Researchers are beginning to identify patterns in sleep that may indicate early signs of dementia or Parkinson’s disease.
Some clear indicators that a family member might be experiencing early symptoms of dementia include difficulty learning new tasks, trouble maintaining focus, challenges in participating in conversations, confusing objects, and experiencing unusual emotional reactions or fear. Dementia impacts nearly seven million people in the United States.
Recently, scientists have found a connection between a specific sleep disorder and the onset of these progressive neurological conditions, which experts suggest could be present in almost ‘all cases’.
However, diagnosing this particular sleep disorder is notably challenging.
Healthcare providers might misidentify it as another condition, or those affected may not even realize they have it since it occurs during sleep.
Research indicates that individuals displaying ‘abnormal movements’ such as talking, shouting, laughing, swearing, moving, or thrashing while asleep might have REM sleep behavior disorder (RBD).
A key sign of RBD is ‘acting out’ dreams, which involves physically moving or speaking during sleep.
In some instances, the thrashing associated with RBD can be so intense that individuals may injure themselves or their partners.
Those affected might also experience grogginess upon waking and an increased likelihood of daytime sleepiness.
RBD affects over one million people in the United States and approximately 80 million individuals worldwide. According to Mount Sinai researchers, it is an early indicator of Parkinson’s disease or dementia ‘in nearly all cases’.
The connection is drawn from the observation that individuals with RBD exhibit increased brain inflammation in areas where dopamine is produced. Parkinson’s and dementia patients also experience reduced dopamine levels due to the death of dopamine-producing nerve cells.
To detect these conditions early, US researchers are employing artificial intelligence to analyze clinical sleep tests, improving RBD diagnoses and identifying patients at higher risk of cognitive decline.
The team utilized automated machine learning to analyze sleep movements, expanding on prior research from the Medical University of Innsbruck in Austria.
They employed video-polysomnogram technology for RBD diagnosis, achieving an accuracy rate of 92 percent.
Dr. Emmanuel During, an associate professor of neurology at Mount Sinai School of Medicine in New York, stated: “This automated approach could be integrated into clinical workflow during the interpretation of sleep tests to enhance and facilitate diagnosis, and avoid missed diagnoses.
“This method could also be used to inform treatment decisions based on the severity of movements displayed during the sleep tests and, ultimately, help doctors personalize care plans for individual patients.”