Abdominal fat and metabolic risk in obese children and adolescents

The aim of this study was to investigate fat distribution, mainly abdominal fat, and its relationship with metabolic risk variables in a group of 126 children and adolescents (60 males and 66 females) aged 5.0 to 14.9. According to IOTF criteria, 46 were classified as normal weight, 28 overweight and 52 obese. Weight, height, waist (WC) and hip circumferences were measured. The body mass index (BMI) was calculated. Total body fat, trunkal and abdominal fat were also assessed by dual energy x-ray absorptiometry (DXA). Glucose, insulin, HDL-Cholesterol, triglycerides (TG), ferritine, homocystein and C-reactive protein (CRP) were measured. Obesity status was related with insulin concentrations, CRP, TG and HDL. Obese patients had higher abdominal fat and higher CRP values than overweight and normal subjects. All markers of central body adiposity were related with insulin and lipid metabolism; however, they were not related with homocystein or ferritin. A simple anthropometric measurement, like waist circumference, seems to be a good predictor of the majority of the obesity related metabolic risk variables.     

Comparison of the Relative Contributions of Intra‐Abdominal and Liver Fat to Components of the Metabolic Syndrome

Abdominally obese individuals with the metabolic syndrome often have excess fat deposition both intra‐abdominally (IA) and in the liver, but the relative contribution of these two deposits to variation in components of the metabolic syndrome remains unclear. We determined the mutually independent quantitative contributions of IA and liver fat to components of the syndrome, fasting serum (fS) insulin, and liver enzymes and measures of hepatic insulin sensitivity in 356 subjects (mean age 42 years, mean BMI 29.7 kg/m²) in whom liver fat and abdominal fat volumes were measured. IA and liver fat contents were correlated (r = 0.65, P < 0.0001). In multivariate linear regression analyses including either liver or IA fat, liver fat or IA fat explained variation in fS‐triglyceride (TG) and high‐density lipoprotein (HDL) cholesterol, plasma glucose, insulin and liver enzyme concentrations, and hepatic insulin sensitivity independent of age, gender, subcutaneous (SC) fat, and/or lean body mass (LBM). Including both liver and IA fat, liver and IA fat both explained variation in TG, HDL cholesterol, insulin and hepatic insulin sensitivity independent of each other and of age, gender, SC fat, and LBM. Liver fat independently predicted glucose and liver enzymes. SC fat and age explained variation in blood pressure. In conclusion, both IA and liver fat independently of each other explain variation in serum TG, HDL cholesterol, insulin concentrations and hepatic insulin sensitivity, thus supporting that both fat depots are important predictors of these components of the metabolic syndrome.     

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.     

No apparent progress in bioelectrical impedance accuracy: validation against metabolic risk and DXA

Bioelectrical impedance (BIA) is quick, easy, and safe when quantifying fat and lean tissue. New BIA models (Tanita BC-418 MA, abbreviated BIA(8)) can perform segmental body composition analysis, e.g., estimate %trunkal fatness (%TF). It is not known, however, whether new BIA models can detect metabolic risk factors (MRFs) better than older models (Tanita TBF-300, abbreviated BIA(4)). We therefore tested the correlation between MRF and percentage whole-body fat (%BF) from BIA(4) and BIA(8) and compared these with the correlation between MRF and dual-energy X-ray absorptiometry (DXA, used as gold standard), BMI and waist circumference (WC). The sample consisted of 136 abdominally obese (WC >or= 88 cm), middle-aged (30-60 years) women. MRF included fasting blood glucose and insulin; high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides; high sensitive C-reactive protein, plasminogen activator inhibitor-1 (PAI-1), and fibrinogen; and alanine transaminase (ALT) liver enzyme. We found that similar to DXA, but in contrast to BMI, neither %BF BIA(4) nor %BF BIA(8) correlated with blood lipids or ALT. In the segmental analysis of %TF, BIA(8) only correlated with inflammatory markers, but not insulin, blood lipids, or ALT liver enzyme (in contrast to WC and %TF DXA). %TF DXA was associated with homeostatic model assessment insulin resistance (HOMA-IR) independently of WC (P = 0.03), whereas %TF BIA(8) was not (P = 0.53). Receiver-operating characteristic (ROC) curves confirmed that %TF BIA(8) did not differ from chance in the detection of insulin resistance (P = 0.26). BIA estimates of fatness were, at best, weakly correlated with obesity-related risk factors in abdominally obese women, even the new eight-electrode model. Our data support the continued use of WC and BMI.     

