amyloid burden was assessed globally using the Centiloid (CL) method. MRI‐derived regional volumes and thickness were harmonized across sites with neuroCombat (Fortin et al., 2018); all variables were Z‐scored. Linear mixed‐effect models were used to investigate the effect of amyloid burden at baseline and its interaction with time on regional volume and thickness measurements. Models were corrected for age, sex, and CDR. P‐values were FDR‐adjusted. Result: Cohort characteristics are shown in Table 1. Decreasing cortical thickness and volumes were observed over time in most regions. At baseline, higher amyloid burden was related to smaller volumes of the hippocampus, amygdala, putamen, and lateral ventricles; and thinner entorhinal cortex, precuneus and posterior cingulate cortex. At follow‐up, subjects with higher baseline amyloid burden showed greater loss of volume and thickness in mainly temporal and parietal regions, as well as amygdala and hippocampal volume (Figure 1). Conclusion: In the largely asymptomatic AMYPAD PNHS cohort, we show that amyloid burden at baseline is predictive of future neurodegeneration, especially affecting temporal volume and parietal thickness. Cortical thickness was slightly more sensitive towards amyloid burden at baseline, while loss of cortical volume was greater than loss of thickness. [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value. Method: Cross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer's Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer's Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD). Result: The best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1). Conclusion: We established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population. [ABSTRACT FROM AUTHOR]

Background: Remote monitoring technologies (RMTs), such as smartphone apps and smartwatches, are changing the way functional and cognitive performance are measured in Alzheimer's disease (AD). Due to their sensitivity, objectivity, and the option of long‐term and continuous measurement, RMTs have the potential to detect a subtle decline in the earliest stages of AD. Here, we present the results of the European RADAR‐AD project (Remote Assessment of Disease and Relapse – Alzheimer's disease), which aims to test feasibility, acceptability and validity of RMT measures across all stages of AD, from cognitively normal to mild dementia. Method: Four study groups (amyloid negative healthy controls, and amyloid positive preclinical AD, prodromal AD, mild‐to‐moderate AD) were included in this cross‐sectional study (N = 175). During 8 weeks, participants wore two activity trackers (Fitbit and Axivity) measuring physical activity, heart rate and sleep continuously, and used two interactive smartphone apps (Mezurio and Altoida's research algorithm: DNS‐MCI) measuring cognition daily/weekly. At baseline, participants underwent extensive neuropsychological, physical examinations, and did two sensor‐based tests (banking app and walk test). Features were extracted for all RMTs (Figure 1) and compared across groups using ANCOVA, with adjustment for relevant confounders. This study is part of an ongoing investigation into high‐end multimodal analyses for real‐world functional performance of continuous RMT data streams. Result: Compliance was high, but decreased with cognitive impairment (feasibility). User experience did not differ between groups but was lower for smartwatches compared to interactive smartphone apps (Table 1) (acceptability). Various individual sensors discriminated symptomatic AD participants from asymptomatic participants (p<0.05), for example the two active apps, but did not discriminate preclinical AD from healthy controls (Table 1) (validity). Conclusion: The RADAR‐AD study provides unique insights in the feasibility, acceptability, and validity of remote monitoring of functional abilities in AD and their potential to differentiate between syndromic stages. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999) and their associated partners. www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. [ABSTRACT FROM AUTHOR]

Background: Different tau‐PET patterns have been observed in Alzheimer's disease (AD) which may be associated with demographic factors, copathology and resilience. We and others previously found that not all Aβ‐positive AD patients are tau‐PET positive. It is currently not known whether tau pathology develops in these groups over time. Therefore, we aimed to examine longitudinal tau binding in tau‐PET negative individuals with symptomatic AD. Method: We included 97 Aβ‐positive participants with clinical diagnosis of mild cognitive impairment or dementia due to AD (MCI/AD) of which 12 (13%) were tau‐PET negative. To compare, we included 131 tau‐PET negative cognitively unimpaired individuals (CU) of which 93 (71%) were Aβ‐negative and 38 (29%) Aβ‐positive. Tau‐status was defined by [18F]flortaucipir PET consensus visual read of two nuclear physicians according to US Food and Drug Administration‐approved guidelines. [18F]flortaucipir SUVr at baseline (N = 225) and after ±2 years (N = 81) was quantified in an early tau region reflecting Braak I (entorhinal cortex), the medial temporal lobe (MTL), and a combined visual read region of interest (ROI) (parietal, occipital, and posterior lateral temporal lobe). We used age‐and sex‐adjusted linear mixed models to compare tau accumulation. Result: Demographics are shown in table 1. Compared to MCI/AD A+T+, MCI/AD A+T‐ were older (p<0.001) and more often male (p = 0.015). MCI/AD A+T‐ did not differ from CU A‐T‐ in baseline tau‐PET SUVr, but had a slightly lower SUVr in Braak I compared to CU A+T‐ (p = 0.040) (figure 1). After ±2 years, all MCI/AD A+T‐ participants who underwent follow up remained visual read tau negative. However, MCI/AD A+T‐ showed a steeper increase in Braak I and MTL tau‐PET SUVr compared to CU A‐T‐ (figure 2A,B) and in Braak I compared to CU A+T‐ (β = 0.31, p = 0.036). Interestingly, MCI/AD A+T‐ showed a significantly steeper tau‐PET SUVr increase compared to MCI/AD A+T+ in Braak I (β = 0.29, p = 0.029) (figure 2A). Conclusion: Although visual read tau‐PET status remains stable over time, our results indicate that tau‐negative MCI/AD patients do accumulate tau over time, most pronounced in early tau regions. This highlights the relevance of quantifying tau regionally over time in tau‐PET negative individuals with clinical AD diagnosis. [ABSTRACT FROM AUTHOR]

Background: Lower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer's Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics. Method: Data from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p<0.05) were identified and examined for their biological relevance according to Gene Ontology. Result: Connectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls. Conclusion: Proteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD. [ABSTRACT FROM AUTHOR]

Background: Depression has been associated with an increased risk of Alzheimer's disease (AD), but the biological mechanisms are still not fully understood. Plasma biomarkers, such as amyloid‐beta, tau, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL), have been associated with AD pathophysiology. However, the relationship with depression is unclear. Assessing these biomarkers in the blood allows for the opportunity to assess if AD pathology is related to depressive symptoms on a large‐scale. We hypothesized if depression is part of the AD process, then these markers will be associated with depressive symptoms. Method: A two‐stage IPD meta‐analysis was performed based on eight cohorts of individuals without dementia as part of the Netherlands Consortium of Dementia Cohorts (NCDC). We examined the cross‐sectional association between each plasma marker for AD (amyloid‐beta42/40 ratio, p‐tau181, NfL, and GFAP) with depressive symptoms (the GDS‐15, PHQ‐9, CES‐D, and SF‐36). Plasma markers were assessed using Single Molecular Array (Simoa; Quanterix) assays. Both plasma markers and depressive symptoms were standardized. We estimated the effect per plasma marker with depressive symptoms using linear regressions, correcting for age, sex, education, and APOE e4 allele presence, in each cohort. The effect estimates were entered into a random‐effects meta‐analysis. We also performed subgroup analyses assessing sex differences and between those with and without an APOE e4 allele. Result: This study involved 7210 participants with an age range of 38 to 102 years. Based on clinical cut‐offs per questionnaire, high depressive symptomology ranged from one to 22% per cohort. None of the plasma markers were associated with depressive symptoms in the meta‐analysis. However, subgroup analyses found an association with NfL and depressive symptoms in women (beta 0.07; 95% CI: 0.03‐0.10, p < 0.001) and in those with an APOE e4 allele (beta 0.11; 95% CI: 0.05‐0.17, p = 0.001). Conclusion: AD pathology did not show an overall relationship with depressive symptoms. However, in women and in those with a genetic risk for AD, NfL showed an association. As NfL is a marker of overall neurodegeneration, this pathology in plasma may be specific to depressive symptoms in certain subgroups. [ABSTRACT FROM AUTHOR]

