Transcriptomic age and transcriptomic age acceleration
DISEASE
Both groups began the trial with positive transcriptomic age acceleration. In other words, mean transcriptomic age was higher than mean chronological age in both groups at baseline. In the exercise group, TA and TAaccel decreased following the HIIT protocol, while both measures increased in the control group over the same time frame. Although the observed changes in both groups were relatively large considering the trial's length, changes in TA and TAaccel only achieved statistical significance within the exercise group. Specifically, a 3.59‐year reversal of TA was observed in the exercise group, which can be interpreted as the average gene expression pattern among exercise participants changing to reflect that of a person 3.59 years younger than their mean baseline TA.Interestingly, changes in TA and TAaccel were the only statistically significant Group X Time observations among all measured variables. This may suggest a focal effect of HIIT on biological aging processes; however, it is important to acknowledge that the correlation between changes in TA and long‐term aging processes is unknown. While this metric is shown to be associated with chronological age, transcriptional processes are rapidly modifiable and the durability of short‐term changes in TA over time is not clear.This trial supports our prespecified hypothesis that HIIT was capable of beneficially modulating transcriptomic age, but future research should obtain TA measurements over longer time frames, utilize a larger sample size, include multiple TA measurement intervals, and include longitudinal follow up to establish the durability of TA change over time and to ascertain its correlation with aging and disease processes. Future studies may additionally include numerous biological age prediction models to establish convergent validity across different methodologies.The TRAP model consistently overestimated participant age in all blood samples. The authors believe this is due to differences in data type between the TRAP training dataset and our sample. The TRAP model was developed and trained using microarray data (Peters et al., 