Metabolic Adaptations and Substrate Oxidation are Unaffected by Exogenous Testosterone Administration during Energy Deficit in Men : Medicine & Science in Sports & Exercise

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Metabolic Adaptations and Substrate Oxidation are Unaffected by Exogenous Testosterone Administration during Energy Deficit in Men


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Medicine & Science in Sports & Exercise 55(4):p 661-669, April 2023. | DOI: 10.1249/MSS.0000000000003089


Severe periods of energy deficit (e.g., % energy intake below energy expenditure) during arduous military training (1,2) and combat sports (3,4), causing weight loss, are known to suppress endogenous testosterone synthesis. Such declines in the synthesis of testosterone and concentrations may lead to loss in skeletal muscle mass (1,2). Exogenous testosterone administration is well documented to increase concentrations of free and total testosterone, leading to muscle accretion in younger and older men under energy balance conditions (5,6) Similarly, weekly testosterone administration during 28 d of 55% energy deficit increases circulating testosterone, which is associated with gains in muscle mass (7,8). Increased muscle mass with testosterone administration seems to result from molecular adaptations within skeletal muscle (9–11). Previous work from our laboratory (8) reported that testosterone administration increases androgen receptor protein content, decreases transcriptional markers of muscle proteolysis, and upregulates muscle translational capacity in young healthy mean during energy deficit. Furthermore, greater fractional synthetic rate across the muscle proteome is maintained, contributing to gains in muscle mass, when exogenous testosterone is administrated during periods of energy deficit compared with placebo in young healthy men (12).

Testosterone administration may elicit whole-body and skeletal muscle energy metabolic adaptations in older men during energy balance conditions (13,14). In older men with low testosterone (hypogonadism), exogenous testosterone decreases resting respiratory exchange ratio (RER) as a result of increased fat oxidation (13). Changes in whole-body substrate oxidation may, in part, be due to testosterone-induced erythropoiesis, increasing the oxygen-carrying capacity to peripheral tissue (15–18). Greater oxygen delivery may account for increases in skeletal muscle mitochondrial transcription factor (TFAM) and peroxisome proliferator–activated receptor-gamma coactivator-1α (PGC-1α) gene expression, and protein content of citrate synthase and oxidative phosphorylation complex IV and V observed with 6 wk of testosterone administration in older men (14). The young, healthy participants in the current study were previously shown to have higher concentrations of erythropoietin and hemoglobin, as well as greater hematocrit with testosterone administration compared with placebo during 28 d of 55% energy deficit (18). Whether increases in oxygen delivery capacity with testosterone administration alter whole-body energy expenditure and substrate oxidation, and skeletal muscle transcriptional regulation of energy metabolism during energy deficit remains unknown.

The objective of this study was to assess the effects of weekly testosterone enanthate injections (200 mg·wk−1) versus placebo on energy expenditure and energy substrate oxidation after 28 d of diet- and exercise-induced energy deficit (55% total energy needs). In addition, the effects of exogenous testosterone versus placebo on skeletal muscle transcriptional regulation of energy, carbohydrate, fat, and protein metabolism genes were also assessed. We hypothesized that whole-body energy expenditure and fat oxidation would increase in men receiving testosterone compared with those receiving placebo. We also hypothesized that testosterone administration would increase the transcription of energy metabolism and fat oxidation–related genes compared with placebo after 28 d of 55% energy deficit.



Participants were part of a parent proof-of-concept, single-center, randomized, double-blind, placebo-controlled trial (; NCT02734238) assessing the impact of exogenous testosterone administration during 28 d of diet- and exercise-induced energy deficit on body composition and physical performance (19). Inclusion and exclusion criteria, as well as body composition and safety data have been previously published (7,19). In brief, 50 healthy, physically active (≥2 d·wk−1 aerobic and/or resistance exercise) men age 18–39 yr who had total testosterone concentrations within the normal physiological range (10.4–34.7 nmol·L−1 (20)) were recruited to participate. This study was approved by the Pennington Biomedical Research Center (PBRC; Baton Rouge, LA) Institutional Review Board and the Human Research Protection Office of the US Army Medical Research and Development Command. Participants provided written informed consent and completed the study between April 2016 and September 2017. All study activities took place at the PBRC.

