New state-of-the-art technology that uses of a form of artificial intelligence is able to estimate the internal body clock. Researchers in the United Kingdom say this breakthrough could potentially pave the way for personalized sleeping and meal plans.
This innovative method uses machine learning — a branch of AI — to predict body clock timing. It analyzes metabolites in blood to predict the timing of an individual’s internal circadian rhythm, a system regulating our 24-hour sleep/wake cycles. Understanding these cycles could help scientists determine optimal bedtimes and durations for every person.
“Smart devices and wearables offer helpful guidance on sleep patterns – but our research opens the way to truly personalized sleep and meal plans, aligned to our personal biology, with the potential to optimize health and reduce the risks of serious illness associated with poor sleep and mistimed eating,” says study co-author Professor Debra Skene from the University of Surrey in a media release.
Studying body rhythms
Prior to this breakthrough, the conventional approach to determine the body’s rhythms involved measuring the timing of natural melatonin production, known as dim light melatonin onset (DLMO).
“We are excited but cautious about our new approach to predicting DLMO. It is more convenient and requires less sampling than the tools currently available. While our approach needs to be validated in different populations, it could pave the way to optimize treatments for circadian rhythm sleep disorders and injury recovery,” adds Prof. Skene.
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The research team collected a series of blood samples from 24 healthy participants, consisting of 12 men and 12 women. All participants maintained regular sleep schedules for seven days before testing. By measuring over 130 metabolite rhythms, the team was able to predict each person’s specific circadian timing.
“Our results could help to develop an affordable way to estimate our own circadian rhythms that will optimize the timing of behaviors, diagnostic sampling, and treatment,” emphasizes study co-author Professor Roelof Hut from the University of Groningen.