| Title: |
Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach |
| Authors: |
Jeppe, K; Ftouni, S; Nijagal, B; Grant, LK; Lockley, SW; Rajaratnam, SMW; Phillips, AJK; McConville, MJ; Tull, D; Anderson, C |
| Publisher Information: |
AMER ASSOC ADVANCEMENT SCIENCE |
| Publication Year: |
2024 |
| Collection: |
The University of Melbourne: Digital Repository |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
2375-2548 |
| Relation: |
Jeppe, K., Ftouni, S., Nijagal, B., Grant, L. K., Lockley, S. W., Rajaratnam, S. M. W., Phillips, A. J. K., McConville, M. J., Tull, D. & Anderson, C. (2024). Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach. Science Advances, 10 (10), https://doi.org/10.1126/sciadv.adj6834.; https://hdl.handle.net/11343/352986 |
| Availability: |
https://hdl.handle.net/11343/352986 |
| Rights: |
https://creativecommons.org/licenses/by/4.0 ; CC BY |
| Accession Number: |
edsbas.E9BE87A5 |
| Database: |
BASE |