Kg Of Fat-free Mass Research Articles

Differences in Loading and Morphological Structure of the Take-off and Non-take-off Limb in Athletics Jumping Events.

The objective of the study was to assess differences between the take-off and non-take-off limbs of athletes in track-and-field jumping events based on a segmental analysis of body composition as well as kinetic analysis. The research included 19 participants (10 males, 9 females) with an average age of 18.1 ± 2.8 years. We measured body height, body mass, body composition (body fat, fat free mass, bone mineral content and bone density) and segmental distribution of these variables. To assess strength of the lower limbs, we performed reaction force analysis during take-off and run symmetry. The difference in the representation of soft tissues between the take-off and non-take-off limbs was not significant; the differences were 0.06%, 0 kg in body fat and 0.01 kg in fat free mass. The differences in the values of bone matter were significant. The bone mineral content was 0.05 kg higher in the take-off limb (p < 0.001), and bone density was 0.07 g/cm2 higher (p < 0.001); the practical significance of the difference was intermediate (d = 0.5). Kinetic analysis showed that athletes exerted greater force on the pad with the take-off limb than the non-take-off limb when taking off while using arms in the first peak of the vertical force. The difference determined in this type of take-off was statistically significant (p < 0.05); the practical significance of the difference was medium (d = 0.7). The difference in the second peak of the vertical reaction force in the take-off arm was not statistically significant. The differences were reflected in the different bone matter compositions.

Field-Based Performance Tests Are Related to Body Fat Percentage and Fat-Free Mass, But Not Body Mass Index, in Youth Soccer Players.

The primary aim of this study was to determine the association between body composition and performance outcomes in youth soccer players. Twenty-five competitive male youth soccer players (age = 13.7 ± 0.8 years, height = 167.4 ± 9.7 cm, weight = 57.6 ± 12.1 kg) volunteered to participate in this study. Height and weight were used to calculate body mass index (BMI). Body fat percentage (BF%) and fat-free mass (FFM) were determined with dual-energy X-ray absorptiometry. Each athlete performed the Pacer test, vertical jump, and t-test drill. Participants were predominantly normal weight (20.4 ± 2.7 kg·m−2). The body composition results were 20.3 ± 4.9% for BF% and 46.5 ± 8.7 kg for FFM. The results of the performance tests indicated a mean ± standard deviation (SD) of 1418 ± 332 m for Pacer, 57.2 ± 7.4 cm for vertical jump, 11.6 ± 0.7 s for t-test. Body mass index was not associated with any performance measure (r = 0.02 to −0.21, all p > 0.05). Body fat percentage was associated with the Pacer, vertical jump, and t-test (r = −0.62, −0.57, 0.61, respectively; all p < 0.01) and remained after accounting for BMI. Fat-free mass was only significantly related to t-test (r = −0.43, p < 0.01). However, after controlling for BMI, FFM was related to all three performance tests. Body fat percentage and FFM are associated with performance in youth soccer players, with stronger relationships reported in the former metric. The findings highlight the need for accurate body composition measurements as part of an assessment battery in young athletes.

The muscle anabolic effect of protein ingestion during a hyperinsulinaemic euglycaemic clamp in middle-aged women is not caused by leucine alone.

