This article was first published in MASS Research Review and is a review and breakdown of a recent study. The study reviewed is Energy Deficiency Impairs Resistance Training Gains in Lean Mass but Not Strength: A Meta-Analysis and Meta-Regression by Murphy et al. (2021). Graphics in this review are by Kat Whitfield.
- The presently reviewed meta analysis (1) quantified the impact of an energy deficit on strength and lean mass gains in response to resistance training.
- Energy deficits led to significant impairment of lean mass gains (effect size [ES] = -0.57, p = 0.02) and non-significant impairment of strength gains (ES = -0.31, p = 0.28). As the energy deficit grew by 100kcals/day, lean mass effect size tended to drop by 0.031 units; a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0).
- “Recomposition” (simultaneous fat loss and muscle gain) is possible in certain scenarios, but a sizable calorie deficit typically makes lean mass accretion an uphill battle.
Three of the most common goals among lifters are to lose fat, gain muscle, and get stronger. This presents a noteworthy challenge, as these goals can lead to contradictory recommendations for total energy intake. Lifters with fat loss goals are virtually always advised to establish a caloric deficit (2), whereas a caloric surplus is typically recommended to support recovery and anabolic processes for lifters aiming to get stronger and more muscular (3). If similar hypertrophy could occur in the presence of a calorie deficit, then this apparent dilemma would be resolved.
That brings us to the presently reviewed meta-analysis (1), which sought to determine if calorie deficits impair gains in strength and lean mass in response to resistance training. Compared to a control diet, energy deficits led to significantly smaller gains in lean mass (effect size [ES] = -0.57, p = 0.02). Energy deficits also led to smaller gains in strength, but the effect size was smaller, and the effect was not statistically significant (ES = -0.31, p = 0.28). Impairment of lean mass gains became more pronounced as the caloric deficit got larger, and a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0). Meta-analyses are great for identifying a general, overall effect, but the feasibility of body recomposition (simultaneous fat loss and muscle gain) is impacted by a number of nuanced contextual factors. Read on to learn more about who might be able to achieve substantial lean mass gains during a calorie deficit, and how to maximize the likelihood of success when pursuing fat loss, hypertrophy, strength, or recomposition goals.
Purpose and Hypotheses
The primary purpose of the presently reviewed meta-analysis (1) was “to quantify the discrepancy in lean mass accretion between interventions prescribing resistance training in an energy deficit and interventions prescribing resistance training without an energy deficit.” The secondary purpose was to investigate the same question, but with a focus on strength gains rather than lean mass gains. The researchers also conducted additional analyses to determine if effects were meaningfully impacted by potentially important variables including age, sex, BMI, and study duration.
The researchers hypothesized that “lean mass gains, but not strength gains, would be significantly attenuated in interventions conducted in an energy deficit compared to those without.”
Search and Study Selection
These researchers wanted to do a meta-analysis comparing resistance training in a caloric deficit to resistance training with a control diet. However, they knew ahead of time that there would be a limited number of studies directly comparing both types of diets in longitudinal research designs. So, they cast a broad net with their literature search and committed to doing two separate analyses. The search strategy aimed to identify English-language studies evaluating relevant resistance training adaptations (lean mass or fat-free mass measured via DXA or hydrostatic weighing, and strength measured via low-repetition strength tests [e.g., 1RM or 3RM] or maximal voluntary contraction). In order to be considered for inclusion, studies needed to implement resistance training protocols that were at least three weeks long, utilized a training frequency of at least two sessions per week, and did not involve aerobic training.
Analysis A involved only studies that directly compared two groups within the same longitudinal resistance training study, with one group consuming a calorie deficit, and another group consuming a control diet. Seven such studies were identified; six involved female participants only, while the seventh involved a mixed-sex sample of males and females. A total of 282 study participants were represented across 16 treatment groups, with an average age of 60 ± 11 years old. Participants were generally sedentary or physically inactive prior to study participation, but one of the studies did not specify activity level. In terms of study characteristics, the researchers described that the studies in analysis A included full-body resistance training programs that “lasted between 8 and 20 weeks (13.3 ± 4.4 weeks) and involved 2-3 sessions per week (2.9 ± 0.3 sessions) with 4-13 exercises per session (8.3 ± 2.4 exercises), 2-4 sets per exercise (2.7 ± 0.4 sets), and 8-20 repetitions per set (11.3 ± 4.1 repetitions).” The researchers used standard meta-analytic techniques to separately compare the effects of calorie deficits and control diets on strength gains and lean mass gains.
