There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Yuqing Lei |
| Institution: | University of Pennsylvania |
| Department: | University of Pennsylvania |
| Country: | |
| Proposed Analysis: | Specific Aims: Develop semi-competing risk model to characterize the progression from MCI to AD/ADRD or death Background: AD is a fatal neurodegenerative disease with a wide range of risk factors, including genetic contributors, clinical characteristics, environmental exposures, and social determinants of health. Despite decades of investment, there is still no effective treatment for AD or AD-related dementias (ADRDs), and while identifying novel therapeutics remains a goal of AD research, there has been a recent increase in efforts to explore ‘drug repositioning’ (identifying existing medications for treatment of other diseases) using computational approaches. A well-accepted framework for drug repositioning in AD/ADRDs is to assess the associations between exposures to existing drugs and change in a clinical outcome of interest, such as a reduced or delayed progression from mild cognitive impairment (MCI) to AD/ADRD. However, there are at least two major challenges in drug repositioning for AD/ADRD: 1) Substantial complexity and heterogeneity of AD/ADRD. There are multiple genetic risk factors and pathways to brain pathologies that contribute to the development of cognitive impairment leading to dementia, and AD is often comorbid with other ADRDs. In addition, many risk factors (e.g., hypertension, hyperlipidemia, diabetes, concomitant medications, and physical inactivity) as well as resilience factors (e.g. social and behavioral determinants of health) are important confounders and/or effect modifiers of brain responses to drug exposures. 2) Lack of statistical power in identifying drug-repositioning signals. An important factor in the lack of power is the ‘curse’ of multi-dimensionality (i.e., multiple tests in this setting) due to the large search space for drug-AD associations. Furthermore, when using real world data, large numbers of confounders and effect modifiers that are related to AD/ADRD need to be carefully controlled in the modeling of drug-AD associations. Traditional methods based on regression analyses of associations between drug exposures and AD/ADRD outcomes are underpowered. Methods that properly account for these challenges are critically needed. The analyses proposed here are part of a recently-funded R56 (1R56AG074604; 09/30/2021-05/31/2023) (Title: “CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRD”, aka CICADA) from NIA to perform preliminary exploratory analyses on AD/ADRD studies with available genotypic and extensive phenotypic data, including information on prescription drug use, which will be the preliminary basis for a publication(s) and will be used to support our next R01 application. For the component of the project that would utilize Alzheimer’s Disease Genetics Consortium (ADGC) data, the goal of this analysis is to construct a semi-competing risk model to explore differences in rate of progression from unaffected to AD or MCI to AD by prescription drug usage. |
| Additional Investigators |
