New machine learning methods can help measure time in our body clocks, potentially helping us all make better health decisions, such as when to sleep and how long to sleep.
The study, conducted by the University of Surrey and the University of Groningen, used a machine learning program to analyze metabolites in the blood to predict times in the body’s circadian timing system.
To date, the standard method of determining the timing of the circadian system is to measure the timing of our natural melatonin rhythms, specifically the timing of initiation of melatonin production, known as twilight melatonin expression (DLMO). .
Co-author of the study, Professor Debra Skeen of the University of Surrey, said:
“After taking two blood samples from the participants, our method was able to predict an individual’s DLMO with accuracy equal to or better than previous, more onerous estimation methods.”
The research team collected time series blood samples from 24 individuals, 12 males and 12 females. All participants were healthy, non-smokers, and had a regular sleep schedule seven days prior to visiting the university’s clinical research facility. The research team then used a targeted metabolomics approach to measure the rhythms of over 130 metabolites. These metabolite data were used in a machine learning program to predict circadian timing.
Professor Skene continued:
“We are excited about our new approach to predicting DLMO, but we are cautious because it is more convenient than currently available tools and requires less sampling. This may pave the way for optimizing the treatment of patients with circadian rhythm sleep disorders and injury recovery.
“Smart devices and wearables provide valuable guidance on sleep patterns, but our research paves the way for truly personalized sleep and meal planning to suit your individual biology to optimize your health. and has the potential to reduce the risk of serious illnesses related to lack of sleep.” ”
Co-author of the study, Professor Roelof Hatt of the University of Groningen, said:
“Our results may help develop affordable methods of estimating our own circadian rhythms that optimize behavior, diagnostic sampling, and timing of treatments.”
