Bioelectrical impedance analysis (BIA) may be used to assess fat free mass (FFM) with reasonable validity based on mean-level comparisons, but differences between BIA and DXA may vary by about 4 kg in an individual patient. These results require confirmation in a larger sample of HNC (Head and neck cancer) patients.
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Background & aims: In dietary practice, it is common to estimate protein requirements on actual bodyweight, but corrected bodyweight (in cases with BMI <20 kg/m2 and BMI ≥30 kg/m2) and fat free mass (FFM) are also used. Large differences on individual level are noticed in protein requirements using these different approaches. To continue this discussion, the answer is sought in a large population to the following question: Will choosing actual bodyweight, corrected bodyweight or FFM to calculate protein requirements result in clinically relevant differences? Methods: This retrospective database study, used data from healthy persons ≥55 years of age and in- and outpatients ≥18 years of age. FFM was measured by air displacement plethysmography technology or bioelectrical impedance analysis. Protein requirements were calculated as 1) 1.2 g (g) per kilogram (kg) actual bodyweight or 2) corrected bodyweight or 3) 1.5 g per kg FFM. To compare these three approaches, the approach in which protein requirement is based on FFM, was used as reference method. Bland–Altman plots with limits of agreement were used to determine differences, analyses were performed for both populations separately and stratified by BMI category and gender. Results: In total 2291 subjects were included. In the population with relatively healthy persons (n = 506, ≥55 years of age) mean weight is 86.5 ± 18.2 kg, FFM is 51 ± 12 kg and in the population with adult in- and outpatients (n = 1785, ≥18 years of age) mean weight is 72.5 ± 18.4 kg, FFM is 51 ± 11 kg. Clinically relevant differences were found in protein requirement between actual bodyweight and FFM in most of the participants with overweight, obesity or severe obesity (78–100%). Using corrected bodyweight, an overestimation in 48–92% of the participants with underweight, healthy weight and overweight is found. Only in the Amsterdam UMC population, protein requirement is underestimated when using the approach of corrected bodyweight in participants with severe obesity. Conclusion: The three approaches in estimation of protein requirement show large differences. In the majority of the population protein requirement based on FFM is lower compared to actual or corrected bodyweight. Correction of bodyweight reduces the differences, but remain unacceptably large. It is yet unknown which method is the best for estimation of protein requirement. Since differences vary by gender due to differences in body composition, it seems more accurate to estimate protein requirement based on FFM. Therefore, we would like to advocate for more frequent measurement of FFM to determine protein requirements, especially when a deviating body composition is to be expected, for instance in elderly and persons with overweight, obesity or severe obesity.
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Assessment and monitoring of fat-free mass (FFM) is of clinical importance, because FFM is reflective of body cell mass, the total mass of protein-rich, metabolically active cells which is affected during malnutrition and therefore related to clinical outcome.Bioelectrical impedance analysis (BIA) is a non-invasive, portable and inexpensive method to assess body composition. Currently validity of BIA in head and neck cancer patients is unknown. Therefore, we tested our hypothesis that BIA, using the Geneva equation, is a valid method to assess FFM in head and neck cancer patients.
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Background & aim The aim of this study was to describe a decrease in resting energy expenditure during weight loss that is larger than expected based on changes in body composition, called adaptive thermogenesis (AT), in overweight and obese older adults. Methods Multiple studies were combined to assess AT in younger and older subjects. Body composition and resting energy expenditure (REE) were measured before and after weight loss. Baseline values were used to predict fat free mass and fat mass adjusted REE after weight loss. AT was defined as the difference between predicted and measured REE after weight loss. The median age of 55 y was used as a cutoff to compare older with younger subjects. The relation between AT and age was investigated using linear regression analysis. Results In this study 254 (M = 88, F = 166) overweight and obese subjects were included (BMI: 31.7 ± 4.4 kg/m2, age: 51 ± 14 y). The AT was only significant for older subjects (64 ± 185 kcal/d, 95% CI [32, 96]), but not for younger subjects (19 ± 152 kcal/d, 95% CI [−9, 46]). The size of the AT was significantly higher for older compared to younger adults (β = 47, p = 0.048), independent of gender and type and duration of the weight loss program. Conclusions We conclude that adaptive thermogenesis is present only in older subjects, which might have implications for weight management in older adults. A reduced energy intake is advised to counteract the adaptive thermogenesis.
