How sleep and heart rate data may help detect Alzheimer’s risk

Originally published on November 19, 2019

Sleep and cardiovascular data may help detect Alzheimer’s risk earlier, long before memory loss becomes obvious. In Episode 49 of the WHOOP Podcast, Dr. Richard Isaacson, founder of the Alzheimer’s Prevention Clinic at Weill Cornell Medicine, explains why Alzheimer’s disease often starts 20 to 30 years before symptoms, how deep sleep and REM sleep relate to brain health, what a WHOOP-based study found in people with a family history of the disease, where passive monitoring may fit alongside cognitive testing and imaging, and which habits he focuses on to lower risk. The conversation answers five practical questions, from early disease stages to the day to day behaviors most connected to prevention.

Note: This article covers WHOOP Strap 3.0. For the latest hardware, see WHOOP.

To listen to episode 49 in full, head to the WHOOP Podcast on Spotify.

How early can Alzheimer’s disease start changing the brain?

Alzheimer’s disease can begin decades before a person has obvious memory symptoms. Isaacson says the most important shift in the field is recognizing that dementia is the late stage of a disease process that starts much earlier.

Instead of thinking about Alzheimer’s as a condition that appears once someone can no longer live independently, Isaacson lays out a staged model. Stage 1 is preclinical or presymptomatic Alzheimer’s disease, when pathology is developing in the brain but daily function still looks normal. Stage 2 is mild cognitive impairment due to Alzheimer’s disease, when a person may notice subtle issues but can still care for themselves. Stage 3 is Alzheimer’s disease dementia, when cognitive decline starts disrupting independent living.

That longer timeline is why Isaacson focuses on prevention rather than waiting for clear symptoms. He notes that about 5.8 million Americans had Alzheimer’s disease dementia at the time of the conversation, while more than 46 million may have Alzheimer’s-related brain changes without symptoms. He also describes a practical Stage 0, before measurable disease is present, when risk reduction may have the most room to work.

In the conversation, Isaacson puts the timing plainly:

“We now know it starts 20 to 30 years before the first symptom of memory loss begins.”

He also explains how his clinic thinks about early risk through the “ABCs of Alzheimer’s prevention management.” A stands for anthropometrics, including body composition and central adiposity. B stands for blood-based biomarkers, including cholesterol, inflammation, blood sugar, nutrition markers, and genetics. C stands for cognitive testing, often with computer-based assessments designed to detect subtle changes before daily life is obviously affected.

This framework matters because it moves Alzheimer’s screening away from a single late diagnosis and toward a pattern of earlier signals. Isaacson argues that clinicians need faster, less invasive ways to spot those signals than a costly amyloid PET scan alone.

What you should take away

• Alzheimer’s disease can begin 20 to 30 years before the first obvious memory symptoms appear.
• Dementia is the late stage of Alzheimer’s disease, not the starting point.
• Isaacson’s prevention framework combines body composition, blood biomarkers, and cognitive testing.
• The earlier a risk signal appears, the more time there may be for lifestyle and medical intervention.

If you want to hear Isaacson unpack why Alzheimer’s starts long before diagnosis, listen to the full episode on Spotify.

Why are sleep and cardiovascular patterns relevant to Alzheimer’s risk?

Once Alzheimer’s is understood as a decades-long process, the next question is which signals may change before memory complaints show up. Isaacson’s answer is sleep and cardiovascular health.

He argues that both are biologically plausible markers. Cardiovascular disease appears to accelerate the path toward dementia, while sleep influences two core brain processes that matter for Alzheimer’s risk. During deep sleep, the brain carries out physical cleanup. During REM sleep, recently formed memories are processed and consolidated.

Isaacson describes deep sleep as the period when amyloid, the protein that accumulates in Alzheimer’s disease, is cleared more effectively. He describes REM sleep as the phase when short-term memories are translated into long-term memories. That means different parts of nightly Sleep may reflect different aspects of brain function, even before someone reports major forgetfulness.

His observation also came from the clinic, not only from theory. Isaacson says he repeatedly saw sleep-deprived people, including high-functioning professionals, show weaker processing speed, executive function, and memory performance than expected. That pattern pushed him to look for an objective, passive way to track what patients were already telling him subjectively.

Isaacson gives the mechanism in concrete terms:

“Deep sleep is the garbage disposal time of the brain. During deep sleep [...] the amyloid gets removed. REM sleep is when the short-term memories that happen during the day get translated into long-term memories.”

That line of thinking aligns with the related WHOOP summary of the Weill Cornell sleep study in people at risk for Alzheimer’s, which also highlights slow wave sleep as a key signal. It also explains why Isaacson was interested in metrics such as sleep stages, resting heart rate, and heart rate variability instead of step counts alone.

