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.

Estimation of percentage body fat in 6- to 13-year-old children by skinfold thickness, body mass index and waist circumference.

We evaluated the accuracy of skinfold thicknesses, BMI and waist circumference for the prediction of percentage body fat (PBF) in a representative sample of 372 Swiss children aged 6-13 years. PBF was measured using dual-energy X-ray absorptiometry. On the basis of a preliminary bootstrap selection of predictors, seven regression models were evaluated. All models included sex, age and pubertal stage plus one of the following predictors: (1) log-transformed triceps skinfold (logTSF); (2) logTSF and waist circumference; (3) log-transformed sum of triceps and subscapular skinfolds (logSF2); (4) log-transformed sum of triceps, biceps, subscapular and supra-iliac skinfolds (logSF4); (5) BMI; (6) waist circumference; (7) BMI and waist circumference. The adjusted determination coefficient (R² adj) and the root mean squared error (RMSE; kg) were calculated for each model. LogSF4 (R² adj 0.85; RMSE 2.35) and logSF2 (R² adj 0.82; RMSE 2.54) were similarly accurate at predicting PBF and superior to logTSF (R² adj 0.75; RMSE 3.02), logTSF combined with waist circumference (R² adj 0.78; RMSE 2.85), BMI (R² adj 0.62; RMSE 3.73), waist circumference (R² adj 0.58; RMSE 3.89), and BMI combined with waist circumference (R² adj 0.63; RMSE 3.66) (P < 0.001 for all values of R² adj). The finding that logSF4 was only modestly superior to logSF2 and that logTSF was better than BMI and waist circumference at predicting PBF has important implications for paediatric epidemiological studies aimed at disentangling the effect of body fat on health outcomes.

Gender differences in whole body fat oxidation kinetics during exercise

Introduction Discrepancies appear in studies comparing fat oxidation between men and women during exercise (1). Therefore, this study aimed to quantitatively describe and compare whole body fat oxidation kinetics between genders during exercise using a sinusoidal model (SIN) (2). Methods Twelve men and 11 women matched for age, body mass index (23.4±0.6 kg.m-2 and 21.5±0.8 kg.m-2, respectively) and aerobic fitness [maximal oxygen uptake ( ) (58.5±1.6 mL.kg FFM-1.min-1 and 55.3±2.0 mL.kg FFM-1.min-1, respectively) and power output ( ) per kilogram of fat-free mass (FFM)] performed submaximal incremental tests (Incr) with 5-min stages and 7.5% increment on a cycle ergometer. Respiratory and HR values were averaged over the last 2 minutes of each stage. All female study participants were eumenorrheic, reported regular menstrual cycles (28.6 ± 0.8 days) and were not taking oral contraceptives (OC) or other forms of exogenous ovarian hormones. Women were studied in the early follicular phase (FP) of their menstrual cycle (between days 3 and 8, where day 1 is the first day of menses). Fat oxidation rates were determined using indirect calorimetry and plotted as a function of exercise intensity. The SIN model (2), which includes three independent variables (dilatation, symmetry, translation), was used to mathematically describe fat oxidation kinetics and to determine the intensity (Fatmax) eliciting the maximal fat oxidation (MFO). Results During Incr, women exhibited greater fat oxidation rates from 35 to 85% , MFO (6.6 ± 0.9 vs. 4.5 ± 0.3 mgkg FFM-1min-1) and Fatmax (58.1 ± 1.9 vs. 50.0 ± 2.7% ) (P<0.05) than men. While men and women showed similar global shapes of fat oxidation kinetics in terms of dilatation and symmetry (P>0.05), the fat oxidation curve tended to be shifted towards higher exercise intensities in women (rightward translation, P=0.08). Conclusion These results showed that women, eumenorrheic, not taking OC and tested in FP, have a greater reliance on fat oxidation than men during submaximal exercise, but they also indicate that this greater fat oxidation is shifted towards higher exercise intensities in women compared with men. References 1. Blaak E. Gender differences in fat metabolism. Curr Opin Clin Nutr Metab Care 4: 499-502, 2001. 2. Cheneviere X, Malatesta D, Peters EM, and Borrani F. A mathematical model to describe fat oxidation kinetics during graded exercise. Med Sci Sports Exerc 41: 1615-1625, 2009.

