The Sleep Research Centre at the University of Surrey in England had 36 adults skip a night of sleep. Then the researchers, led by Derk-Jan Dijk, PhD, took blood samples and measured changes in the expression levels of thousands of genes.
A machine learning algorithm identified a subset of 68 genes and with 92% accuracy could detect whether a sample was from a sleep-deprived or well-rested individual.
This discovery paves the way for a future test that will be able to assess if a driver was sleep deprived. Previous research in this area from the AAA Foundation for Traffic Safety has shown that drivers who get just one to two hours less than the recommended daily allowance in a 24-hour period nearly double their risk for a car crash.
Emma Laing, BSc, MSc, PhD, PGCAP, senior lecturer in Bioinformatics at the University of Surrey, says in a release, “We all know that insufficient sleep poses a significant risk to our physical and mental health, particularly over a period of time. However, it is difficult to independently assess how much sleep a person has had, making it difficult for the police to know if drivers were fit to drive, or for employers to know if staff are fit for work.”
Simon Archer, BSc, PhD, FRSB, professor of Molecular Biology of Sleep at the University of Surrey, says, “Identifying these biomarkers is the first step to developing a test which can accurately calculate how much sleep an individual has had. The very existence of such biomarkers in the blood after only a period of 24-hour wakefulness shows the physiological impact a lack of sleep can have on our body.”
Dijk, director of the Surrey Sleep Research Centre at the University of Surrey, says, “This is a test for acute total sleep loss; the next step is to identify biomarkers for chronic insufficient sleep, which we know to be associated with adverse health outcomes.”
The findings are published in SLEEP.