Time Restricted Feeding Mitigates High-Fat-Diet Induced Sleep Disruption and Amplifies NREM Substates

Area of Science:

• Neuroscience
• Chronobiology
• Sleep Science

Background:

• Diet quality and timing significantly impact sleep, yet their effects on sleep microarchitecture are not fully understood.
• Traditional sleep scoring methods overlook the detailed patterns within non-rapid eye movement (NREM) sleep.
• High-fat diets (HFD) and feeding schedules are known to influence metabolic and sleep health, but the precise mechanisms are unclear.

Purpose of the Study:

• To investigate how a high-fat diet (HFD) and time-restricted feeding (TRF) affect sleep microarchitecture in mice.
• To develop and apply a novel machine-learning pipeline for resolving discrete NREM sleep substates.
• To determine if TRF can mitigate HFD-induced sleep disruptions and modulate specific NREM substates.

Main Methods:

• Utilized electroencephalography (EEG) and electromyography (EMG) for continuous sleep monitoring in mice.
• Applied a machine-learning pipeline combining EEG feature extraction and unsupervised clustering to identify NREM substates.
• Compared sleep patterns between mice fed a standard diet, HFD, and HFD with active-phase TRF.

Main Results:

• HFD increased sleep latency and fragmentation, which were ameliorated by active-phase TRF.
• Identified two distinct NREM substates (Cluster 1 and Cluster 6) sensitive to TRF, characterized by specific EEG power profiles.
• TRF increased the occurrence of these NREM substates, particularly during longer sleep episodes in the light phase.

Conclusions:

• TRF mitigates HFD-induced sleep macro-architecture disruption and selectively enhances specific NREM sleep substates.
• The developed machine-learning framework provides an objective method for quantifying murine sleep microarchitecture.
• Aligning caloric intake with the circadian active window holds potential for improving both metabolic and sleep health.