AD/ADRD Data Sources: Access and Utilization to Advance Research for Improving Human Health

Abstract

AbstractBackgroundAlzheimer’s Disease and Related Dementia (AD/ADRD) cases affect more than 6 million Americans and more than 55 million people globally, leading to the deaths of 1 in every 3 older adults (Alzheimer’s Association, 2023). By 2050, these numbers are projected to climb. The National Institute on Aging is leading efforts to effectively prevent and treat AD/ADRD to improve human health and advance research enabled by accessible, utilizable, and shareable AD/ADRD data sources and investigated the utilization of AD/ADRD genomic datasets for secondary research from generalist and disease‐specific repositories. Multifactorial approaches about the use of the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) and Database of Genotypes and Phenotypes (dbGaP) were investigated to understand efficacies in secondary data useMethodA correlational study was conducted of AD/ADRD data access requests approved in NIAGADS and dbGaP (2020‐2022). NIAGADS (n = 12) and dbGaP (n = 12) datasets (Figures 1 and 2) were obtained for inclusion in the final analysis. A literature review was also conducted of studies published in PubMed, ScienceDirect, PLOS One, and gray literature (2013‐2023) to understand AD/ADRD data access and utilization key drivers (Figure 3). Hypotheses: H1: There is no difference in the approval rates of data access requests from NIAGADS and dbGaP between 2020‐2022; H2: Utilization of AD/ADRD secondary data sources, NIAGADS and dbGaP, have contributed to growth rates of AD/ADRD publications. Independent two‐sample t‐tests were performed to test the hypotheses.ResultH1: dbGaP data access requests approved (M = 0.793, SD = 0.036) compared to NIAGADS data access requests approved (M = 0.887, SD = 0.032) demonstrated no significance in approval rates between 2020‐2022, t(9) = 2.26, p = 0.160. H2: Publication rates from dbGaP (M = 18.8, SD = 20.8) compared to NIAGADS (M = 2.33, SD = 6.24) demonstrated a significance in publication growth rates, t(13) = 2.16, p = 0.002.ConclusionResearchers trust the use of dbGaP and NIAGADS, and other generalist and disease‐specific data sources (such as ADNI, AlzPathway, ALSOD, dbGaP, NCRAD, OMIM, and UPDB), for secondary data analyses. Researchers utilizing these data sources discovered hidden disease‐gene associations, AD/ADRD pathophenotypes, genetic variants affecting gene transcripts, fMRI functional connectivity changes, relationship modeling of inflammatory factors, candidate gene prioritization, and drug therapies. Researchers recognize the value of using data sources for secondary research.