The addition of β-Hydroxy β-Methylbutyrate (HMB) to creatine monohydrate supplementation does not improve anthropometric and performance maintenance across a collegiate rugby season | Journal of the International Society of Sports Nutrition

The athletic gut microbiota | Journal of the International Society of Sports Nutrition
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Experimental design

Collegiate, male rugby players were recruited for this double-blind, randomized investigation. Following a 5-day creatine monohydrate loading phase, all athletes arrived at the human performance laboratory (HPL) in the morning, fasted (8–10 h), and after having refrained from unaccustomed (i.e., extreme deviation from normal training) or vigorous exercise (48 h). The athletes initially provided a blood sample before completing assessments of body composition, muscle morphology, and isometric strength testing. Within 48–72 h, participants reported to their normal training facility for sprint testing, which completed all testing prior to the fall season (PREFALL). The athletes were then matched for fat-free lean mass and randomly assigned to consume one of two supplementation regimens: HMB and creatine monohydrate (HMB-Cr) or creatine monohydrate and placebo (Cr) for 6 weeks. All PREFALL-assessments were repeated following the 6-wk fall competitive season (POSTFALL) and prior to- (PRESPRING) and following (POSTSPRING) the spring competitive season. Between the fall and spring seasons, the athletes completed a 10-wk wash-out period where they discontinued all supplementation. Prior to PRESPRING, returning athletes once again completed a 5-day creatine monohydrate loading period before being reassigned to consume the opposite supplementation regimen from their fall assignment (i.e., cross-over design). All warm-ups and maximal efforts during strength and sprint testing were completed under the supervision of a Certified Strength and Conditioning Specialist. Figure 1 illustrates the study design and timeline.

Fig. 1

Study design and timeline


A priori analysis (effect size f = 0.25, ß = 0.80, α = 0.05) using sprinting performance as the most relevant and sport-specific measure indicated 16 participants would be necessary to observe an effect in this repeated-measures design. Following an explanation of all study procedures risks, and benefits, a convenience sample of 16 collegiate (Division 1-AA) American rugby players (21.1 ± 1.6 years [range = 19.0–24.2 years]; 178 ± 6 cm; 88.3 ± 14.2 kg) from the University’s team and who were free of any physical limitations (determined by medical history questionnaire and physical activity readiness questionnaire) provided their written informed consent to participate in this investigation. During the fall season, three athletes left the study prior to POSTFALL testing for the following reasons: personal, scheduling conflict, and illness (athlete self-reported swollen lymph nodes, no medical diagnosis reported). Of the remaining 13 players who completed POSTFALL testing, three did not return to the team in the spring and two did not complete POSTSPRING testing due to injury (n = 1) or non-compliance (n = 1). Consequently, eight players completed the cross-over portion of this study. This investigation was approved by the Kennesaw State University Institutional Review Board (#17–058).

Supplementation intervention

Prior to enrollment, several potential participants had indicated that they regularly or periodically (i.e., only on training days) consumed creatine monohydrate as a dietary supplement. To standardize creatine monohydrate consumption, all enrolled athletes were given a 5-day supply (twenty 5 g packets for a total of 100 g) of creatine monohydrate in powder form (Bulk, Hard Eight Nutrition LLC, Henderson, NV). The athletes were instructed to consume four 5 g packets with water (375 mL) per day for 5 days to standardize muscle creatine content [10] and prevent changes to the athlete’s normal supplementation habits (totaling 20 g per day for 5-days). Subsequently, the athletes were divided into HMB-Cr (5 g HMB [Bulk, Hard Eight Nutrition LLC, Henderson, NV] + 5 g creatine monohydrate) or Cr (5 g creatine monohydrate + 5 g maltodextrin [Now Health Group Inc., Bloomingdale, IL]). Both supplements were similar in appearance, texture, and taste. Regardless of which supplementation regimen they were instructed to follow, the athletes ingested their given supplement, in powder form, with water (375 mL) once each day for two separate six-week periods. A member of the research team, who did not participate in data collection except to assist in obtaining blood samples, maintained the double blind (i.e., both remaining research team members and athletes did not know supplementation assignments) until after all data had been collected and analyzed. To aid in compliance, members of the research team met with participating athletes at practice on each week of the study to deliver a week’s worth of supplement and to collect used supplement packets. All athletes who respectively completed fall and spring testing returned all used supplement packets each week and did not report missing any dosages; indicating 100% compliance in those athletes.

