Associations of leukocyte telomere length with body anthropometric indices and weight change in chinese women

Objective: This study evaluated associations of telomere length with various anthropometric indices of general and abdominal obesity, as well as weight change. Design and Methods: The study included 2,912 Chinese women aged 40‐70 years. Monochrome multiplex quantitative polymerase chain reaction was applied to measure relative telomere length. Results: Telomere length was inversely associated with body mass index (BMI), waist circumference, waist‐to‐height ratio, weight, and hip circumference (P_trend = 0.005, 0.004, 0.004, 0.010, and 0.026, respectively), but not waist‐to‐hip ratio (P_trend = 0.116) or height (P_trend = 0.675). Weight change since age 50 was further evaluated among women over age 55. Women who maintained their weight within ±5% since age 50, particularly within a normal range (BMI = 18.5‐24.9 kg/m²), or reduced their weight from overweight (BMI = 25‐29.9 kg/m²) or obesity (BMI ≥30 kg/m²) to normal range, had a longer mean of current telomere length than women who gained weight since age 50 (P_trend = 0.025), particularly those who stayed in obesity or gained weight from normal range or overweight to obesity (P = 0.023). Conclusion: Our findings show that telomere shortening is associated with obesity and that maintaining body weight within a normal range helps maintain telomere length.     

Obesity‐susceptibility loci and the tails of the pediatric BMI distribution

Objective: To determine whether previously identified adult obesity susceptibility loci were associated uniformly with childhood BMI across the BMI distribution. Design and Methods: Children were recruited through the Children's Hospital of Philadelphia (n = 7,225). Associations between the following loci and BMI were assessed using quantile regression: FTO (rs3751812), MC4R (rs12970134), TMEM18 (rs2867125), BDNF (rs6265), TNNI3K (rs1514175), NRXN3 (rs10146997), SEC16B (rs10913469), and GNPDA2 (rs13130484). BMI z‐score (age and gender adjusted) was modeled as the dependent variable, and genotype risk score (sum of risk alleles carried at the 8 loci) was modeled as the independent variable. Results: Each additional increase in genotype risk score was associated with an increase in BMI z‐score at the 5th, 15th, 25th, 50th, 75th, 85th, and 95th BMI z‐score percentiles by 0.04 (±0.02, P = 0.08), 0.07 (±0.01, P = 9.58 × 10^?7), 0.07 (±0.01, P = 1.10 × 10^?8), 0.09 (±0.01, P = 3.13 × 10^?22), 0.11 (±0.01, P = 1.35 × 10^?25), 0.11 (±0.01, P = 1.98 × 10^?20), and 0.06 (±0.01, P = 2.44 × 10^?6), respectively. Each additional increase in genotype risk score was associated with an increase in mean BMI z‐score by 0.08 (±0.01, P = 4.27 × 10^?20). Conclusion: Obesity risk alleles were more strongly associated with increases in BMI z‐score at the upper tail compared to the lower tail of the distribution.     

Central obesity as a risk factor for prostatic hyperplasia

Objective: Obesity‐related metabolic diseases may influence prostatic hyperplasia. This study examined the impact of obesity on prostate volume in men without overt obesity‐related metabolic diseases. Research Methods and Procedures: We recruited 146 men over the age of 40 years who did not have overt obesity‐related diseases, such as diabetes, impaired fasting glucose, hypertension, or dyslipidemia. Transrectal ultrasonography was performed on all subjects. The subjects were divided into three groups according to their BMI: normal (18.5 to 22.9 kg/m²), overweight (23 to 24.9 kg/m²), and obese (≥25 kg/m²), and two groups according to their waist circumference: normal waist (≤90 cm) and central obesity (>90 cm). The classification of the subgroups was based on the Asia‐Pacific criteria of obesity. We compared the prostate volume among subgroups and assessed factors related to prostatic hyperplasia. Results: Mean prostate volume was 18.8 ± 5.0, 21.8 ± 7.2, and 21.8 ± 5.6 mL in the normal, overweight, and obese groups, respectively, and was 20.0 ± 5.9 and 23.7 ± 5.3 mL in the normal waist and central obesity group, respectively. Prostate volume was significantly greater in the obese group than in the normal group (P = 0.03) and in the central obesity group compared with the normal waist group (P = 0.002). Prostate volume was positively correlated with BMI and waist circumference after adjustment for age. After adjusting for confounding factors, central obesity was an independent factor affecting prostatic hyperplasia, which was defined as a prostate volume >20 mL (odds ratio = 3.37, p = 0.037). Relative to men with both low BMI (18.5 to 22.9 kg/m²) and normal waist circumference, those with high BMI (≥25 kg/m²) and central obesity were at significantly increased risk of prostatic hyperplasia (odds ratio = 4.88, p = 0.008). However, those with high BMI (≥25 kg/m²) and normal waist circumference were not at significantly increased risk. Discussion: Prostate volume was greater in the obese and central obesity groups than in the normal group after patients with overt obesity‐related metabolic diseases were excluded. Although both BMI and waist circumference were positively correlated with prostate volume, central obesity was the only independent factor affecting prostate hyperplasia. We suggest that central obesity is an important risk factor for prostatic hyperplasia.     