It has been suggested that leucine is primarily responsible for the increase in muscle protein synthesis after protein ingestion because leucine uniquely activates the mTOR-p70S6K signalling cascade. We compared the effects of ingesting protein or an amount of leucine equal to that in the protein during a hyperinsulinaemic-euglycaemic clamp (to eliminate potential confounding as a result of differences in the insulinogenic effect of protein and leucine ingestion) on muscle anabolic signalling and protein turnover in 28 women. We found that protein, but not leucine, ingestion increased muscle p-mTORSer2448 and p-p70S6KThr389 , although only protein, and not leucine, ingestion decreased muscle p-eIF2αSer51 and increased muscle protein synthesis. It has been suggested that leucine is primarily responsible for the increase in muscle protein synthesis (MPS) after protein ingestion because leucine uniquely activates the mTOR-p70S6K signalling cascade. We tested this hypothesis by measuring muscle p-mTORSer2448 , p-p70S6KThr389 and p-eIF2αSer51 , as well as protein turnover (by stable isotope labelled amino acid tracer infusion in conjunction with leg arteriovenous blood and muscle tissue sampling), in 28 women who consumed either 0.45g proteinkg-1 fat-free mass (containing 0.0513g leucinekg-1 fat-free mass) or a control drink (n= 14) or 0.0513g leucinekg-1 fat-free mass or a control drink (n= 14) during a hyperinsulinaemic-euglycaemic clamp procedure (HECP). Compared to basal conditions, the HECP alone (without protein or leucine ingestion) suppressed muscle protein breakdown by ∼20% and increased p-mTORSer2448 and p-p70S6KThr389 by >50% (all P< 0.05) but had no effect on p-eIF2αSer51 and MPS. Both protein and leucine ingestion further increased p-mTORSer2448 and p-p70S6KThr389 , although only protein, and not leucine, ingestion decreased (by ∼35%) p-eIF2αSer51 and increased (by ∼100%) MPS (all P< 0.05). Accordingly, leg net protein balance changed from negative (loss) during basal conditions to equilibrium during the HECP alone and the HECP with concomitant leucine ingestion and to positive (gain) during the HECP with concomitant protein ingestion. These results provide new insights into the regulation of MPS by demonstrating that leucine and mTOR signalling alone are not responsible for the muscle anabolic effect of protein ingestion during physiological hyperinsulinaemia, most probably because they fail to signal to eIF2α to initiate translation and/or additional amino acids are needed to sustain translation.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes.

Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875-1881, 2018-The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.

Endocrine Effects of Relative Energy Deficiency in Sport.

The term Relative Energy Deficiency in Sport was introduced by the International Olympic Committee in 2014. It refers to the potential health and performance consequences of inadequate energy for sport, emphasizing that there are consequences of low energy availability (EA; typically defined as <30kcal·kg-1 fat-free mass·day-1) beyond the important and well-established female athlete triad, and that low EA affects populations other than women. As the prevalence and consequences of Relative Energy Deficiency in Sport become more apparent, it is important to understand the current knowledge of the hormonal changes that occur with decreased EA. This paper highlights endocrine changes that have been observed in female and male athletes with low EA. Where studies are not available in athletes, results of studies in low EA states, such as anorexia nervosa, are included. Dietary intake/appetite-regulating hormones, insulin and other glucose-regulating hormones, growth hormone and insulin-like growth factor 1, thyroid hormones, cortisol, and gonadal hormones are all discussed. The effects of low EA on body composition, metabolic rate, and bone in female and male athletes are presented, and we identify future directions to address knowledge gaps specific to athletes.

Developmental Changes in Isometric Strength: Longitudinal Study in Adolescent Soccer Players.

This study aimed to examine longitudinal changes in isometric strength of the knee extensors (ImKE) and knee flexors (ImKF) at 30° and 60°. The sample was composed of 67 players aged 11.0-13.9 years at baseline over five years. Stature, body mass, skinfolds, and isometric strength (ImKE30°, ImKF30°, ImKE60° and ImKF60°) were measured. Fat mass and fat-free mass (FFM) were derived from skinfolds. Skeletal age was obtained using TW2 RUS. Multilevel random effects regression analyses extracted developmental polynomial models. An annual increment on chronological age (CA) corresponded to 5.6 N (ImKE30°: ), 2.7 N (ImKF30°: ), 4.6 N (ImKE60°: ) and 1.5 N (ImKF60°). An increment of 1 kg in FFM predicted isometric strength as follows: 1.2 N (ImKE30°), 2.1 N (ImKF30°), 3.1 N (ImKE60°) and 2.0 N (ImKF60°). The following equations were obtained: ImKE30°=5.759×CA+1.163×FFM; ImKF30°=-19.369+2.691×CA+0.693×CA2+2.108×FFM; ImKE60°=4.553×CA+3.134×FFM; and, ImKF60°=-19.669+1.544×CA+2.033×FFM. Although skeletal maturity had a negligible effect on dependent variables, age and body size, based on FFM, were relevant longitudinal predictors. During adolescence, systematic assessment of knee extensors and knee flexors are strongly recommended to prevent impairment of knee muscle groups.

