Acute exercise activates p38 MAPK and increases the expression of telomere-protective genes in cardiac muscle.

NEW FINDINGS: What is the central question of this study? A positive association between telomere length and exercise training has been shown in cardiac tissue of mice. It is currently unknown how each bout of exercise influences telomere-length-regulating proteins. We sought to determine how a bout of exercise altered the expression of telomere-length-regulating genes and a related signalling pathway in cardiac tissue of mice. What is the main finding and its importance? Acute exercise altered the expression of telomere-length-regulating genes in cardiac tissue and might be related to altered mitogen-activated protein kinase signalling. These findings are important in understanding how exercise provides a cardioprotective phenotype with ageing. Age is the greatest risk factor for cardiovascular disease. Telomere length is shorter in the hearts of aged mice compared with young mice, and short telomere length has been associated with an increased risk of cardiovascular disease. One year of voluntary wheel-running exercise attenuates the age-associated loss of telomere length and results in altered gene expression of telomere-length-maintaining and genome-stabilizing proteins in heart tissue of mice. Understanding the early adaptive response of the heart to an endurance exercise bout is paramount to understanding the impact of endurance exercise on heart tissue and cells. To this end, we studied mice before (BL), immediately after (TP1) and 1 h after a treadmill running bout (TP2). We measured the changes in expression of telomere-related genes (shelterin components), DNA-damage-sensing (p53 and Chk2) and DNA-repair genes (Ku70 and Ku80) and mitogen-activated protein kinase (MAPK) signalling. The TP1 animals had increased TRF1 and TRF2 protein and mRNA levels, greater expression of DNA-repair and -response genes (Chk2 and Ku80) and greater protein content of phosphorylated p38 MAPK compared with both BL and TP2 animals. These data provide insights into how physiological stressors remodel the heart tissue and how an early adaptive response mediated by exercise may be maintaining telomere length and/or stabilizing the heart genome through the upregulation of telomere-protective genes.

Real-world walking economy: can laboratory equations predict field energy expenditure?

We addressed a practical question that remains largely unanswered after more than a century of active investigation: can equations developed in the laboratory accurately predict the energy expended under free-walking conditions in the field? Seven subjects walked a field course of 6,415 m that varied in gradient (-3.0 to +5.0%) and terrain (asphalt, grass) under unloaded (body weight only, Wb) and balanced, torso-loaded (1.30 × Wb) conditions at self-selected speeds while wearing portable calorimeter and GPS units. Portable calorimeter measures were corrected for a consistent measurement-range offset (+13.8 ± 1.8%, means ± SD) versus a well-validated laboratory system (Parvomedics TrueOne). Predicted energy expenditure totals (mL O2/kg) from four literature equations: ACSM, Looney, Minimum Mechanics, and Pandolf, were generated using the speeds and gradients measured throughout each trial in conjunction with empirically determined terrain/treadmill factors (asphalt = 1.0, grass = 1.08). The mean energy expenditure total measured for the unloaded field trials (981 ± 91 mL O2/kg) was overpredicted by +4%, +13%, +17%, and +20% by the Minimum Mechanics, ACSM, Pandolf, and Looney equations, respectively (corresponding predicted totals: 1,018 ± 19, 1,108 ± 26, 1,145 ± 37, and 1,176 ± 24 mL O2/kg). The measured loaded-trial total (1,310 ± 153 mL O2/kg) was slightly underpredicted by the Minimum Mechanics equation (-2%, 1,289 ± 22 mL O2/kg) and overpredicted by the Pandolf equation (+13%, 1,463 ± 32 mL O2/kg). Computational comparisons for hypothetical trials at different constant speeds (range: 0.6-1.8 m/s) on variable-gradient loop courses revealed between-equation prediction differences from 0% to 37%. We conclude that treadmill-based predictions of free-walking field energy expenditure are equation-dependent but can be highly accurate with rigorous implementation.NEW & NOTEWORTHY Here, we investigated the accuracy with which four laboratory-based equations can predict field-walking energy expenditure at freely selected speeds across varying gradients and terrain. Empirical tests involving 6,415-m trials under two load conditions indicated that predictions are significantly equation dependent but can be highly accurate (i.e., ±4%). Computations inputting identical weight, speed, and gradient values for different theoretical constant-speed trials (0.6-1.8 m/s) identified between-equation prediction differences as large as 37%.

Walking economy is predictably determined by speed, grade, and gravitational load.