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Background: Our aim was to identify dietary patterns by the level of maternal education that contribute to BMI, fat mass index (FMI), and fat-free mass index (FFMI) in children at age 5 and to assess if these dietary patterns are related to BMI at age 10. Methods: Per group (low/middle/high level), Reduced Rank Regression (RRR) was used to derive dietary patterns for the response variables BMI z-score, FMI, and FFMI in 1728 children at age 5 in the Amsterdam Born Children and their Development (ABCD) cohort. Regression analyses were then used to determine the association with BMI at age 10. Results: In each group, pattern 1 was characterized by its own cluster of food groups. Low: water/tea, savory snacks, sugar, low-fat meat, and fruits; middle: water/tea, low-fat cheese, fish, low-fat dairy, fruit drink, low-fat meat, and eggs; and high: low-fat cheese, fruits, whole-grain breakfast products, and low-fat and processed meat. Additionally, in each group, pattern 1 was positively associated with BMI z-scores at age 10 (low: β ≤ 0.43 [95% CI ≤ 0.21; 0.66], p < 0.001, middle: β ≤ 0.23 [0.09; 0.36], p ≤ 0.001, and high: β ≤ 0.24 [0.18; 0.30], p < 0.001). Conclusions: The dietary patterns stratified by the level of maternal education are characterized by different food groups. But in all the groups, pattern 1 is positively associated with BMI at age 10.
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Rationale: Malnutrition is a common problem in patients with Chronic Obstructive Pulmonary Disease (COPD). Whereas estimation of fat-free muscle mass index (FFMi) with bio-electrical impedance is often used, less is known about muscle thickness measured with ultrasound (US) as a parameter for malnutrition. Moreover, it has been suggested that in this population, loss of muscle mass is characterized by loss of the lower body muscles rather than of the upper body muscles.1 Therefore, we explored the association between FFMi, muscle thickness of the biceps brachii (BB) and the rectus femoris (RF), and malnutrition in patients with COPD. Methods: Patients were assessed at the start of a pulmonary rehabilitation program. Malnutrition was assessed with the Scored Patient-Generated Subjective Global Assessment (PG-SGA). Malnutrition was defined as PG-SGA Stage B or C. FFMi (kg/m²) was estimated with bio-electrical impedance analysis BIA 101® (Akern), using the Rutten equation. Muscle thickness (mm) of the BB and the RF was measured with the handheld BodyMetrix® device (Intelametrix). Univariate and multivariate logistic regression analyses were performed to analyse associations between FFMi and muscle thickness for BB and RF, and malnutrition. Multivariate analysis corrected for sex, age, and GOLD-stage. Odds ratios (OR) and 95% confidence intervals (CI) were presented. A p-level of <0.05 was considered significant. Results: In total, 27 COPD patients (age 64±8.1 years; female 60%, GOLD-stage 3, interquartile range=3-4, BMI 27±6.6 kg/m2) were included in the analyses. In the univariate analysis, FFMi (p=0.014; OR=0.70, 95%CI: -0.12—0.15), RF thickness (p=0.021; OR=0.79, 95%CI: -0.09—0.01), and BB thickness (p=0.006; OR=0.83, 95%CI: -0.06—0.01) were all significantly associated with malnutrition. In the multivariate analysis, FFMi (p=0.031; OR=0.59, 95%CI: -0.18—0.01) and BB thickness (p=0.017; OR=0.73, 95%CI:-0.09—0.01) were significantly associated with malnutrition. None of the co-variables were significantly associated with malnutrition. Conclusion: In this relatively small sample of patients with severe COPD, low FFMi and low BB muscle thickness were both robustly associated with increased odds of being malnourished. BB muscle thickness measured with US may provide added value to the toolbox for nutritional assessment. The results of this exploratory study suggest that upper body muscles may reflect nutritional status more closely than lower body muscles. Reference: 1 Shrikrishna D, Patel M, Tanner RJ, Seymour JM, Connolly BA, Puthucheary ZA, et al. Quadriceps wasting and physical inactivity in patients with COPD. Eur Respir J. 2012;40(5):1115–22.)
