Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.
Sci Adv
; 10(10): eadj6834, 2024 Mar 08.
Article
de En
| MEDLINE
| ID: mdl-38457492
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 (R2 = 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Sommeil
/
Privation de sommeil
Limites:
Humans
Langue:
En
Journal:
Sci Adv
Année:
2024
Type de document:
Article
Pays d'affiliation:
Australie
Pays de publication:
États-Unis d'Amérique