However, the aforementioned studies in this section demonstrating that different surrogates of hypertrophy seemingly disagree with each other prompted our laboratory to examine VL myofibrillar protein concentration differences between high versus low anabolic responders following a week full body resistance training program Roberts M.
Table 2. Associations between macro-, micro-, and ultrastructural surrogates of hypertrophy following 12 weeks of resistance training. It is apparent that ultrastructural indices of skeletal muscle hypertrophy typically agree well with one another.
However, microscopic assessments do not agree well with ultrastructural assessments or macroscopic assessments, and vice versa. To this point, we also performed simple correlations for each of the surrogate measures of hypertrophy on data from all subjects regardless of cluster for greater statistical power aside from myofibrillar and sarcoplasmic protein concentration changes due to lack of available sample from this study.
Considering these findings against the background of the current hypothetical model of RT-induced skeletal muscle hypertrophy presents a conundrum. For decades, it has been assumed that the deposition of sarcomeres in parallel in existent myofibrils, or the genesis of new myofibrils in existent muscle fibers results in the observed expansion of fCSA and macroscopic assessments of muscle size in response to RT interventions. However, it is clear from the above data that this assumption lacks consistent and explicit empirical support.
After performing an extensive search of the scientific literature, it is apparent that no studies have directly quantified sarcomere number in parallel prior to or following resistance training in human fibers. Furthermore, the few studies employing TEM methods to provide myofibril densities were severely underpowered having analyzed few fibers and few subjects, and although critical first steps, do not allow confident population-wide inferences.
Strikingly, of the available evidence surveyed wherein molecular and microscopic measurements occurred prior to and following resistance training, a reduction in myofibrillar protein concentrations concomitant to observed increases in fCSA has been a more common finding.
According to the widely assumed model of resistance training-induced hypertrophy, a maintenance of myofibrillar protein concentration should coincide with an increase in fCSA. Moreover, this occurrence should produce an increase in macroscopic measures of muscle size including muscle thickness, muscle mass, and muscle volume. Yet, the current state of the evidence tells a different and relatively inconsistent story.
Based upon the current available evidence, Figure 4 summarizes how resistance training-induced skeletal muscle hypertrophy occurs at the macroscopic level and may occur at the ultrastructural level.
Figure 4. Mechanisms of resistance training-induced skeletal muscle hypertrophy. Likewise, numerous studies have reported that fCSA increases occur with resistance training B. However, the ultrastructural and molecular adaptations to resistance training remain largely unresolved C. The first and most obvious drawback when comparing biopsy data to regional or whole body metrics is that the biopsy specimen is small e.
In relation to characterizing ultrastructural indices using TEM, inadequate sampling is an imminent concern given that dozens not hundreds of partial fiber areas are typically analyzed Alway et al.
Another viable concern with small samples from both fCSA and TEM is a regression to the mean phenomenon from sampling sites, since it is impossible to biopsy the same location twice. This can make repeated measures difficult to correlate because large or small values can skew results Barnett et al.
Further compounding this issue is the lack of true controls in most training studies to help correct for repeated measures sampling complexity. However, it is currently difficult to decipher how resistance training affects subcellular protein concentrations given that multiple methods have been used Willoughby et al.
In addition to these points, although mitochondrial volume has been consistently shown to decrease in response to resistance training Groennebaek and Vissing, , alterations in sarcoplasmic reticulum and t-tubule volumes have been largely unexplored in human skeletal muscle in response to training interventions; and changes in these structures could contribute to observed increases or decreases in fCSA.
Moreover, disproportionate increases in ICF volume may occur with resistance training, albeit there is no current microscopic or biochemical method to directly decipher this phenomenon. Limitations to various muscle imaging techniques also exist. For instance, although MRI can estimate total fluid content within a scan Ogino et al. This is a critical point that is oftentimes underappreciated given that ECF, which does not represent the intracellular milieu and contributes to mass and thickness changes, significantly increases with higher-volume resistance training Haun et al.
Thus, the tandem use of regional bioelectrical impedance spectroscopy BIS with regional e. Hence, using methods that accurately account for ICF and ECF shifts which occur during resistance training may provide more insight as to whether true hypertrophy occurs.
