ECSS Paris 2023: OP-PN36
INTRODUCTION: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly prescribed for weight loss. However, concomitant loss of muscle mass is a documented concern. The extent of muscle loss outside of controlled clinical trials remains poorly characterized¹. METHODS: We examined 3-month changes in muscle mass and evaluated baseline predictors of this change in 51 (39 female) patients receiving GLP-1 treatment at Mayo Clinic. Body composition was assessed via bioelectrical impedance analysis (BIA; Tanita Corporation, Inc.) at baseline and after 3 months of GLP-1RA treatment. Baseline characteristics were: age, 49±12 (mean±SD) years; body weight 112 ± 27 kg; body mass index (BMI), 39±8 kg/m²; muscle mass, 58±12 kg; and body fat, 44±7 %. Backward stepwise multiple linear regression was used to evaluate predictors of muscle mass changes, including age, sex, medication type and achieved dose, and baseline anthropometrics (body weight, BMI, muscle mass, and fat mass). Model assumptions were assessed using residual plots, multicollinearity was evaluated with variance inflation factors, and model fit was summarized using R². A minimal clinically important decrease in muscle mass at 3 months was estimated as 0.1 multiplied by the baseline between-subject SD². RESULTS: Mean muscle mass decreased significantly at 3 months (58±12 vs. 56±12 kg; P = 0.0163). Regression analysis revealed that higher baseline muscle mass (P = 0.0013) and female sex (P = 0.0087) were associated with greater muscle loss, whereas higher baseline body weight was associated with muscle preservation (P = 0.0021). Age, medication type, and achieved dose were not significant predictors of muscle changes (P > 0.05). The final model explained approximately 37% of the variance (R² = 0.37), indicating that 63% of inter-individual variability was not accounted for by baseline anthropometrics. Based on our clinically conservative threshold of important decrease in muscle mass, 27 participants experienced significant muscle loss. Notably, 13 participants demonstrated an increase in muscle mass over the same period, illustrating divergent responses of muscle mass changes during GLP-1RA treatment. CONCLUSION: In a real-world setting, GLP-1RA therapy is associated with an overall decline in muscle mass, yet individual responses may vary considerably. These findings suggest that key determinants beyond baseline anthropometrics, particularly physical activity/exercise and nutrition, established factors regulating muscle mass, should be studied over the course of GLP-1RA treatment to better explain potential changes in muscle mass. ¹ Karakasis, P., et al.(2025). Metabolism: clinical and experimental, 164, 156113. https://doi.org/10.1016/j.metabol.2024.156113 ² Mirzadeh, P., et al. (2026). Nutrition, Metabolism and Cardiovascular Diseases. https://doi.org/10.1016/j.numecd.2026.104615
Read CV Kailin JohnssonECSS Paris 2023: OP-PN36
INTRODUCTION: Ageing is associated with progressive declines in skeletal muscle mass and strength, increasing the risk of sarcopenia, functional impairment, and reduced quality of life. Resistance training is a key intervention to counteract these changes and is commonly prescribed using well-defined parameters such as frequency, intensity, duration, and type. However, current recommendations do not address the timing of training. Emerging evidence suggests that training responses may vary across the day due to circadian regulation of physiological functions. Given pronounced interindividual differences in the timing of peak performance, this study examined whether tailoring resistance training to an individual's peak performance time of day enhances skeletal muscle adaptations in older adults. METHODS: To determine individual peak and nadir times of day, participants completed standardised strength tests at four timepoints (08:00, 12:00, 16:00, and 20:00) on four separate days after a familiarisation test. Participants were then block-randomised (2:2:1) to train at their individual peak time of day, their nadir time of day, or to a non-training control group. The 12-week intervention comprised two supervised, progressive whole-body resistance training sessions (moderate-to-high loads, 8-15 repetitions) and one moderate-intensity endurance cycling session per week. Primary outcomes were isometric mid-thigh pull strength, handgrip strength and skeletal muscle mass assessed by dual-energy X-ray absorptiometry. Between-group differences at follow-up were analysed using baseline-adjusted analyses of covariance, with additional adjustment for age and sex. Effect sizes were standardised to baseline standard deviations. RESULTS: Of the 108 randomised participants, 93 (41% female; mean [standard deviation] age 67 [5] years; BMI 24.3 [2.7] kg/m²) were included in the analyses. Training adherence exceeded 91% for resistance and 93% for endurance sessions in both exercise groups. Absolute training loads increased progressively across all resistance exercises, with large relative increases (approximately two- to threefold) from the first to final training session. These increases were comparable between the peak and nadir groups. Across all outcomes, comparisons between peak, nadir, and control groups showed trivial effect sizes (<0.20), with no meaningful differences between training at peak versus nadir times of day. CONCLUSION: There is little evidence that tailoring resistance training to an individual's peak time of day provides additional benefits in terms of skeletal muscle adaptations in older adults. Similar adherence, load progression and training adaptations in the peak and nadir groups indicate that the effectiveness of resistance training is largely independent of the time of day at which it is performed. These findings suggest that older adults may prioritise flexibility in exercise timing to support adherence without compromising muscular adaptations.