Background: Individuals with Alzheimer's disease (AD) show variability in cognitive decline. Little is known about the underlying biological processes. Using an untargeted CSF proteomics approach, we explored the biological processes associated with cognitive decline throughout the AD continuum. Method: We included 280 AD individuals from the Amsterdam Dementia Cohort (median age 66 (IQR:60‐70) years, 124 (44%) female, n = 34 preclinical AD, n = 77 MCI due to AD, n = 169 AD‐dementia, all abnormal CSF amyloid, Table 1). The CSF proteome was quantified at baseline using LC‐MS/MS in an untargeted manner based on TMT labelingWe included n = 2318 proteins measured in ≥50% individuals. We used the MMSE score as a proxy for global cognition, and created summary scores for cognitive domains (i.e., episodic memory, executive functioning, attention and language) using neuropsychological test scores. We applied linear mixed models to test the association between each protein and cognitive decline over time, with age, sex and education as covariates, a random intercept for patient ID, and a random slope for time; stratified for syndrome diagnosis. We performed pathway enrichment analysis on proteins associated with cognitive decline. Result: Individuals with preclinical AD showed most decline on episodic memory compared to other domains. In preclinical AD, 150 proteins, enriched for cell death and cell adhesion, were associated with memory decline. Individuals with MCI showed most decline on episodic memory and executive functioning. Proteins associated with decline on memory (n = 188, Figure 1) and executive functioning (n = 213) were enriched for complement and coagulation cascade and innate immune processes. Proteins associated with memory decline were also enriched for synaptic plasticity. Individuals with AD‐dementia showed more decline on attention and language compared to other AD‐stages. In AD‐dementia, 69 proteins, enriched for innate immune processes, were associated with decline on the MMSE and 127 proteins, enriched for synaptic plasticity, were associated with decline on language. Conclusion: Proteins involved in innate immune processes, synaptic plasticity and cell death were associated with cognitive decline in AD, with different processes involved in different stages. These results provide insight into the pathophysiology of cognitive decline and hint towards potential therapeutic targets for prevention of cognitive decline in AD. [ABSTRACT FROM AUTHOR]

Background: Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in‐home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer's disease (AD) patients. Here, we present results from the European RADAR‐AD study on the use of multimodal data streams for the identification of functional deficits across all syndromic stages of AD. Method: Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross‐sectional study. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in‐clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, Pre.AD = 26, Pro.AD = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross‐validation (5‐fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR‐AD study. Result: The multimodal combination of RMTs achieved a mean AUC of > 0.60 in all pairwise comparisons with a mean AUC > 0.65 for HC vs (Pre.AD, Pro.AD and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs Pre.AD. Conclusion: Our results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. [ABSTRACT FROM AUTHOR]

Background: Augmented reality apps merge real world with virtual experiences and can be used to remotely assess complex instrumental activities of daily living (iADL) that are affected early in Alzheimer's disease (AD). Our aim was to compare standard clinical measures with an augmented reality app to assess iADL that are related to memory and spatial navigation in early AD and its feasibility in the home‐setting. Method: We administered an augmented reality app (Altoida Inc., Washington DC, USA) in an on‐going cross‐sectional study (RADAR‐AD: Remote Assessment of Disease and Relapse – Alzheimer's Disease) in three groups: 1) amyloid negative healthy controls (HC, N = 49); and amyloid positive 2) preclinical AD (PreAD, N = 17); and 3) prodromal AD (ProAD, N = 29) (Table 1). Altoida's research algorithm DNS‐MCI (Digital Neuro Signature) produces the outcome of a machine learning model trained to identify cognitively normal individuals from those with cognitive impairment). DNS‐MCI reflects performance in app‐based tasks assessing memory and visuo‐spatial function (placing and finding virtual objects, fire drill simulation) further including attention and motor performance (reaction time, finger tapping, navigational trajectory). At baseline, app‐based tasks were performed in the clinic together with a standard neuropsychological assessment and iADL questionnaires (Figure 1). Participants were furthermore given the option of using Altoida in the home environment. Result: The DNS‐MCI score could significantly distinguish HC and PreAD participants from the ProAD group and was correlated with all neuropsychological tests and iADL questionnaires (Figures 1 and 2). Participants used the app on average 3‐4 times at home (Table 1). Baseline in‐clinic assessments were strongly correlated with at‐home assessments (r = 0.53, p<.001). Conclusion: App‐based augmented reality tasks are applicable in the home setting and successful in capturing cognitive impairment in early AD. Future research should focus on fine graining algorithms to also detect possible subtle impairment in preAD. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. [ABSTRACT FROM AUTHOR]

Background: The choroid plexus (CP) is responsible for the production of cerebrospinal fluid (CSF), transport of proteins to the CSF and clearance of proteins from the CSF. The CP becomes compromised during aging, which is exacerbated in AD. Nonetheless, the relation between CP and AD pathophysiology remains unclear. We aim to investigate the pathophysiology underlying the CP involvement in AD, utilizing human CSF proteomics and mouse CP transcriptomics. Method: We included 526 individuals from EMIF‐AD MBD, St Louis Knight ADRC and Maastricht BB‐ACL studies. Based on CSF Aβ1‐42 (A, data‐driven cut‐offs), individuals were classified as controls (cognitively normal (CN) A‐, n = 171) or as amyloid‐positive (CN A+, n = 120; mild cognitive impairment (MCI) A+, n = 154; AD A+, n = 81). CSF proteomic data were generated using TMT spectrometry and protein concentrations were compared using ANOVA adjusted for age and sex. Transcriptomic was performed in CP tissue of APPNL‐G‐F (n = 17) and wild‐type C57BL/6J (n = 17) mice using Illumina RNA‐Seq and differential expression analysis was conducted. Next, Gene Ontology and Ingenuity Pathway analyses were performed. For proteomics, high expression of proteins in the CP was identified based on Allen Brain Atlas and the R package ABAEnrichment. Result: In CN A+, 51 proteins were increased and 21 decreased relative to controls. Out of those increased proteins, 57% were enriched for expression in the CP (ABAEnrichment P<0.001, Figure 1) and were associated with lysosomes and the CLEAR pathway, extracellular matrix and coagulation. A similar pattern was observed for both MCI A+ and AD A+ compared to controls, with a significant percentage of increased proteins enriched for expression in the CP (22 to 30%), which are related to lysosomes and the CLEAR pathway, extracellular matrix and coagulation. In APPNL‐G‐F compared to wild‐type mice, CP transcriptomics revealed 313 upregulated and 198 downregulated genes (Figure 2). The upregulated genes were also associated with the lysosomes and the CLEAR pathway, as well as energy metabolism and lipids. Conclusion: Human proteomics and mice transcriptomics studies suggest an association between amyloid pathology, CP functioning and lysosomes. Dysfunction of lysosomes in AD could relate to compromised clearing events. This is important for future research and AD treatment development. [ABSTRACT FROM AUTHOR]

