Predicting dementia outcomes using simple, non-invasive assessments: a prospective population-based study
Award Number
MR/P001823/1Award Type
FellowshipStatus / Stage
ActiveDates
1 August 2016 -31 July 2019
Duration (calculated)
02 years 11 monthsFunder(s)
MRC (UKRI)Funding Amount
£207,015.76Funder/Grant study page
MRC UKRIContracted Centre
University of EdinburghContracted Centre Webpage
Principal Investigator
Timothy WilkinsonPI Contact
tdw13@cam.ac.ukPI ORCID
0000-0001-8885-1288WHO Catergories
Development of clinical assessment of cognition and functionHigh quality epidemiological data
Tools and methodologies for interventions
Disease Type
Alzheimer's Disease (AD)Dementia (Unspecified)
Vascular Dementia (VD)
CPEC Review Info
| Reference ID | 259 |
|---|---|
| Researcher | Reside Team |
| Published | 12/06/2023 |
Data
| Award Number | MR/P001823/1 |
|---|---|
| Status / Stage | Active |
| Start Date | 20160801 |
| End Date | 20190731 |
| Duration (calculated) | 02 years 11 months |
| Funder/Grant study page | MRC UKRI |
| Contracted Centre | University of Edinburgh |
| Contracted Centre Webpage | |
| Funding Amount | £207,015.76 |
Abstract
I will obtain the UKB baseline data for all 503,000 participants as well as coded data for the entire cohort from linked health datasets (hospital admissions, death registrations and primary care). I will perform univariate logistic regression to identify the variables that most strongly predict dementia over a 5-10 year period and then use multivariate logistic regression to combine independently predictive variables to create the prediction models. I will investigate the incremental value of the addition of the cognitive testing data in “high risk” participants. Power calculation: It is generally accepted that 10-20 outcomes are required per variable in a multivariate logistic regression analysis, meaning that 100-200 cases would be sufficient for a 10-variable model. Our scoping work has identified a predicted 4000 all-cause dementia cases by 2017. Model validation: I will comprehensively validate the models using surplus UKB outcomes (internal) and using data from Generation Scotland, CPRD and SAIL databases (external). Outputs: My research will fill a key gap in the pathway to development of better interventions for dementia prevention and treatment, and will equip me with research skills that will provide the foundations of an ongoing clinical academic career in dementia research.
Aims
To develop a model that predicts the risk of developing dementia over a 5-10 year period in an asymptomatic population using simple, readily available, non-invasive measures and brief, touchscreen cognitive assessments Objectives: 1. Obtain and thoroughly explore the UK Biobank (UKB) baseline and follow up data 2. Identify the variables that most strongly predict incident dementia diagnosis (all-cause dementia, Alzheimer’s disease and vascular dementia) 3. Create multivariate risk prediction models by combining the independently predictive variables 4. Validate the model using surplus UKB outcomes and data from external sources