Anabolic signaling responses to exercise and recovery whey protein are suppressed at high altitude

Lowlanders sojourning to high‐altitude (HA) often experience muscle mass losses that are attributable to negative energy balance and potential dysregulation of intramuscular anabolic signaling. The combined effects of exercise and high‐quality protein ingestion can ameliorate the effects of short‐term, moderate negative energy balance (10‐d, ≤ 40 % total energy requirements) on intramuscular anabolic signaling at sea level (SL); however, the ability to sustain this effect at HA is unknown. This study examined the combined effects of HA (4300 m) and severe negative energy balance (21‐d, 70% of SL total energy requirements) on mTORC1 signaling responses after aerobic exercise and recovery whey protein (WP) ingestion in recreationally active, native lowlanders (n = 8 males). Total body mass and fat‐free mass were assessed using dual energy x‐ray absorptiometry before and after the 21‐d energy deficit at HA. Muscle biopsies of the vastus lateralis were obtained at SL, acute HA (3‐h HA exposure, day 0), and chronic HA (day 21), before (BASE) and after completing 80‐min of steady‐state aerobic exercise followed by a 2 mile time trial (POST), and 3 h into recovery (REC). In each trial, 25 g WP was consumed as a drink immediately after the POST biopsy. Intramuscular anabolic signaling was determined using Western blot and RT‐qPCR. Participants lost 7.5 ± 0.8 kg of total body mass (4.3 ± 1.0 kg of fat‐free mass, P &lt; 0.05). POST and REC p‐mTORSer2448, p‐70S6KSer424/421 and p‐rpS6Ser235/236 were 2‐, 23‐, and 7‐fold higher (P &lt; 0.05) than BASE at SL. At acute HA, POST p‐mTORSer2448, and POST and REC p‐70S6KSer424/421 were not different (P &gt; 0.05) from BASE and were 1‐ and 20‐fold lower than SL (P &lt; 0.05). However, POST and REC p‐rpS6Ser235/236 at acute HA was 4‐fold higher (P &lt; 0.05) than BASE and not different from SL. At chronic HA, POST and REC p‐mTORSer2448 and p‐70S6KSer424/421 were not different from BASE and were 1‐ and 19‐fold lower (P &lt; 0.05) than SL. POST and REC p‐rpS6Ser235/236 at chronic HA was 2‐fold higher (P &lt; 0.05) than BASE, but overall phosphorylation was 4‐fold lower (P &lt; 0.05) than SL. BASE HIF‐1α mRNA expression tended to be higher (P = 0.07) at acute HA than SL, whereas REC HIF‐1α mRNA expression at acute and chronic HA were lower (P &lt; 0.05) than SL. REDD1 mRNA expression and REDD1/14‐3‐3 coupling were not affected by study phase. These findings indicate that acute HA exposure induces intramuscular anabolic resistance that is further impaired with chronic HA exposure concomitant with severe negative energy balance, suggesting a mechanism for muscle loss at HA.Support or Funding InformationThis material is based on the work supported by US Army Medical Research and Material Command and the US Army Natick Soldier Research Development and Engineering Center. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Army.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Skeletal muscle mass in human athletes: What is the upper limit?

To examine the amount of absolute and relative skeletal muscle mass (SM) in large sized athletes to investigate the potential upper limit of whole body muscle mass accumulation in the human body. Ninety-five large-sized male athletes and 48 recreationally active males (control) had muscle thickness measured by ultrasound at nine sites on the anterior and posterior aspects of the body. SM was estimated from an ultrasound-derived prediction equation. Body density was estimated by hydrostatic weighing technique, and then body fat percentage and fat-free mass (FFM) were calculated. We used the SM index and FFM index to adjust for the influence of standing height (ie, divided by height squared). Ten of the athletes had more than 100 kg of FFM, including the largest who had 120.2 kg, while seven of the athletes had more than 50 kg of SM, including the largest who had 59.3 kg. FFM index and SM index were higher in athletes compared to controls and the percentage differences between the two groups were 44% and 56%, respectively. The FFM index increased linearly up to 90 kg of body mass, and then the values leveled off in those of increasing body mass. Similarly, the SM index increased in a parabolic fashion reaching a plateau (approximately 17 kg/m2 ) beyond 120 kg body mass. SM index may be a valuable indicator for determining skeletal muscle mass in athletes. A SM index of approximately 17 kg/m2 may serve as the potential upper limit in humans.

