Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y.

OBJECTIVE: To determine reference values for fat-free mass index (FFMI) and fat mass index (FMI) in a large Caucasian group of apparently healthy subjects, as a function of age and gender and to develop percentile distribution for these two parameters. DESIGN: Cross-sectional study in which bioelectrical impedance analysis (50 kHz) was measured (using tetrapolar electrodes and cross-validated formulae by dual-energy X-ray absorptiometry in order to calculate FFMI (fat-free mass/height squared) and FMI (fat mass/height squared). SUBJECTS: A total of 5635 apparently healthy adults from a mixed non-randomly selected Caucasian population in Switzerland (2986 men and 2649 women), varying in age from 24 to 98 y. RESULTS: The median FFMI (18-34 y) were 18.9 kg/m(2) in young males and 15.4 kg/m(2) in young females. No difference with age in males and a modest increase in females were observed. The median FMI was 4.0 kg/m(2) in males and 5.5 kg/m(2) in females. From young to elderly age categories, FMI progressively rose by an average of 55% in males and 62% in females, compared to an increase in body mass index (BMI) of 9 and 19% respectively. CONCLUSIONS: Reference intervals for FFMI and FMI could be of practical value for the clinical evaluation of a deficit in fat-free mass with or without excess fat mass (sarcopenic obesity) for a given age category, complementing the classical concept of body mass index (BMI) in a more qualitative manner. In contrast to BMI, similar reference ranges seems to be utilizable for FFMI with advancing age, in particular in men.

Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index.

OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m(2)), a body fat mass index (BFMI; kg/m(2)), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI. METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated. RESULTS: FFMI values were 16.7 to 19.8 kg/m(2) for men and 14.6 to 16.8 kg/m(2) for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m(2) for men and 3.9 to 8.2 kg/m(2) for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m(2) in men and women, respectively, for obese BMI (>30 kg/m(2)). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively. CONCLUSION: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.

Prevalence of low fat-free mass index and high and very high body fat mass index following lung transplantation.

The aim of this study was to determine the prevalence of low fat-free mass index (FFMI) and high and very high body fat mass index (BFMI) after lung transplantation (LTR). A total of 37 LTR patients were assessed prior to and at 1 month, 1 year and 2 years for FFM and compared to 37 matched volunteers (VOL). FFM was calculated by the Geneva equation and normalized for height (kg/m(2)). Subjects were classified as FFMI "low", <or=17.4 in men and <or=15.0 in women; BFMI "high", 5.2-8.1 in men and 8.3-11.7 in women; or "very high" >8.2 kg/m(2) in men and >11.8 kg/m(2) in women. In 23 M/14 F, body mass index (BMI) was 22.3+/-4.4 and 20.1+/-4.9 kg/m(2), respectively. The prevalence of low FFMI was 80% at 1 month and 33% at 2 years after LTR. Prevalence of very high BFMI increased and was higher in patients than VOL after LTR. The prevalence of low FFMI was high prior to and remained important 2 years after LTR, whereas BFMI was lower prior to and higher 2 years after LTR.

Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98 years.

OBJECTIVE: Body mass index does not discriminate body fat from fat-free mass or determine changes in these parameters with physical activity and aging. Body fat mass index (BFMI) and fat-free mass index (FFMI) permit comparisons of subjects with different heights. This study evaluated differences in body mass index, BFMI, and FFMI in physically active and sedentary subjects younger and older than 60 y and determined the association between physical activity, age, and body composition parameters in a healthy white population between ages 18 and 98 y. METHODS: Body fat and fat-free mass were determined in healthy white men (n = 3549) and women (n = 3184), between ages 18 and 98 y, by bioelectrical impedance analysis. BFMI and FFMI (kg/m2) were calculated. Physical activity was defined as at least 3 h/wk of endurance-type activity for at least 2 mo. RESULTS: Physically active as opposed to sedentary subjects were more likely to have a low BFMI (men: odds ratio [OR], 1.4; confidence interval [CI], 0.7-2.5; women: OR 1.9, CI 1.6-2.2) and less likely to have very high BFMI (men: OR, 0.2; CI, 0.1-0.2; women: OR, 0.1; CI, 0.02-0.2), low FFMI (men: OR, 0.5; CI, 0.3-0.9; women: OR, 0.7; CI, 0.6-0.9), or very high FFMI (men: OR, 0.6; CI, 0.4-0.8; women: OR, 0.7; CI, 0.5-1.0). Compared with subjects younger than 60 y, those older than 60 y were more like to have very high BFMI (men: OR, 6.5; CI, 4.5-9.3; women: OR, 14.0; CI, 9.6-20.5), and women 60 y and older were less likely to have a low BFMI (OR, 0.4; CI, 0.2-0.5). CONCLUSIONS: A clear association was found between low physical activity or age and height-normalized body composition parameters (BFMI and FFMI) derived from bioelectrical impedance analysis. Physically active subjects were more likely to have high or very high or low FFMI. Older subjects had higher body weights and BFMI.