Disentangling basal and accumulated body mass for cross‐population comparisons

Measures of human body mass confound 1) well‐established population differences in body form and 2) exposure to obesogenic environments, posing challenges for using body mass index (BMI) in cross‐population studies of body form, energy reserves, and obesity‐linked disease risk. We propose a method for decomposing population BMI by estimating basal BMI (bBMI) among young adults living in extremely poor, rural households where excess body mass accumulation is uncommon. We test this method with nationally representative, cross‐sectional Demographic and Health Surveys (DHS) collected from 69,916 rural women (20–24 years) in 47 low‐income countries. Predicting BMI by household wealth, we estimate country‐level bBMI as the average BMI of young women (20–24 years) living in rural households with total assets <400 USD per capita. Above 400 USD per capita, BMI increases with both wealth and age. Below this point, BMI hits a baseline floor showing little effect of either age or wealth. Between‐country variation in bBMI (range of 4.3 kg m^?2) is reliable across decades and age groups (R² = 0.83–0.88). Country‐level estimates of bBMI show no relation to diabetes prevalence or country‐level GDP (R² < 0.05), supporting its independence from excess body mass. Residual BMI (average BMI minus bBMI) shows better fit with both country‐level GDP (R² = 0.55 vs. 0.40) and diabetes prevalence (R² = 0.23 vs. 0.17) than does conventional BMI. This method produces reliable estimates of bBMI across a wide range of nationally representative samples, providing a new approach to investigating population variation in body mass. Am J Phys Anthropol 153:542–550, 2014. © 2013 Wiley Periodicals, Inc.     

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.     

Low 25‐Hydroxyvitamin D Does Not Affect Insulin Sensitivity in Obesity after Bariatric Surgery

Objective: A positive correlation between levels of 25‐hydroxyvitamin D [25(OH)D] and insulin sensitivity has been shown in healthy subjects. We aimed to test the hypothesis that concentration of 25(OH)D influences insulin sensitivity in obesity before and after weight loss. Research Methods and Procedures: We investigated the relation between serum 25(OH)D and insulin sensitivity (estimated by euglycemic‐hyperinsulinemic clamp) in 116 obese women (BMI ≥ 40 kg/m²) evaluated before and 5 and 10 years after biliopancreatic diversion (BPD). Body composition was estimated by the isotope dilution method. Results: Prevalence of hypovitaminosis D was 76% in the obese status and 91% and 89% at 5 and 10 years after BPD, respectively, despite ergocalciferol supplementation. 25(OH)D concentration decreased from 39.2 ± 22.3 in obesity (p = 0.0001) to 27.4 ± 16.4 and 25.1 ± 13.9 nM 5 and 10 years after BPD, respectively. Whole‐body glucose uptake increased from 24.27 ± 4.44 at the baseline to 57.29 ± 11.56 and 57.71 ± 8.41 μmol/kg_{fat free mass} per minute 5 and 10 years after BPD, respectively (p = 0.0001). Predictor of 25(OH)D was fat mass (R² = 0.26, p = 0.0001 in obesity; R² = 0.20, p = 0.02 after BPD). Parathormone correlated with fat mass (R² = 0.19; p = 0.0001) and BMI (R² = 0.053; p = 0.01) and inversely with M value (R² = 0.16; p = 0.0001), but only in obese subjects. Discussion: A high prevalence of hypovitaminosis D was observed in morbid obesity both before and after BPD. Low 25(OH)D did not necessarily imply increased insulin resistance after BPD, a condition where, probably, more powerful determinants of insulin sensitivity overcome the low circulating 25(OH)D levels. However, the present data cannot exclude some kind of influence of vitamin D status on glucose and insulin metabolism.     