Insulin resistance and cardiovascular outcomes in the ORIGIN trial.

In the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial (Clinicaltrials.gov: NCT000069784), titrated doses of basal insulin glargine targeting fasting normoglycaemia had a neutral effect on cardiovascular outcomes. The dose of insulin required to achieve normoglycaemia provides a unique measurement of each individual's resistance to insulin's action, and was therefore used to examine the link between insulin resistance and cardiovascular outcomes. Self-titration of insulin doses targeting a fasting plasma glucose ≤5.3 mmoL/L (95 mg/dL) was promoted at every visit and cardiovascular and other serious health outcomes were ascertained. All analyses were restricted to participants allocated to insulin glargine, who added it to lifestyle or 1 glucose-lowering oral agent at randomization. Normoglycaemia was defined as a fasting plasma glucose <5.6 mmol/L and HbA1c <6% at the 2-year visit. The median of the natural logarithm of insulin doses (expressed per kg of fat-free mass), recorded at every visit from randomization until either the penultimate visit or the first occurrence of a cardiovascular outcome, was analysed. Higher median insulin doses did not reflect incident cardiovascular events overall or in the subset that achieved normoglycaemia. When the dose taken before a cardiovascular event or the penultimate visit was analysed, the adjusted hazard of the composite of cardiovascular death, myocardial infarction or stroke was 0.94 (95% CI 0.88, 1.00) per unit higher dose overall, and 0.91 (95% CI 0.81, 1.01) in the normoglycaemic subset. Insulin resistance may not promote cardiovascular outcomes in individuals with dysglycaemia.

Relationship Of Fat-free Mass And Fat Mass To Body Weight In College Female Athletes

Body mass differs widely across the sports spectrum in female athletes. While some sports are dominated by large athletes, other sports participants benefit from a smaller size and/or are limited by weight class. The most traditional measure of body composition has been %fat with less frequent emphasis on the major components of body composition: fat-free mass (FFM) and fat mass (FM). FFM is the functional component since as it is closely associated with maximal voluntary strength, while FM plays an important role in energy balance. With the ever increasing emphasis on control and manipulation of these components in modern sports, closer observation should be given to the relationship between FFM and FM accumulation. PURPOSE: To determine the relationship of FFM and FM to height and body mass across a wide-range of size typically observed in college female athletes. METHODS: One hundred and thirty-two NCAA Division-II female athletes (age = 19.9 ± 1.2 y, height =169.1 ± 7.9 cm, body mass = 67.8 ± 12.2 kg) volunteered to participate. Body composition was assessed using dual-energy x-ray absorptiometry (DEXA) to assess regional estimates of lean (bone, muscle, etc.) and fat tissue which allowed determination FFM and FM. RESULTS: Height was linearly correlated with body mass (r=0.58). FFM and FM were significantly related to height (r = 0.63 and 0.40, respectively) and body mass (r = 0.91 and 0.93, respectfully), with the relationships being linear in all four cases. FFM accumulated at approximately 0.35 kg/cm height and 0.50 kg/kg body mass; accounting for 91% of the variance in body mass, while FM accounted for only 9%. The greatest accumulation of FFM was at a height of 169 cm (0.402 kg/cm height) and body mass of 105 kg (0.663 kg/kg). The relationship between FFM and FM was significant (r = 0.71) and curvilinear. Accumulation of FM per kg of FFM increased at FFM > 55 kg. CONCLUSIONS: In female athletes, body mass appears to be the significant determinant of FFM accumulation. The accumulation of FM is determined by FFM, while the rate of accumulation of FM increases significantly at a threshold of 55 kg FFM.

Comparison of bioelectrical impedance and DXA for measuring body composition among adults with Down syndrome

BackgroundIndividuals with Down syndrome (DS) have been shown to display high levels of adiposity and a unique body shape. Laboratory methods used to evaluate body composition might be too cumbersome for this special population. Therefore, field methods are desired due to their non-invasive nature.Objective: to determine the agreement between dual energy x-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) for measuring body fat percentage BF% and fat-free mass (FFM) among adults with DS. MethodsTwenty-one adults (male: n = 10; female: n = 11) with DS participated in this study. BF% and FFM were determined by DXA and BIA. ResultsThere was a significant mean difference between DXA and BIA for BF% (41.33 ± 8.98% and 34.23 ± 9.22%, respectively) and FFM (41.80 ± 8.74 kg and 46.95 ± 9.92 kg, respectively). The correlation between the two devices for BF% and FFM were significant (r = 0.89 and r = 0.94, respectively, p < 0.001 for both). The standard error of estimate and total error values were 4.38% and 8.27%, respectively, for BF% and 3.04 kg and 6.13 kg, respectively, for FFM. The 95% limits of agreement ranged from −15.64% below to 1.46% above the constant error (CE) of −7.09% for BF% and from −1.52 kg below to 11.83 kg above the CE of 5.15 kg for FFM. ConclusionsThe significant mean differences and large amount of individual error suggest that BIA may not be an appropriate surrogate body composition measure compared to DXA in adults with DS.

The effect of 8 days of strict bed rest on the incretin effect in healthy volunteers.

Bed rest and physical inactivity are the consequences of hospital admission for many patients. Physical inactivity induces changes in glucose metabolism, but its effect on the incretin effect, which is reduced in, e.g., Type 2 diabetes, is unknown. To investigate how 8 days of strict bed rest affects the incretin effect, 10 healthy nonobese male volunteers underwent 8 days of strict bed rest. Before and after the intervention, all volunteers underwent an oral glucose tolerance test (OGTT) followed by an intravenous glucose infusion (IVGI) on the following day to mimic the blood glucose profile from the OGTT. Blood glucose, serum insulin, serum C-peptide, plasma incretin hormones [glucagon-like peptide (GLP-1) and glucose-dependent insulinotropic peptide (GIP)], and serum glucagon were measured serially during both the OGTT and the IVGI. The incretin effect is calculated as the relative difference between the area under the curve for the insulin response during the OGTT and that of the corresponding IVGI, respectively. Concentrations of glucose, insulin, C-peptide, and GIP measured during the OGTT were higher after the bed rest intervention (all P < 0.05), whereas there was no difference in the levels of GLP-1 and Glucagon. Bed rest led to a mean loss of 2.4 kg of fat-free mass, and induced insulin resistance evaluated by the Matsuda index, but did not affect the incretin effect (P = 0.6). In conclusion, 8 days of bed rest induces insulin resistance, but we did not see evidence of an associated change in the incretin effect.

Use of prediction equations to determine the accuracy of whole-body fat and fat-free mass and appendicular skeletal muscle mass measurements from a single abdominal image using computed tomography in advanced cancer patients.

Measurements of body composition using dual-energy X-ray absorptiometry (DXA) and single abdominal images from computed tomography (CT) in advanced cancer patients (ACP) have important diagnostic and prognostic value. The question arises as to whether CT scans can serve as surrogates for DXA in terms of whole-body fat-free mass (FFM), whole-body fat mass (FM), and appendicular skeletal muscle (ASM) mass. Predictive equations to estimate body composition for ACP from CT images have been proposed (Mourtzakis et al. 2008; Appl. Physiol. Nutr. Metabol. 33(5): 997-1006); however, these equations have yet to be validated in an independent cohort of ACP. Thus, this study evaluated the accuracy of these equations in estimating FFM, FM, and ASM mass using CT images at the level of the third lumbar vertebrae and compared these values with DXA measurements. FFM, FM, and ASM mass were estimated from the prediction equations proposed by Mourtzakis and colleagues (2008) using single abdominal CT images from 43 ACP and were compared with whole-body DXA scans using Spearman correlations and Bland-Altman analyses. Despite a moderate to high correlation between the actual (DXA) and predicted (CT) values for FM (rho = 0.93; p ≤ 0.001), FFM (rho = 0.78; p ≤ 0.001), and ASM mass (rho = 0.70; p ≤ 0.001), Bland-Altman analyses revealed large range-of-agreement differences between the 2 methods (29.39 kg for FFM, 15.47 kg for FM, and 3.99 kg for ASM mass). Based on the magnitude of these differences, we concluded that prediction equations using single abdominal CT images have poor accuracy, cannot be considered as surrogates for DXA, and may have limited clinical utility.

