Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women

Objective: To compare estimates of total and truncal fatness from eight‐electrode bioelectrical impedance analysis equipment (BIA₈) with those from DXA in centrally obese women. The secondary aim was to examine BMI and waist circumference (WC) as proxy measures for percentage total body fat (%TBF) and truncal body fat percentage (tr%BF). Research Methods and Procedures: This was a cross‐sectional study of 136 women (age, 48.1 ± 7.7 years; BMI, 30.4 ± 2.9 kg/m²; %TBF_DXA, 46.0 ± 3.7%; WC, 104 ± 8 cm). Fatness was measured by DXA and Tanita BC‐418 equipment (Tanita Corp., Tokyo, Japan). Agreement among methods was assessed by Bland‐Altman plots, and regression analysis was used to evaluate anthropometric measures as proxies for total and abdominal fatness. Results: The percentage of overweight subjects was 41.9%, whereas 55.9% of the subjects were obese, as defined by BMI, and all subjects had a WC exceeding the World Health Organization cut‐off point for abdominal obesity. Compared with DXA, the BIA₈ equipment significantly underestimated total %BF (?5.0; ?3.6 to ?8.5 [mean; 95% confidence interval]), fat mass (?3.6; ?3.9 to ?3.2), and tr%BF (?8.5; ?9.1 to ?7.9). The discrepancies between the methods increased with increasing adiposity for both %TBF and tr%BF (both p < 0.001). Variation in BMI explained 28% of the variation in %TBF_DXA and 51% of %TBF_BIA8. Using WC as a proxy for truncal adiposity, it explained only 18% of tr%BF_DXA variance and 27% of tr%BF_BIA8 variance. The corresponding figures for truncal fat mass were 49% and 35%, respectively. No significant age effects were observed in any of the regressions. Discussion: BIA₈ underestimated both total and truncal fatness, compared with DXA, with higher dispersion for tr%BF than %TBF. The discrepancies increased with degree of adiposity, suggesting that the accuracy of BIA is negatively affected by obesity.     

Assessing Body Composition among 3‐ to 8‐Year‐Old Children: Anthropometry,BIA, and DXA

Objective: To examine the inter‐relationships of body composition variables derived from simple anthropometry [BMI and skinfolds (SFs)], bioelectrical impedance analysis (BIA), and dual energy x‐ray (DXA) in young children. Research Methods and Procedures: Seventy‐five children (41 girls, 34 boys) 3 to 8 years of age were assessed for body composition by the following methods: BMI, SF thickness, BIA, and DXA. DXA served as the criterion measure. Predicted percentage body fat (%BF), fat‐free mass (FFM; kilograms), and fat mass (FM; kilograms) were derived from SF equations [Slaughter (SL)1 and SL2, Deurenberg (D) and Dezenberg] and BIA. Indices of truncal fatness were also determined from anthropometry. Results: Repeated measures ANOVA showed significant differences among the methods for %BF, FFM, and FM. All methods, except the D equation (p = 0.08), significantly underestimated measured %BF (p < 0.05). In general, correlations between the BMI and estimated %BF were moderate (r = 0.61 to 0.75). Estimated %BF from the SL2 also showed a high correlation with DXA %BF (r = 0.82). In contrast, estimated %BF derived from SFs showed a low correlation with estimated %BF derived from BIA (r = 0.38); likewise, the correlation between DXA %BF and BIA %BF was low (r = 0.30). Correlations among indicators of truncal fatness ranged from 0.43 to 0.98. Discussion: The results suggest that BIA has limited utility in estimating body composition, whereas BMI and SFs seem to be more useful in estimating body composition during the adiposity rebound. However, all methods significantly underestimated body fatness as determined by DXA, and, overall, the various methods and prediction equations are not interchangeable.     