Study design

This study included a 14-d energy balance phase, followed by a 28-d inpatient phase of controlled diet- and exercise-induced energy deficit (Fig. 1). During balance, participants were free-living and maintained their normal physical activity levels. Participants were provided a eucaloric diet providing 1.6 g protein·kg−1·d−1, 30% total energy intake from fat, with the remaining energy coming from carbohydrate. This macronutrient distribution was maintained throughout the study. Daily energy requirements for diet prescriptions was initially determined using the average of Mifflin St. Jeor Equation with an activity factor of 1.3 to account for activities of daily living, and results from 7-d accelerometer data and 3-d activity logs collected during initial screening (7,19). Diet and physical activity adherence during balance were verified by research dietitians, accelerometer data, and daily measurements of metabolic body weight on a calibrated digital scale (GSE Inc. model 450; GSE Scale Systems, Novi, MI) after an overnight fast and morning void. Energy intake was adjusted when necessary to maintain body weight within ±2% during the balance phase.

Study design.

After completing the balance phase, participants were admitted to an inpatient unit at the PBRC for the 28-d controlled diet- and exercise-induced energy deficit (55% below total energy needs) phase of the study. Participants were randomized to receive weekly intramuscular injections of 200 mg of testosterone enanthate (TEST; n = 24) or 1 mL of sesame oil placebo (PLA; n = 26) on study days 15, 21, 28, and 35. A total of 800 mg of testosterone enanthate was injected during deficit. The testosterone dose was based on previous dose–response studies shown to enhance skeletal muscle mass (6,16,21). During deficit, daily energy expenditure was increased by 3–4 sessions of varied-intensity (40%–85% of predetermined V̇O2peak) aerobic exercise per day. These increases in daily exercise resulted in aerobic exercise-induced energy expenditures of 1335 ± 258 kcal·d−1 in TEST and 1396 ± 353 kcal·d−1 in PLA (7). Exercise modalities included elliptical, stationary bike, treadmill or outdoor walking, running, and load carriage (weighted backpack ~30% of body mass). Exercise intensity was verified biweekly using open-circuit indirect calorimetry (ParvoMedics TrueOne 2400, East Sandy, UT) and adjusted when needed to maintain the prescribed total daily energy expenditures of 4045 ± 698 kcal·d−1 in TEST and 3868 ± 555 kcal·d−1 in PLA (7,19). Light calisthenics were also incorporated every 3–4 d during deficit. Calisthenics were not performed within 48 h of muscle biopsies.

Body composition

Body composition was determined by dual-energy x-ray absorptiometry (DXA; Lunar iDXA; GE Healthcare, Madison, WI). Scans were analyzed using Lunar Encore software (version 13.6). Total body mass, fat-free mass, and fat mass were primary outcomes of the parent study (7) and are reported in this article as a change from balance to deficit. DXA scans were conducted by trained personnel on days 11 (balance) and 39 (deficit) after an overnight fast and morning void. Conditions for the DXA were standardized across time and participants. Participants were positioned in the center of the scanner table with their head positioned approximately 3 cm below the top of the scanner field of view. Arms were positioned palms down, without touching the legs. If the participant was too large to fit entirely in the scanner field of view with this positioning, then the palms were turned inward toward, but not touching, the legs. Velcro straps at the ankles and lower leg were used to secure the legs and feet, with the greater toes touching but not overlapping, in place. Standard error of the estimate for DXA scans is ~1%–2% (22). Coefficients of variation for machine calibrations were 0.23%.

Blood analytes

Fasted blood samples were collected on study days 14 (balance) and 42 (deficit) after an overnight fast. Samples were collected between 0600 and 0900 h to limit circadian rhythm confounding total testosterone concentrations. Total testosterone was measured using a Siemens Immulite 2000 (Llanbeis, United Kingdom). Free testosterone was determined by calculation (23).

