Principal Investigator: Dr Ville-Petteri Makinen
South Australian Health and Medical Research InstituteTags: 29890, Cognitive-decline, depression, diabetes, genetics, subtypes
A person with depression has diabetes more often than expected by chance, and vice versa. We hypothesize that the two disorders form a vicious cycle that triggers further devastating long-term health consequences. In particular, depression and diabetes stress the brain; this is a high-priority research area due to the soaring prevalence of dementia in ageing populations. We will investigate how depression and diabetes accompany cognitive decline, and how genetics affects the disease co-occurrence. Knowing the genetic interactions is important because it will help us elucidate the complex origins of dementia and reveal new treatment opportunities. Depression, diabetes and dementia affect hundreds of millions of people worldwide. Accurate characterization of the genetic and environmental risk factors is essential for the continued progress in the development and deployment of treatments to those who need them the most. This is fully compatible with the UK Biobank mission `to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society`. We will define subgroups of vulnerable individuals based on their clinical and biochemical profiles (better focusing of public health resources). We will also integrate genetics and genomics resources to identify potential targets for therapies. The project comprises two parts. First, we have developed a statistical method that allows us to analyse multiple confounding factors simultaneously, and to divide a population into smaller more ‘environmentally’ homogeneous subgroups. This makes it easier to determine genetic effects on cognition that are interacting with the underlying circumstances. Second, we will compare the genetics of cognitive decline within the subgroups against known genetic variants for psychiatric disorders and metabolic diseases (i.e. diabetes). This will elucidate shared susceptibility genes between the three diseases, and provide new context-specific information on the synergistic risk factors to cognitive decline. We are requesting access to data related to mental health, obesity, diabetes, basic laboratory tests and cognitive performance in the entire cohort (n = 500,000). In addition, we are requesting access to the available neuroimaging data for the cohort (projected n > 100,000). For genetic analyses, we aim to use the genome-wide genotypes of all individuals (n = 500,000).
In order to achieve a higher accuracy in phenotyping homogeneous subgroups described in the current scope of research, we consider it is relevant to include also the time varying behavioral and physiological stress patterns along with the environmental factors of the UK Biobank participants in the self-organizing map (SOM) clustering framework. We are particularly interested to identify subgroups with distinctive temporal pattern features in the 7-day raw acceleration signals and heart rate variability (HRV) (e.g. variability in circadian rhythm, span of moderate-to-vigorous intensity exercise throughout the week, type of daily activity patterns). The temporal patterns and variability in activity, assessed based the 7-day raw acceleration signals, have been shown to vary with mental health measures. Moreover, heart rate variability (HRV), reflecting the activity of the autonomous nervous system, varies by the severity of psychological distress. The addition of these variables will thus help us identify and include features for subgroups. We will assess HRV-based chronic stress levels from the raw ECG signals.
Last updated Oct 22, 2018