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INTRODUCTION: Although frontotemporal dementia (FTD) with right anterior temporal lobe (RATL) predominance has been recognized, a uniform description of the syndrome is still missing. This multicenter study aims to establish a cohesive clinical phenotype. METHODS: Retrospective clinical data from 18 centers across 12 countries yielded 360 FTD patients with predominant RATL atrophy through initial neuroimaging assessments. RESULTS: Common symptoms included mental rigidity/preoccupations (78%), disinhibition/socially inappropriate behavior (74%), naming/word‐finding difficulties (70%), memory deficits (67%), apathy (65%), loss of empathy (65%), and face‐recognition deficits (60%). Real‐life examples unveiled impairments regarding landmarks, smells, sounds, tastes, and bodily sensations (74%). Cognitive test scores indicated deficits in emotion, people, social interactions, and visual semantics however, lacked objective assessments for mental rigidity and preoccupations. DISCUSSION: This study cumulates the largest RATL cohort unveiling unique RATL symptoms subdued in prior diagnostic guidelines. Our novel approach, combining real‐life examples with cognitive tests, offers clinicians a comprehensive toolkit for managing these patients. Highlights: This project is the first international collaboration and largest reported cohort.Further efforts are warranted for precise nomenclature reflecting neural mechanisms.Our results will serve as a clinical guideline for early and accurate diagnoses. [ABSTRACT FROM AUTHOR]

Background: The prevention of symptomatic Alzheimer's disease (AD) is a major endeavor currently in the field, with the prevalence of subjects on the AD continuum (i.e. presence of amyloid‐β [Aβ] pathology) being much higher than previously estimated. However, the extent to which Aβ pathology and its interaction with common risk factors in the aging population relates to disease progression is still limited. The AMYPAD Prognostic and Natural History Study (PNHS) aims to evaluate the value of amyloid‐PET for predicting AD‐related disease progression. Method: A total of 1423 non‐demented subjects were included from the AMYPAD‐PNHS pan‐European cohort (17 sites across 10 Parent Cohorts), for who a baseline [18F]flutemetamol or [18F]florbetaben amyloid‐PET scans that passed quality control was available (Table‐1). Amyloid burden was assessed using the Centiloid (CL) method (whole cerebellum reference region). Independent linear mixed‐effect models were used to assess the predictive value of continuous CL, and its interaction with time, across cognitive domains: mini‐mental state examination (MMSE), memory, attention, language fluency, visuospatial, and executive functions. Years from baseline, age, sex, years of education, cognitive dementia rating (CDR), and APOE‐ε4 were used as independent variables. Clinical follow‐up was 3.4 ± 1.8 years (up to ±10 years) on average. Result: At baseline, 90% of the subjects were classified as cognitively unimpaired (CDR = 0). Based on CL, most subjects were categorized as amyloid‐negative (59%, 0±9CL), followed by grey‐zone (24%, 25±11CL), and amyloid‐positive (17%, 82±21CL). Baseline CL and its interaction with time were predictive of MMSE scores, memory, attention, and language fluency. Baseline CL, but not its interaction with time, was predictive of executive function. Finally, the interaction of baseline CL with time, but not its main effect, was predictive of visuospatial functioning (Figure‐1). Age and sex were significant contributors to nearly all models. Conclusion: Higher baseline CL was associated with lower cognitive performance in all explored domains, suggesting that emerging cerebral amyloid accumulation in non‐demented individuals is predictive of overall cognitive function. Also, higher CL values were predictive of decline across cognitive domains, except for executive function. Further analysis is ongoing, including different confounding factors, longitudinal PET outcomes, and other biomarkers of the disease. [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: Prognostic and monitoring capabilities of blood‐based biomarkers for early cognitive decline, amyloid‐β (Aβ) accumulation and grey matter (GM) loss in the asymptomatic phase of Alzheimer disease require further investigation over extended time periods. This longitudinal study investigated serum glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and Aβ1‐42/Aβ1‐40 as potential prognostic and monitoring biomarkers in cognitively unimpaired (CU) elderly. Method: We included 184 CU older adults (Table 1) who underwent baseline serum sampling and 2‐yearly neuropsychological assessment for up to 10 years (median: 6 years, Figure 1). A subset (N = 109) underwent serial blood sampling, Aβ‐PET and structural MRI at 2 (N = 73), 3 (N = 34), or 4 (N = 2) time points (time interval: 2‐10 years, median: 6 years). Cognition was evaluated in memory, language and executive functioning domains through conversion of neuropsychological tests scores to standardised z‐scores and subsequent averaging per domain. Aβ‐accumulation and GM loss were analysed in a voxelwise manner using Voxelstats with cluster‐level pRFT<.05 and voxel‐level puncorrected<.001 significance thresholds.[1] Aβ1‐42/Aβ1‐40 was inverted to facilitate biomarker comparison. Linear‐mixed effects models were used to (i) identify the predictive value of serum biomarkers for longitudinal cognitive decline, Aβ‐accumulation and GM loss, (ii) calculate annual serum biomarker rates of change and (iii) investigate associations between longitudinal biomarker changes and longitudinal changes in cognition, Aβ‐PET and GM volume. Result: High baseline GFAP and NfL predicted a longitudinal decline in memory (βGFAP*time = ‐0.020, pFDR = 0.03; βNfL*time = ‐0.031, pFDR = 0.002) and language (βGFAP*time = ‐0.025, pFDR<0.001, βNfL*time = ‐0.017, pFDR = 0.04), and longitudinal GM loss within the hippocampus. Low Aβ1‐42/Aβ1‐40 predicted memory decline (βAβ1‐42/Aβ1‐40*time = ‐0.022, pFDR = 0.03) as well as Aβ accumulation within Aβ‐vulnerable regions (Figure 1). Serum GFAP (βtime = 0.022, pFDR<.001) and NfL (βtime = 0.040, pFDR<.001) increased over time. Longitudinal serum NfL increases were associated with a concomitant decline in memory (β∆NfL*time = ‐0.031, pFDR=.004), language (β∆NfL*time = ‐0.022, pFDR=.01) and GM volume (Figure 2). Serum GFAP increases were associated with precuneal Aβ accumulation. Aβ1‐42/Aβ1‐40 decreases were stronger in Aβ‐ subjects (pFDR<.001) and did not associate with Aβ accumulation, nor any other longitudinal marker. Conclusion: Serum GFAP, NfL and Aβ1‐42/Aβ1‐40 are prognostic and/or monitoring biomarkers providing complementary information by reflecting different Aβ‐dependent or ‐independent pathophysiological processes. REFERENCE [1] Mathotaarachchi et al. Front Neuroinform 2016. [ABSTRACT FROM AUTHOR]

Background: The prevention of symptomatic Alzheimer's disease (AD) is a major endeavor currently in the field, with the prevalence of subjects on the AD continuum (i.e. presence of amyloid‐ß [Aß] pathology) being much higher than previously estimated. However, the extent to which Aß pathology and its interaction with common risk factors in the aging population relates to disease progression is still limited. The AMYPAD Prognostic and Natural History Study (PNHS) aims to evaluate the value of amyloid‐PET for predicting AD‐related disease progression. Method: A total of 1423 non‐demented subjects were included from the AMYPAD‐PNHS pan‐European cohort (17 sites across 10 Parent Cohorts), for who a baseline [18F]flutemetamol or [18F]florbetaben amyloid‐PET scans that passed quality control was available (Table‐1). Amyloid burden was assessed using the Centiloid (CL) method (whole cerebellum reference region). Independent linear mixed‐effect models were used to assess the predictive value of continuous CL, and its interaction with time, across cognitive domains: mini‐mental state examination (MMSE), memory, attention, language fluency, visuospatial, and executive functions. Years from baseline, age, sex, years of education, cognitive dementia rating (CDR), and APOE‐e4 were used as independent variables. Clinical follow‐up was 3.4 ± 1.8 years (up to ±10 years) on average. Result: At baseline, 90% of the subjects were classified as cognitively unimpaired (CDR = 0). Based on CL, most subjects were categorized as amyloid‐negative (59%, 0±9CL), followed by grey‐zone (24%, 25±11CL), and amyloid‐positive (17%, 82±21CL). Baseline CL and its interaction with time were predictive of MMSE scores, memory, attention, and language fluency. Baseline CL, but not its interaction with time, was predictive of executive function. Finally, the interaction of baseline CL with time, but not its main effect, was predictive of visuospatial functioning (Figure‐1). Age and sex were significant contributors to nearly all models. Conclusion: Higher baseline CL was associated with lower cognitive performance in all explored domains, suggesting that emerging cerebral amyloid accumulation in non‐demented individuals is predictive of overall cognitive function. Also, higher CL values were predictive of decline across cognitive domains, except for executive function. Further analysis is ongoing, including different confounding factors, longitudinal PET outcomes, and other biomarkers of the disease. [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 present study aimed to explore the association between Apolipoprotein E (APOE) genotype, the main genetic risk factor for sporadic Alzheimer's Disease (AD), age, and cerebral β‐amyloid (Aβ) aggregation, the earliest pathological event in AD in a large, multicenter, European cohort, the AMYPAD study. Method: We included 876 participants from the AMYPAD cohort who had known APOE genotype and went through at least one Aβ PET imaging. We first looked at the association between Aβ deposition and age according to sex, APOE genotype, and cognitive status. We then used linear regression models to look at the association between Aβ aggregation and APOE genotype in different cerebral regions with age, sex, history of dementia, MMSE score, CDR score, depression, hypercholesterolemia, diabetes and center as covariates. Result: Our participants had a mean age of 67 years old with a mean MMSE score of 28.9/30 and counted 60% of women. We found a positive association between Aβ deposition and age with a gradually stronger association in heterozygous ԑ4 carriers (β (SD) = 1.94 (0.27)) and homozygous ԑ4 carriers (3.18 (0.52)) compared to non‐carriers (0.99 (0.12), p<0.001; Figure). We found similar results when restricting the analyses to cognitively unimpaired participants (heterozygous ԑ4: 1.65 (0.27), p<0.001; homozygous ԑ4: 3.01 (0.52), p<0.001), however Aβ aggregation was less associated to age in participants with mild cognitive impairment (heterozygous ԑ4: 1.33 (1.4), p = 0.33; homozygous ԑ4: 2.34 (1.9), p = 0.29). Moreover, we found an effect of sex on the association between Aβ aggregation and age in ԑ4 non‐carriers characterized by a stronger association in men (1.61 (0.22)) compared to women (0.63 (0.13)). Finally, we observed that the effect of APOE genotype on Aβ aggregation remained similar in the different cerebral regions, with less discernable differences between genotypes in the medial temporal and occipital regions. Conclusion: The number of APOE4 alleles has an impact on the association between age and Aβ aggregation, such that high amyloid was observed at younger ages in homozygous APOE4 carriers than in heterozygous APOE4 carriers and in non‐carriers. The effect of APOE4 on Aβ deposition was global rather than regional in our analysis. [ABSTRACT FROM AUTHOR]

