Causal interactions in physical activity and premature cardiometabolic mortality
Approved Research ID: 53710
Approval date: June 12th 2020
Physical activity has been highlighted as a cost-effective strategy for prevention of cardiometabolic diseases. To date policy actions and treatments are based on an assumption of a similar beneficial effect from physical activity on the population. Human disease development is, however, a complex interplay between genetic inheritance, life style and environmental factors. This project investigates how genetics modulate level of physical activity and disease risk. We will also examine if and how physical activity mediates the realization of genetic risk in cardiometabolic diseases. Results can be used in developing targeted and effective lifestyle interventions.
GenActive project aims to generate polygenic risk scores (PRSs) PA and muscle strength using UK Biobank data (including self-reports and objectively measured physical activity). By using PRSs, we will study whether genetic risk for low physical activity (PA) and cardiometabolic diseases overlap or add to each other in individual risk analysis using Finnish cohorts (Finngen). Our second aim is to study if and how PA mediates the realization of genetic risk in morbidity and mortality outcomes using longitudinal twin designs (Finnish twin cohort). With these designs, we aim to examine causal disease and disability pathways in subjects and twin pairs with varying PRSs. The designs ensure that any clustering detected in PRSs or environmental factors/lifestyle is truly reflective of etiological heterogeneity and does not simply reflect different stages in disease evolution or other temporal variation.
We are familiar with the published physical activity and muscle strength GWAS studies from UK biobank data (links below), but expect that we need to do some modifications to the study sample and PA calculations for our needs.
GenActive project investigates associations between physical activity/muscle strength genetics with common diseases and premature mortality. The purpose is to examine causal disease and disability pathways. We are expanding our scope to develop new better measures for physical activity, before we continue to constructing polygenic scores and conducting associations analysis with adverse health outcomes. Novel ways to measure physical activity are:
Exploration of fundamental human activity patterns
It has been speculated that human behaviour follows a Levy-flight pattern-like structure where most bursts of activity are short lasting with very occasional sustained bouts interspersed. This idea has not been explicitly explored and therefore the purpose of the proposed project is to explore how well human activity patterns map onto Levy-flight-like structure and whether an analytical formula can be developed to describe the probability distribution of activity bursts. The results could be used to individualize activity prompting using a wearable devise whereby the structure of the individual's activity pattern is extracted from multiple day recordings and used in targeting activity prompts to help maximize physical activity for health.
Unsupervised day segmentation
Segmentation of the day into activities such as commuting, working, lunch break etc. is a difficult task based on accelerometry recordings because the tasks may involve varying acceleration characteristics between individuals. A similar challenge of breaking a time-series into meaningful segments has been tackled in music research where an automated method of annotating the verse, chorus etc. has been required. The purpose of the proposed project is to apply an unsupervised segmentation algorithm used to segment music pieces onto daily accelerometry signals. A method for automated segmentation of day based on accelerometry would be useful in evaluating human activity behaviour patterns on the population level particularly in datasets that do not include time use diaries. Physical activity research regarding wearable devices has developed during the past years. According to current understanding the variables used before to describe physical activity levels were too general and need to be developed. If we can better measure daily activity, we can next develop new polygenic scores for physical activity and study how physical activity genetics associates with adverse outcomes such as morbidity and premature mortality.