In order to expand the pool of studies, analysis B included studies with participants completing resistance training in an energy deficit or completing resistance training without an energy deficit. It’s easy to do a meta-analysis when you’ve got two different diets tested within the same study, because the two diet groups are effectively matched in terms of key subject characteristics and training programs. However, it’s not quite as easy when you’re analyzing separate studies that involve one type of diet or the other. In order to ensure that results from studies with and without energy deficits were being compared on approximately equal footing, the researchers began by identifying studies that assessed the effects of resistance training with an energy deficit and met the previously listed inclusion criteria (they found 31). Then, they scoured the much, much larger body of research assessing the effects of resistance training without an energy deficit. The purpose of this expanded search was to find suitable “matches” for the 31 energy deficit studies based on age, sex, BMI, and characteristics of the resistance training interventions completed.
They weren’t able to find perfect matches for every study, but they ended up with 52 total studies that were effectively matched for age, sex, study duration, and resistance training characteristics (but not BMI). One study included resistance-trained participants, one study did not specify the training status of their participants, and the rest of them included participants that were sedentary or physically inactive prior to study participation. This collection of 52 studies included 10 with male subjects, 24 with female subjects, and 18 with mixed-sex samples, for a total of 57 treatment groups and 1,213 participants with an average age of 51 ± 16 years. The researchers described that the studies in analysis B included full-body resistance training programs that “lasted between 3 and 28 weeks (15.8 ± 6.0 weeks) and involved 2-4 sessions per week (2.9 ± 0.5 sessions) with 4-14 exercises per session (8.2 ± 2.6 exercises), 1-4 sets per exercise (2.7 ± 0.6 sets), and 1-16 repetitions per set (10.1 ± 1.9 repetitions).”
Analysis B began with a visual comparison of changes in lean mass and strength. For each treatment group among the included studies, an effect size was calculated, and the effect sizes from each group were plotted in a “waterfall plot.” This type of plot arranges the effect sizes from smallest (or most negative) to largest (or most positive), which allows for some surface-level inferences based on visual assessment. Analysis B also included a meta-regression component, in which the energy deficit in each treatment group was calculated based on the assumption that each kilogram of fat lost in the study represented a cumulative calorie deficit of ~9,441kcals (4). As such, the daily energy deficit was back-calculated based on the cumulative energy deficit and the length of the trial, and meta-regression was used to assess the relationship between daily energy deficits and changes in lean mass, while controlling for age, sex, study duration, and BMI.
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In analysis A, energy deficits led to significantly smaller gains in lean mass when compared to a control diet (effect size [ES] = -0.57, p = 0.02). Energy deficits also led to smaller gains in strength, but the effect size was smaller, and the effect was not statistically significant (ES = -0.31, p = 0.28). Forest plots for both analyses are presented in Figure 1.
The waterfall plots for analysis B are presented in Figure 2. For studies involving an energy deficit, the pooled effect size for lean mass was negative (ES = -0.11, p = 0.03), while it was positive for studies that did not involve an energy deficit (ES = 0.20, p < 0.001). For strength gains, effect sizes were positive and similar in magnitude whether studies did (ES = 0.84, p < 0.001) or did not (ES = 0.81, p < 0.001) involve an energy deficit.
As for the meta-regression component of analysis B, the relationship between energy deficits and changes in lean mass (when controlling for age, sex, study duration, and BMI) is presented in Figure 3. The slope of the line was -0.00031 (p = 0.02), which means there was a statistically significant negative relationship between the size of the energy deficit and the magnitude of changes in lean mass. As the energy deficit grew by 100kcals/day, the effect size for lean mass tended to drop by 0.031 units. By extension, a deficit of ~500kcals/day was predicted to fully blunt lean mass gains (ES = 0), and estimated changes in lean mass became negative for energy deficits beyond ~500kcals/day.
Criticisms and Statistical Musings
I wouldn’t call these “criticisms,” but there are a few important limitations and contextual factors to keep in mind when interpreting these results. The first point pertains to the pool of participants for this meta-analysis. In analysis A, the majority of participants were untrained individuals in their 50s, 60s, or 70s. Compared to a young, healthy, resistance-trained “control” subject, their untrained status boosts their propensity for short-term hypertrophy, while their age (specifically combined with their untrained status) might limit their propensity for short-term hypertrophy. The participant pool for analysis B is a little more heterogeneous in terms of age, but the untrained status is still a factor to consider when generalizing these findings to well-trained people. More advanced lifters tend to require greater optimization of training and nutrition variables to promote further training adaptations, so the untrained participants in this meta-analysis might theoretically be able to achieve better growth in suboptimal conditions (in this case, a caloric deficit). On the other hand, this analysis did not account for protein intake and did not require included studies to achieve any particular threshold for minimum protein intake. Insufficient protein consumption would impair hypertrophy and make recomposition less feasible, which could potentially exaggerate the impact of caloric deficits on lean mass accretion.
