Sleep is a cornerstone of systemic health, and its disruption directly triggers or exacerbates chronic illness.

In healthy individuals, quality sleep regulates metabolism, immune responses, cellular repair, and cognitive consolidation. Conversely, poor or fragmented sleep drives cardiovascular disease, metabolic dysfunction, mental health disorders, and neurodegeneration.

Sleep measures—such as sleep structure analysis via polysomnography—indicate ongoing disease processes and serve as markers for future disease risk. AI models, including U-Sleep and SleepFM, identify specific changes in sleep structure that signal disease development. These models can track how different biological systems fall out of sync, mapping disease progression long before symptoms appear. They help us understand the relationships between physiological signals and health outcomes. For example, they spot subtle anomalies, such as when brainwaves show deep slow-wave sleep while the cardiovascular system shows high alertness. This reveals early organ-specific changes and prodromal diseases like Parkinson’s.

This insight can be built into simpler consumer systems to track health and predict potential risks.