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Original Research

Exercise-Induced Hormone Elevations Are Related to Muscle Growth

Mangine, Gerald T.1; Hoffman, Jay R.2; Gonzalez, Adam M.2; Townsend, Jeremy R.2; Wells, Adam J.2; Jajtner, Adam R.2; Beyer, Kyle S.2; Boone, Carleigh H.2; Wang, Ran2; Miramonti, Amelia A.2; LaMonica, Michael B.2; Fukuda, David H.2; Witta, E. Lea2; Ratamess, Nicholas A.3; Stout, Jeffrey R.2

Author Information
Journal of Strength and Conditioning Research: January 2017 - Volume 31 - Issue 1 - p 45-53
doi: 10.1519/JSC.0000000000001491
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Abstract

Introduction

The moderately high-intensity and high-volume (8–12 repetition maximum [RM] or more), short-rest interval (1–2 minutes) resistance training model has long been used by individuals striving to increase muscle mass and is thought to be ideal for skeletal muscle hypertrophy (3,26). In part, the scientific support for this hypothesis relied on evidence demonstrating greater postexercise concentrations in anabolic hormones, whose postexercise elevation would potentially improve the likelihood of hormone–receptor binding and initiating a cascade of intracellular reactions that affects muscle growth (2,18,19). However, the validity of this concept is questionable. Hormonal influence is dependent on receptor availability in activated muscle (19), and androgen receptor content has been reported to decline after moderately high-intensity, high-volume (10RM) resistance training (27). Exercise-induced elevations in circulating anabolic hormones (i.e., testosterone and insulin-like growth factor 1 [IGF-1]) have been demonstrated to be comparable between high-volume and high-intensity training protocols (12,21,29). Recent evidence has also shown that increases in muscle hypertrophy may be comparable between these training paradigms and seem to occur without significant elevations (from baseline) in several endocrine measures (i.e., testosterone, growth hormone, cortisol, and IGF-1) (38,39). Several investigators have suggested that the exercise-induced hormone response may not influence muscle growth (24,37–39). Furthermore, evidence in resistance-trained men does not support the high-volume, short-rest interval model as being more advantageous (in comparison to a high-intensity, longer-rest interval model) for developing muscle strength and hypertrophy (4,21,28). Consequently, evidence demonstrating a clear relationship between postexercise hormone concentrations and muscle hypertrophy is lacking.

Few studies have attempted to relate the postexercise endocrine response to changes in muscle hypertrophy (1,23,35,40). However, the findings of these studies are contradictory and may have been influenced by differences in techniques used to measure relationships between the acute hormonal responses and subsequent muscle hypertrophy. For instance, 2 of these studies used Pearson's product moment correlation coefficients to assess the aforementioned relationships in small sample populations (n = 8–10) (1,23), which increases the odds of spurious observations (34). Recently, Walker et al. (35) accounted for this limitation by using a nonparametric statistical measure (i.e., Spearman's rank correlation coefficients), but lost contextual information through the statistic's ranking process. Similarly, a significant amount of information is lost when using either of these statistical procedures for assessing the relationships between concepts that exist across time (i.e., hypertrophy, multiple endocrine responses) because the statistics can only assess the relationship between 2 sets of values. To examine the correlation between hypertrophy and the endocrine response, either muscle hypertrophy (1,23,35,40) or the endocrine response (1,23,35) from baseline and posttesting must be transformed into a single value (i.e., change score, average score). Furthermore, the validity of this type of relationship (i.e., correlating one variable to one other) is based on the assumption that the related variables were collected without systematic or random error (6,14). In each of these studies (1,23,35,40), data were collected or analyzed using procedures (e.g., magnetic resonance imaging, dual-energy x-ray absorptiometry, assay analysis, etc.) that were dependent on technician reliability and were therefore subject to error (36). Consequently, previously observed relationships between the endocrine response to resistance training and muscle hypertrophy must be viewed with caution.