Whole-room calorimetry

Participants’ 24-h and sleep energy expenditure, as well as carbohydrate, fat, and protein oxidation were assessed on study days 11 (balance) and 39 (deficit) using a whole-room indirect calorimeter. Participants entered the chamber at ~0800 h after an overnight fast and exited at ~0800 h the following morning. Meals were prepared by the metabolic kitchen and served according to a fixed schedule (breakfast at 0900 h, lunch at 1200 h, and dinner at 1800 h). Participants received the same type of meals, snacks, and beverages at balance and deficit to control for potential differences between menu days (Supplemental Table 1, Supplemental Digital Content, Sample menus, Twenty-four-hour energy expenditures and the oxidation of carbohydrate, fat, and protein were calculated (24). RER was measured as CO2 production divided by O2 consumption and used in conjunction with 24-h urinary nitrogen excretion to calculate substrate oxidation rates. Sleep energy expenditure and sleep RER were assessed between 0200 and 0500 h when spontaneous activity was zero. During deficit, participants completed multiple exercise sessions on a cycle ergometer to maintain the increase in total daily energy expenditure prescribed during this phase of the study. No exercise was conducted in the chamber during balance. Activity factor was determined as 24-h energy expenditure divided by sleep energy expenditure (25).

Urinary nitrogen excretion

Total nitrogen content of the urine was determined from a single pooled 24-h urine sample using pyrochemiluminescence (Antek 9000; Antek Instruments, Houston, TX) to assess urinary nitrogen excretion during balance and deficit. Nitrogen balance was calculated as the difference of nitrogen intake minus urinary nitrogen excretion plus miscellaneous (estimated at 5 mg·kg−1) and fecal (estimated at 2 g·d−1) losses (26).

Oxygen-carrying capacity

Rested/fasting blood samples were collected on study days 14 (balance) and 42 (deficit). As previously reported (18), hemoglobin was measured using the Beckman Coulter DxH (Beckman Coulter, Brea, CA). Hematocrit was calculated by multiplying red blood cell count by mean cell volume. Bound oxygen-carrying capacity was estimated as hemoglobin multiplied by 1.34 mL with an assumed oxygen saturation of 97% for all volunteers.

Skeletal muscle biopsies

Percutaneous muscle biopsies of the vastus lateralis were collected under rested/fasted conditions on study days 14 (BAL) and 42 (DEF). Muscle biopsies were collected from one incision on one leg per biopsy protocol day using a 5-mm Bergström needle with manual suction under local anesthesia (1% lidocaine). Muscle biopsies were snap-frozen in liquid nitrogen and stored at −80°C until analysis.

Skeletal muscle glycogen

Glycogen concentration was determined in ~3 mg (dry weight) freeze-dried muscle homogenized in water using a TissueLyser II (Qiagen, Valencia, CA) with 2-mm zirconium oxide beads. Homogenates were boiled at 100°C for 5 min and centrifuged at 13,000g for 5 min at room temperature. Supernatants were used to assess muscle glycogen concentrations using an endpoint fluorometric assay (cat. no. MAK016; Sigma-Aldrich, St. Louis, MO). A subset of participants from TEST (n = 18) and PLA (n = 22) were included in the muscle glycogen analysis because of limited sample availability.

Skeletal muscle mRNA expression

Total RNA was isolated from ~20 mg muscle samples using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA). RNA quantity and quality were assessed using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific). Equal amounts of total RNA (500 ng) were reverse-transcribed using the high-capacity cDNA reverse transcription kit (cat. no. 4368814; Applied Biosystems, Foster City, CA). Reverse transcriptions and quantitative reverse transcription–polymerase chain reaction amplifications were conducted using a T100™ (Bio-Rad, Hercules, CA) and QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). mRNA analyses were performed using commercially available individual TaqMan® probes (cat. no. 4331182; Applied Biosystems) and TaqMan® Fast Advanced Master Mix (cat. no. 4444556; Applied Biosystems). mRNA was normalized to ACTB (Supplemental Fig. 1, Supplemental Digital Content, ACTB cycle threshold count, Fold change for mRNA was calculated using the ΔΔ cycle threshold (ΔΔCT) method (27) and expressed relative to balance values within a treatment.