Background: Pathogenic mutations in the progranulin gene (GRN) are a key cause of frontotemporal dementia (FTD), inducing a reduced biofluid concentration of the progranulin protein (PGRN). PGRN is a cysteine‐rich glycoprotein with essential roles in inflammation and lysosomal function, made up of 7 granulin peptides and 1 paragranulin. The role of these peptides is unclear, but existing data suggests they may have contradictory roles to full‐length PGRN. With the development of numerous clinical trials aiming to treat progranulin‐associated FTD (FTD‐GRN) by increasing full‐length PGRN, it is important to establish effective outcome measures to assess treatment success and further our understanding of PGRN's biology. Here, we aimed to develop an assay to quantify granulin peptides in cerebrospinal fluid (CSF) and determine whether they contribute to the pathology of FTD‐GRN. Method: Based on previously published explorative data of endogenous CSF peptides, 12 peptides spanning the progranulin sequence, were selected for the development of targeted assays using a quadrupole Orbitrap hybrid mass spectrometer (Fusion Tribrid, Thermo). An analytical protocol was optimised involving reduction, alkylation, molecular weight cut‐off filtration and solid phase extraction, and using isotope labelled heavy standards for quantification. Additionally, tandem mass tag (TMT) proteomics was used to analyse tryptic peptides spanning granulin and paragranulin sequence regions in 248 CSF samples from the Genetic FTD initiative (GENFI) including 56 GRN mutation carriers and 76 mutation‐negative controls. Result: Preliminary results reveal the presence of three endogenous peptides in CSF, which based on sequence matching, likely represent granulin 6 and 7 alongside the paragranulin peptide. TMT results showed significantly reduced relative peptide intensity across the granulin regions in GRN carriers CSF compared to controls (p<0.0001), but no significant difference in the paragranulin region (p>0.33). Conclusion: These findings indicate that two granulins and paragranulin are quantifiable in CSF and may have key roles in progranulin biology and potentially FTD pathology. This is supported by TMT results of differential CSF paragranulin levels compared to granulins. Continuing work will quantify CSF granulin concentrations in the GENFI cohort to assess whether these peptides have key roles in FTD‐GRN's underlying biology and as potential outcome measures in trials. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐ß (Aß) 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 Aß 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 Aß 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 Aß 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 Aß‐positive and Aß‐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 Aß‐negative group showed a significant change in Aß pathology for SUVR and SRTM2 (both ‐0.004/year). For [18F]florbetaben, only the Aß‐positive group showed a significant change in Aß 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 Aß‐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 Aß pathology in the Aß‐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 Aß pathology was observed in the Aß‐positive‐group, except for MRTM2. [ABSTRACT FROM AUTHOR]

Introduction: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD‐related biomarker traits indicative of AD‐relevant pathophysiological processes. Methods: We performed whole‐exome gene‐based rare‐variant association studies (RVASs) of 17 AD‐related traits on whole‐exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF‐AD MBD) study (n = 450) and whole‐genome sequencing (WGS) data from ADNI (n = 808). Results: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene‐based RVAS revealed the exome‐wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. Discussion: The identification of these novel gene–trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets. [ABSTRACT FROM AUTHOR]

Introduction: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). Methods: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. Results: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. Discussion: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. Highlights: We investigated brain network predictors of dementia symptom onsetFrontotemporal dementia results in characteristic dynamic network patternsAlterations in network dynamics are associated with neuropsychological impairmentNetwork dynamic changes predict symptomatic conversion in presymptomatic carriersNetwork dynamic changes are associated with longitudinal cognitive decline [ABSTRACT FROM AUTHOR]

Introduction: Imaging studies indicated basal forebrain reduction in primary progressive aphasia (PPA), which might be a candidate marker for cholinergic treatment. Nucleus basalis of Meynert (nbM) neuronal loss has been reported, but a systematic quantitative neuropathological assessment including the three clinical PPA variants is lacking. Methods: Quantitative assessment of neuronal density and pathology was performed on nbM tissue of 47 cases: 15 PPA, constituting the different clinicopathological phenotypes, 14 Alzheimer's disease (AD), and 18 cognitively normals. Results: Group‐wise, reduced nbM neuronal density was restricted to AD. At the individual level, semantic variant PPA with underlying AD neuropathological change (ADNC) had lower neuronal densities, while those with frontotemporal lobar degeneration (FTLD) transactive response DNA binding protein 43 kDa (TDP‐43) type C pathology were unaffected. Higher Braak stages and increased numbers of nbM‐related pretangles were associated with nbM neuronal loss. Discussion: nbM neuronal loss in PPA is related to ADNC. This study cautions against overinterpreting MRI‐based basal forebrain volumes in non‐AD PPA as neuronal loss. [ABSTRACT FROM AUTHOR]

Background: Suspected non‐Alzheimer's disease pathophysiology (SNAP) is a biomarker concept that encompasses individuals with neuronal injury but without amyloidosis. We aim to investigate the pathophysiology of SNAP, defined as abnormal tau without amyloidosis, in individuals with mild cognitive impairment (MCI) by cerebrospinal fluid (CSF) proteomics. Methods: Individuals were classified based on CSF amyloid beta (Aβ)1‐42 (A) and phosphorylated tau (T), as cognitively normal A—T– (CN), MCI A–T+ (MCI‐SNAP), and MCI A+T+ (MCI‐AD). Proteomics analyses, Gene Ontology (GO), brain cell expression, and gene expression analyses in brain regions of interest were performed. Results: A total of 96 proteins were decreased in MCI‐SNAP compared to CN and MCI‐AD. These proteins were enriched for extracellular matrix (ECM), hemostasis, immune system, protein processing/degradation, lipids, and synapse. Fifty‐one percent were enriched for expression in the choroid plexus. Conclusion: The pathophysiology of MCI‐SNAP (A–T+) is distinct from that of MCI‐AD. Our findings highlight the need for a different treatment in MCI‐SNAP compared to MCI‐AD. [ABSTRACT FROM AUTHOR]

Background: The AMYPAD Prognostic and Natural History Study aims to evaluate the value of (semi‐) quantitative amyloid PET imaging for predicting progression within an Alzheimer's disease risk probability spectrum. The project is actively recruiting non‐demented participants from 17 sites across Europe, with a particular interest in those with emerging amyloid pathology. Methods: As of 17th January 2022, a total of 1,460 participants had been included. Of those, clinical data of 1,015 were processed, with 688 (68%) having at least a quantified amyloid PET at baseline, with [18F]flutemetamol or [18F]florbetaben. The primary metric used to assess amyloid burden was the Centiloid (CL), measured from the ratio of the GAAIN cortical target and whole cerebellum. Scans were categorized as negative (CL≤12), gray‐zone (1250). The distribution of these groups was assessed using a Gaussian mixture model. Independent linear mixed‐effect models were used to explore the predictive value of CL, and its interaction with time, for several cognitive outcomes: mini‐mental state examination (MMSE), memory (immediate recall, IR), attention (digit‐span forward, DSF), language, visuospatial, and executive functions. In addition, years from baseline, parent cohort, age, sex, education, APOE‐ε4, and the Clinical Dementia Rating (CDR) score were used as independent variables. Results: Demographics are shown in Table 1. At baseline, 88% of the subjects were classified as cognitively unimpaired (CDR=0). A majority of 384 (56%) was categorized as negative (1±7CL), 210 (30%) as grey‐zone (14±11CL), and 94 (14%) as positive (58±31CL) [Figure 1]. Baseline CL (‐0.007 [‐0.010; ‐0.004]) and its interaction with time (‐0.002 [‐0.003; ‐0.001]) were predictive of IR. Baseline CL, but not its interaction with time, predicted MMSE (‐0.007, 95%CI [‐0.010; ‐0.003]) and DSF (‐0.003 [‐0.005; ‐0.001]) [Figure 2]. No significant relationships were observed for executive, language, or visuospatial function. Conclusions: Higher baseline CL were associated with lower global cognitive function and attention scores. Whereas, higher CL were associated with a steeper memory decline over time. Current results suggest that cerebral amyloid accumulation is predictive of future overall cognitive function, attention, and memory decline. Further analysis is assured, including more participants, longitudinal PET outcomes, and other biomarkers of the disease. [ABSTRACT FROM AUTHOR]

