David J Tomlinson1, Robert M Erskine2,3, Christopher I Morse1, Gladys L Onambélé1. 1. Health, Exercise, and Active Living Research Center, Manchester Metropolitan University, Crewe, United Kingdom. 2. Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom. 3. Institute of Sport, Exercise, and Health, University College London, London, United Kingdom.
Abstract
Background: The impact, within a single cohort, of independent modulators of skeletal muscle quality, including age, adiposity and obesity, habitual nutritional intake, and physical activity (PA), is unclear. Objective: We examined the bivariate associations between age, adiposity, habitual nutritional intake, and PA against 11 key intrinsic muscle-tendon unit (MTU) characteristics to identify the strongest predictors. We also compared overall profile differences between MTU categories with the use of z scores shown in radar graphs. Methods: Fifty untrained independently living men (n = 15) and women (n = 35) aged 43-80 y (mean ± SD: 64 ±9 y) were categorized by adiposity [men: normal adiposity (NA) <28%, high adiposity (HA) ≥28%; women: NA <40%, HA ≥40%] and body mass index [BMI (in kg/m2); normal: 18 to <25; overweight: ≥25 to <30; and obese: ≥30]. Group differences were examined by body composition assessed with the use of dual-energy X-ray absorptiometry, habitual nutritional intake through a 3-d food diary, PA (work, leisure, sport) using the Baecke questionnaire, 14 serum cytokine concentrations using multiplex luminometry, and 11 MTU characteristics of the gastrocnemius medialis using a combination of isokinetic dynamometry, electromyography, and ultrasonography. Results: Interestingly, classification by BMI highlighted differences between normal and obese individuals in 6 of 11 MTU characteristics (P < 0.001 to P = 0.043). No significant differences were reported in serum cytokine concentrations between adiposity and BMI classifications. BMI predicted 8 of 11 (r = 0.62-0.31, P < 0.001 to P = 0.032), daily energy intake predicted 7 of 11 (r = 0.45-0.34, P = 0.002-0.036), age predicted 5 of 11 (r = -0.49-0.32, P < 0.001 to P = 0.032), work-based PA predicted 5 of 11 (r = 0.43-0.32, P = 0.003-0.048), and adiposity predicted 4 of 11 (r = 0.51-0.33, P < 0.001 to P = 0.022) MTU characteristics. Mathematical z scores and radar graphs showed how endocrine and dietary profiles, but not PA, differed between the top and bottom ∼20% of muscle unit size and specific force. Conclusions: Given the number of factors associated with MTU, education should be targeted to both adequate food quantity and quality (especially protein intake) and increasing habitual moderate to vigorous PA while decreasing sedentary behavior. Specific endocrine variables are also proposed as key pharmaceutical targets.
Background: The impact, within a single cohort, of independent modulators of skeletal muscle quality, including age, adiposity and obesity, habitual nutritional intake, and physical activity (PA), is unclear. Objective: We examined the bivariate associations between age, adiposity, habitual nutritional intake, and PA against 11 key intrinsic muscle-tendon unit (MTU) characteristics to identify the strongest predictors. We also compared overall profile differences between MTU categories with the use of z scores shown in radar graphs. Methods: Fifty untrained independently living men (n = 15) and women (n = 35) aged 43-80 y (mean ± SD: 64 ±9 y) were categorized by adiposity [men: normal adiposity (NA) <28%, high adiposity (HA) ≥28%; women: NA <40%, HA ≥40%] and body mass index [BMI (in kg/m2); normal: 18 to <25; overweight: ≥25 to <30; and obese: ≥30]. Group differences were examined by body composition assessed with the use of dual-energy X-ray absorptiometry, habitual nutritional intake through a 3-d food diary, PA (work, leisure, sport) using the Baecke questionnaire, 14 serum cytokine concentrations using multiplex luminometry, and 11 MTU characteristics of the gastrocnemius medialis using a combination of isokinetic dynamometry, electromyography, and ultrasonography. Results: Interestingly, classification by BMI highlighted differences between normal and obese individuals in 6 of 11 MTU characteristics (P < 0.001 to P = 0.043). No significant differences were reported in serum cytokine concentrations between adiposity and BMI classifications. BMI predicted 8 of 11 (r = 0.62-0.31, P < 0.001 to P = 0.032), daily energy intake predicted 7 of 11 (r = 0.45-0.34, P = 0.002-0.036), age predicted 5 of 11 (r = -0.49-0.32, P < 0.001 to P = 0.032), work-based PA predicted 5 of 11 (r = 0.43-0.32, P = 0.003-0.048), and adiposity predicted 4 of 11 (r = 0.51-0.33, P < 0.001 to P = 0.022) MTU characteristics. Mathematical z scores and radar graphs showed how endocrine and dietary profiles, but not PA, differed between the top and bottom ∼20% of muscle unit size and specific force. Conclusions: Given the number of factors associated with MTU, education should be targeted to both adequate food quantity and quality (especially protein intake) and increasing habitual moderate to vigorous PA while decreasing sedentary behavior. Specific endocrine variables are also proposed as key pharmaceutical targets.
Authors: Christian M Girgis; Roderick J Clifton-Bligh; Mark W Hamrick; Michael F Holick; Jenny E Gunton Journal: Endocr Rev Date: 2012-11-20 Impact factor: 19.871
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