An alternative approach to assessing the relationships between the postexercise endocrine response and muscle hypertrophy is to use structural equation modeling. This manner of statistical procedure uses multiple independent and dependent variables to describe latent constructs, whose relationships are then statistically assessed (6,11,14). The statistical assessment can be co-variance or variance-based, though a variance-based procedure does not rely on typical parametric assumptions (e.g., normal distribution, large sample size, etc.). Additionally, the use of multiple indicator variables (e.g., muscle thickness and cross-sectional area) to describe a single latent variable (e.g., muscle hypertrophy) maximizes their variance and permits systematic and random modeling errors (14). Partial least squares structural equation modeling (PLS-SEM) is a variance-based procedure that uses bootstrapping to statistically assess the relationships between multiple latent variables that are developed from several collected indicator variables (6,14). Previously, PLS-SEM and bootstrapping have been used to assess relationships within the biomedical sciences (5,20,41), but it has not yet been used to assess the relationships between the postexercise endocrine response and muscle hypertrophy. Therefore, the purpose of this investigation was to use PLS-SEM to assess the relationships between muscle hypertrophy and the endocrine responses to resistance exercise across 8 weeks of training in resistance-trained men. Furthermore, we aimed to determine whether any observed relationships remained consistent when using either a high-volume, short-rest interval or a high-intensity, long-rest interval resistance training protocol.

Methods

Experimental Approach to the Problem

Full details of the experimental protocol and training design are described elsewhere (21). Briefly, participants were required to complete a 2-week preparatory training program before the onset of the actual training program. During this phase, 4 participants removed themselves from the study for reasons unrelated to the investigation. After the preparatory period (week 0), pre-training (PRE) assessments of muscle thickness and cross-sectional area (CSA) were performed at a standardized time of day and before initiating any physical activity. Participants were then randomly assigned to one of the 2 training groups: a high-intensity, low-volume training group (INT; n = 13; 24.7 ± 3.7 years; 89.5 ± 15.9 kg; 180.1 ± 5.8 cm) or a high-volume, moderate intensity training group (VOL; n = 13; 24.2 ± 2.7 years; 90.1 ± 11.7 kg; 169.5 ± 30.1 cm). During training, participants were required to complete at least 28 resistance training sessions (∼90%) of an 8-week resistance-training program (4 sessions per week−1) that included 6 upper- and lower-body exercises during each session, under the direct supervision of certified strength and conditioning specialists. Kilocalorie and macronutrient intake was monitored for changes through 3-day food diaries collected weekly, whereas postexercise nutrition was standardized by providing ∼235 ml of chocolate milk (170 calories; 2.5 g Fat; 29 g Carbohydrate; 9 g protein) or Lactaid (150 calories; 2.5 g Fat; 24 g Carbohydrate; 8 g protein) to each participant immediately after each workout. Pre-exercise and postexercise blood samples were collected during the first and last weeks of the 8-week training program. Posttesting (POST) occurred during the week 9.

Subjects

A complete description of the methods, study design, and participant characteristics has been previously reported (21). However, the participant characteristics presented here reflect data collapsed across groups since they were analyzed as a single group for a segment of this investigation. Additionally, the characteristics of each group reflect subsamples of the original groups because 2 participants (one member from each group) chose not to provide blood samples for analysis, and the data set for one other participant (intensity group) was not complete. Briefly, 33 physically-active, resistance-trained men who had been regularly participating (at the time of recruitment) in resistance training for a minimum of 2 years (5.7 ± 2.2) and free of any physical limitations (determined by medical history questionnaire and PAR-Q) were recruited to participate in an 8-week full-body resistance training program. Before participating, all participants were informed of all procedures, risks, and benefits associated with the study and each participant provided his written informed consent to participate in the study. This investigation was approved by the New England Institutional Review Board. The age range of the participants was 19.3 years to 33.0 years.

