ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset

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. Methods: We systematically addressed several limitations of the originally shared data and provide additional unreleased data to enhance the patient-level dataset. Results: In this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1702 study participants and is accessible to the research community via a centralized portal. Conclusions: 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. ANMerge can be downloaded here: https://doi.org/10.7303/syn22252881

Multi-Platform Proteomic Analysis of Alzheimer’s Disease Cerebrospinal Fluid and Plasma Reveals Network Biomarkers Associated with Proteostasis and the Matrisome

“…To compare how proteomic measurements in our discovery cohort compared to Olink and SomaScan measurements in other AD cohorts, we performed correlation analyses with plasma Olink data from a Hong Kong-based cohort 18 , CSF and plasma Olink data from the BioFinder cohort 19 , and SomaScan plasma data from the AddNeuroMed cohort 20 ( Supplementary Figure 6 ). Correlations were restricted to proteins significantly altered in AD in each biofluid to maximize S:N. In AD plasma, correlation of Olink measurements in the Hong Kong cohort with our discovery cohort Olink measurements was excellent ( r =0.82), with lower but strong correlation with SomaScan ( r =0.57) and MS ( r =0.63) measurements in our cohort ( Supplementary Figure 6A ).…”

“…The individual proteins are colored according to whether they are correlated across paired measurements of the same subjects in depleted and undepleted measurements. 20 Proteins are colored by the brain co-expression module in which they reside, as described in Johnson et al (A-H) Individual and total protein signal levels as measures of relative abundance in CSF and plasma were analyzed on the SomaScan (A, B), Olink (C, D), undepleted tandem mass tag mass spectrometry (TMT-MS) (E, F), and depleted TMT-MS platforms (G, H) in control (n=18) and AD (n=18) subjects. (A) Aptamer relative fluorescence units (RFUs) were ranked and analyzed by the contribution of each aptamer RFU to the cumulative RFU in CSF (left) or plasma (right).…”

Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data‐driven disease modeling

“…Six longitudinal datasets stemming from the Alzheimer's Disease Neuroimaging Initiative (ADNI), 25 AddNeuroMed (ANMerge), 26 Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), 27 Japanese Alzheimer's Disease Neuroimaging (J‐ADNI), 28 National Alzheimer's Coordinating Center (NACC), 29 and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) 30 were used as training datasets for our progression models. All of these studies obtained ethical approval for human data collection and informed patient consent for data sharing.…”

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

“…Some of the data used for the validation of WHASA are publicly available (N = 30) [17]. We used the consensus derived from the WMH manual segmentation of three anonymous expert raters reported in the manuscript of Lesjak and colleagues [16]. On the remaining data (N = 100), segmentations were first initiated using the Lesion Segmentation Toolbox (LST) [31], then three expert neuroradiologists (rater's initials: C.D., D.H., N.P.)…”

“…Neuroimaging Initiative (ADNI) [10], the Open Access Series of Imaging Studies (OASIS) [14], the KIKI2009/ Kirby [15], the Parkinson Progression Markers Initiative (PPMI) [11], the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI), the ANMerge dataset [16], and a public segmentation database of Multiple Sclerosis patients (LIMTS) [17]. Data from three additional cohorts were incorporated to enrich the sample: the Clinically Isolated Syndrome-COGnitive (SCI-COG) cohort, the REAC-TIV database, and the MEMORA cohort.…”