New Machine Learning Method Predicts Body Clock Timings to Improve Sleep and Health Decisions

New machine learning techniques can help time our body clocks, helping us all make better health decisions, such as when and how long to sleep.

This 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.The research is published in the journal Proceedings of the National Academy of Sciences.

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 to initiate melatonin production, known as the dim melatonin onset (DLMO).

Professor Debra Skene, co-author of the study at the University of Surrey, said, “After taking two blood samples from the participants, our method was able to demonstrate similar or better accuracy than previous more intrusive estimates. We were able to predict the DLMO of the method.”

The research team collected time-series blood samples from 24 people (12 men and 12 women). All participants were healthy, did not smoke, and slept regularly for 7 days prior to his visit to the university’s clinical research facility. The research team then used a targeted metabolomics approach to measure more than 130 metabolic rhythms. This metabolite data was then used in a machine learning program to predict circadian timing.

Professor Skene said: Optimizes treatment of circadian rhythm sleep disorders and injury recovery.

“While smart devices and wearables provide valuable guidance on sleep patterns, our research has the potential to help individuals optimize their health and reduce their risk of serious illnesses associated with poor sleep. It paves the way for a truly personalized sleep and meal plan that’s aligned with the biology of your child: Did you eat at the wrong time?”

Professor Roelof Hut, co-author of the study at the University of Groningen, said, “Our results demonstrate an affordable approach to estimating our own circadian rhythms, optimizing behavior, diagnostic sampling, and timing of treatment.” It may help develop a method.”