A team of researchers from the University of Cambridge has built a model based on theoretical physics methods to predict the amount of effort and time required to make a muscle grow and thus optimize training aimed at gaining muscle mass. The results, published in the Biophysical Journal , suggest that there is an optimal weight to perform resistance training for each person and each muscle growth goal.
And it is that, although it may not seem like it, there is hardly any objective data that helps us know when we are exercising less … or too much . “Surprisingly, not much is known about the pathways through which exercise strengthens muscles: there is a lot of anecdotal or experiential knowledge, but little solid and proven data,” explains Eugene Terentjev, one of the authors of the job.
Muscles are made up of a series of individual filaments just two micrometers long and less than one micrometer wide. “Because of this, at least part of the explanation for muscle growth should be at the molecular scale,” says co-author Neil Ibata. “It was only 50 years since the description of the interactions between the main structural molecules in muscle. It is not yet well known what the role of all the proteins involved is ”. In previous work, the same researchers had already observed that one of these proteins, titin, generates certain chemical signals that are related to muscle growth.
Using this knowledge of the signaling pathway that titin protein activates, the authors began by developing a very simple mathematical model that tracked titin molecules that were activated by force and began the cell signaling cascade. Later, they added complexity to the model by adding additional information such as metabolic energy exchange, or the duration of repetition and recovery . The model was validated using long-term studies on muscle hypertrophy.
And the optimal effort is …
“While there are experimental data showing similar muscle growth with values as small as 30% of maximum load, our model suggests that 70% loads are the most efficient method to stimulate growth, ” explains Terentjev. “Below above that value, the titin kinase opening rate drops precipitously and prevents mechanosensitive signaling from occurring. Above all, rapid depletion prevents a good result, which our model has predicted quantitatively “.
The researchers’ idea is to develop an application based on their model that can provide individualized optimal exercise tables for specific goals. Ultimately, it is about not exercising too much or too little, as we have already seen that overload also prevents muscle growth. Additionally, the researchers hope to improve their model by expanding their analysis with detailed data for both men and women, as many exercise studies are heavily biased toward male athletes.
Referencia: Neil Ibata and Eugene M. Terentjev. ‘Why exercise builds muscles: Titin mechanosensing controls skeletal muscle growth under load.’ Biophysical Journal (2021). DOI: 10.1016/j.bpj.2021.07.023
Text: Cambridge University
