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

Laing, Emma, Moller-Levet, Carla, Dijk, Derk-Jan and Archer, Simon (2018) Identifying and validating blood mRNA biomarkers for acute and chronic insufficient sleep in humans: a machine learning approach Sleep, zsy186.

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Abstract

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 entire and pre-filtered transcriptome samples collected in healthy young adults during acute total sleep deprivation and following 1 week of either chronic insufficient (< 6 h) or sufficient sleep (~8.6 h) to identify panels of mRNA biomarkers of sleep debt status. The size of identified panels ranged from 9-74 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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
NameEmailORCID
Laing, EmmaE.Laing@surrey.ac.uk
Moller-Levet, CarlaC.Moller-Levet@surrey.ac.uk
Dijk, Derk-JanD.J.Dijk@surrey.ac.uk
Archer, SimonSimon.Archer@surrey.ac.uk
Date : 24 September 2018
DOI : 10.1093/sleep/zsy186
Copyright Disclaimer : © Sleep Research Society 2018. Published by Oxford University Press [on behalf of the Sleep Research Society]. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords : Sleep deprivation; Biomarkers; Blood; Transcriptomics; Elastic-net; Regression; Multivariate
Depositing User : Melanie Hughes
Date Deposited : 21 Aug 2018 09:31
Last Modified : 16 Nov 2018 11:54
URI: http://epubs.surrey.ac.uk/id/eprint/849055

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