What you should take away

• Isaacson links Alzheimer’s risk to both sleep quality and cardiovascular health.
• Deep sleep is connected to overnight amyloid clearance, while REM sleep is connected to memory consolidation.
• Repeated shortfalls in sleep may show up as weaker executive function and processing speed before major symptoms appear.
• Passive sleep and cardiovascular tracking can add objective context to what people already feel subjectively.

If you want to hear Isaacson go deeper on how sleep stages map to brain health, listen to the full episode on Spotify.

What did the Weill Cornell study using WHOOP actually find?

After the mechanism comes the harder question: did the signal actually show up in data? According to Isaacson, yes.

The study enrolled 34 people at risk for Alzheimer’s disease because of family history, and 33 completed the protocol after one participant lost the device early. Participants wore WHOOP bands for about six months while the Weill Cornell team collected detailed cognitive testing and blood-based biomarkers. Isaacson credits neurologist and bioinformatics researcher Dr. Peter Yan for leading the machine learning work needed to sort through the larger WHOOP dataset beyond what appears in the app.

The team used clustering methods to divide participants into two groups based on WHOOP-collected measures, then compared those groups against cognitive performance. Isaacson says the key result was that the wearable data could separate people with stronger executive function from people whose executive function was below expected. In the paper, published in The Journal of Prevention of Alzheimer’s Disease, the group with more slow wave sleep also performed better on a cognitive task tied to prefrontal cortex function.

Isaacson summarizes the finding this way:

“Using the WHOOP-collected measures, we were able to predict whether or not a person was going to fall into two buckets [...] someone with excellent or above average executive function [...] or someone whose cognitive function was below expected.”

Will Ahmed highlights the central claim of the study during the episode: this was the first study to show that slow wave sleep identified by a wrist-worn wearable device correlated with performance on cognitive testing. That does not make WHOOP a diagnostic tool for Alzheimer’s disease. It does show that passively collected physiological data may carry clinically relevant information about cognition.

The conversation also reinforces that this was a small study. Isaacson treats it as proof of concept, not a finished answer. Even so, it adds to the case for following sleep trends over time rather than viewing one bad night in isolation.

What you should take away

• The Weill Cornell study followed 34 at-risk participants, with 33 completing the protocol.
• WHOOP-collected data separated people with stronger executive function from those with lower-than-expected executive function.
• Slow wave sleep was the clearest sleep-stage signal linked to better cognitive performance in the study.
• Isaacson describes the results as proof of concept for passive cognitive risk tracking, not a stand-alone diagnosis.

If you want to hear Isaacson unpack the study design and what Peter Yan’s analysis added, listen to the full episode on Spotify.

How could passive wearable data change Alzheimer’s research?

If a wearable signal tracks with cognitive performance, the next step is deciding what it could replace, reduce, or prioritize. Isaacson’s case for passive monitoring is built around scale, comfort, and safety.

He points out that current early-detection tools can be expensive, invasive, or hard to repeat frequently. Amyloid PET scans can cost around $5,000, are not broadly covered in the way a screening tool would need to be, and expose people to radiation. Computer-based cognitive testing and blood work can help, but Isaacson keeps coming back to a simpler idea: if a peripheral biosensor can flag risk earlier, clinicians can reserve more intensive testing for the people who need it most.

That is also where adherence matters. Isaacson says 89% of participants wanted to keep wearing WHOOP after the six-month study ended, and many kept the team leaderboard active long after the formal research period closed. For a long-horizon disease like Alzheimer’s, that kind of sustained use is a practical research advantage.

Isaacson also says the next study he wants to run is larger, around 100 patients, with a new hypothesis: that WHOOP-collected measures may help predict whether a person has amyloid in the brain. He says his team applied for funding through the Alzheimer’s Drug Discovery Foundation after new interest in digital biomarkers opened a path that did not exist when the original study started.

Isaacson makes the limitation of current imaging clear:

“You can’t even have more than two or three PET scans a year because it increases potentially risk for cancer.”

This interest in passive monitoring also fits a broader pattern across WHOOP research, including work on COVID-19 recovery tracking at Duke University, where researchers explored how continuous physiological data might support clinical follow-up outside the hospital.

What you should take away

• Isaacson sees passive monitoring as a way to screen more people before moving to expensive imaging or intensive testing.
• Wearable adherence matters in Alzheimer’s research because risk patterns develop over long periods of time.
• Isaacson says 89% of participants wanted to keep wearing WHOOP after the formal study ended.
• The next research question is whether wearable signals may help identify amyloid-positive people for follow-up testing.

For Isaacson’s full take on why passive monitoring could scale earlier Alzheimer’s screening, listen to the full episode on Spotify.

Which habits does Dr. Richard Isaacson focus on to reduce Alzheimer’s risk?

The final piece of the conversation moves from detection to action. Isaacson is careful not to present a single magic fix. He talks about stacked risk reduction, where several measurable behaviors each move the odds in a better direction.