Severe Obesity Is Associated With Impaired Arterial Smooth Muscle Function in Young Adults

The degree of arterial dilatation induced by exogenous nitrates (nitrate‐mediated dilatation, NMD) has been similar in obese and normal‐weight adults after single high‐dose glyceryl trinitrate (GTN). We examined whether NMD is impaired in obesity by performing a GTN dose‐response study, as this is a potentially more sensitive measure of arterial smooth muscle function. In this cross‐sectional study, subjects were 19 obese (age 31.0 ± 1.2 years, 10 male, BMI 44.1 ± 2.1) and 19 age‐ and sex‐matched normal‐weight (BMI 22.4 ± 0.4) young adults. Blood pressure (BP), triglycerides, high‐density lipoprotein (HDL), and low‐density lipoprotein (LDL)‐cholesterol, glucose, insulin, high‐sensitivity C‐reactive protein (hs‐CRP), carotid intima‐media thickness (CIMT), and flow‐mediated dilatation (FMD) were measured. After incremental doses of GTN, brachial artery maximal percent dilatation (maximal NMD) and the area under the dose‐response curve (NMD AUC) were calculated. Maximal NMD (13.4 ± 0.9% vs. 18.3 ± 1.1%, P = 0.002) and NMD AUC (54,316 ± 362 vs. 55,613 ± 375, P = 0.018) were lower in obese subjects. The obese had significantly higher hs‐CRP, insulin, and CIMT and lower HDL‐cholesterol. Significant bivariate associations existed between maximal NMD or NMD AUC and BMI‐group (r = ?0.492, P = 0.001 or r = ?0.383, P = 0.009), hs‐CRP (r = ?0.419, P = 0.004 or r = ?0.351, P = 0.015), and HDL‐cholesterol (r = 0.374, P = 0.01 or r = 0.270, P = 0.05). On multivariate analysis, higher BMI‐group remained as the only significant determinant of maximal NMD (r² = 0.242, β = ?0.492, P = 0.002) and NMD AUC (r² = 0.147, β = ?0.383, P = 0.023). In conclusion, arterial smooth muscle function is significantly impaired in the obese. This may be important in their increased cardiovascular risk.     

Increased Whole‐Body Adiposity Without a Concomitant Increase in Liver Fat is Not Associated With Augmented Metabolic Dysfunction

Aim of this study was to determine whether an increase in adiposity, without a concomitant increase in intrahepatic triglyceride (IHTG) content, is associated with a deterioration in metabolic function. To this end, multiorgan insulin sensitivity, assessed by using a two‐stage hyperinsulinemic–euglycemic clamp procedure in conjunction with stable isotopically labeled tracer infusion, and very low‐density lipoprotein (VLDL) kinetics, assessed by using stable isotopically labeled tracer infusion and mathematical modeling, were determined in 10 subjects with class I obesity (BMI: 31.6 ± 0.3 kg/m²; 37 ± 2% body fat; visceral adipose tissue (VAT): 1,225 ± 144 cm³) and 10 subjects with class III obesity (BMI: 41.5 ± 0.5 kg/m²; 43 ± 2% body fat; VAT: 2,121 ± 378 cm³), matched on age, sex, and IHTG content (14 ± 4 and 14 ± 3%, respectively). No differences between class I and class III obese groups were detected in insulin‐mediated suppression of palmitate (67 ± 3 and 65 ± 3%, respectively; P = 0.635) and glucose (67 ± 3 and 73 ± 5%, respectively; P = 0.348) rates of appearance in plasma, and the insulin‐mediated increase in glucose disposal (218 ± 18 and 193 ± 30%, respectively; P = 0.489). In addition, no differences between class I and class III obese groups were detected in secretion rates of VLDL‐triglyceride (6.5 ± 1.0 and 6.0 ± 1.4 µmol/l·min, respectively; P = 0.787) and VLDL‐apolipoprotein B‐100 (0.40 ± 0.05 and 0.41 ± 0.04 nmol/l·min, respectively; P = 0.866), and plasma clearance rates of VLDL‐triglyceride (31 (16–59) and 29 (18–46) ml/min, respectively; P = 0.888) and VLDL‐apolipoprotein B‐100 (15 (11–19) and 17 (11–25) ml/min, respectively; P = 0.608). We conclude that increased adiposity without a concomitant increase in IHTG content does not cause additional abnormalities in adipose tissue, skeletal muscle, and hepatic insulin sensitivity, or VLDL metabolism.     