Waist circumference correlates with metabolic syndrome indicators better than percentage fat

Objective : Percent fat is often considered the reference for establishing the magnitude of adipose tissue accumulation and the risk of excess adiposity. However, the increasing recognition of a strong link between central adiposity and metabolic disturbances led us to test whether waist circumference (WC) is more highly correlated with metabolic syndrome components than percent fat and other related anthropometric measures such as BMI. Research Methods and Procedures : BMI, WC, and percent fat, measured by DXA, were evaluated in 1010 healthy white and African‐American men and women [age, 48.3 ± 17.2 (standard deviation) years; BMI, 27.0 ± 5.3 kg/m²]. The associations of BMI, WC, and percent fat with age and laboratory‐adjusted health risk indicators (i.e., serum glucose, insulin, triglycerides, high‐density lipoprotein cholesterol, blood pressure) in each sex and ethnicity group were examined. Results : For 18 of 24 comparisons, the age‐ and laboratory‐adjusted correlations were lowest for percent fat and in 16 of 24 comparisons were highest for WC. Fifteen of the between‐method differences reached statistical significance. With health risk indicator as the dependent variable and anthropometric measures as the independent variable, the contribution of percent fat to the WC regression model was not statistically significant; in contrast, adding WC to the percent fat regression model did make a significant independent contribution for most health risk indicators. Discussion : WC had the strongest associations with health risk indicators, followed by BMI. Although percent fat is a useful measure of overall adiposity, health risks are best represented by the simply measured WC.     

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.     

Plasma Adiponectin Levels and Metabolic Factors in Nondiabetic Adolescents

Objectives: The relationship of plasma adiponectin levels with various anthropometric and metabolic factors has been surveyed extensively in adults. However, how plasma adiponectin levels are related to various anthropometric indices and cardiovascular risk factors in adolescents is not as vigorously studied. In this study, we investigated this among healthy nondiabetic adolescents. Research Methods and Procedures: Two hundred thirty nondiabetic subjects (125 boys and 105 girls, ～10 to 19 years old) were included. The plasma adiponectin, fasting plasma glucose, insulin, lipids and anthropometric indices including body height, weight, waist circumference, and hip circumference were examined. Body fat mass (FM) and percentage were obtained from DXA scan. The homeostasis model assessment was applied to estimate the degree of insulin resistance. Results: The plasma adiponectin levels were significantly higher in girls (30.79 ± 14.48 μg/mL) than boys (22.87 ± 11.41 μg/mL). The plasma adiponectin levels were negatively related to BMI, FM, FM percentage, waist circumference, waist‐to‐hip ratio, insulin resistance, plasma insulin, triglycerides, and uric acid levels, but positively with high‐density lipoprotein cholesterol (HDL‐C) with the adjustment for age and gender. Using different multivariate linear regression models, only age and HDL‐C were consistently related to the plasma adiponectin levels after adjustment for the other variables. Discussion: The relationship between plasma adiponectin and various anthropometric indices and metabolic factors, especially HDL‐C, previously reported in adults was present in the healthy nondiabetic adolescents. Whether variation of plasma adiponectin levels in healthy nondiabetic adolescents may influence their future coronary artery disease risk warrants further investigation.     