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BACKGROUND: Intentional weight loss in obese older adults is a risk factor for accelerated muscle mass loss. We investigated whether a high protein diet and/or resistance exercise preserves fat free mass (FFM) during weight loss in overweight and obese older adults.METHODS: We included 100 overweight and obese adults (55-80 year) in a randomized controlled trial (RCT) with a 2 × 2 factorial design and intention-to-treat analysis. During a 10-week weight loss program all subjects followed a hypocaloric diet. Subjects were randomly allocated to either a high protein (1.3 g/kg body weight) or normal protein diet (0.8 g/kg), with or without a resistance exercise program 3 times/week. FFM was assessed by air displacement plethysmography.RESULTS: At baseline, mean (±SD) BMI was 32 ± 4 kg/m(2). During intervention, protein intake was 1.13 ± 0.35 g/kg in the high protein groups vs. 0.98 ± 0.29 in the normal protein groups, which reflects a 16.3 ± 5.2 g/d higher protein intake in the high protein groups. Both high protein diet and exercise did not significantly affect change in body weight, FFM and fat mass (FM). No significant protein*exercise interaction effect was observed for FFM. However, within-group analysis showed that high protein in combination with exercise significantly increased FFM (+0.6 ± 1.3 kg, p = 0.011).CONCLUSION: A high protein diet, though lower than targeted, did not significantly affect changes in FFM during modest weight loss in older overweight and obese adults. There was no significant interaction between the high protein diet and resistance exercise for change in FFM. However, only the group with the combined intervention of high protein diet and resistance exercise significantly increased in FFM.TRIAL REGISTRATION: Dutch Trial Register, number NTR4556, date 05-01-2014.
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BACKGROUND & AIMS: Sufficient protein intake is of great importance in hemodialysis (HD) patients, especially for maintaining muscle mass. Daily protein needs are generally estimated using bodyweight (BW), in which individual differences in body composition are not accounted for. As body protein mass is best represented by fat free mass (FFM), there is a rationale to apply FFM instead of BW. The agreement between both estimations is unclear. Therefore, the aim of this study is to compare protein needs based on either FFM or BW in HD patients.METHODS: Protein needs were estimated in 115 HD patients by three different equations; FFM, BW and BW adjusted for low or high BMI. FFM was measured by multi-frequency bioelectrical impedance spectroscopy and considered the reference method. Estimations of FFM x 1.5 g/kg and FFM x 1.9 g/kg were compared with (adjusted)BW x 1.2 and x 1.5, respectively. Differences were assessed with repeated measures ANOVA and Bland-Altman plots.RESULTS: Mean protein needs estimated by (adjusted)BW were higher compared to those based on FFM, across all BMI categories (P < 0.01) and most explicitly in obese patients. In females with BMI >30, protein needs were 69 ± 17.4 g/day higher based on BW and 45 ± 9.3 g/day higher based on BMI adjusted BW, compared to FFM. In males with BMI >30, protein needs were 51 ± 20.4 g/day and 23 ± 20.9 g/day higher compared to FFM, respectively.CONCLUSIONS: Our data show large differences and possible overestimations of protein needs when comparing BW to FFM. We emphasize the importance of more research and discussion on this topic.
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The aim of the study was to investigate whether an increased risk of injury occurrence can be determined through frequent anthropometric measurements in elite-standard youth soccer players. Over the course of one season, we followed 101 male elite-standard youth soccer players between 11 and 19 years of age. Height and body mass were monitored at monthly measurement intervals and fat percentage was assessed every 3 months by use of the sum of skinfold method. Growth in height (cm), alternations in body mass index (kg/m(2)), fat percentage and fat-free mass index (kg/m(2)) were calculated. Injuries were recorded in accordance with the recommendations of the FIFA Consensus Model for Injury Registration. Odds ratio scores and 95% confidence intervals were calculated using binary logistic regression analyses. The following anthropometric injury risk factors were identified: ≥ 0.6 centimeter growth per month (p=0.03; OR=1.63; 95% CI: 1.06-2.52), ≥ 0.3 kg/m(2) increase of body mass index value per month (p=0.03; OR=1.61; 95% CI: 1.04-2.49) and low fat percentage; i. e., < 7% for players aged 11-16 and < 5% for players over 16 years (p=0.01; OR=1.81; 95% CI: 1.18-2.76). Individual monitoring of anthropometrics provides useful information to determine increased risk of injury occurrence in elite-standard youth soccer.
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