Body weight as an indicator of fat-free mass in active elderly women

Objective: Fat-free mass (FFM) reduction and the tendency for a reduction in surrounding fatty issue and increase in the middle are a natural consequence of growing old and should be studied in order to gain a better understanding of the aging process. This study set out to find the FFM differences between active elderly women in two age groups (60-69 and 70-80 years) and to determine which of the anthropometric measurements, body weight (BW), abdominal circumference (AC), or body mass index (BMI) are the best predictors of FFM variation within the group. Methods: Eighty-one (n = 81) active elderly women of the Third Age willingly signed up to participate in the research during the activities at the University of the Third Age (UTA) in Brazil. The research was approved by the Research Ethics Committee of the Faculty of Medical Sciences of the State University of Campinas (UNICAMP). Body weight (BW), height (H) and the BMI were measured according to the international standards. The AC was measured in centimetres at the H of the navel and body composition was ascertained using bioimpedance analysis. The SAS program was used to perform the statistical analysis of independent samples and parametric data. Results: The results showed FFM values with significant differences between the two groups, with the lowest values occurring among the women who were over 70 years of age. In the analysis, the Pearson`s Correlation Coefficient for each measured independent variable was ascertained, with the BW measurement showing the highest ratio (0.900). Conclusions: The BW measurement was regarded as reliable, low-cost and easy to use for monitoring FFM in elderly women who engage in physical activities. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

PREDICTION OF FAT-FREE MASS BY BIOELECTRICAL IMPEDANCE ANALYSIS IN OLDER ADULTS FROM DEVELOPING COUNTRIES: A CROSS-VALIDATION STUDY USING THE DEUTERIUM DILUTION METHOD

Objective: Several limitations of published bioelectrical impedance analysis (BIA) equations have been reported. The aims were to develop in a multiethnic, elderly population a new prediction equation and cross-validate it along with some published BIA equations for estimating fat-free mass using deuterium oxide dilution as the reference method. Design and setting: Cross-sectional study of elderly from five developing countries. Methods: Total body water (TBW) measured by deuterium dilution was used to determine fat-free mass (FFM) in 383 subjects. Anthropometric and BIA variables were also measured. Only 377 subjects were included for the analysis, randomly divided into development and cross-validation groups after stratified by gender. Stepwise model selection was used to generate the model and Bland Altman analysis was used to test agreement. Results: FFM = 2.95 - 3.89 (Gender) + 0.514 (Ht(2)/Z) + 0.090 (Waist) + 0.156 (Body weight). The model fit parameters were an R(2), total F-Ratio, and the SEE of 0.88, 314.3, and 3.3, respectively. None of the published BIA equations met the criteria for agreement. The new BIA equation underestimated FFM by just 0.3 kg in the cross-validation sample. The mean of the difference between FFM by TBW and the new BIA equation were not significantly different; 95% of the differences were between the limits of agreement of -6.3 to 6.9 kg of FFM. There was no significant association between the mean of the differences and their averages (r = 0.008 and p = 0.2). Conclusions: This new BIA equation offers a valid option compared with some of the current published BIA equations to estimate FFM in elderly subjects from five developing countries.