Background: Many genetic risk variants have been identified for Alzheimer's disease (AD), but their functional consequence on molecular pathways are unclear. Quantitative trait loci (QTL) studies help elucidate the functional effect of genetic variance on (neuro)biological processes. The understanding of AD mechanisms has been limited due to the relative inaccessibility of in vivo brain tissue and its surroundings. In this study, we integrated genetic and metabolomics cerebrospinal fluid (CSF) data, to understand how AD risk variants influence neurobiological processes. Method: We performed a genome‐wide mQTL study on 5,543 CSF metabolite levels using data from 977 subjects (age 52.7±16.6 years, 63.8%female) from the Amsterdam Dementia Cohort (n = 487; 220 controls, 87 mild cognitive impairment, 180 AD‐type dementia) and the Utrecht cohort (n = 490 controls). We measured CSF metabolites with three platforms: GC‐TOF MS (primary metabolism, 393 metabolites), CSH‐QTOF MS/MS (complex lipids, 3,532 metabolites), and HILIC‐QTOF MS/MS (biogenic amines, 1,618 metabolites). Association signals between genetic variants and CSF metabolite levels were tested using linear regression, adjusted for principal components, age, sex and site (Amsterdam/Utrecht) using a Bonferroni‐corrected genome‐wide significance threshold (5.0e−8 /754 independent CSF signals = 6.0e−11). To identify metabolites with predicted CSF levels associated with AD, we integrated metabolite summary statistics with AD summary statistics (Kunkle et al., 2019; Bellenguez et al., 2022), using a metabolome‐wide association study (MWAS) approach (FUSION software). Result: We identified 89 genome‐wide CSF mQTLs, including 89 independent loci for 62 CSF metabolite levels. Most mQTLs were biogenic amines (81), followed by mQTLs for the primary (5) metabolism and complex lipids (3) (Figure 1). MWAS results included 24 metabolite‐AD associations with P<0.05. The strongest associations included variants proximal to the CANX gene with unannotated CSF metabolite 1.09_397.32 (PBellenguez = 2.4e−6; PKunkle = 3.4e−3), and CCDC124 variants with 8.76_225.06 (PBellenguez = 2.9e−3; PKunkle = 2.8e−5) (Figure 2). Conclusion: We identified novel metabolite‐AD risk associations, including CANX and CCDC124 with unknown CSF metabolites. In future work we aim to identify these CSF metabolites using raw spectra and intensity data. This study shows that the multi‐modal integration of genetics with CSF metabolomics helps in the functional interpretation of genetic variance, and could therefore serve as useful approach for other brain‐related disorders. [ABSTRACT FROM AUTHOR]

Background: Rare TREM2 variants are major risk factors for Alzheimer's disease (AD), but TREM2 variants may also confer a higher risk for other neurodegenerative diseases and a lower probability for cognitively healthy aging. We studied the TREM2 burden associated with different types of dementia and cognitively healthy centenarians (CHCs). Method: We included participants of the 100‐Plus Study, Amsterdam Dementia Cohort, Longitudinal Aging Study Amsterdam and various Dutch memory clinics. TREM2 variants (R47H, R62H, Q33X, R62C, T96K) were genotyped with a high density array or imputed using standard methods (TOPMED reference panel). To avoid family bias and ethnic differences, related individuals and those of non‐European ancestry were excluded. Frequencies of individual variants and the burden of TREM2 variants were compared in AD dementia, dementia with Lewy Bodies (DLB), frontotemporal dementia (FTD), cognitively normal controls and CHCs (correcting for 5 principal components). Result: We identified 242 carriers of TREM2 mutations and 6,683 controls without a TREM2 mutation with a mean age of 69 years (SD ±14 years), 50% female (Table 1). TREM2 variants were significantly associated with AD dementia (OR 1.4, 95%CI 1.1‐1.8, p = 0.01) (Table 2). R47H conferred the highest risk for AD (OR 2.8, 95%CI 1.4‐5.6, p = 0.004), compared to 1.3‐fold increased risk of R62H (95%CI 0.9‐1.7, p = 0.11). The carrier frequency was not increased in DLB (OR 1.4, 95%CI 0.7‐2.6, p = 0.34) and was not increased in FTD compared to controls (OR 0.59, 95%CI 0.3‐1.3, p = 0.18). Of interest, the allele frequency of TREM2 variants in centenarians is half of the expected frequency (OR = 0.46, 95%CI 0.1‐1.1, p = 0.10), and only concerns R62H. Conclusion: We replicate the association of TREM2 with increased risk of AD. In contrast to previous reports no association was observed with DLB or FTD. Cognitively healthy centenarians show a negative trend for TREM2 allele frequency suggesting that carrying a TREM2 variant decreases chances of survival to extreme age. [ABSTRACT FROM AUTHOR]

Background: Little is known about the genetic factors and downstream molecular pathways determining individual variability in fluid and imaging biomarkers associated with Alzheimer's disease(AD). We studied polygenic risk scores (PRS) and pathway‐specific PRS in relationship with AD fluid and imaging biomarkers, in non‐demented individuals from the European Prevention of Alzheimer's Dementia (EPAD) cohort. Method: EPAD inclusion criteria were age>50 and Clinical Dementia Rating≤0.5 (n = 1886, Table 1). AD‐PRS was based on 85 previously identified loci, including and excluding APOE (PRSAPOE, PRSnoAPOE)[1]. Using gene‐variant and variant‐pathway mapping[2], six pathway‐specific PRSnoAPOE were identified:1) immune‐activation, 2) signal‐transduction, 3) inflammatory‐response, 4) migration, 5) amyloid‐production, and 6) clearance. Linear models were used to assess the relationship of all PRS with fluid AD biomarkers, including Aβ1‐42, p‐Tau181, and t‐tau; and several imaging biomarkers, including hippocampal volume, global and lobar white matter hyperintensities (WMH) volumes, fractional anisotropy (FA) in 14 regions of interest from diffusion tensor imaging, and 10 resting‐state network connectivity from functional MRI. Models were adjusted for age, sex, site, and multiple comparisons. Result: Models' coefficients are reported in Table 2. PRSAPOE was significantly associated with decreased Aβ1‐42, and increased p‐Tau181 and t‐Tau. PRSnoAPOE showed a weaker, but still significant, negative association with Aβ1‐42 levels, and a significant positive association with p‐Tau181 and t‐Tau. Aβ1‐42 was only significantly associated with the migration, amyloid, and clearance pathways, while all pathways were significantly positively associated with p‐Tau181 and t‐Tau. Hippocampal volume was only significantly associated with PRSAPOE. Regarding WMH load, PRSAPOE showed a positive association with global, frontal periventricular, and parietal deep lobes. Furthermore, the clearance pathway was related to WMH load in all regions, most strongly in periventricular areas. Only the migration pathway was related to increases in FA in the splenium, body, and genu of the corpus callosum. Only the PRSAPOE was related to reduced functional connectivity (FC) in the default mode, control, and visual networks. Conclusion: We show that genetic risk beyond APOE facilitates the manifestation of AD related pathologies. Moreover, clearance and migration pathways were associated with neuroimaging measures of white matter integrity, while FC and hippocampal volume were associated with overall AD genetic risk. [ABSTRACT FROM AUTHOR]