The first two priorities are straightforward: exercise and sleep. Isaacson says they are easily two of the five most important lifestyle factors he watches for prevention. He describes exercise as the number one thing a person can do to reduce risk, then adds that the dose should fit the person’s body composition, conditioning, and goals. That emphasis lines up with other WHOOP reporting on exercise and cognitive function with Dr. Tommy Wood and healthy aging with Dr. Linda Fried.

Nutrition comes next. Isaacson says a Mediterranean-style diet matters, and he shares a personal observation from his own WHOOP data: sugar intake disrupted his physiology more than he expected. He also describes his own pattern of time-restricted eating, often waiting 12 to 14 hours, and sometimes 16 hours, between dinner and the next meal on most days. In the episode, he presents that as a personal practice he believes supports brain longevity, not as a universal prescription.

He also returns to the idea of “know your numbers.” For Isaacson, that means more than body weight or BMI. He wants people paying attention to blood pressure, resting heart rate, HRV, cholesterol, and blood sugar alongside sleep and exercise habits.

His strongest example is the SPRINT MIND trial, which he says showed that reducing blood pressure from 140 over 80 to 120 over 70 for three years lowered the likelihood of mild cognitive impairment by 19%.

Isaacson distills the daily priorities simply:

“Exercise and sleep are easily two of the five most important things.”

For people who want to keep learning, Isaacson points listeners to Alzheimer’s Universe, a free education platform with courses for the public, students, clinicians, and caregivers.

What you should take away

• Isaacson puts exercise and sleep at the top of his Alzheimer’s prevention list.
• Nutrition, including a Mediterranean-style pattern and attention to added sugar, is part of the prevention picture.
• “Know your numbers” includes blood pressure, resting heart rate, HRV, cholesterol, and blood sugar.
• Isaacson cites a 19% reduction in mild cognitive impairment risk in SPRINT MIND with tighter blood pressure control over three years.

The bottom line

• Alzheimer’s disease can begin 20 to 30 years before the first obvious memory symptoms appear.
• Isaacson separates Alzheimer’s disease into preclinical disease, mild cognitive impairment, and dementia, which shifts the focus toward earlier prevention.
• Deep sleep is relevant to Alzheimer’s research because it is tied to overnight amyloid clearance, while REM sleep is tied to memory consolidation.
• A Weill Cornell study using WHOOP-collected measures found that wearable data could distinguish between participants with stronger executive function and those with lower-than-expected executive function.
• Slow wave sleep was the clearest sleep-stage signal linked to better cognitive performance in the study discussed in Episode 49 of the WHOOP Podcast.
• Passive monitoring may help researchers identify who should receive more intensive testing, including cognitive assessment, blood work, or amyloid imaging.
• Long-term adherence is a practical advantage in Alzheimer’s research, and Isaacson says 89% of study participants wanted to keep wearing WHOOP after six months.
• Exercise, sleep, blood pressure control, and tracking personal physiology over time are the prevention priorities Isaacson returns to most often.

Frequently asked questions about things discussed in this episode

How does WHOOP measure sleep stages that matter in this research?

WHOOP tracks sleep duration, timing, and stage estimates to show how much time you spend in stages such as deep sleep and REM sleep. In the study Isaacson discusses, slow wave sleep, often grouped with deep sleep, was the sleep-stage signal most closely linked to better executive function.

What does WHOOP do for long-term trend tracking related to brain health?

WHOOP makes long-term trend tracking possible by collecting sleep and cardiovascular data passively over time. That matters in Alzheimer’s research because risk patterns may develop years before obvious symptoms appear, so one night of data is less useful than a stable pattern across weeks or months.

How does WHOOP show cardiovascular signals discussed in this episode?

WHOOP displays cardiovascular signals such as resting heart rate and heart rate variability, or HRV, alongside Sleep and Recovery. Isaacson describes those signals as useful context because cardiovascular health and brain health are closely connected in Alzheimer’s prevention research.

What does WHOOP help you notice about habits like alcohol, sugar, and sleep timing?

WHOOP helps you notice how specific behaviors show up in next-day physiology. Isaacson says his own data made the effects of alcohol, sugar, and sleep preparation more obvious, which helped him change habits he might otherwise have underestimated.

How does WHOOP fit into Alzheimer’s research today?

WHOOP fits into Alzheimer’s research today as a passive monitoring tool that may help identify meaningful physiological patterns before intensive testing begins. Isaacson presents the wearable data as a screening and research input, not as a stand-alone diagnosis for Alzheimer’s disease.

What does WHOOP do for motivation and adherence in long studies?

WHOOP supports adherence by giving people feedback they can use day to day while also making group accountability possible through shared teams and trends. Isaacson says that practical engagement mattered in his study because most participants wanted to keep wearing the device after the formal six-month period ended.

For a topic as long-horizon as Alzheimer’s risk, seeing your Sleep, Recovery, HRV, and resting heart rate trends over time can make prevention feel measurable instead of abstract.