0214 Effects of Simulated Night-Shiftwork Induced Circadian Misalignment on the Human Plasma Metabolome. (25th May 2022) - Kubicki, Michelle; McHill, Andrew; Melanson, Edward; Reisdorph, Nichole; Wright, Kenneth; Depner, Christopher
Abstract: Introduction: Circadian misalignment occurs when behaviors such as food intake and sleep happen at inappropriate endogenous circadian times, is common among shift-workers, and is a risk factor for cardiometabolic disease. Identifying mechanisms underlying adverse cardiometabolic risk linked to circadian misalignment could help develop new countermeasure strategies to mitigate health consequences of shiftwork. Thus, we analyzed 24-hour profiles of plasma metabolites during circadian alignment and misalignment in a night-shiftwork protocol. Methods: 14 healthy adults (6M/8F), aged 26.4±1.2y (mean±SD), completed a 6 day in-laboratory simulated night-shiftwork study. The protocol comprised 2 baseline days (circadian alignment) followed by a transition day to shiftwork, and then 2 simulated night-shiftwork days (circadian misalignment). Participants consumed energy-balanced diets throughout the protocol, starting 3 days prior to laboratory admission. Plasma was collected every 4h during circadian alignment and misalignment conditions and was analyzed via untargeted metabolomics (liquid chromatography/mass-spectrometry). A model selection approach (best fit) with circadian and behavioral cosine models identified metabolites influenced by circadian (central circadian clock measured via dim-light melatonin onset) versus behavioral cycles (food intake/sleep). Results: 5, 171 metabolites were detected with average abundance of 424 ( 8%) changing between conditions (false discovery rate (FDR)<0.05). 380 metabolites had significant (FDR<0.05) 24-h time-of-day patterns. Of these 380 metabolites, 248 had 24-h time-of-day patterns during circadian alignment, 283 had 24-h time-of-day patterns during misalignment, and 141 had 24-h time-of-day patterns during both conditions. Of the metabolites with 24-h time-of-day patterns in both conditions, 50 were circadian-influenced (e.g., arachidinoyl-serine) and 91 were behaviorally-influenced (e.g., propionylcarnitine). Conclusion: Almost twice as many metabolites were influenced by the behavioral versus circadian cycle suggesting altered timing of behaviors like food intake and sleep have a significant impact on 24-h time-of-patterns in the human plasma metabolome. For example, propionylcarnitine is involved in energy metabolism and likely reflects 24h time-of-day patterns altered by food intake. Alternatively, arachidinoyl-serine was circadian influenced, is involved in vasodilation, and thus may contribute to circadian regulation of blood pressure. Further understanding circadian and behavioral regulation of the metabolome could have application in identifying mechanisms of physiological dysregulation and targets for intervention to mitigate health consequences of circadian misalignment. Support (If Any): NIH-DK092624, NIH-TR001082, NIH-DK048520, NIH-HL132150, NIH-HL145099, NIH-DK111161