Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans. Issue 4 (August 2021)

• Record Type: Journal Article
• Authors: Cogswell, D.; Bisesi, P.; Markwald, R. R.; Cruickshank-Quinn, C.; Quinn, K.; McHill, A.; Melanson, E. L.; Reisdorph, N.; Wright, K. P.; Depner, C. M.
• Is Part Of: Journal of biological rhythms. Volume 36:Issue 4(2021)
• Page Start: 369
• Page End: 383
• Publication Date: 2021-08
• Subjects: circadian rhythm -- circadian misalignment -- personalized medicine -- sleep restriction -- sleep loss -- biomarker -- metabolomics; Biological rhythms -- Periodicals; Circadian rhythms -- Periodicals; 571.77
• DOI: 10.1177/07487304211025402
• Languages: English
• ISSNs: 0748-7304

Abstract

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R 2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly ( p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R 2 ; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R 2 ; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.