Background: Posterior cortical atrophy (PCA) is a clinically defined syndrome characterized by impairment in higher‐order visual processing. The underlying pathology in PCA is most commonly Alzheimer's disease (AD), but large‐scale biomarker and neuropathological studies are lacking. In this ongoing project we aim to describe demographic, clinical, biomarker and neuropathological correlates of PCA in a large‐scale international cohort. Method: We contacted 55 research centers conducting PCA research identified in a literature review (n=1353 papers) as well as additional sites recruited through the ISTAART Atypical AD Professional Interest Group (n=7 sites) requesting deidentified, single‐subject data from PCA patients (published and unpublished). Inclusion criteria were: 1. clinical diagnosis of PCA, 2. availability of AD biomarkers (PET or CSF) or 3. availability of autopsy diagnosis. Single‐subject demographic, clinical, fluid, neuroimaging and neuropathological data were collected. Result: As of January 2022, we have collected individual patient data from 390 participants evaluated at 14 sites in 10 countries (Table 1). In the preliminary sample, mean age at symptom onset was 63.1 ± 8.8 years, 62.3% of participants were female, and 78.3% presented with a "PCA‐pure" clinical syndrome by Crutch 2017 diagnostic criteria. APOE4 genotype was present in 45.6% (n=125). Preliminary results show that 82.9% (n=270) display predominant MRI atrophy and 91.9% (n=185) predominant FDG hypometabolism in posterior cortical regions. CSF amyloid markers were positive in 80.2% participants (n=187), while CSF phosphorylated tau markers were positive in 61.4% (n=184). Amyloid PET was positive in 85.2% (n=129) while tau PET was only available in 65 patients (positive in all, and all were amyloid positive). At autopsy (n=36, data from 4 centers), high AD neuropathologic changes were found in all but one patient, who had primary frontotemporal lobar degeneration with TDP‐43 type A pathology. Lewy bodies, argyrophilic grains and cerebral amyloid angiopathy were common co‐pathologies (Figure 1). Conclusion: In a large international cohort, PCA is strongly associated with positive AD biomarkers and neuropathology. Data collection is ongoing, and we further aim to identify clinical features associated with non‐AD underlying causes of PCA. [ABSTRACT FROM AUTHOR]

Background: The AMYPAD Prognostic and Natural History Study aims to evaluate the value of (semi‐)quantitative amyloid PET imaging for predicting progression within an Alzheimer's disease risk probability spectrum. The project is actively recruiting non‐demented participants from 17 sites across Europe, with a particular interest in those with emerging amyloid pathology. Method: As of 17th January 2022, a total of 1,460 participants had been included. Of those, clinical data of 1,015 were processed, with 688 (68%) having at least a quantified amyloid PET at baseline, with [18F]flutemetamol or [18F]florbetaben. The primary metric used to assess amyloid burden was the Centiloid (CL), measured from the ratio of the GAAIN cortical target and whole cerebellum. Scans were categorized as negative (CL≤12), gray‐zone (1250). The distribution of these groups was assessed using a Gaussian mixture model. Independent linear mixed‐effect models were used to explore the predictive value of CL, and its interaction with time, for several cognitive outcomes: mini‐mental state examination (MMSE), memory (immediate recall, IR), attention (digit‐span forward, DSF), language, visuospatial, and executive functions. In addition, years from baseline, parent cohort, age, sex, education, APOE‐ε4, and the Clinical Dementia Rating (CDR) score were used as independent variables. Result: Demographics are shown in Table 1. At baseline, 88% of the subjects were classified as cognitively unimpaired (CDR=0). A majority of 384 (56%) was categorized as negative (1±7CL), 210 (30%) as grey‐zone (14±11CL), and 94 (14%) as positive (58±31CL) [Figure 1]. Baseline CL (‐0.007 [‐0.010; ‐0.004]) and its interaction with time (‐0.002 [‐0.003; ‐0.001]) were predictive of IR. Baseline CL, but not its interaction with time, predicted MMSE (‐0.007, 95%CI [‐0.010; ‐0.003]) and DSF (‐0.003 [‐0.005; ‐0.001]) [Figure 2]. No significant relationships were observed for executive, language, or visuospatial function. Conclusion: Higher baseline CL were associated with lower global cognitive function and attention scores. Whereas, higher CL were associated with a steeper memory decline over time. Current results suggest that cerebral amyloid accumulation is predictive of future overall cognitive function, attention, and memory decline. Further analysis is assured, including more participants, longitudinal PET outcomes, and other biomarkers of the disease. [ABSTRACT FROM AUTHOR]

Background: Approximately a third of frontotemporal dementia (FTD) is genetic with mutations in three genes accounting for the majority of the inheritance: C9orf72, GRN and MAPT. Synaptic dysfunction is a common mechanism in all of them and the use of fluid biomarkers could be helpful to improve the diagnostic accuracy and useful as a readout of cellular dysfunction within therapeutic trials. Method: A total of 193 cerebrospinal fluid samples from the GENetic FTD Initiative including 77 presymptomatic (31 C9orf72, 23 GRN, 23 MAPT), 55 symptomatic mutation carriers (26 C9orf72, 17 GRN, 12 MAPT) and 61 mutation‐negative controls were measured using a microflow LC PRM‐MS set‐up targeting 15 synaptic proteins: 14‐3‐3 proteins (eta, zeta/delta and epsilon), AP‐2 complex subunit beta, beta‐synuclein, gamma‐synuclein, complexin‐2, neurogranin, neuronal pentraxin receptor (NPTXR), neuronal pentraxin 1 (NPTX1), neuronal pentraxin 2 (NPTX2), phosphatidylethanolamine‐binding protein 1 (PEBP‐1), rab GDP dissociation inhibitor α (rab GDIα), syntaxin‐1B and syntaxin‐7. Mutation carrier groups were compared to each other and to controls using a bootstrapped linear regression model, adjusting for age and sex. Result: Eight proteins were increased only in symptomatic MAPT mutation carriers (compared with controls) and not in symptomatic C9orf72 or GRN mutation carriers: 14‐3‐3‐eta, beta‐synuclein, gamma‐synuclein, neurogranin, PEBP‐1, rab GDIα, syntaxin‐1B and syntaxin‐7. In contrast, NPTX1 and NPTX2 were affected in all three genetic groups (decreased compared to controls), with NPTXR being affected in C9orf72 and GRN mutation carriers only (decreased compared to controls). No changes were seen in presymptomatic mutation carriers in these proteins. Figure 1 contains p‐values for all significant changes. Conclusion: Differential involvement of synaptic markers is seen in the genetic forms of FTD, with impairment particularly in those with MAPT mutations, with only the neuronal pentraxins affected in GRN and C9orf72 mutation carriers. Further work is needed to explore correlations with clinical and imaging biomarkers, whether there are changes in the late presymptomatic period, and how these markers change over time. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disorder that is the leading cause of dementia, affecting more than 50 million people worldwide. In the search for new biomarkers, increasing attention has been dedicated to the retina. As part of the central nervous system, the retina manifests many of the characteristic pathological processes that occur in the AD brain. Moreover, it can be imaged at low‐cost and non‐invasively at high resolution, and therefore it may be used for AD diagnosis and monitoring. In this context, emerging evidence suggests the potential of in vivo hyperspectral retinal imaging (HSRI) as a potential biomarker for brain amyloid‐β accumulation. Therefore, this project aims at studying HSRI as a novel biomarker that could lead to inexpensive population screening, thereby removing one of the principal bottlenecks to therapeutic advances in AD. Method: In this study, we seek to define the molecular basis, sensitivity, and specificity of the HSRI signal of AD, and whether it can distinguish AD from other dementias in a translational pre‐clinical and clinical research project. We perform HSRI of synthetic protein aggregates, and different cell and mouse models of neurodegenerative proteinopathies, and correlate these data with molecular biology assays, to study the molecular basis and pathological correlates of HSRI signals. In a parallel clinical study, we assess the correlation between amyloid status and HSRI and evaluate whether the diagnostic power of HSRI can be increased via a multimodal imaging approach. Result: Our preliminary results demonstrate that HSRI can be used to quantify retinal amyloid in AD animal models and post‐mortem tissues as well as discriminate between AD patients and controls, with improvement in classification accuracy by including optical coherence tomography (OCT) data. Conclusion: This research study covers out‐of‐the‐box thinking to transform the detection of AD. It collects evidence of the specificity and molecular basis of HSRI which is essential for its rational clinical use as an AD imaging biomarker. [ABSTRACT FROM AUTHOR]

