Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: cross-sectional data from a population-based survey.

ABSTRACT: BACKGROUND: Although smokers tend to have a lower body-mass index than non-smokers, smoking may favour abdominal body fat accumulation. To our knowledge, no population-based studies have assessed the relationship between smoking and body fat composition. We assessed the association between cigarette smoking and waist circumference, body fat, and body-mass index. METHODS: Height, weight, and waist circumference were measured among 6,123 Caucasians (ages 35-75) from a cross-sectional population-based study in Switzerland. Abdominal obesity was defined as waist circumference>=102 cm for men and >=88 cm for women. Body fat (percent total body weight) was measured by electrical bioimpedance. Age- and sex-specific body fat cut-offs were used to define excess body fat. Cigarettes smoked per day were assessed by self-administered questionnaire. Age-adjusted means and odds ratios were calculated using linear and logistic regression. RESULTS: Current smokers (29% of men and 24% of women) had lower mean waist circumference, body fat percentage, and body-mass index compared with non-smokers. Age-adjusted mean waist circumference and body fat increased with cigarettes smoked per day among smokers. The association between cigarettes smoked per day and body-mass index was non-significant. Compared with light smokers, the adjusted odds ratio (OR) for abdominal obesity in men was 1.28 (0.78-2.10) for moderate smokers and 1.94 (1.15-3.27) for heavy smokers (P=0.03 for trend), and 1.07 (0.72-1.58) and 2.15 (1.26-3.64) in female moderate and heavy smokers, respectively (P<0.01 for trend). Compared with light smokers, the OR for excess body fat in men was 1.05 (95% CI: 0.58-1.92) for moderate smokers and 1.15 (0.60-2.20) for heavy smokers (P=0.75 for trend) and 1.34 (0.89-2.00) and 2.11 (1.25-3.57), respectively in women (P=0.07 for trend). CONCLUSION: Among smokers, cigarettes smoked per day were positively associated with central fat accumulation, particularly in women.

Relationship of physical activity with motor skills, aerobic fitness and body fat in preschool children: a cross-sectional and longitudinal study (Ballabeina).

Adiposity, low aerobic fitness and low levels of activity are all associated with clustered cardiovascular disease risk in children and their high prevalence represents a major public health concern. The aim of this study is to investigate the relationship of objectively measured physical activity (PA) with motor skills (agility and balance), aerobic fitness and %body fat in young children. This study is a cross-sectional and longitudinal analyses using mixed linear models. Longitudinal data were adjusted for baseline outcome parameters. In all, 217 healthy preschool children (age 4-6 years, 48% boys) participated in this study. PA (accelerometers), agility (obstacle course), dynamic balance (balance beam), aerobic fitness (20-m shuttle run) and %body fat (bioelectric impedance) at baseline and 9 months later. PA was positively associated with both motor skills and aerobic fitness at baseline as well as with their longitudinal changes. Specifically, only vigorous, but not total or moderate PA, was related to changes in aerobic fitness. Higher PA was associated with less %body fat at baseline, but not with its change. Conversely, baseline motor skills, aerobic fitness or %body fat were not related to changes in PA. In young children, baseline PA was associated with improvements in motor skills and in aerobic fitness, an important determinant of cardiovascular risk.

Consequences of smoking for body weight, body fat distribution, and insulin resistance

Our aim was to critically evaluate the relations among smoking, body weight, body fat distribution, and insulin resistance as reported in the literature. In the short term, nicotine increases energy expenditure and could reduce appetite, which may explain why smokers tend to have lower body weight than do nonsmokers and why smoking cessation is frequently followed by weight gain. In contrast, heavy smokers tend to have greater body weight than do light smokers or nonsmokers, which likely reflects a clustering of risky behaviors (eg, low degree of physical activity, poor diet, and smoking) that is conducive to weight gain. Other factors, such as weight cycling, could also be involved. In addition, smoking increases insulin resistance and is associated with central fat accumulation. As a result, smoking increases the risk of metabolic syndrome and diabetes, and these factors increase risk of cardiovascular disease. In the context of the worldwide obesity epidemic and a high prevalence of smoking, the greater risk of (central) obesity and insulin resistance among smokers is a matter of major concern

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Differences in whole-body fat oxidation kinetics between cycling and running.