Genetic Variability of Adult Body Mass Index: A Longitudinal Assessment in Framingham Families

Objective: To explore the contribution of genetics to the mean, SD, maximum value, maximum less the mean, and change over time in body mass index (BMI) and the residual of body weight after adjustment for height. BMI is frequently used as a general indicator of obesity because of its ease and reliability in ascertainment. Cross‐sectional twin and family studies have shown a moderate‐to‐substantial genetic component for BMI. However, the contribution of genetics to the long‐term average, variability, or change over time in BMI is less clear. Research Methods and Procedures: Longitudinal data from the Framingham heart study were used to create pedigrees of age‐matched individuals. Heritability estimates were derived using variance‐decomposition methods on a total of 1051 individuals from 380 extended pedigrees followed for a period of 20 years. All subjects were followed from approximately age 35 to 55 years. Results: Moderate heritability estimates were found for the mean BMI (h² = 0.37), maximum BMI (h² = 0.40), and the mean residual of body weight (h² = 0.36). Low heritability estimates (h² ? 0.20) were found for the maximum less the mean in BMI and the SDs of BMI and residual of body weight. No additive genetic contribution was found for the average change over time in BMI or the residual of body weight. Discussion: These findings suggest that there is a significant genetic component for the magnitude of BMI throughout an individual's middle‐adult years; however, little evidence was found for a genetic contribution to the variability or rate of change in an individual's BMI.     

Solvent‐dependent conformation of amylose tris(phenylcarbamate) as deduced from scattering and viscosity data

The z‐average mean‐square radius of gyration 〈S²〉_z, the particle scattering function P(k), the second virial coefficient, and the intrinsic viscosity [η] have been determined for amylose tris(phenylcarbamate) (ATPC) in methyl acetate (MEA) at 25°C, in ethyl acetate (EA) at 33°C, and in 4‐methyl‐2‐pentanone (MIBK) at 25°C by light and small‐angle X‐ray scattering and viscometry as functions of the weight‐average molecular weight in a range from 2 × 10⁴ to 3 × 10⁶. The first two solvents attain the theta state, whereas the last one is a good solvent for the amylose derivative. Analysis of the 〈S²〉_z, P(k), and [η] data based on the wormlike chain yields h (the contour length or helix pitch per repeating unit) = 0.37 ± 0.02 and λ^?1 (the Kuhn segment length) = 15 ± 2 nm in MEA, h = 0.39 ± 0.02 and λ^?1 = 17 ± 2 nm in EA, and h = 0.42 ± 0.02 nm and λ^?1 = 24 ± 2 nm in MIBK. These h values, comparable with the helix pitches (0.37–0.40 nm) per residue of amylose triesters in the crystalline state, are somewhat larger than the previously determined h of 0.33 ± 0.02 nm for ATPC in 1,4‐dioxane and 2‐ethoxyethanol, in which intramolecular hydrogen bonds are formed between the C?O and NH groups of the neighbor repeating units. The slightly extended helices of ATPC in the ketone and ester solvents are most likely due to the replacement of those hydrogen bonds by intermolecular hydrogen bonds between the NH groups of the polymer and the carbonyl groups of the solvent. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 729–736, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

