During this unique study 36 participants skipped one night of sleep. During this 40-hour period of sleep deprivation, blood samples were taken and changes in the expression levels of thousands of genes were measured.
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 breakthrough discovery paves the way for a future test which will be able to assess if a driver was sleep deprived. Previous research in this area 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.
Biomarkers for sleep debt status showed little overlap with previously identified biomarkers for circadian phase. Biomarkers for acute and chronic sleep loss also showed little overlap but were associated with common functions related to the cellular stress response, such as heat shock protein activity, the unfolded protein response, protein ubiquitination and endoplasmic reticulum associated protein degradation, and apoptosis. This characteristic response of whole blood to sleep loss can further aid our understanding of how sleep insufficiencies negatively affect health.
The lead author said: "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."
The senior author said: "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."
A blood test for drowsy driving?
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