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Updated: Jul 2, 2025

Skeletal Muscle Gender Dimorphism from Proteomics
Published on: December 14, 2011
Disaggregating the influence of maturity status on training, anthropometric, performance, skeletal periphery, and
Blair T Crewther1, Anna Pastuszak2, Christian J Cook3
1Institute of Sport - National Research Institute, Warsaw, Poland; Biomedical Science, School of Science and Technology, University of New England, Armidale, Australia.
Abstract:
Maturity offset (i.e., age from peak height velocity [PHV]) is widely used to assess maturational status among youth athletes, but details on the skeletal periphery, hormones and training factors are lacking. More precision is also needed to explicate the timing, tempo, and sequence of growth-related events. These gaps were addressed in a cross-sectional study. One hundred and two athletic boys (aged 14.1 ± 0.5 years) were evaluated for training details, salivary testosterone and cortisol, height, body mass, body mass index, body fat, fat-free mass (FFM), hand and carpal bone area, bone mineral content (BMC) and density (BMD), and countermovement jump performance. Participants were assigned to circa-PHV (N = 56) and post-PHV (N = 46) groups for comparisons, before data pooling and disaggregation using generalized additive and linear regression models. The older post-PHV group had a larger body size, more testosterone, and better performance and skeletal outcomes, whilst weekly training and school-based exercise favored the younger circa-PHV group (all p < 0.001). Smoother plots verified these differences via linear, or weakly non-linear, associations. Maturity offset was predicted (R2 = 0.848) by the linear combination of FFM (42.0 % relative contribution), hand BMD (31.5 %), chronological age (16.0 %), testosterone (6.7 %), and training hours (3.8 %). In conclusion, athletic boys who presented at different stages of maturity (-0.98 to 2.84 years from PHV) also differed on many developmental features. Most variables increased at constant, or near-constant, tempo with a higher maturity offset, with FFM and hand BMD emerging as the strongest linear predictors of maturational status.
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