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: | Wenyuan Li |
| Institution: | Zhejiang University |
| Department: | ChuangYiDaLou A311 |
| Country: | |
| Proposed Analysis: | In the last decade, with the drastic growth of population and prolonged lifespan, a growing number of populations were suffered from neurodegenerative disorders such as cognitive impairment and dementia. Due to the slow and occult onset of neurodegenerative diseases, there are few effective treatments available for patients who have suffered. Modern medicine can manage only to halt the progression of diseases mentioned above after being diagnosed. Thus, methods in neurodegenerative disease subtyping, diagnosis, and prognosis seem necessary. However, there was little to no in-depth quantitative knowledge regarding the brain aging process. While cost-effective high-throughput technologies provide an increasing amount of data, the analysis of a single layer of data hardly provided reliable causal relations. Analysis of multi-omics data along with clinical information had now taken the front seat in deriving useful insights into the mechanisms of various diseases including neurodegenerative diseases such as cognition impairment and Alzheimer’s disease. Integration of multi-omics data sets has been widely demonstrated in recent years to aid in deciphering the underlying mechanisms of multiple omics levels. Various studies have shown that merging omics datasets result in a better understanding and a clearer picture of the system under the study. For instance, a study that integrated metabolomics and transcriptomics yielded molecular abnormalities associated with prostate cancer and showed higher sensitivity and specificity in identifying prostate cancer from benign prostatic hyperplasia. Our long-term goal is to determine new strategies for subtyping and evaluating the risk of subjects with mild cognitive impairment to progress to AD on an individual level via ADNI datasets. To do so, we plan to build up a cross-scale, multi-model, and multi-omics prediction model. Our central hypothesis is that clinical information, and biomarkers from individuals’ radioactive images and biospecimens all serve as key parameters impacting individuals’ progression from healthy or mild cognitive impaired to AD diagnosis in the next few years. This hypothesis is based on a synthesis of our own and others’ findings and publications. The rationale is that by the time the studied completed, we will be able to identify key components facilitating early identification of individuals with a higher risk of developing MCI or AD. We will then validate the finding using a domestic dataset when the data is ready for analysis. We will test our hypothesis and attain our objective via the following specific aims: 1. Subtyping and classification of neurodegenerative diseases based on the ADNI participants' multi-omics profiles. To accomplish this, we will leverage current samples provided by ADNI to build up omics profiles for each participant. As a result, we may classify samples into different subtypes and identify suitable, individualized interventions for patients belonging to different risk groups. Working hypothesis: the possibility for healthy people to turn into MCI or AD yields to multiple factors. Multi-omics profiling makes it possible to study the underlying mechanism to predict the possibility for symptoms or disease to occur. 2. Prediction of biomarkers for prediction of disease onset and progression. For this, we can study biomarkers provided from participants’ radioactive images, genetic samples, and biospecimens. Together with the clinical information, we can build up a cross-scale, multi-model, and multi-omics prediction model. Working hypothesis: We can employ biomarkers as molecular footprints of the biological pathway that provides the flow of information, thereby elucidating the underlying mechanism of the onset and progression (in 4~5 years) of MCI and AD. The model is an integrated analytical method that offers wider opportunities to identify reliable causal biomarkers based on the data from multiple molecular events and to provide dependable predictions of the end-point events. |
| Additional Investigators |