DXA‐derived Abdominal Fat Mass,Waist Circumference,and Blood Lipids in Postmenopausal Women

The purpose of this study was to determine the utility of dual‐energy X‐ray absorptiometry (DXA)‐derived fat mass indices for predicting blood lipid profile in postmenopausal women. A secondary purpose was to determine whether waist circumference is comparable with DXA‐derived measurements in predicting blood lipid profile. Subjects were 423 postmenopausal women (age 58.1 ± 6.3 years). Fat mass was assessed at abdomen, trunk, and total body using DXA. Anthropometric measurements included BMI and waist circumference. Blood samples were analyzed for total cholesterol (TC), triglyceride (TAG), high‐density lipoprotein (HDL), low‐density lipoprotein (LDL), and cholesterol/HDL ratio. Of the DXA‐derived measures, abdominal‐fat mass was the best predictor of blood lipid profiles. DXA‐derived abdominal fat mass and waist girth explained 20 and 16.5% of variation in TC/HDL ratio, respectively, in univariate analysis, with no difference between the slopes of the regression coefficients. Eighty‐four percent of subjects were common to the top quartiles of waist circumference and abdominal fat mass, and blood lipid profiles generally worsened across increasing quartiles. DXA‐derived abdominal fat mass and waist circumference are of equivalent utility for predicting alterations in blood lipids. Waist circumference is, therefore, ideal as an inexpensive means in primary health‐care services for predicting risk of cardiovascular diseases in postmenopausal women.     

Impact of Obesity and Body Fat Distribution on Cardiovascular Risk Factors in Hong Kong Chinese

Objective: Body fat distribution has been reported to differentially contribute to the development of cardiovascular risk. We report the relative associations between general and central obesity and risk factors in 2893 Chinese subjects recruited from the Hong Kong population. Research Methods and Procedures: Anthropometric parameters [waist circumference (WC) and BMI], surrogate measures of insulin resistance (fasting plasma glucose and insulin, oral glucose tolerance test, 2 hours glucose and insulin), fasting lipids (total, low‐density lipoprotein‐cholesterol, high‐density lipoprotein‐cholesterol, and triglycerides) and systolic and diastolic blood pressure were measured. General obesity was classified as BMI ≥25.0 kg/m² and central obesity as a WC ≥80 or ≥90 cm in women and men, respectively. Results: A total of 39.2% of the population was found to be obese. Obesity per se increased the levels of the risk factors, but central adiposity contributed to a greater extent to adverse high‐density lipoprotein‐cholesterol, triglyceride, and insulin resistance levels. There was a continuous relationship between increasing obesity, both general and central, and cardiovascular risk, with lowest risk associated with the lowest indices of obesity. In the 1759 nonobese subjects divided into quartiles of BMI or WC, the levels of the cardiovascular risk factors still significantly increased with increasing quartiles of adiposity. Discussion: Central adiposity appears to contribute to a greater extent than general adiposity to the development of cardiovascular risk in this population. The relationship between obesity parameters and risk is a continuum, with risk factors significantly increasing even at levels usually considered nonobese. These observations support the proposed redefinition of overweight and obesity in Asian populations using lower cut‐off points.     

Identifying Metabolically Healthy but Obese Individuals in Sedentary Postmenopausal Women

