European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS): study protocol.

INTRODUCTION: The European Prevention of Alzheimer's Dementia (EPAD) project is funded initially by the Innovative Medicines Initiative and has been established to overcome the major hurdles hampering drug development for secondary prevention of Alzheimer's dementia, by conducting the EPAD Longitudinal Cohort Study (LCS) in alignment with the Bayesian adaptive designed EPAD Proof-of-Concept (PoC) trial. METHODS AND ANALYSIS: EPAD LCS is an ongoing prospective, multicentre, pan-European longitudinal cohort study. Participants are recruited mainly from existing parent cohorts across Europe to form a 'probability-spectrum' population covering the entire continuum of anticipated probability for Alzheimer's dementia development. The primary objective of the EPAD LCS is to be a readiness cohort for the EPAD PoC trial though a second major objective is to generate a comprehensive and large data set for disease modelling of preclinical and prodromal Alzheimer's disease. This characterisation of cognitive, biomarker and risk factor (genetic and environmental) status of research participants over time will provide the necessary well-phenotyped population for developing accurate longitudinal models for Alzheimer's disease covering the entire disease course and concurrently create a pool of highly characterised individuals for the EPAD PoC trial. ETHICS AND DISSEMINATION: The study has received the relevant approvals from numerous Institutional Review Boards across Europe. Findings will be disseminated to several target audiences, including the scientific community, research participants, patient community, general public, industry, regulatory authorities and policy-makers. Regular and coordinated releases of EPAD LCS data will be made available for analysis to help researchers improve their understanding of early Alzheimer's disease stages and facilitate collaborations. TRIAL REGISTRATION NUMBER: NCT02804789.EU/EFPIA Innovative Medicines Initiative Joint Undertaking EPAD grant agreement 11573

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses

Prediction of Alzheimer's disease biomarker status defined by the ‘ATN Framework’ among cognitively healthy individuals: Results from the EPAD longitudinal cohort study

Background: The Amyloid/Tau/Neurodegeneration (ATN) framework has been proposed as a means of evidencing the biological state of Alzheimer’s disease (AD). Predicting ATN status in pre-dementia individuals therefore provides an important opportunity for targeted recruitment into AD interventional studies. We investigated the extent to which ATN-defined biomarker status can be predicted by known AD risk factors as well as vascular-related composite risk scores. Methods: 1010 cognitively healthy older adults were allocated to one of five ATN-defined biomarker categories. Multinomial logistic regression tested risk factors including age, sex, education, APOE4, family history of dementia, cognitive function, vascular risk indices (high systolic blood pressure, body mass index (BMI), high cholesterol, physical inactivity, ever smoked, blood pressure medication, diabetes, prior cardiovascular disease, atrial fibrillation and white matter lesion (WML) volume), and three vascular-related composite scores, to predict five ATN subgroups; ROC curve models estimated their added value in predicting pathology. Results: Age, APOE4, family history, BMI, MMSE, and white matter lesions (WML) volume differed between ATN biomarker groups. Prediction of Alzheimer’s disease pathology (versus normal AD biomarkers) improved by 7% after adding family history, BMI, MMSE and WML to a ROC curve that included age, sex and APOE4. Risk composite scores did not add value. Conclusions: ATN-defined Alzheimer’s disease biomarker status prediction among cognitively healthy individuals is possible through a combination of constitutional and cardiovascular risk factors but established dementia composite risk scores do not appear to add value in this context.</p

Cross-sectional and longitudinal association of sleep and Alzheimer biomarkers in cognitively unimpaired adults

