# Exploratory development of biomarkers for neurobehavioral performance impairment during sleep loss: comparison across multiple types of sleep deprivation

## TL;DR

This study explores how sleep loss affects neurobehavioral performance and identifies potential biomarkers for fatigue-related impairment.

## Contribution

The study identifies reproducible gene expression biomarkers for attention impairment during total sleep deprivation.

## Key findings

- Reduced sleep quantity and altered timing significantly affect sleep stages and neurobehavioral performance.
- Transcriptomic changes in gene expression correlate with impaired neurobehavioral performance.
- Candidate biomarkers for fatigue were identified that respond to specific and multiple performance metrics.

## Abstract

Inter-individual variation in response to insufficient or altered timing of sleep presents a challenge for the development of personalized approaches to fatigue monitoring and mitigation. Insufficient sleep has adverse health impacts, can result in impaired neurobehavioral performance, and can substantially increase the risk of injury and even mortality in safety–critical operations such as transportation. The present study provides a detailed exploration of physiological, neurobehavioral, and gene expression changes during sleep restriction, acute total sleep deprivation, and altered timing of sleep among 59 healthy volunteer participants who completed a 10-day inpatient study.

Reducing the quantity or altering the timing of sleep significantly impacts self-reported estimates of sleep duration, polysomnography-recorded sleep stages, and neurobehavioral performance test results. Impaired neurobehavioral performance was associated with transcriptomic changes in gene expression. A comparison of current and prior research on total sleep deprivation indicated that reproducible candidate gene expression biomarkers exist for at least one metric of attention, specifically, Psychomotor Vigilance Test (PVT) lapses.

Candidate biomarkers of fatigue-related impairment were identified that responded to single neurobehavioral performance endpoints, as well as those that responded to multiple types or metrics of performance. Reproducible identification of biomarker candidates for PVT lapses during total sleep deprivation increases confidence in the ability to develop a molecular approach to fatigue-related impairment detection, while novel discoveries expanded the list of candidate genes to other impairment metrics. Molecular biomarkers for neurobehavioral performance impairment represent a potentially valuable tool to more precisely monitor the neurobehavioral performance deficits resulting from sleep loss, and further research and validation could eventually augment fatigue risk management practices that historically emphasize scheduling and rest opportunities. The data generated from self-assessment, polysomnography, neurobehavioral performance, and molecular investigations provide a wealth of information made publicly available for further data mining and scientific advancements.

ClinicalTrials.gov, TRN: NCT04211506, Registered 23 December 2019.

The online version contains supplementary material available at 10.1186/s12864-025-12193-6.