Resistance training exercise is clearly recognized as the primary method for increasing muscle mass. Despite its popularity, not all people respond the same way to resistance training stimuli, nor do they experience the same degree of chronic adaptations such as hypertrophy. This hypertrophic variability may be affected by gender, age, diet, physical activity level, previous training status and various factors related to genetic predisposition. Although it would be convenient to assume that one specific training method could generate the same response without variance among different people, conclusive evidence shows that this is not true. It has been previously distinguished that those people who positively respond to a training program/stimuli fall under a “responders” category, while those that do not experience any chronic adaptation after identical training processes have been identified as “non-responders”. This area of research is currently receiving a great deal of interest due to the possibility of increasing the effectiveness of prevention programs at the general health level as well as athletic programs to expedite recovery from injury or improved performance.

One way to clarify genetic predisposition for hypertrophic adaptations from resistance training stimuli is by analyzing non-coding RNA, considering their importance in adaptation of skeletal muscle at the molecular level. MicroRNA (miRNA) in particular is accepted as a primary regulator of mammalian cell phenotype. One of the central functions of miRNA is the development and differentiation of skeletal muscle tissue. One emerging term related to the identification of microRNA in skeletal muscle is “myomirs”, which are a collective group of several miRNA that are regulated during muscle development (including miR-133a/b, miRNA-206 and miR-1). In a recent research it was demonstrated that high responders to resistance training exercise have a differential regulation of skeletal muscle microRNA expression. In this study, 56 young men who undertook a 5 day/wk resistance training protocol for 12 weeks to increase muscle mass were divided in two group; those that increased muscle mass (upper 20% of the group) and those that showed moderate or no changes (lower 20% of the group). These two groups were analyzed for expression levels of 21 different microRNAs by performing a biopsy in their vastus lateralis. Lead author Peter Davidsen reported that out of the 21 miRNA examined, 17 were stable in both groups and four were differentially expressed. Low responders showed a downregulation in miR-378, miR-29a and miR-26a while miR-451 was upregulated. These results indicate that specific genetic characteristics are needed for maximum results following a resistance training program with the goal of hypertrophy.

Many fitness professionals have realized by now that the Elliptical Trainer and other static modalities do not predict energy expenditure accurately and based on usage dynamics commonly over-predict the caloric expenditure – giving clients a false sense of accomplishment and frustration at the lack of results at the same time. To correct this issue the first step is identifying how many calories a person can actually expend. Walk and run tests can do this with acceptable accuracy and present viable numbers to work with for weight loss. Below are two formulas for calculating the predicted VO2max and subsequent caloric expenditure potential. The one mile walk test best serves the less fit population, whereas the 1.5 mile run/jog test is designed for those in better shape. One caveat to both is the individual needs to perform at maximal effort and the distance must be accurate. Therefore, measuring the distance and practicing the test for pace is important for optimal validity.

Trainers should still aim to use the most efficient methods for optimizing adaptation relative to their client’s goal(s), but it must be kept in mind that genetic predisposition is obviously a major determinant of the rate and degree of possible adaptation. One must not misuse this information as a crutch however, stating that poor results are solely due to genetic-related factors (as it may be due to inadequate programming). The capacity to classify individuals as responders or non-responders could help to develop the most appropriate exercise prescription as well as realistic goals.