The next points pertain to analysis B. This analysis was a bit unconventional when compared to the typical meta-analysis, but I really like it and feel that it strengthens the paper. It’s important to recognize that the energy deficit quantified in analysis B is estimated based on the energy value of changes in fat mass. While this analysis did not incorporate the energy value of changes in lean mass, the researchers provided an excellent explanation for this choice, and confirmed that the choice did not meaningfully impact outcomes of the analysis. As noted previously, analysis B included a pool of 52 studies that were effectively matched for age, sex, study duration, and resistance training characteristics, but the researchers were unable to match the studies based on BMI. The studies involving an energy deficit reported an average BMI of 32.7 ± 3.0, while the studies without an energy deficit reported an average BMI of 27.5 ± 3.6. The meta-regression analysis did identify a relationship between BMI and changes in lean mass, but I am neglecting to interpret that as a meaningful relationship due to the confounding effect of this study matching discrepancy.
Finally, a general note on meta-analyses. They sit atop our hierarchy of evidence, which means we consider them to be the most robust type of evidence available (when done correctly). However, we still have to apply their findings carefully and judiciously. For example, if a meta-analysis finds no benefit of micronutrient supplementation but virtually all of the studies recruited participants with adequate baseline levels of the nutrient in question, we can’t use that evidence to conclude that supplementation would be ineffective for individuals with a deficiency. For many research questions, context is critically important; some meta-analyses are well suited to sort through those contextual factors, while others are not. A lot of people will scan the presently reviewed study, see that predicted lean mass gains reached zero at a deficit of 500kcals/day, and will interpret that cutoff point as a widely generalizable “rule.” We should resist that temptation, and hesitate before applying a literal interpretation of these results for individuals who are substantially leaner or substantially more trained than the participants included in this meta-analysis.
A surface-level interpretation of analysis A is pretty straightforward: if gaining lean mass is your priority, you should avoid a calorie deficit. This general concept is easy to digest; low energy status leads to increased activation of 5’-adenosine monophosphate-activated protein kinase (AMPK), which generally promotes catabolic processes and impedes anabolic processes (5). Further, as reviewed by Slater and colleagues (3), maximizing hypertrophy is an energy-intensive process. The process of building muscle involves the energy cost of resistance training, the energy cost of post-exercise elevations in energy expenditure, the energy cost of increased protein turnover (which includes both degradation and synthesis), and several other aspects of increased expenditure that result from gaining more metabolically active tissue and consuming more calories to fuel training. As such, muscle hypertrophy is an energy-intensive process that is optimally supported by a state of sufficient energy availability. Having said that, a deeper interpretation of analysis B suggests that our conclusions probably require a little more nuance regarding how much energy is “enough.”
Figure 3 shows the relationship between estimated energy deficits and gains in lean mass. The regression line crosses zero at about 500kcals/day, which is informative. It tells us that, in a sample of people who are mostly untrained and have BMIs in the overweight-to-obese categories, a daily energy deficit of ~500kcals/day is predicted to fully attenuate gains in lean mass. However, Figure 3 includes individual data points from studies, which adds further depth and nuance to our interpretation. With exactly one exception, all of the studies reporting fairly substantial gains in lean mass involved an estimated deficit of no more than 200-300 kcals/day. Furthermore, every study reporting an effect size clearly below zero (that is, a loss of lean mass) involved an estimated deficit larger than 200-300 kcals/day. As such, we should acknowledge and understand that the ~500kcals/day number is not a rigid cutoff; the relationship between energy deficits and lean mass changes is continuous in nature, and there appears to be (for example) a substantive difference between 100 and 400 kcals/day.