Statistical analysis

Normality of the data was assessed using Shapiro–Wilk tests for dependent variables. Because mRNA expressions were not normally distributed (P < 0.05), data were log transformed (log2) for statistical analysis. Unpaired t-tests were used to assess for differences in changes (deficit minus balance) in body mass, fat-free mass, fat mass, hemoglobin, and hematocrit between TEST and PLA. Mixed-model ANOVA with fat mass and fat-free mass as covariates was used to assess energy expenditure main effects of time (balance vs deficit), treatment (TEST vs PLA), and their interactions. Mixed-model ANOVA was used to assess main effects of time, treatment, and their interactions for total and free testosterone, oxygen-carrying capacity, energy and macronutrient intake, energy balance, substrate oxidation, nitrogen balance, glycogen, and mRNA expressions. Bonferroni adjustments for multiple comparisons were performed if significant interactions were observed. Stepwise linear regression analysis was conducted to determine the relationship of 24-h energy expenditure to fat-free mass, fat mass, activity factory, oxygen-carrying capacity, and treatment during balance, deficit, and deltas (deficit minus balance). All data are presented as mean ± SD. The α level for significance was set at P < 0.05. Data were analyzed using IBM SPSS Statistics for Windows Version 26.0 (IBM Corp., Armonk, NY). Figures were constructed using GraphPad Prisma 9 (GraphPad Software, Inc, San Diego, CA).


Circulating testosterone and body composition

As previously reported (7), after 28 d of deficit, total and free testosterone concentrations increased (P < 0.05) during deficit compared with balance in TEST and were higher than PLA (Table 1). Body mass decreased to a greater extent (P < 0.01) in PLA (−5.0 ± 1.1 kg) compared with TEST (−2.2 ± 2.1 kg). Change in fat-free mass was greater (P < 0.01) in TEST (2.5 ± 1.8 kg) than PLA (−0.3 ± 1.1 kg). Fat mass decreased similarly in TEST (−4.7 ± 1.3 kg) and PLA (−4.6 ± 1.0 kg).

TABLE 1 - Fasting testosterone concentrations.
Placebo (n = 26) Testosterone (n = 24) P
Energy Balance Energy Deficit Energy Balance Energy Deficit Time Treatment T × T
Total testosterone (ng·dL−1) 402 ± 101 319 ± 131** 472 ± 153 1016 ± 246* <0.01 <0.01 <0.01
Free testosterone (ng·dL−1) 8.8 ± 2.3 5.0 ± 2.5*,** 9.7 ± 2.4 24.4 ± 8.0* <0.01 <0.01 <0.01
Values are mean ± SD. Twenty-four-hour relative macronutrient intake during energy balance (day 14) and energy deficit (day 42).
*Different from balance, P < 0.05.
**Different from TEST, P < 0.05.
T × T, time-by-treatment.

Energy and macronutrient intake, energy expenditure, and energy balance

Per protocol design, energy intake (Fig. 2A) and relative carbohydrate and fat intakes (Table 2) decreased (P < 0.01) in both TEST and PLA during deficit compared with balance. Because of greater body mass loss in PLA, relative protein intake increased (P = 0.01) from balance to deficit in PLA and was greater (P < 0.01) compared with TEST during deficit. Sleep energy expenditure was maintained in both PLA (−67 ± 19 kcal·d−1) and TEST (−4 ± 32 kcal·d−1) during deficit compared with balance (Fig. 2B). Per protocol design, 24-h energy expenditure increased (P < 0.01) similarly in PLA (1387 ± 27 kcal·d−1) and TEST (1482 ± 44 kcal·d−1) during deficit compared with balance (Fig. 2C). Energy balance decreased (P < 0.01) similarly from balance in both PLA (−2242 ± 132 kcal·d−1) and TEST (−2371 ± 169 kcal·d−1) during deficit (Fig. 2D).

