Science is one step closer to being able to accurately predict your lifespan based on your daily habits, thanks to a new study from Stanford University.
Researchers monitored the behaviour of 81 African turquoise killifish in camera-monitored tanks over the course of their entire lifespans – that is, four to eight months.
After analysing billions of frames of video footage, the scientists could then link daily behaviour patterns to the likelihood of living a longer life.
“One major finding from this study is that behaviour is a non-invasive readout of the ageing process,” co-lead author Dr Claire Bedbrook, a bioengineer and neuroscientist, told BBC Science Focus.
“This suggests that tracking simple metrics, such as activity and sleep, throughout a 24-hour cycle can inform us about where we are in our ageing progression – and even predict our future lifespan.”
Bedbrook explained that, since smartwatches were already commonplace, scientists could soon be able to “understand and quantify where an individual is on their journey from adulthood to death.”
In the meantime, the findings from this study can teach us about the ageing process of animals with complex brains.
One such finding was that, by early midlife – around 70–100 days into their lifespan – fish that would go on to live longer lives were already behaving differently to fish that died more quickly.
“I think it’s so interesting that we were able to take an animal’s behaviour at a relatively young age, and accurately estimate its age and whether it would live a long or short life,” co-lead author Dr Ravi Nath, a neuroscientist and geneticist, told BBC Science Focus.
In particular, the scientists found differences in sleeping patterns. Longer-life fish mainly slept at night, whereas shorter-life fish increasingly slept during the day as young adults.
At the same time, more active fish – those that swam more vigorously, quickly and spontaneously around their tanks during daylight hours – were likely to live for longer.
The scientists managed to identify a total of 100 distinct ‘behavioural syllables’, referring to short actions that make up the building blocks of killifish behaviour, many of which they could link to lifespan.
Using machine learning models, the researchers could accurately forecast the lifespan of an individual fish, based on just a few days of behavioural data from the middle of its life.
The scientists also found that the fish aged in around two to six stages, rather than continuously – a finding that mirrors recent research on humans.
“I initially hypothesised that ageing is a gradual process, with each day slightly worse than the last,” said Bedbrook. “Instead, behavioural tracking reveals long periods of stability punctuated by abrupt transitions, during which animals rapidly age and enter a new life stage.”
Future studies could analyse behaviour in a more naturalistic setting, the scientists said, allowing the fish to have more social contact with each other.
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