Pleiotropic Effects of Genes for Insulin Resistance on Adiposity in Baboons

Objective: Previous research has suggested a genetic contribution to the development of insulin resistance and obesity. We hypothesized that the same genes influencing insulin resistance might also contribute to the variation in adiposity. Research Methods and Procedures: A total of 601 (200 male, 401 female) adult baboons (Papio hamadryas) from nine families with pedigrees ranging in size from 43 to 121 were used in this study. Plasma insulin, glucose, C‐peptide, and adiponectin were analyzed, and homeostasis model assessment of insulin resistance (HOMA IR) was calculated. Fat biopsies were collected from omental fat tissue, and triglyceride concentration per gram of fat tissue was determined. Body weight and length were measured, and BMI was derived. Univariate and bivariate quantitative genetic analyses were performed using SOLAR. Results: Insulin, glucose, C‐peptide, and adiponectin levels, HOMA IR, triglyceride concentration of fat tissue, body weight, and BMI were all found to be significantly heritable, with heritabilities ranging from 0.15 to 0.80. Positive genetic correlations (r_Gs) were observed for HOMA IR with C‐peptide (r_G = 0.88 ± 0.10, p = 0.01), triglyceride concentration in fat tissue (r_G = 0.86 ± 0.33, p = 0.02), weight (r_G = 0.50 ± 0.20, p = 0.03), and BMI (r_G = 0.64 ± 0.22, p = 0.02). Discussion: These results suggest that a set of genes contributing to insulin resistance also influence general and central adiposity phenotypes. Further genetic research in a larger sample size is needed to identify the common genes that constitute the genetic basis for the development of insulin resistance and obesity.     

Differential Effects of Abdominal Adipose Tissue Distribution on Insulin Sensitivity in Black and White South African Women

Black South African women are more insulin resistant than BMI‐matched white women. The objective of the study was to characterize the determinants of insulin sensitivity in black and white South African women matched for BMI. A total of 57 normal‐weight (BMI 18–25 kg/m²) and obese (BMI > 30 kg/m²) black and white premenopausal South African women underwent the following measurements: body composition (dual‐energy X‐ray absorptiometry), body fat distribution (computerized tomography (CT)), insulin sensitivity (S_I, frequently sampled intravenous glucose tolerance test), dietary intake (food frequency questionnaire), physical activity (Global Physical Activity Questionnaire), and socioeconomic status (SES, demographic questionnaire). Black women were less insulin sensitive (4.4 ± 0.8 vs. 9.5 ± 0.8 and 3.0 ± 0.8 vs. 6.0 ± 0.8 × 10^?5/min/(pmol/l), for normal‐weight and obese women, respectively, P < 0.001), but had less visceral adipose tissue (VAT) (P = 0.051), more abdominal superficial subcutaneous adipose tissue (SAT) (P = 0.003), lower SES (P < 0.001), and higher dietary fat intake (P = 0.001) than white women matched for BMI. S_I correlated with deep and superficial SAT in both black (R = ?0.594, P = 0.002 and R = 0.495, P = 0.012) and white women (R = ?0.554, P = 0.005 and R = ?0.546, P = 0.004), but with VAT in white women only (R = ?0.534, P = 0.005). In conclusion, body fat distribution is differentially associated with insulin sensitivity in black and white women. Therefore, the different abdominal fat depots may have varying metabolic consequences in women of different ethnic origins.     

Genome-wide scan of plasma cholecystokinin in baboons shows linkage to human chromosome 17

Objective: Cholecystokinin (CCK) is known to inhibit food intake and is an important signal for controlling meal volume, indicating a possible role in weight regulation. Our objective was to investigate genetic influences on plasma CCK in baboons. Research Methods and Procedures: Subjects were 376 baboons (males = 113, females = 263) from the Southwest National Primate Research Center, housed at the Southwest Foundation for Biomedical Research, San Antonio, Texas. Anthropometric and biochemical parameters were analyzed. Genetic effects on plasma CCK were estimated by the maximum likelihood‐based variance components method implemented in the software program SOLAR (Sequential Oligogenic Linkage Analysis Routines). Results: Male baboons (32.7 ± 6 kg) were much heavier than females (20.2 ± 4 kg). Similarly, mean (± standard deviation) plasma CCK values were also higher in male baboons (13.8 ± 6 pM) than female baboons (12.5 ± 4 pM). Significant heritabilities were observed for plasma CCK (0.14 ± 0.1, p < 0.05), body weight (h² = 0.62 ± 0.15, p < 10^?8), and glucose (h² = 0.68 ± 0.17, p < 10^?7). A genome‐wide scan of plasma CCK detected a strong signal for a quantitative trait locus (QTL) on chromosome 17p12–13 [logarithm of the odds (LOD) = 3.1] near marker D17S804. Suggestive evidence of a second QTL was observed on chromosome 4q34–35 (LOD = 2.3) near marker D4S2374. Discussion: A substantial contribution of additive genetic effects to the variation in plasma levels of CCK was demonstrated in baboons. The identification of a QTL for plasma CCK on chromosome 17p is significant, as several obesity‐related traits such as BMI, leptin, adiponectin, and acylation stimulating protein have already been mapped to this region.     

