Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans.

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 R2 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 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 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.

Role of sleep and circadian disruption on energy expenditure and in metabolic predisposition to human obesity and metabolic disease.

Weight gain, obesity and diabetes have reached alarming levels in the developed world. Traditional risk factors such as over-eating, poor nutritional choices and lack of exercise cannot fully account for the high prevalence of metabolic disease. This review paper examines the scientific evidence on two novel risk factors that contribute to dys-regulated metabolic physiology: sleep disruption and circadian misalignment. Specifically, fundamental relationships between energy metabolism and sleep and circadian rhythms and the impact of sleep and circadian disruption on metabolic physiology are examined. Millions of individuals worldwide do not obtain sufficient sleep for healthy metabolic function, and many participate in shift work and social activities at times when the internal physiological clock is promoting sleep. These behaviours predispose an individual for poor metabolic health by promoting excess caloric intake in response to reduced sleep, food intake at internal biological times when metabolic physiology is not prepared, decreased energy expenditure when wakefulness and sleep are initiated at incorrect internal biological times, and disrupted glucose metabolism during short sleep and circadian misalignment. In addition to the traditional risk factors of poor diet and exercise, disturbed sleep and circadian rhythms represent modifiable risk factors for prevention and treatment of metabolic disease and for promotion of healthy metabolism.