Genes influence levels of physical activity, according to a study – Life Current – Latest news from Uruguay and the world updated



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The research, developed by the University of Oxford's Big Data Institute, reports, for example, the time we sit down, sleep or move with our genes, in one of the most complete works in this area.

Experts programmed an "automatic learning machine" to differentiate sedentary and active lives (and various intermediate levels) in 200 volunteers who carried a camera and a bracelet for two days, monitoring their activity every 20 seconds.

They then compared this information with that of 91,105 individuals registered in the Biobank UK database who carried the same type of bracelet for a week in prior periods.

"How and why we move does not just depend on genes, but understanding the role they play will help us improve our understanding of the causes and consequences of physical activity," project director Aiden Doherty said in a statement.

Only through studying large amounts of data, he said, will he be able to decipher "the complex genetic foundations" of some of the most basic functions, "like movement, rest or sleep."

The WHO recommends that adults have at least 150 minutes of physical activity per week. Photo: Pixabay
Photo: Pixabay

In addition to detecting 14 related genetic regions, seven of them new, the scientists noted for the first time, thanks to the big data, that "increased physical activity spontaneously reduces blood pressure."

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

Physical inactivity, according to experts, is a threat to global public health, with a broad spectrum of diseases associated with physical inactivity such as obesity, diabetes or cardiovascular problems.

Also changes in sleep are related to heart disease, metabolic and psychiatric disorders.

The study's experts emphasized that the use of the "automatic learning machine" to analyze large amounts of health data is advancing rapidly and this conditions the kind of studies that can be developed.

"We designed machine learning models to teach machines to analyze complex functions such as activity," explained Karl Smith-Byrne, one of the participants in this paper.

"They could help us, for example, determine if inactivity is a cause or consequence of obesity," added Michael Holmes of the British Heart Foundation at Oxford University.

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