| Title: | Dementia use case for 'iASiS' platform |
| Lead Institution: | St George's, University of London |
| Principal investigator: | Professor Peter Garrard |
Application 35916
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About
This work will contribute to the creation of a computational platform (iASiS) for integrating various data types (e.g. demographic, cognitive, imaging, genotyping) in dementia patients. The questions to be answered include: 1) how many patients who have been diagnosed with Alzheimer's disease have (genetic) maternal/paternal family history and how many don't? 2) Are there comorbidities that interact with the presumed pathology or the effects of family history and genetic determinants? 3) Are there any genetic markers of clinical variants (e.g. early onset; PCA; amnestic; MCI; psychiatric; language)? 4) Are there variant-specific prodromal syndromes (e.g. depression; anxiety)? The results of this study may lead to the identification of new biomarkers and (via the iASiS platform) interactions among known biomarkers, which will help improve the prevention, diagnosis and treatment of dementias and promotion of health throughout society. The potential of identifying and integrating new biomarkers will also enrich the quality and quantity of information stored in UK Biobank and support future research. 1) Identification and clinical/radiological characterisation of dementia patients (Alzheimer's Disease, Lewy Body Dementia, Frontotemporal Dementia) and age/gender matched controls with comorbidities, such as depression. 2) Stratification according to the type of dementia (primary) and the type of comorbidity (secondary). 3) When AD patients have coincident or premorbid depression, incidences of chromosome 3 p25-26 variant (linked to depression), and one or more APOE4 (linked to Alzheimer's) alleles. 4) Presence of maternal/paternal history of dementia. 5) Comparisons of the proportions of maternal, paternal or no family history in each group. A subset of the full cohort consisting of people diagnosed with any form of dementia will be sufficient.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12627 | Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease | Magdalena Arnal Segura (+10) | 2022 | Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring |
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