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. 2020 Dec 16;6(1):e12102.
doi: 10.1002/trc2.12102. eCollection 2020.

Evaluating the Alzheimer's disease data landscape

Colin Birkenbihl  1   2 , Yasamin Salimi  1   2 , Daniel Domingo-Fernándéz  1   2 , Simon Lovestone  3 ; AddNeuroMed consortium; Holger Fröhlich  1   2 , Martin Hofmann-Apitius  1   2 ; Japanese Alzheimer's Disease Neuroimaging Initiative; and the Alzheimer's Disease Neuroimaging Initiative
Affiliations

Evaluating the Alzheimer's disease data landscape

Colin Birkenbihl et al. Alzheimers Dement (N Y). .

Abstract

Introduction: Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital.

Methods: Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies.

Results: The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de).

Discussion: Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.

Keywords: Alzheimer's disease; FAIR data; biomarker; clinical study; cohort; cohort study; data; data access; data set; data sharing; data viewer; data‐driven; dementia; disease modeling; magnetic resonance imaging; open‐science; patient level data.

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Figures

FIGURE 1
FIGURE 1
Combined ethnoracial diversity found across the investigated AD cohorts. Table S2 shows the individual compositions of each cohort
FIGURE 2
FIGURE 2
Interoperability of AD data sets. A, Availability of data modalities scored based on the defined criteria. The criteria are explained in Supplementary Table 1. B, Equivalence of clinical assessment variables across cohorts. PET = positron emission tomography
FIGURE 3
FIGURE 3
Longitudinal follow‐up as the proportion of participants at study baseline (ie, participants were aligned based on their first visit). A, At least one variable measured. B, CSF amyloid beta. C, MMSE scores. CSF = cerebrospinal fluid. MMSE = Mini Mental State Examination
See this image and copyright information in PMC

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