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Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.
- Source :
-
Science advances [Sci Adv] 2024 Mar 08; Vol. 10 (10), pp. eadj6834. Date of Electronic Publication: 2024 Mar 08. - Publication Year :
- 2024
-
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 ( R <superscript>2</superscript> = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.
- Subjects :
- Humans
Wakefulness
Metabolomics
Machine Learning
Sleep Deprivation metabolism
Sleep
Subjects
Details
- Language :
- English
- ISSN :
- 2375-2548
- Volume :
- 10
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- Science advances
- Publication Type :
- Academic Journal
- Accession number :
- 38457492
- Full Text :
- https://doi.org/10.1126/sciadv.adj6834