Since we can’t treat every deficit below 500kcals/day as being functionally equivalent, a dieter with ambitions related to recomposition will have to decide exactly how large of a deficit they can manage without meaningfully impairing hypertrophy potential. As Slater and colleagues have noted (3), simultaneous fat loss and skeletal muscle hypertrophy is “more likely among resistance training naive, overweight, or obese individuals.” Along those lines, readers who are well-trained or substantially leaner than the participants in this meta-analysis might need to adjust their interpretation and expectations, erring toward a smaller daily energy deficit if they wish to accomplish appreciable hypertrophy along the way. While an untrained individual with a BMI over 30 is an obvious candidate for successful recomposition, it would be inaccurate to suggest that body recomposition is completely unattainable for individuals with leaner physiques or more training experience.
As reviewed by Barakat and colleagues (6), there are several published examples of resistance-trained individuals achieving simultaneous fat loss and lean mass accretion in the absence of obesity. Nonetheless, these researchers also acknowledged that the feasibility and magnitude of recomposition are impacted by training status and baseline body composition, and that trained individuals have an increased need to optimize training variables, nutrition variables, and other tertiary variables (such as sleep quality and quantity) in order to achieve practically meaningful recomposition. While having some resistance training experience or a BMI below 30 does not automatically render recomposition impossible, it’s also important to acknowledge that significant recomposition might not be attainable for people who have already optimized (more or less) their approach to training and nutrition and are absolutely shredded or near their genetic ceiling for muscularity.
I think this meta-analysis was conducted very effectively, and its results are quite informative for setting energy intake guidelines that are suitable for a wide range of goals. So, to wrap up this article, I want to concisely review how to adjust energy intake for lifters with strength goals, recomposition goals, hypertrophy goals, and fat loss goals. Please note that these recommended targets for rates of weight loss and weight gain throughout the following section are admittedly approximate and imprecise, as hypertrophic responses to training can be quite variable. There are innumerable “edge cases” and circumstances in which these recommendations start to become less advisable; unfortunately, I can’t (at this time) think of a way to provide a totally robust set of concise recommendations without an individualized assessment of body composition, diet history, training experience, and responsiveness to training.
Practical Guidance for Adjusting Energy Intake
For Strength Goals
The results of the presently reviewed meta-analysis could be perceived as suggesting that energy restriction does not meaningfully impair strength gains. However, the analysis generally included untrained participants in relatively short-term trials. As we know, much of the early strength adaptations experienced by novice lifters can be attributed to factors that are entirely unrelated to hypertrophy, such as neural adaptations and skill acquisition (7). When it comes to long-term capacity for strength, creating an environment suitable for hypertrophy plays an important role in maximizing muscle mass, and creating an environment suitable for rigorous training and recovery plays an important role in maximizing longitudinal training adaptations. In both cases, a state of chronic energy insufficiency counters these goals, so lifters should generally aim to spend the majority of their training career in a state that reflects adequate energy status. Energy status is reflected by both short-term energy availability and long-term energy stores (i.e., fat mass), so lifters with higher body-fat levels can probably make considerable strength gains while losing fat, as long as the acute deficit isn’t large enough to threaten hypertrophy, training performance, or recovery capacity. This is particularly true for lifters who are relatively new to training or have a lot of room for additional strength gains.
So, lifters with relatively high body-fat levels should not feel like they’re unable to cut to their ideal weight if it happens to be lower than their current weight. I would expect that many lifters can maintain a satisfactory rate of progress while losing up to (roughly) 0.5% of body mass per week. However, as one gets leaner and leaner, stored body energy is reduced, and the acute presence of an energy deficit probably has a larger impact on the body’s perceived energy status. Once a strength-focused lifter is at their ideal body-fat level, they’ll want to shift their focus away from fat loss and toward hypertrophy, training capacity, and recovery. In this context, they’ll generally want to minimize their time spent in an energy deficit and set their calorie target at a level that allows for weight maintenance or modest weight gain over time (for example, ~0.1% of body mass per week for relatively experienced lifters, or ~0.25% of body mass per week for relatively inexperienced lifters). As they get closer to their genetic limits for strength and muscularity, they might find it difficult to make continued progress at approximately neutral energy balance, and then might shift toward oscillating phases of bulking (a caloric surplus) and cutting (a modest caloric deficit). This approach is also suitable for less experienced lifters who simply prefer to see more rapid increases in strength and hypertrophy during their bulking phases, and are comfortable with the tradeoff of requiring occasional cutting phases. It’s also important to note that strength-focused lifters don’t always need to be in neutral or positive energy balance; in fact, short-term energy restriction is commonly implemented in order to make the weight class that offers the lifter their greatest competitive advantage. Fortunately, these transient periods of energy restriction don’t tend to have a huge impact on strength performance (8), provided that the lifter is adequately refueled and recovered in time for competition.
