Studies of the dose-response curve for exercise have typically shown that little exercise is much better than no exercise, and gains continue at a slowing pace up to a fair way above the recommendation of 150 hours of moderate to vigorous physical activity per week. See research showing that two to four times that amount further improves outcomes, for example. At some point, however, diminishing returns tip over into harm. There is such as thing as too much physical activity, though few people reach that point. The study here, in which researchers generate a brain aging metric derived from neuroimaging data and correlate its progression with physical activity, is interesting for producing a dose-response curve that looks very similar to those derived from data on physical activity versus incidence of age-related disease or mortality.
A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. A total of 16,972 eligible participants with both valid T1-weighted neuroimaging and accelerometer data from the UK Biobank were studied. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM).
Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders.
The brain age estimated by LightGBM achieved an appreciable performance (r = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (r = 0.90, MAE = 3.03). We found that LPA (F = 2.47), MPA (F = 6.49), VPA (F = 4.92), and MVPA (F = 6.45) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Improved brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.
Link: https://doi.org/10.34133/hds.0257
View the full article at FightAging
