Scientists use ‘sleep age’ to infer long-term health

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The numbers tell a story. From your credit score to your age, the metrics predict a variety of outcomes, whether it’s your likelihood of getting a loan or your risk of heart disease. Now, Stanford Medicine researchers have described another revealing metric, one that can predict mortality. It’s called sleep age.

Sleep age is a projected age that correlates with one’s health based on sleep quality. So, for example, if you analyze the sleep characteristics of dozens of 55-year-olds and average them out, you’ll get an idea of ​​what sleep is like at that age. For example, someone who is 55 years old and sleeps soundly through the night with good quality REM cycles could theoretically have a sleep age of 45 years.

Sleep expert Emmanuel Mignot, MD, Ph.D., and colleagues analyzed some 12,000 studies, each of which focused on an individual, who reported characteristics of their sleep, such as chin movement and legs, breathing and heartbeat. Their goal was to develop a system that maps the age of sleep and, using machine learning, identifies the variations in sleep most closely related to mortality.

Generally speaking, people sleep differently at different ages, with changes in sleep quality being one of the first and best documented signs of aging and poor health. The good news: the dream age is not set in stone. We have the power to make it better.

The study, led by Mignot, the Craig Reynolds Professor of Sleep Medicine at Stanford Medicine, appeared July 22 in npj digital medicine. I spoke with Mignot, who has studied sleep for 30 years, about why sleep age matters, how it’s calculated, and what the study’s findings mean for our health.

Why study the age of sleep?

When you sleep, you are disconnected from sensory inputs—ideally you should not be disturbed by the noisy outside world or bright lights.

During sleep, it’s not just the brain that goes through an automatic program, but heart rate and breathing also change, and variations in these can be early predictors of a health disorder. We spend about a third of our lives sleeping, making it a substantial component of our overall well-being.

It is well known that in almost any disorder, sleep is one of the first things to be disturbed. For example, about five to ten years before other symptoms appear in patients with Parkinson’s disease, a specific sleep disturbance occurs during which the patient acts out violently in their dreams, screams, or hits a wall.

What was the most important finding of the study?

Our main finding was that sleep fragmentation—when people wake up multiple times during the night for less than a minute without remembering—was the strongest predictor of mortality. Although 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 like insomnia.

Determining why sleep fragmentation is so detrimental to health is something we plan to study in the future.

Can we measure our own sleep age? It can improve?

The code is available to clinicians and researchers, but the Normal person you would probably have trouble running it through a computer. Regardless, it is not deterministic. There is a huge variation. Even if you have a higher sleep age than your chronological age, it doesn’t mean your mortality risk is going to be higher. You see people chained to smoking and drinking alcohol in their 90s and wonder, “How does this person survive this long?” There is always a great natural variation.

Going to bed and waking up at regular times is key to improving sleep. This means not falling asleep but making sure you are fully rested. It’s a different amount for everyone and often the window varies slightly, for example being a night owl versus an early bird.

Get solid light exposure, preferably outside light, during the day, keep your sleeping environment dark at night, exercise regularly but not too close to bedtime, don’t drink alcohol or caffeine at bedtime and avoiding large meals at night contribute to healthy sleep. And of course, be sure to treat any sleep disorders.

How did you calculate the age of sleep in this study?

We use an machine learning program to predict sleep age by feeding sleep study data and the age of each person in these programs. This tells us what an average sleep is like at a particular age. The algorithm recognizes patterns in the data and uses that information to predict the age of sleep. Once the algorithm has been built, we can use it to assign additional sleep ages. For some people, their dream age seems much older than their chronological age.

We can use the difference between your chronological age and your dream age to predict mortality, based on the idea that older sleeping age it is an indicator of a health problem. And, in fact, we found that people with older sleep ages compared to their actual age have a higher risk of mortality, based on the sleep of patients who died later. From other studies, we know that sleep deprivation is found in a variety of conditions such as sleep apnea, neurodegeneration, obesity, and chronic pain. How lack of sleep causes, exacerbates, or results in these conditions is unknown.

What are the next steps with your investigation?

I hope to use sleep studies to better predict and treat disease before it manifests itself in death. This study included only 12,000 people. In the future, we will try to predict the future occurrence of heart attacks, strokes, and Alzheimer’s disease that cause mortality.

We’re working with scientists at Harvard University to collect 250,000 sleep studies. Much of the data in this larger set was collected 10 years ago, allowing us to make better mortality predictions.

Imagine if we could use sleep studies to predict a person’s heart attack risk and then use that information to start early interventions? That would be a big problem.

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More information:
Andreas Brink-Kjaer et al, Age estimation from deep learning sleep studies predicts life expectancy, npj digital medicine (2022). DOI: 10.1038/s41746-022-00630-9

Citation: Scientists use ‘sleep age’ to infer long-term health (2022, September 1) Retrieved September 6, 2022 from

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