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      Can Absolute and Proportional Anthropometric Characteristics Distinguish Stronger and Weaker Powerlifters? :

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          Abstract

          This study sought to compare the anthropometric profiles of 17 weaker and 17 stronger Australasian and Pacific powerlifters who had competed in a regional-, national-, or international-level powerlifting competition in New Zealand. Stronger lifters were defined as those having a Wilks score greater than 410, whereas those in the weaker group had a Wilks score less than 370. Each powerlifter was assessed for 37 anthropometric dimensions by International Society for the Advancement of Kinanthropometry (ISAK) level II and III accredited anthropometrists. Because all powerlifters were highly mesomorphic and possessed large girths and bone breadths, both in absolute terms and when expressed as Phantom-Z scores compared through the Phantom, relatively few significant anthropometric differences were observed. However, stronger lifters had significantly greater muscle mass and larger muscular girths in absolute terms as well as greater Brugsch Index (chest girth/height) and "Phantom"-normalized muscle mass, upper arm, chest, and forearm girths. In terms of the segment lengths and bone breadths, the only significant difference was that stronger lifters had a significantly shorter lower leg than weaker lifters. Because the majority of the significant differences were for muscle mass and muscular girths, it would appear likely that these differences contributed to the stronger lifters' superior performance. Powerlifters may therefore need to devote some of their training to the development of greater levels of muscular hypertrophy if they wish to continue to improve their performance. To better understand the anthropometric determinants of muscular strength, future research should recruit larger samples (particularly of elite lifters) and follow these subjects prospectively.

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          Most cited references 11

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          Relative body fat and anthropometric prediction of body density of male athletes

          Two hundred and seven male members of South Australian representative squads in 18 sports (mean +/- s = 24.2 +/- 4.7 years) were tested in order to provide descriptive data on relative body fat (% BF), develop a population specific equation and cross-validate existing equations. Measurements were taken of 10 circumferences, 2 diameters and 8 skinfolds; body density (BD) was measured by underwater weighing with the residual volume (RV) being determined by He dilution. The overall mean BD was 1.0761 g X cm-3 (s = 0.0085 g X cm-3; range = 1.0465-1.0968 g X cm-3) which corresponded to 10.0% BF according to Siri (s = 3.7%; range = 1.3-23.0%). The games players (n = 129) registered an overall mean of 10.3% BF (s = 3.7%; range = 2.2-23.0%). There were significant differences (p less than 0.05) for % BF between the lacrosse players (mean = 12.3%) an both the Australian Rules footballers (mean = 8.0%) and track and field athletes (mean = 8.7%). A stepwise multiple regression on 185 subjects yielded the following equation, which had an R of 0.787: BD = 1.078865-0.000419 (sigma abdominal, medial calf, front thigh and juxta-nipple skinfolds in mm) +0.000948 (neck circumference in cm) -0.000266 (age in decimal years) -0.000564 (ankle circumference in cm). Only those predictors which resulted in a significantly increased correlation (p less than or equal to 0.05) were included. The standard error of estimate of 0.00537 g X cm-3 was equivalent to 2.3% BF at the mean. This equation was satisfactorily cross-validated against the BD of a separate sample (n = 22) from the same population. However, cross-validation of 11 previously published equations indicated that they have limited applicability to State representative sportsmen.
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            The role of FFM accumulation and skeletal muscle architecture in powerlifting performance.

            The purpose of this study was to determine the distribution and architectural characteristics of skeletal muscle in elite powerlifters, and to investigate their relationship to fat-free mat (FFM) accumulation and powerlifting performance. Twenty elite male powerlifters (including four world and three US national champions) volunteered for this study. FFM, skeletal muscle distribution (muscle thickness at 13 anatomical sites), and isolated muscle thickness and fascicle pennation angle (PAN) of the triceps long-head (TL), vastus lateralis, and gastrocnemius medialis (MG) muscles were measured with B-mode ultrasound. Fascicle length (FAL) was calculated. Best lifting performance in the bench press (BP), squat lift (SQT), and dead lift (DL) was recorded from competition performance. Significant correlations (P < or = 0.01) were observed between muscle distribution (individual muscle thickness from 13 sites) and performance of the SQT (r = 0.79 to r = 0.91), BP (r = 0.63 to r = 0.85) and DL (r = 0.70 to r = 0.90). Subscapular muscle thickness was the single best predictor of powerlifting performance in each lift. Performance of the SQT, BP, and DL was strongly correlated with FFM and FFM relative to standing height (r = 0.86 to 0.95, P < or = 0.001). FAL of the triceps long head and vastus lateralis were significantly correlated with FFM (r = 0.59, P < or = 0.01; 0.63, P < or = 0.01, respectively) and performance of the SQT (r = 0.45; r = 0.50, respectively; P < or = 0.05), BP (r = 0.52; r = 0.56, respectively; P < or = 0.05), and DL (r = 0.56; r = 0.54, respectively; P < or = 0.01). A significant positive correlation was observed between isolated muscle thickness and PAN for triceps long-head (r = 0.64, P < or = 0.01) and gastrocnemius medialis (r = 0.48, P < or = 0.05) muscles, but not for vastus lateralis (r = 0.35). PAN was negatively correlated with powerlifting performance. Our results indicate that powerlifting performance is a function of FFM and, therefore, may be limited by the ability to accumulate FFM. Additionally, muscle architecture appears to play an important role in powerlifting performance in that greater fascicle lengths are associated with greater FFM accumulation and powerlifting performance.
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              Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload.

              The response to strength training varies widely between individuals and is considerably influenced by genetic variables, which until now, have remained unidentified. The deletion (D), rather than the insertion (I), variant of the human angiotensin-converting enzyme (ACE) genotype is an important factor in the hypertrophic response of cardiac muscle to exercise and could also be involved in skeletal muscle hypertrophy - an important factor in the response to functional overload. Subjects were 33 healthy male volunteers with no experience of strength training. We examined the effect of ACE genotype upon changes in strength of quadriceps muscles in response to 9 weeks of specific strength training (isometric or dynamic). There was a significant interaction between ACE genotype and isometric training with greater strength gains shown by subjects with the D allele (mean +/- S.E.M.: II, 9.0+/-1.7 %; ID, 17.6 +/-2.2 %; DD, 14.9+/-1.3 %, ANOVA, P 0.05). A consistent genotype and training interaction (ID DD II) was observed across all of the strength measures, and both types of training. ACE genotype is the first genetic factor to be identified in the response of skeletal muscle to strength training. The association of the ACE I/D polymorphism with the responses of cardiac and skeletal muscle to functional overload indicates that they may share a common mechanism. These findings suggest a novel mechanism, involving the renin-angiotensin system, in the response of skeletal muscle to functional overload and may have implications for the management of conditions such as muscle wasting disorders, prolonged bed rest, ageing and rehabilitation, where muscle weakness may limit function.
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                Author and article information

                Journal
                Journal of Strength and Conditioning Research
                Journal of Strength and Conditioning Research
                Ovid Technologies (Wolters Kluwer Health)
                1064-8011
                2009
                November 2009
                : 23
                : 8
                : 2256-2265
                Article
                10.1519/JSC.0b013e3181b8d67a
                19826300
                © 2009

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