In a recent finding, researchers from the Universities of Surrey and Groningen utilized a new machine-learning method to predict and manage our internal circadian timing clock. 

Most prevalent method for determining the circadian system timing has been to assess the time of our natural melatonin rhythm, specifically when we begin producing melatonin, known as dim light melatonin onset (DLMO).

The study involved evaluating and analyzing a time series of blood samples from 24 healthy people comprising 12 men and 12 women. All the participants had standard sleeping patterns ad did not smoke for seven days before the start of the study. Researchers first used a focused metabolomics technique to evaluate more than 130 metabolite rhythms, and then these data were employed in a machine-learning program to predict circadian timing.

According to experts, the approach must be validated in several groups before being utilized to optimize therapy for circadian rhythm sleep issues and injury rehabilitation. 

The study thus paves the path for genuinely personalized sleep and nutrition programs that are tailored to our biology, with the potential to improve health and lower the risks of serious illness linked to insufficient sleep and improper diet. The findings contribute to the development of a low-cost approach for measuring human circadian rhythms, which would allow us to better time behaviors, diagnostic samples, and therapy.

(Source: https://www.sciencedaily.com/releases/2023/04/230424162854.htm)