0050 Identification of a Plasma Metabolome-Based Biomarker for Dim-Light Melatonin Offset and Onset in Humans. (27th May 2020)

• Record Type: Journal Article
• Authors: Cogswell, D T; Bisesi, P J; Markwald, R R; Cruickshank-Quinn, C; Quinn, K; McHill, A W; Melanson, E L; Reisdorph, N; Wright, K P; Depner, C M
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A20
• Page End: A21
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.049
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Easily measuring individual circadian timing is increasingly important to inform personalized chronotherapy, screen for circadian disorders and circadian misalignment, and advance circadian research. Findings from multiple studies show that transcriptomics is a viable method to estimate dim-light melatonin onset (DLMO), but no published omics-based findings have predicted dim-light melatonin offset (DLMOff), and only one known study has used metabolomics to predict DLMO. Here, we developed and tested a plasma metabolomics-based biomarker of circadian phase using DLMO and DLMOff as phase markers. Methods: Sixteen (8M/8F) healthy participants aged 22.4 ± 4.8y (mean ± SD) completed an in-laboratory study with 3 baseline days (9h sleep opportunity/night), followed by a randomized cross-over protocol with 9h sleep and 5h sleep conditions, each lasting 5 days. Blood was collected every 4h on the final 24h of each condition for untargeted metabolomics analyses. DLMO and DLMOff were determined during the final 24h of each condition. Samples from all conditions were randomly split into training (68%) and test (32%) datasets. DLMO and DLMOff models were developed using partial least squares regression in the training dataset and validated in the test dataset. Results: When validating with the test dataset, R 2 for the DLMO model was 0.60, median absolute error (MdAE) was 2.2 ± 2.8h (± interquartile range), and 44% of samples had MdAE under 2h. R 2 for the DLMOff model was 0.62, MdAE was 1.8 ± 2.6, and 51% of samples had MdAE under 2h. The DLMOff model predicted baseline samples, under conditions of 9h sleep and controlled food intake, with an R 2 of 0.91 and MdAE 1.1 ± 1.1h. Conclusion: These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need for biomarker efforts to predict multiple circadian phase markers. Additional analyses with an independent validation dataset will help advance these initial findings. Support: NIH-R01HL085705, NIH-R01HL109706, NIH-R01HL132150, NIH-K01HL145099, NIH-F32DK111161, and NIH-UL1TR000154; and Sleep Research Society Foundation 011-JP-16;