the difference between the two numbers, referred to as the mileage delta, was used to determine if a person was metabolically older or younger than their chronological age, helping researchers identify who may be aging at an accelerated rate and more at risk for chronic diseases and mortality earlier in life.
the study was the first of its kind to compare all the different machine learning algorithms to come to a general result based on the capabilities of each one.
the results
out of the 225,000 participants, those who were tested as being older than their actual age were found to have a higher likelihood of developing chronic illness or already having a chronic disease. they also had a higher risk of early mortality, as well as were in worsened health or “frailer” than those in the group that was found to be of a similar age or lower age metabolically.
these individuals were put into the accelerated aging category. they were also found to have markers that indicated a higher incidence of increased cellular aging and increased risk for age-related diseases.
good health was apparent for those in the decelerated aging group or those who were found to be the same age or younger metabolically. still, the link was not as strong in this group as it was in those who fell into the accelerated aging group. thus, while aging clocks can be used to decrease accelerated aging through early detection and intervention methods, they cannot reverse aging in those who are doing so at a typical rate.