Background: A novel panel of 14 proteins measured in the CSF could separate individuals with genetic frontotemporal dementia (FTD) from controls, with most significant findings observed for neurofilament medium (NEFM), neuronal pentraxin 2 (NPTX2), neurosecretory protein VGF (VGF) and aquaporin 4 (AQP4) [1]. However, it is currently unknown whether these altered protein levels in the CSF reflect neurodegenerative changes in the brain. The aim of this study was to explore the cross‐sectional associations between the previously identified CSF biomarker candidates and cortical thickness in presymptomatic and symptomatic mutation carriers, and whether those associations differ by FTD mutation type. Method: We have analyzed T1 MRI scans alongside concurrent CSF samples from 202 individuals from the GENFI cohort, belonging to families that carry FTD mutations in either C9orf72, GRN or MAPT genes. The study sample included symptomatic mutation carriers, presymptomatic mutation carriers and non‐carrier controls. Cortical thickness was estimated with FreeSurfer and CSF protein levels were measured via a multiplexed antibody‐based suspension bead array. The correlations between regional cortical thickness and protein levels were calculated via linear regression. Result: Altered levels of NEFM, AQP4, APOA1, PTPRN2, CTSS, SERPINA3, C4, AMPH and CD14 were all correlated with increased atrophy of at least one cortical region. Some effects were mutation specific, but NEFM, AQP4 and APOA1 correlated with atrophy in all mutation groups. We also observed mutation specific effects for 10 of the proteins. CTSS levels were only correlated with cortical atrophy in C9orf72 mutation carriers while NPTX2, VGF and PTPRN2 correlated with atrophy in GRN mutation carriers. In MAPT mutation carriers, 6 different proteins correlated with atrophy in the right temporal pole. Conclusion: The proposed fluid biomarker candidates continue to show promise and further longitudinal studies will contribute to elucidate their relationship to cortical atrophy and their prognostic value in genetic FTD. [1] Bergström et al. Mol Neurodegener. 2021; 16(1):79 [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) co‐pathology is a common feature in dementia with Lewy bodies (DLB) and associated with a more rapid decline. Here, we aimed to evaluate plasma biomarkers as alternatives for CSF biomarkers for the detection of amyloid pathology, and for their diagnostic and prognostic value in DLB. Method: Plasma Aβ42/40, GFAP, NfL, pTau181 and pTau231, were measured by SIMOA in patients with DLB (n = 342), AD (n = 131), and healthy controls (n = 89) from the E‐DLB consortium cohort. Associations of biomarkers with CSF Aβ42 status were assessed with ANCOVA corrected for age and sex. ROC analyses were performed to assess biomarker's performance for amyloid detection and differential diagnosis. Associations of biomarkers with cognitive impairment and decline were determined by linear regression and mixed effects models. Result: Altered Aβ42/40 (β = ‐0.0076, 95%CI:‐0.0137 to ‐0.0016, p<.05), pTau181 (β = 0.2503, 95%CI: 0.0555 to 0.4451, p<.05) and pTau231 levels (β = 0.2269, 95%CI:0.0347 to 0.4191, p<.05) were associated with amyloid pathology in DLB, as assessed by CSF Aβ42. The combination of these biomarkers differentiated amyloid normal from abnormal DLB patients with an AUC of 0.728 (sensitivity = 64.6%, specificity = 75.0%). pTau181 best differentiated these groups as a single marker (AUC = 0.700, sensitivity = 62.5%, specificity = 73.1%). The combination of Aβ42/40, NfL and pTau231 differentiated DLB from AD with an AUC of 0.726 (sensitivity = 73.3%, specificity = 68.8%). In DLB, increased GFAP (β = ‐8.9, 95%CI:‐11.7 to ‐6.1, p<.001), NfL (β = ‐7.2, 95%CI:‐10.0 to ‐4.4, p<.001), pTau181 (β = ‐2.6, 95%CI:‐4.0 to ‐1.2, p<.001) and pTau231 (β = ‐2.4, 95%CI:‐3.7 to ‐1.2, p<.001) were associated with lower baseline MMSE scores and increased GFAP (annual change of ‐2.1 MMSE points (95%CI:‐2.9 to ‐1.3, p<.001) and NfL (annual change of ‐2.1 MMSE points (95%CI:‐3.0 to ‐1.3, p<.001) predicted faster cognitive decline. Conclusion: Plasma Aβ42/40, pTau181 and pTau231 were associated with amyloid abnormality in DLB. Their utility for differential diagnosis and identifying amyloid pathology in DLB was modest. Our findings suggest a prognostic value of GFAP and NfL for cognitive decline in DLB. [ABSTRACT FROM AUTHOR]

Background: There is a need for scalable, robust and precise Alzheimer's Disease (AD) plasma phosphorylated Tau (pTau) biomarker tests to support clinical trials and clinical practice. This study evaluated the performance of novel plasma pTau181 and pTau217 assays developed on the LUMIPULSE G1200 automated immunoassay platform and compared them to Quanterix homebrew assays. Method: Homebrew Quanterix Simoa assays for plasma pTau181 and pTau217 were transferred to the LUMIPULSE G instrument using recombinantly expressed versions of respectively α‐pTau181 mAb ADx252 and α‐pTau217 mAb RD‐084 (capture) both combined with an ALP‐conjugated Fab fragment digested from α‐N‐terminal Tau recombinant ADx204/RD‐073 (detector). Analytical performance evaluated specificity, LLOQ and precision. Performance in patient samples was evaluated using a prospective memory cohort of 42 biomarker confirmed AD patients in the prodromal or dementia stage as well as 35 healthy spouse controls. LUMIPULSE G results were compared with results from the Quanterix homebrew assays designed by ADx using the same antibody configuration. Result: Robust LUMIPULSE G1200 measurements (mean intra‐run CV 3%) distinguished AD patients from spouse controls with an area under the curve (AUC) of 0.81 and 0.88 for pTau181 and pTau217, respectively. The Quanterix homebrew assays demonstrated higher AUC of 0.98 for pTau181 (pDeLong = 0.002) and of 0.95 for pTau217 (Figure 1A). Addition of age, sex and ApoE4 status to the LUMIPULSE G‐based models increased the performance of both pTau181 and pTau217 to predict AD diagnosis – relatively to univariate models ‐ with AUCs of respectively 0.91 and 0.95 (Figure 1B, both pDeLong < 0.05), approximating Simoa‐based pTau performance. PTau values measured on both platforms correlated significantly (p<0.0001) for pTau181 (r = 0.66) and pTau217 (r = 0.77). Conclusion: The LUMIPULSE G platform is a scalable, automated, real‐time‐testing platform that is globally available. These data demonstrate that two assays on this platform, pTau181 and pTau217, are capable of distinguishing AD from controls in plasma. The sensitivity and specificity are slightly less on LUMIPULSE G when compared to an assay using the same antibodies developed on the Simoa instrument. Thus, additional experiments are required on the LUMIPULSE G to optimize analytical performance of these assays using an IVD‐grade automated system. [ABSTRACT FROM AUTHOR]

Background: Blood‐based biomarkers have shown good prognostic performance to predict cognitive decline in well‐controlled asymptomatic cohorts. We now investigated whether preclinical serum levels (sampled on average five years prior to Alzheimer's Disease (AD) diagnosis) or longitudinal changes in GFAP, a glial activation marker, or NfL, a marker of neurodegeneration could predict the rate of cognitive decline rate following initial memory clinic presentation. Method: Preclinical serum samples from 115 clinical AD patients (mean age at diagnosis = 74 years, 58% female, 40% biomarker‐proven) were retrospectively retrieved from the Leuven university hospital long‐term sample storage. All preclinical samples were collected during routine clinical visits on average 5 years (range: 2‐10 years) prior to AD diagnosis. GFAP and NfL were measured using Simoa at VUmc Amsterdam. For a subset of 51 AD patients, longitudinal serum biomarker data were available with follow‐up samples taken either before (N = 16), after (N = 16) or at the time of diagnosis (N = 40). Using linear mixed‐effects models, we assessed (i) the relationship between preclinical baseline biomarker levels and MMSE decline following memory clinic presentation, (ii) the annual rates in longitudinal serum biomarker change and (iii) the relationship between longitudinal serum biomarker changes and MMSE decline. Biomarker levels were natural log‐transformed for statistical analyses and reported p‐values were Bonferroni corrected. Result: High preclinical GFAP serum levels were predictive of faster MMSE decline after initial memory clinic presentation (βs = ‐0.97, p < 0.001). Preclinical NfL levels demonstrated no such predictive value (Figure 1). Across the total time period, serial NfL levels (βs = 0.091, 95% CI: 0.060‐0.13) increased more steeply than GFAP levels (βs = 0.052, 95% CI: 0.035‐0.072). However, within the preclinical time window, only serial GFAP levels (βs = 0.057, p = 0.03), and not NfL, displayed a significant time‐dependent rise (Figure 2). Not longitudinal GFAP nor NfL changes since preclinical sampling were associated with subsequent MMSE decline. Conclusion: Serum GFAP levels measured on average five years prior to clinical diagnosis are informative of the rate of global cognitive decline once patients present at the memory clinic and serum GFAP levels increase towards diagnosis. [ABSTRACT FROM AUTHOR]

Background: The presence of multiple brain pathologies in patients with late‐onset dementia was shown to be a rule rather than an exception. Studies have shown that over half of the patients with Lewy bodies (LB) in the brain also have concurrent Alzheimer's disease pathology (ADNC). The estimates of pTDP‐43 proteinopathy prevalence vary greatly between studies, ranging from 0‐50%. This disparity may be attributed to different patterns of LB distribution, including brainstem, limbic, and neocortical. In this study, we aimed to investigate the hypothesis that the severity of pTDP‐43 pathology and hippocampal neuronal loss vary depending on the distribution of LBs, independent of co‐existing ADNC. Method: Paraffin‐embedded sections from 270 brains of older adults with and without dementia were histologically evaluated for amyloid‐beta, pTau, pTDP‐43, and alpha‐synuclein. The severity of ADNC was assessed according to NIA‐AA guidelines. We differentiated cases positive for alpha‐synuclein in the amygdala or neocortex (cerebral LB) from those with pathology restricted to brainstem regions (brainstem LB). The CA1 region from the posterior hippocampus was quantitatively analyzed for pTDP‐43 pathology and overall neuronal density. Result: Our findings revealed that pTDP‐43 neuronal inclusions were present in the CA1 region in more than 80% of subjects with cerebral LB and high ADNC. The percentage of pTDP‐43 positive neurons was higher in the cerebral LB/AD group than in AD cases without LBs (β = 0.11, p<10−4). We detected a decreased neuronal density in the CA1 regions in cases with cerebral LBs regardless of AD status (β = ‐35.43, p<10−4). LB restricted to brainstem regions had no impact on pTDP‐43 pathology (β = 0, p = 0.99) and neuronal density in the hippocampus (β = ‐12.26, p = 0.15). Conclusion: Our results support the existence of a distinct subgroup of late‐onset dementia cases characterized by cerebral LBs, ADNC, and severe pTDP‐43 pathology. This finding may explain the variability of pTDP‐43 prevalence estimations in LB patients. By identifying the association between cerebral LBs and neuronal loss in the hippocampus, we also emphasize the importance of stratifying dementia patients based on the pattern of LB distribution. Further research is needed to clarify the functional interactions between pTDP‐43, alpha‐synuclein (LB), and ADNC. [ABSTRACT FROM AUTHOR]

