ANMerge: A Comprehensive and Accessible Alzheimer’s Disease Patient-Level Dataset
Colin Birkenbihl, Sarah Westwood, Liu Shi, Alejo Nevado‐Holgado, Eric Westman, Simon Lovestone, Martin Hofmann‐Apitius
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.