Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain's circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA-based predictor of melatonin phase which requires few samples. Transcriptome data were collected under normal, sleep-deprivation and abnormal sleep-timing conditions to assess robustness of the predictor. Partial least square regression (PLSR), applied to the transcriptome, identified a set of 100 biomarkers primarily related to glucocorticoid signaling and immune function. Validation showed that PLSR-based predictors outperform published blood-derived circadian phase predictors. When given one sample as input, the R2 of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R2 was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples.
Keywords: biomarker; chronotherapy; computational biology; human; machine learning; neurodegeneration; neuroscience; sleep disorders; systems biology; transcriptomics.