The metabolic energy that human walking requires can vary by more than 10-fold, depending on the speed, surface gradient, and load carried. Although the mechanical factors determining economy are generally considered to be numerous and complex, we tested a minimum mechanics hypothesis that only three variables are needed for broad, accurate prediction: speed, surface grade, and total gravitational load. We first measured steady-state rates of oxygen uptake in 20 healthy adult subjects during unloaded treadmill trials from 0.4 to 1.6 m/s on six gradients: -6, -3, 0, 3, 6, and 9°. Next, we tested a second set of 20 subjects under three torso-loading conditions (no-load, +18, and +31% body weight) at speeds from 0.6 to 1.4 m/s on the same six gradients. Metabolic rates spanned a 14-fold range from supine rest to the greatest single-trial walking mean (3.1 ± 0.1 to 43.3 ± 0.5 ml O2·kg-body-1·min-1, respectively). As theorized, the walking portion (V̇o2-walk = V̇o2-gross - V̇o2-supine-rest) of the body's gross metabolic rate increased in direct proportion to load and largely in accordance with support force requirements across both speed and grade. Consequently, a single minimum-mechanics equation was derived from the data of 10 unloaded-condition subjects to predict the pooled mass-specific economy (V̇o2-gross, ml O2·kg-body + load-1·min-1) of all the remaining loaded and unloaded trials combined (n = 1,412 trials from 90 speed/grade/load conditions). The accuracy of prediction achieved (r2 = 0.99, SEE = 1.06 ml O2·kg-1·min-1) leads us to conclude that human walking economy is predictably determined by the minimum mechanical requirements present across a broad range of conditions.NEW & NOTEWORTHY Introduced is a "minimum mechanics" model that predicts human walking economy across a broad range of conditions from only three variables: speed, surface grade, and body-plus-load mass. The derivation/validation data set includes steady-state loaded and unloaded walking trials (n = 3,414) that span a fourfold range of walking speeds on each of six different surface gradients (-6 to +9°). The accuracy of our minimum mechanics model (r2 = 0.99; SEE = 1.06 ml O2·kg-1·min-1) appreciably exceeds that of currently used standards.

Energy expenditure during level human walking: seeking a simple and accurate predictive solution.

Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n = 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o2 rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O2·kg(-1)·min(-1), respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o2-speed model types: linear (∝V(1.0)), exponential (∝V(2.0)), and exponential/height (∝V(2.0)/Ht). We found that the proportion of variance (R(2)) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O2·kg(-1)·min(-1)). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o2 total = V̇o2 rest + 3.85 + 5.97·V(2)/Ht (where V is measured in m/s, Ht in meters, and V̇o2 in ml O2·kg(-1)·min(-1)).

Do telomeres adapt to physiological stress? Exploring the effect of exercise on telomere length and telomere-related proteins.

Aging is associated with a tissue degeneration phenotype marked by a loss of tissue regenerative capacity. Regenerative capacity is dictated by environmental and genetic factors that govern the balance between damage and repair. The age-associated changes in the ability of tissues to replace lost or damaged cells is partly the cause of many age-related diseases such as Alzheimer's disease, cardiovascular disease, type II diabetes, and sarcopenia. A well-established marker of the aging process is the length of the protective cap at the ends of chromosomes, called telomeres. Telomeres shorten with each cell division and with increasing chronological age and short telomeres have been associated with a range of age-related diseases. Several studies have shown that chronic exposure to exercise (i.e., exercise training) is associated with telomere length maintenance; however, recent evidence points out several controversial issues concerning tissue-specific telomere length responses. The goals of the review are to familiarize the reader with the current telomere dogma, review the literature exploring the interactions of exercise with telomere phenotypes, discuss the mechanistic research relating telomere dynamics to exercise stimuli, and finally propose future directions for work related to telomeres and physiological stress.

Predicting metabolic rate across walking speed: one fit for all body sizes?

We formulated a "one-size-fits-all" model that predicts the energy requirements of level human walking from height, weight, and walking speed. Our three-component model theorizes that the energy expended per kilogram per stride is independent of stature at mechanically equivalent walking speeds. We measured steady-state rates of oxygen uptake of 78 subjects who spanned a nearly twofold range of statures (1.07-2.11 m) and sevenfold range of body masses (16-112 kg) at treadmill speeds from 0.4 to 1.9 m/s. We tested the size independence of the model by deriving best-fit equations in the form of the model on four stature groups (n ≥ 15): short, moderately short, moderately tall, and tall. The mean walking metabolic rates predicted by these four independently derived equations for the same set of reference subjects (n = 16; stature range: 1.30-1.90 m) agreed with one another to within an average of 5.2 ± 3.7% at the four intermediate speeds in our protocol. We next evaluated the model's gross predictive accuracy by dividing our 78 subjects into 39 stature-matched pairs of experimental and validation group subjects. The model best-fit equation derived on the experimental group subjects predicted the walking metabolic rates of the validation group subjects to within an average of 8.1 ± 6.7% (R(2) = 0.90; standard error of estimate = 1.34 ml O2·kg(-1)·min(-1)). The predictive error of the American College of Sports Medicine equation (18.0 ± 13.1%), which does not include stature as a predictor, was more than twice as large for the same subject group. We conclude that the energy cost of level human walking can be accurately predicted from height, weight, and walking speed.