For Recomposition Goals
I’d like to mention two caveats before providing recommendations for recomposition. First, you should assess the feasibility of recomping before you set up a recomposition diet. If you’ve got plenty of body-fat to lose and are untrained, your recomp potential is very high. If you’re shredded and near your genetic ceiling for muscularity, your recomp potential is extremely low. Everyone else will find themselves somewhere in the middle, but the general idea is that you can get away with a larger energy deficit during recomposition if you have higher body-fat or less advanced training status. Second, these recommendations are going to seem a bit superficial. The presently reviewed meta-analysis discussed the specific caloric value of energy deficits, but I will focus on the rate of body weight changes. This is because the recommendations are intended to be practical in nature; few people will have the ability to perform serial DXA scans to allow for up-to-date energy deficit calculations based on changes in total body energy stored as lean mass and fat mass. Plus, and even if they could, the margin of error for DXA (and other accessible body composition measurement devices) is so large as to render this calculation functionally unreliable at the individual level.
One factor that could guide your approach to recomposition is hypertrophy potential. If you’ve got plenty of body-fat to lose and you’re relatively untrained, you should be able to recomp very effectively with an energy intake that allows for a slow rate of weight loss (up to 0.5% of body mass per week), weight maintenance, or even a slow rate of weight gain (up to 0.1% of body mass per week). I know it seems paradoxical to suggest that you could be gaining weight while in a caloric deficit, but the math works out. If, for example, you gain 1.5kg of lean mass while losing 1kg of fat mass, the estimated cumulative change in body energy would be in the ballpark of around -6,700 kcals (so, body weight increased, but the total metabolizable energy content of the body decreased, thereby representing a caloric deficit). For lifters with lower body-fat levels or more advanced training status, it becomes increasingly critical to optimize diet and training variables in order to promote hypertrophy. Even when these variables are optimized, the anticipated rate of hypertrophy shrinks. As a result, the “energy window” for recomposition most likely tightens; even a moderate energy deficit has potential to threaten hypertrophy, and the anticipated rate of hypertrophy becomes too low to suggest that rapidly trading a few pounds of fat for several pounds of muscle is a realistic goal. So, for these individuals, I would advise keeping body weight as steady as is feasible.
A separate factor that could guide your approach to recomposition is the degree to which you prioritize fat loss versus hypertrophy. In many cases, a lifter interested in recomposition might have goals that are a bit skewed. In other words, some lifters might feel that recomposition would be fantastic if possible, but they’re particularly adamant about losing fat, even if it comes at the expense of optimizing hypertrophy along the way. Conversely, others will be particularly adamant about making some big strides toward lean mass accretion, even if it comes at the expense of losing fat along the way. For a lifter who wishes to recomp but prioritizes fat loss, aiming for a relatively slow rate of weight loss would be a sensible approach (for example, losing somewhere between 0.1% and 0.5% of body mass per week).
For a lifter who wishes to recomp but prioritizes hypertrophy, aiming for a relatively slow rate of weight gain would be advisable (for example, gaining somewhere between 0.05% and 0.1% of body mass per week). It’s obviously difficult to track some small changes in weekly intervals without using some method of data smoothing, but just to contextualize those numbers, a 180lb lifter would gain between 4.32-8.64 pounds over the course of a year if gaining between 0.05% and 0.1% of body mass per week. Within this set of recommendations, a lifter with lower perceived potential for recomping would be advised to aim for the lower ends of the weight gain and weight loss ranges, or to simply aim for approximate weight stability.
For Hypertrophy Goals (Bulking)
Finally, moving on to simpler stuff. For hypertrophy-focused lifters who are relatively experienced and comparatively closer to their genetic limit for muscularity, aiming to gain around 0.1% of body mass per week is a decent starting point. For hypertrophy-focused lifters who are relatively inexperienced and pretty far from their genetic limit for muscularity, aiming to gain around 0.25% of body mass per week is a good place to start. Obviously, if one were adamant about avoiding unnecessary fat gain, they could go a little below these recommended rates. You’ll notice that the guidelines for a hypertrophy-focused recomp and a very conservative bulk are not mutually exclusive. Sometimes, people will embark on a conservative bulking phase and find that they ended up losing a little fat along the way (as Bob Ross would call it, a happy accident). Conversely, a lifter who was eager to maximize their rate of hypertrophy and unconcerned about fat gain could push their rate of weight gain a little higher. There are probably diminishing returns for the hypertrophy-supporting effects of a caloric surplus as the surplus grows larger and larger, but to my knowledge, the “ideal surplus” for hypertrophy has not yet been conclusively identified (3).