Comparison of body composition methods in obese African-American women

Objective : To compare the accuracy of percentage body fat (%BF) estimates between bioelectrical impedance analysis (BIA) and DXA in obese African‐American women. Research Methods and Procedures : Fifty‐five obese African‐American women (mean age, 45 years; mean BMI, 38; mean %BF, 48%) were studied. BF was assessed by both BIA (RJL Systems BIA 101Q; RJL Systems, Clinton Township, MI) and DXA (Hologic QDR‐2000 Bone Densitometer; Hologic Inc., Bedford, MA). Generalized and ethnicity‐ and obese‐specific equations were used to calculate %BF from the BIA. Bland‐Altman analyses were used to compare the agreement between the BIA and the DXA, with the DXA serving as the criterion measure. Results : Two of the generalized equations provided consistent estimates across the weight range in comparison with the DXA estimates, whereas most of the other equations increasingly underestimated %BF as BF increased. One of the generalized and one of the ethnicity‐specific equations had mean differences that were not significantly different from the DXA value. Discussion : The findings show that the Lukaski equation provided the most precise and accurate estimates of %BF in comparison with the QDR 2000 and provide preliminary support for the use of this equation for obese African‐American women.     

Greater Abdominal Fat Accumulation Is Associated with Higher Metabolic Risk in Chinese than in White People: An Ethnicity Study

Introduction

Chinese are reported to have a higher percent body fat (%BF) and a higher percent trunk fat (%TF) than whites for a given body mass index (BMI). However, the associations of these ethnic differences in body composition with metabolic risks remain unknown.

Methods and Procedures

A total of 1 029 Chinese from Hangzhou, China, and 207 whites from New York, NY, USA, were recruited in the present study. Body composition was measured using dual-energy X-ray absorptiometry (DXA). Analysis of covariance was used to assess the ethnic differences in fat, fat distribution, and metabolic risk factors.

Results

After adjusting for BMI, age, and height, Chinese men had an average of 3.9% more %BF and 12.1% more %TF than white men; Chinese women had an average of 2.3% more %BF and 11.8% more %TF than white women. Compared with whites, higher metabolic risks were detected in Chinese for a given BMI after adjusting for age and height. Further adjustment for %BF did not change these ethnic disparities. However, after adjusting for %TF, the ethnic differences decreased and become insignificant in triglyceride, high-density lipoprotein cholesterol, and blood pressure (except for systolic blood pressure in men). For fasting plasma glucose, the ethnic differences persisted after adjustment for %BF, but decreased significantly from 0.910 to 0.686 mmol/L among men, and from 0.629 to 0.355 mmol/L among women, when the analyses were further controlled for %TF.

Discussion

Chinese have both higher %BF and %TF than white people for a given BMI. However, only %TF could in part account for the higher metabolic risk observed in Chinese men and women.     

Assessing body composition with DXA and bioimpedance: effects of obesity, physical activity, and age

Objective: This study evaluated to what extent dual‐energy X‐ray absorptiometry (DXA) and two types of bioimpedance analysis (BIA) yield similar results for body fat mass (FM) in men and women with different levels of obesity and physical activity (PA). Methods and Procedures: The study population consisted of 37–81‐year‐old Finnish people (82 men and 86 women). FM% was estimated using DXA (GE Lunar Prodigy) and two BIA devices (InBody (720) and Tanita BC 418 MA). Subjects were divided into normal, overweight, and obese groups on the basis of clinical cutoff points of BMI, and into low PA (LPA) and high PA (HPA) groups. Agreement between the devices was calculated by using the Bland–Altman analysis. Results: Compared to DXA, both BIA devices provided on average 2–6% lower values for FM% in normal BMI men, in women in all BMI categories, and in both genders in both HPA and LPA groups. In obese men, the differences were smaller. The two BIA devices provided similar means for groups. Differences between the two BIA devices with increasing FM% were a result of the InBody (720) not including age in their algorithm for estimating body composition. Discussion: BIA methods provided systematically lower values for FM than DXA. However, the differences depend on gender and body weight status pointing out the importance of considering these when identifying people with excess FM.     