Sleep abnormalities are prevalent in Alzheimer's disease, with sleep quality already impaired at its preclinical stage. Epidemiological and experimental data point to sleep abnormalities contributing to the risk of Alzheimer's disease. However, previous studies are limited by either a lack of Alzheimer's disease biomarkers, reduced sample size or cross-sectional design. Understanding if, when, and how poor sleep contributes to Alzheimer's disease progression is important so that therapies can be targeted to the right phase of the disease. Using the largest cohort to date, the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study, we test the hypotheses that poor sleep is associated with core Alzheimer's disease CSF biomarkers cross-sectionally and predicts future increments of Alzheimer's disease pathology in people without identifiable symptoms of Alzheimer's disease at baseline. This study included 1168 adults aged over 50 years with CSF core Alzheimer's disease biomarkers (total tau, phosphorylated tau and amyloid-beta), cognitive performance, and sleep quality (Pittsburgh sleep quality index questionnaire) data. We used multivariate linear regressions to analyse associations between core Alzheimer's disease biomarkers and the following Pittsburgh sleep quality index measures: total score of sleep quality, binarized score (poor sleep categorized as Pittsburgh sleep quality index > 5), sleep latency, duration, efficiency and disturbance. On a subsample of 332 participants with CSF taken at baseline and after an average period of 1.5 years, we assessed the effect of baseline sleep quality on change in Alzheimer's disease biomarkers over time. Cross-sectional analyses revealed that poor sleep quality (Pittsburgh sleep quality index total > 5) was significantly associated with higher CSF t-tau; shorter sleep duration (9 versus 0) was associated with lower CSF amyloid-beta. Longitudinal analyses showed that greater sleep disturbances (1-9 versus 0 and >9 versus 0) were associated with a decrease in CSF Aβ42 over time. This study demonstrates that self-reported poor sleep quality is associated with greater Alzheimer's disease-related pathology in cognitively unimpaired individuals, with longitudinal results further strengthening the hypothesis that disrupted sleep may represent a risk factor for Alzheimer's disease. This highlights the need for future work to test the efficacy of preventive practices, designed to improve sleep at pre-symptomatic stages of disease, on reducing Alzheimer's disease pathology.O.G.-R. receives funding from the Alzheimer’s Association Research Fellowship Program (2019-AARF-644568). J.D.G. is supported by the Spanish Ministry of Science and Innovation (RYC-2013-13054). M.S.-C. receives funding from Instituto de Salud Carlos III (PI19/00155) and from the Spanish Ministry of Science, Innovation and Universities (Juan de la Cierva programme grant IJC2018-037478-I). N.V.-T. is funded by a Spanish Ministry of Science, Innovation and Universities postdoctoral grant (Juan de la Cierva programmegrant FJC2018-038085-I). E.M.A.-U. holds a ‘Ramón y Cajal’ fellowship (RYC2018-026053-I) and a grant of the Ministry of Science and Innovation (PID2019-111514RA-I00). J.B. receives funding from Alzheimer’s Research UK (supported by the Margaret Jost Fellowship and the Don Thoburn Memorial Scholarship) and the David Telling Charitable Trust, and E.C. has received funding from BRACE and ARUK (Bristol & Bath Network)

Data_Sheet_1_Modifiable dementia risk factors and AT(N) biomarkers: findings from the EPAD cohort.docx

IntroductionModifiable risk factors account for a substantial proportion of Alzheimer’s disease (AD) cases and we currently have a discrete AT(N) biomarker profile for AD biomarkers: amyloid (A), p-tau (T), and neurodegeneration (N). Here, we investigated how modifiable risk factors relate to the three hallmark AT(N) biomarkers of AD.MethodsParticipants from the European Prevention of Alzheimer’s Dementia (EPAD) study underwent clinical assessments, brain magnetic resonance imaging, and cerebrospinal fluid collection and analysis. Generalized additive models (GAMs) with penalized regression splines were modeled in the AD Workbench on the NTKApp.ResultsA total of 1,434 participants were included (56% women, 39% APOE ε4+) with an average age of 65.5 (± 7.2) years. We found that modifiable risk factors of less education (t = 3.9, p DiscussionThis cross-sectional study provides further support for modifiable risk factors displaying neuroprotective associations with the characteristic AT(N) biomarkers of AD.</p

Regional associations of white matter hyperintensities and early cortical amyloid pathology

White matter hyperintensities (WMHs) have a heterogeneous aetiology, associated with both vascular risk factors and amyloidosis due to Alzheimer's disease. While spatial distribution of both amyloid and WM lesions carry important information for the underlying pathogenic mechanisms, the regional relationship between these two pathologies and their joint contribution to early cognitive deterioration remains largely unexplored. We included 662 non-demented participants from three Amyloid Imaging to Prevent Alzheimer's disease (AMYPAD)-Affiliated cohorts: EPAD-LCS (N = 176), ALFA+ (N = 310), and EMIF-AD PreclinAD Twin60++ (N = 176). Using PET imaging, cortical amyloid burden was assessed regionally within early accumulating regions (medial orbitofrontal, precuneus, and cuneus) and globally, using the Centiloid method. Regional WMH volume was computed using Bayesian Model Selection. Global associations between WMH, amyloid, and cardiovascular risk scores (Framingham and CAIDE) were assessed using linear models. Partial least square (PLS) regression was used to identify regional associations. Models were adjusted for age, sex, and APOE-e4 status. Individual PLS scores were then related to cognitive performance in 4 domains (attention, memory, executive functioning, and language). While no significant global association was found, the PLS model yielded two components of interest. In the first PLS component, a fronto-parietal WMH pattern was associated with medial orbitofrontal-precuneal amyloid, vascular risk, and age. Component 2 showed a posterior WMH pattern associated with precuneus-cuneus amyloid, less related to age or vascular risk. Component 1 was associated with lower performance in all cognitive domains, while component 2 only with worse memory. In a large pre-dementia population, we observed two distinct patterns of regional associations between WMH and amyloid burden, and demonstrated their joint influence on cognitive processes. These two components could reflect the existence of vascular-dependent and-independent manifestations of WMH-Amyloid regional association that might be related to distinct primary pathophysiology

Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort

Background: We classified non-demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles. Materials and methods: From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut-offs of 1000 pg/mL for amyloid beta (Aß)1-42 and 27 pg/mL for p-tau181 were validated using Gaussian mixture models. Given strong correlation of p-tau and t-tau (R 2 = 0.98, P < 0.001), neurodegeneration was defined by age-adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages. Results: Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A–T–N–, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T–N– compared to A–T–N– (P < 0.001). Discussion: In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages