A New Ageing Clock To Predict Life Expectancy

How old are you really? You may know your chronological age – the number of years since you were born – but what about biological age? Not everyone ages at the same rate. Scientists are working to develop new metrics to accurately measure true biological age, predict healthy lifespan and, in turn, figure out what drives ageing and how to combat it.

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A team led by David Sinclair, professor of genetics in the Blavatnik Institute at Harvard Medical School, has just taken another step toward this goal by developing two artificial intelligence-based clocks that use established measures of frailty to gauge both chronological and biological age in mice.

“We are working to predict mouse health spans so we can quickly assess the effectiveness of interventions intended to extend life and move toward doing the same one day in humans,” said Sinclair, senior author of the study, published Sept. 15 in Nature Communications.

“It can take up to three years to complete a longevity study in mice to see if a particular drug or diet slows the aging process,” said co-first author Alice Kane, HMS research fellow in genetics in the Sinclair lab. “Predictive biometrics can accelerate such research by indicating whether an intervention is likely to work.”

After training two computer models (called FRIGHT and AFRAID) to learn from the mouse data, researchers monitored the frailty of mice given lifespan-enhancing treatments or diets. They found that the models were able to accurately predict biological age and life expectancy based on the mice’s frailty. These models have been made freely available to other scientists.

It should be possible to develop similar predictive models for humans. This would provide a less expensive, less invasive way to assess biological age than current approaches. This may not be as simple as it sounds, however:

Since frailty indices already exist for people, in principle “it would be relatively straightforward” to develop a life expectancy clock like AFRAID for humans—”if we had the proper data set,” said Schultz. “However, such a large, longitudinal dataset that tracks people from their 60s into their 90s with significant mortality follow-up data is not available, to our knowledge.”

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