Are MPS statistics useless as a predictor of hypertrophy?

richln

MPS (muscle protein synthesis)
MPB (muscle protein breakdown [proteolysis])
RT (resistance training)

In the last decade or so, I have seen many people misuse acute MPS statistics (MPS measurements directly following RT) as a sort of reductionist proxy for muscle hypertrophy. The argument usually sounds something like this:

“Studies show that net muscle protein balance (MPS-MPB) is increased immediately after RT, then returns to baseline about 48 hours after training (for trainees not using PEDs). Therefore, you should train the muscle about every 48 hours to optimize hypertrophy. You should also consume at least 20 g of protein within a couple of hours after RT to support peak MPS response timing.”

The first statement in the above argument is demonstrably true (in general) [1]. However, the second and third statements above may be nothing more than a misapplied logical fallacy.

It is convenient and desirable to simplify a biochemical process as complex as hypertrophy to a singleton-dimension factor such as cumulative response to acute MPS-stimulating events. After all, it seems intuitive and logical that highly-specific biomarkers such as MPS can be optimized to subsequently optimize muscle growth. As such, we derive the assumed concept that whatever stimulus causes most time spent in positive net muscle protein balance results in the most hypertrophy [max(MPS-MPB) = max(bigness)]. However, a 2014 paper from Mitchell et. al. [3] created quite a stir by concluding that acute MPS measurements resulting from chronic RT are not correlated with muscle hypertrophy. An important limitation of the study is that it only measured MPS over 6 hours after the training event, but it is the first study (that I am aware of) that empirically demonstrated the lack of correlation between RT-induced MPS and muscle hypertrophy.

This paper was not the first that started punching holes in the hypothesis that acute MPS measurements are predictive of muscle hypertrophy. Compared to RT acute MPS response, a similar MPS response is also observed in non-RT exercise bouts where hypertrophy does not occur [5]. Stimulus-specific fractional synthesis rates (i.e., myofibrillar vs. mitochondrial MPS), have been proposed to explain the former, [5,2], yet this proposal seems to be refuted empirically by [3], which isolated and quantified myofibrillar MPS specifically. The elderly have shown ~70% of the acute MPS response to RT as compared to young people [6], although hypertrophy response is severely blunted in the elderly [7]. To date, there are still strong gaps in hypertrophy knowledge as “the mechanisms regulating MPS and adaptation to exercise [hypertrophy] still remains poorly defined” [2].

In addition to known mechanisms that are the suspected master regulators of MPS (AKT-mTOR signaling) and MPB (ubiquitin-proteasome system), mRNA levels of MAFbx and MuRF1 may contribute to atrophy beyond the proteolysis pathway, and observations have been made of additional pathways initiating MPS independently of AKT-mTOR signaling [8]. Furthermore, Murton and Greenhaff posit “… it has become commonplace to extrapolate from data generated by studies investigating acute responses to resistance exercise to explain chronic training adaptations. However, no evidence exists to validate this practice and the need for research on the temporal changes of muscle to resistance exercise training continues to be of paramount importance” [8]. It has been proposed that acute MPS/MPB and molecular signaling following RT may be more appropriately considered as a part of a localized stress/inflammation response than an indicator of future lean mass gain [8,9]. A newer paper from Camera et. al. proposes that the direct relationship between the acute response from the cellular signaling network and chronic adaptation is order of magnitudes more complex than our current understanding, citing repeated conflicting results observed between signaling events and adaptation outcome, and anomalies such as “normal responses and adaptations to both acute exercise and chronic exercise training can be seen when one or more key pathways are absent, are blocked with drugs, or are otherwise attenuated.” [10]

Some further flaws in the reasoning that acute MPS is a predictor of hypertrophy: acute MPS returns to baseline sooner for trained lifters after RT [4] implying that trained lifters would benefit from higher frequency training than untrained lifters, but this does not account for recovery factors resulting from greater training load used by trained lifters; MPS is elevated by meal timing but modern meta-analyses conclude that meal timing is a relatively insignificant predictor of hypertrophy as other confounders appear to dominate [11,12]; and does not model supercompensation effects.