Blood sampling and biochemical analysis

Fasted blood samples were obtained on each visit to the HPL prior to any physical activity. All samples were obtained from an antecubital vein using a needle by a research team member who was trained and experienced in phlebotomy. Approximately 15 mL of blood was drawn into SST tubes (for serum collection) and EDTA-treated Vacutainer® tubes (for plasma). SST tubes were allowed to clot for 10 min prior to centrifugation, while EDTA treated tubes were centrifuged immediately for 10 min at 3600 rpms at 4 °C. The resulting serum and plasma were aliquoted and stored at − 80 °C until analysis.

All samples were analyzed for circulating concentrations of cortisol (ng·mL− 1) and creatine kinase (μ·L− 1). Concentrations of cortisol were analyzed via enzyme-linked immunosorbent assays (ELISA) via a 96-well spectrophotometer (SpectraMax M3 Multi-Mode Microplate Reader, Molecular Devices) using a commercially available kit. Concentrations of creatine kinase were determined against an enzymatic approach in serum samples using commercially available reagents and a single-cuvette spectrophotometer (SpectraMax M3 Multi-Mode Microplate Reader, Molecular Devices) at a wavelength of 340 and 450 nm (nm), respectively. To eliminate inter-assay variance, all samples were thawed once and analyzed in duplicate in the same run by a single technician with an average coefficient of variation of 4.2% for cortisol and 5.8% for creatine kinase.

Body composition

Initially, height (± 0.1 cm) and body mass (± 0.1 kg) were determined using a stadiometer (WB-3000, TANITA Corporation, Tokyo, Japan) with the athletes standing barefoot, feet together, in athletic attire. Subsequently, body composition was assessed by three methods (i.e., dual energy X-ray absorptiometry [iDXA, Lunar Corporation, Madison, WI], air displacement plethysmography [BodPod, COSMED USA Inc., Chicago, IL], and bioelectrical impedance analysis [770 Body Composition and Body Water Analyzer, InBody, Seoul, South Korea]) using standardized procedures. Briefly, iDXA scanning required the athletes to remove any metal or jewelry and lay supine on the iDXA table prior to an entire body scan in “standard” mode using the company’s recommended procedures and supplied algorithms. Quality assurance was assessed by daily calibrations performed prior to all scans using a calibration block provided by the manufacturer. All iDXA measurements were performed by the same researcher using standardized positioning procedures. For air displacement plethysmography, the device and associated scale were calibrated daily using a known volume and mass provided by the manufacturer. During testing, the athletes were asked to wear a tight-fitting bathing suit or compression shorts and swim cap before entering the device. Two trials were performed for each athlete to obtain two measurements of body volume within 150 mL. A third trial was performed if body volume estimates from the first two trials were not within 150 mL, and values from the two closest trials were averaged. Thoracic lung volume was estimated [36]. Bioelectrical impedance analysis required the athletes to stand barefoot on two metal sensors located at the base of the device and hold two hand grips for approximately 30–60 s. Prior to stepping onto the device, the athletes cleaned the soles of their feet with alcohol wipes provided by the manufacturer.

Following testing, body mass, bone mineral content (BMC; from iDXA), body volume (from BodPod), and total body water (from bioelectrical impedance analysis) were entered into a 4-compartment model (Eq. 1), to estimate body fat percentage (BF%) [37] and fat-free mass (± 0.1 kg).

$$ BF%=frac{left(2.748 x volumeright)-left(0.699 x waterright)+left(1.129 x BMCright)-left(2.051 x Body Massright)}{Body Mass}x 100 $$


Muscle morphology

Non-invasive skeletal muscle ultrasound images were collected from the right thigh of each athlete. Prior to image collection, all anatomical locations of interest were identified using standardized landmarks for the rectus femoris (RF; 50% of the distance from the proximal border of the patella to the anterior, inferior suprailiac crest) and vastus lateralis (VL; 50% of the distance from the lateral condyle of the tibia to the most prominent point of the greater trochanter of the femur) muscles. After completion of all measurements, the athlete laid supine on the examination table for a minimum of 15 min to allow fluid shifts to occur before images were collected [38]. The same investigator performed all landmark measurements for each participant.

A 12 MHz linear probe scanning head (General Electric Vivid i BT09, Wauwatosa, WI, USA) was coated with water soluble transmission gel to optimize spatial resolution and used to collect all ultrasound images. Image collection began with the probe being positioned on (and perpendicular to) the surface of the skin to provide acoustic contact without depressing the dermal layer. Subsequently, two consecutive images were collected with the probe oriented longitudinal to the muscle tissue interface using Brightness Mode (B-mode; Gain = 50 dB; image depth = 5–6 cm) ultrasound [39]. Each of these images included a horizontal line (approximately 1 cm), located below the image, which was used for calibration purposes when analyzing the images offline [40]. To capture images of the RF, the athlete laid supine with his legs extended but relaxed. A rolled towel was placed beneath the popliteal fossa of the right leg to allow a 10° bend in the knee and the foot was secured [41]. For the VL, the athlete was placed on his side, legs together except for a rolled towel between the knees, and positioned to allow a 10° bend at the knees [41]. The same investigator positioned each athlete and collected all images.

