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From denying that age is important to obsessively monitoring the calorific content of our diets, humans obsess one way or another about getting older. How we define ageing or when you become “old” is not trivial. In biomedical studies, particular those focused on the molecular mechanisms of growing older, loss of function and biomarkers of disease are used to define how much you have aged. From my perspective, this is almost certainly misleading.
In humans, chronological age defines the time since you were born. Whether you are considered old within your community, depends on many factors. For example, when I grew up in Glasgow in the 1970s, a 60-year-old man was very old, but a 60-year-old man in Stockholm today could be almost considered middle-aged. An alternative definition for old age, articulated by Warren Sanderson and Sergei Scherbov defines when, for example, you have ten years of life expectancy left.
So the average life expectancy varies from community to community, reflecting a mix of genetics, environment and randomly determined factors. Why you wish to define “old” also depends on what you might do with the information. It could be to plan future pension liabilities or ensure adequate management of the healthcare system (or to predict when a “free” public health service goes bust). It is also important when planning human medical research, when the prevalence of a variety of diseases, for example diabetes, dementia and heart disease increases with chronological age.
You cannot use someone’s chronological age to diagnose their health status – measuring symptoms of age-associated disease is not the same as measuring ageing. Rather than chronological age, your “biological” age is what is going to determine when you show clinical symptoms of disease. And which “age-associated” disease will depend on your genetic, epigenetic and environmental risks factors (and of course those random factors).
Our new study, published in Genome Biology, provides the first reliable tool to define this “biological age” in humans; one that is very distinct from chronological age.
We used a process called RNA-profiling to measure and compare gene expression in thousands of human tissue samples. Rather than looking for genes associated with disease or extreme longevity, we instead looked at the activation of 150 genes in the blood, brain and muscle tissue that were a hallmark of good health at the age of 65. We then used this to create a formula for “healthy ageing” that can tell us how well a person is ageing compared to others born in the same year.
Many people born in the same year can have a very different “biological age” score, which means that this is very different to measuring age just based on chronology. Another interesting discovery we made was that having a low score (something that could be considered as accelerated ageing) was associated with cognitive decline, which means that we could potentially use our work to predict those at risk of developing Alzheimer’s disease or other dementia. This was not the case for so-called “lifestyle diseases” such as heart disease and diabetes.
Some researchers have attempted to use epigenetic changes to describe ageing in relation to outside factors. Tim Spector, an epigeneticist from King’s College London, describes epigenetics as “a mechanism that describes how genes can be switched on or off by chemical signals, a bit like a dimmer switch on a light, without altering the DNA structure … These signals can alter the way genes produce proteins or signal other genes and importantly, they can last months or years.”
One process involved in epigenetics is DNA methylation (DNAm), where the addition or removal of a methyl chemical group can alter what DNA does. But the DNAm clock has modest correlations with health outcomes and variation in the “ticking rate” is not very dramatic, though is altered by some common lifestyle factors.
Common lifestyle factors (exercise, diabetes and so on) did not alter the 150 genes that we used to define biological age and our diagnostic for human biological age is distinct from the epigenetic model, stratifies people of the same chronological age more widely and so has substantially greater practical use. For now we cannot suggest that a person’s choices influence their biological age. Likewise, the link between biological age and extreme longevity has not been studied.
The challenge now is to understand how each persons risk profile for (prevalent) “disease” interacts with their “biological” age to determine which age-related illnesses they are most likely to suffer from (first).
If we can put a number on an individual’s biological age then arguably we can apply the logic articulated by Sanderson and Scherbov, that defining how we treat and support an individual as they age might be better reflected on their project life expectancy than their chronological age. This means personalised health surveillance plans or personalised pension plans. Whether this is acceptable or embraced is an entirely different question.