Further recommended reading: https://www.reddit.com/r/ResearchReview/comments/4eg7we/research_review_6_does_muscle_protein_synthesis/

1. Phillips, Stuart M., et al. "Mixed muscle protein synthesis and breakdown after resistance exercise in humans." American journal of physiology-endocrinology and metabolism 273.1 (1997): E99-E107.
2. Atherton, P. J., and K. Smith. "Muscle protein synthesis in response to nutrition and exercise." The Journal of physiology 590.5 (2012): 1049-1057.
3. Mitchell, Cameron J., et al. "Acute post-exercise myofibrillar protein synthesis is not correlated with resistance training-induced muscle hypertrophy in young men." PloS one 9.2 (2014): e89431.
4. Damas, Felipe, et al. "A review of resistance training-induced changes in skeletal muscle protein synthesis and their contribution to hypertrophy." Sports Medicine 45.6 (2015): 801-807.
5. Kumar, Vinod, et al. "Human muscle protein synthesis and breakdown during and after exercise." Journal of Applied Physiology 106.6 (2009): 2026-2039.
6. Kumar, Vinod, et al. "Age‐related differences in the dose–response relationship of muscle protein synthesis to resistance exercise in young and old men." The Journal of physiology 587.1 (2009): 211-217.
7. Welle, Stephen, Saara Totterman, and Charles Thornton. "Effect of age on muscle hypertrophy induced by resistance training." The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 51.6 (1996): M270-M275.
8. Murton, A. J., and P. L. Greenhaff. "Resistance exercise and the mechanisms of muscle mass regulation in humans: acute effects on muscle protein turnover and the gaps in our understanding of chronic resistance exercise training adaptation." The international journal of biochemistry & cell biology45.10 (2013): 2209-2214.
9. Phillips, Bethan E., et al. "Molecular networks of human muscle adaptation to exercise and age." PLoS genetics 9.3 (2013): e1003389.
10. Camera, Donny M., William J. Smiles, and John A. Hawley. "Exercise-induced skeletal muscle signaling pathways and human athletic performance." Free Radical Biology and Medicine 98 (2016): 131-143.
11. Aragon, Alan Albert, and Brad Jon Schoenfeld. "Nutrient timing revisited: is there a post-exercise anabolic window?." Journal of the international society of sports nutrition 10.1 (2013): 5.
12. Schoenfeld, Brad Jon, Alan Albert Aragon, and James W. Krieger. "The effect of protein timing on muscle strength and hypertrophy: a meta-analysis." Journal of the International Society of Sports Nutrition 10.1 (2013): 53.

TLDR: There is significant evidence suggesting that one should be skeptical of any claims linking hypertrophy to acute MPS statistics.

MFPLiftWeightsRun

Cool. So..training every two days is not proven best training schedule?
I didn't think so either (extra rest days sometimes are key)
But what is the optimized schedule?

Gallowmere1984

PhilipABullen wrote: »

Cool. So..training every two days is not proven best training schedule?
I didn't think so either (extra rest days sometimes are key)
But what is the optimized schedule?

I have a feeling that as with most things, optimal will end up being very individual. There are so many factors such as stress, how one manages said stress, sleep, nutrition, recovery practices, genetic variability in hypertrophic potential based in hormone profiles, etc.

richln

I agree with Gallowmere. Unfortunately, I don't think there is an optimal training frequency. If there is, then it is not static.

In addition to the short-term factors that Gallowmere mentioned, every individual will have a different recovery response to intensity, load, and volume from any given workout, coupled with daily variances in NEAT. Smart programming will attempt to compensate for these factors, but as discussed in a couple of those references I linked, chronic adaptation results in the scientific literature are pretty all over the place, even for people placed on the same programming.

Even if an individual found an optimal short-term frequency (or perhaps, cycled frequencies), it would likely change over long-term due to accumulated adaptation, physical age and training age.