Background: TDP‐43 is known to co‐accumulate in the brains of most Alzheimer's Disease (AD) cases, in addition to amyloid‐β plaques and tau tangles, and it has been recently defined as limbic‐predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC). TDP‐43 pathology is thought to worsen cognitive decline and neurodegeneration, but it is not yet understood whether TDP‐43 expression impacts AD pathogenesis, specifically tau pathology. Method: We performed a histological evaluation of 85 human cases including controls, symptomatic AD cases with (ADNC+LATENC) and without (ADNC) LATE‐NC using paraffin‐embedded post‐mortem tissue. We quantitatively assessed pTau load and neuronal density. Additionally, we measured brain levels of p‐tauT181 and total TDP‐43 (1‐300aa.) through ELISA using fresh frozen tissue of controls, ADNC, ADNC+LATENC and FTLD‐TDP cases. Moreover, we tested the seeding potential of ADNC+LATENC and AD seeds in vivo, using a TDP‐43A315T transgenic mouse model as well as in vitro, using a tau biosensor cell line (HEK293T cells transfected with a tau PS301S mutation). Result: We observed that ADNC+LATENC cases presented increased neuronal loss (p = 0.0231) and hippocampal tau pathology (p<0.0001) compared to ADNC and controls. Moreover, the ADNC+LATENC group showed increased levels of the p‐tauT181 in frontal (p = 0.0212) and entorhinal cortex (p = 0.0003) compared to the ADNC group. Total TDP‐43 levels were decreased in ADNC+LATENC and FTLD‐TDP cases compared to controls in entorhinal (p = 0.0151 and p = 0.0041, respectively) but not frontal cortex. Moreover, we observed that TDP‐43A315T mice injected with ADNC+LATENC seeds exhibited significantly increased p‐tau seeding compared to ADNC (p = 0.0163) and control‐injected animals (p = 0.0007). In line with these findings, ADNC+LATENC seeds also triggered increased p‐tau cell seeding in tau biosensor cells compared to ADNC seeds (p = 0.0048). Conclusion: These results highlight the synergy between TDP‐43 and tau proteins in the context of AD pathogenesis. Specifically, our data suggest that the presence of comorbid LATE‐NC in AD aggravates tau pathology, increasing its aggregation and seeding. Moreover, a decrease in total TDP‐43 in ADNC+LATENC and FTLD‐TDP groups points to a loss of function of this protein. We highlight the importance of considering comorbid co‐pathologies in the context of clinical diagnosis and patient stratification. [ABSTRACT FROM AUTHOR]

Background: The presence of multiple brain pathologies in patients with late‐onset dementia was shown to be a rule rather than an exception. Studies have shown that over half of the patients with Lewy bodies (LB) in the brain also have concurrent Alzheimer's disease pathology (ADNC). The estimates of pTDP‐43 proteinopathy prevalence vary greatly between studies, ranging from 0‐50%. This disparity may be attributed to different patterns of LB distribution, including brainstem, limbic, and neocortical. In this study, we aimed to investigate the hypothesis that the severity of pTDP‐43 pathology and hippocampal neuronal loss vary depending on the distribution of LBs, independent of co‐existing ADNC. Method: Paraffin‐embedded sections from 270 brains of older adults with and without dementia were histologically evaluated for amyloid‐beta, pTau, pTDP‐43, and alpha‐synuclein. The severity of ADNC was assessed according to NIA‐AA guidelines. We differentiated cases positive for alpha‐synuclein in the amygdala or neocortex (cerebral LB) from those with pathology restricted to brainstem regions (brainstem LB). The CA1 region from the posterior hippocampus was quantitatively analyzed for pTDP‐43 pathology and overall neuronal density. Result: Our findings revealed that pTDP‐43 neuronal inclusions were present in the CA1 region in more than 80% of subjects with cerebral LB and high ADNC. The percentage of pTDP‐43 positive neurons was higher in the cerebral LB/AD group than in AD cases without LBs (β = 0.11, p<10−4). We detected a decreased neuronal density in the CA1 regions in cases with cerebral LBs regardless of AD status (β = ‐35.43, p<10−4). LB restricted to brainstem regions had no impact on pTDP‐43 pathology (β = 0, p = 0.99) and neuronal density in the hippocampus (β = ‐12.26, p = 0.15). Conclusion: Our results support the existence of a distinct subgroup of late‐onset dementia cases characterized by cerebral LBs, ADNC, and severe pTDP‐43 pathology. This finding may explain the variability of pTDP‐43 prevalence estimations in LB patients. By identifying the association between cerebral LBs and neuronal loss in the hippocampus, we also emphasize the importance of stratifying dementia patients based on the pattern of LB distribution. Further research is needed to clarify the functional interactions between pTDP‐43, alpha‐synuclein (LB), and ADNC. [ABSTRACT FROM AUTHOR]

Background: TDP‐43 is known to co‐accumulate in the brains of most Alzheimer's Disease (AD) cases, in addition to amyloid‐β plaques and tau tangles, and it has been recently defined as limbic‐predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC). TDP‐43 pathology is thought to worsen cognitive decline and neurodegeneration, but it is not yet understood whether TDP‐43 expression impacts AD pathogenesis, specifically tau pathology. Method: We performed a histological evaluation of 85 human cases including controls, symptomatic AD cases with (ADNC+LATENC) and without (ADNC) LATE‐NC using paraffin‐embedded post‐mortem tissue. We quantitatively assessed pTau load and neuronal density. Additionally, we measured brain levels of p‐tauT181 and total TDP‐43 (1‐300aa.) through ELISA using fresh frozen tissue of controls, ADNC, ADNC+LATENC and FTLD‐TDP cases. Moreover, we tested the seeding potential of ADNC+LATENC and AD seeds in vivo, using a TDP‐43A315T transgenic mouse model as well as in vitro, using a tau biosensor cell line (HEK293T cells transfected with a tau PS301S mutation). Result: We observed that ADNC+LATENC cases presented increased neuronal loss (p = 0.0231) and hippocampal tau pathology (p<0.0001) compared to ADNC and controls. Moreover, the ADNC+LATENC group showed increased levels of the p‐tauT181 in frontal (p = 0.0212) and entorhinal cortex (p = 0.0003) compared to the ADNC group. Total TDP‐43 levels were decreased in ADNC+LATENC and FTLD‐TDP cases compared to controls in entorhinal (p = 0.0151 and p = 0.0041, respectively) but not frontal cortex. Moreover, we observed that TDP‐43A315T mice injected with ADNC+LATENC seeds exhibited significantly increased p‐tau seeding compared to ADNC (p = 0.0163) and control‐injected animals (p = 0.0007). In line with these findings, ADNC+LATENC seeds also triggered increased p‐tau cell seeding in tau biosensor cells compared to ADNC seeds (p = 0.0048). Conclusion: These results highlight the synergy between TDP‐43 and tau proteins in the context of AD pathogenesis. Specifically, our data suggest that the presence of comorbid LATE‐NC in AD aggravates tau pathology, increasing its aggregation and seeding. Moreover, a decrease in total TDP‐43 in ADNC+LATENC and FTLD‐TDP groups points to a loss of function of this protein. We highlight the importance of considering comorbid co‐pathologies in the context of clinical diagnosis and patient stratification. [ABSTRACT FROM AUTHOR]

Introduction: Neurofilament light (NfL), chitinase‐3‐like protein 1 (YKL‐40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Methods: We performed genome‐wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF‐AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. Results: We identify novel genome‐wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL‐40. We confirm previous work suggesting that YKL‐40 levels are associated with DNA variants in CHI3L1. Discussion: Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD‐related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD. [ABSTRACT FROM AUTHOR]

Introduction: This study sought to discover and replicate plasma proteomic biomarkers relating to Alzheimer's disease (AD) including both the "ATN" (amyloid/tau/neurodegeneration) diagnostic framework and clinical diagnosis. Methods: Plasma proteins from 972 subjects (372 controls, 409 mild cognitive impairment [MCI], and 191 AD) were measured using both SOMAscan and targeted assays, including 4001 and 25 proteins, respectively. Results: Protein co‐expression network analysis of SOMAscan data revealed the relation between proteins and "N" varied across different neurodegeneration markers, indicating that the ATN variants are not interchangeable. Using hub proteins, age, and apolipoprotein E ε4 genotype discriminated AD from controls with an area under the curve (AUC) of 0.81 and MCI convertors from non‐convertors with an AUC of 0.74. Targeted assays replicated the relation of four proteins with the ATN framework and clinical diagnosis. Discussion: Our study suggests that blood proteins can predict the presence of AD pathology as measured in the ATN framework as well as clinical diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods: Brain 18F‐fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB‐consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age‐ and education‐sensitive maps corrected for Mini‐Mental State Examination score, sex (and either education or age). Results: Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB‐typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion: These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non–disease‐related variables such as age and cognitive reserve. [ABSTRACT FROM AUTHOR]

Proteome profile changes in Alzheimer's disease (AD) brains have been reported. However, it is unclear whether they represent a continuous process, or whether there is a sequential involvement of distinct proteins. To address this question, we used mass spectrometry. We analyzed soluble, dispersible, sodium dodecyl sulfate, and formic acid fractions of neocortex homogenates (mainly Brodmann area 17‐19) from 18 pathologically diagnosed preclinical AD, 17 symptomatic AD, and 18 cases without signs of neurodegeneration. By doing so, we identified four groups of AD‐related proteins being changed in levels in preclinical and symptomatic AD cases: early‐responding, late‐responding, gradually‐changing, and fraction‐shifting proteins. Gene ontology analysis of these proteins and all known AD‐risk/causative genes identified vesicle endocytosis and the secretory pathway‐related processes as an early‐involved AD component. In conclusion, our findings suggest that subtle changes involving the secretory pathway and endocytosis precede severe proteome changes in symptomatic AD as part of the preclinical phase of AD. The respective early‐responding proteins may also contribute to synaptic vesicle cycle alterations in symptomatic AD. [ABSTRACT FROM AUTHOR]

Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non‐carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD‐related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD. [ABSTRACT FROM AUTHOR]

Introduction: The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). Methods: We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data‐driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. Results: There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure‐function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non‐carriers, and increased with proximity to the expected onset of disease. Discussion: Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance. [ABSTRACT FROM AUTHOR]

Background: Understanding the role of amyloid imaging in the earliest stages of Alzheimer's Disease (AD) becomes increasingly relevant for secondary prevention. In this context, the AMYPAD Prognostic and Natural History Study (PNHS) is an open‐label, prospective, multi‐centre cohort study (http://amypad.eu/) to determine the value of quantitative amyloid PET imaging in determining AD dementia risk. The study aims at enrolling 2000 non‐demented individuals from several European cohorts, with a focus on those with emerging amyloid pathology. Method: As of January 31th 2020, 438 participants have consented into AMYPAD PNHS across 15 active sites. Of those, 347 are from EPAD LCS, 9 from ALFA+ and 82 from EMIF‐AD (Figure 1). To date, 329 subjects have been scanned with [18F]flutemetamol or [18F]florbetaben, and 108 PET scans have been analyzed with IXICO's LEAP pipeline. The primary variable is the Centiloid value (CL) using the whole cerebellum as reference region. Scans were categorized as negative (CL<12); gray‐zone (1250) for amyloid burden, and associations between CL values and age, CDR and RBANS were assessed. Result: Site activation is almost complete, with two remaining sites to be activated by February 2020. In addition to current recruitment sources, two additional cohorts (FACEHBI and FPACK) have started recruitment, and at least one more is expected in February. Current AMYPAD PNHS participants have a mean age of 67.08 ± 7.71 years and 59% of them are female. The distribution of CL values ranged from ‐12.75 to 141.79 (Figure 2), and showed a positive association with age (p<0.001) as expected, with similar behavior for the each radiotracer (Figure 3). Forty‐five scans (42%) were classified as negative, 40 (37%) gray‐zone, and 23 (21%) positive. Within EPAD LCS participants, increasing amyloid burden was related to lower global scores on RBANS (ANOVA, F=3.6, p<0.05), and CDR=0.5 was most frequently observed in the gray‐zone (9.5%) and positive (12.0%) groups compared to amyloid‐negative subjects (2.1%). Conclusion: The AMYPAD Prognostic and Natural History Study is currently ongoing and a total of 17 sites will be actively recruiting by February 2020. Preliminary quantitative results indicate that the trial is effectively recruiting the intended target population. [ABSTRACT FROM AUTHOR]

Background: Mutations in the MAPT gene are known to cause frontotemporal dementia (FTD), but there is heterogeneity in FTD phenotype across individuals. Here we used an unsupervised learning algorithm – Subtype and Stage Inference (SuStaIn) – to relate phenotypic heterogeneity to specific mutations in the MAPT gene. Method: SuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence (set of stages) in which biomarkers transition between different z‐scores. We applied SuStaIn to cross‐sectional regional brain volumes extracted from T1‐weighted MRI data from MAPT carriers in the GENFI study to find the best stratification of the data into subtypes, and the temporal progression of each subtype. We used data from 82 MAPT carriers (57 presymptomatic and 25 symptomatic) to identify subtypes and data from a control group of 300 non‐carriers to derive z‐scores. We subtyped and staged individuals at up to five annual follow‐up visits to assess the consistency of the subtypes longitudinally. We compared the specific mutations and clinical and neuropsychological test scores of individuals assigned to each subtype. Result: SuStaIn identified two groups of MAPT carriers with distinct atrophy patterns (Figure 1), which we termed a 'temporal' subtype and a 'frontotemporal' subtype. The subtype assignments were consistent at follow‐up visits (Table 1): there were no individuals that changed from the temporal to the frontotemporal subtype or vice‐versa. Subtype assignment was strongly associated with IVS10+16, R406W and P301L mutations (Table 2): there was a one‐to‐one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one‐to‐one mapping between P301L mutations and the frontotemporal subtype. The temporal subtype was associated with memory problems, whereas the frontotemporal subtype was associated with worse performance on tests of attention and visuospatial skills (Table 3). Conclusion: Our results demonstrate the utility of SuStaIn for identifying disease subgroups and associating imaging patterns with genetics and cognition. We show that different MAPT mutations give rise to distinct atrophy patterns and clinical syndromes, providing insights into the underlying disease biology, and potential utility for patient stratification. [ABSTRACT FROM AUTHOR]

Background: The National Institute on Aging‐Alzheimer's Association (NIA‐AA) proposed the ATN framework as a classification system for Alzheimer's disease. The ATN framework helps to inform participant inclusion and potentially trial outcomes as clinical trials are increasingly targeting a range of pathologies. However, it is limited by biomarkers that are either not yet fully qualified or are relatively invasive and where access can be difficult. A blood‐based version of the ATN framework would be of considerable value and recent progress suggests such an objective is realizable. Method: To identify blood‐based biomarkers predicting different ATN profiles, we used SOMAscan assay platform to measure 4001 proteins in 785 subjects selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study (EMIF‐AD MBD) study, all of whom had measures of amyloid, CSF total tau (T‐tau) and phosphorylated tau (P‐tau). We firstly performed linear regression to identify single proteins associated with the ATN framework. Then we constructed protein co‐expression network to identify co‐expressed protein modules. We further rank‐ordered modules based upon their relevance to ATN. Using the proteins within the module with the highest relevance, we performed machine learning to differentiate different ATN profiles from non‐pathological controls (NPC). Result: The proteins identified from linear regression were enriched with AD related pathways. Seven modules were identified from co‐expression analysis, among which blue module was highly associated with the ATN framework (Figure 1). Using machine learning, we identified a subset of proteins within the blue module, along with age and apolipoprotein E ε4, that discriminated NPC from amyloid pathology dementia including A+T‐N‐, A+T+N‐, A+T‐N+ and A+T+N+ profile with high area under the curve (AUC, 0.72, 0.80, 0.84, 0.84 respectively) (Figure 2). However, these proteins could not differentiate NPC from Suspected Non‐Alzheimer Pathology (SNAP), or non‐amyloid dementia (A‐T‐N+, A‐T+N‐ and A‐T+N+). Conclusion: The results suggest that high‐dimensional plasma protein testing could be a useful and reproducible approach for discriminating NPC from amyloid pathology dementia. A minimally invasive and cost‐effective blood biomarker of the ATN framework could facilitate clinical trials by contributing to rapid and effective selection of participants. [ABSTRACT FROM AUTHOR]

Background: Ketones, the brain's alternative fuel to glucose, bypass the brain glucose deficit in mild cognitive impairment (MCI). The BENEFIC trial conducted in MCI showed improved measures of cognition in multiple domains after a 6‐month ketogenic intervention [Fortier et al. 2019]. Functional connectivity in the dorsal‐attention network (DAN) deteriorates early in MCI, and prior to changes in the ventral‐attention network [Zhang et al. 2015]. We aimed to assess whether improved cognitive outcomes in the BENEFIC Trial were linked to improved brain functional connectivity. Method: Participants in this 6‐month intervention were randomized to a placebo (n= 15) or ketogenic medium chain triglyceride (kMCT; n= 17) drink. A neurocognitive battery and brain imaging were performed pre‐ and post‐intervention. Functional connectivity was computed from resting‐state functional magnetic resonance images. Independent component analysis (FSL Melodic) was used to segment seven major resting‐state networks (DAN, ventral‐attention, frontoparietal, limbic, somatomotor, default‐mode and visual) [Yeo et al. 2011]. Regional cortical ketone (11C‐acetoacetate) and glucose (18F‐fluorodeoxyglucose) metabolic rates were also quantified using positron emission tomography (PET). Result: Changes (post ‐ pre) in DAN functional connectivity was significantly higher in the kMCT group vs placebo (P= 0.024). After correction for age, sex, education, ApoE status and intracranial volume, the significant difference between groups was still present, with a large effect size (partial h2= 0.16; P= 0.043; Figure 1). Connectivity in no other networks was significantly different between groups. Changes in attention and processing speed composite Z‐score (r= 0.42; P= 0.021) and specific tests targeting attention (Trail Making visual scanning and number sequencing conditions) were significantly associated with the increased DAN functional connectivity. Change in ketone uptake specifically in DAN cortical regions (Figure 2C) was significantly higher in the kMCT vs placebo group (P= 0.001) and was directly associated with the improved DAN functional connectivity, with a large effect size (r= 0.69; P= 0.002; Figure 3). Conclusion: We show that a 6‐month ketogenic intervention improves DAN functional connectivity in MCI, an improvement that is directly linked to increased energy (ketone) supply to the DAN. The beneficial effect of ketones on cognition in MCI may be linked to improved DAN connectivity. [ABSTRACT FROM AUTHOR]

