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  3. Current Page:A Possible Correlation Between Sleep Disorders and Alzheimer’s Disease

A Possible Correlation Between Sleep Disorders and Alzheimer’s Disease

27/08/2026
Neurologists from ULiège have analyzed the sleep patterns of more than 500 healthy individuals in order to investigate the link between sleep disorders and the risk of developing Alzheimer’s disease.
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For years, scientists around the world have been studying the relationship between sleep disorders and neurodegenerative diseases. Today, Belgian researchers are shedding new light on this issue by suggesting that this connection may emerge even before any clinical signs of the disease appear.

The study conducted by the GIGA Neurosciences team at ULiège demonstrated that, among middle-aged participants, a higher frequency of nocturnal micro-arousals is associated with an increased genetic risk of developing Alzheimer’s disease, whereas no such link was observed in young adults. These findings, published in the journal Sleep, suggest that sleep studies could eventually contribute to the early identification of vulnerable individuals.

The results show that the genetic risk of developing Alzheimer’s disease is linked to the frequency of nocturnal micro-arousals, brief periods of brain activity that do not fully awaken the sleeper but can disrupt the normal course of sleep. No association was found among younger participants. In contrast, among older participants, more frequent micro-arousals were associated with a higher genetic risk, even though they were still relatively young and showed no signs of the disease.

At the same time, the Belgian researchers focused on the locus coeruleus, a region located in the brainstem, about the size of a grain of rice, which plays a role in wakefulness, attention, and sleep regulation. “This region is difficult to observe, but it appears to play a role in the early mechanisms associated with the disease,” explains Gilles Vandewalle, Research Director at the FNRS and professor at ULiège.

In the long term, sleep analysis could therefore complement existing tools used to identify at-risk individuals before the onset of symptoms.

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MedicineUniversité de Liège

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