Not getting enough sleep costs more than just bad feelings and trouble concentrating.
While it’s well-established that poor sleep quality can lead to a lack of focus and negative moods, regular sleep deprivation can also increase the risk of developing serious medical conditions such as obesity, heart disease, and diabetes, as well as shorten life expectancy.
It’s now clear that getting enough high-quality sleep is crucial for overall health and longevity.
While there are many sleep hacks that people try to improve their sleep, experts suggest that understanding and adjusting our body clock timing can have a significant impact on our sleep quality and overall health.
In a recent study, researchers utilized machine learning to determine the timing of individuals’ internal body clocks, which could help people make informed decisions regarding their sleep schedules and improve their overall health.
The study was done by the University of Surrey and the University of Groningen. They used a machine learning tool to look at molecules in the blood to try to figure out when our internal circadian clock works.
Until now, the widely accepted approach for identifying the timing of our circadian system has been through the measurement of our natural melatonin rhythm.
This involves determining the onset of melatonin production in response to dim light, which is commonly referred to as dim light melatonin onset (DLMO).
“After taking two blood samples from our participants,” adds Professor Debra Skene, “our method was able to predict the DLMO of individuals with an accuracy comparable or better than previous, more intrusive estimation methods.”
The team of researchers gathered a sequence of blood samples from a total of 24 individuals, consisting of 12 men and 12 women.
All participants were in good health, did not smoke, and maintained a consistent sleep schedule for seven days prior to their visit to the University’s clinical research center.
Using a targeted metabolomics method, the team then assessed more than 130 metabolic rhythms. These metabolite measurements were subsequently used in a machine-learning algorithm to forecast circadian timing, yielding promising results.
“We are excited but cautious about our new approach to predicting DLMO,” Professor Skene adds “as 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 optimise treatments for circadian rhythm sleep disorders and injury recovery.
“Smart devices and wearables offer helpful guidance on sleep patterns – but our research opens the way to truly personalised sleep and meal plans, aligned to our personal biology, with the potential to optimise health and reduce the risks of serious illness associated with poor sleep and mistimed eating.”
The findings “could help to develop an affordable way to estimate our own circadian rhythms that will optimize the timing of behaviors, diagnostic sampling, and treatment,” adds Professor Roelof Hut, co-author of the study from the University of Groningen.
Source: 10.1073/pnas.2212685120
Image Credit: Getty