Cross-mode bioelectrical impedance analysis in a standing position for estimating fat-free mass validated against dual-energy x-ray absorptiometry

Bioelectrical impedance analysis (BIA) is commonly used to assess body composition. Cross-mode (left hand to right foot, ZCR) BIA presumably uses the longest current path in the human body, which may generate better results when estimating fat-free mass (FFM). We compared the cross-mode with the hand-to-foot mode (right hand to right foot, ZHF) using dual-energy x-ray absorptiometry (DXA) as the reference. We hypothesized that when comparing anthropometric parameters using stepwise regression analysis, the impedance value from the cross-mode analysis would have better prediction accuracy than that from the hand-to-foot mode analysis. We studied 264 men and 232 women (mean ages, 32.19 ± 14.95 and 34.51 ± 14.96 years, respectively; mean body mass indexes, 24.54 ± 3.74 and 23.44 ± 4.61 kg/m2, respectively). The DXA-measured FFMs in men and women were 58.85 ± 8.15 and 40.48 ± 5.64 kg, respectively. Multiple stepwise linear regression analyses were performed to construct sex-specific FFM equations. The correlations of FFM measured by DXA vs FFM from hand-to-foot mode and estimated FFM by cross-mode were 0.85 and 0.86 in women, with standard errors of estimate of 2.96 and 2.92 kg, respectively. In men, they were 0.91 and 0.91, with standard errors of the estimates of 3.34 and 3.48 kg, respectively. Bland-Altman plots showed limits of agreement of −6.78 to 6.78 kg for FFM from hand-to-foot mode and −7.06 to 7.06 kg for estimated FFM by cross-mode for men, and −5.91 to 5.91 and −5.84 to 5.84 kg, respectively, for women. Paired t tests showed no significant differences between the 2 modes (P > .05). Hence, cross-mode BIA appears to represent a reasonable and practical application for assessing FFM in Chinese populations.

Changes in weight, body composition and physical fitness after 1.5 years at university.

This study examined changes in Belgian students' weight, body composition and physical fitness after 1.5 years at university. Furthermore, this study investigated whether these changes differed by gender and weight status. In this longitudinal study, 172 students' weight, body mass index (BMI) and waist circumference (WC) were objectively measured, whereas fat%, fat mass, fat-free mass, dynamic leg strength, handgrip strength, hamstring flexibility and cardiorespiratory fitness were estimated using validated instruments. Measurements were conducted at the start of the first semester (T0), after the first semester (T1) and after 1.5 years (T2) at university. Female students' weight and BMI did not change, whereas male students gained 2.7 kg of weight and showed a 0.7 kg/m(2) BMI increase after 1.5 years. After the first semester, an increase in fat% was observed in the total group of students, whereas this time effect did not remain significant when comparing T0 and T2. In contrast to females, increases in 2.1 kg of fat-free mass and 1.8 cm of WC were found in males after 1.5 years. Higher baseline BMI and WC predicted greater BMI and fat% increases in males. Handgrip strength improved for both sexes, whereas no changes in other physical fitness components were found across the 1.5-year period. The greatest weight and BMI gains as well as unfavourable changes in body composition were found in male students with higher baseline BMI and WC. The observed changes in body composition did not cohere with changes in physical fitness.

Relationship between maternal pre-pregnancy body mass index, gestational weight gain and childhood fatness at 6-7 years by air displacement plethysmography.

