The paradox of obesity with normal weight; a cross-sectional study.

Objective: To evaluate the prevalence of excessive adiposity among normal-weight individuals, and their cardiometabolic risk. Methods: This cross-sectional study included 3,001 participants (ages 20-95, 52% men, BMI 28.0 ± 5.5 kg/m2) who completed an anthropometric evaluation, dual x-ray absorptiometry (DXA) scan to measure body composition, and cardiometabolic blood markers. Excess adiposity was defined as ≥25% for men and ≥ 35% for women. Results: Of the entire study participants, 967 were in normal BMI (18.5-24.9 kg/m2) with a wide body fat distribution (4-49%). Of them, 26% of men and 38% of women were classified with excess adiposity. As compared to normal-weight lean participants, normal-weight obese men and women had higher triglycerides (76.5 ± 37.3 vs. 101.2 ± 50.3 mg/dL, p = 0.004 and 84 ± 44.2 vs. 101.4 ± 91.1 mg/dL, p = 0.030; respectively) and elevated low-density lipoprotein cholesterol (103.3 ± 31.7 vs. 119.6 ± 45.5 mg/dL, p = 0.011) and total cholesterol (171.5 ± 40.3 vs. 190.2 ± 39 mg/dL, p = 0.007) for men only. Among NWO, abdominal circumference was prevalent in 60% of the females with NWO (≥88 cm), but only in 4% of males (≥102 cm). Conclusion: Higher adiposity, even within normal weight, increases cardiometabolic risk, and abdominal waist circumference misclassified obesity in normal-weight individuals. This study highlights the need for a body composition evaluation to determine cardiometabolic risk for adults with normal body weight.

Cross-Validation of a New General Population Resting Metabolic Rate Prediction Equation Based on Body Composition.

Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m2; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.

Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults.

BACKGROUND: Although over one hundred equations have been developed to predict the energy expenditure of individuals, none are sensitive to weight change in assessment of resting metabolic rate (RMR) before and after weight loss. OBJECTIVE: To formulate adjusted equations for overweight and obese individuals and to compare their accuracy with existing prediction RMR equations before and after weight loss. SUBJECTS/MATERIALS: This is historical prospective study. Participants included 39 overweight and obese men and women before and after losing 10-20% from baseline weight on a diet and physical activity regimen for at least three months. Pre and post weight loss measured RMR results were compared to estimated RMR using several existing prediction equations: Harris and Benedict, Ravussin and Bogardus, and Mifflin et al. To improve the accuracy of these prediction equations, we suggest new equations adjusted for weight loss, based on measured RMR and evaluated their accuracy. RESULTS: Pre and post weight loss data indicated: significant fat reduction in both genders; reduction in free-fat mass only in men, and a significant decrease in measured RMR only in women. Our suggested equations were the most accurate and closest to measured RMR in both genders, in comparison to the Harris and Benedict, Ravussin and Bogardus, and Mifflin et al equation results. Estimated RMR using the latter equations was significantly lower than measured RMR in both genders at pre and post weight loss (P < 0.01). CONCLUSIONS: This study highlights the need for adjusting RMR equations before and after weight loss in overweight and obese individuals. Further research is needed to validate our suggested equations.

Comparison of body composition assessment across body mass index categories by two multifrequency bioelectrical impedance analysis devices and dual-energy X-ray absorptiometry in clinical settings.

BACKGROUND: InBody-770 and SECA mBCA 515 are multifrequency bioelectrical impedance analysis (BIA) devices, which are commonly used in the clinic to assess fat-free mass (FFM) and body fat (BF). However, the accuracy between devices in clinical settings, across different body mass index (BMI) groups remains unclear. METHODS: Body composition for 226 participants (51% men, aged 18-80 years, BMI 18-56 kg/m²) was assessed by two commercial multifrequency BIA devices requiring standing position and using eight-contact electrodes, InBody 770 and SECA mBCA 515, and compared to results from dual-energy X-ray absorptiometry (DXA). Measurements were performed in a random order, after a 3 h fast and no prior exercise. Lin's-concordance correlation and Bland-Altman analyses were used to compare between devices, and linear regression to assess accuracy in BF% across BMI groups. RESULTS: We found strong correlation between DXA results for study population BF% and those obtained by InBody (ρc = 0.922, 95% confidence interval (CI) 0.902, 0.938) and DXA and SECA (ρc = 0.940, CI 0.923, 0.935), with 95% limits of agreements between 2.6 and -8.9, and 7.1 and -7.6, respectively. BF% assessment by SECA was similar to DXA (-0.3%, p = 0.267), and underestimated by InBody (-3.1%, p < 0.0001). InBody deviations were largest among normal weight people and decreased with increasing BMI group, while SECA measurements remained unaffected. CONCLUSIONS: Both BIA devices agreed well with BF% assessment obtained by DXA. Unlike SECA, InBody underestimated BF% in both genders and was influenced by BMI categories. Therefore, in clinical settings, individual assessment of BF% should be taken with caution.