Muscle Cross-Sectional Area and Thickness

Noninvasive skeletal muscle ultrasound images were collected from the dominant thigh of each participant using previously described procedures (21) to measure muscle cross-sectional area (CSA; ±0.1 cm2) and muscle thickness (MT; ±0.1 cm). Briefly, the same investigator identified all anatomical locations of interest using standardized landmarks for the rectus femoris (RF) and vastus lateralis (VL) and collected all images using a 12 MHz linear probe scanning head (General Electric LOGIQ P5; Wauwatosa, WI, USA). For all images, the extended field of view mode (Gain = 50 dB; Image Depth = 5 cm) was used to capture 2 consecutive panoramic images of the muscular regions of interest. After image collection, the ultrasound data were transferred to a personal computer and analyzed by the same investigator using Image J (version 1.45s; National Institutes of Health, Bethesda, MD, USA). The averaged values from both images within a specific region were used for statistical analysis. Using these procedures, measures of reliability had been determined for assessing the RF (MT: ICC3, K = 0.93, SEM3, K = 0.17, MD = 0.45 cm; CSA: ICC3, K = 0.88, SEM3, K = 1.78, MD = 4.60 cm2) and VL (MT: ICC3, K = 0.88, SEM3, K = 0.16, MD = 0.42 cm; CSA: ICC3, K = 0.99, SEM3, K = 1.11, MD = 3.05 cm2) musculature on ten active, resistance-trained men (25.3 ± 2.0 year; 90.8 ± 6.8 kg; 180.3 ± 7.1 cm). Individual measures (MT and CSA) of the RF and VL muscles are presented in Table 1.

Table 1.
Table 1.:
Individual measures of muscle size before and after 8 weeks of resistance training.

Blood Sampling and Hormonal Analyses

During the resistance training period, blood samples were collected on the first day of week 1 (week 1) and week 8 (week 8), as previously described (21). Briefly, during each blood collection trial, participants reported to the Human Performance Laboratory (HPL) 3 hours postprandial, at a time of day consistent with their normal training schedule. After a 15-minute equilibration period, baseline (BL) samples were collected and participants were then provided ∼235 ml of chocolate milk or Lactaid (Lactaid(R) Chocolate Lowfat Milk; McNeil Nutritionals LLC, Ft. Washington, PA). Subsequently, participants completed their respective resistance exercise protocol. On completion of the resistance exercise protocol, an immediately postexercise (IP) blood sample was collected and then participants were provided their normal ∼235 ml of chocolate milk or Lactaid post-exercise. Participants then remained in the HPL, lying in the supine position for the remaining blood sample collection time points: 30 minutes postexercise (30P) and 60 minutes postexercise (60P). All blood samples were analyzed for circulating concentrations of testosterone (T), cortisol (CORT), insulin-like growth factor (IGF-1), 22-kD growth hormone (GH), and insulin (INSL). To eliminate inter-assay variance, all samples for each assay were analyzed in duplicate in the same assay run by a single technician resulting in an average coefficient of variation of 3.74% for T, 4.03% for CORT, 6.77% IGF-1, 3.50% for GH, and 6.54% for INSL. The area under the curve (AUC), expressed in arbitrary units (au) through the trapezoidal method was calculated and used for statistical analysis. The individual endocrine responses to resistance exercise during week 1 and week 8 are presented in Table 2.

Table 2.
Table 2.:
Individual area-under-the-curve endocrine responses to exercise at the onset and conclusion of 8-weeks of resistance training.*

Statistical Analyses

Partial least squares structural equation modeling (PLS-SEM) was used to assess the relationships between muscle thickness and CSA and the endocrine response to resistance exercise. Figure 1 illustrates the inner and outer relationship models under investigation. Initially, the outer measurement model, which described the relationships between the collected indicator variables (i.e., endocrine response at week 1 and week 8, muscle thickness/CSA measures at PRE and POST) and their associated latent constructs was assessed using factor analysis or multiple linear regression for all reflective (i.e., collected measures of muscle size) and formative (i.e., collected endocrine response measures) constructs, respectively. Subsequently, the inner structural model (i.e., relationships between latent constructs) was evaluated using a consistent PLS-SEM algorithm (8). The quality of the model was assessed by its ability to explain variance (R2; r-squared) and the statistical significance of the path coefficients (15). Interpretations of R2 values were evaluated at the following levels: weak (0.190), moderate (0.333), and substantial (0.670). Statistical significance for each path coefficient was determined through bootstrapping using 1,000 iterations (9,31). Group differences in the model's quality were examined by an independent t-test using the means and standard error for R2 from bootstrap analysis from each group and Equation 1 (22):

Figure 1.
Figure 1.:
Model for the relationship between changes in muscle size and the endocrine response to resistance exercise. IGF-1 = insulin-like growth factor 1; RF_CSA = rectus femoris cross-sectional area; RF_MT = rectus femoris muscle thickness; VL_CSA = vastus lateralis cross-sectional area; VL_MT = vastus lateralis muscle thickness; WK 1 = week 1; WK 8 = week 8.

