Summary: New machine-learning method predicts the body clock, helping to improve sleep and health. The technology offers personalized sleep and meal plans aligned to our personal biology, reducing risks of illness. The approach uses blood samples to predict circadian timing and offers an easy way to estimate our own circadian rhythms.
Source: University of Surrey
A new machine-learning method could help us gauge the time of our internal body clock, helping us all make better health decisions, including when and how long to sleep.
The research, which has been conducted by the University of Surrey and the University of Groningen, used a machine learning programme to analyse metabolites in blood to predict the time of our internal circadian timing system.
To date the standard method to determine the timing of the circadian system is to measure the timing of our natural melatonin rhythm, specifically when we start producing melatonin, known as dim light melatonin onset (DLMO).
Professor Debra Skene, co-author of the study from the University of Surrey, said:
“After taking two blood samples from our participants, our method was able to predict the DLMO of individuals with an accuracy comparable or better than previous, more intrusive estimation methods.”
The research team collected a time-series of blood samples from 24 individuals – 12 men and 12 women. All participants were healthy, did not smoke and had regular sleeping schedules seven days before they visited the University clinical research facility. The research team then measured over 130 metabolite rhythms using a targeted metabolomics approach. These metabolite data were then used in a machine learning programme to predict circadian timing.
Professor Skene continued:
“We are excited but cautious about our new approach to predicting DLMO – 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.”
Professor Roelof Hut, co-author of the study from University of Groningen, said:
“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.”
About this machine learning and circadian rhythm research news
Author: Dalitso Njolinjo
Source: University of Surrey
Contact: Dalitso Njolinjo – University of Surrey
Image: The image is in the public domain
Original Research: Closed access.
“Machine learning estimation of human body time using metabolomic profiling” by Debra Skene et al. PNAS
Abstract
Machine learning estimation of human body time using metabolomic profiling
Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders.
Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions.
We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods.
Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.