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.

Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women.

Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ~2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1 × 10E-09), previously identified in association with waist-hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9 × 10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6 × 10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist-hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not previously uncovered in large-scale GWAS of anthropometric traits.

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

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.

Effects of 2 different prior endurance exercises on whole-body fat oxidation kinetics: light vs. heavy exercise.

This study aimed to compare the effects of 2 different prior endurance exercises on subsequent whole-body fat oxidation kinetics. Fifteen men performed 2 identical submaximal incremental tests (Incr2) on a cycle ergometer after (i) a ∼40-min submaximal incremental test (Incr1) followed by a 90-min continuous exercise performed at 50% of maximal aerobic power-output and a 1-h rest period (Heavy); and (ii) Incr1 followed by a 2.5-h rest period (Light). Fat oxidation was measured using indirect calorimetry and plotted as a function of exercise intensity during Incr1 and Incr2. A sinusoidal equation, including 3 independent variables (dilatation, symmetry and translation), was used to characterize the fat oxidation kinetics and to determine the intensity (Fat(max)) that elicited the maximal fat oxidation (MFO) during Incr. After the Heavy and Light trials, Fat(max), MFO, and fat oxidation rates were significantly greater during Incr2 than Incr1 (p < 0.001). However, Δ (i.e., Incr2-Incr1) Fat(max), MFO, and fat oxidation rates were greater in the Heavy compared with the Light trial (p < 0.05). The fat oxidation kinetics during Incr2(Heavy) showed a greater dilatation and rightward asymmetry than Incr1(Heavy), whereas only a greater dilatation was observed in Incr2(Light) (p < 0.05). This study showed that although to a lesser extent in the Light trial, both prior exercise sessions led to an increase in Fat(max), MFO, and absolute fat oxidation rates during Incr2, inducing significant changes in the shape of the fat oxidation kinetics.

Beneficial effects of combinatorial micronutrition on body fat and atherosclerosis in mice.

AIMS: More than two billion people worldwide are deficient in key micronutrients. Single micronutrients have been used at high doses to prevent and treat dietary insufficiencies. Yet the impact of combinations of micronutrients in small doses aiming to improve lipid disorders and the corresponding metabolic pathways remains incompletely understood. Thus, we investigated whether a combination of micronutrients would reduce fat accumulation and atherosclerosis in mice. METHODS AND RESULTS: Lipoprotein receptor-null mice fed with an original combination of micronutrients incorporated into the daily chow showed reduced weight gain, body fat, plasma triglycerides, and increased oxygen consumption. These effects were achieved through enhanced lipid utilization and reduced lipid accumulation in metabolic organs and were mediated, in part, by the nuclear receptor PPARα. Moreover, the micronutrients partially prevented atherogenesis when administered early in life to apolipoprotein E-null mice. When the micronutrient treatment was started before conception, the anti-atherosclerotic effect was stronger in the progeny. This finding correlated with decreased post-prandial triglyceridaemia and vascular inflammation, two major atherogenic factors. CONCLUSION: Our data indicate beneficial effects of a combination of micronutritients on body weight gain, hypertriglyceridaemia, liver steatosis, and atherosclerosis in mice, and thus our findings suggest a novel cost-effective combinatorial micronutrient-based strategy worthy of being tested in humans.

Diagnostic accuracy of body mass index, body fat and waist in defining low aerobic fitness in adolescents