Validation of DXA Body Composition Estimates in Obese Men and Women

The aim of this study was to determine the accuracy of dual‐energy X‐ray absorptiometry (DXA)‐derived percentage fat estimates in obese adults by using four‐compartment (4C) values as criterion measures. Differences between methods were also investigated in relation to the influence of fat‐free mass (FFM) hydration and various anthropometric measurements. Six women and eight men (age 22–54 years, BMI 28.7–39.9 kg/m², 4C percent body fat (%BF) 31.3–52.6%) had relative body fat (%BF) determined via DXA and a 4C method that incorporated measures of body density (BD), total body water (TBW), and bone mineral mass (BMM) via underwater weighing, deuterium dilution, and DXA, respectively. Anthropometric measurements were also undertaken: height, waist and gluteal girth, and anterior‐posterior (A‐P) chest depth. Values for both methods were significantly correlated (r² = 0.894) and no significant difference (P = 0.57) was detected between the means (DXA = 41.1%BF, 4C = 41.5%BF). The slope and intercept for the regression line were not significantly different (P > 0.05) from 1 and 0, respectively. Although both methods were significantly correlated, intraindividual differences between the methods were sizable (4C‐DXA, range = ?3.04 to 4.01%BF) and significantly correlated with tissue thickness (chest depth) or most surrogates of tissue thickness (body mass, BMI, waist girth) but not FFM hydration and gluteal girth. DXA provided cross‐sectional %BF data for obese adults without bias. However, individual data are associated with large prediction errors (±4.2%BF). This error appears to be associated with tissue thickness indicating that the DXA device used may not be able to accurately account for beam hardening in obese cohorts.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

Percentage of Body Fat Assessment Using Bioelectrical Impedance Analysis and Dual-Energy X-ray Absorptiometry in a Weight Loss Program for Obese or Overweight Chinese Adults

The current study aimed to compare the estimates of body fat percentage (%BF) by performing bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in a sample of obese or overweight Chinese adults who participated in a weight-loss randomized control trial stratified by gender to determine whether or not BIA is a valid measurement tool. Among 189 adults [73 males, 116 females; age  = 41 to 74 years; mean body mass index (BMI)  = 27.3 kg/m²], assessments of %BF at the baseline and six months from the baseline were conducted by performing BIA and DXA. Bland-Altman analyses and multiple regression analyses were used to assess the relationships between %BF_BIA and %BF_DXA. Compared with DXA, BIA underestimated %BF [in males: 4.6, –2.4 to 11.7 (mean biases, 95% limit of agreement) at the baseline, 1.4, –7.4 to 10.2 at the endpoint, and 3.2, –4.8 to 11.3 in changes; in females: 5.1, –2.4 to 12.7; 2.2, –6.1 to 10.4; and 3.0, –4.8 to 10.7, respectively]. For males and females, %BFDXA proved to be a significant predictor of the difference between DXA and BIA at the baseline, the endpoint, and in changes when BMI and age were considered (in males: p<0.01 and R ²  = 23.1%, 24.1%, 20.7%, respectively; for females: p<0.001 and R ²  = 40.4%, 48.8%, 25.4%, respectively). The current study suggests that BIA provides a relatively accurate prediction of %BF in individuals with normal weight, overweight, or obesity after the end of weight-loss program, but less accurate prediction of %BF in obese individuals at baseline or weight change during the weight-loss intervention program.     

The Prevalence of Metabolically Healthy Obese Subjects Defined by BMI and Dual‐Energy X‐Ray Absorptiometry

Nearly one‐third of obese (OB) people are reported to be metabolically healthy based on BMI criteria. It is unknown whether this holds true when more accurate adiposity measurements are applied such as dual‐energy X‐ray absorptiometry (DXA). We compared differences in the prevalence of cardiometabolic abnormalities among adiposity groups classified using BMI vs. DXA criteria. A total of 1,907 adult volunteers from Newfoundland and Labrador participated. BMI and body fat percentage (%BF; measured using DXA) were measured following a 12‐h fasting period. Subjects were categorized as normal weight (NW), overweight (OW), or OB based on BMI and %BF criteria. Cardiometabolic abnormalities considered included elevated triglyceride, glucose, and high‐sensitivity C‐reactive protein (hsCRP) levels, decreased high‐density lipoprotein (HDL) cholesterol levels, insulin resistance, and hypertension. Subjects were classified as metabolically healthy (0 or 1 cardiometabolic abnormality) or abnormal (≥2 cardiometabolic abnormalities). We found low agreement in the prevalence of cardiometabolic abnormalities between BMI and %BF classifications (κ = 0.373, P < 0.001). Among NW and OW subjects, the prevalence of metabolically healthy individuals was similar between BMI and %BF (77.6 vs. 75.7% and 58.8 vs. 62.5%, respectively) however, there was a pronounced difference among OB subjects (34.0 vs. 47.7%, P < 0.05). Similar trends were evident using three additional definitions to characterize metabolically healthy individuals. Our findings indicate that approximately one‐half of OB people are metabolically healthy when classified using %BF criteria which is significantly higher than previously reported using BMI. Caution should therefore be taken when making inferences about the metabolic health of an OB population depending on the method used to measure adiposity.     

