Discussion
We used data from a rigorous randomized controlled trial to determine if a blood metabolomics-based biomarker can distinguish between participants that received a week of adequate sleep versus a week of insufficient sleep. We also examined the impact of a weekend of recovery sleep on our top-performing biomarker. Our final biomarker model consists of 25 plasma compounds and is designed to differentiate participants between 7 nights of 9h versus 5h sleep opportunities. As part of our biomarker development and analysis workflow we compared the performance of 8 different machine learning algorithms. The top four algorithms, based on G-mean, were Random Forest, Key Nearest Neighbors, Support Vector Machine and Generalized Linear Model with Stepwise Feature Selection. These four algorithms had similarly better G-mean values compared to the bottom four performing algorithms, highlighting the importance of considering different machine learning algorithms in future study design. Using these four best-performing algorithms, we built a voting ensemble which performed better than the individual models based on G-mean. This final ensemble model showed a similar range of accuracy as our previously published metabolomics-based biomarker of insufficient sleepTwenty-four of the 25 compounds in our biomarker model are lipids, primarily consisting of glycerophospholipids and sphingolipids. Comparing the compounds in the current biomarker panel with the compounds in our previously published biomarker panel, the broad compound class representations of glycerophospholipids and sphingolipids were similar, and two compounds are common between the two studies: SM(43:2) and PE(35:3), each showing decreased abundance during sleep restriction versus adequate sleep. Unfortunately, due to a large number of unidentified compounds in our prior 65-compound biomarker signature of insufficient sleep it is not possible to directly evaluate the performance of that biomarker signature in this dataset. This issue exists across untargeted metabolomics studies and highlights the need for development of targeted biomarker assays in large-scale clinical trials. Despite limitations, we compare our biomarker compounds against prior metabolomics-based biomarkers of sleep duration. Several prior metabolomics studies with total sleep deprivation have been published. In a study with twelve healthy participants that were given controlled meals, the metabolome was analyzed every two hours during 24 h of total sleep deprivation. Compared to the first day with an 8h sleep opportunity, several plasma glycerophospholipids and sphingolipids were significantly increased during total sleep deprivation, opposite the direction of our findingsMetabolomics analyses from studies of experimental sleep restriction have also been published. In a unique study with parallel sleep restriction in rats and humans (n = 10; 5 women), 5 days of experimental sleep restriction was compared against baseline adequate sleep. A global shift in lipid compounds was observed, with a noted decrease in oxalic acid and diacylglycerol 36:3 in both rats and humans following sleep deprivation with ad libitum access to foodIn general, findings of these prior studies and the present work have in common the identification of an impact of total sleep deprivation and sleep restriction on plasma metabolomics, and most commonly an effect on lipids. However, the specific findings are mixed, sometimes appearing contradictory, which may be explained by the diversity in targeted versus untargeted metabolomics methods, timing of sample collection, controlled versus ad libitum food availability, medications and drugs, experimental sleep restriction protocols, sleep laboratory settings, methods of statistical analysis, and participant characteristics including age, sex, and health status. This also emphasizes that the precise pathways through which sleep restriction may impact lipid metabolism remain to be identified and likely include many different classes of lipids.We further assessed our biomarker model by examining the impact of a workweek of insufficient sleep followed by weekend recovery sleep, using completely naive samples from the WR group that did not contribute to model training. Overall, ~ 55% of the samples from the WR group were classified as adequate sleep and ~ 45% classified as insufficient sleep. These data are visually represented by the PCA (Fig. Additionally, it is worth noting that in clinical practice, blood collections typically occur in the fasted state. As the field progresses, it is conceivable that most blood draws will occur in the morning fasted state in clinical settings. Consequently, any variation in performance of samples collected across the day may have less clinical relevance. Intriguingly, if we focus exclusively on the fasting T1 samples for the WR participants, the overall classification for WR-ADEQUATE and WR_INSUFFICIENT would change for just one participant. Consequently, our exploratory analyses in Fig. There are important limitations to the current studies on sleep and the metabolome, including our present study. First, many findings are based on a combination of fasting and non-fasting samples and it is not precisely defined how fasting status may impact the biomarkers. Ideally, large clinical trials will consistently use fasting samples to minimize this potential source of variability. Second, we previously showed a delay in circadian melatonin timing in the SR and WR groups following the sleep restriction and weekend recovery portions of the protocol but no change in the CON groupIn conclusion, our findings provide additional evidence that the human plasma metabolome is affected by insufficient sleep, including variations of lipid levels such as glycerophospholipids, sphingolipids, and diacylglycerols. These compounds can be used to develop machine learning models that differentiate samples between adequate and insufficient sleep using a between-participants laboratory-controlled design. These findings help set the stage for larger fully powered trials to identify biomarkers that differentiate people between habitual adequate sleep versus habitual insufficient sleep. Finally, our data further suggest weekend recovery sleep may only partially compensate the effects of a workweek of insufficient sleep and highlight potential interindividual differences in the physiological response to weekend recovery sleep.