This study aimed to quantitatively describe and compare whole-body fat oxidation kinetics in cycling and running using a sinusoidal mathematical model (SIN). Thirteen moderately trained individuals (7 men and 6 women) performed two graded exercise tests, with 3-min stages and 1 km h(-1) (or 20 W) increment, on a treadmill and on a cycle ergometer. Fat oxidation rates were determined using indirect calorimetry and plotted as a function of exercise intensity. The SIN model, which includes three independent variables (dilatation, symmetry and translation) that account for main quantitative characteristics of kinetics, provided a mathematical description of fat oxidation kinetics and allowed for determination of the intensity (Fat(max)) that elicits maximal fat oxidation (MFO). While the mean fat oxidation kinetics in cycling formed a symmetric parabolic curve, the mean kinetics during running was characterized by a greater dilatation (i.e., widening of the curve, P < 0.001) and a rightward asymmetry (i.e., shift of the peak of the curve to higher intensities, P = 0.01). Fat(max) was significantly higher in running compared with cycling (P < 0.001), whereas MFO was not significantly different between modes of exercise (P = 0.36). This study showed that the whole-body fat oxidation kinetics during running was characterized by a greater dilatation and a rightward asymmetry compared with cycling. The greater dilatation may be mainly related to the larger muscle mass involved in running while the rightward asymmetry may be induced by the specific type of muscle contraction.

Reproducibility and within-day variability of body fat measurements using segmental bipolar bioelectrical impedance in women

BACKGROUND AND AIMS: little is known regarding the reproducibility of body fat measuring devices; hence, we assessed the between and within-device reproducibility, and the within-day variability of body fat measurements. METHODS: body fat percentage was measured twice on seventeen female students aged between 18 and 20 with a body mass index of 21.9 ± 2.5 kg/m2 (mean ± SD) using seven bipolar bioelectrical impedance devices. Each participant was also measured each hour between 7:00 and 22:00. RESULTS: the correlation between first and second measurements was very high (Spearman r between 0.985 and 1.000, p<0.001), as well as between devices (Spearman r between 0.916 and 0.991, p<0.001). Repeated measurements analysis showed no differences were between devices (p=0.59) or readings (first vs. second: p=0.74). Conversely, significant differences were found between assessment periods throughout the day, measurements made in the morning being lower than those made in the afternoon (F test for repeated values= 6.58, p<0.001). CONCLUSIONS: the between and within-device reproducibility for measuring body fat is high, enabling the use of multiple devices in a single study. Conversely, small but significant changes in body fat measurements occur during the day, urging body fat measurements to be performed at fixed times.

Gender differences in whole-body fat oxidation kinetics during exercise.

Discrepancies appear in studies comparing fat oxidation between men and women. Therefore, this study aimed to quantitatively describe and compare whole-body fat oxidation kinetics between genders during exercise, using a sinusoidal (SIN) model. Twelve men and 11 women matched for age, body mass index, and aerobic fitness (maximal oxygen uptake and maximal power output per kilogram of fat-free mass (FFM)) performed submaximal incremental tests (Incr) with 5-min stages and a 7.5% maximal power output increment on a cycle ergometer. Fat oxidation rates were determined using indirect calorimetry, and plotted as a function of exercise intensity. The SIN model, which includes 3 independent variables (dilatation, symmetry, translation) that account for the main quantitative characteristics of kinetics, was used to mathematically describe fat oxidation kinetics and to determine the intensity (Fatmax) eliciting the maximal fat oxidation (MFO). During Incr, women exhibited greater fat oxidation rates from 35% to 85% maximal oxygen uptake, 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% maximal oxygen uptake) than men (p < 0.05). 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 toward higher exercise intensities in women (rightward translation, p = 0.08). These results support the idea that women have a greater reliance on fat oxidation than men during submaximal exercise, but also indicate that this greater fat oxidation is shifted toward higher exercise intensities in women than in men.

