A new blood test has been developed that can detect with remarkable accuracy whether an individual has not slept for 24 hours. This innovative blood test, described in a publication in Science Advances, utilizes a combination of biomarkers found in the blood, achieving a 99% probability of correctly identifying sleep deprivation when compared to a well-rested state. This discovery holds significant implications for enhancing safety in critical situations, where sleep deprivation greatly increases the risk of accidents and injuries.
The motivation behind this research stems from the global concern over the consequences of sleep deprivation, particularly in fields where alertness is crucial, such as transportation and healthcare. With approximately 20% of road accidents worldwide linked to sleep deprivation, the need for a reliable method to identify sleep-deprived individuals has never been more pressing. Traditional methods for assessing sleep loss, while useful, can be influenced by various factors and do not provide the objectivity or precision that a biological marker could offer.
Senior author Clare Anderson led the research while she was with the Monash University School of Psychological Sciences and Turner Institute for Brain and Mental Health. She is now a professor of sleep and circadian science at the University of Birmingham.
“Objective tests that identify individuals who present as a risk to themselves or others are urgently needed in situations where the cost of a mistake is fatal,” she said. “Alcohol testing was a game changer for reducing road crashes and associated serious injuries and fatalities, and it is possible that we can achieve the same with fatigue. But much work is still required to meet this goal.”
The study involved two main experiments, each comprising 40 hours of extended wakefulness under controlled conditions, known as constant routine (CR), to standardize external factors and focus solely on the effects of sleep deprivation. A matched control experiment was also conducted, where participants followed a more typical 8-hour sleep/16-hour wake schedule to compare against the sleep deprivation conditions.
The participants were young, healthy adults recruited from the general public, ensuring they had regular sleep schedules and no known medical, psychiatric, or sleep disorders. The sample size for the sleep deprivation experiments comprised 23 participants in total, with 12 participants (average age 25.6 years, one female) in experiment 1, and 11 participants (average age 25.2 years, four females) in experiment 2. An additional control group, consisting of 5 participants (all male, average age 24 years), was also included to provide a baseline for comparison.
Participants prepared for the study by maintaining an 8-hour sleep schedule for two weeks prior to the laboratory phase, verified through sleep diaries and wrist actigraphy. Upon entering the laboratory phase, participants underwent comprehensive monitoring, including full polysomnography on the first night to rule out sleep disorders. The sleep deprivation protocol began on day 3, with participants staying awake for 40 hours in a dimly lit environment, under constant supervision, and following a fixed schedule of hourly snacks to maintain consistent nutritional intake.
Blood samples were collected at regular intervals throughout the CR and control periods, starting 2 hours after wake time and then every 2 hours thereafter. These samples were immediately processed to extract plasma. The analysis utilized untargeted liquid chromatography–mass spectrometry (LC-MS) to identify a wide range of metabolites present in the samples.
By applying machine learning to the data collected from plasma samples, the researchers were able to identify a biomarker consisting of just five metabolites. This biomarker demonstrated remarkable accuracy in predicting whether an individual had been awake for more than 24 hours.
In a within-participant analysis, where the model compared an individual’s sleep-deprived state against their well-rested baseline, the biomarker predicted sleep deprivation with a 99.2% probability of being correct. This level of accuracy is exceptionally high and suggests that the biomarker is highly sensitive and specific to the effects of sleep deprivation on the body’s metabolite profile.
However, when considering the potential for practical applications such as diagnostic blood tests (where an individual’s well-rested sample might not be available for comparison), the accuracy dropped slightly to 89.1%. Despite this decrease, the accuracy remains impressively high.
The researchers also explored the individual metabolites that comprise the biomarker, shedding light on the biological pathways affected by sleep deprivation. These included metabolites related to liver function, antioxidant activity, and lipid metabolism, among others. Such insights not only validate the biomarker’s efficacy but also contribute to our understanding of the physiological impact of sleep loss. This could, in turn, inform future research into the health consequences of sleep deprivation and potential interventions to mitigate its effects.
The researchers said that this biomarker could have significant applications in identifying sleep-deprived individuals in safety-critical situations. Given that sleep deprivation is a known factor in many road accidents and occupational hazards, the ability to objectively and accurately detect sleep deprivation could have profound implications for public safety.
The research team hopes that this discovery could lead to the development of simple, quick tests to identify sleep-deprived drivers or workers in critical roles, thereby reducing the risk of accidents and injuries related to sleep deprivation.
“This is a really exciting discovery for sleep scientists, and could be transformative to the future management of health and safety relating to insufficient sleep,” Anderson said. “While more work is required, this is a promising first step. There is strong evidence that less than five hours’ sleep is associated with unsafe driving, but driving after 24 hours awake, which is what we detected here, would be at least comparable to more than double the Australian legal limit of alcohol performance wise.”
The findings represent a major advancement in the objective detection of sleep deprivation. But the study, like all research, has limitations. The research focused on a relatively small and specific cohort, primarily young, healthy adults with more males than females, which may affect the generalizability of the findings. Future research will need to explore the biomarker’s effectiveness across a broader and more diverse population, as well as in less controlled environments, to fully understand its potential.
The study, “Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach,” was authored by Katherine Jeppe, Suzanne Ftouni, Brunda Nijagal, Leilah K. Grant, Steven W. Lockley, Shantha M. W. Rajaratnam, Andrew J. K. Phillips, Malcolm J. McConville, Dedreia Tull, and Clare Anderson.