Researchers from Massachusetts General Hospital and Beth Israel Deaconess Medical Center found that the quantity of brain wave data acquired during nighttime sleep studies might predict future health outcomes, bolstering the idea of sleep as a “window” into health.
The findings reveal that a model based on quantitative sleep data may accurately predict the 10-year risk of all 11 health outcomes studied. Dementia (RR = 6.2), death (RR = 5.7), and moderate cognitive impairment or dementia (RR = 4.0) were the three outcomes with the greatest risk ratios linked with poor-to-average sleep. “It’s surprising that sleep can predict the future incidence of 11 outcomes spanning neurological, psychiatric, cardiovascular, and mortality conditions even before the actual diagnosis,” said lead author Haoqi Sun, a doctorate in machine learning and computational neuroscience and an instructor in neurology at Massachusetts General Hospital. “We already know that sleep provides health information, but it’s still surprising that brain activity during sleep may reflect so much information, let alone other critical signals like breathing and heart rates.”
Quantitative studies of sleep microstructure in 8,673 persons who had a diagnostic sleep investigation in a sleep centre were performed as part of the study.
Quantitative assessments of sleep microstructure were performed on 8,673 persons who received a diagnostic sleep study using polysomnography at a sleep centre. The participants were 51 years old on average, with 51 percent of them being female.
The data acquired by nocturnal EEG, a recording that detects brain waves, yielded 86 characteristics, according to the researchers. They divided the individuals into three groups based on their sleep quality: bad, average, and good, and used a statistical model to estimate health risks. Medical codes, brain imaging reports, drugs, and/or cognition scores were used to assess health outcomes. Age, sex, BMI, and the usage of specific prescription medications were all taken into account as potential confounders.
While sleep medicine doctors commonly utilise sleep studies to diagnose sleep problems like obstructive sleep apnea, Sun believes the findings indicate that deciphering sleep data might play a bigger role in health care.
“The capacity to predict future incident health outcomes using noninvasive physiologic measures of sleep would be crucial,” he added, “since it would allow early treatments to avert adverse consequences.”
