There are four key pathophysiological endotypes that contribute to sleep apnea. The main one is a narrow airway or a collapsible upper airway, or as we call it, the critical closing pressure, the upper airway. That’s how we measure it experimentally. So anatomy is the main driver of sleep apnea, but the extent to which sleep apnea is driven by anatomy varies a lot between patients. The other three main endotypes are a respiratory arousal threshold...
There are four key pathophysiological endotypes that contribute to sleep apnea. The main one is a narrow airway or a collapsible upper airway, or as we call it, the critical closing pressure, the upper airway. That’s how we measure it experimentally. So anatomy is the main driver of sleep apnea, but the extent to which sleep apnea is driven by anatomy varies a lot between patients. The other three main endotypes are a respiratory arousal threshold. So that is simply how easily you wake up when your airway narrows during sleep. Some people are just really sensitive or light sleepers, and that can contribute to sleep apnea because your breathing is much worse during lighter versus deeper sleep. The other two factors are how well the muscles around your airway respond to airway narrowing. Some people just can’t activate their muscles around the airway to open up that airway during sleep. And then finally, the last non-anatomical endotype is what we call loop gain or unstable control of breathing. And this is simply how responsive you are to carbon dioxide during sleep. So carbon dioxide’s the main driver of breathing, and some people are just really too sensitive to very small changes of CO2 during sleep, and that can set them up for cyclical breathing patterns and close off the airway. So those four key causes are really what drive sleep apnea pathogenesis and we’re now developing different approaches to actually estimate them or biomarkers if you like that we can estimate them from standard sleep studies either using signal processing approaches or from the sleep study outputs themselves with machine learning approaches, what we call a palm scale estimated approach. And they can help us figure out why people get their sleep apnea to help us drive targeted therapy.
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