Category: Publications

“ADataViewer: exploring semantically harmonized Alzheimer’s disease cohort datasets”

Authors: Yasamin Salimi, Daniel Domingo-Fernández, Carlos Bobis-Álvarez, Martin Hofmann-Apitius & Colin Birkenbihl for the Alzheimer’s Disease Neuroimaging Initiative, the Japanese Alzheimer’s Disease Neuroimaging Initiative, for the Aging Brain: Vasculature, Ischemia, and Behavior Study, the Alzheimer’s Disease Repository Without Borders Investigators, for the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Abstract:

Background: Currently, Alzheimer’s disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial.

Methods: We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets.

Results: Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets.

Conclusions: In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/.

DOI: doi.org/10.1186/s13195-022-01009-4

Published online: 21 May 2022 in the journal Alzheimer’s Research & Therapy

“Age differences in the association between sleep and Alzheimer’s disease biomarkers in the EPAD cohort”

Authors: Sharon L. Naismith, Yue Leng, Jake R. Palmer, Brendan P. Lucey

Abstract:

Introduction: We aimed to determine the independent association between sleep quality and Alzheimer’s disease (AD) biomarkers, and whether the associations differ with age.

Methods: We included 1240 individuals aged ≥50, without dementia from the European Prevention of Alzheimer’s Disease v1500.0 dataset. Linear regression was used to examine Pittsburgh Sleep Quality Index (PSQI) scores against cerebrospinal fluid (CSF) phosphorylated tau/β-amyloid ratio (p-tau/Aβ42) for the entire sample and via age tertiles. Models controlled for demographic, clinical, genetic, vascular, and neuroimaging variables.

Results: For the youngest age tertile, shorter sleep duration and higher sleep efficiency were associated with greater p-tau/Aβ42 ratio. For the oldest tertile, longer sleep latency was associated with greater p-tau/Aβ42.

Discussion: Differential relationships between sleep and AD pathology depend on age. Short sleep duration and sleep efficiency are relevant in middle age whereas time taken to fall asleep is more closely linked to AD biomarkers in later life.

Highlights: This study shows age differences in the link between sleep and AD biomarkers.Shorter sleep was associated with greater p-tau/Aβ42 ratio in middle age.The association was independent of genetic, vascular, and neuroimaging markers of AD.

DOI: 10.1002/dad2.12380

Published online: 25 November 2022 in the Journal Alzheimer’s & Dementia

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

Associations Between Multimorbidity and Cerebrospinal Fluid
Amyloid: A Cross-Sectional Analysis of the European
Prevention of Alzheimer’s Dementia (EPAD) V500.0 Cohort

At, with and beyond risk: expectations of living with
the possibility of future dementia

Brain Amyloid Pathology and Cognitive Function
Alzheimer Disease Without Dementia?

Cognitive Dispersion is not Associated with Cerebrospinal Fluid
Biomarkers of Alzheimer’s Disease: Results from the European
Prevention of Alzheimer’s Dementia (EPAD) v500.0 Cohort

Cognitive Functions as Predictors of Alzheimer’s Disease
Biomarker Status in the European Prevention of Alzheimer’s
Dementia Cohort

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

Authors: Jonathan Blackman, Laura Stankeviciute, Eider M Arenaza-Urquijo, Marc Suárez-Calvet, Gonzalo Sánchez-Benavides, Natalia Vilor-Tejedor, Alejandro Iranzo, José Luis Molinuevo, Juan Domingo Gispert, Elizabeth Coulthard, Oriol Grau-Rivera, for the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Abstract:

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 (<7 h) was associated with higher CSF p-tau and t-tau; and a higher degree of sleep disturbance (1-9 versus 0 and >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.

DOI: 10.1093/braincomms/fcac257

Published online: 3 November 2022 in the Journal Brain communications

“Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets”

Authors: Y. Salimi, D. Domingo-Fernández, M. Hofmann-Apitius, C. Birkenbihl, the Alzheimer’s Disease Neuroimaging Initiative, the Japanese Alzheimer’s Disease Neuroimaging Initiative, the Alzheimer’s Disease Repository Without Borders Investigators & the European Prevention of Alzheimer’s Disease (EPAD) Consortium

Abstract:

Background: While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer’s disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts.

Objectives: To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets.

Design and Setting: We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile.

Participants and Measurements: Participants with available CSF amyloid-β 1–42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies.

Results: We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles.

Conclusion: Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.

DOI: https://doi.org/10.14283/jpad.2023.100

Published online: 6 September 2023 in the Journal of Prevention of Alzheimer’s Disease