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Approved Research

Vitamin D and neurocognitive dysfunction: new insights from environmental and genetic factors

Principal Investigator: Dr David Llewellyn
Approved Research ID: 9462
Approval date: August 26th 2016

Lay summary

1a: We aim to better understand the role of vitamin D and associated environmental and genetic factors in the development of stroke and dementia. We will address the following key research questions:

1) Are geographic location and sunlight exposure linked to stroke and dementia?

2) Are vitamin D-related genetic variants linked to stroke and dementia?

3) Are any observed associations explained by differences in circulating vitamin D levels?

4) Do genetic variants modify the relationship between circulating vitamin D levels and stroke and dementia?

1b: Our research directly addresses the aim of UK Biobank to improve the prevention, diagnosis and treatment of serious and life-threatening illnesses – including stroke and dementia. Our research will take advantage of the wealth of UK Biobank data available to provide important new insights to elucidate the role may vitamin D play in the maintenance of ‘brain health’. Low vitamin D levels are common in the general population in the UK and further afield, and the recently established associations with stroke and dementia are therefore a significant public health concern.

1c: Using information from the UK Biobank and linked databases we will first investigate whether people who live further north and are exposed to lower levels of sunlight have a higher risk of stroke and dementia. We will then investigate whether genetic markers linked to vitamin D modify the risk of stroke and dementia. Lastly we will investigate the possibility that these environmental and genetic factors increase the risk of stroke and dementia by reducing vitamin D levels found in the bloodstream.

1d: We require access to data from the full cohort. This will include follow-up data relevant to stroke and dementia (in subsets as appropriate) as this becomes available.

 

Project extension:

“We would like to extend our project 9462 by incorporating multiple risk factors in addition to vitamin D.  We intend to construct polygenic risk scores for each of these risk factors in order to facilitate Mendelian randomization studies.  We also intend to construct a mathematical dynamic model of these risk factors and possible confounders in order to develop a system to enhance future neurocognitive dysfunction prevention trials.

We would like to focus on the following risk factors in addition to vitamin D: depression, hypertension, physical inactivity, diabetes, obesity, hyperlipidemia, smoking, coronary heart disease, renal dysfunction, diet, cognitive inactivity.  They have been identified as key modifiable dementia risk factors or highlighted for further research by a recent systematic review and Delphi consensus study by Deckers et al. (2015).”

Project extension 2018:

Original scope

 

We aim to better understand the role of vitamin D and associated environmental and genetic factors in the development of stroke and dementia. We will address the following key research questions:

1)            Are geographic location and sunlight exposure linked to stroke and dementia?

2)            Are vitamin D-related genetic variants linked to stroke and dementia?

3)            Are any observed associations explained by differences in circulating vitamin D levels?

4)            Do genetic variants modify the relationship between circulating vitamin D levels and stroke and dementia?

 

New scope

 

We aim to better understand the role of environmental and genetic factors in the development of stroke and dementia. We will address the following key research questions:

1)            Which geographic, environmental and lifestyle factors are linked to stroke and dementia?

2)            Which genetic variants are linked to stroke and dementia?

3)            How do these genetic and environmental risk factors interact with each other?

4)            How can these risk factors be best understood using contemporary machine learning approaches?

5)    How can we use these predictive models to inform evidence-based personalized