Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling

Abstract: Metabolism plays a crucial role in sleep regulation, yet its effects are challenging to track in real time. This study introduces a machine learning-based framework to analyze sleep patterns and identify how metabolic changes influence sleep at specific time points. We first established that sleep periods in Drosophila melanogaster function independently, with no causal relationship between different sleep episodes. Using gradient boosting models and explainable artificial intelligence techniques, we quantified the influence of time-dependent sleep features. Causal inference and autocorrelation analyses further confirmed that sleep states at different times are statistically independent, providing a robust foundation for exploring metabolic effects on sleep. Applying this framework to flies with altered monocarboxylate transporter 2 expression, we found that changes in ketone transport modified sleep stability and disrupted transitions between day and night sleep. In an Alzheimers disease model, metabolic interventions such as beta hydroxybutyrate supplementation and intermittent fasting selectively influenced the timing of day to night transitions rather than uniformly altering sleep duration. Autoencoder based similarity scoring and wavelet analysis reinforced that metabolic effects on sleep were highly time dependent. This study presents a novel approach to studying sleep-metabolism interactions, revealing that metabolic states exert their strongest influence at distinct time points, shaping sleep stability and circadian transitions.