Background: Understanding the role of amyloid imaging in the earliest stages of Alzheimer’s Disease (AD) becomes increasingly relevant for secondary prevention. In this context, the AMYPAD Prognostic and Natural History Study (PNHS) is an open‐label, prospective, multi‐centre cohort study (http://amypad.eu/) to determine the value of quantitative amyloid PET imaging in determining AD dementia risk. The study recruits from several European cohorts and aims at enrolling 2000 non‐demented individuals with a particular focus on those with emerging amyloid pathology. Method: As of January 25th, 2020, AMYPAD PNHS had 17 actively recruiting sites across Europe, and 782 non‐demented participants (>95% cognitively unimpaired) had consented to participate in the study. Of those, 448 are from EPAD LCS, 129 from EMIF‐AD Twin 60++, 111 from FACEHBI, 70 from ALFA+, 17 from FPACK, and 7 from the UCL‐2010‐412 cohort. Of the consented subjects, 334 already had an amyloid PET scan before this study. Within AMYPAD PNHS, 588 subjects have hitherto been scanned with either [18F]flutemetamol or [18F]florbetaben. Of note, 53.6% of the total number of scans have been acquired with a dual‐time window dynamic protocol. The primary outcome measure is the Centiloid value (CL) using the whole cerebellum as reference region, to enable quantitative comparability between the two tracers in the study. Also, images have been visually assessed by local raters. Of the 588 acquired scans, 296 have been quantified with IXICO’s LEAP pipeline and passed Quality Control, thus rendering valid CL values. Subjects were categorized as amyloid‐negative (CL≤12); as in a “gray‐zone” (1250) and a Gaussian Mixture Model was used to assess the population’s Centiloid distribution. Result: Based on visual assessment, 28% of the fully 296 quantified scans were visually rated as positive and 72% as negative. Quantitatively, 50% of them were categorized as negative, 33% in the gray‐zone, and 17% were positive (Figure 1). Centiloid values ranged from ‐30.3 to 154.2 (Mean±SD: 22.5±32.0 CL) (Figure 2). Conclusion: Preliminary quantitative results indicate that PNHS is being successful in recruiting a cohort mostly composed of cognitively unimpaired individuals who are enriched for early amyloid abnormalities, which is the intended target population. [ABSTRACT FROM AUTHOR]

Background: We identified 13 unrelated index patients carrying the presenilin 1 (PSEN1) missense mutation, p.Cys263Phe in a Flanders‐Belgian cohort of Alzheimer's disease (AD) patients. Seven affected relatives in three different families also carried the PSEN1 p.Cys263Phe mutation (n=20). We aimed to delineate a clinicopathological phenotype of p.Cys263Phe carriers. We compared the p.Cys263Phe genotype–phenotype with that of AD patients carrying autosomal dominant mutations i.e. PSEN1 (n=29), PSEN2 (n=1), and APP (n=5). Methods: Reviewing of medical records of autosomal dominant mutation carriers to obtain clinical and pathological data for defining genotype‐phenotype data. Results: Mean onset age of the p.Cys263Phe carriers is 63.9±5.5 years (range 53‐79), with a disease duration of 7.0±3.5 years (range 4‐13). APOE genotypes have no significant effects on onset age. A positive familial history is present in 92.9% of the carriers. Segregation with disease is present in two families with autosomal dominant inheritance of AD (figure 1). In all carriers the clinical presentation is predominantly amnestic, although 35.7% (5/14) of the patients also show significant frontal symptoms. Structural neuroimaging displays diffuse (sub)cortical atrophy with evident hippocampal atrophy in 26.7% (4/15) of the carriers. In 40.0% (6/15) of the patients we observe severe signs of small vessel disease. Brain perfusion SPECT or fluorodeoxyglucose PET imaging shows bilateral temporoparietal involvement in 57.1% (4/7) of carriers. Cerebrospinal fluid AD biomarkers were analyzed in eight carriers, and all are characteristic for AD. Neuropathological examination in three carriers shows severe levels of hallmark AD lesions combined with pronounced cerebral amyloid angiopathy (CAA) and multiple intracerebral hemorrhages in one patient. Carriers of p.Cys263Phe have a later age at onset (63.9 years) compared to PSEN1 carriers (50.9 years) and other PSEN1, PSEN2 and APP mutation carriers (51.4 years) (figure 2). Conclusion: PSEN1 p.Cys263Phe carriers present with early‐onset familial AD with autosomal dominant co‐segregation. Important is that frontal symptoms are seen in 35.7% of the carrier while neuropathological examination reveals severe levels of AD neuropathology with prominent presence of CAA. The disease onset of PSEN1 p.Cys263Phe carriers is later in comparison with other autosomal dominant gene mutation carriers. [ABSTRACT FROM AUTHOR]

Background: Mutations in MAPT are associated with frontotemporal dementia (FTD), but little is known about the progression in its early stages. We aimed at identifying the presence of early brain changes in MAPT mutation carriers. Method: We included 3T MRIs from 84 MAPT carriers [27 symptomatic: mean(SD) age 58(8) years; 57 presymptomatic: 40(11) years] from the Genetic FTD Initiative (GENFI) and from 77 age‐matched non‐carrier healthy controls (44(14) years). Based on their expected years to symptom onset (EYO), we divided the presymptomatic carriers into early (n=35, <‐10 years) and late (n=22, >‐10 years) groups. First, we performed voxel‐based morphometry (VBM) comparing 24 symptomatic carriers with 32 controls to identify the regions of interest (ROIs) which were atrophic in the symptomatic stage of MAPT. We then used automated and manual segmentations to extract these ROI volumes in all carriers. To remove the effect of age, gender, total intracranial volume and scanner type, we transformed the volumes into w‐scores, considering the controls as the reference group. A w‐score of <‐1.28 (corresponding to the 10th percentile) was considered abnormal. Result: From the VBM we identified seven structures significantly atrophic in symptomatic carriers: the nucleus accumbens, amygdala, hippocampus, orbitofrontal cortex, temporal pole, anterior insula and hypothalamus. The percentage of early presymptomatic carriers with abnormal w‐scores was 14% for nucleus accumbens and hippocampus, and 20% orbitofrontal cortex. In the late group, 36% showed abnormal amygdala and temporal pole, 27% abnormal hippocampus, 32% abnormal anterior insula and 9% abnormal hypothalamus. In the symptomatic group, the percentages were higher: nucleus accumbens (41%), amygdala, hippocampus and temporal pole (85%), orbitofrontal cortex (56%), anterior insula (93%) and hypothalamus (48%). Conclusion: Abnormal limbic regions are a frequent feature in presymptomatic MAPT carriers, showing early structural changes before symptom onset. Further investigations on the associated cognitive and white matter changes are ongoing. [ABSTRACT FROM AUTHOR]

Introduction: Plasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins. Methods: 4001 plasma proteins were measured in two groups of participants (discovery group = 516, replication group = 365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid. Results: A panel of proteins (n = 44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve = 0.78) and the replication group (area under the curve = 0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization. Discussion: The results suggest that high‐dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition. [ABSTRACT FROM AUTHOR]

Background: Neurodegenerative brain diseases (NBDs) ultimately result in neuronal loss and subsequent cognitive decline. In Alzheimer's disease, tau neurofibrillary tangles (NFT) and amyloid plaques are considered as the hallmark lesions, but there are a number of other distinct features that are not exclusive to AD and do occur to a certain extent in other NBDs. Method: We extracted DNA from a cohort of 382 post‐mortem brains of NBD patients and controls with detailed neuropathological characterization. Genotyping of 85 genetic loci, previously found to be associated with AD, is ongoing using an in‐house developed oxford nanopore technologies (ONT) multiplex assay, allowing simultaneous sequencing of all variants on an ONT Flongle platform. Base‐ calling, alignment to Hg38 and variant calling were performed followed by QC on the data and regression analysis. Result: Association testing has currently been performed for 59 of the total 85 SNPs in the assay using either linear or logistic regression. This revealed significant association for an AD risk variant in the SIGLEC11 locus and Braak NFT staging (p = 0.003) and NIA_AA score (p = 0.002). Significance was also found for a risk variant in the EPHA1 locus and Hirano body score (p = 0.008). Furthermore, we found numerous nominally significant associations between risk loci and TDP43 LATE, granulovacuolar degeneration, phases of amyloid plaque distribution and CAA severity. Conclusion: These results possibly highlight biological pathways involved in generating these additional features. Our ongoing work is currently focusing on increasing our sample size to further validate the associations we discovered and optimizing our ONT assay. [ABSTRACT FROM AUTHOR]

Background: Neurodegenerative brain diseases (NBDs) ultimately result in neuronal loss and subsequent cognitive decline. In Alzheimer's disease, tau neurofibrillary tangles (NFT) and amyloid plaques are considered as the hallmark lesions, but there are a number of other distinct features that are not exclusive to AD and do occur to a certain extent in other NBDs. Method: We extracted DNA from a cohort of 382 post‐mortem brains of NBD patients and controls with detailed neuropathological characterization. Genotyping of 85 genetic loci, previously found to be associated with AD, is ongoing using an in‐house developed oxford nanopore technologies (ONT) multiplex assay, allowing simultaneous sequencing of all variants on an ONT Flongle platform. Base‐ calling, alignment to Hg38 and variant calling were performed followed by QC on the data and regression analysis. Result: Association testing has currently been performed for 59 of the total 85 SNPs in the assay using either linear or logistic regression. This revealed significant association for an AD risk variant in the SIGLEC11 locus and Braak NFT staging (p = 0.003) and NIA_AA score (p = 0.002). Significance was also found for a risk variant in the EPHA1 locus and Hirano body score (p = 0.008). Furthermore, we found numerous nominally significant associations between risk loci and TDP43 LATE, granulovacuolar degeneration, phases of amyloid plaque distribution and CAA severity. Conclusion: These results possibly highlight biological pathways involved in generating these additional features. Our ongoing work is currently focusing on increasing our sample size to further validate the associations we discovered and optimizing our ONT assay. [ABSTRACT FROM AUTHOR]

Introduction: A critical and as‐yet unmet need in Alzheimer's disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers. Methods: This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD‐type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis. Results: Eight metabolites were associated with amyloid β and one with t‐tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory. Discussion: PFAMs have been found increased and associated with amyloid β burden in CSF and clinical measures. [ABSTRACT FROM AUTHOR]

Introduction: Unlike for glucose, uptake of the brain's main alternative fuel, ketones, remains normal in mild cognitive impairment (MCI). Ketogenic medium chain triglycerides (kMCTs) could improve cognition in MCI by providing the brain with more fuel. Methods: Fifty‐two subjects with MCI were blindly randomized to 30 g/day of kMCT or matching placebo. Brain ketone and glucose metabolism (quantified by positron emission tomography; primary outcome) and cognitive performance (secondary outcome) were assessed at baseline and 6 months later. Results: Brain ketone metabolism increased by 230% for subjects on the kMCT (P <.001) whereas brain glucose uptake remained unchanged. Measures of episodic memory, language, executive function, and processing speed improved on the kMCT versus baseline. Increased brain ketone uptake was positively related to several cognitive measures. Seventy‐five percent of participants completed the intervention. Discussion: A dose of 30 g/day of kMCT taken for 6 months bypasses a significant part of the brain glucose deficit and improves several cognitive outcomes in MCI. [ABSTRACT FROM AUTHOR]

