These are studies which are now taking place in the Data Portal analysis environment.
Predictors of successful ageing in European super-agers
Principal investigator: Willemijn Jansen, Maastricht University
Successful brain function in the very old (90+ years) may inform understanding of how to maintain healthy brain function across the lifespan.
The predictors of healthy cognition in older age is of utmost socioeconomic and medical importance. This European collaborative project will test the relationships between demographic, behavioural, physical, clinical, imaging and biological variables, with healthy brain function (ie absence of dementia), using Dementias Platform UK (DPUK) and EMIF (European Medical Information Framework) cohorts who have participants aged 90 + years.
The impact of this work is two-fold: a derived 90+ dataset for continued population research and to enhance understanding of the predictors of healthy cognition in super-old age.
Identifying sub-groups of individuals who are at risk of cognitive decline
Principal investigator: Brian Tom, University of Cambridge
Selective recruitment of high-risk participants into clinical trials for dementia research is both cost effective and highly informative.
Identifying subgroups of individuals who are at high risk of cognitive decline in the short to medium term or is important for informing recruitment into clinical trials and clinical decision-making. As part of the DPUK project, this study aims to develop and apply state-of-the-art risk stratification methods to identify such subgroups in DPUK cohorts.
The impact of this work is important for informing highly specific recruitment into clinical trials.
Using longitudinal data for predicting Alzheimer’s disease
Principal investigator: Terry Lyons, University of Oxford
Understanding the predictors of Alzheimer’s disease will aid early diagnosis.
This study makes use of the DPUK cross-cohort population studies to better predict whether a specified individual will develop Alzheimer’s disease within a given future period. The primary goal is to use machine learning for data analysis to support the efficient design of clinical trials. The prediction will be in the form of a score which represents a likelihood of a positive diagnosis within that period.
The impact of this research is to find patterns that are characteristic of the disease process and use them for prediction and insight into the causes of Alzheimer’s disease.
Validating disease progression models using real world data
Principal investigator: John Gallacher, University of Oxford
Disease models which use ‘real world data’ from existing population cohorts can be used to improve efficiency of drug treatment strategies.
The 'Real world Outcomes across the Alzheimer's Disease spectrum for better care initiative' – ROADMAP – was created to facilitate the open collaboration among a variety of stakeholders to efficiently use real world evidence (RWE) for the benefit of patients with Alzheimer’s disease and their carers. The objective of the study is to validate natural disease progression models in people with Alzheimer's disease.
The impact of this research is to build a disease progression model that can be used for the health economic evaluation of the efficiency of different treatment pathways.
Big data for precision medicine
Principal investigator: Eleftherios Samaras, St George’s University of London
Unified sources of data from cohort studies will aid diagnosis and treatment of dementia.
This study – Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients (IASIS) – proposes to design a unified conceptual schema to represent diverse sources of available data. The ability to integrate large volumes of clinical and laboratory data may provide evidence for potentially important associations that emerge from cohort studies.
This project provides a major scientific opportunity to identify or confirm associations with dementia types and responses to dementia treatment, as well as dementia prognoses and outcomes.
Body Mass Index and cognitive function
Principal investigator: Graciela Muniz-Terrera, University of Edinburgh
Body Mass Index (BMI) may be used to aid dementia diagnosis.
Research suggests higher BMI in midlife is associated with lower cognitive function in later life. However, this association is not found to be consistently observed across age groups; with mixed findings reported when BMI has been assessed in later midlife and in the very-old age group (defined as people older than 85 years). Some studies show that in oldest groups, those who are underweight and/or normal weight are at higher risk of cognitive impairment compared to those with a higher BMI.
The impact of this research will be important for informing the development of accurate protocols for calculating dementia risk in different age groups.