If you watched a bunch of animals living in identical conditions, you’d expect them to age in roughly similar ways.
But in a new study from Stanford, short-lived fish with similar genetics still aged very differently – and the weird part is that the differences showed up early. By “midlife,” the way a fish swam and slept could already signal whether it was headed for a long life or a short one.
The study was led by postdoctoral scholars Claire Bedbrook and Ravi Nath, and grew out of a collaboration between the labs of geneticist Anne Brunet and bioengineer Karl Deisseroth.
The big idea is simple but kind of mind-bending: behavior might be one of the earliest, most sensitive readouts of how aging is unfolding – not just in fish, but possibly in other species too.
Watching aging the hard way
Most aging studies compare groups of young animals with groups of old animals. That’s useful, but it’s also like comparing childhood photos to retirement photos and trying to guess what happened in between.
You miss the personal timeline – the twists, the plateaus, and the moments when things suddenly change.
Bedbrook and Nath wanted to do something more interesting: follow the same individuals continuously, day and night, across their whole adult lives.
To pull that off, they used the African turquoise killifish – a lab favorite for aging studies because it lives only about four to eight months, but still has many biological features shared with longer-lived vertebrates, including humans.
Capturing the daily routines of fish
The researchers built an automated setup where each fish lived in its own tank under constant camera surveillance. In total, they tracked 81 individuals and collected billions of video frames.
From all that footage, the team extracted detailed measures of posture, speed, movement, and rest.
Behavior was broken down into 100 distinct “behavioral syllables” – small, repeatable actions that make up the fish’s daily routine.
“Behavior is a wonderfully integrated readout, reflecting what’s happening across the brain and body,” Brunet said.
“Molecular markers are essential, but they capture only slices of biology. With behavior, you see the whole organism, continuously and non-invasively.”
Fish diverged early, not late
After the fish lived out their full lives, the researchers grouped them by lifespan – who lived longer, who died earlier – and then basically rewound the tape to see when the differences began.
They found the separation started surprisingly early: by about 70 to 100 days of age, which is early midlife for a killifish, the “short-life” and “long-life” fish were already behaving differently.
Sleep was one of the clearest signals. Fish that died sooner tended to sleep not only at night, but more and more during the day. Fish that lived longer mostly slept at night, keeping a more normal day-night rhythm.
Movement also mattered. The longer-lived fish tended to swim with more vigor. When they darted around the tank, they hit higher speeds. They were also generally more active during daylight hours.
And the really striking part: these weren’t just patterns you could notice after the fact. The team used machine-learning models and found that just a few days of behavioral data from a middle-aged fish could predict its eventual lifespan.
“Behavioral changes pretty early on in life are telling us about future health and future lifespan,” Bedbrook noted.
Aging wasn’t a smooth slide
Another surprise was the shape of aging. The researchers expected slow, gradual decline. Instead, most fish went through two to six rapid behavioral transitions – each lasting only a few days – followed by longer stable stages that lasted weeks.
The fish didn’t bounce back and forth randomly between these stages. They mostly moved forward in sequence, like passing through chapters.
“We expected aging to be a slow, gradual process. Instead, animals stay stable for long periods and then transition very quickly into a new stage,” Bedbrook said.
“Seeing this staged architecture appear from continuous behavior alone was one of the most exciting discoveries.”
The team likens the pattern to a Jenga tower. You can pull out a lot of blocks with little obvious change – until one removal forces a sudden reshuffling.
This “stepwise” aging idea also lines up with some human research suggesting aging markers change in waves, especially in midlife and older age. The killifish study adds a behavioral version of that story.
The biology behind it
The researchers didn’t stop at behavior. At a point in adulthood when behavior could reliably predict lifespan, they looked at gene activity across eight organs.
Instead of obsessing over single genes, they looked for coordinated shifts across groups of genes involved in shared processes.
The biggest differences showed up in the liver. Fish on shorter aging paths had higher activity in genes related to protein production and cellular maintenance.
It’s not a full explanation, but it’s a molecular hint that the internal biology is shifting alongside the behavioral changes.
Why this could matter for people
This is a fish study, so nobody’s saying your smartwatch can predict how long you’ll live tomorrow. But the idea has obvious human echoes.
We already track movement and sleep constantly through phones and wearables. If subtle changes in activity patterns signal early health shifts – long before disease is obvious – that could be a powerful tool for prevention.
“Behavior turns out to be an incredibly sensitive readout of aging,” Nath said. “You can look at two animals of the same chronological age and see from their behavior alone that they’re aging very differently.”
Sleep, in particular, is a big focus for the team moving forward. In humans, sleep quality often declines with age, and disrupted sleep has been linked to cognitive decline and neurodegenerative disease.
Nath wants to explore whether sleep itself can be manipulated to support healthier aging – and whether early intervention could nudge someone onto a better aging trajectory.
Future research directions
The researchers also plan to test whether aging paths can be altered through interventions like diet changes or genetic tweaks that might slow the pace of aging.
Bedbrook is interested in making the system more “real life,” too – letting fish interact socially and experience richer environments, rather than living alone in a tank.
“We now have the tools to map aging continuously in a vertebrate,” she said. “With the rise of wearables and long-term tracking in humans, I’m excited to see whether the same principles – early predictors, staged aging, divergent trajectories – hold true in people.”
And Deisseroth’s lab adds another frontier: tracking brain activity continuously over long periods to see how neural changes line up with these behavioral aging stages.
If the brain’s activity shifts in step with aging – or even helps drive it – that could open a whole new way of thinking about what sets the pace of aging in the first place.
The study is published in the journal Science.
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