A criterion alpha level of p ≤ 0.05 was used to determine statistical significance. Statistical Software (SmartPLS version 3.2.1; SmartPLS GmbH, Bönningstedt, Germany) was used for analyses of the statistical models. Post hoc power analyses of each significant model were performed using a publicly available calculator (32).

Results

Resistance Training Program Comparisons

Programming characteristics, estimated dietary intakes, all training program outcomes and comparisons, as well as the endocrine response to resistance exercise at week 1 and week 8 have been previously reported (21). Briefly, the average training volume load was significantly greater (28.4%) for VOL over INT, but no group differences were observed in relative caloric intake, relative protein intake, or changes in lower-body muscle size over the course of 8 weeks of resistance training. Significant group differences were observed in the endocrine response to training. At the onset of training (week 1), significantly greater GH (42.4%) and CORT (29.2%) responses to exercise were observed for VOL, whereas no group differences were observed for T, IGF-1, or INSL. At week 8, only CORT remained significantly greater (21.6%) for VOL.

Assessment of Model Quality

The consistent PLS-SEM algorithm revealed a substantial ability (R2 = 0.734, p < 0.001) for muscle thickness/CSA (PRE) and the entire endocrine response in explaining variance in muscle thickness/CSA (POST). The power to observe this was 0.783 and variance inflation factors were all below 10, indicating no issues relating to multicollinearity. Bootstrap analysis indicated that significant (p ≤ 0.05) pathways existed from muscle size at PRE to the T response and from the T response to muscle size at POST. No other pathways were observed to be significant. When the model was reanalyzed without T, the ability of the remaining model to explain variance in muscle size (POST) was reduced (R2 = 0.426, p = 0.014). Further, no significant individual pathways were observed. Conversely, separate analyses of the model with GH (R2 = 0.75, p < 0.001), CORT (R2 = 0.73, p < 0.001), IGF-1 (R2 = 0.73, p < 0.001), or INSL (R2 = 0.64, p < 0.001) removed did not affect the model's ability to explain variance in muscle size (POST). All pathway coefficients, their standard error, and significance of the original model are presented in Table 3.

Table 3.
Table 3.:
Significant pathways between muscle size and the endocrine response to resistance exercise.*

When the quality of the model was assessed in each group independently, improvements (p < 0.001) were observed for both VOL (R2 = 0.987) and INT (R2 = 0.882). However, no group differences in model quality were observed (p = 0.496). Group-wise comparisons of pathway coefficients and their standard error are presented in Table 4.

Table 4.
Table 4.:
Group differences in pathway coefficients for the relationships between the endocrine response to resistance exercise and muscle size at POST.*

Discussion

This study demonstrates the use of PLS-SEM to identify the relationships between several measures collected across multiple time points. Our major finding indicated that the endocrine response to resistance exercise, specifically testosterone, is related to muscle hypertrophy across 8 weeks of training. Previously, a strong correlation (r = 0.76) between the testosterone response to resistance exercise and muscle hypertrophy had been reported in one study (1), but not in others (r = 0.06–0.14; p > 0.05) (23,40). The lack of consistency between studies might be explained, in part, by differences in how the relationships were assessed. Ahtiainen et al. (1) related change scores (muscle size and testosterone response), whereas the other 2 studies related the change in muscle size to an averaged testosterone response (23) or the response from a single time point (23,40). The important distinction is that relating 2 sets of change scores assumes that initial scores lack importance, whereas the later studies ignore potentially important variations in the endocrine response across the training period. In contrast, PLS-SEM does not alter the data when assessing the relationships between indicator variables and their latent concept or between latent concepts (6,14). Consequently, PLS-SEM provides a more accurate assessment of the relationships in question. To date, this is the first study to examine the relationships between the endocrine response to resistance exercise and muscle hypertrophy in this manner.