Background: We recently identified three distinct spatial‐temporal trajectories of amyloid deposition through the application of a machine‐learning model (i.e., Subtype and Stage Inference [SuStaIn]) to amyloid positron emission tomography (PET) data. These included Frontal, Parietal, and Occipital subtypes, defined by the earliest regions to show abnormality. The current study aimed to determine whether these subtypes of amyloid accumulation hold value in predicting baseline and longitudinal cognitive functioning in addition to a validated measure of global amyloid burden, i.e. Centiloid (CL) units. Method: The Frontal, Parietal, and Occipital subtypes were applied to the amyloid‐PET standard uptake value ratios (SUVr) of 2,510 subjects from 3 cohorts (ADNI, EMIF‐AD, OASIS‐3). Of these, 570 subjects with a high subtype probability assignment (>50%), assigned stage ≥ 1, and available cognition were included in the analyses. Subtypes were compared on global cognition and several cognitive domains including memory, attention, executive functioning, language, and visuospatial functioning. To assess whether subtype assignment added to baseline CL in predicting baseline and longitudinal cognition (range follow‐up=0.4‐11 years), linear models (predictors: subtype and CL) and linear mixed models (predictors: subtype*time and CL*time) were performed, respectively. All models were adjusted for baseline age, sex, education, and baseline cognitive stage. Result: Participant characteristics are shown in Table‐1. The majority was assigned to the Frontal subtype (57%), followed by Occipital (22%) and Parietal (20%). After adjusting for baseline CL, the Occipital subtype showed lower baseline language scores than the Parietal subtype (β=‐0.34, p=.046) (Fig‐1A) and lower baseline attention scores than both Frontal (β=‐0.39, p=.047) and Parietal (β=‐0.73, p=.002) subtypes (Fig‐1B, Table‐2). In addition, the Occipital subtype declined faster over time on global cognition (Fig‐2A) and language (Fig‐2B) as compared to the Frontal (βglobal cognition=‐0.35, pglobal cognition=.007; βlanguage=‐0.10, planguage=.015) and Parietal (βglobal cognition=‐0.50, pglobal cognition=.002; βlanguage=‐0.13, planguage=.015) subtypes. Conclusion: Our results show the added value of amyloid subtype assignment on top of CL measures on baseline and longitudinal cognition. Specifically, the Occipital subtype was associated with lower baseline performance and showed faster rates of cognitive decline. These findings suggest that subtype assignment based on amyloid PET might improve prognosis and patient stratification in clinical trials. [ABSTRACT FROM AUTHOR]

Background: Grey matter covariance networks become disrupted early in Alzheimer's disease (AD), when amyloid starts aggregating, and before the onset of irreversible atrophy. However, the precise relationship of grey matter network breakdown and the AD pathophysiological process remains unclear. Here, we studied to what extent shared familial (genes and family environment) or environmental factors unique to a twin contribute to these relationships in a sample of older monozygotic twins with intact cognition. Method: From the EMIF‐AD PreclinAD study we included monozygotic twins who had at least an MRI scan (n=197) and CSF available (n=124). We assessed the upper limit of genetic contribution to grey matter network measures (size, degree, connectivity density, clustering, path length, small‐world measures) with within‐twin pair correlations. Associations between CSF AD markers (Aβ42/40, t‐tau, p‐tau) and other markers related to AD pathophysiology (neurogranin, Aβ38, Aβ40, BACE1) with grey matter network disruptions were assessed with linear mixed models. Twin analyses (cross‐twin cross‐trait, twin difference analyses) were used to assess the contribution of shared familial and/or unique environmental factors to these associations. Result: Twenty individuals (16%) had abnormal CSF Aβ42/40 levels. Grey matter network measures were highly correlated within twin pairs (r=0.54‐0.97; all p<0.001, Fig.1), suggesting a moderate to strong genetic background. Across the whole sample, more abnormal Aβ42/40 ratios were moderately related to lower connectivity density (β±SE = 0.176±0.09, p=0.055, Fig.2). The strongest associations with grey matter network measures were observed for t‐tau and p‐tau levels (range β=‐0.344‐0.182), followed by Aβ production markers (Aβ38, Aβ40, BACE1) (range β=‐0.202‐0.165). We observed no relationships between neurogranin and grey matter network measures. Cross‐twin cross‐trait analyses showed that levels for t‐tau, p‐tau and Aβ38 in one twin were associated with grey matter network measures in their co‐twin (r=‐0.27 to ‐0.21; p range=0.02 to 0.07, Fig.3). Within‐pair differences in BACE1 were related to within‐pair differences in normalized path length values (r=0.35; p=0.01; Fig.4). Conclusion: Our results suggest a shared familial background for the relationship of grey matter network disruptions and AD‐related processes as measured in CSF. In addition, unique environmental factors influencing BACE1 may also affect grey matter network disruptions. [ABSTRACT FROM AUTHOR]

Background: To improve prognostic procedures, we aimed to explore whether diabetes mellitus and Alzheimer's disease (AD) biomarkers are synergistically associated with cognitive decline in persons without dementia. Method: We included 1.737 persons with normal cognition (NC) and 634 with mild cognitive impairment (MCI) from 8 European memory clinic and aging cohorts as part of the PRIME project. We examined cognitive decline in MMSE (raw scores), memory (delayed recall), attention, executive functioning, and language domains (Z‐scores). AD biomarkers included CSF amyloid‐beta42 (data‐driven cut‐offs) or amyloid PET (center‐specific cut‐offs) as measure of amyloid positivity, CSF p‐tau as measure of tau tangle positivity and CSF t‐tau as measure of neurodegeneration (both center‐specific cut‐offs). We performed generalized linear mixed models with random intercepts and slopes, all adjusted for demographics. Result: Mean age was 65.9 (SD=8.9) years, 54% were women, and diabetes was diagnosed in 7% of NC and 12% of MCI. Mean follow‐up time was 1.8 years (range 0.5‐8). In NC, we found decline in memory and attention in persons with both diabetes and t‐tau positivity. This was not shown in persons with only diabetes or only t‐tau positivity (Figure 1). We also found this interaction between diabetes and p‐tau on attention decline (Table 1). When stratifying for amyloid status, we found that this was only significant in amyloid negative individuals (memory t‐tau: p=0.005, attention p‐tau: p=0.033, attention t‐tau: p=0.039). A higher baseline language performance was found in persons with both diabetes and p‐tau or t‐tau positivity (p=0.011; p=0.034) compared to those with only diabetes or tau positivity. We found steeper decline in language in persons with both diabetes and amyloid positivity, compared to other groups (Figure 2). A similar synergism with diabetes was found for p‐tau. No interaction was found for executive functioning. In persons with MCI with both diabetes and t‐tau positivity, we found higher baseline MMSE scores (p=0.041) compared to other groups. Diabetes was not associated with cognitive decline in any domain in MCI. Conclusion: Diabetes may affect non‐AD‐related decline in memory and attention as well as AD‐related decline in language at an early clinical stage. This may help improve prognostic procedures. [ABSTRACT FROM AUTHOR]