Which Obesity Indicators Are Better Predictors of Metabolic Risk?: Healthy Twin Study

No consensus exists as to the most sensitive and specific obesity indicator associated with metabolic risk factors. We aimed to validate anthropometry as the predictor for obesity-related metabolic risk factors through comparison with direct body composition measures in Korean adults. A total of 995 Korean women and 577 Korean men who participated in the Healthy Twin study were the subjects. Anthropometric measurements included BMI, waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHTR). Direct body composition measures included the percentage of body fat (%BF) measured using dual-energy X-ray absorptiometry scanners and bioelectrical impedance analyzer (BIA). The following criteria were used to define abnormal metabolic risk factors: blood pressure > or = 130/85 mm Hg, fasting glucose (> or = 100 mg/dl), insulin (> or = 25 microU/ml), homeostasis model assessment (HOMA) (> or = 2.61), high-density lipoprotein (HDL) (<40 mg/dl for men or <50 mg/dl for women), triacylglycerol (> or = 150 mg/dl), uric acid (>7 mg/dl for men or >6 mg/dl for women), high-sensitivity C-reactive protein (hs-CRP) (> or = 2.11 mg/l). In multiple regression analyses (adjusted for age, education, smoking, alcohol, exercise and past/current medical history, and treated families as a random effect), WC, WHTR, and BMI were consistently associated with all metabolic risk factors regardless of the subject's gender. Some of the areas under the receiver-operating characteristic curves regarding abnormal metabolic risk factors were significantly higher for the three indicators of central obesity than for %BF. Our study validates the usefulness of anthropometry over direct body fat measures to predict metabolic risks.     

Reliability and intermethod agreement for body fat assessment among two field and two laboratory methods in adolescents

To increase knowledge about reliability and intermethods agreement for body fat (BF) is of interest for assessment, interpretation, and comparison purposes. It was aimed to examine intra- and inter-rater reliability, interday variability, and degree of agreement for BF using air-displacement plethysmography (Bod-Pod), dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and skinfold measurements in European adolescents. Fifty-four adolescents (25 females) from Zaragoza and 30 (14 females) from Stockholm, aged 13-17 years participated in this study. Two trained raters in each center assessed BF with Bod-Pod, DXA, BIA, and anthropometry (DXA only in Zaragoza). Intermethod agreement and reliability were studied using a 4-way ANOVA for the same rater on the first day and two additional measurements on a second day, one each rater. Technical error of measurement (TEM) and percentage coefficient of reliability (%R) were also reported. No significant intrarater, inter-rater, or interday effect was observed for %BF for any method in either of the cities. In Zaragoza, %BF was significantly different when measured by Bod-Pod and BIA in comparison with anthropometry and DXA (all P < 0.001). The same result was observed in Stockholm (P < 0.001), except that DXA was not measured. Bod-Pod, DXA, BIA, and anthropometry are reliable for %BF repeated assessment within the same day by the same or different raters or in consecutive days by the same rater. Bod-Pod showed close agreement with BIA as did DXA with anthropometry; however, Bod-Pod and BIA presented higher values of %BF than anthropometry and DXA.     