Furthermore, Reidy et al. When interpreting and contrasting the results of different gross-level measurements, the dimensionality of each needs to be considered. Of course, volumes, areas, and thickness are three-, two-, and one-dimensional, respectively, but understanding what this means in the context of hypertrophy requires deeper thought.
Starting from a unidimensional level, muscle can grow in three different, orthogonal directions. For instance, it may be possible that a muscle grows wider but not thicker, or has differential growth in different directions or parts of the muscle. A unidimensional measure, such as thickness via ultrasound, is inherently limited in capturing these possibilities; changes in off-axis lengths will not be captured. Area can, to some extent, account for some of these differences, in that it captures growth across an entire plane.
If absolute growth does occur in off-axis directions e. If thickness predominates, then a linear relationship between thickness and area should hold Franchi et al. Conversely, volume not only takes into account changes in muscle length, but also heterogeneities in area along the muscle. For instance, a muscle may grow more distally than proximally. Such heterogeneities are best captured by taking advantage of the full dimensionality of the construct itself — muscle is three-dimensional.
This may be why thickness changes scale with area, but not volume Franchi et al. Unfortunately, our understanding about the dimensionality and heterogeneity of growth itself is limited; these properties may be muscle and protocol-specific.
The former can be assessed at any point along the muscle, and is often defined to be the CSA orthogonal to the longitudinal axis of the segment on which the muscle is located i.
Therefore, PCSA is a measure of the average CSA orthogonal to the fiber orientation and is seemingly related to the number of sarcomeres in parallel Lieber and Ward, With intent to provide future research directions and improve the likelihood of fruitful discovery moving forward, it is necessary to operationally define skeletal muscle hypertrophy in an objective manner.
To serve as rationale for our proposed definition and types of skeletal muscle hypertrophy, we encourage readers to consider that various types of hypertrophy have been characterized in both cardiac and smooth muscle Johansson, ; Mihl et al. We propose that skeletal muscle hypertrophy be generally and simply defined as an increase in skeletal muscle size accompanied by an increase in mineral, protein, or substrate abundance e.
However, considering the reviewed evidence, we encourage the formal adoption of three types of skeletal muscle hypertrophy worthy of further inquiry: a connective tissue hypertrophy, b myofibrillar hypertrophy, and c sarcoplasmic hypertrophy. Connective tissue hypertrophy can be defined as an increase in the volume of the extracellular matrix of skeletal muscle accompanied by an increase in mineral or protein abundance. Considering these definitions, we feel it is critical to appreciate the fact that measurement techniques assess different constructs and these differences do not necessarily make a measurement better or worse, but simply different.
Resource constraints, technical capabilities, and potential risks to participants are factors that inherently affect the selection of assessments of muscle hypertrophy. To dismiss methodologies that provide less resolution of molecular changes based solely on this fact is inappropriate as good reliability of a test can be incredibly useful for examining changes over time and inferring effects of resistance training interventions.
That is to say, while macro- and microscopic tests do not directly assess myofibrillar protein accrual and fCSA, increases in these variables should result in eventual increases in macroscopic indices. Stated differently, while some methods of detecting true hypertrophy are more vivid e. While it can be difficult to reconcile why different methods used to assess skeletal muscle hypertrophy in response to resistance training do not always agree, we posit that certain procedures can be adopted to clarify research findings in the field.
First, if a single measure of hypertrophy is being examined e. Alternatively stated, we feel clear language pertaining to the outcome data of a method should be explicitly reported to better portray the nature of a specific measurement.
Second, if multiple indices of skeletal muscle hypertrophy are being collected, then it would be valuable to include associations between the measures in order to provide the reader greater insight as to how well or poorly the measurements agree. Third, when possible, we posit that using methods to determine regional fluid shifts that account for ICF and ECF changes could better delineate the mode of hypertrophy and whether mass changes in a region of interest were largely due to fluid accumulation.
Certainly, this assumes the research question centers around true protein accretion and not simply an expansion of other microscopic or macroscopic assessments as dependent variables, specifically. Although surface electrode BIS possesses limitations given that greater subcutaneous adipose tissue thickness values can negatively influence skeletal muscle impedance readings Tagliabue et al.
