Resistance exercise training and circulatory responses to feeding and skeletal muscle protein anabolism in older men
Bibliographic record
Abstract
Sarcopenia, the age-related loss of muscle mass, contributes to frailty and the loss of independence in older adults. It is well known that ageing results in a decreased sensitivity of skeletal muscle to the anabolic effects of dietary protein, and one explanation for this may be the age-related reduction in muscle leg blood flow and microvascular blood volume (MBV). Low MBV could compromise the postprandial transport of key amino acids and insulin to skeletal muscle where they are known to stimulate muscle protein synthesis (MPS) and arrest rates of muscle protein breakdown (MPB), respectively. As a result, strategies that enhance MBV in older adults may augment the delivery of amino acids and insulin to skeletal muscle following a meal and thus serve as a viable means to combat sarcopenia. Resistance exercise training (RET) is a well-known and potent medicine for enhancing muscle protein mass as well as improving a variety of health risk factors in older adults. Yet, the physiological mechanisms underpinning these effects remain unclear. Specifically, the impact of RET on MBV and protein turnover in older adults remains largely unknown. A recent article published by Phillips et al. (2015) in The Journal of Physiology provides a unique insight into this area of research. In this study the authors compared two groups of 10 older men. One group had completed 20 weeks of supervized whole-body RET prior to testing, and the other group served as an untrained control group. Phillips et al. (2015) measured leg blood flow (LBF) by Doppler ultrasound and muscle MBV by contrast enhanced ultrasound (CEUS) in the fasted and fed state (fed-state measurements were obtained following 2 h of intravenous amino acid (Glamin) and dextrose infusion). The authors also employed stable isotopic tracers and performed multiple skeletal muscle biopsies to assess fasted and fed rates of MPS. Notably, MPB and net protein balance (NPB) via the arteriovenous balance method were measured as well. This is an important aspect of the present study, since measurements of MPB and NPB are often excluded. The main finding from the investigation was that although the RET group demonstrated greater increases in both fed state LBF and MBV compared to the untrained group, MPS and NPB were not statistically different between groups. In addition, they demonstrated that fed-state MPB was suppressed in the RET group. The authors concluded that the acute suppression of fed-state MPB could confer a positive impact on the maintenance of muscle mass in the long-term and future studies are now required to test this hypothesis. The vast majority of studies to date have examined only a single aspect of muscle protein metabolism in isolation (i.e. MPS or MPB), in response to an exercise or nutrition stimulus. As such, it is difficult to interpret the efficacy of nutritional or exercise interventions to enhance net skeletal muscle mass. Despite the inclusion of gold-standard methods to assess protein turnover, the authors of the current study observed somewhat surprising results. Specifically, that 20 weeks of RET did not increase fed-state myofibrillar MPS, nor did it increase the rate of disappearance of labelled phenylalanine (an estimation of MPS). Indeed, resistance exercise sensitizes skeletal muscle to the anabolic impact of protein ingestion (Yang et al. 2012; Murphy et al. 2015). Therefore, we would have expected older adults who regularly engaged in RET to be more sensitive to feeding compared to untrained older adults. It is important to acknowledge that Phillips et al. (2015) are the first to examine the MPS response to feeding alone in a group of older adults who are resistance trained, compared to an untrained group. However, this experimental approach, whilst minimizing muscle biopsy numbers per participant, does mean that genetic and other dietary/physiological differences between participants may have impacted the outcomes. The authors did attempt to minimize such differences by matching their groups for age, body mass index and lean body mass, but there is still likely to be more variability in this model compared to a repeated measures design in the same individuals. Another interesting finding from this study was that muscle NPB was not elevated in the RET group compared to the untrained group despite a significant reduction in the rate of appearance of phenylalanine, which is suggestive of suppressed MPB. One unfortunate limitation of the arteriovenous method is that it employs a two-pool model of calculation. NPB and MPB are estimated based on the difference in the concentration of phenylalanine in the artery versus the vein, and the dilution of labelled phenylalanine with unlabelled phenylalanine in venous blood, respectively. Since free amino acids in the intracellular pool are not directly measured, it is not possible to determine whether labelled phenylalanine is being retained in the intracellular free amino acid pool, or incorporated into muscle proteins (Volpi et al. 2001). This is likely to explain why the decrease in fed-state MPB does not appear to be reflected in the NPB data. The application of the three-pool model (which includes measuring the free amino acid dilution in the intracellular pool in addition to measuring phenylalanine in the artery and vein) may have provided more sensitive measurements, and an enhanced ability to detect changes in NPB, MPS and MPB due to RET and thus is perhaps a consideration for future work in this area. Somewhat counter-intuitively, Phillips et al. (2015) observed that RET did not enhance whole-body glucose uptake, and as such, inferred that whole body insulin sensitivity was similar between groups. Moreover, they also failed to identify a change in lean body mass in response to RET. The lack of improvement in insulin sensitivity is in contrast to a previous study by Miller et al. (1994), where RET was successful in increasing glucose tolerance and insulin sensitivity (measured using glycaemic clamps) even though lean body mass did not significantly increase in their middle aged-to-older participants. Phillips et al. (2015) attributed the significant increase in fed-state MBV demonstrated only by the RET group to an increase in insulin sensitivity of the vascular endothelium following training. However, it is difficult to determine whether this is the case since endothelial function was not measured. Furthermore, vascular architecture is highly variable between individuals and therefore this method (CEUS) is the most effective when used in a longitudinal model where participants are evaluated multiple times (Phillips, 2015). Unfortunately, due to the invasive nature of this study, it was not feasible to perform repeated measures before and after training. In the future it would be of benefit to repeat CEUS in the same individuals so that true remodelling within the vasculature can be observed. The lack of change in lean body mass in response to RET could be related to a blunted capacity for older adults to accrue muscle mass with resistance exercise, though perhaps the lack of improvement demonstrated in the current study can be attributed to the lower intensity of the training stimulus (2 sets at 70% 1RM versus other studies where participants have trained for 3 sets at 80% 1RM with repetitions performed to volitional fatigue). In summary, we applaud Phillips et al. (2015) for the comprehensive nature of the present study. The integration of multiple measurements to assess muscle anabolism in conjunction with the effects of feeding is the direction in which future research in the area of muscle metabolism needs to progress. The main finding from this study was that RET can effectively enhance fed-state LBF and MBV while adequately attenuating MPB with feeding. Though there are inherent limitations with the study design, this is one of the first investigations into fed-state muscle metabolism that has also examined vascular responses, thus highlighting the importance and difficulties in measuring the necessary variables. Their findings of increased rates of MPS with feeding are congruent with our laboratory though the lack of elevated MPS response following RET is unexpected and warrants further investigation (Yang et al. 2012). None The authors would like to thank Dr. Stuart Phillips for his constructive comments during the preparation of this manuscript.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".