A novel body circumferences-based estimation of percentage body fat.

Anthropometric measures of body composition are often used for rapid and cost-effective estimation of percentage body fat (%BF) in field research, serial measurements and screening. Our aim was to develop a validated estimate of %BF for the general population, based on simple body circumferences measures. The study cohort consisted of two consecutive samples of health club members, designated as 'development' (n 476, 61 % men, 39 % women) and 'validation' (n 224, 50 % men, 50 % women) groups. All subjects underwent anthropometric measurements as part of their registration to a health club. Dual-energy X-ray absorptiometry (DEXA) scan was used as the 'gold standard' estimate of %BF. Linear regressions where used to construct the predictive equation (%BFcal). Bland-Altman statistics, Lin concordance coefficients and percentage of subjects falling within 5 % of %BF estimate by DEXA were used to evaluate accuracy and precision of the equation. The variance inflation factor was used to check multicollinearity. Two distinct equations were developed for men and women: %BFcal (men)=10·1-0·239H+0·8A-0·5N; %BFcal (women)=19·2-0·239H+0·8A-0·5N (H, height; A, abdomen; N, neck, all in cm). Bland-Altman differences were randomly distributed and showed no fixed bias. Lin concordance coefficients of %BFcal were 0·89 in men and 0·86 in women. About 79·5 % of %BF predictions in both sexes were within ±5 % of the DEXA value. The Durnin-Womersley skinfolds equation was less accurate in our study group for prediction of %BF than %BFcal. We conclude that %BFcal offers the advantage of obtaining a reliable estimate of %BF from simple measurements that require no sophisticated tools and only a minimal prior training and experience.

[ERGOGENIC SPORT SUPPLEMENTS FOR ATHLETES].

Use of performance-enhancing supplements occurs at all levels of sports, from recreational athletes to professional athletes. Although some supplements do enhance athletic performance, many have no proven benefits and have adverse effects. Nutritional supplements are categorized into the following categories: I. Apparently Effective. II. Possibly Effective. III. Too Early To Tell. IV. Apparently Ineffective. This article will review 4 ergogenic supplements which are categorized in the first category--"Apparently Effective"--1) Buffer agents 2) Creatine 3) Caffeine and 4 Nitric Oxide. Given the widespread use of performance enhancing supplements, physicians, and dietitians should be prepared to counsel athletes about their effectiveness, safety and legality.

Body fat measurements in elite adolescent volleyball players: correlation between skinfold thickness, bioelectrical impedance analysis, air-displacement plethysmography, and body mass index percentiles.

BACKGROUND: Determination of body composition is an essential parameter in training athletes because low fat-muscle ratio might improve physical performance in many types of sports. Since training is often conducted in the field, it is important to determine whether simple field measurements of body composition assessment correlate with laboratory measurements. OBJECTIVE: Examine the correlation of body fat content as measured using skinfold thicknesses (SF), air-displacement plethysmography (BOD POD), bioelectrical impedance analysis (BIA) and body mass index (BMI) age and gender adjusted percentiles. METHOD: Body mass as measured by SF, BOD POD, BIA, and BMI percentiles were examined in 29 elite, national team level, male and female volleyball players (age range 13 to 18) at the beginning of the training season. RESULTS: Body fat percent measured by SF, BIA and BOD POD were highly positively correlated (r > 0.83). Measurements of body fat by SF, BIA and BOD POD were weakly correlated with BMI percentiles (r < 0.45). CONCLUSIONS: Results suggest that BMI percentile is not a good measure for body fat in adolescent elite male and female volleyball players. SF and measurements of body composition by BIA and BOD POD are essentially interchangeable.