This study aims to investigate the effect of maternal pre‐pregnancy body mass index (BMI) and gestational weight gain (GWG) on offspring body composition. In this prospective cohort study, offspring body composition at 6 years of age was obtained through air displacement plethysmography. Linear regression was used to obtain crude and adjusted coefficients. Information regarding offspring body composition and maternal pre‐pregnancy BMI was available for 3156 children and on offspring body composition and GWG for 3129 children. There was a direct association of maternal pre‐pregnancy BMI and GWG with offspring's fat mass (FM), fat‐free mass (FFM), fat mass index (FMI), fat‐free mass index (FFMI) and body fat percent (BF%) in crude and adjusted analyses. After adjustment for co‐variables, for each kg m−2 of maternal pre‐pregnancy BMI increase, there was a mean increment of 0.13 kg in the offspring FFM, 0.06 kg m−2 in FFMI, 0.11 kg in FM, 0.07 kg m−2 in FMI and 0.18% in BF%. For each kilogram of maternal GWG increase, there was a mean increment of 0.08 kg in offspring's FM, 0.05 kg m−2 in FMI, 0.04 kg in FFM, 0.01 kg m−2 in FFMI and 0.18 % in BF%. Mothers with a higher pre‐pregnancy BMI or GWG tend to have children with greater adiposity at age 6 years. Fetal overnutrition is more likely among mothers with greater BMI during pregnancy; as a consequence, it can accelerate the childhood obesity epidemic.

Nutritional status evaluation in patients affected by bethlem myopathy and ullrich congenital muscular dystrophy.

Collagen VI mutations lead to disabling myopathies like Bethlem myopathy (BM) and Ullrich congenital muscular dystrophy (UCMD). We have investigated the nutritional and metabolic status of one UCMD and seven BM patients (five female, three male, mean age 31 ± 9 years) in order to find a potential metabolic target for nutritional intervention. For this study, we used standard anthropometric tools, such as BMI evaluation and body circumference measurements. All results were compared to dual-energy X-ray absorptiometry (DXA), considered the “gold standard” method. Energy intake of each patient was evaluated through longitudinal methods (7-day food diary) while resting energy expenditure (REE) was predicted using specific equations and measured by indirect calorimetry. Clinical evaluation included general and nutritional blood and urine laboratory analyses and quantitative muscle strength measurement by hand-held dynamometry. BM and UCMD patients showed an altered body composition, characterized by low free fat mass (FFM) and high fat mass (FM), allowing us to classify them as sarcopenic, and all but one as sarcopenic-obese. Another main result was the negative correlation between REE/FFM ratio (basal energy expenditure per kilograms of fat-free mass) and the severity of the disease, as defined by the muscle megascore (correlation coefficient −0.955, P-value <0.001). We postulate that the increase of the REE/FFM ratio in relation to the severity of the disease may be due to an altered and pathophysiological loss of energetic efficiency at the expense of skeletal muscle. We show that a specific metabolic disequilibrium is related to the severity of the disease, which may represent a target for a nutritional intervention in these patients.

Bioelectrical impedance measurement of body composition in obese adolescents

Background: A stand-on bioelectrical impedance analysis (BIA) system is a safe, non-invasive, relatively inexpensive method of measuring body composition. However, little is known about how the body composition parameters measured by BIA relate to those determined by dual-energy X-ray absorptiometry (DXA) in an obese, adolescent population. Objectives: To compare body composition measures from the Tanita stand-on BIA (MC-180MA), determined by (a) in-built equations and (b) published and derived equations using raw data (resistance (R) and reactance (Xc)), with body composition parameters measured by DXA in obese adolescents. Methods: 66 obese adolescents (age: 10—18 y, BMI: 22—48 kg/m2) had body composition measured by both BIA (Tanita MC-180MA) and DXA (Lunar Prodigy). Raw data obtained from the BIA, R and Xc at frequencies of 5, 50, 250 and 500 kHz, were used in published bioimpedance spectroscopy [1] and BIA equations [2]. Raw data was also used to generate a new prediction equation for FFM using a double cross-validation method. Results: Compared to DXA, the BIA in-built equations overestimated fat-free mass (FFM) by 4.3 kg [95% limits of agreement −13.9 to 5.3] and underestimated % body fat (%BF) by 10.8% [−11.1 to 35.3]. There were no significant differences in the estimates of body composition parameters determined by published/derived equations using raw data and DXA; the mean differences in FFM and %BF were 0.4—0.9 kg and 0.5—0.8%, respectively. However, similar to the BIA in-built equations, all estimates had wide limits of agreement of approximately ±7 kg for FFM and ±10% for %BF. Conclusion: The Tanita BIA (MC-180MA) in-built equations cannot be recommended as a measure of body composition in obese adolescents. However, equations using the raw data can be used to measure body composition in groups, but there is the potential for significant error if used for an individual.