Read CV Fabienne BruggisserECSS Paris 2023: OP-PN36
INTRODUCTION: Exercise disrupts hormonal balance, driving distinct adaptations enhancing exercise performance and mitigating against range of health risks (1). Exercise perturbs all major steroid hormone classes, influencing adaptive responses, energy utilisation, and recovery (2). However, inconsistent findings on the effects of exercise-induced hormonal responses exist due to reliance on limited immunoassays (3, 4). Therefore, this study aimed to simultaneously profile steroid hormones via LC-MS/MS in response to three distinct exercise modalities. METHODS: Ten healthy active adult (24.6 ± 2.1 years old) males randomly completed high-intensity interval exercise (HIIE; >80% VO2max), moderate intensity continuous exercise (MICE; <65% VO2max) and whole-body resistance exercise (RE; 3 sets×10 reps at 70% 1RM with 90 s rest). Blood samples were collected pre-, 10 min post-, and 2 h post-exercise; glucose and lactate were measured in whole blood, and 16 plasma hormones by LC–MS/MS. All data were analysed by 2-way ANOVA or non-parametric equivalent. RESULTS: DHEA and androstenedione rose after MICE and HIIE (P<0.01) and RE (P<0.05) but fell below rest by 2 h. Estrone increased 2 h post-RE vs post-exercise (P<0.01) and 2 h post-HIIE vs rest (P<0.01). Only HIIE elevated cortisol (P<0.001) and cortisone (P<0.01) post-exercise, while cortisol fell below rest at 2 h across all modalities (P<0.001). HIIE also raised corticosterone (P<0.001), exceeding MICE (P<0.01); only MICE showed sub-resting corticosterone at 2 h (P<0.01). All exercise modalities increased aldosterone (P<0.01), with greater responses in MICE and HIIE than RE. At 2 h, all protocols elevated the testosterone:cortisol ratio (P<0.05). RE and HIIE decreased testosterone:17β-estradiol and androgens:estrogens and increased estrogens:progesterone ratios (P<0.05). CONCLUSION: While all exercise modalities activated early anabolic precursors only RE and HIIE increased estrogenic signalling, likely reflecting their shared anaerobic demand. These data indicate that exercise intensity, rather than duration or type, orchestrate distinct steroid signatures, providing a mechanistic foundation for manipulating hormonal environments to optimise recovery, performance, and health. Future studies should explore how circulating hormones act on target tissues to drive metabolic regulation and adaptations. References 1. Egan B, Sharples AP. Molecular responses to acute exercise and their relevance for adaptations in skeletal muscle to exercise training. Physiol Rev. 2023;103(3):2057-170. 2. Hackney AC, Lane AR. Exercise and the regulation of endocrine hormones. Prog Mol Biol Transl Sci. 2015; 135:293-311. 3. Leal DV, Taylor L, Hough J. Exercise-induced salivary hormone Responses to High-Intensity, Self-Paced Running. Int J Sports Physiol Perform. 2021;16(9):1319-27. 4. Leal DV, Taylor L, Hough J. Reproducibility of acute steroid hormone responses in men to short-duration running. Int J Sports Physiol Perform. 2019;14(10):1430-7.
Read CV Deaglan McCulloughECSS Paris 2023: OP-PN36