Background: Glial fibrillary acidic protein (GFAP) is a novel Alzheimer's Disease (AD) biomarker that associates with amyloid pathology and pathology‐related changes can be detected in different biofluids. We studied how the performance of GFAP to distinguish between controls, AD and dementia with Lewy bodies (DLB), depends on the studied biofluid (cerebrospinal fluid (CSF), serum or plasma) and the confounding effects of age or sex. Method: From the Amsterdam Dementia Cohort and a local repository of healthy controls we included 372 cognitively normal individuals (CN), 255 patients with AD and 120 patients with DLB (Table 1) and GFAP levels were measured in one (n=322), two (n=416) or three (n=9) biofluids. GFAP was measured using Simoa in CSF (n=473), plasma (n=257) and serum (n=451) samples. For individuals with DLB, GFAP was measured in CSF and serum only. Effects of clinical diagnosis, age and sex on GFAP levels in each body fluid were estimated with linear models. Estimated differences between diagnostic groups were corrected for age and sex, age effects were corrected for sex and diagnostic group, and sex effects were corrected for age and diagnostic group. Result: GFAP levels were increased in AD relative to controls in all biofluids (fold change (FC): 1.6(CSF), 1.9(plasma) and 1.4(serum), p‐values<0.001) and increased in AD relative to DLB (FC: 1.7(CSF) and 1.3(serum), p‐values<0.001, Figure 1). No difference was found between DLB and controls. CSF GFAP levels increased with age in all clinical groups (range of standard deviation protein level increase per year (SD/year): 0.020‐0.024), while plasma GFAP levels increased with age only in CN (SD/year: 0.032). Serum GFAP levels increased with age in CN and DLB (SD/year: 0.058(CN) and 0.074(DLB), Figure 2). CSF GFAP showed sex effects in AD only (FC in males compared to females: 1.1), whereas serum GFAP showed sex effects in all clinical groups (FC in males: 0.83(AD), 0.85(NC) and 0.72(DLB)), and plasma GFAP showed no sex effects (Figure 3A‐C). Conclusion: Differences in GFAP levels between clinical diagnoses showed the same trend for all three matrices. With older age, differences in GFAP levels between controls and AD become harder to detect. [ABSTRACT FROM AUTHOR]

Background: Early amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related to each other remains unclear. Investigating the simultaneous disruptions of functional‐structural brain features within individuals in relation to amyloid deposition may increase our understanding of the neuropathological processes underlying AD. Method: We included 648 non‐demented participants from the European Prevention for Alzheimer's Dementia (EPAD) cohort vIMI baseline data release. The cut‐off for cerebrospinal fluid (CSF) amyloid‐β positivity (A+) was defined at <1000pg/mL (Elecsys assay). Functional connectivity was estimated with functional eigenvector centrality (EC) from the resting state functional MRI. White matter (WM) integrity was estimated with fractional anisotropy (FA), computed on diffusion MRI. Both measures were computed at the voxel level within the gray and white matter, respectively. First, we used linear regression models to investigate differences between A+ and A‐ participants within each modality separately, adjusting for age, sex, and site. In the whole group, we performed sparse canonical correlation analysis (sCCA) on functional and structural measures, identifying shared components between the two modalities. Individual sCCA scores were compared between amyloid groups using a generalized linear model (GLM). To evaluate this association in A+ individuals, the same sCCA analysis was performed normalizing the structural and functional matrices using the mean and SD from A‐ participants. Result: Sample characteristics are provided in Table 1. Differences in voxelwise EC and FA between amyloid groups are shown in Figure 1. In the whole group, sCCA analysis showed that EC in the default mode, executive and cerebellar networks covary with FA in parietal, temporal and cerebellar areas (Figure 2). sCCA projections were differentially expressed according to amyloid status (Table 2). In A+ participants, stronger association of EC in the posterior DMN and cerebellum with FA in the splenium and brainstem were found (Figure 3). Conclusion: Using a data‐driven multivariate analysis, we identified concerted patterns of functional and structural changes that occur with amyloid deposition in the predementia stage. These results highlight the importance of multimodal assessment for early diagnosis. [ABSTRACT FROM AUTHOR]

Background: In preclinical Alzheimer's disease (AD), amyloid accumulates in highly‐functionally connected brain regions. This selective vulnerability is related to the high neuronal fluctuations, typical of these regions. Dynamic functional connectivity (FC) was introduced to investigate network organization over time, with high network variations indicating regional flexibility, hence promoting functional integration. The relation of early amyloid deposition with FC dynamics remains unclear. Eigenvector centrality (EC) evaluates a node's importance in functional networks, both for the whole functional MRI time series ("static" EC) or within sliding‐windows ("dynamic" EC). We studied the association of cerebrospinal fluid (CSF) amyloid load with static and dynamic EC in non‐demented individuals from the European Prevention of Alzheimer's Dementia (EPAD) cohort. Method: Data for 701 non‐demented participants were available. CSF Aβ1‐42 levels <1000 pg/mL were defined as amyloid positive (A+) (Elecsys assay). Both static and dynamic voxel‐wise EC were computed from rs‐fMRI time series. Static EC differences between A+ and A‐ participants were assessed using linear models. Standard deviation and range of dynamic EC were compared between A+ and A‐ participants within significant clusters of static EC differences, and within 10 resting‐state networks (RSN). Linear models were used to determine interaction between amyloid and cognitive performance on dynamic EC variability (Figure 1). Result: Demographics and clinical characteristics are shown in Table 1. A+ participants showed lower static EC values in parietal and occipital clusters, and higher static EC values in a medio‐frontal cluster (Figure 2). The medio‐frontal cluster had also lower dynamic EC variability in A+ (Figure 3). The default mode (Figure 4) and the visual networks of A+ participants also showed lower dynamic EC variability (p < 0.001). Dynamic properties in the DMN and visual networks were differentially associated with cognitive domains in the A‐ and A+ groups, with lower variability found in A+ participants with higher cognitive scores (Figure 5). Conclusion: We demonstrate that early amyloid deposition affects static and dynamic EC, possibly by reducing involvement of functional hubs in different network dynamics and functional integration, thus relating to cognitive dysfunctions. Our data suggest dynamic EC as an early biomarker of the interplay between early amyloid deposition and cognitive decline. [ABSTRACT FROM AUTHOR]

