Tuesday , January 19 2021

Genes influence levels of physical activity, according to research – Vida in practice – Latest news from Uruguay and the world has been updated



The study, developed by the University of Oxford's large data institute, tells, for example, the time when we sit, sleep or move with our genes, in one of the most complete creations in this field.

The experts designed the Automatic Learning Machine to differentiate between active and active lives (and some intermediate levels) for 200 volunteers carrying a camera and bracelet for 2 days, monitoring their activity every 20 seconds.

They then compared this information with that of 91,105 people listed in the UK Ubank database carrying the same type of wristband for a week in previous periods.

"How and why we're moving depends not only on the genes, but understanding the role they play will help us improve our knowledge of the causes and effects of physical activity," said project manager Aiden Doherty.

Only by studying large amounts of data can he decipher the "complex genetic elements" of some of the most basic functions, such as motion, rest, or sleep.

Those who recommend adults have at least 150 minutes of exercise per week. Photo: Pixabay
Photo: Pixabay

In addition to identifying 14 associated genetic areas, seven of which are new, the scientists first observed, thanks to large data, that "the increase in physical activity spontaneously reduces blood pressure."

In addition, the genetic analysis revealed the existence of a "superposition" between neurodegenerative diseases, mental health and brain structure, which illustrates the important role of the central nervous system in physical activity and sleep.

Lack of physical activity, according to experts, poses a threat to global public health, with a variety of diseases associated with sedentary lifestyle, such as obesity, diabetes or cardiovascular problems.

Sleep changes are also linked to heart disease and metabolism and psychiatric disorders.

Research experts emphasized that the use of the "automatic learning machine" for the analysis of large quantities of health data is progressing quickly and is suitable for the type of research that can develop.

"We have designed models for machine learning to teach machines how to analyze complex functions, such as activity," explained Carl Smith, Biran, one of the participants in this work.

"They can help us, for example, determine whether inactivity is a cause or effect of obesity," added Michael Holmes of the University of the Heart of the University of Oxford.


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