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Scientists at the University of Surrey's Sleep Research Center said they use a machine learning algorithm to identify a subset of 68 genes to detect 92% accuracy if a blood sample came from a patient deprived of sleep.
The study ("Identification and Validation of Blood-mRNA Biomarkers for Acute Chronic Inadequate Sleep in Humans: an Automatic Learning Approach") appears in the journal Sleep.
"The lack of acute and chronic sleep is associated with adverse health effects and risks of accidents. There is therefore a need for biomarkers to monitor the status of sleep debt. None are currently available. Elastic-net and Ridge regression were applied to whole and pre-filtered transcriptome samples taken from healthy young adults during total sleep deprivation and after one week of insufficient (<6 h) or sufficient ( ~ 8.6 h). biomarkers of mRNA from the state of sleep debt. The size of the identified panels ranged from 9–74 biomarkers. Panel performance, assessed by cross-validation and independent validation, was weak. Using inter-subject evaluations based on a blood sample, the accuracy of the classification 'Acute sleep loss" was 92%, but only 57% for the classification 'Chronic sleep insufficiency". Reasonable precision to classify 'chronic sleep insufficiency" could only be achieved by an intra-subject comparison of blood samples. The biomarkers of the sleep debt status showed little overlap with previously identified biomarkers for the circadian phase, "the researchers write.
"Biomarkers of acute and chronic sleep losses did not overlap much but were associated with common functions related to cellular stress response: heat shock protein activity, unfolded protein response, ubiquitination of proteins, and endoplasmic reticulum associated with this response. Characteristics of whole blood to sleep loss can help to better understand how sleep deficits negatively affect health. The subsequent development of these new biomarkers for research and clinical practice requires validation in other protocols and age groups.
The discovery paves the way for a future test that will assess whether a driver was deprived of sleep, according to the team. Earlier research in this area conducted by the AAA Foundation for Road Safety has shown that drivers who have only one to two hours less than the recommended daily allowance in a 24-hour period almost double the risk of getting on the road. ;a car accident.
Dr. Emma Laing, Senior Lecturer in Bioinformatics, said, "We all know that lack of sleep is a significant risk to our physical and mental health, especially over a period of time. However, it is difficult to independently assess a person had, making it difficult for the police to know if drivers were fit to drive or for employers to know if the staff is fit to work. "
"The identification of these biomarkers is the first step in developing a test that accurately calculates the amount of sleep an individual has," said Simon Archer, Ph.D., professor of molecular sleep biology. . "The very existence of such biomarkers in the bloodstream after only 24 hours of wakefulness shows the physiological impact that a lack of sleep can have on our body."
Professor Simon Archer, Ph.D., professor of molecular sleep biology at the University of Surrey, said:
Simon Archer, Ph.D., professor of molecular sleep biology at the University of Surrey, said: Director of the Surrey Sleep Research Center at the University of Surrey, said:
"This is an acute total sleep loss test," said Derk-Jan Dijk, Ph.D., director of the Sleep Research Center. "The next step is to identify biomarkers of chronic sleep negative health effects. "
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