Intraperitoneal Fat and Insulin Resistance in Obese Adolescents

Obesity is epidemic among adolescents in the United States. We sought to analyze the anthropometric measures of adiposity and fasting indices of insulin resistance, including insulin‐like growth factor–binding protein‐1 (IGFBP‐1), and to provide a clinical estimate of intraperitoneal (IP) fat in obese adolescents (BMI ≥95th percentile), between ages 13 and 17 years. Subjects had baseline testing to determine eligibility for a subsequent randomized, placebo‐controlled trial of metformin XR therapy. Anthropometry and dual‐energy X‐ray absorptiometry (DXA) were used to quantify total body fat while abdominal computed tomography (CT) was used to measure IP (CT‐IP) and subcutaneous (CT‐SQ) fat. Using anthropometry and fasting laboratory data, we constructed regression models for both CT‐IP and CT‐SQ. A total of 92 subjects, 33 males, were evaluated. Of the 92 subjects, 19 were black. Fasting insulin concentrations were highly associated with other measures of insulin resistance. Median percent body fat across all subjects, as measured by DXA, was 41%. Using CT measures, 67% of abdominal cross‐sectional area was fat, 14% of which was IP fat. In multiple regression analysis, waist circumference (WC) and BMI, jointly and independently, were strongly associated with both CT‐IP and CT‐SQ fat. BMI and WC explained 62% of variance of CT‐SQ fat, but only 26% of variance of CT‐IP fat. Adding triglyceride:high‐density lipoprotein (TG:HDL) ratio and IGFBP‐1 (among nonblacks) to the regression model increased the explained variance for estimating CT‐IP fat to 45%. When evaluating the metabolic morbidity of an obese adolescent, a model using fasting IGFBP‐1, TG:HDL, BMI, and WC may be worthwhile as an estimate of IP fat.     

Prediction of Fatness by Standing 8‐Electrode Bioimpedance: A Multiethnic Adolescent Population

The objective of this study was to validate an 8‐electrode bioimpedance analysis (BIA₈) device (BC‐418; Tanita, Tokyo, Japan) for use in populations of European, Maori, Pacific Island, and Asian adolescents. Healthy adolescents (215 M, 216 F; 129 Pacific Island, 120 Asian, 91 Maori, and 91 European; age range 12–19 years) were recruited by purposive sampling of high schools in Auckland, New Zealand. Weight, height, sitting height, leg length, waist circumference, and whole‐body impedance were measured. Fat mass (FM) and fat‐free mass (FFM) derived from the BIA₈ manufacturer's equations were compared with measurements by dual‐energy X‐ray absorptiometry (DXA). DXA‐measured FFM was used as the reference to develop prediction equations based on impedance. A double cross‐validation technique was applied. BIA₈ underestimated FM by 2.06 kg (P < 0.0001) and percent body fat (%BF) by 2.84% (P < 0.0001), on average. However, BIA₈ tended to overestimate FM and %BF in lean and underestimate FM and %BF in fat individuals. Sex‐specific equations developed showed acceptable accuracy on cross‐validation. In the total sample, the best prediction equations were, for boys: FFM (kg) = 0.607 height (cm)²/impedance (Ω) + 1.542 age (y) + 0.220 height (cm) + 0.096 weight (kg) + 1.836 ethnicity (0 = European or Asian, 1 = Maori or Pacific) ? 47.547, R² = 0.93, standard error of estimate (SEE) = 3.09 kg; and, for girls: FFM (kg) = 0.531 height (cm)²/impedance (Ω) + 0.182 height (cm) + 0.096 weight (kg) + 1.562 ethnicity (0 = non‐Pacific, 1 = Pacific) ? 15.782, R² = 0.91, SEE = 2.19 kg. In conclusion, equations for fatness estimation using BIA₈ developed for our sample perform better than reliance on the manufacturer's estimates. The relationship between BIA and body composition in adolescents is ethnicity dependent.     

Short-Term Overfeeding Increases Circulating Adiponectin Independent of Obesity Status

Background

Adiponectin is an adipose tissue derived hormone which strengthens insulin sensitivity. However, there is little data available regarding the influence of a positive energy challenge (PEC) on circulating adiponectin and the role of obesity status on this response.

Objective

The purpose of this study was to investigate how circulating adiponectin will respond to a short-term PEC and whether or not this response will differ among normal-weight(NW), overweight(OW) and obese(OB).

Design

We examined adiponectin among 64 young men (19-29 yr) before and after a 7-day overfeeding (70% above normal energy requirements). The relationship between adiponectin and obesity related phenotypes including; weight, percent body fat (%BF), percent trunk fat (%TF), percent android fat (%AF), body mass index (BMI), total cholesterol, HDLc, LDLc, glucose, insulin, homeostatic model assessment insulin resistance (HOMA-IR) and β-cell function (HOMA-β) were analyzed before and after overfeeding.