Background: The Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q) is a proxy‐based instrument aimed at measuring difficulties in daily functioning due to cognitive decline in the context of dementia. To facilitate its interpretation and clinical implementation, normative data from the general population are necessary. Here, we aimed to compose normative scores for the A‐IADL‐Q and provide clinical cutoff values for mild cognitive impairment (MCI) and dementia. Method: Cross‐sectional data from three Dutch cohorts (i.e., Dutch Brain Research Registry, European Medial Information Framework‐Alzheimer's Disease (EMIF‐AD) 90+, and Amsterdam Dementia Cohort (ADC)) were used, including 1127 cognitively healthy individuals and 380 individuals with MCI and/or dementia. Regression‐based norms were constructed in the healthy sample, using linear models including age, sex, education(high/low) and their interactions, and the optimal model was selected using backward elimination. Individual differences between observed and expected IADL t‐scores were divided by the residual standard error of the model. Optimal normative score cutoffs to distinguish healthy from MCI and/or dementia were based on bootstrap average estimates(500 repeats) of the Youden Index. Diagnostic accuracy was evaluated using receiver operating curves (ROC), area under the curve (AUC), sensitivity and specificity. Result: Normative data (mean age = 64±13year, 68.6%female, 67.8% highly educated) and memory clinic data (mean age = 66±8year, 43.9%female, 38.9% highly educated) were used (Table1). The best normative model (i.e., with the lowest Akaike information criterion) included age‐squared, sex, education and the interaction between age and education. The optimal normative score cutoffs to distinguish healthy from dementia and MCI/dementia were, respectively, 1.78SD and 1.79SD below the mean, with high AUCs of 0.96 [95%CI 0.95‐0.98] and 0.94 [95%CI 0.92,0.95]. The optimal cutoff to distinguish healthy from MCI was higher; 1.01SD below the mean, also with a high AUC‐score of 0.86 [95%C 0.81‐0.9])(Table2, Figure1). Conclusion: We composed normative scores with clinical cutoff values that can aid in the distinction between cognitively healthy from MCI and/or dementia with high diagnostic accuracy. These normative scores are accessible via an online tool to further facilitate and implement clinical use of the A‐IADL‐Q. Future studies should include participants from different countries, cultures and different socio‐economic background to facilitate world‐wide use. [ABSTRACT FROM AUTHOR]

Background: As clinical trials increasingly focus on the earliest stages of Alzheimer's disease (AD), there is a need for reliable and valid functional measures in these stages. The National Institute of Aging and Alzheimer's Association (NIA‐AA) defined six clinical stages of AD, with stages 1 and 2 covering preclinical AD with normal cognitive and functional performance. Here, we explored the psychometric characteristics of the Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q), separately in stage1‐2 AD and amyloid negative controls. Method: Data from four European memory clinic and community‐based cohorts (SCIENCe, ALFA+, EPAD‐LCS and INSIGHT pre‐AD) were jointly analyzed (Fig.1;selection criteria). Amyloid positivity was determined by cerebrospinal fluid or positron‐emission tomography, using local cut‐offs. We explored three measurement properties:(1) internal consistency, using confirmatory factor analysis and Cronbach's alpha, (2) construct validity, by correlating A‐IADL‐Q performance with demographics and neuropsychological tests covering global cognition, executive functions, memory and attention, using Pearson's r or Kendall's tau, and (3) responsiveness, using linear mixed modelling with time as variable of interest, adjusted for age, sex and education. Result: A total of 396 controls (mean age = 62±7year, 71% female) and 242 stage1‐2 individuals (mean age = 65±9year, 60.7% female) were included (Table1). High internal consistency was supported by a single factor structure (comparative fit index = 0.997, Cronbach's alpha>0.905). In line with previous validation studies, low correlations were found between A‐IADL‐Q performance and demographic characteristics. Low correlations were found with neuropsychological test performance (Table2). Regarding responsiveness (Ncontrols = 219, mean follow‐up = 1.45±0.75 year, Nstage1‐2 = 127, mean follow‐up = 2.16±1.15 year), A‐IADL‐Q showed a decline in stage1‐2, although this did not reach statistical significance (β = ‐0.1, p = 0.45). Controls showed a small increase in performance, again non‐significant (β = 0.06, p = 0.41)(Fig.2). At baseline, stage1‐2 individuals had lower A‐IADL‐Q scores than controls (Welch two‐sample t‐test: t = ‐3.13, p<0.01). Conclusion: Our findings support the psychometric characteristics of the A‐IADL‐Q in stage1‐2 individuals and controls. Construct validity was partially supported, as expected, because cognitive impairments are not evident in this population. In line with this, findings regarding responsiveness may have been affected by the short follow‐up time of one year. Future validation in preclinical AD should include a longer follow‐up duration, to adequately capture potential decline. [ABSTRACT FROM AUTHOR]

Background: Age related hearing loss has been associated with increased prevalence and incidence of dementia. Underlying mechanisms that connect hearing loss with dementia remain largely unclear. Methods: We studied the association of hearing loss and other risk markers for dementia in two cohorts with normal cognition and different age: 65 participants from the EMIF‐AD 90+ study (mean age 92.7 years, 56.9% female) and 60 participants from the EMIF‐AD PreclinAD study (mean age 74.4, 43.3% female). Individuals were selected when they had hearing function ('digits‐in‐noise test') and neuropsychological testing available. Amyloid binding potential (BPND) was derived from dynamic PET scans. We used linear regression analysis and generalized estimating equations to test the association of hearing loss and BPND. We used linear mixed models to test the association of hearing function and cognition over time. In the oldest‐to‐old individuals, magnetic resonance imaging was available at the time of hearing function testing, and we performed mediation analyses to study whether cognitive decline is mediated through regional brain regions. All models included age, sex, and amyloid status as covariates, and longitudinal analyses were corrected for education years. Results: Oldest‐to‐old individuals showed worse hearing functioning than the younger cohort (p<.001). In oldest‐to‐old hearing loss was not associated with BPND (p =.70), whereas younger individuals showed an association of hearing loss with higher BPND (p =.003, figure 1). Oldest‐to‐old individuals showed associations of worse hearing with a steeper decline in memory (β ± SE = ‐0.018 ± 0.007), global cognition (β ± SE = ‐0.017 ± 0.007) and language (β ± SE = ‐0.014 ± 0.007), while in the younger cohort worse hearing was associated with steeper decline in language only (β ± SE = ‐0.086 ± 0.02, figure 2). Mediation analyses demonstrated that the hippocampus and nucleus accumbens fully mediate the effects of hearing loss on memory and global cognition in the older individuals (figure 3‐4). Conclusions: Hearing loss was associated with amyloid burden in younger individuals only, and with cognitive decline in both age groups. These results suggest that mechanisms linking hearing loss with risk for dementia depends on age. [ABSTRACT FROM AUTHOR]