The purpose of this study was to compare different methods to identify metabolically healthy but obese (MHO) individuals in a cohort of obese postmenopausal women. We examined the anthropometric and metabolic characteristics of 113 obese (age: 57.3 ± 4.8 years; BMI: 34.2 ± 2.7 kg/m²), sedentary postmenopausal women. The following methods were used to identify MHO subjects: the hyperinsulinemic–euglycemic clamp (MHO: upper quartile of glucose disposal rates); the Matsuda index (MHO: upper quartile of the Matsuda index); the homeostasis model assessment (HOMA) index (MHO: lower quartile of the HOMA index); having 0–1 cardiometabolic abnormalities (systolic/diastolic blood pressure ≥130/85 mm Hg, triglycerides (TG) ≥1.7 mmol/l, glucose ≥5.6 mmol/l, HOMA >5.13, high‐sensitive C‐reactive protein (hsCRP) >0.1 mg/l, high‐density lipoprotein‐cholesterol (HDL‐C) <1.3 mmol/l); and meeting four out of five metabolic factors (HOMA ≤2.7, TG ≤1.7 mmol/l, HDL‐C ≥1.3 mmol/l, low‐density lipoprotein‐cholesterol ≤2.6 mmol/l, hsCRP ≤3.0 mg/l). Thereafter, we measured insulin sensitivity, body composition (dual‐energy X‐ray absorptiometry), body fat distribution (computed tomography scan), energy expenditure, plasma lipids, inflammation markers, resting blood pressure, and cardiorespiratory fitness. We found significant differences in body composition (i.e., peripheral fat mass, central lean body mass (LBM)) and metabolic risk factors (i.e., HDL‐C, hsCRP) between MHO and at risk individuals using the different methods to identify both groups. In addition, significant differences between MHO subjects using the different methods to identify MHO individuals were observed such as age, TG/HDL, hsCRP, and fasting insulin. However, independently of the methods used, we noted some recurrent characteristics that identify MHO subjects such as TG, apolipoprotein B, and ferritin. In conclusion, the present study shows variations in body composition and metabolic profile based on the methods studied to define the MHO phenotype. Therefore, an expert consensus may be needed to standardize the identification of MHO individuals.     

The relationship of body fat to metabolic disease: Influence of sex and ethnicity

Clinical investigations designed to determine risk profiles for the development of cardiovascular disease (CVD) and type 2 diabetes mellitus (DM) are usually performed in homogenous populations and often focus on body mass index (BMI), waist circumference (WC), and fasting triglyceride (TG) levels. However, there are major ethnic differences in the relationship of these risk factors to outcomes. For example, the BMI risk threshold may be higher in blacks than in whites and higher in women than in men. Furthermore, a WC that predicts an obese BMI in white women only predicts a BMI in the overweight category in black women. In addition, overweight black men have a greater risk of developing type 2 DM than do overweight black women. Although TG levels are excellent predictors of insulin resistance in whites, they are not effective markers of insulin resistance in blacks. Among the criteria sets currently available to predict the development of CVD and type 2 DM, the most well known is the metabolic syndrome. The metabolic syndrome has 5 criteria: central obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) levels, fasting hyperglycemia, and hypertension. To make the diagnosis of the metabolic syndrome, 3 of the 5 factors must be present. For central obesity and low HDL, the metabolic syndrome guidelines are sex specific. Diagnostic guidelines should also take ethnic differences into account, particularly in the diagnosis of central obesity and hypertriglyceridemia.     

Traditional Anthropometric Parameters Still Predict Metabolic Disorders in Women With Severe Obesity

It is well established that fat distribution rather than the total quantity of fat is the major determinant of cardiovascular risk in overweight subjects. However, it is not known whether the concept of fat distribution still makes sense in severely obese subjects. Particularly, the role of visceral fat accumulation and/or of adipocyte hypertrophy in insulin resistance (IR) has not been studied in this population. Therefore, the aim of this study was to clarify the determinants of metabolic disorders in severely obese women. We performed a cross‐sectional study in 237 severely obese women (BMI >35 kg/m²). We assessed total body fat mass and fat distribution by anthropometric measurements (BMI and waist‐to‐hip ratio (WHR)) and by dual‐energy X‐ray absorptiometry (DXA). In 22 women, we measured subcutaneous and visceral adipocyte size on surgical biopsies. Mean BMI was 44 ± 7 kg/m² (range 35–77), mean age 37 ± 11 years (range 18–61). Lipid parameters (triglycerides, high‐density lipoprotein cholesterol) and IR markers (fasting insulin and homeostasis model assessment (HOMA) index) correlated with fat distribution, whereas inflammatory parameters (C‐reactive protein, fibrinogen) correlated only with total fat mass. An association was observed between android fat distribution and adipocyte hypertrophy. Visceral adipocyte hypertrophy was associated with both IR and hypertension, whereas subcutaneous fat‐cell size was linked only to hypertension. Our results obtained in a large cohort of women showed that fat distribution still predicts metabolic abnormalities in severe obesity. Furthermore, we found a cluster of associations among fat distribution, metabolic syndrome (MS), and adipocyte hypertrophy.     

