Researchers identify molecular markers in saliva that could lead to new clinical tests for sleep loss.
Researchers have identified a detectable “sleepiness fingerprint” in the saliva of men who remained awake for 24 hours, marking a step toward the development of a non-invasive clinical test for sleep deprivation. Saliva-based diagnostics have advanced significantly in recent years, with other applications including a highly accurate saliva-based endometriosis test.
The study, published in the Journal of Proteome Research, identified molecular differences in saliva after a full night of rest compared to 24 hours without sleep. While sleep loss is known to dull alertness and coordination, there is currently no widely available clinical test to determine when an individual is dangerously sleep-deprived, though previous efforts have explored blood-based biomarkers for sleep deprivation and blood tests to detect drowsy driving.
“Until now, sleep deprivation has been impossible to measure biochemically—and yet it is one of the greatest burdens of our time,” says Thomas Kraemer, the corresponding author of the study, in a release. “This study introduces the first direct biomarkers of sleep loss in saliva under real-world conditions, marking a milestone in forensic investigations.”
The research team recruited 20 healthy young adult males who usually sleep seven to nine hours per night. Participants completed three sleep scenarios in a random order, each separated by one week: deprivation (one night without sleep), restriction (four nights with two hours less sleep than usual), and well-rested (around eight hours of sleep).
Researchers collected saliva before and after each scenario to analyze metabolite compositions. Using statistical analyses, the team determined 10 molecular differences between sleep-deprived and well-rested samples. In contrast, the sleep-restricted state showed no significant metabolic difference from the rested state.
The team developed and trained a predictive model based on these varying saliva metabolites, which correctly identified samples from sleep-deprived individuals 94% of the time. Errors made by the model were likely attributable to individual metabolic processes. For example, some participants did not return to a fully rested metabolic profile even after eight hours of sleep following a day of wakefulness.
These findings suggest that a collection of salivary metabolites holds potential for use in clinical settings or roadside checks where accurate detection of sleep deprivation is needed. Accurate diagnostics are crucial for sleep-related disorders, which can also be assessed through tools like a blood test to predict the risk of sleep apnea. The team is now undertaking a large-scale international assessment of the predictive model, expanding tests to more than 1,000 samples collected from shift workers, women, and frequent drivers, says Kraemer in a release.
ID 94897049 © Fizkes | Dreamstime.com