Background: Genome‐wide association studies (GWAS) on dementia have successfully identified variants conferring risk to disease. Still, how such variants impact exact biological mechanisms underlying disease remains largely unclear. Protein quantitative trait loci (pQTLs) measured in cerebrospinal fluid (CSF) may provide insight into these neurobiological mechanisms, since CSF protein levels can reflect ongoing processes in the brain and have been related to genetic variants. We used a CSF pQTL approach in individuals along the Alzheimer's disease (AD) spectrum to reveal intermediate molecular pathways by which genetic variance influences neurobiological processes. Improving the functional interpretation of genetic variants in a matrix relevant to neurological disease helps deciphering biological mechanisms underlying neurological traits. Method: We selected 243 subjects (106 controls/67 mild cognitive impairment/70 AD‐dementia, age 66.8±8.1 years, 53.9%female) from the EMIF‐AD MBD study, who had genetic data and CSF data (n = 1,351 proteins, tandem mass tag (TMT) mass spectrometry) available. Association signals between genetic variants and CSF proteins were tested using linear regression, adjusted for principal components (PC1‐3), age and sex using the genome‐wide significance threshold (P<5.0e−8). CSF pQTLs were identified as novel if the association has not been previously reported in Yang et al., (2021), Sasayama et al., (2017) or Kauwe et al., (2014). Results: Twenty‐four out of 1,351 CSF proteins (1.78%) showed genome‐wide significant associations with in total 147 SNPs (Table 1; Figure 1). We identified 25 CSF pQTLs, representing three cis‐acting (IGH, LTF and ADAMTS8) and 22 trans‐acting pQTLs. Results included mostly novel CSF pQTL associations, exception for the cis association of LTF with LTF protein levels. Two novel cis‐acting pQTLs were previously reported in a study on human lymphocytes (IGH on IGHV2‐70D) (Theusch et al., 2020) and prefrontal cortex brain data (ADAMTS8 on ADAMTS8) (Ng et al., 2017). Conclusions: These results contribute to the functional interpretation of genetic variance on neurobiological processes, by pinpointing inflammation and tissue remodeling as potential contributing mechanisms in AD. [ABSTRACT FROM AUTHOR]

Introduction: The Models of Patient Engagement for Alzheimer's Disease (MOPEAD) project was conceived to explore innovative complementary strategies to uncover hidden prodromal and mild Alzheimer's disease (AD) dementia cases and to raise awareness both in the general public and among health professionals about the importance of early diagnosis. Methods: Four different strategies or RUNs were used: (a) a web‐based (WB) prescreening tool, (2) an open house initiative (OHI), (3) a primary care–based protocol for early detection of cognitive decline (PC), and (4) a tertiary care–based pre‐screening at diabetologist clinics (DC). Results: A total of 1129 patients at high risk of having prodromal AD or dementia were identified of 2847 pre‐screened individuals (39.7%). The corresponding proportion for the different initiatives were 36.8% (WB), 35.6% (OHI), 44.4% (PC), and 58.3% (DC). Conclusion: These four complementary pre‐screening strategies were useful for identifying individuals at high risk of having prodromal or mild AD. [ABSTRACT FROM AUTHOR]

Background: Higher physical activity is associated with better global cognition and a lower risk of dementia (Rojer et al., 2021). Using questionnaires to assess physical activity is difficult in an Alzheimer's disease (AD) population, since questionnaires rely on retrospective subjective recall. Wearables measure physical activity objectively and continuously. A variety exists, with some delivering raw high‐frequency research‐grade movement data, others providing summary variables only. The aim of this study is to compare simple activity metrics from two activity trackers monitoring physical activity in AD, and relate these to standard questionnaires. Method: Participants in the RADAR‐AD study (Remote Assessment of Disease and Relapse – Alzheimer's Disease) were asked to wear two activity trackers (dominant hand: Axivity AX3, non‐dominant hand: Fitbit Charge 3) for 8 weeks. Features calculated from the Axivity's raw accelerometer data were: acceleration magnitude, time spent in sedentary, light, moderate‐to‐vigorous activity, and sleeping. Fitbit features included: step count and heart rate. Self‐reported activity levels (Godin Leisure Time Questionnaire), depression (Geriatric Depression Scale), social functioning (Social Functioning Scale), global cognition (Mini‐Mental State Examination), partner‐reported function (ADCS‐ADL) and neuropsychiatric symptoms (Neuropsychiatric Inventory) were assessed and related to activity levels using regression models. Result: We included amyloid negative controls (n = 36) and amyloid positive preclinical (n = 13), prodromal (n = 15) and mild‐to‐moderate (n = 10) AD participants (Table 1). Expected associations were found between sedentary activity time and age, and between self‐reported activity and both moderate‐to‐vigorous activity time and step count (Table 2, Figure 1). Small group sizes currently make further comparisons of exploratory value only. Conclusion: Wearable devices have the potential to accurately capture clinically relevant aspects of daily activity and function of interest in AD population. On‐going research in the RADAR‐AD study will focus on developing disease‐specific methods of feature extraction based on raw movement data, and combining features from multiple devices, to explore this further. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999).www.imi.europa.eu. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. [ABSTRACT FROM AUTHOR]

Background: Biomarkers for Alzheimer's disease (AD), including amyloid‐beta (Abeta) and phosphorylated‐tau have recently been accurately detected in blood. Non‐specific biomarkers such as neurofilament light (NFL) and glial fibrillary acidic protein (GFAP) have been added as complementary biomarkers to fully investigate AD etiology in vivo. This has increased the possibility to examine relationships with other pathways to AD, such as white matter hyperintensities (WMH) and brain atrophy on MRI. Our aim was to explore the relationship between plasma AD biomarkers and MRI markers of cerebral small vessel disease and neurodegeneration in mid‐ and late life. Method: Data from 594 individuals (mean (SD) age: 64 (8) years; 17% female) were included from the SMART‐MR Study, a prospective cohort study of non‐demented individuals with a history of vascular disease from the UMC Utrecht in the Netherlands. MRI markers of CSVD included WMH, presence of infarcts, total brain volume (TBV), and hippocampal volume (HV) assessed on 1.5T MRI. AD plasma markers (Abeta1‐40, Abeta1‐42, pTau‐181, NFL, and GFAP) were assessed using Single Molecular Array (Simoa; Quanterix) assays. Linear regressions were performed for each plasma marker, as well as Abeta1‐42/Abeta1‐40 ratio, with age, sex, education, and intracranial volume, with WMH volume, TBV, and HV. Additionally, logistic regressions were performed for the presence of infarcts, including lacunar infarcts and cortical infarcts, corrected for age, sex, and education. Plasma AD levels were converted to z‐scores. P < 0.01 was considered statistically significant. Result: Higher NFL and pTau‐181 were associated with larger WMH volume (B NFL=0.17, 95% CI=0.06;0.29, p=0.003; B pTau‐181=0.16, 95% CI=0.06;0.26, p=0.001). Higher Abeta1‐40 (B=‐0.11, 95% CI=‐0.17;‐0.05, p<0.001) levels were associated with lower HV. Higher NFL (B=‐5.63, 95% CI=‐8.95;‐2.31, p<0.001) and Abeta1‐42 (B=‐3.61, 95% CI=‐6.26;‐0.96, p=0.008) were associated with lower TBV. Regarding infarcts, higher NFL was associated with any infarct (OR=1.42, 95% CI=1.13;1.78, p=0.003). Higher GFAP was marginally associated only with cortical infarcts (OR=1.45, 95% CI=1.09;1.92, p=0.01). Conclusion: Within non‐demented adults with a history vascular disease, plasma AD markers differentially mapped to MRI markers. NFL and pTau‐181 were associated with markers reflecting vascular damage, whereas amyloid markers were associated with atrophy in the TBV and HV. [ABSTRACT FROM AUTHOR]