Background: Suspected non‐Alzheimer's disease pathophysiology (SNAP) is a biomarker‐defined concept that encompasses individuals with Alzheimer's disease (AD) neuronal injury but without amyloidosis. We have previously shown that 24% of mild cognitive impairment (MCI) individuals with SNAP will progress within 3 years to the AD dementia stage. Nonetheless, it remains unclear what explains the MCI‐SNAP biomarker profile and whether it reflects an early stage of AD, atypical AD or non‐AD pathology. We aimed to investigate the underlying pathophysiology of MCI‐SNAP using proteomics in cerebrospinal fluid (CSF). Method: We included 206 individuals from the European EMIF‐AD MBD study and Maastricht BB‐ACL study. Based on CSF Aβ1‐42 (A) and p‐tau (T), individuals were classified as cognitively normal with normal A and T (CN, n=100), MCI with normal A and abnormal T (MCI‐SNAP, n=32), and MCI with abnormal A and T (MCI‐AD, n=74). We centrally generated CSF proteomic data for 1761 proteins using tandem tag mass spectrometry. We compared protein concentrations between groups using ANOVA adjusted for age and sex (1/3 observations per group minimum). We performed Gene Ontology enrichment analyses with false discovery rate to uncover the biological pathways of MCI‐SNAP. We investigated the expression profile of proteins in fetal/mature astrocytes, neurons, oligodendrocytes, microglia and endothelial cells. Result: Sample characteristics are presented in Table 1. In MCI‐SNAP, we found 83 decreased and 28 increased proteins compared to CN and 177 decreased and 21 increased proteins compared to MCI‐AD. In MCI‐AD, we found 13 decreased and 193 increased proteins compared to CN. Proteins with decreased levels in MCI‐SNAP compared to both CN and MCI‐AD (n=55) were enriched for extracellular matrix, hemostasis, immune system, proteolysis and lysosome (Figure 1). A high percentage of these decreased proteins in MCI‐SNAP are expressed by mature astrocytes (51%) and endothelial cells (49%). Proteins increased in MCI‐SNAP and MCI‐AD compared to CN (n=17) were related to cytoskeleton organization and mainly expressed by neurons (88%). Conclusion: Our results show that the pathophysiology of SNAP is distinct from that of AD. Further studies are needed to clarify the role of extracellular matrix, hemostasis, immune system, proteolysis and lysosome processes in SNAP. [ABSTRACT FROM AUTHOR]

Background: Memory loss is central to Alzheimer's disease (AD), but the precise underlying mechanisms remain unclear. Post‐mortem research suggests that synaptic loss best explains symptoms. Here, we investigated in cerebrospinal fluid (CSF) whether levels of synaptic proteins are related with memory scores in older individuals with normal cognition and in preclinical AD. Method: Participants were selected from ADNI and EMIF‐AD MBD if they had CSF proteomics and memory scores available. In total, we selected 85 controls (i.e., normal cognition (CN) and normal CSF amyloid‐1‐42 and tau levels; mean±sd age 69±9 years, 58% female) and 46 preclinical AD (i.e. normal cognition and abnormal CSF amyloid‐1‐42 level, normal or abnormal CSF t‐tau and p‐tau; mean±sd age 74±7 years, 50% female). Age‐, sex‐ and education‐specific norms were used to normalize memory tests (ADNI: 30‐minute delayed Rey auditory verbal learning test (ADNI), EMIF: centre‐specific delayed memory tests). We selected 37 proteins available in both cohorts that were specific for synapses according to synGO (https://www.syngoportal.org/), Z‐transformed protein levels relative to controls, and pooled these across cohorts. Protein‐memory associations were assessed with linear models; model1: including covariates education, sex and age; model2: model1 + covariate amyloid status; and model3: model2 + protein level‐amyloid status interaction. Result: Memory scores were similar in the preclinical AD and control groups (P=0.52, Figure 1). Three proteins (NCSTN, FGA and NRGN) had increased levels in preclinical AD vs. controls. Across the total group, no proteins were associated with memory functioning. Repeating models including an interaction term with amyloid status showed that 9 synaptic proteins (24.3%) were associated with memory depending on amyloid status. In controls, lower protein levels related with better memory, while in preclinical AD, lower protein levels tended to be related with worse memory (Figure 2). Conclusion: Of the synaptic proteins tested, associations with memory were mediated by amyloid status. The finding that lower synaptic protein levels related to better memory in controls, but to worse memory in preclinical AD suggests that synaptic protein levels may reflect different biological processes in these groups. [ABSTRACT FROM AUTHOR]

Background: In a previous study, we showed the sensitivity of the Buschke Selective Reminding test Total Retention (BSRT TR) for detecting and predicting 4‐year cognitive change in cognitively intact older adults. Here, we examined how BSRT TR related to tau load. Method: The study sample consisted of 29 cognitively intact participants of the Flemish Prevent AD cohort KU Leuven (CDR global=0, MMSE=28‐30, mean age=68.5 year) and eight memory‐clinic recruited patients with prodromal AD (CDR global=0.5, MMSE=21‐30, mean age=71 year). All participants underwent neuropsychological testing, MRI and 18F‐MK6240 PET. Structural T1‐weighted MRI images were processed using CAT12. Tau load was quantified as 18F‐MK6240 DVR with the inferior cerebellum used as reference region. The primary analysis was based on voxel‐wise regression analysis between 18F‐MK6240 DVR scans and BSRT TR mean scores, with age as covariate. The second primary endpoint was the correlation between BSRT TR mean scores and tau DVR in an entorhinal and perirhinal volume of interest (VOI) based on the Brainnetome atlas. The secondary analysis was a VOI analysis based on an early metaVOI also containing hippocampus, parahippocampus, fusiform gyrus and inferior temporal cortex. Results were compared to those obtained for other episodic memory scores (BSRT delayed recall (DR), Auditory Verbal Learning Test (AVLT) total learning (TL) and AVLT DR), and an MRI volumetric analysis. Result: Voxel‐wise regression analysis showed a negative correlation between BSRT TR mean and DVR in ento‐ and perirhinal cortex bilaterally (x=22, y=‐4, z=‐32, extent=612 voxels, cluster level pcorr<0.001) (x=‐30, y=‐22, z=‐20, extent=2910 voxels, cluster level pcorr<0.001) (Figure A). This was confirmed by the analysis of the entorhinal/perirhinal VOI (rho=‐0.44, p=0.0063; Figure B). Significant correlations with ento‐ and perirhinal DVR were also present for BSRT DR (rho=‐0.39, p=0.017) and AVLT TL (rho=‐0.41, p=0.012), but not for AVLT DR (rho=‐0.29, p=0.084). No significant correlation was found with tau in the early metaVOI for any of the verbal episodic memory measures. Voxel‐wise regression analysis between gray matter volume and BSRT TR mean scores did not show significant results. Conclusion: The earliest measurable tau aggregate levels in ento‐ and perirhinal cortex correlate with classical episodic memory parameters. [ABSTRACT FROM AUTHOR]

Background: Plasma phosphorylated tau‐181 (p‐tau181) is one of the most reliable blood‐based biomarkers for Alzheimer's Disease (AD), yet has been consistently quantified with assays employing an antibody cross‐reacting with p‐tau175. We investigated the ability of a novel phospho‐specific plasma p‐tau181 assay to predict longitudinal amyloid accumulation in cognitively unimpaired (CU) elderly. Methods: Plasma p‐tau181 was quantified with a novel Simoa‐based assay using a p‐tau181 specific antibody (ADx252) in a prospective longitudinal cohort of 78 CU elderly (baseline age[mean] = 70 years, 47% female, 51% APOE‐ε4 carriers). Plasma sampling, amyloid‐β (Aβ)‐PET ([18F]flutemetamol or [11C]PiB) and episodic memory tests (average Buschke Selective Reminding total retention /12) were performed at baseline and Aβ‐PET was repeated on average 5 years later (SD: 2, range 1‐10 years). Longitudinal amyloid accumulation was modelled through calculation of Aβ‐PET rate of change by dividing the difference between baseline and follow‐up tracer uptake (in Centiloids) by the time interval between scans (in years). A Spearman correlation matrix between longitudinal amyloid accumulation, plasma p‐tau181, baseline amyloid load, age and baseline episodic memory was calculated. Z‐scores of all variables were entered into a hierarchical regression analysis in a stepwise manner based on correlation strength and finally sex and APOE were added. Results: Baseline plasma p‐tau181 correlated with longitudinal amyloid accumulation (ρ = 0.28, Figure 1A) as well as age (ρ = 0.31, Figure 1B). A negative correlation was found between age and baseline episodic memory scores (ρ = ‐0.33), and between p‐tau181 and baseline episodic memory scores (ρ = ‐0.19), although the latter did not reach significance. Apart from plasma p‐tau181, none of the other baseline variables correlated significantly with longitudinal amyloid accumulation (Figure 2). Hence, plasma p‐tau181 was entered into the base regression model. This base model (F(1,76) = 7.78, p = 0.007) explained 27% of the variance (R2) in longitudinal amyloid accumulation. Including additional predictors like demographics, clinical data or baseline amyloid load, did not further improve the model. Conclusion: Plasma p‐tau181 measured with the novel phospho‐specific Simoa assay best reflects longitudinal amyloid accumulation in asymptomatic Alzheimer's Disease with no additional predictive value of the other baseline factors included. [ABSTRACT FROM AUTHOR]