When animals reach middle age, their daily habits can provide clues as to how long they are likely to live. This is the conclusion of a new study supported by the Knight Initiative for Brain Resilience at Stanford University’s Wu Tsai Neurosciences Institute. The researchers observed dozens of short-lived fish continuously over their entire lifespan to better understand how behavior is related to aging.
Study with Fish
Although the fish had similar genetic characteristics and lived under the same controlled conditions, they aged in very different ways. Even in early adulthood, these differences were evident in how they swam and rested. These patterns were so pronounced that it was possible to predict whether a fish would ultimately have a shorter or longer lifespan.
Although the study focused on fish, the results suggest that recording subtle everyday behaviors such as movement and sleep – which are now often recorded by wearable devices – could provide insights into the aging process in humans.
The study, published in Science, was led by postdoctoral researchers Claire Bedbrook and Ravi Nath from Wu Tsai Neuro. It grew out of a Knight Initiative-supported collaboration between the Stanford labs of geneticist Anne Brunet and bioengineer Karl Deisseroth, the study’s lead authors.
Tracking Ageing in Real Time
Most aging studies compare young animals with older ones. While this approach is useful, it can ignore how ageing occurs over time in individuals and how differences develop between individuals. Bedbrook and Nath wanted to track aging continuously over the entire lifespan. Even animals reared under almost identical conditions can age differently and have very different lifespans. The team wanted to find out whether natural behavior could shed light on when these differences begin.
They used the African turquoise killifish, a species with a lifespan of only four to eight months. Despite its short lifespan, it shares important biological features with humans, including a complex brain, which makes it a valuable model for ageing research. The Brunet lab has played a leading role in establishing the killifish as a model organism. This study was the first to follow individual vertebrates continuously, day and night, throughout their adult life.
The researchers developed an automated system in which each fish lived in its own tank under constant camera surveillance. Similar to a real-life version of “The Truman Show”, the system recorded every moment of each animal’s life. In total, the team tracked 81 fish and collected billions of video images. Using this huge data set, they analyzed posture, speed, resting phases and movement. They identified 100 different “behavioral syllables” – short, repetitive actions that form the basic elements of the fish’s movement and resting patterns. With this detailed record, the researchers began to ask new questions: When do individuals begin to age differently? What early characteristics determine these trajectories? And can behavior alone predict lifespan?
Early Behavioral Signals for Longevity
One of the most striking discoveries was how early the ageing pathways begin to differ. After tracking each fish throughout its life, the team grouped them by lifespan and then looked back to see when behavioral differences first appeared. They found that fish that would later live longer or shorter lives already behaved differently in early midlife (at 70 to 100 days of age).
Sleep patterns stood out as a decisive factor. Fish that ultimately had a shorter lifespan tended to sleep not only at night, but increasingly during the day as well. In contrast, fish that lived longer mostly slept at night. Activity level also played a role. Fish with longer lifespans swam more vigorously and reached higher speeds when moving around the tank. They were also more active during the day. This type of spontaneous movement was also associated with longevity in other species.
Importantly, these behavioral differences were not only descriptive, but also predictive. Using machine learning models, the researchers showed that just a few days of behavioral data from middle-aged fish were sufficient to estimate lifespan.
Ageing Takes Place in Clearly Defined Phases
The study also showed that ageing does not progress slowly and evenly. Instead, most fish underwent two to six rapid behavioral changes, each lasting only a few days. These transitions were followed by longer phases of stability that lasted for weeks. The fish usually went through these phases one after the other, rather than switching back and forth between them.
“We had expected aging to be a slow, gradual process,” Bedbrook said. “Instead, the animals remain stable for long periods of time and then move very quickly into a new phase. Seeing this gradual structure emerge just from the continuous behavior was one of the most exciting discoveries.” This gradual pattern is consistent with findings from human studies that suggest that molecular changes occur in waves with age, particularly in midlife and old age. The results in killifish provide a behavioral perspective on this phenomenon.
The researchers hypothesize that aging may involve long periods of relative stability interrupted by short, rapid changes. They liken this to a Jenga tower, where many blocks can be removed without much effect until a crucial change triggers a sudden shift. To explore the biology behind these patterns, the team studied gene activity in eight organs at a stage where behavior could reliably predict lifespan. Instead of focusing on individual genes, they looked at coordinated changes across groups of genes involved in common processes.
The most striking differences were found in the liver. Genes related to protein production and cell maintenance were more active in fish with shorter lifespans. This suggests that with advancing age, internal biological changes occur in addition to behavioral differences.
Behavior Provides Insight into the Aging Process
“Behavior turns out to be an incredibly sensitive indicator of aging,” Nath said. “You can look at two animals of the same chronological age and tell just by their behavior that they are aging very differently.” This sensitivity is evident in many aspects of daily life, particularly sleep. In humans, sleep quality and sleep-wake cycles often decline with age, and these changes are associated with cognitive decline and neurodegenerative diseases. Nath plans to investigate whether improving sleep could support healthier ageing and whether early interventions could influence the ageing process. The researchers also want to investigate whether ageing processes can be influenced by targeted strategies, including dietary changes and genetic interventions that could influence the pace of ageing.
For Bedbrook, the findings raise broader questions: What drives the transition between the different stages of aging, and can these changes be delayed or reversed? She is also interested in moving to more natural environments where animals can interact socially and experience more realistic conditions. “We now have the means to continuously track the aging process in a vertebrate,” she said. “With the proliferation of wearables and long-term monitoring in humans, I’m excited to see if the same principles – early predictors, staged aging, divergent trajectories – apply in humans.”
Another important area of research concerns the brain. Deisseroth’s lab is developing tools to continuously monitor neuronal activity over long periods of time, which could shed light on how changes in the brain relate to or potentially influence the rate of aging in the rest of the body. Ultimately, this research aims to explain why aging occurs so differently and to find new ways to enable a healthier, longer life.