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: | Zonghua Li |
| Institution: | Mayo Clinic |
| Department: | Neuroscience |
| Country: | |
| Proposed Analysis: | The ε4 allele of the apolipoprotein E (APOE) gene is the strongest genetic risk factor for late-onset AD (LOAD), whereas the ε2 allele confers the protective effect against AD. However, whether APOE genotype also impacts the conversion from the mild cognitive impairment (MCI) stage to AD is less clear. This study aims to understand how carrying different APOE genotypes in MCI individuals affect their chances of converting to AD. We hypothesize that 1) APOE4 carriers (ε4/ε4, ε4/ε3) have higher while APOE2 carriers (ε2/ε2, ε3/ε2) have lower risk of MCI to AD conversion compared with APOE3 homozygous carriers (ε3/ε3); 2) APOE genotype impacts differential risk for MCI to AD conversion independent of the amyloid-β (Aβ) deposition. Given that MCI represents the early clinical phase of AD where patients still for the most part have intact cognitive functions, our study may provide invaluable insights as to how to uniquely target this dementia population for therapy. This study will explore three main objectives. Objective 1: To obtain the basic statistics for MCI → AD conversion Objective 1.1: We intend to construct a model to describe the progression of AD in relation to Aβ deposition (with PiB-PET imaging data) and CSF t-Tau/p-Tau. In addition, we aim to understand how enrolled subjects may fall into different demographic characteristics (gender, races), APOE genotype and MCI/AD status (healthy, MCI-AD conversion, MCI-AD non-conversion) with the Alluvial diagram. Objective 1.2: We will attempt to identify the predictive factors (demographic characteristics, behavioural factors, biomarkers) for MCI → AD conversion via comprehensive correlational analysis combined with machine learning (both supervised and unsupervised learning). Objective 2: To assess the risk of MCI → AD conversion in individuals carrying different APOE genotypes Objective 2.1: With survival analysis (Cox regression), we will estimate the median years for MCI → AD conversion for individuals carrying different APOE genotypes. In doing so, we will adjust the potential confounding factors such as gender, race, education years et al. Alternatively, we may stratify the data based on the cofounding factors (given that the sample size is adequate). We may also incorporate these confounding factors into the statistical model. Additionally, we will select a subset of subjects with similar PiB-PET signals for assessing the dependency on Aβ pathology for the roles of APOE genotype in conferring differential risks for MCI → AD conversion. The Cox regression would also allow us to compare the risk for MCI → AD conversion among different APOE genotypes with hazard ratios. Objective 2.2: We plan to calculate the variance for MCI → AD conversion that can be accounted for by APOE genotype with logistic regression. Potential confounding factors would be incorporated into the model. Objective 2.3: A post-hoc analysis will be performed with the neuropathological data from the postmortem samples. We will select the subset of subjects with minimal Aβ deposition for analysis. This analysis aims to further test our hypothesis that APOE genotype-related risk for MCI → AD/dementia conversion is Aβ-independent. Objective 3: Lastly, we intend to conduct correlational analysis between APOE genotype and the identified predictive biomarkers. This analysis should pave the road for further investigation on the mechanisms underlying the differential effect of APOE genotypes on MCI → AD conversion using animal models and/or iPSC-derived cellular or organoid models. |
| Additional Investigators |