Assessment of Abdominal Fat Compartments Using DXA in Premenopausal Women From Anorexia Nervosa to Morbid Obesity

Objective: To test a newly developed dual energy X‐ray absorptiometry (DXA) method for abdominal fat depot quantification in subjects with anorexia nervosa (AN), normal weight, and obesity using CT as a gold standard. Design and Methods: 135 premenopausal women (overweight/obese: n = 89, normal‐weight: n = 27, AN: n = 19); abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and total adipose tissue (TAT) areas determined on CT and DXA. Results: There were strong correlations between DXA and CT measurements of abdominal fat compartments in all groups with the strongest correlation coefficients in the normal‐weight and overweight/obese groups. Correlations of DXA and CT VAT measurements were strongest in the obese group and weakest in the AN group. DXA abdominal fat depots were higher in all groups compared to CT, with the largest % mean difference in the AN group and smallest in the obese group. Conclusion: A new DXA technique is able to assess abdominal fat compartments including VAT in premenopausal women across a large weight spectrum. However, DXA measurements of abdominal fat were higher than CT, and this percent bias was most pronounced in the AN subjects and decreased with increasing weight, suggesting that this technique may be more useful in obese individuals.     

Sex Differences in the Association of Thigh Fat and Metabolic Risk in Older Adults

We have previously shown a favorable association of subcutaneous leg fat with markers of insulin resistance and dyslipidemia in postmenopausal women. It is not known whether there is a sex dimorphism in the association of lower‐body adiposity with reduced metabolic risk. Thus, our primary aim was to determine whether the favorable association of thigh subcutaneous fat, independent of abdominal fat, is also observed in older men. Mid‐thigh and abdominal fat areas were measured by computed tomography (CT) in 108 older men and postmenopausal women (mean ± s.d.; 69 ± 7 years). Additionally, trunk and leg fat mass (FM) were measured by dual‐energy X‐ray absorptiometry (DXA). Markers of insulin resistance and dyslipidemia were determined from oral glucose tolerance tests and lipid and lipoprotein measurements, respectively. Outcomes were fasted and postchallenge (area under the curve, AUC) insulin (INS_AUC) and glucose (GLU_AUC), product of the insulin and glucose AUC (INS_AUC × GLU_AUC), triglycerides (TG), and high‐density lipoprotein (HDL)‐cholesterol. Consistent with our previous findings in postmenopausal women, adjusting for DXA trunk FM revealed a favorable association of DXA leg FM with the metabolic risk outcomes in both older men and postmenopausal women. Likewise, adjusting for CT abdominal visceral fat generally revealed a favorable association of CT thigh fat with metabolic risk outcomes in women, but not men. The discordance between the DXA and CT results in men is unclear but may be due to sex differences in visceral fat accrual. The mechanisms underlying the protective effect of thigh fat on metabolic risk factors need to be elucidated.     

A pathophysiological approach to metabolic syndrome using factor analysis in an adult Romanian population

The aim of the study was to examine the role of insulin resistance in etiopathogenesis of metabolic syndrome in an adult Romanian population using exploratory factor analysis. We analyzed 228 non-diabetic subjects randomized in respect to the age and sex distribution of the general population. For each patient, age, sex, body mass index (BMI), systolic and diastolic blood pressure (SBP, DBP), HDL-cholesterol (HDL), plasma triglycerides (TG), fasting plasma glucose (FPG) and fasting insulin were obtained. Factor analysis was performed using principal component analysis, with Varimax rotation of the major determinants of metabolic syndrome. Mean age was 48.9 +/- 12.7 years; 107 (46.9%) were men and 121 (53.1%) women. We found three major factors, which are correlated with metabolic syndrome and may explain its variance. Factor 1 comprises SBP and DBP in men and SBP, DBP and BMI in women. Factor 2 comprises BMI, HDL, TG and FPG in men and BMI, TG and FPG in women. Factor 3 comprises fasting insulin in men and fasting insulin, TG and HDL in women. The finding of more than one factor suggests that insulin resistance is not the only pathophysiological mechanism involved. These factors appear to work independently of each other in men, but they intersect in women, suggesting that the pathophysiology of metabolic syndrome may be different in women compared with men.     

