Identifying and validating blood mRNA biomarkers for acute and chronic insufficient sleep in humans: a machine learning approach

Acute and chronic insufficient sleep are associated with adverse health outcomes and risk of accidents. There is therefore a need for biomarkers to monitor sleep debt status. None are currently available. We applied elastic net and ridge regression to transcriptome samples collected in 36 healthy young adults during acute total sleep deprivation and following 1 week of either chronic insufficient (< 6 hr) or sufficient sleep (similar to 8.6 hr) to identify panels of mRNA biomarkers of sleep debt status. The size of identified panels ranged from 9 to 74 biomarkers. Panel performance, assessed by leave-one-subject-out cross-validation and independent validation, varied between sleep debt conditions. Using between-subject assessments based on one blood sample, the accuracy of classifying acute sleep loss was 92%, but only 57% for classifying chronic sleep insufficiency. A reasonable accuracy for classifying chronic sleep insufficiency could only be achieved by a within-subject comparison of blood samples. Biomarkers for sleep debt status showed little overlap with previously identified biomarkers for circadian phase. Biomarkers for acute and chronic sleep loss also showed little overlap but were associated with common functions related to the cellular stress response, such as heat shock protein activity, the unfolded protein response, protein ubiquitination and endoplasmic reticulum-associated protein degradation, and apoptosis. This characteristic response of whole blood to sleep loss can further aid our understanding of how sleep insufficiencies negatively affect health. Further development of these novel biomarkers for research and clinical practice requires validation in other protocols and age groups. Statement of Significance Insufficient sleep poses a significant risk to physical and mental health, and the general safety of individuals. The development of an objective assessment is important for treatment and diagnosis, with applications in many real world situations, for example, suspected sleepy (drowsy) driver accidents, assessing fitness for duty, interpretation and diagnosis of sleep disorders, and evaluation of therapeutic sleep interventions. Here, we show that sets of blood mRNA transcripts, related to biological functions associated with cellular stress responses, can accurately predict acute sleep loss but detecting chronic sleep insufficiency is more challenging.