Many nutrition practices often used by bodybuilders lack scientific support and can be detrimental to health. Recommendations during the dieting phase are provided in the scientific literature, but little attention has been devoted to bodybuilders during the off-season phase. During the off-season phase, the goal is to increase muscle mass without adding unnecessary body fat. This review evaluated the scientific literature and provides nutrition and dietary supplement recommendations for natural bodybuilders during the off-season phase. A hyper-energetic diet (~10–20%) should be consumed with a target weight gain of ~0.25–0.5% of bodyweight/week for novice/intermediate bodybuilders. Advanced bodybuilders should be more conservative with the caloric surplus and weekly weight gain. Sufficient protein (1.6–2.2 g/kg/day) should be consumed with optimal amounts 0.40–0.55 g/kg per meal and distributed evenly throughout the day (3–6 meals) including within 1–2 hours pre- and post-training. Fat should be consumed in moderate amounts (0.5–1.5 g/kg/day). Remaining calories should come from carbohydrates with focus on consuming sufficient amounts (≥3–5 g/kg/day) to support energy demands from resistance exercise. Creatine monohydrate (3–5 g/day), caffeine (5–6 mg/kg), beta-alanine (3–5 g/day) and citrulline malate (8 g/day) might yield ergogenic effects that can be beneficial for bodybuilders.

Resistance training is commonly prescribed to enhance strength/power qualities and is achieved via improved neuromuscular recruitment, fiber type transition, and/ or skeletal muscle hypertrophy. The rate and amount of muscle hypertrophy associated with resistance training is influenced by a wide array of variables including the training program, plus training experience, gender, genetic predisposition, and nutritional status of the individual. Various dietary interventions have been proposed to influence muscle hypertrophy, including manipulation of protein intake, specific supplement prescription, and creation of an energy surplus. While recent research has provided significant insight into optimization of dietary protein intake and application of evidence based supplements, the specific energy surplus required to facilitate muscle hypertrophy is unknown. However, there is clear evidence of an anabolic stimulus possible from an energy surplus, even independent of resistance training. Common textbook recommendations are often based solely on the assumed energy stored within the tissue being assimilated. Unfortunately, such guidance likely fails to account for other energetically expensive processes associated with muscle hypertrophy, the acute metabolic adjustments that occur in response to an energy surplus, or individual nuances like training experience and energy status of the individual. Given the ambiguous nature of these calculations, it is not surprising to see broad ranging guidance on energy needs. These estimates have never been validated in a resistance training population to confirm the “sweet spot” for an energy surplus that facilitates optimal rates of muscle gain relative to fat mass. This review not only addresses the influence of an energy surplus on resistance training outcomes, but also explores other pertinent issues, including “how much should energy intake be increased,” “where should this extra energy come from,” and “when should this extra energy be consumed.” Several gaps in the literature are identified, with the hope this will stimulate further research interest in this area. Having a broader appreciation of these issues will assist practitioners in the establishment of dietary strategies that facilitate resistance training adaptations while also addressing other important nutrition related issues such as optimization of fuelling and recovery goals. Practical issues like the management of satiety when attempting to increase energy intake are also addressed.

