Genetic changes associated with physical activity emerge thanks to machine learning pioneers

In one of the most detailed projects of its kind, the scientists studied the activity of 91,105 UK Biobank participants who had previously worn an activity monitor on their wrist for a week.

The scientists taught machines to automatically identify active and sedentary life from the huge amounts of activity monitor data.

They then combined this data with UK Biobank genetic information to reveal 14 genetic regions related to activity, seven new to science, they report in Nature Communications. The work paves the way for better understanding of sleep, physical activity, and their health consequences.

Physical inactivity is a global public health threat and is associated with a range of common diseases including obesity, diabetes and heart disease. Changes in sleep duration are linked to heart and metabolic diseases and psychiatric disorders. Analysis of the human genetic data showed for the first time that increased physical activity causally lowers blood pressure.

The genetic analysis also showed overlap with neurodegenerative diseases, mental health wellbeing and brain structure, showing an important role for the central nervous system with respect to physical activity and sleep.

The study was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, and the British Heart Foundation Centre of Research Excellence at Oxford. The study was collaborative, and performed by a multidisciplinary team of scientists from a diverse set of fields, including machine learning, genetics, statistics, and epidemiology.

To help identify the types of activity recorded on the wrist monitors, the researchers turned to 200 volunteers who wore a special camera that captured their activity every 20 seconds over two days. The images were compared with the activity data captured by the wrist worn monitors, providing a guide to interpreting the data.