Values are mean ± SD. Energy intake (A), 24-h energy expenditure (B), sleep energy expenditure (C), energy balance (D), and nitrogen balance (E) during energy balance (day 11) and energy deficit (day 39). Oxygen-carrying capacity (F) during energy balance (14) and energy deficit (day 42). *Different from balance, P < 0.05. +Different from TEST, P < 0.05.
TABLE 2 - Relative macronutrient intake.
Placebo (n = 26) Testosterone (n = 24) P
Energy Balance Energy Deficit Energy Balance Energy Deficit Time Treatment T × T
Carbohydrate (g·kg−1·d−1) 4.61 ± 1.10 2.72 ± 0.86* 4.57 ± 1.00 2.59 ± 0.62* <0.01 0.73 0.51
Fat (g·kg−1·d−1) 1.16 ± 0.21 0.84 ± 0.17 1.14 ± 0.17 0.80 ± 0.12* <0.01 0.59 0.24
Protein (g·kg−1·d−1) 1.67 ± 0.10 1.74 ± 0.07*,** 1.65 ± 0.05 1.67 ± 0.05 <0.01 0.01 0.02
Values are mean ± SD. Twenty-four hour relative macronutrient intake during energy balance (day 11) and energy deficit (day 39).
*Different from balance, P < 0.05.
**Different from TEST, P < 0.05.
T × T, time-by-treatment.

Substrate metabolism

Twenty-four-hour RER and carbohydrate oxidation decreased (P < 0.05) in both PLA and TEST during deficit compared with balance (Table 3). Protein and fat oxidation increased (P < 0.05) in PLA and TEST during deficit compared with balance. Nitrogen balance decreased (P < 0.05) in PLA (−54.0 ± 71.1 mg·kg−1·d−1), but was maintained in TEST (−15.7 ± 66.1 mg·kg−1·d−1) during deficit compared with balance (Fig. 2E). Nitrogen balance tended (P = 0.056) to be lower in PLA compared with TEST during deficit. There was no difference in glycogen between PLA (balance, 320 ± 120 mmol·kg−1 dry wt; deficit, 270 ± 89 mmol·kg−1 dry wt) and TEST (balance, 352 ± 127 mmol·kg−1 dry wt; deficit, 313 ± 103 mmol·kg−1 dry wt). Independent of treatment, glycogen tended to be lower (P = 0.06) during deficit compared with balance.

TABLE 3 - Substrate oxidation.
Placebo (n = 26) Testosterone (n = 24) P
Energy Balance Energy Deficit Energy Balance Energy Deficit Time Treatment T × T
RER 0.86 ± 0.02 0.79 ± 0.03 0.87 ± 0.05 0.80 ± 0.04 <0.01 0.28 0.61
Carbohydrate oxidation (g·d−1) 247 ± 49 201 ± 101 267 ± 81 255 ± 120 0.03 0.11 0.19
Fat oxidation (g·d−1) 79 ± 22 237 ± 65 79 ± 46 238 ± 74 <0.01 0.95 0.92
Protein oxidation (g·d−1) 105 ± 22 124 ± 22 114 ± 34 117 ± 27 0.01 0.86 0.05
Values are mean ± SD. Twenty-four hour RER (V̇CO2/V̇O2) and substrate oxidation during energy balance (day 11) and energy deficit (day 39).
T × T, time-by-treatment.

Oxygen-carrying capacity

Hematocrit and hemoglobin had a greater (P < 0.05) reduction from balance to deficit in PLA (−1.3% ± 4.0% and −0.6 ± 1.5 g·dL−1) compared with TEST (0.6% ± 2.2% and 0.0 ± 0.7 g·dL−1). Estimated oxygen-carrying capacity decreased (P < 0.05) in PLA (−0.8 ± 2.0 mL O2·dL−1), but was maintained in TEST (0.0 ± 0.9 mL O2·dL−1) during deficit compared with balance (Fig. 2F). Estimated oxygen-carrying capacity was lower (P < 0.05) in PLA compared with TEST during deficit.

