ArticleScience advances2024
Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Leveraging the Metabolic Fingerprint of Sleep Deprivation and Sleep Restriction for Forensic Applications: A Machine Learning Study in Oral Fluid Metabolomics.Journal of proteome research · 2026Article
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Lifestyles, metabolome and diabetic kidney disease: a cohort study.QJM : monthly journal of the Association of Physicians · 2026Article
- Occupational and Driving Challenges Within Sleep Medicine.Journal of sleep research · 2025Review
- Circadian ontogenetic metabolomics atlas: an interactive resource with insights from rat plasma, tissues, and feces.Cellular and molecular life sciences : CMLS · 2025Article
- Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.Scientific reports · 2025Article
- Potential non-invasive biomarkers of chronic sleep disorders identified by salivary metabolomic profiling among middle-aged Japanese men.Scientific reports · 2025Article
- Associating EEG functional networks and the effect of sleep deprivation as measured using psychomotor vigilance tests.Scientific reports · 2024Article
- Higher central circadian temperature amplitude is associated with greater metabolite rhythmicity in humans.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.