Results

Analysis of variance (ANOVA) and partial correlations were used to compute the effect of overfeeding on adiponectin and its association with adiposity measurements, respectively. Circulating Adiponectin levels significantly increased after the 7-day overfeeding in all three adiposity groups. Moreover, adiponectin at baseline was not significantly different among NW, OW and OB subjects defined by either %BF or BMI. Baseline adiponectin was negatively correlated with weight and BMI for the entire cohort and %TF, glucose, insulin and HOMA-IR in OB. However, after controlling for insulin resistance the correlation of adiponectin with weight, BMI and %TF were nullified.

Conclusion

Our study provides evidence that the protective response of adiponectin is preserved during a PEC regardless of adiposity. Baseline adiponectin level is not directly associated with obesity status and weight gain in response to short-term overfeeding. However, the significant increase of adiponectin in response to overfeeding indicates the physiological potential for adiponectin to attenuate insulin resistance during the development of obesity.     

Body composition assessment in extreme obesity and after massive weight loss induced by gastric bypass surgery

Body composition methods were examined in 20 women [body mass index (BMI) 48.7 +/- 8.8 kg/m(2)] before and after weight loss [-44.8 +/- 14.6 (SD) kg] after gastric bypass (GBP) surgery. The reference method, a three-compartment (3C) model using body density by air displacement plethysmography and total body water (TBW) by H(2)18O dilution (3C-H(2)18O), showed a decrease in percent body fat (%BF) from 51.4 to 34.6%. Fat-free mass hydration was significantly higher than the reference value (0.738) in extreme obesity (0.756; P < 0.001) but not after weight reduction (0.747; P = 0.16). %BF by H(2)18O dilution and air displacement plethysmography differed significantly from %BF by 3C-H(2)18O in extreme obesity (P < 0.05) and 3C models using (2)H(2)O or bioelectrical impedance analysis (BIA) to determine TBW improved mean %BF estimates over most other methods at both time points. BIA results varied with the equation used, but BIA better predicted %BF than did BMI at both time points. All methods except BIA using the Segal equation were comparable to the reference method for determining changes over time. A simple 3C model utilizing air displacement plethysmography and BIA is useful for clinical evaluation in this population.     

Prevalence of insulin resistance in a young adult population. Relationship with weight status

AimTo estimate the prevalence of insulin resistance using both the Homeostatic Model Assessment (HOMA) index and basal insulinemia, and to analyze its relationship to overweight, as measured by body mass index (BMI) and waist circumference (WC).Patients and methodsA series of 118 non-diabetic young adults aged 18 and 19 years attending a primary care health center were studied. They were contacted by telephone, and their BMI, WC, HOMA and basal insulinemia were measured, among other parameters.ResultsHOMA values ≥ P90 (HOMA ≥3.15) were found in 9.3% of the sample (50% in the obesity group). Insulinemia ≥ P90 (16,9) was found in 11%. Based on BMI, 17.8% were overweight (26.5% of men, 11.6% of women), and 6.8% were obese (6.1% of men, 7.2% of women). Based on WC, 5.71% were obese when waist was measured at the midpoint and 15.38%, when measured at the iliac crest. HOMA was found to be significantly correlated to weight increase, BMI, WC, systolic blood pressure, triglycerides, and blood glucose, while correlation was only found between insulinemia and increased WC and decreased high lipoprotein cholesterol (HDL) levels.ConclusionIn this young adult sample, increased BMI and WC were associated to increased insulin resistance. High HOMA values were found in 9.3% of subjects.     

Validation of five simple models estimating body fat in white postmenopausal women: use in clinical practice and research

The aim of this study was to validate noninvasive models, retrieved from the literature, estimating body fat in white women. The cohort used for the validation consisted of 277 postmenopausal women, and the reference method was dual-energy X-ray absorptiometry (DXA). Five models were retrieved containing anthropometric measurements such as bicep and tricep skinfolds, waist circumference (WC), height, and body weight. Models including only BMI were found to be less biased and more valid than others including skinfolds and circumferences. The model by Visser et al., estimating body density (BD = 0.0226 × sex - 0.0022 × BMI + 1.0605) with the subsequent use of Brozek's (and not Siri's) equation to estimate body fat percentage (%BF), was found to be more valid than the other models for this cohort. In conclusion, it seems that Visser's et al. model, including only BMI, with Brozek's equation, is a fast, noninvasive, and valid method to assess body composition in white postmenopausal women in clinical practice and research.     

