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

Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach | Science Advances

article
Sleep deprivation can be detected in your blood

 

Young healthy participants with regular sleep schedules were recruited from the general public as described previously (26, 58). Participants either followed the protocol of a sleep deprivation experiment [experiment 1: n = 12 (25.6 ± 3.9 years old, one female), experiment 2: n = 11 (25.2 ± 7.4 years old, four females)], or the matched control [n= 5 (24 ± 2.5 years old, all male)], and LC-MS processing was conducted as three separate experiments.

A total of 28 participants, all relatively young. Not quite enough to use it for anything IRL, but and interesting concept regardless.

I wonder if the results would replicate in a larger and more heterogeneous population.

 

I wonder what the results would look like in the ME population.