Impact of Information About Obesity Genomics on the Stigmatization of Overweight Individuals: An Experimental Study

Advances in genomic technologies are rapidly leading to new understandings of the roles that genetic variations play in obesity. Increasing public dissemination of information regarding the role of genetics in obesity could have beneficial, harmful, or neutral effects on the stigmatization of obese individuals. This study used an online survey and experimental design to examine the impact of genetic versus non‐genetic information on obesity stigma among self‐perceived non‐overweight individuals. Participants (n = 396) were randomly assigned to read either genetic, non‐genetic (environment), or gene—environment interaction obesity causal information. A total of 48% of participants were female; mean age was 42.7 years (range = 18–86 years); 75% were white; 45.2% had an annual household income of less than $40,000; mean BMI was 23.4 kg/m². Obesity stigma was measured using the Fat Phobia Scale — short form (FPS‐S). After reading the experimental information, participants in the genetic and gene—environment conditions were more likely to believe that genetics increase obesity risk than participants in the non‐genetic condition (both P < 0.05), but did not differ on obesity stigma. Obesity stigma was higher among whites and Asians than Hispanics and African Americans (P = 0.029), and associated with low self‐esteem (P = 0.036). Obesity stigma was also negatively associated with holding 'germ or virus' (P = 0.033) and 'overwork' (P = 0.016) causal beliefs about obesity, and positively associated with 'diet or eating habits' (P = 0.001) and 'lack of exercise' (P = 0.004) causal beliefs. Dissemination of brief information about the role of genetics in obesity may have neither a beneficial nor a harmful impact on obesity stigmatization compared with non‐genetic information among self‐perceived non‐overweight individuals.     

Liver attenuation, pericardial adipose tissue, obesity, and insulin resistance: the Multi-Ethnic Study of Atherosclerosis (MESA)

Insulin resistance is linked to general and abdominal obesity, but its relation to hepatic lipid content and pericardial adipose tissue is less clear. The purpose of this study was to examine cross‐sectional associations of liver attenuation, pericardial adipose tissue, BMI, and waist circumference with insulin resistance. We measured liver attenuation and pericardial adipose tissue using the existing cardiac computed tomography scans in 5,291 individuals free of clinical cardiovascular disease and diabetes in the Multi‐Ethnic Study of Atherosclerosis (MESA) during the study's baseline visit (2000–2002). Low liver attenuation was defined as the lowest quartile and high pericardial adipose tissue as the upper quartile of volume (cm³). We used standard clinical definitions for obesity and abdominal obesity. Insulin resistance was assessed by the homeostasis model assessment of insulin resistance (HOMA_IR) index. In multivariate linear regression with all adiposity measures in the model simultaneously, all adiposity measures were significantly (P < 0.0001) associated with insulin resistance: regression coefficients (±s.e.) were 0.31 (±0.02) for low liver attenuation, 0.27 (±0.02) for high pericardial adipose tissue, 0.27 (±0.02) for obesity, and 0.32 (±0.02) for abdominal obesity. We found significant differences (P = 0.003) between standardized liver attenuation and insulin resistance by ethnicity: regression coefficients per 1 s.d. increment were 0.10 ± 0.01 for whites, 0.11 ± 0.02 for Chinese, 0.08 ± 0.2 for blacks, and 0.14 ± 0.01 for Hispanics. Liver attenuation and pericardial adipose tissue were associated with insulin resistance, independent of BMI and waist circumference.     