Skeletal muscle mRNA expression

In both TEST and PLA, expression of fatty acid metabolism (CD36/FAT, FABP, CPT1b, and ACOX1) and storage (FASN), energy sensing (PRKAB1 and TP53), mitochondria (TFAM and COXIV), and amino acid metabolism (BCAT2 and BCKHDA) mRNA were increased (P < 0.05) during deficit compared with balance (Table 4). There were no differences in LARS, GSY1, PGC-1α, GSK3α, PRKAB2, ACACA, UCP3, NRF1, or LAT1 (Supplemental Table 2, Supplemental Digital Content, Nonsignificant mRNA expression,

TABLE 4 - mRNA expression.
mRNA Placebo (n = 26) Testosterone (n = 24) P
Energy Balance Energy Deficit Energy Balance Energy Deficit Time Treatment T × T
FABP 0.00 ± 0.96 1.11 ± 0.90 0.00 ± 0.74 1.46 ± 0.78 <0.001 0.301 0.339
CPT1B 0.00 ± 0.84 1.17 ± 0.95 0.00 ± 0.94 1.32 ± 1.06 <0.001 0.693 0.723
CD36/FAT 0.00 ± 0.83 0.81 ± 0.90 0.00 ± 1.04 1.01 ± 1.03 <0.001 0.600 0.623
COX IV 0.00 ± 0.94 0.67 ± 0.69 0.00 ± 1.10 0.77 ± 1.13 <0.001 0.765 0.812
TP53 0.00 ± 1.20 0.68 ± 0.90 0.00 ± 1.22 0.72 ± 0.86 0.007 0.769 0.529
BCKHDA 0.00 ± 1.10 1.00 ± 1.09 0.00 ± 1.11 0.63 ± 1.28 <0.001 0.487 0.359
PRKAB1 0.00 ± 1.06 0.49 ± 0.99 0.00 ± 1.05 0.59 ± 1.01 0.012 0.803 0.801
FASN 0.00 ± 0.68 0.42 ± 0.98 0.00 ± 1.03 0.51 ± 1.27 0.025 0.843 0.849
BCAT2 0.00 ± 0.70 0.75 ± 0.91 0.00 ± 0.68 0.43 ± 1.04 <0.001 0.437 0.245
ACOX1 0.00 ± 0.96 0.35 ± 1.01 0.00 ± 1.00 0.40 ± 1.01 0.042 0.865 0.939
TFAM 0.00 ± 0.94 0.48 ± 0.97 0.00 ± 0.98 0.38 ± 1.04 0.019 0.867 0.717
Values are mean ± SD. mRNA expression from rested/fasted muscle biopsies collected during energy balance (day 14) and energy deficit (day 42).
T × T, time-by-treatment.

Regression analysis

Fat-free mass accounted for the majority of the variance in 24-h energy expenditure during balance (Figs. 3A, D). During deficit, fat-free mass and activity factor were identified as the two primary variables accounting for the variance in 24-h energy expenditure in TEST and PLA (Figs. 3B, E). Change (∆; deficit minus balance) in fat-free mass was not associated with∆24-h energy expenditure (Fig. 3C). ∆Activity factor was the primary variable associated with ∆24-h energy expenditure (Fig. 3F).

Associations between 24-h energy expenditure and fat-free mass during energy balance (A) and energy deficit (B). Association between ∆24-h energy expenditure and ∆fat-free mass (C). Associations between 24-h energy expenditure and activity level during energy balance (D) and energy deficit (E). Association between ∆24-h energy expenditure and ∆activity level (F).


The primary findings from this study were that despite higher oxygen-carrying capacity, there was no effect of testosterone on energy expenditures or whole-body substrate oxidation during 28 d of diet- and exercise-induced energy deficit. Similarly, transcriptional regulation of energy, fat, and protein metabolism in skeletal muscle was not affected by testosterone treatment. Regression analysis identified a decrease in the strength of the association between fat-free mass and 24-h energy expenditure during deficit in both TEST and PLA. Concurrently, the strength of the association between activity factor and 24-h energy expenditure increases during energy deficit in both treatments. The lack of an effect of testosterone on energy expenditure and substrate oxidation may, in part, have been the result of increases in physical activity driving metabolic adaptations, masking any potential treatment effect during diet- and exercise-induced energy expenditure.

