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. 2021;79(1):423-431.
doi: 10.3233/JAD-200948.

ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset

Colin Birkenbihl  1   2 , Sarah Westwood  3 , Liu Shi  3 , Alejo Nevado-Holgado  3 , Eric Westman  3 , Simon Lovestone  3 ; AddNeuroMed Consortium; Martin Hofmann-Apitius  1   2
Affiliations

ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset

Colin Birkenbihl et al. J Alzheimers Dis. 2021.

Abstract

Background: Accessible datasets are of fundamental importance to the advancement of Alzheimer's disease (AD) research. The AddNeuroMed consortium conducted a longitudinal observational cohort study with the aim to discover AD biomarkers. During this study, a broad selection of data modalities was measured including clinical assessments, magnetic resonance imaging, genotyping, transcriptomic profiling, and blood plasma proteomics. Some of the collected data were shared with third-party researchers. However, this data was incomplete, erroneous, and lacking in interoperability.

Objective: To provide the research community with an accessible, multimodal, patient-level AD cohort dataset.

Methods: We systematically addressed several limitations of the originally shared resources and provided additional unreleased data to enhance the dataset.

Results: In this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1,702 study participants and is accessible to the research community via a centralized portal.

Conclusion: ANMerge is an information rich patient-level data resource that can serve as a discovery and validation cohort for data-driven AD research, such as, for example, machine learning and artificial intelligence approaches.

Keywords: AddNeuroMed; Alzheimer’s disease; biomarkers; cohort analysis; cohort studies; data-driven science; dataset; dementia; genome wide association studies; magnetic resonance imaging; multimodal.

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Conflict of interest statement

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/20-0948r2 ).

Figures

Fig. 1
Fig. 1
Overview on longitudinal data collection per modality. Proteomics, Proteomic data from blood plasma. Transcriptomics, Transcriptomic data from blood plasma. MRI, Structural magnetic resonance imaging.
Fig. 2
Fig. 2
Participant overlap across modalities. The numbers illustrate the number of participants with available information for the intersection of the respective modalities.
Fig. 3
Fig. 3
Longitudinal follow-up and patient drop-out throughout study runtime per diagnosis group. CTL, healthy controls; MCI, mild cognitive impaired participants; AD, Alzheimer’s disease patients.
See this image and copyright information in PMC

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