Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.

Autor:	Jeppe K; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia., Ftouni S; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia., Nijagal B; Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia., Grant LK; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia.; Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA., Lockley SW; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia.; Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA., Rajaratnam SMW; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia.; Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA., Phillips AJK; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia., McConville MJ; Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia., Tull D; Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia., Anderson C; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.; Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia.; Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, UK.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2024 Mar 08; Vol. 10 (10), pp. eadj6834. Date of Electronic Publication: 2024 Mar 08.
DOI:	10.1126/sciadv.adj6834
Abstrakt:	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 2 = 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.
Databáze:	MEDLINE
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