Relationship of Insulin Sensitivity and Left Ventricular Mass in Uncomplicated Obesity

Objective: We studied uncomplicated obesity as a model to evaluate the influence of insulin sensitivity per se on left ventricular mass (LVM) and geometry. Research Methods and Procedures: We selected 50 obese subjects (BMI > 30 kg/m²; 38 women and 12 men; mean age, 38.4 ± 10 years; BMI, 36.4 ± 10.5 kg/m²) with normal blood pressure, glucose tolerance, and plasmatic lipid levels. Thirty lean subjects formed the control group. Each subject underwent euglycemic insulin clamp (7 pmol/min per kg) to evaluate whole body glucose use (M index) and echocardiogram to calculate LVM and indexed LVM. Results: Insulin‐resistant obese subjects had higher LVM, LVM/h^2.7, LVM/body surface area, and LVM/fat‐free mass_kg (p = 0.001; p = <0.001 p = 0.001, and p = 0.04, respectively) than obese subjects with normal insulin sensitivity. Multivariate regression analysis showed that M index was the strongest independent correlate of LVM (r² = 0.34; p = 0.03). Discussion: Our findings showed that insulin resistance, in uncomplicated obesity, is associated with an increased LVM and precocious changes of left ventricular geometry, whereas preserved insulin sensitivity is not associated with increased LVM.     

Eating Frequency is Associated With Energy Intake but Not Obesity in Midlife Women

Midlife women tend to gain weight with age, thus increasing risk of chronic disease. The purpose of this study was to examine associations between overweight/obesity and behavioral factors, including eating frequency, in a cross‐sectional national sample of midlife women (n = 1,099) (mean age = 49.7 years, and BMI = 27.7 kg/m²). Eating behaviors and food and nutrient intakes were based on a mailed 1‐day food record. BMI was calculated from self‐reported height and weight, and level of physical activity was assessed by self‐reported questionnaire. After exclusion of low‐energy reporters (32% of sample), eating frequency was not associated with overweight/obesity (P > 0.05) and was not different between BMI groups (normal, 5.21 ± 1.79; overweight, 5.16 ± 1.74; obese, 5.12 ± 1.68, P = 0.769). Adjusted logistic regression showed that eating frequency, snacking frequency, breakfast consumption, eating after 10 pm and consuming meals with children or other adults were not significantly associated with overweight/obesity. Total energy intake increased as eating frequency increased in all BMI groups, however, obese women had greater energy intake compared to normal weight women who consumed the same number of meals and snacks. Intake of fruit and vegetables, whole grains, dietary fiber, dairy, and added sugars also increased as eating frequency increased. While eating frequency was not associated with overweight/obesity, it was associated with energy intake. Thus, addressing total energy intake rather than eating frequency may be more appropriate to prevent weight gain among midlife women.     

Early Detrimental Metabolic Outcomes of rs17300539‐A Allele of ADIPOQ Gene Despite Higher Adiponectinemia

Minor allele A of single‐nucleotide polymorphism (SNP) 11391 G/A of ADIPOQ gene (rs17300539) has been consistently associated with higher adiponectin levels in adults and children. The aim of this study was to investigate the metabolic role of this variant in a large cohort of children of European origin. A total of 1,852 children from two general populations in Verona and in Fleurbaix–Laventie and from the Lille childhood obesity cohort, were genotyped and pooled together after checking for the absence of genetic heterogeneity for rs17300539 between Italian and French children. The genotype of rs17300539 was studied in relation to circulating adiponectin levels, BMI, fasting plasma glucose, fasting serum insulin (FSI), insulin resistance index (homeostasis model assessment of insulin resistance (HOMA_IR)), high‐density lipoprotein cholesterol, and triglycerides. After adjustment for known confounders, rs17300539 GA+AA carriers had 1.6 µg/ml higher adiponectin levels (P = 6 × 10^?8) than GG carriers. They also showed higher BMI (B = 0.97, P = 0.015) and higher prevalence of obesity (OR = 1.35 (1.06–1.85), P = 0.015) than GG carriers. Before adjusting for obesity status, GA+AA carriers had higher FSI (B = 1.10, P = 0.040) and higher HOMA_IR (B = 0.31, P = 0.020) than GG carriers. After adjustment for obesity status, they did not differ from GG carriers for any metabolic parameter, either among obese or nonobese children. The rs17300539‐A variant, though consistently associated with higher adiponectin levels, does not exert any appreciable protective metabolic effect in children, either in the presence or absence of obesity. In contrast, this SNP may increase the risk for childhood obesity and related insulin resistance.     

