Lower-Limb Dominance, Performance, and Fiber Type in Resistance-trained Men

Introduction Large imbalances between limbs are common and potentially dangerous, yet few studies have simultaneously examined performance and physiological asymmetries. The current study examined the associations between lower-limb dominance, drop-jumping kinematics, maximal strength, and myosin heavy-chain (MHC) fiber type in the vastus lateralis. Methods Thirteen resistance-trained men (age, 24.3 2.7 yr; height, 181.4 6.6 cm; mass, 87.7 +/- 11.3 kg) identified their dominant (DOM) and nondominant (ND) limb, performed drop jumps (30 cm) and maximal knee extensions (1-repetition maximum, or 1RM), and provided biopsies from both vastus lateralis muscles for single-fiber (109 +/- 36 per limb per person) MHC fiber-type identification (FT%). Results All participants selected right as the preferred kicking limb (DOM). DOM displayed a trend for a greater eccentric knee angular velocity (EKV; P = 0.083) and a significantly greater concentric knee angular velocity (CKVl P = 0.002) during drop jump. DOM also tended to be stronger than ND (64.3 +/- 11.3 vs 61.0 +/- 8.8 kg, P = 0.063). Slow-twitch (MHC I) fibers were more prevalent in DOM (P < 0.025), whereas ND contained more fast-twitch (MHC IIa; P < 0.025). No correlations existed between categories (jumping, 1RM, and FT%). Asymmetries of >5% were present in 6 of 12 participants for EKV, 2 of 12 for CKV, 6 of 13 for 1RM, 12 of 13 for MHC I, and 11 of 13 for MHC IIa. However, only a single participant expressed asymmetries of >5% in all dependent variables (EKV, CKV, 1RM, MHC I, and MHC IIa). Conclusions Several statistically and clinically relevant asymmetries were identified. The FT% differences between lower limbs were large and common. The findings also seem to conclude that DOM was stronger, moved faster, and contained more MHC I. However, only 23% of participants actually displayed that result. This highlights the need to analyze and report both group and individual data, particularly when interpreting findings across multiple related, but not necessarily causal, measurements.