During 28 d of energy deficit, there were no differences in 24-h and sleep energy expenditures between TEST and PLA. The lack of a treatment effect on 24-h energy expenditure resulted in no differences in the severity of diet- and exercise-induced energy deficit between TEST and PLA. These data seem to indicate that use of testosterone enanthate (200 mg·wk−1) does not exacerbate energy deficits. These results are consistent with previous work by Welle et al. (28), who reported that when correcting for increases in fat-free mass, energy expenditure under resting/fasted conditions was unchanged after 3 months of weekly intramuscular injections of testosterone enanthate in healthy men. Conversely, Bauman et al. (29) reported increases in energy expenditure under resting/fasted conditions in hypogonadal males with chronic spinal cord injury receiving 5 to 10 mg transdermal T patch daily for 12 months. Interestingly, in participants with muscular dystrophy, Welle et al. (28) also reported an increase in energy expenditure under resting/fasted conditions when correcting for increases in fat-free mass. Together, these data may suggest that, although testosterone administration increases energy expenditure in individuals with compromised muscle mass, it may not impact energy expenditure in eugonadal healthy men.

Despite differences in estimated oxygen-carrying capacity between treatments, 24-h energy expenditure and fat oxidation increased, whereas RER and carbohydrate oxidation decreased similarly in both TEST and BAL during deficit compared with balance. Transcriptional adaptations in skeletal muscle were in agreement with changes in whole-body substrate oxidation. Specifically, energy sensing (PRKAB1 and TP53), mitochondria transcription factor (TFAM), marker of mitochondria content (COXIV), and fatty acid metabolism (CD36/FAT, FABP, CPT1b, and ACOX1) and storage (FASN) mRNA increased in skeletal muscle independent of treatment. These data contradict previous studies that reported alterations in substrate oxidation and metabolic gene expression after testosterone administration (13,14). However, others (30,31) have similarly reported no changes in substrate oxidation with exogenous testosterone compared with placebo. Discordant results across studies can likely be attributed to differences in study population (e.g., younger vs older participants, eugonadal vs hypogonadal participants) and study design (e.g., exercise vs no exercise intervention). For the current study, as previously reported (1,7,8,12,32), increases in 24-h energy expenditure in these young healthy men were the result of daily aerobic exercise–induced energy expenditures of 1335 kcal·d−1 in TEST and 1396 kcal·d−1 in PLA. Given the high level of daily aerobic exercise and increases associations between activity factor and 24-h energy expenditure during deficit, shifts in substrate oxidation seem to be primarily driven by exercise-induced adaptations (33,34). Increases in transcriptional regulators of energy and fat metabolism in the current study are also consistent with molecular adaptations that have previously been documented to increase fat oxidation with aerobic exercise training (35–38).

Decreased carbohydrate and increased fat oxidation may also be reflective, in part, of changes in energy and substrate availability during deficit (39,40). Although glycogen content was not statistically different, a result that was driven by one outlier in TEST at deficit, it was numerically lower during deficit compared with balance. Our laboratory (41) and others (42–45) have shown that reductions in glycogen availability increase transcriptional regulation of fatty acid metabolism. Lack of a difference in these outcomes between TEST and PLA may also be due to the magnitude of decline in oxygen-carrying capacity. Although hemoglobin concentrations were lower in PLA, the previously reported (18) mean value of 13.8 g·dL−1 during deficit remained within the normal range (13.8–17.2 g·dL−1) of hemoglobin for males. Maintenance of hemoglobin within the normal range in PLA and the lack of change of hemoglobin concentrations and oxygen-carrying capacity in TEST likely contributed to the lack of a treatment effects on energy expenditure and substrate oxidation during the 28 d of 55% energy deficit in the current study. These data suggest that in the context of aerobic exercise training and energy deficit, testosterone does not seem to alter substrate oxidation at the whole-body or skeletal muscle level compared with placebo.