Bio-electrical impedance analysis for estimation of fat-free mass and muscle mass in cystic fibrosis patients.

The nutritional status of cystic fibrosis (CF) patients has to be regularly evaluated and alimentary support instituted when indicated. Bio-electrical impedance analysis (BIA) is a recent method for determining body composition. The present study evaluates its use in CF patients without any clinical sign of malnutrition. Thirty-nine patients with CF and 39 healthy subjects aged 6-24 years were studied. Body density and mid-arm muscle circumference were determined by anthropometry and skinfold measurements. Fat-free mass was calculated taking into account the body density. Muscle mass was obtained from the urinary creatinine excretion rate. The resistance index was calculated by dividing the square of the subject's height by the body impedance. We show that fat-free mass, mid-arm muscle circumference and muscle mass are each linearly correlated to the resistance index and that the regression equations are similar for both CF patients and healthy subjects.

Adjusting for fat-free mass in metabolic studies

in a recent publication, Eriksson et al. [1] explored the relationship between size at birth and resting metabolic rate and body composition in adulthood in a cohort of over 300 men and women. They reported an unexpected finding that people of both sexes who had a low birth weight also had a higher metabolic activity per unit muscle tissue. This conclusion was drawn from an analysis where resting metabolic rate (expressed as kcal/kg fat-free mass) in adulthood was examined relative to the birth weight of the subject. One explanation that they suggested was that the apparent increased activity of muscle tissue resulted from an increased sympathetic drive associated with low birth weight. There may be a less physiological reason for the findings of Eriksson et al. Whilst the data are not given specifically in the text, it can be seen clearly from Fig. 1 in the paper that the mean fat-free mass measured in adulthood increased, in both sexes, from the lightest birth weight group to the heaviest birth weight group when the cohort were divided into tertiles based on birth weight. The crux of the issue is that in many - indeed most - cases, expressing resting energy expenditure as kcal/kg fat-free mass does not totally adjust for fat-free mass [2 - 5], and a bias is introduced so that those who have a higher fat-free mass will tend to have a lower resting energy expenditure when expressed per kg fat-free mass. This bias found when expressing many physiological parameters relative to body size, body weight or body composition has long been known [6], and should be carefully considered by appropriate adjustment and hence analysis.

Prediction of fat-free mass using bioelectrical impedance analysis in young adults from five populations of African origin.

Background/objectives:Bioelectrical impedance analysis (BIA) is used in population and clinical studies as a technique for estimating body composition. Because of significant under-representation in existing literature, we sought to develop and validate predictive equation(s) for BIA for studies in populations of African origin.Subjects/methods:Among five cohorts of the Modeling the Epidemiologic Transition Study, height, weight, waist circumference and body composition, using isotope dilution, were measured in 362 adults, ages 25-45 with mean body mass indexes ranging from 24 to 32. BIA measures of resistance and reactance were measured using tetrapolar placement of electrodes and the same model of analyzer across sites (BIA 101Q, RJL Systems). Multiple linear regression analysis was used to develop equations for predicting fat-free mass (FFM), as measured by isotope dilution; covariates included sex, age, waist, reactance and height(2)/resistance, along with dummy variables for each site. Developed equations were then tested in a validation sample; FFM predicted by previously published equations were tested in the total sample.Results:A site-combined equation and site-specific equations were developed. The mean differences between FFM (reference) and FFM predicted by the study-derived equations were between 0.4 and 0.6âeuro0/00kg (that is, 1% difference between the actual and predicted FFM), and the measured and predicted values were highly correlated. The site-combined equation performed slightly better than the site-specific equations and the previously published equations.Conclusions:Relatively small differences exist between BIA equations to estimate FFM, whether study-derived or published equations, although the site-combined equation performed slightly better than others. The study-derived equations provide an important tool for research in these understudied populations.

Prediction of resting energy expenditure from fat-free mass and fat mass.