For Fat Loss Goals (Cutting)
Choosing a rate of fat loss involves striking a balance; as mentioned in a previous MASS article, favoring a slower rate of weight loss confers plenty of benefits. However, going too slow with the process can delay goal completion, threaten motivation, and lead to unnecessary time spent in a deficit. If maintaining strength, lean mass, and training capacity is of utmost importance, losing up to 0.5% of body mass per week would be advisable. Once again, the guidelines for a recomp that prioritizes fat loss and a very conservative cut are not mutually exclusive, and some individuals will embark on a conservative fat loss phase and be pleasantly surprised to find that they gained a little bit of muscle along the way. If you’re in a bit of a hurry, you could bump your rate of weight loss closer to 1% of body mass per week. However, it’s important to note that the higher this rate gets, the higher the potential to negatively impact strength, lean mass, and training capacity, especially for lifters with less fat mass to lose. From a practical perspective, it might not be a bad idea to cap weight loss at around a kilogram or so per week, even if that ends up being <1% of body mass. Losing a kilogram of fat requires establishing a cumulative energy deficit of ~9,441kcals, which would equate to a daily energy deficit of ~1350kcals/day. As such, when lifters who weigh over 100kg or so aim for 1% of body mass loss per week, they can often find themselves in a scenario that demands daily calorie intakes that might be considered unsustainably low relative to their body size.
Rates of weight gain and weight loss appear to be quite impactful, and they’re topics of considerable interest in the fitness world. As a result, the dearth of studies directly comparing different rates of weight gain and weight loss in resistance-trained participants is a bit surprising. In the short term, we could probably gain some useful insight related to this question if researchers took an approach like the meta-regression component of “analysis B” in the presently reviewed study, but restricted the search to studies with resistance-trained samples and included studies assessing caloric surpluses and caloric deficits of varying magnitudes. An even better way to address this topic would involve a series of well controlled trials directly comparing different rates of weight loss and gain within the same study. These types of studies would yield more robust results, but it would take a while to run enough of these studies to develop nuanced conclusions with a high level of confidence.
Application and Takeaways
The most direct path to fat loss is a caloric deficit, and a caloric surplus offers the smoothest path to gains in strength and lean mass. Nonetheless, we want the best of both worlds from time to time. Large energy deficits threaten lean mass accretion, and extended periods of excessive energy restriction can impair strength gains as well. However, these issues can largely be circumvented by utilizing a caloric deficit that is appropriately scaled to the individual’s goal, training status, and body-fat level. Simultaneous fat loss and muscle gain is indeed possible, although it becomes less feasible as an individual’s body-fat level decreases and training status increases. “Recomping” can theoretically be achieved in the context of weight loss, gain, or maintenance, but the dietary approach should be individualized based on the lifter’s body composition, training status, and priorities.
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- Murphy C, Koehler K. Energy deficiency impairs resistance training gains in lean mass but not strength: A meta-analysis and meta-regression. Scand J Med Sci Sports. 2021 Oct 8; ePub ahead of print.
- Roberts BM, Helms ER, Trexler ET, Fitschen PJ. Nutritional Recommendations for Physique Athletes. J Hum Kinet. 2020 Jan;71:79–108.
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- Thomson DM. The Role of AMPK in the Regulation of Skeletal Muscle Size, Hypertrophy, and Regeneration. Int J Mol Sci. 2018 Oct 11;19(10):3125.
- Barakat C, Pearson J, Escalante G, Campbell B, De Souza EO. Body Recomposition: Can Trained Individuals Build Muscle and Lose Fat at the Same Time? Strength Cond J. 2020 Oct;42(5):7–21.
- Taber CB, Vigotsky A, Nuckols G, Haun CT. Exercise-Induced Myofibrillar Hypertrophy is a Contributory Cause of Gains in Muscle Strength. Sports Med. 2019 Jul;49(7):993–7.
- Helms ER, Zinn C, Rowlands DS, Naidoo R, Cronin J. High-protein, low-fat, short-term diet results in less stress and fatigue than moderate-protein moderate-fat diet during weight loss in male weightlifters: a pilot study. Int J Sport Nutr Exerc Metab. 2015 Apr;25(2):163–70.