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These are the studies currently being facilitated by the DPUK Data Portal.

Predictors of successful ageing in European super-agers (90+): a cross-platform investigation of research and real-world evidence

Principal investigator: Willemijn Jansen, Maastricht University

Successful brain function in the very old (90+ years) may inform understanding of maintaining 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 biomarker 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 epidemiological research and to enhance understanding of the predictors of healthy cognition in super-old age (90+).

Developing risk stratification models for identifying sub-groups of individuals who are at high risk of short to long term cognitive decline or dementia

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 of having dementia 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 characterised recruitment into clinical trials.

Using longitudinal data from multiple studies 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.

 

Validation of a model to predict MMSE in incident AD dementia cases

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 AD spectrum for better care - ROADMAP evidence initiative – 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 (AD) and their carers. The objective of the study is to validate natural disease progression models in people with AD.

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.

Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients (IASIS)

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 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 have emerged 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 (BMI) and cognitive function: an investigation of its nonlinear association

Principal investigator: Graciela Muniz-Terrera, University of Edinburgh

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 persons aged ≥85 years). Some studies show that in the very old segment of the population, 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.

Deciphering associations of mental health and cognitive function with air pollution and built environment

Principal investigator: Chinmoy Sarkar, University of Hong Kong

Investigating the effects of urban development and pollution on cognition and mental health.

The proposed study aims to contribute to the environmental epidemiology of mental health disorders and cognitive decline. Evidence from the study will inform strategies and policies towards minimising exposures from health-inhibiting urban environments, especially harmful air pollutants. The proposed study will employ UK Biobank mental health, cognitive, air pollution and built environment data (N=500 000).

The impact of this study will be internationally relevant once outcomes can be compared with other urban cities such as China.  

Associations between mortality and access to healthcare services in the UKBUMP database

Principal investigator: Chinmoy Sarkar, University of Hong Kong

Accessibility to healthcare facilities may reduce mortality and chronic disease.

Optimising healthcare access is associated with significant public health benefits. Centralized access to healthcare services have been evidenced to have beneficial effects upon health outcomes including reduction in mortality and chronic disease prevalence. The proposed project aims to test this hypothesis among the UK Biobank participants (N=500 000) employing the healthcare accessibility metrics within the UK Biobank Urban Morphometrics Platform (UKBUMP).

The impact of this research will help optimise healthcare service allocation with an aim to minimise mortality and healthcare expenditure. 

Building a virtual dementia e-cohort using routinely-collected health datasets

Principal investigator: Tim Wilkinson, University of Edinburgh

This study aims to build on the UK’s strong record of collecting and using healthcare data to create a ‘virtual’ cohort to facilitate dementia research.

It will use anonymised primary care, hospital admissions and mortality data from the Welsh SAIL Databank to build a ‘virtual’ dementia e-cohort. These datasets contain complex, detailed, coded healthcare data for the Welsh population, meaning they have great potential for dementia research. The objective is to convert these ‘real-world’, coded data into a series of simplified variables, in order to create a resource with which scientists can conduct studies that provide crucial insights into the risk factors and natural history of dementia.