Muscle Wins Again: Examining The Relationship Between Maximal Strength And Body Composition Metrics

Maximal strength is the greatest force a muscle group can produce during a single maximal effort. Strength is a key determinant of both athletic performance and functional capacity, and understanding its relationship with body composition offers valuable insights for optimizing overall health and physical performance. PURPOSE: The purpose of this study was to assess the relationships between maximal strength and various body composition measurements, with the objective of determining whether fat-related or muscle-related metrics are stronger predictors of strength outcomes. METHODS: Per the available subject pool, trained biological males (n = 20; 21.6 ± 1.9 yrs; 175.2 ± 5.5 cm; 80.3 ± 9.5 kg; Dots: 268.7 ± 39.2au) participated in a BodPod analysis to assess body fat percentage (BF%), fat mass (FM), and fat-free mass (FFM). Body mass index (BMI), Fat-mass index (FMI), Fat-free mass index (FFMI), lean body mass (LBM), skeletal muscle estimation (SME), and skeletal muscle quality (SMQ) were computed using basic physiological computations. Maximal strength was assessed through 1-repetition-maximum back squat, bench press, and deadlift, then summed together for a maximal strength total (TOT). Pearson correlations and Hopkins effect size were used to evaluate relationships between TOT maximal strength and body composition metrics. Alpha level was set at p ≤ 0.05. RESULTS: Significant muscle-related variables demonstrated very large to moderate relationships against TOT, including SMQ (ES = 0.880, p < 0.001), FFM (ES = 0.61, p = 0.004), LBM (ES = 0.57, p = 0.009), and SME (ES = 0.57, p = 0.008). Significant very large and moderate relationships were also observed for indexed variables FFMI (ES = 0.85, p < 0.001) and BMI (ES = 0.51, p = 0.024), but not for FMI (ES = 0.18, p = 0.441). No meaningful relationships were found fat-related variables FM (ES = 0.18, p = 0.440) and BF% (ES = 0.06, p = 0.792). CONCLUSION: These findings show that muscle-related metrics, such as SMQ and FFMI, are stronger predictors of strength outcomes when considering body composition variables in males. Incorporating SMQ and FFMI into health assessments offers a free non-invasive way to evaluate functional strength, metabolic health, aging, and disease risk, supporting early detection and intervention.