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Related Experiment Video

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Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
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Blood transcriptome based biomarkers for human circadian phase.

Emma E Laing1, Carla S Möller-Levet2, Norman Poh3

  • 1Department of Microbial Sciences, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.

Elife
|February 21, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method using blood mRNA to predict the body's internal clock (circadian phase). This approach is more accurate and requires fewer samples than traditional melatonin tests for diagnosing sleep disorders.

Keywords:
biomarkerchronotherapycomputational biologyhumanmachine learningneurodegenerationneurosciencesleep disorderssystems biologytranscriptomics

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Area of Science:

  • Chronobiology
  • Molecular Biology
  • Sleep Medicine

Background:

  • Accurate assessment of circadian phase is crucial for diagnosing and treating circadian rhythm sleep-wake disorders.
  • The current gold-standard involves lengthy 24-hour plasma melatonin measurements, which is impractical for routine clinical use.

Purpose of the Study:

  • To develop and validate a novel, efficient method for predicting circadian phase using whole-blood mRNA.
  • To create a multivariate predictor that requires minimal blood samples, improving upon existing methods.

Main Methods:

  • Collected transcriptome data from whole blood under various conditions (normal, sleep deprivation, abnormal timing) to ensure predictor robustness.
  • Utilized Partial Least Square Regression (PLSR) on transcriptome data to identify 100 key biomarkers.
  • Focused on biomarkers related to glucocorticoid signaling and immune function for phase prediction.

Main Results:

  • The developed PLSR-based predictor demonstrated superior performance compared to previously published blood-derived circadian phase predictors.
  • Achieved an R² of 0.74 for predicting circadian phase with a single blood sample.
  • Improved prediction accuracy to an R² of 0.90 when using two samples taken 12 hours apart.

Conclusions:

  • A robust, multivariate whole-blood mRNA-based model can accurately predict circadian phase.
  • This blood transcriptome approach offers a practical and efficient alternative to melatonin assays for clinical applications.
  • The findings facilitate improved diagnosis and treatment strategies for circadian rhythm sleep-wake disorders.