Determining the waist circumference in african americans which best predicts insulin resistance

Total body size and central fat distribution are important determinants of insulin resistance. The BMI and waist circumference (WC) thresholds in African Americans that best predict insulin resistance are unknown. Our goal was to determine the BMI and WC values in African Americans, which optimally predict insulin resistance. The subjects were African Americans (68 men, 63 women), aged 35 +/- 8 years (mean +/- s.d.), with a BMI of 30.9 +/- 7.5, in the range of 18.5-54.7 kg/m(2), and with a WC of 98 +/- 18, in the range of 69-173 cm. Insulin resistance was defined by the lowest tertile of the insulin sensitivity index (S(I)). The Youden index was calculated to determine the WC and BMI thresholds that predict insulin resistance with an optimal combination of sensitivity and specificity. In men the thresholds that optimally predicted insulin resistance were a BMI > or =30 kg/m(2) or a WC > or =102 cm. For women, insulin resistance was best predicted by a BMI > or =32 kg/m(2) or a WC > or =98 cm. In African Americans, insulin resistance (in men) was best predicted by a WC > or =102 cm, and in women by a WC > or =98 cm, or by a BMI value that fell in the obese category (men: > or =30 kg/m(2), women: > or =32 kg/m(2)).     

New percentage body fat prediction equations for Japanese females

Anthropometry is a simple and cost-efficient method for the assessment of body composition. However prediction equations to estimate body composition using anthropometry should be 'population-specific'. Most popular body composition prediction equations for Japanese females were proposed more than 40 years ago and there is some concern regarding their usefulness in Japanese females living today. The aim of this study was to compare percentage body fat (%BF) estimated from anthropometry and dual energy x-ray absorptiometry (DXA) to examine the applicability of commonly used prediction equations in young Japanese females. Body composition of 139 Japanese females aged between 18 and 27 years of age (BMI range: 15.1-29.1 kg/m(2)) was measured using whole-body DXA (Lunar DPX-LIQ) scans. From anthropometric measurements %BF was estimated using four equations developed from Japanese females. The results showed that the traditionally employed prediction equations for anthropometry significantly (p<0.01) underestimate %BF of young Japanese females and therefore are not valid for the precise estimation of body composition. New %BF prediction equations were proposed from the DXA and anthropometry results. Application of the proposed equations may assist in more accurate assessment of body fatness in Japanese females living today.     

Relationship of visceral adiposity to cardiovascular disease risk factors in black and white teens

Objective: We tested the hypothesis that visceral adiposity, compared with general adiposity, would explain more of the variance in cardiovascular disease (CVD) risk factors. Research Method and Procedures: Subjects were 464 adolescents (238 black and 205 girls). Adiposity measures included visceral adipose tissue (VAT; magnetic resonance imaging), percent body fat (%BF; DXA), BMI, and waist girth (anthropometry). CVD risk factors were fasting insulin, fibrinogen, total to high‐density lipoprotein‐cholesterol ratio, triglycerides (TGs), systolic blood pressure, and left ventricular mass indexed to height^2.7. Results: After adjustment for age, race, and sex, all adiposity indices explained significant proportions of the variance in all of the CVD risk factors; %BF tended to explain more variance than VAT. Regression models that included both %BF and VAT found that both indices explained independent proportions of the variance only for total to high‐density lipoprotein‐cholesterol ratio. For TGs, the model that included both %BF and VAT found that only VAT was significant. For systolic blood pressure and left ventricular mass indexed to height^2.7, anthropometric measures explained more of the variance than VAT and %BF. Discussion: The hypothesis that visceral adiposity would explain more variance in CVD risk than general adiposity was not supported in this relatively large sample of black and white adolescents. Only for TGs did it seem that VAT was more influential than %BF. Perhaps the deleterious effect of visceral adiposity becomes greater later in life as it increases in proportion to general adiposity.