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: | Marco Fernandes |
| Institution: | University of Oxford |
| Department: | The Wellcome Centre for Human Genetics |
| Country: | |
| Proposed Analysis: | The past decade has seen major progress in unravelling the genes, protein and metabolic pathways implicated in neurodegenerative disorders (NDD) including Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). The gap in our knowledge is how these pathways translate in progression of disease, both in the preclinical and in the clinical stage. AD, PD, and ALS are heterogeneous disorders. The interplay between genes, proteins, metabolites and clinical, pathological, radiological, and epidemiological characteristics is far from understood. Dissecting each of these NDD into defined clinical and molecular subgroups will empower future trials and allow more precise treatment/prevention. We plan to integrate multiple data (e.g. UKB, ADNI, AMPAD, AMPPD, ALS challenges, IMI programs) into one platform and our aims are to develop: • Novel, multi-dimensional, instruments by which patients with NDD including AD, PD or ALS can be segmented pre-symptomatically or at an early stage of symptomatic disease. • Predictive biomarkers of progression of NDD in clinically relevant endophenotypes. • Biomarkers of NDD target engagement that allow patient stratification. Cellular and animal models have led to major advances in our understanding of the neurobiology of NDD. These advances have been paralleled by unprecedented investments (public – private) in human research of NDD using -omics, CSF /plasma biomarkers and imaging (e.g., UKB, ADNI, AMPAD, AMPPD, ALS challenges and the various IMI programs in Europe). However, there remains a large and fundamental bi-directional knowledge gap: · From bedside to bench connecting individual patients to biological mechanisms, so that their underlying pathology can be targeted with specificity and sensitivity. · From bench to bedside linking neurobiology to the different phenotypic presentations in patients with AD, PD and ALS. Basic research has brought to surface pathways of interest but we do not know of the predictive value for disease progression in humans. Our aim is to integrate existing clinical, biomarker, omics and imaging data across basic and human research. We will build upon the successes of biomarkers for NDD (e.g., NFL, p-Tau, beta-amyloid and GFAP), neuro imaging, genomics, proteomics and metabolomics and aim to develop novel instruments to dissect the complexity of NDD in the pre-symptomatic, early clinical and clinical stage using state-of-the-art multi-omics data integration. The ultimate goal is to improve tracking of disease progression and predict molecular subgroups that are clinically relevant. This requires big data analysis and follow-up studies of pre-symptomatic and symptomatic patients in an early stage of disease. To achieve these goals, we aim to integrate existing clinical and functional data from basic and human research across multiple data sets (see A4) with that of UK Biobank. A4. A brief description of the method(s) to be used (up to 5000 characters or 300 words): We will use state of the art cross-omics analysis (classical statistics and deep learning) to connect the dots, across: • Clinical, Radiological and epidemiological data of various data banks: UK Biobank, AMPAD, ADNI, IMI, EADB/IGAP/ADSP, FinnGen • -Omics: genetics, epigenetics, transcriptomics, proteomics, metabolomics and microbiome – directly measured or imputed data based on the genome. The analysis will commence with a comprehensive analysis of pathways informed by genetic association studies. We will evaluate the evidence across studies for genes, metabolites and proteins in the pathway, thus expanding the pathway. Where possible, we will impute –omics data into UK Biobank based on the genome. We will use classical statistics (Cox and linear regression) and machine learning (Random Forrest, Gradient Boosting; Neural Networks) to impute and model which pathways link these genes to AD, PD, and ALS progression and subgroups using the data of the UK Biobank and target iPSC data from genetics. We recognize ALS is a rare disease and that the power for the analysis of this subgroup is low in UK Biobank. However, we are interested in the progression of patients with dementia scoring high for the polygenic risk score for ALS. |
| Additional Investigators | |
| Investigator's Name: | Shahzad Ahmad |
| Proposed Analysis: | The past decade has seen major progress in unravelling the genes, protein and metabolic pathways implicated in neurodegenerative disorders (NDD) including Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). The gap in our knowledge is how these pathways translate in progression of disease, both in the preclinical and in the clinical stage. AD, PD, and ALS are heterogeneous disorders. The interplay between genes, proteins, metabolites and clinical, pathological, radiological, and epidemiological characteristics is far from understood. Dissecting each of these NDD into defined clinical and molecular subgroups will empower future trials and allow more precise treatment/prevention. We plan to integrate multiple data (e.g. UKB, ADNI, AMPAD, AMPPD, ALS challenges, IMI programs) into one platform and our aims are to develop: • Novel, multi-dimensional, instruments by which patients with NDD including AD, PD or ALS can be segmented pre-symptomatically or at an early stage of symptomatic disease. • Predictive biomarkers of progression of NDD in clinically relevant endophenotypes. • Biomarkers of NDD target engagement that allow patient stratification. Cellular and animal models have led to major advances in our understanding of the neurobiology of NDD. These advances have been paralleled by unprecedented investments (public – private) in human research of NDD using -omics, CSF /plasma biomarkers and imaging (e.g., UKB, ADNI, AMPAD, AMPPD, ALS challenges and the various IMI programs in Europe). However, there remains a large and fundamental bi-directional knowledge gap: · From bedside to bench connecting individual patients to biological mechanisms, so that their underlying pathology can be targeted with specificity and sensitivity. · From bench to bedside linking neurobiology to the different phenotypic presentations in patients with AD, PD and ALS. Basic research has brought to surface pathways of interest but we do not know of the predictive value for disease progression in humans. Our aim is to integrate existing clinical, biomarker, omics and imaging data across basic and human research. We will build upon the successes of biomarkers for NDD (e.g., NFL, p-Tau, beta-amyloid and GFAP), neuro imaging, genomics, proteomics and metabolomics and aim to develop novel instruments to dissect the complexity of NDD in the pre-symptomatic, early clinical and clinical stage using state-of-the-art multi-omics data integration. The ultimate goal is to improve tracking of disease progression and predict molecular subgroups that are clinically relevant. This requires big data analysis and follow-up studies of pre-symptomatic and symptomatic patients in an early stage of disease. To achieve these goals, we aim to integrate existing clinical and functional data from basic and human research across multiple data sets (see A4) with that of UK Biobank. A4. Methods: We will use state of the art cross-omics analysis (classical statistics and deep learning) to connect the dots, across: • Clinical, Radiological and epidemiological data of various data banks: UK Biobank, AMPAD, ADNI, IMI, EADB/IGAP/ADSP, FinnGen • -Omics: genetics, epigenetics, transcriptomics, proteomics, metabolomics and microbiome – directly measured or imputed data based on the genome. The analysis will commence with a comprehensive analysis of pathways informed by genetic association studies. We will evaluate the evidence across studies for genes, metabolites and proteins in the pathway, thus expanding the pathway. Where possible, we will impute –omics data into UK Biobank based on the genome. We will use classical statistics (Cox and linear regression) and machine learning (Random Forrest, Gradient Boosting; Neural Networks) to impute and model which pathways link these genes to AD, PD, and ALS progression and subgroups using the data of the UK Biobank and target iPSC data from genetics. We recognize ALS is a rare disease and that the power for the analysis of this subgroup is low in UK Biobank. However, we are interested in the progression of patients with dementia scoring high for the polygenic risk score for ALS. |