Another viable strategy worthy of consideration is to include multiple indices of skeletal muscle hypertrophy at various levels. As an example, our laboratory has implemented K-means cluster analysis based solely upon changes in VL thickness to generate low, moderate, and high anabolic response clusters to 12 weeks of resistance training Mobley et al. As discussed earlier, we subsequently adopted a different approach in these same subjects by generating clusters based upon a composite hypertrophy score which entailed percent changes in DXA TBMM which only considers appendicular lean mass changes , VL thickness using ultrasound, and fCSA, and selected high and low hypertrophic responders whose composite scores existed in the upper and lower quartiles Roberts M.
Likewise, a similar multi-variable cluster approach has been adopted by Davidsen et al. Thus, if clustering subjects in hopes of characterizing specific phenotypes or predicting adaptive responses thereof is the nature of the research question, we feel using a composite hypertrophy score containing multiple indices e. This approach can be thought of as a form of dimensionality reduction; hypertrophic responses can be measured in a number of ways, so procedures such as principal component analysis can yield values that are linear combinations of the predictor variables of interest, and a way that can account for much of the variance.
However, as pointed out by Rucker et al. Often, if explaining variation in hypertrophic responses to resistance training is the primary aim via predictors of interest, researchers would likely be better served to include all subjects and their raw, continuous scores in the analysis for greater statistical power. Finally, calculating the test-retest reliability of hypertrophic assessments to establish standard errors of measurement is also a powerful strategy to more confidently conclude if hypertrophy occurred and direct further exploration.
Conceptually, changes beyond calculated measurement error can allow researchers to infer that the specific construct being assessed by the employed technique changed beyond the error of the measurement, regardless of the extent. Often, test-retest reliability is unreported, and depending on the calculated error of measurement, changes within various ranges after resistance training interventions may be better explained by measurement error rather than true variation in the construct being assessed due to the research intervention.
As an example, reliability statistics from our laboratory for macroscopic, microscopic, and molecular assessments of hypertrophy are shown in Table 3. Both absolute values expressed in the unit of the measurement and relative values expressed as percentages of the measurement can be used to construct confidence intervals beyond which changes scores may be more associated with factors other than measurement error.
Importantly, even the calculation of measurement error possesses a certain degree of confidence, and this is not posited to negate changes within the calculated range of error, as these changes could still be real. Nevertheless, the combination of the calculated measurement error and the measured change itself provide more information to the researcher, and reader, and can improve the interpretation and presentation of data. Ideally, in the case of a well-controlled resistance training intervention, hypertrophic outcomes could be better associated with the effects of training rather than measurement error itself.
Table 3. Test-retest reliability statistics for macroscopic, microscopic, and molecular assessments of hypertrophy. To summarize, we propose the following strategies for consideration:.
By understanding the expected error of measurement with certain confidence, changes in molecular, microscopic, and macroscopic assessments of muscle hypertrophy can be more accurately surmised with improved confidence. Assessments on different scales are not inherently better or worse, but they are different insofar as their construct validity. Assessing the macroscopic, microscopic, and molecular adaptations to resistance training has been a widespread research goal for exercise physiologists since the 19th century.
Given the current knowledge-gap regarding the ultrastructural adaptations to resistance training, we hope that future research will better characterize the biochemical and ultrastructural underpinnings of skeletal muscle hypertrophy.
While different assessment techniques seem to disagree with one another, we posit that this conundrum provides tremendous opportunity for future researchers to build upon current methods or generate newer and more valid methods to better assess skeletal muscle hypertrophy. Publicly available datasets were analyzed in this study. CH was the primary architect of this work. All co-authors substantially contributed, edited and approved the final version of this manuscript.
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This includes building more strength when repeated stress to the tissue indicates a need to accommodate the new, higher loads. This is exactly what the process of strength training does. It has to be triggered by a physiological need. Hypertrophy can be thought of as a thickening of muscle fibers, which occurs when the body has been stressed just the right amount to indicate that it must create larger, stronger muscles that can tolerate this new, increased load.
This need causes a cellular response , leading to cells synthesizing more materials. For muscles to grow, two things have to happen: stimulation and repair. Dormant cells called satellite cells, which exist between the outer and basement membranes of a muscle fiber become activated by trauma, damage or injury — all possible responses to the stress of weight training. An immune system response is triggered, leading to inflammation, the natural clean up and repair process that occurs on a cellular level.
Concurrently, a hormonal response is triggered, causing the release of growth factor, cortisol, and testosterone. These hormones help regulate cell activity.
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