After all images were collected, the ultrasound data were transferred to a personal computer for analysis via Image J (National Institutes of Health, Bethesda, MD, USA, version 1.45 s) by the same technician. Muscle thickness (± 0.01 cm; perpendicular distance between the superficial and deep aponeuroses) and pennation angle (± 0.1°; intersection of the fascicles with the deep aponeurosis) were measured from each image and averaged. Intraclass correlation coefficients (ICC3,k = 0.77–0.99) for determining muscle thickness (RF: ICC3,K = 0.93, VL: ICC3,K = 0.88) and pennation angle (RF: ICC3,K = 0.99, VL: ICC3,K = 0.84) was previously determined in ten active, resistance-trained men (25.3 ± 2.0 years, 180 ± 7 cm, 90.8 ± 6.8 kg) using the methodology described above.

Maximal and rapid torque production

Following anthropometric assessments, the athletes completed a general warm-up that included riding a cycle ergometer for 5 min at their preferred resistance and cadence followed by a dynamic stretching: 10 body weight squats, 10 alternating lunges, 10 walking knee hugs and 10 walking butt kicks. The athletes then completed three maximal voluntary isometric contractions (MVICs) of the right knee extensors and flexors on a Biodex System 4 dynamometer (Biodex Medical Systems, Inc. Shirley, NY, USA) at a knee joint angle of 120° and 150° (180° = full knee extension), respectively [42, 43]. The order of testing (e.g., knee flexion before knee extension) was randomized at PREFALL and remained constant for each athlete on subsequent visits. Athletes were seated on the dynamometer with their hands across their chest and restraining straps placed over their trunk, pelvis, and thigh. The axis of knee rotation was aligned with the input axis of the dynamometer. Initially, athletes performed two submaximal isometric contractions at 50 and 75% of their perceived maximal effort prior to maximal testing. For maximal testing, the athletes were instructed to “push” or “pull”, “as hard and fast as possible” for 3–4 s for the knee extensors and knee flexors, respectively [44, 45]. A one-minute rest interval separated each trial. Athletes were instructed to keep the leg musculature relaxed and to avoid a countermovement prior to each MVIC. An additional trial was performed if any preceding activity occurred during baseline. Strong verbal encouragement and visual biofeedback was provided during all maximal strength tests. The dynamometer chair settings were recorded to ensure that identical settings were used for each subsequent visit.

The raw torque data from the dynamometer was processed using custom written software (LabVIEW, National Instruments, Austin, TX, USA). Peak torque (Nm) was considered the highest 50 ms rolling average. Rate of torque development (RTD; Nm·sec− 1) was derived from the linear slope of the torque-time curve (Δtorque / Δtime) and calculated from 0 to 100 ms and 0–200 ms. Impulse (Nm·sec) was calculated as the area under the torque-time curve (∫Torque dt) for the same time intervals. A torque onset of 7.5 Nm and 4 Nm was used for the knee extensors and knee flexors, respectively [45]. The trial producing the highest peak torque was used for subsequent analysis.

Sprinting performance

Sprinting time and kinetics (i.e., force, velocity, and power) were assessed during 40-m sprints using an isokinetic sprinting device (1080 Sprint, 1080 Motion, Lidingo, Sweden) tethered to athletes via waist belt (Power Systems LLC, Knoxville, TN, USA). All sprints were performed in cleats on a grass surface located adjacent to their training facility. A pair of cones were positioned approximately 5 m from the device to denote the “starting line”, while another pair of cones were placed parallel to the starting line at a distance of 40 m to denote the “finish line”. Three additional sets of cones were placed approximately 10 m apart between the starting and finish lines to clearly indicate the sprinting path. Prior to testing, the athletes performed their usual pre-practice warm-up routine, which included approximately 5 min of light jogging followed by dynamic stretching. Then the athletes completed a specific warm-up that included two untethered submaximal sprints and one maximal familiarization 40-m sprint while tethered to the device. Subsequently, they completed one maximal sprint trial against the low-resistance (9.81 N; the minimum resistance necessary for the device to measure kinetics) and a second against high-resistance (147.1 N; the maximal resistance setting). The athletes were allotted 3–5 min of rest between each sprint. All sprint tests were conducted in the “Isotonic” mode (i.e., load is constant and independent of acceleration) to provide the smoothest resistance during testing, as per the manufacturer recommendations. The athletes were instructed to take their preferred starting stance at the starting line and to begin each sprint at their ready. The Quantum software used to control the device was set to detect the athlete’s position and initiate data collection on his first movement and continue to collect data until the athlete reached a distance of 40 m from his starting position. Athletes were verbally encouraged throughout each maximal sprint. The starting leg was recorded during the first sprint and made constant throughout all future sprints. Upon completion of each testing session, sprinting data were stored on a password-protected computer for subsequent analysis.