Our results indicated that 73.4% of the variance in muscle size after 8 weeks of resistance training could be explained by baseline muscle size and the endocrine response (i.e., CORT, GH, IGF-1, INSL, and T) across training. However, T was the only significant pathway. During rest, exogenously elevated concentrations in circulating testosterone have been demonstrated to enhance protein synthesis and inhibit protein breakdown within skeletal muscle (7), and to suppress the catabolic effects of elevated cortisol concentrations (42). In response to resistance exercise, the specific role of transitory elevations in testosterone for promoting muscle growth remains unclear. Previously, West and colleagues (39) reported no enhancement on anabolic signaling or acute postexercise muscle protein synthesis from exercise-induced elevations in testosterone. Although this may be true, it does not eliminate the testosterone (or any hormone) response from being physiologically important for muscle hypertrophy. Our results provide evidence that the exact training stimulus may not change the relationship between the exercise-induced endocrine response, specifically testosterone, and muscle hypertrophy. Some investigators have suggested a nongenomic (i.e., independent of its receptor) role where testosterone stimulates transient increases in intracellular calcium (10), which may temporarily elevate maximal force production (16). In this capacity, exercise-induced elevations in testosterone might enable greater training intensity and volume load during resistance exercise, which seems to be more beneficial for stimulating muscle hypertrophy in a resistance-trained population (21). Regardless of the mechanism, our findings suggest that postexercise testosterone elevations do influence muscle hypertrophy.

The remaining model (without testosterone) was capable of explaining 42.6% of the variance in muscle thickness/CSA after 8 weeks of resistance training. However, there were no significant pathways observed amongst the remaining hormones (i.e., CORT, GH, IGF-1, or INSL), which may imply that a synergistic endocrine response is more influential of muscle hypertrophy than the individual responses from these specific hormones. Though evidence of interdependent relationships between hormones supports this notion, there is no evidence of muscle hypertrophy being affected. For instance, growth hormone concentrations may be partially responsible for IGF-1 release (13), but circulating IGF-1 concentrations have not been previously related to muscle hypertrophy (23,40). Rather, its anabolic effect may be dependent on its uptake into skeletal muscle (25), which was not examined in this study. Circulating cortisol concentrations are also known to affect protein synthesis by competing with testosterone within the muscle cell (42). However, transient changes in cortisol concentrations have not been shown to affect protein synthesis (30). Finally, insulin is known to regulate the same protein synthesis pathways affected by T and IGF-1 (33), but its role immediately after exercise seems to be highly dependent on dietary intake surrounding the workout (17). Thus, it is possible that an ideal exercise-induced hormonal milieu for muscle hypertrophy exists, but the present model was not capable of distinguishing it beyond the influence of T. This may have been the consequence of data collection times (i.e., blood samples were only collected during week 1 and week 8) affecting the sensitivity of the model to detect the influence of measures with limited contributions. Assessing the endocrine response on a greater number of occasions (>2) may afford a better opportunity for PLS-SEM to maximize the variance amongst these latent constructs (e.g., CORT, GH, IGF-1, and INSL) and thus determine their relationship to muscle hypertrophy (6,14).

Practical Applications

Traditional statistical measures do not adequately assess the relationships between multiple variables that exist across time. This investigation demonstrates a unique method for analyzing these types of relationships without the need for transforming data. Our findings indicate that baseline muscle size and the hormonal response to resistance exercise are related to muscle hypertrophy after 8 weeks of training. In particular, exercise-induced testosterone concentrations seem to be more influential of hypertrophy in comparison to the cortisol, growth hormone, IGF-1, and insulin responses to resistance exercise. Further, the observed relationships seem to remain consistent regardless of whether resistance training emphasizes training volume or intensity.

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Keywords:

structural equation modeling; partial least squares regression; endocrine response; hypertrophy

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