Training with low carbohydrate availability (LCHO) has been shown to acutely enhance endurance training skeletal muscle response, but the concomitant energy deficit (ED) in LCHO interventions has represented a confounding factor in past research. This study aimed at determining if achieving energy balance with high fat (EB-HF) acutely enhances the adaptive response in LCHO compared to ED with low fat (ED-LF). In a crossover design, nine well-trained males completed a 'sleep-low' protocol: on day 1 they cycled to deplete muscle glycogen while reaching a set energy expenditure (30 kcal (kg of fat free mass (FFM)) −1). Post-exercise, low carbohydrate, proteinmatched meals completely (EB-HF, 30 kcal (kg FFM) −1) or partially (ED-LF, 9 kcal (kg FFM) −1) replaced the energy expended, with the majority of energy derived from fat in EB-HF. In the morning of day 2, participants exercised fasted, and skeletal muscle and blood samples were collected and a carbohydrate-protein drink was ingested at 0.5 h recovery. Muscle glycogen showed no treatment effect (P < 0.001) and decreased from 350 ± 98 to 192 ± 94 mmol (kg dry mass) −1 between rest and 0.5 h recovery. Phosphorylation status of the mechanistic target of rapamycin and AMP-activated protein kinase pathway proteins showed only time effects. mRNA expression of p53 increased after exercise (P = 0.005) and was higher in ED-LF at 3.5 h compared to EB-HF (P = 0.027). Plasma glucose and insulin area under the curve (P < 0.04) and peak values (P ≤ 0.05) were higher in EB-HF after the recovery drink. Achieving energy balance with a high-fat meal in a 'train-low' ('sleep-low') model did not enhance markers of skeletal muscle adaptation and impaired glycaemia in response to a recovery drink following training in the morning. K E Y W O R D S endurance, energy availability, high-fat feeding, muscle glycogen, train low This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The syndrome of Relative Energy Deficiency in Sport (RED-S) includes wide-ranging effects on physiological and psychological functioning, performance, and general health. However, RED-S is understudied among male athletes at the highest performance levels. This cross-sectional study aimed to investigate surrogate RED-S markers prevalence in Norwegian male Olympic-level athletes. Athletes (n = 44) aged 24.7 ± 3.8 years, body mass 81.3 ± 15.9 kg, body fat 13.7% ± 5.8%, and training volume 76.1 ± 22.9 hr/month were included. Assessed parameters included resting metabolic rate (RMR), body composition, and bone mineral density by dual-energy X-ray absorptiometry and venous blood variables (testosterone, free triiodothyronine, cortisol, and lipids). Seven athletes (16%) grouped by the presence of low RMR (RMRratio < 0.90) (0.81 ± 0.07 vs. 1.04 ± 0.09, p < .001, effect size 2.6), also showed lower testosterone (12.9 ± 5.3 vs. 19.0 ± 5.3 nmol/L, p = .020) than in normal RMR group. In low RMRratio individuals, prevalence of other RED-S markers (—subclinical—low testosterone, low free triiodothyronine, high cortisol, and elevated low-density lipoprotein) was (N/number of markers): 2/0, 2/1, 2/2, 1/3. Low bone mineral density (z-score < −1) was found in 16% of the athletes, all with normal RMR. Subclinical low testosterone and free triiodothyronine levels were found in nine (25%) and two (5%) athletes, respectively. Subclinical high cortisol was found in 23% of athletes while 34% had elevated low-density lipoprotein cholesterol levels. Seven of 12 athletes with two or more RED-S markers had normal RMR. In conclusion, this study found that multiple RED-S markers also exist in male Olympic-level athletes. This highlights the importance of regular screening of male elite athletes, to ensure early detection and treatment of RED-S.

Background: To detect longitudinal changes of resting metabolic rate (RMR) resulting from the effects of energetic stress, reliable RMR measurements are crucial. The Vyntus CPX is a new automated indirect calorimetry system for which RMR reliability has not been determined. Additionally, its agreement with common predictive RMR formulas is unknown. Aim: To determine the within and between-day reliability of RMR measurements using the Vyntus CPX system and its agreement with predictive RMR formulas. Methods: Young (31  ±  7 years) healthy participants (n = 26, 12 females, 14 males) completed three measurements of RMR, two consecutive measures on the same day, one the day before/after, all under standardised conditions. Reliability was assessed with pairwise comparisons of between-day at the same time (BDST), within day consecutive measurements (WDCM) and between-day different time (BDDT), for parameters of reliability (mean change (MC), intraclass correlation (ICC) and typical error of measurement (TEM)). Measured RMR values (kcal/day) were compared against predictive values of 4 common formulas. Results: Parameters of reliability (mean, (95% confidence interval)) were: -BDST: MC, 0.2(-2.3—2.7)% (p = 0.67); ICC, 0.92(0.84—0.97); TEM, 4.5(3.5—6.2)%. -WDCM: MC, −2.5(-6.2—1.3)% (p = 0.21); ICC, 0.88(0.74—0.88); TEM, 7.0(5.4—9.8)%. -BDDT: MC, −1.5(-4.8—1.9)% (p = 0.57); ICC, 0.90(0.76—0.95); TEM, 6.1(4.8—8.5)%. RMRratios (measured/predicted) were: 1.04  ±  0.14 (Nelson, p = 0.13), 1.03  ±  0.10 (Mifflin, p = 0.21), 0.98  ±  0.09 (Harris-benedict, p = 0.30), 0.95  ±  0.11 (Cunningham1980, p = 0.01), 1.00  ±  0.12 (Cunningham1991, p = 0.90) and 0.96  ±  0.13 (DXA, p = 0.03). Conclusions: The Vyntus CPX is reliable and measured RMR values agreed with four predictive formulas but are lower than Cunningham1980 and DXA RMR estimates for this population.