Lipid Profile in the Severely Obese: Changes with Weight Loss after Lap‐Band Surgery

Objective: To characterize dyslipidemia before and after weight loss in the severely obese. Research Methods and Procedures: Five hundred fifteen subjects who had Lap‐Band surgery were followed with yearly conventional lipid profiles for up to 4 years. Preoperative data were collected from the most recent 381 subjects, and predictors of dyslipidemia were sought. One hundred seventy‐one subjects completed a 1‐year review, providing data to assess predictors of change in lipids. Results: Favorable changes in fasting triglycerides (TG), high‐density lipoprotein‐cholesterol (HDL‐C), and total cholesterol (TC):HDL‐C ratio occurred within 1 year. All improvements were maintained up to 4 years. Male gender, central obesity, elevated fasting glucose, and insulin resistance were associated with less favorable lipid levels. Fasting plasma glucose best predicted TG (r = 0.46, p < 0.001), whereas insulin sensitivity using the homeostatic model assessment (HOMA %S) correlated best with the HDL‐C (r =0.34, p < 0.001). Higher preoperative fasting glucose best predicted the decrease in TG; improved HOMA %S with weight loss correlated best with HDL‐C. The extent of weight loss had limited influence on lipid changes. However, low preoperative HOMA %S was associated with lower weight loss. Greater weight loss was associated with more favorable lipid measures after controlling for preoperative HOMA %S. Discussion: Dyslipidemia of obesity is related to weight distribution, insulin sensitivity, and impaired glucose tolerance. Improvement with weight loss is related to the decrease in fasting glucose, improvement in insulin sensitivity, and the extent of weight lost. Improvement in dyslipidemia is sustained with long‐term weight loss.     

Determinants of insulin-resistant phenotypes in normal-weight and obese Black African women

Objective: Subsets of metabolically “healthy obese” and “at‐risk” normal‐weight individuals have been previously identified. The aim of this study was to explore the determinants of these phenotypes in black South African (SA) women. Methods and Procedures: From a total of 103 normal‐weight (BMI ≤ 25 kg/m²) and 122 obese (BMI ≥ 30 kg/m²) black SA women, body composition, fat distribution, blood pressure, fasting glucose levels, insulin resistance, and lipid profiles were measured. Questionnaires relating to family history, physical activity energy expenditure (PAEE), and socio‐demographic variables were administered. The subjects were classified as insulin sensitive or insulin resistant according to the homeostasis model assessment of insulin resistance (HOMA‐IR) (≥1.95 insulin resistant). Results: Our study showed that 22% of the normal‐weight women were insulin resistant and 38% of the obese women were insulin sensitive. Increased visceral adipose tissue (VAT) (P = 0.001) and decreased VAT/leg fat mass (P ≤ 0.001), independent of total body fatness, distinguished between the phenotypes. Moreover, the insulin‐sensitive women were of higher socioeconomic status, did more leisure and vigorous PAEE and were less likely to use injectable contraceptives. Using a regression model, body fat distribution, percent body fat, age, log leisure PAEE, and use of injected contraception accounted for 35% of the variance in HOMA‐IR in the normal‐weight women. In the obese women, 34% of the variance in HOMA‐IR was explained by the same variables, excluding PAEE. No differences in smoking status or family history of metabolic disease were found between the phenotypes. Discussion: Central fat distribution, total adiposity, socioeconomic status, leisure PAEE, and use of injectable contraceptives distinguished between insulin‐sensitive and insulin‐resistant black SA women.     