Background: In dementia with Lewy bodies (DLB), Alzheimer co‐pathology is common, and associated with an unfavorable prognosis. For targeted treatment development it is crucial to understand the biological underpinnings of DLB with Alzheimer co‐pathology, and how it differs from Alzheimer disease (AD). We performed a cerebrospinal fluid (CSF) proteome profiling to biologically characterize DLB with and without AD co‐pathology in vivo. Method: Patients from Amsterdam Dementia Cohort, Twin60+, and UPENN, we selected individuals with DLB (n=109), and subcategorized them in CSF Aβ42 (A‐/A+) and pTau‐181 (T‐/T+) biomarker groups (A‐T‐ n=29; A‐T+ n=16; A+T‐ n=29; A+T+ n=36), and patients with AD dementia (A+T+ n=230) and cognitively normal controls (A‐T‐ n=246). CSF levels of 810 proteins were measured with the Olink proximity extension assay. We compared protein profiles from the DLB subgroups and AD dementia with controls with ANCOVA adjusted for age and sex. Proteins with significant FDR‐controlled p‐values were used to perform functional enrichment analyses in KEGG and GO Biology and Cellular components using STRING‐DB. Result: DLB A‐T‐ exhibited least abnormalities compared to controls, with 6 proteins dysregulated (Figure 1). DLB A‐T+ had 12 upregulated proteins. DLB A+T‐ exhibited the most distinct protein dysregulation, with 16 upregulated proteins, and 312 downregulated proteins. DLB A+T+ had 3 downregulated and 18 upregulated proteins. Of the DLB groups, DLB A+T+ had with 15 proteins the largest overlap in dysregulated proteins with AD dementia patients (Figure 1). In functional enrichment analysis for DLB A‐T‐ no enrichment was present. DLB A‐T+ and DLB A+T+ groups were enriched for proteins involved cytokine‐cytokine interactions. The DLB A+T‐ group had top enrichment for basement membrane components and phenylalaline metabolism upregulation, and cell adhesion molecules, VEGF, and axon guidance and morphogenesis downregulation. Conclusion: The CSF proteome in DLB varied depending on the CSF AD profile. The overlap in CSF proteome between AD dementia and DLB with signs of AD seems to cover mostly immune‐mediated processes. The biological abnormalities in DLB with isolated abnormal CSF Aβ42 were most distinct, possibly pointing to a unique subtype. Using biomarkers and proteomics for understanding the biology behind AD co‐pathology could guide treatment development for DLB. [ABSTRACT FROM AUTHOR]

Background: The amyloid‐cascade hypothesis of Alzheimer's disease posits that there is a causal relationship between amyloid‐β (Aβ) and tau pathology. However, the mechanism through which Aβ would cause tau remains unclear. We cannot exclude the possibility of an additional underlying factor influencing Aβ at one time‐point and tau at a later time‐point. One such potential underlying factor can be genetic variation among individuals. This study aimed to examine the relationship between Aβ and tau, taking potential genetic confounding into account using a genetically identical twin design. Methods: We included 70 identical twins (35 pairs) from the EMIF‐AD PreclinAD study with normal baseline cognition. We extracted baseline global [18F]flutemetamol (Aβ)‐PET binding potential (BPND) and 4‐year follow‐up [18F]flortaucipir (tau)‐PET BPND in a temporal meta‐region‐of‐interest. If Aβ is causally related to tau, the twin with more Aβ should also have more tau compared to the co‐twin. We tested this using regional and voxel‐wise within‐pair difference models, in which the within‐pair difference in Aβ is regressed on the within‐pair difference in tau. The resulting effect represents an association that is free of confounding due to factors that are shared between twins of the same pair, which includes genetic factors (twins are genetically identical), but also shared demographic (e.g. sex, age) and shared environmental (e.g. raised in the same family) factors. Results: Participant characteristics are shown in Table 1. Total group analysis showed that global Aβ‐PET BPND was associated with temporal tau‐PET BPND (β=0.49, p<0.001) (Fig‐1A). The twin‐difference model replicated this. We observed a highly comparable effect size for the association between within‐pair differences in global Aβ‐PET and within‐pair differences in temporal tau‐PET (β=0.56, p<0.001) (Fig‐1B). Compared to the total group voxel‐wise analysis, we observed an overlapping (and more widespread) pattern when regressing within‐pair differences in global Aβ‐PET on within‐pair differences in voxel‐wise tau‐PET, except for effects on tau in the medial temporal lobe (Fig‐2). Conclusion: Our within‐pair difference results show that the association between Aβ and tau is minimally affected by confounding by genetic variation. Our voxel‐wise results support a causal association between global Aβ and tau outside of the medial temporal lobe. [ABSTRACT FROM AUTHOR]

Background: Plasma levels of amyloid‐β1‐42/1‐40, phosphorylated‐tau (P‐tau) and glial fibrillary acidic protein (GFAP) can be used to predict amyloid‐β pathology in elderly cognitively unimpaired individuals, but their prognostic value in early Alzheimer's Disease (AD) stages in the normal aging population needs further study. We aimed to investigate the value of these plasma biomarkers for predicting memory change in cognitively unimpaired individuals in a population‐based cohort. Method: From the EMIF‐AD PreclinAD study we selected 188 individuals with normal cognition, who had plasma samples, amyloid‐β status and at least one cognitive follow‐up available (total follow‐up years (mean(SD) = 4.3(.42)) (Table 1). Amyloid‐β status was defined using visual read of dynamic [18F]flutemetamol‐PET images or CSF amyloid‐β1‐42/1‐40<0.066 (Euroimmun). Simoa assays were used to measure plasma amyloid‐β1‐42/1‐40 (AMYBLOOD), P‐tau181 (Eli Lilly) and GFAP (Quanterix) levels. Linear mixed models, adjusted for age, sex and years of education, were applied to test associations of baseline plasma biomarker levels with subsequent change in memory (plasma*time), and its interaction with amyloid‐β status (plasma*time*amyloid‐β status). Result: From the total cohort tested, 31 subjects (16.5%) were amyloid‐positive. These individuals were older, showed worse baseline memory performance, lower plasma amyloid‐β1‐42/1‐40, and higher plasma P‐tau181 and GFAP levels compared to amyloid‐negative individuals (Table 1). Higher plasma GFAP levels at baseline were significantly associated with worse memory performance over time (GFAP*time: β=‐.03, p=.01). Interestingly, this relation was significantly stronger in amyloid‐positive compared to amyloid‐negative individuals (GFAP*time*amyloid‐β status: β=‐.07, p=.001) (Table 2, Figure 1). No significant associations were observed for baseline plasma amyloid‐β1‐42/1‐40 and P‐tau181 levels with change in memory over time (P‐tau181*time: β=‐.02, p=.07; amyloid‐β1‐42/1‐40*time: β=.02, p=.12) (Table 2). Conclusion: Our data suggest plasma GFAP can be used as a predictor of future memory decline in cognitively unimpaired, amyloid‐positive, individuals in the normal aging population. This supports the use of plasma GFAP as a prognostic marker for disease and treatment‐effect monitoring in the early stages of AD development. [ABSTRACT FROM AUTHOR]