Sprinting kinetic data were downloaded from the Quantum software into a comma-separated values file and organized into a spreadsheet (Excel 2016, Microsoft, Redmond, WA, USA). Data from each file was used to calculate sprinting kinetic variables as previously described [46]. Briefly, changes in acceleration were used to identify the onset and duration of each step and then force (N), velocity (m·sec− 1), and power (W) were averaged for each step. Calculated values were then separated into the first and second steps, the acceleration phase (i.e., from the 3rd step to the step when peak velocity was achieved), and the peak velocity phase (i.e., the remainder of the sprint). Since the acceleration phase is marked by increases in velocity, the rate of increase for each kinetic variable was used for all group comparisons. In contrast, the average across all steps in the peak velocity phase was used for group comparisons due to the consistency seen in velocity during this phase. The repeatability (± 0.7%) and accuracy of the 1080 Sprint for measuring position (± 0.5%), velocity (± 0.5%) and force (± 4.8 N) have been provided by the manufacturer [47], while we have also demonstrated consistent measures of kinetics following two consecutive sprints [46].

Dietary intake and training

Due to the known influence on muscle size and performance, athletes were instructed to maintain their normal kilocaloric intake and training habits throughout the course of the investigation. The athletes were asked to record all food and beverage intake over the course of 3 days (two weekdays and one weekend day) on two consecutive weeks at the onset (i.e., weeks 1 and 2) and conclusion (i.e., weeks 5 and 6) of the fall and spring competitive seasons. Food records were collected weekly and reviewed by a research team member before entering the information into a publicly-available online database (MyFitnessPal) to determine total kilocaloric (kcals) and protein (g) intake. For statistical analysis, average kilocaloric and protein intake over each consecutive 2-wk period were analyzed relative to body mass.

Records of the occurrence and details of resistance training were also requested of athletes because the team did not utilize a standard training program. The athletes were asked to provide a list of the exercises they completed, as well as the number of sets, repetitions, intensity load, and approximate rest intervals that they used for each session. These records were collected on two consecutive weeks at the onset and conclusion of each season. Since each athlete utilized his own training regimen, there was considerable variability in the number of training days completed each week, exercises performed, set and repetition paradigms, and whether rest intervals were tracked. Briefly, the athletes reported participating in training sessions on 1–7 days each week, they utilized a various assortment of structural (e.g., bench press, shoulder press, squat, deadlift), core (e.g., lat pulldowns, seated rows, upright rows), and assistance exercises (e.g., biceps curls, triceps pushdowns) with barbells, dumbbells, and cables. The number of sets and repetitions completed varied considerably between typical muscular strength, hypertrophy, and endurance paradigms; though each athlete remained fairly consistent with their own training pattern. To quantify training, frequency (days·wk.− 1) and upper- and lower-body volume (sets·repetitions·load) were averaged over each 2-week period at the beginning and end of each season and then used for statistical comparisons.

Statistical analysis

The Shapiro-Wilk test was used to determine normality and indicated that several of the variables were not distributed normally. Consequently, changes in all variables were separately examined across time using linear mixed models with maximum likelihood estimation and an autoregressive-heterogenous repeated covariance to account for the dependent relationships existing between time points. Additionally, a grouping factor (i.e., HMB-Cr or Cr) was added to the model to examine the effect of supplementation. Following any significant F-ratio, specific differences between time points within each group were further assessed by applying Bonferonni adjustments to confidence intervals while differences between groups at each time point were examined using independent t-tests. For all statistical tests, a criterion alpha level of p ≤ 0.05 was used to determine significance. Differences between and within groups were further assessed by effect sizes calculated according to Cohen’s d and Cohen’s dz., respectively [48]. As previously suggested for recreationally-trained individuals [49], interpretations of effect size were evaluated at the following levels: trivial (< 0.35), small (0.35–0.80), moderate (0.80–1.50), and large (> 1.50). Data are represented as mean (or mean difference) ± standard deviation with 95% confidence intervals (95% C.I.).

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