During the 28-d energy deficit, nitrogen balance was maintained in TEST, but decreased in PLA compared with the baseline energy balance phase. More severe negative nitrogen balance during energy deficit has previously been associated with greater losses in fat-free mass (46). As such, negative nitrogen balance in PLA during energy deficit likely contributed to differences in fat-free mass between treatments reported here and elsewhere (1,7,8,12,32). Negative nitrogen balance during energy deficit has been shown to be the result of increased reliance on endogenous protein stores for substrate oxidation because of declines in glycogen and energy availability (47). Although not statistically different (P = 0.05), PLA had a 19 g·d−1 increase in protein oxidation during deficit compared with balance. A portion of this increase may be attributed to higher protein intake in the PLA group during deficit compared balance. However, protein intake only increased ~5 g·d−1; the remaining 14 g·d−1 of protein oxidation may, in part, have contributed to the negative protein balance observed in PLA. Conversely, there was no numerical difference in protein oxidation in TEST from balance to deficit. Despite numerical increases in protein oxidation in PLA, transcriptional regulators of branched-chain amino acid breakdown were higher during deficit compared with balance, regardless of treatment. Lack of difference in transcriptional regulation of amino acid breakdown may suggest that sparing of endogenous protein for energy production does not fully explain differences in nitrogen balance between TEST and PLA. Previous results from muscle analysis of these participants indicate that the maintenance of nitrogen balance may be the result of greater synthetic potential and inhibited muscle protein breakdown with TEST compared with PLA, which may have also contributed to the maintenance of nitrogen balance in TEST during deficit (8,12).


In conclusion, weekly intramuscular injections of 200 mg of testosterone enanthate during 28 d of a 55% diet- and exercise-induced energy deficit resulted in higher fat-free mass and maintenance of oxygen-carrying capacity and nitrogen balance compared with placebo. Despite these differences, there was no effect of testosterone on energy expenditure and carbohydrate, fat, or protein oxidation during energy deficit. Similarly, increases in skeletal muscle transcriptional regulation of energy sensing, mitochondrial biogenesis, and fat and protein metabolism in TEST and PLA during energy deficit corroborate changes in whole-body substrate oxidation. Overall, these data indicate that in the context of diet- and exercise-induced energy deficit, whole-body and skeletal muscle metabolic adaptations are largely independent of testosterone. Observed metabolic adaptations in the current study were likely the result of high levels of daily aerobic exercise and reduced energy availability.

The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The authors wish to acknowledge Dr. Andrew Young for his critical review of this article, as well as the participants of this study. The authors would like to thank Dr. Shalender Bhasin for sharing his expertise and assisting the research team on the design of the testosterone intervention. The authors would also like to thank Ms. Marissa DiBella for her assistance in quantitative reverse transcription–polymerase chain reaction analysis and the study team at Pennington Biomedical Research Centre for their significant contributions to study management and conduct, data collection, and analysis.

This research was supported by the Collaborative Research to Optimize Warfighter Nutrition II and III projects, funded by the US Department of Defense, and the Joint Program Committee-5, and the Military Operational Research Program, funded by the US Army Medical Research and Materiel Command. This research was also supported by the NIDDK-sponsored Ruth L. Kirschstein National Research Service T32 Research Training Grant (T32-DK064584; K. L. M.).

The investigators adhered to the policies for protection of human subjects as prescribed in Army Regulation 70-25, and the research was conducted in adherence with the provisions of 32 CFR part 219. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the Army or the Department of Defense. Any citations of commercial organizations and trade names in this report do not constitute an official Department of the Army endorsement of approval of the products.

The authors have nothing to disclose. J. C. R. reports that her institution (Pennington Biomedical Research Center) received a grant from the Department of Defense for work association with this publication.

Data safety monitoring board: Timothy Church (Chair, ACAP Health, Dallas, TX); Michael Switzer (Behavior Technology Lab, Pennington Biomedical Research Centre, Baton Rouge, LA); William Johnson (Biostatistics, Pennington Biomedical Research Centre, Baton Rouge, LA); Brian Irvine (School of Kinesiology, Louisiana State University, Baton Rouge, LA).


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