Sleep quality is the strongest indicator of the lifespan, study finds

Researchers created a system employing machine learning to forecast a person's "sleep age."

Their objective was to create a system that can determine a person's sleep age and pinpoint the sleep patterns that are most closely associated with mortality.

Why is sleep age that significant?

Before the significance of sleep age, we need to learn about what sleep age is. Sleep age roughly means a projected age that correlates to a person's health based on their sleep quality.

More to explain if needed, while we sleep, not only does our brain undergo an automatic program, but also our heart rate and breathing change. Changes in these parameters might serve as early warning signs of a health problem, the study underlines.

Apart from these, there are simple things we can do to improve our sleep age. For instance, getting the necessary sunlight during the day, exercising regularly but not too close to bedtime, not drinking alcohol and caffeine around bedtime, and avoiding heavy nighttime meals all contribute to healthy sleep.

The strongest predictor of mortality

"Our main finding was that sleep fragmentation — when people wake up multiple times throughout the night for less than a minute without remembering it — was the strongest predictor of mortality," says Emmanuel Mignot.

"Though we see a link in the data, how it contributes to mortality is unknown. This is different from a person realizing they were waking up, which happens during sleep disorders such as insomnia."

What will be the next step?

Emmanuel Mignot and his team are currently working with scientists from Harvard University. Studies on 250.000 people will help to collect more data.

Mignot also adds that they are working to understand whether sleep quality can increase the risk for heart attack and Alzheimer's.

Abstract:

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with an increased all-cause mortality rate of 29% (95% confidence interval: 20–39%). An increase from −10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1–11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.