Objective: to assess the diagnostic accuracy of different anthropometric markers in defining low aerobic fitness among adolescents. Methods: cross-sectional study on 2,331 boys and 2,366 girls aged 10 - 18 years. Body mass index (BMI) was measured using standardized methods; body fat (BF) was assessed by bioelectrical impedance. Low aerobic fitness was assessed by the 20-meter shuttle run using the FITNESSGRAMR criteria. Waist was measured in a subsample of 1,933 boys and 1,897 girls. Overweight, obesity and excess fat were defined according to the International Obesity Task Force (IOTF) or FITNESSGRAMR criteria. Results: 38.5% of boys and 46.5% of girls were considered as unfit according to the FITNESSGRAMR criteria. In boys, the area under the ROC curve (AUC) and 95% confidence interval were 66.7 (64.1 - 69.3), 67.1 (64.5 - 69.6) and 64.6 (61.9 - 67.2) for BMI, BF and waist, respectively (P<0.02). In girls, the values were 68.3 (65.9 - 70.8), 63.8 (61.3 - 66.3) and 65.9 (63.4 - 68.4), respectively (P<0.001). In boys, the sensitivity and specificity to diagnose low fitness were 13% and 99% for obesity (IOTF); 38% and 86% for overweight + obesity (IOTF); 28% and 94% for obesity (FITNESSGRAMR) and 42% and 81% for excess fat (FITNESSGRAMR). For girls, the values were 9% and 99% for obesity (IOTF); 33% and 82% for overweight + obesity (IOTF); 22% and 94% for obesity (FITNESSGRAMR) and 26% and 90% for excess fat (FITNESSGRAMR). Conclusions: BMI, not body fat or waist, should be used to define low aerobic fitness. The IOTF BMI cut-points to define obesity have a very low screening capacity and should not be used.

Body fat and cholecalciferol supplementation in elderly homebound individuals

Vitamin D deficiency, observed mainly in the geriatric population, is responsible for loss of bone mass and increased risk of bone fractures. Currently, recommended doses of cholecalciferol are advised, but since there are few studies evaluating the factors that influence the serum levels of 25-hydroxyvitamin D (25(OH)D) following supplementation, we analyzed the relationship between the increase in serum 25(OH)D after supplementation and body fat. We studied a group of 42 homebound elderly subjects over 65 years old (31 women) in order to assess whether there is a need for adjustment of the doses of cholecalciferol administered to this group according to their adipose mass. Baseline measurements of 25(OH)D, intact parathyroid hormone and bone remodeling markers (osteocalcin and carboxy-terminal fraction of type 1 collagen) were performed. Percent body fat was measured by dual-energy X-ray absorptiometry. The patients were divided into three groups according to their percent body fat index and were treated with cholecalciferol, 7,000 IU a week, for 12 weeks. The increases in serum levels of 25(OH)D were similar for all groups, averaging 7.46 ng/mL (P < 0.05). It is noteworthy that this increase only shifted these patients from the insufficiency category to hypovitaminosis. Peak levels of 25(OH)D were attained after only 6 weeks of treatment. This study demonstrated that adipose tissue mass does not influence the elevation of 25(OH)D levels following vitamin D supplementation, suggesting that there is no need to adjust vitamin D dose according to body fat in elderly homebound individuals.

Percent body fat is a better predictor of cardiovascular risk factors than body mass index

The objective of the present study was to evaluate the predictive values of percent body fat (PBF) and body mass index (BMI) for cardiovascular risk factors, especially when PBF and BMI are conflicting. BMI was calculated by the standard formula and PBF was determined by bioelectrical impedance analysis. A total of 3859 ambulatory adult Han Chinese subjects (2173 males and 1686 females, age range: 18-85 years) without a history of cardiovascular diseases were recruited from February to September 2009. Based on BMI and PBF, they were classified into group 1 (normal BMI and PBF, N = 1961), group 2 (normal BMI, but abnormal PBF, N = 381), group 3 (abnormal BMI, but normal PBF, N = 681), and group 4 (abnormal BMI and PBF, N = 836). When age, gender, lifestyle, and family history of obesity were adjusted, PBF, but not BMI, was correlated with blood glucose and lipid levels. The odds ratio (OR) and 95% confidence interval (CI) for cardiovascular risk factors in groups 2 and 4 were 1.88 (1.45-2.45) and 2.06 (1.26-3.35) times those in group 1, respectively, but remained unchanged in group 3 (OR = 1.32, 95%CI = 0.92-1.89). Logistic regression models also demonstrated that PBF, rather than BMI, was independently associated with cardiovascular risk factors. In conclusion, PBF, and not BMI, is independently associated with cardiovascular risk factors, indicating that PBF is a better predictor.