AI can identify a person's biological clock with a blood sample

Researchers have developed a new application of artificial intelligence that can have a high impact on people’s lives. Through blood analysis, a machine learning algorithm can identify a patient’s circadian rhythm, which can leverage a more personalized approach to medical treatments.

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The circadian rhythm it is the cycle that governs our physiological activities and metabolism, more popularly known as the “biological clock”. It is through this that the body knows when it is time to feel hungry and when it is time to wake up and feel sleepy.

Until today, there was no effective method to measure the circadian rhythm of patients accurately, which prevented the use of this information in the personalization of treatments. Doctors could use this information to recommend the best times to eat, rest and take your medication.

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The study has the potential to change this scenario. Published in the Journal of Biological Rhythms, it involved 16 participants, who were followed for two weeks in a sleep laboratory.

Just a little blood can reveal a lot about your biological clock (Image: Karolina Grabowska/Pexels)

The volunteers had blood samples taken regularly to analyze levels of melatonin, the hormone that governs the sleep cycle, and others 4. metabolites, substances produced by chemical reactions in the body.

With the help of machine learning, it was possible to identify which metabolites are associated with the different phases of the circadian rhythm. With the improvement of the algorithm, it was possible to limit the analysis and measure the cycle with only 16 metabolites.

The study concluded that the test is more efficient than existing melatonin measurement methods. The old model required blood sampling every hour throughout the day to measure sleep-inducing hormone levels, with accurate but impractical results.

Researchers make the caveat that the new method has some limitations. It works best with people who have had adequate sleep and controlled eating. In addition, the system would be more efficient if fewer analyzed metabolites were needed to achieve the results.

Source: Massive Science

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