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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.

Validating a model of cognitive decline and time to first diagnosis of mild cognitive impairment or dementia due to Alzheimer's disease

Principal Investigator: John Gallacher, University of Oxford

The ROADMAP 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 and their carers. The first objective of the study is to validate a simple natural disease progression model in people with Alzheimer's disease.

It additionally supports a pilot exercise of data extraction, harmonisation, integration and analysis. Results from this exercise will be used to establish a set of procedures for repeated access to different types of data for the validation of different disease models.

Prevail Parkinson's Disease natural history

Principal Investigator: Herve Rhinn, Prevail Therapeutics

Prevail Therapeutics is a biotech company that is developing gene-therapy for brain disorders such as Parkinson's disease. The research team are using the Data Portal to understand in more detail the natural history of the disease in terms of its genetics in order to: identify clinical endpoints and biomarkers that evolve with disease course in the most predictable fashion in longitudinal studies; define sub-populations of interest for closer study, and estimate the effect of changes to appropriately design clinical studies.

Longitudinal relationships between mental health and cognitive change

Principal Investigator: Sarah Bauermeister, University of Oxford

We know from previous studies that poor mental health can have a harmful effect on cognitive function in old age. In this study we are using the existing mental health and cognitive data in five cohorts to examine anxiety, depression and stress. We will explore the association of these disorders with cognition using latent cognitive and mental health constructs. We will also examine these associations over time.

Assessing individual differences in cognitive change over time

Principal Investigator: Sarah Bauermeister, University of Oxford

Assessing the trajectory of cognitive change over time is of utmost importance in the pursuit of understanding cognitive ageing and dementia. In this study we will individual differences in cognitive change over time using different measures of cognition across cognitive-rich DPUK cohorts with three or more longitudinal waves of data. We will first assess different cognitive tasks as cognitive performance indicators of individual cognitive change and second understand the other associated indicators of cognitive change using latent change score modelling.

DPUK data discovery tools categorisation and coding programme

Principal Investigator: Sarah Bauermeister, University of Oxford

Dementias Platform UK (DPUK) proposes to develop its own data discovery tools so that data can be highly categorised, enabling researchers to more clearly identify cohorts which are applicable for their research proposals. We will summarise detailed information about all the variables, and tabulate this into interactive tools which will be integrated into the existing Data Portal providing cross-cohort search capabilities for researchers. This will be a data variable investigation, not an analytical investigation and no data will be analysed and no summary statistics will be computed.

Investigating the links between early adult lifestyle activities and later-life cognition

Principal Investigator: Andrey Kormilitzin, University of Oxford

The correlation between lifestyle, social and mental activities, and increased brain function in older age is well established. So too is the ‘cognitive reserve’ hypothesis which suggests that educational experiences have an enhancement mechanism on cognitive decline and brain pathology in old age. We propose to conduct multiple individual analyses to investigate associations between lifestyle activities, physical fitness, social engagement, biomedical measures, mental health on later life cognition in the population cohort, ELSA. The aim of this study is to gain a clearer understanding of the predictors of ‘successful’ cognitive ageing and cognitive decline using an existing population cohort with a wide range of variables.

Creating patient segments by examining the early signs of Alzheimer's disease

Principal Investigator: Dominic DeBiaso

We aim to help clinicians better help patients during the early 'prodromal' phases of Alzheimer’s Disease. Behavioural data, biomarkers, cognitive testing, and genetic information are all useful in determining the state of Alzheimer’s that the patient is currently at. We will use the ELSA dataset which contains a significant number of cognitive tests and survey data. Trends discovered in the analysis of the rich ELSA data set will then be used to create patient segments and profiles. We will then begin analysis and deep predictive modelling to construct a model to classify patients that may be susceptible to Alzheimer’s Disease. This model could then be applied to patients that may be at risk for developing Alzheimer’s Disease so that clinicians could take early intervention.