ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Martina Sattlecker |
| Institution: | King's College, London |
| Department: | Institute of Psychiatry |
| Country: | |
| Proposed Analysis: | I have developed classification/regression models AD diagnostics and consequently biomarker discovery using protein measures in plasma derived from our in-house samples. Thus, I am interested in using the Rules Based Medicine Plasma Multiplex data sets as a replication data set for my models. |
| Additional Investigators | |
| Investigator's Name: | Stephen Newhouse |
| Proposed Analysis: | 1. We calculated the rate of cognitive decline using MMSE scores from our own cohort. During this investigation we also assessed which clinical covariates (e.g. age of onset, education etc) are associated with the rate of cognitive decline in AD patients. Now we plan apply the same methods for the ADNI data and compare the findings to our data. 2. Additionally we assessed if protein data can predict the previously calculated rate of cognitive decline in AD patients. We would like to use the ADNI RBM panel rates of decline calculated for the ANDI cohort (in point 1) . Similarly we want to replicate our finding in our genetic data and plan to run a rate of decline QTL. 3. We plan to run a protein QTL with the ADNI data set. Again this would be a replication of results we obtained from our own cohort. |
| Investigator's Name: | Richard Dobson |
| Proposed Analysis: | 1. We calculated the rate of cognitive decline using MMSE scores from our own cohort. During this investigation we also assessed which clinical covariates (e.g. age of onset, education etc) are associated with the rate of cognitive decline in AD patients. Now we plan apply the same methods for the ADNI data and compare the findings to our data. 2. Additionally we assessed if protein data can predict the previously calculated rate of cognitive decline in AD patients. We would like to use the ADNI RBM panel rates of decline calculated for the ANDI cohort (in point 1) . Similarly we want to replicate our finding in our genetic data and plan to run a rate of decline QTL. 3. We plan to run a protein QTL with the ADNI data set. Again this would be a replication of results we obtained from our own cohort. |
| Investigator's Name: | Jennifer Mollon |
| Proposed Analysis: | I have carried out a large-scale protein QTL (pQTL) analysis using 300 samples, split into roughly equal groups of Alzeimer's cases (AD), healthy controls (CTL) and people with mild cognitive impairment (MCI). The protein assay was done using SomaLogic's aptamer-based scan for approximately 1000 proteins. I have analysed pQTLs in the whole cohort of 300 and also looked for differences in SNP effects between the AD and CTL cohorts. I would like to replicate our findings for the overlapping proteins, using the ADNI genotype data and the RBM proteomics data. Replication in an independent cohort using a different technology will be strong evidence for the veracity of the results. Although the RBM proteins are a subset of those in the SomaLogic assay, I feel that replicating the results for proteins that are available will strengthen the evidence for all of my findings. |