On the basis of literature values, the relationship between fat-free mass (FFM), fat mass (FM), and resting energy expenditure [REE (kJ/24 h)] was determined for 213 adults (86 males, 127 females). The objectives were to develop a mathematical model to predict REE based on body composition and to evaluate the contribution of FFM and FM to REE. The following regression equations were derived: 1) REE = 1265 + (93.3 x FFM) (r2 = 0.727, P < 0.001); 2) REE = 1114 + (90.4 x FFM) + (13.2 x FM) (R2 = 0.743, P < 0.001); and 3) REE = (108 x FFM) + (16.9 x FM) (R2 = 0.986, P < 0.001). FM explained only a small part of the variation remaining after FFM was accounted for. The models that include both FFM and FM are useful in examination of the changes in REE that occur with a change in both the FFM and FM. To account for more of the variability in REE, FFM will have to be divided into organ mass and skeletal muscle mass in future analyses.

Reexamination of the relationship of resting metabolic rate to fat-free mass and to the metabolically active components of fat-free mass in humans.

The ratio of resting metabolic rate (RMR) to fat-free mass (FFM) is often used to compare individuals of different body sizes. Because RMR has not been well described over the full range of FFM, a literature review was conducted among groups with a wide range of FFM. It included 31 data sets comprising a total of 1111 subjects: 118 infants and preschoolers, 323 adolescents, and 670 adults; FFM ranged from 2.8 to 106 kg. The relationship of RMR to FFM was found to be nonlinear and average slopes of the regression equations of the three groups differed significantly (P less than 0.0001). For only the youngest group did the intercept approach zero. The lower slopes of RMR on FFM, at higher measures of FFM, corresponded to relatively greater proportions of less metabolically active muscle mass and to lesser proportions of more metabolically active nonmuscle organ mass. Because the contribution of FFM to RMR is not constant, an arithmetic error is introduced when the ratio of RMR to FFM is used. Hence, alternative methods should be used to compare individuals with markedly different FFM.

Anthropometric midarm measurements can detect systemic fat-free mass depletion in patients with chronic obstructive pulmonary disease

Our objective was to determine whether anthropometric measurements of the midarm (MA) could identify subjects with whole body fat-free mass (FFM) depletion. Fifty-five patients (31% females; age: 64.6 ± 9.3 years) with mild/very severe chronic obstructive pulmonary disease (COPD), 18 smokers without COPD (39% females; age: 49.0 ± 7.3 years) and 23 never smoked controls (57% females; age: 48.2 ± 9.6 years) were evaluated. Spirometry, muscle strength and MA circumference were measured. MA muscle area was estimated by anthropometry and MA cross-sectional area by computerized tomography (CT) scan. Bioelectrical impedance was used as the reference method for FFM. MA circumference and MA muscle area correlated with FFM and biceps and triceps strength. Receiver operating characteristic curve analysis showed that MA circumference and MA muscle area cut-off points presented sensitivity and specificity >82% to discriminate FFM-depleted subjects. CT scan measurements did not provide improved sensitivity or specificity. For all groups, there was no significant statistical difference between MA muscle area [35.2 (29.3-45.0) cm²] and MA cross-sectional area values [36.4 (28.5-43.3) cm²] and the linear correlation coefficient between tests was r = 0.77 (P < 0.001). However, Bland-Altman plots revealed wide 95% limits of agreement (-14.7 to 15.0 cm²) between anthropometric and CT scan measurements. Anthropometric MA measurements may provide useful information for identifying subjects with whole body FFM depletion. This is a low-cost technique and can be used in a wider patient population to identify those likely to benefit from a complete body composition evaluation.

Changes in fat, fat-free mass and body water in human normal pregnancy

Measurements of body weight, total body water and total body potassium (40K) were made serially on three occasions during pregnancy and once post partum in 27 normal pregnant women. Skinfold thickness and fat cell diameter were also measured. A model of body composition was formulated to permit the estimation of changes in fat, lean tissue and water content of the maternal body. Total maternal body fat increased during pregnancy, reaching a peak towards the end of the second trimester before diminishing. Serial measurements of fat cell diameter showed poor correlation, whilst total body fat calculated from skinfold thickness correlated well with our estimated values for total body fat in pregnancy.