Long-Term Resistance Trained Human Muscles Have More Fibres, More Myofibrils and Tighter Myofilament Packing than Untrained.
Med Sci Sports Exerc
; 2024 Jun 19.
Article
in En
| MEDLINE
| ID: mdl-38875487
ABSTRACT
INTRODUCTION:
Increases in skeletal muscle size occur in response to prolonged exposure to resistance training that is typically ascribed to increased muscle fibre size. Whether muscle fibre number also changes remains controversial, and a paucity of data exists about myofibrillar structure. This cross-sectional study compared muscle fibre and myofibril characteristics in long-term resistance-trained (LRT) versus untrained (UNT) individuals.METHODS:
The maximal anatomical cross-sectional area (ACSAmax) of the biceps brachii muscle was measured by MRI in 16 LRT (5.9 ± 3.5 years' experience) and 13 UNT males. A muscle biopsy was taken from the biceps brachii to measure muscle fibre area, myofibril area and myosin spacing. Muscle fibre number, myofibril number in total and per fibre were estimated by dividing ACSAmax by muscle fibre area or myofibril area, and muscle fibre area by myofibril area, respectively.RESULTS:
Compared to UNT, LRT individuals had greater ACSAmax (+70%, P < 0.001), fibre area (+29%, P = 0.028), fibre number (+34%, P = 0.013), and myofibril number per fibre (+49%, P = 0.034) and in total (+105%, P < 0.001). LRT individuals also had smaller myosin spacing (-7%, P = 0.004; i.e. greater packing density) and a tendency towards smaller myofibril area (-16%, P = 0.074). ACSAmax was positively correlated with fibre area ( r = 0.526), fibre number ( r = 0.445) and myofibril number (in total r = 0.873 and per fibre r = 0.566), and negatively correlated with myofibril area ( r = -0.456) and myosin spacing ( r = -0.382) (all P < 0.05).CONCLUSIONS:
The larger muscles of LRT individuals exhibited more fibres in cross-section and larger muscle fibres, which contained substantially more total myofibrils and more packed myofilaments than UNT participants, suggesting plasticity of muscle ultrastructure.
Full text:
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Database:
MEDLINE
Language:
En
Year:
2024
Type:
Article