Reproducibility and within-day variability of body fat masurements using segmental bipolar bioelectrical impedance in women

Objective: to assess the between and within-device reproducibility, as well as within-day variability of body fat measurements. Methods: body fat percentage (%BF) was measured twice on seventeen female students aged between 18 and 20 with a body mass index of 21.9 22.6 kg/m2 (mean SD) using seven bipolar bioelectrical impedance devices (BF-306) according to the manufacturer's recommendations. Each student was also measured each hour between 7:00 and 22:00. Statistical analysis was conducted using a general linear model for repeated measurements. Results: the correlation between first and second measurements was very high (Pearson r between 0.985 and 1.000, p<0.001), as well as the correlation between devices (Pearson r between 0.986 and 0.999, all p<0.001). Repeated measurements analysis showed no differences were between devices (F test=0.83, p=0.59) or readings (first vs. second: F test=0.12, p=0.74). Conversely, significant differences were found between assessment periods throughout the day, measurements made in the morning being lower than those made in the afternoon. Assuming an overall daily average of 100 (based on all measurements), the values were 95.8 3.2 (mean SD) at 8:00 versus 101.3 3.0 at 20:00, corresponding to a mean change of 2.2 1.1 in %BF (F test for repeated values=6.58, p<0.001). Conclusions: the between and within-device reproducibility for measuring body fat is high, enabling the use of multiple devices in a single study. Conversely, small but significant changes in body fat measurements occur during the day, urging body fat measurements to be performed at fixed times.

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.

Topographical body fat distribution links to amino acid and lipid metabolism in healthy non-obese women.

Visceral adiposity is increasingly recognized as a key condition for the development of obesity related disorders, with the ratio between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) reported as the best correlate of cardiometabolic risk. In this study, using a cohort of 40 obese females (age: 25-45 y, BMI: 28-40 kg/m(2)) under healthy clinical conditions and monitored over a 2 weeks period we examined the relationships between different body composition parameters, estimates of visceral adiposity and blood/urine metabolic profiles. Metabonomics and lipidomics analysis of blood plasma and urine were employed in combination with in vivo quantitation of body composition and abdominal fat distribution using iDXA and computerized tomography. Of the various visceral fat estimates, VAT/SAT and VAT/total abdominal fat ratios exhibited significant associations with regio-specific body lean and fat composition. The integration of these visceral fat estimates with metabolic profiles of blood and urine described a distinct amino acid, diacyl and ether phospholipid phenotype in women with higher visceral fat. Metabolites important in predicting visceral fat adiposity as assessed by Random forest analysis highlighted 7 most robust markers, including tyrosine, glutamine, PC-O 44∶6, PC-O 44∶4, PC-O 42∶4, PC-O 40∶4, and PC-O 40∶3 lipid species. Unexpectedly, the visceral fat associated inflammatory profiles were shown to be highly influenced by inter-days and between-subject variations. Nevertheless, the visceral fat associated amino acid and lipid signature is proposed to be further validated for future patient stratification and cardiometabolic health diagnostics.

Correction: Topographical Body Fat Distribution Links to Amino Acid and Lipid Metabolism in Healthy Obese Women.

This corrects the article on p. e73445 in vol. 8.]. This corrects the article "Topographical Body Fat Distribution Links to Amino Acid and Lipid Metabolism in Healthy Non-Obese Women" , e73445. There was an error in the title of the article. The correct version of the title in the article is: Topographical Body Fat Distribution Links to Amino Acid and Lipid Metabolism in Healthy Obese Women The correct citation is: Martin F-PJ, Montoliu I, Collino S, Scherer M, Guy P, et al. (2013) Topographical Body Fat Distribution Links to Amino Acid and Lipid Metabolism in Healthy Obese Women. PLoS ONE 8(9): e73445. doi:10.1371/journal.pone.0073445

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.