Association Between the 4 bp Proinsulin Gene Insertion Polymorphism (IVS‐69) and Body Composition in Black South African Women

The objective of the study was to examine the association between a functional 4 bp proinsulin gene insertion polymorphism (IVS‐69), fasting insulin concentrations, and body composition in black South African women. Body composition, body fat distribution, fasting glucose and insulin concentrations, and IVS‐69 genotype were measured in 115 normal‐weight (BMI <25 kg/m²) and 138 obese (BMI ≥30 kg/m²) premenopausal women. The frequency of the insertion allele was significantly higher in the class 2 obese (BMI ≥35kg/m²) compared with the normal‐weight group (P = 0.029). Obese subjects with the insertion allele had greater fat mass (42.3 ± 0.9 vs. 38.9 ± 0.9 kg, P = 0.034) and fat‐free soft tissue mass (47.4 ± 0.6 vs. 45.1 ± 0.6 kg, P = 0.014), and more abdominal subcutaneous adipose tissue (SAT, 595 ± 17 vs. 531 ± 17 cm², P = 0.025) but not visceral fat (P = 0.739), than obese homozygotes for the wild‐type allele. Only SAT was greater in normal‐weight subjects with the insertion allele (P = 0.048). There were no differences in fasting insulin or glucose levels between subjects with the insertion allele or homozygotes for the wild‐type allele in the normal‐weight or obese groups. In conclusion, the 4 bp proinsulin gene insertion allele is associated with extreme obesity, reflected by greater fat‐free soft tissue mass and fat mass, particularly SAT, in obese black South African women.     

Total and Regional Fat and Serum Cardiovascular Disease Risk Factors in Lean and Obese Children and Adolescents

Objective: This study was conducted to evaluate the association of total and central adiposity with serum cardiovascular disease (CVD) risk factors in lean and obese Portuguese children and adolescents. Research Methods and Procedures: A total of 87 girls (13.2 ± 1.6 years old, 29.9 ± 6.4% body fat [mean ± SD]) and 72 boys (13.2 ± 1.6 years old, 20.8 ± 9.9% body fat) volunteered for the study. Whole‐body composition and fat distribution, from DXA and anthropometry, and serum lipids, lipoproteins, and apolipoproteins were evaluated. Results: The sum of three trunk skinfolds (STS) was highly correlated with total trunk fat mass measured by DXA (p < 0.001). Body mass index, DXA‐measured percentage of body fat, trunk fat mass, STS, and the waist‐to‐height ratio were generally found to be associated with triacylglycerol, the ratio of total cholesterol (TC) to high density lipoprotein‐cholesterol (HDL‐C), low density lipoprotein‐cholesterol (LDL‐C), and apolipoprotein B levels, (significant age‐adjusted r between 0.16 and 0.27, p < 0.05). Body mass index, STS, and the waist circumference were also associated with HDL‐C (p < 0.05), whereas no body composition variable significantly correlated with TC or apolipoproteins A‐I. The STS was significantly correlated with HDL‐C (p < 0.01), TC/HDL‐C (p < 0.05), and apolipoproteins A‐I (p < 0.05) independently of whole‐body fatness. Obese subjects (n = 73) had higher TC, LDL‐C, TC/HDL‐C, and apolipoprotein B than did non‐obese subjects (n = 86), and significant associations between central adiposity and some lipid variables (triacylglycerol and HDL‐C) were found in obese children and adolescents that were not present in leaner individuals. Discussion: DXA‐ and anthropometry‐based whole‐body and central fat measures are associated with serum CVD risk factors in Portuguese boys and girls. Obese children and adolescents have a poorer lipid profile than do their leaner counterparts. Trunk skinfolds, which are easy to obtain even in large samples, predict CVD risk factors to the same extent as DXA‐based variables, in some cases, independently of total fatness.     

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.