Chronological definitions of aging are easy, yet intuitively we all know that age is not just a number. It’s common to see people who “don’t look their age”, whether it’s a 91-year-old grandmother completing a marathon or a lifelong smoker. These people don’t look their age, and biologically they likely aren’t. How can we explain this? Well, like most of biology, physiological aging is due to a combination of genetics and lifestyle, and is extremely complex.
To understand physiological aging, first we need to find a single physiological parameter that predicts chronological aging in a homogenous population. Some studies, such as a recent one that studied aging in mice, develop an aging signature, which consists of a profile of biomarkers that can predict age. However, a single aging parameter would allow aging researchers to conduct simple and more efficient tests to determine whether specific interventions (pharmacological, lifestyle, or otherwise) slow or speed up the aging process. That’s the challenge Dr. Ross Pollock and colleagues addressed in a recent paper published in The Journal of Physiology titled “An investigation into the relationship between age and physiological function in highly active older adults.”
In order to maximize their chances to find a physiological function that represented aging, the scientists needed to minimize genetic and lifestyle factors in a cross section of subjects spanning various ages. This idea is critical to the concept of biological versus chronological aging. So let me repeat it another way: in order to best study chronological aging in a cohort of subjects, those subjects must have similar biological aging rate. The scientists decided on a homogenous cohort of non-elite, highly active, older cyclists. Once subjects were recruited, the scientists put these cyclists through a battery of physiological tests to measure muscle strength, explosive cycling power, aerobic capacity (VO2max), neuromuscular junction health, bone density, metabolic health, blood markers, body fat distribution, and cognitive health. So what did they find?
The physiological trait that was most correlated with age (i.e. could best predict age) was aerobic capacity, also known as VO2max. Aerobic capacity is a measure of how much oxygen your body can use. The test is performed by scientists monitoring the exhaled oxygen levels of a subject performing some sort of exercise, typically either on a stationary bike or treadmill. As the exercise intensity is increased, subjects use more and more oxygen to produce energy aerobically (or with oxygen) until they reach their maximal levels. While VO2max only directly measures oxygen consumption, it indirectly measures much more. It represents how strong the heart is, it’s ability to pump oxygen rich blood to the muscles, the blood supply to skeletal muscle, and how well muscle can use that oxygen to create the energy needed for movement. VO2max is also correlated with a lower risk of developing many chronic diseases, and more importantly, a lower risk of death. So perhaps it wasn’t that surprising that VO2max was correlated with age after all. Now scientists can use VO2max to predict chronological age, right? Well not quite…
Most cross-sectional studies with a large number of subjects can see statistically significant correlations, but most of those correlations are not predictive in any clinically meaningful way. These scientists were only interested in a correlation of 80% or greater, which would mean that the measurement could predict 80% of the variation in chronological age. The strongest correlation they found, between age and VO2max, could only explain 40% of age in men and 30% in women. That’s not even close to the 80% correlation that would be predictive in any clinically meaningful way. Still VO2max was better at predicting age than metabolic health, bone density, or neuromuscular health, all of which were correlated with age, but showed even lower predictive utility.
The authors concluded by stating, “The biological ageing process, even when free from confounding factors, is thus likely to be highly individualistic.” The complexity they refer to is in part what makes aging so difficult to study. People are different; different from animal models and different from other individuals, even when they seem so much a like as they were in this study. In the future, scientists will need to follow individual people over a long period of time to have any hope of discovering a physiological measurement that can predict aging. I don’t know about you, but I find myself asking, “Where do I sign up?”