A machine-learning analysis of brain waves recorded during sleep may help identify people at high risk of developing dementia, according to a study led by UC San Francisco and Beth Israel Deaconess Medical Center in Boston.
The study found that when a person’s “brain age” — estimated from sleep signals using EEG — exceeded their actual age, the risk of dementia increased.
For every 10-year increase in brain age versus actual age, dementia risk rose by nearly 40%. Conversely, if brain age was lower than actual age, dementia risk was lower.
The study published in JAMA Network Open on March 19.
What conventional sleep metrics miss
Researchers used a machine-learning model that integrates 13 microstructural features of brain waves from EEG recordings. Data came from approximately 7,000 participants who had been enrolled in five studies.
The participants were 40 to 94 years old and none had dementia at the start of the study. They were followed between 3.5 and 17 years, during which time about 1,000 participants developed the disorder.
The researchers found that analyzing fine-scale patterns in brain waves during sleep provided insights that conventional sleep metrics often miss. Earlier pooled analyses of several participant cohorts found no significant links between dementia risk and traditional sleep measures, such as time spent in different sleep stages or overall sleep efficiency.
“Broad sleep metrics don’t fully capture the complex multidimensional nature of sleep physiology,” said senior author Yue Leng, MBBS, Ph.D., associate professor of psychiatry at the UCSF School of Medicine.
Brain waves linked to cognitive health
Several sleep EEG patterns that contribute to brain age are known to play roles in brain health and memory. These include delta waves, which form a rolling wave pattern associated with deep sleep, and sleep spindles, short, fast-frequency brain activity associated with memory consolidation.
Among the most notable findings was that sudden large spikes seen on EEG, known as kurtosis, were associated with a lower risk of dementia.
The researchers also found that the relationship between “older” brain age and dementia risk remained significant after accounting for factors such as education, smoking, body mass index, and physical activity, as well as other health conditions and genetic risk factors.
Potential for early detection
Because sleep EEG signals can be collected noninvasively, the researchers said that brain age could eventually help detect dementia risk in nonclinical settings, such as by using wearable technologies.
“Brain age is calculated from sleep brain waves,” said Leng. “We know that brain activity during sleep provides a measurable window into how well the brain is aging.”
The findings also raise the possibility that improving sleep health could influence brain aging. Leng noted that earlier studies have found treating sleep disorders can change sleep-related brain-wave patterns.
“Better body management, such as lowering body mass index and increasing exercise to reduce the likelihood of apnea, may have an impact,” said first author Haoqi Sun, Ph.D., assistant professor of neurology at Beth Israel Deaconess Medical Center, who developed the model with two co-authors*. “But there’s no magic pill to improve brain health.”
Co-authors:* Robert J. Thomas, M.D., and M. Brandon Westover, M.D., Ph.D., of Beth Israel Deaconess Medical Center, developed the machine-learning model with Sun. For other authors, please see the paper.
Funding: National Institutes of Health (R01NS102190, R01NS102574, R01NS107291, RF1AG064312, RF1NS120947, R01AG073410, RF1AG064312, R01NS102190, R01AG062531); National Institute on Aging (R21AG085495 and R01AG083836); National Science Foundation (2014431); National Health and Medical Research Council (GTN2009264); American Academy of Sleep Medicine.
Disclosures: Please see the paper.