Waist Circumference,BMI, and Visceral Adipose Tissue in White Women and Women of African Descent

Although waist circumference (WC) is a marker of visceral adipose tissue (VAT), WC cut‐points are based on BMI category. We compared WC‐BMI and WC‐VAT relationships in blacks and whites. Combining data from five studies, BMI and WC were measured in 1,409 premenopausal women (148 white South Africans, 607 African‐Americans, 186 black South Africans, 445 West Africans, 23 black Africans living in United States). In three of five studies, participants had VAT measured by computerized tomography (n = 456). Compared to whites, blacks had higher BMI (29.6 ± 7.6 (mean ± s.d.) vs. 27.6 ± 6.6 kg/m², P = 0.001), similar WC (92 ± 16 vs. 90 ± 15 cm, P = 0.27) and lower VAT (64 ± 42 vs. 101 ± 59 cm², P < 0.001). The WC‐BMI relationship did not differ by race (blacks: β (s.e.) WC = 0.42 (.01), whites: β (s.e.) WC = 0.40 (0.01), P = 0.73). The WC‐VAT relationship was different in blacks and whites (blacks: β (s.e.) WC = 1.38 (0.11), whites: β (s.e.) WC = 3.18 (0.21), P < 0.001). Whites had a greater increase in VAT per unit increase in WC. WC‐BMI and WC‐VAT relationships did not differ among black populations. As WC‐BMI relationship did not differ by race, the same BMI‐based WC guidelines may be appropriate for black and white women. However, if WC is defined by VAT, race‐specific WC thresholds are required.     

High‐normal tsh values in obesity: Is it insulin resistance or adipose tissue's guilt?

Objective:

Clinical evidences reported subclinical alterations of thyroid function in obesity, although the relationship between thyroid status and obesity remains unclear. We cross‐sectionally investigated the influence of metabolic features on hypothalamic–pituitary–thyroid axis in obesity.

Design and Methods:

We enrolled 60 euthyroid subjects with no history of type 2 diabetes mellitus and assessed the relationship of thyroid function with insulin resistance, measured using euglycemic clamp, and abdominal fat volume, quantified by computed tomography scan (CT scan). Thyroid stimulating hormone (TSH) correlated with BMI (r = 0.46; P = 0.02), both visceral (r = 0.58; P = 0.02) and subcutaneous adipose tissue volumes (r = 0.43; P = 0.03) and insulin resistance (inverse relationship with insulin sensitivity–glucose uptake: r = ?0.40; P = 0.04).

Results:

After performing multivariate regression, visceral adipose tissue volume was found to be the most powerful predictor of TSH (β = 3.05; P = 0.01), whereas glucose uptake, high‐density lipoprotein (HDL) cholesterol, low‐density lipoprotein (LDL) cholesterol, subcutaneous adipose tissue volume, and triglycerides were not. To further confirm the hypothesis that high‐normal TSH values could be dependent on adipose tissue, and not on insulin resistance, we restricted our analyses to moderately obese subjects' BMI ranging 30‐35 kg/m². This subgroup was then divided as insulin resistant and insulin sensitive according to the glucose uptake (≤ or >5 mg·kg^?1·min^?1, respectively). We did not find any statistical difference in TSH (insulin resistant: 1.62 ± 0.65 µU/ml vs. insulin sensitive: 1.46 ± 0.48; P = not significant) and BMI (insulin resistant: 32.2 ± 1.6 kg/m² vs. insulin sensitive: 32.4 ± 1.4; P = not significant), thus confirming absence of correlation between thyroid function and insulin sensitivity per se.

Conclusion:

Our study suggests that the increase in visceral adipose tissue is the best predictor of TSH concentration in obesity, independently from the eventual concurrent presence of insulin resistance.