The association between growth response to growth hormone and baseline body composition of children with growth hormone deficiency

ObjectiveWe wanted to examine the relationship between initial growth response to recombinant human Growth Hormone (rhGH) treatment and body composition in children with growth hormone deficiency (GHD). Design and methodsForty-two patients (21 boys and 21 girls) aged between 5.7–15.5years (mean age: 10.8±2.6years) with isolated GHD. The auxological and laboratory data (GH and IGF-I levels) and results of bioelectrical impedance analyses were evaluated. Children with GHD were followed up for 12months and categorized according to growth response to rhGH into good and poor responders (change in height of >0.7 SDS or <0.7 SDS over one year respectively). Mean doses of rhGH per kg of fat free mass (FFM) were calculated. ResultsForty-eight percent of patients showed a good growth response to rhGH therapy. At study entry, mean age, height SDS, weight SDS, serum IGF-1 SDS, IGFBP-3 SDS, growth velocity prior to rhGH therapy, GH after clonidine and l-dopa were similar in the two groups. At baseline, BMI SDS and waist–hip ratio were significantly higher in good responders (p=0.02 and p=0.006, respectively). Good responders had lower percentages of FFM (73.4±8.9 vs. 83.1±5.9) and total body water (TBW) (56.5±5.3 vs. 63.1±4.4), compared to poor responders (p<0.05). There were significant correlations between changes in height SDS over one year and baseline body composition in children with GHD on rhGH treatment (r=−0.617 for percentage of FFM, r=−0.629 for percentage of TBW, p<0.001). A correlation between BMI SDS, waist–hip ratio, mean rhGH dose per FFM and growth response was observed only in prepubertal subjects. ConclusionBaseline body composition data in children with GHD can be used to predict the growth response to rhGH treatment. A management strategy that involves titrating rhGH dose according to FFM as a means of optimizing the growth response to intervention requires further study.

Body composition in male physical education university students in view of their physical activity level

PurposeBody composition and fat distribution is specific for particular populations and social groups. However, one factor that significantly affects body composition is physical activity. The aim of the study was to assess the various components of body composition in male physical education students with regard to their physical activity level.MethodsA detailed questionnaire survey on physical activity was administered to 252 male students. Based on their responses, the participants were placed into two groups engaged in either moderate or vigorous physical activity. Anthropometric measurements included measures of body height and mass and also skinfold thickness. Body composition was assessed by bioelectrical impedance analysis. Statistical analysis was performed by comparing the groups’ mean values, standard deviations, and percentages of the components of body composition.ResultsThe groups did not differ significantly for mean body height and mass. No statistically significant differences were found in the absolute amounts of the various components of body composition (except for fat mass) between the groups. Both groups had 61.5 kg of fat-free mass (constituting 80.6% of body mass for the vigorously active and 78.7% of body mass for the moderately active students) and both had 44 kg of muscle mass (constituting 58.3% and 56.1% of body mass, respectively). Students who declared to be involved in vigorous physical activity had 2 kg less and 2% lower fat mass than those involved in moderate physical activity (based on BIA measurements). Measures of skinfold thickness found more subcutaneous fat tissue in the vigorously active group, but the use of a fat index based on body height found them to present less fat.ConclusionsThe difference in fat content between physical education students who were more or less physically active was found to be 2 kg and 2%. The results found that physical activity level was not associated with body height, body mass, and the absolute amounts of the other studied components of body composition.