Evidence of linkage and association with body fatness and abdominal fat on chromosome 15q26

Objective: In the present study, we undertook a two‐step fine mapping of a 20‐megabase region around a quantitative trait locus previously reported on chromosome 15q26 for abdominal subcutaneous fat (ASF) in an extended sample of 707 subjects from 202 families from the Quebec Family Study. Research Methods and Procedure: First, 19 microsatellites (in addition to the 7 markers initially available on 15q24‐q26; total = 26) were genotyped and tested for linkage with abdominal total fat, abdominal visceral fat, and ASF assessed by computed tomography and with fat mass (FM) using variance component‐based approach on age‐ and sex‐adjusted phenotypes. Second, 16 single nucleotide polymorphisms (SNPs) were genotyped and tested for association using family‐based association tests. Results: After the fine mapping, the peak logarithm of odds ratio (LOD) score (marker D15S1004) increased from 2.79 to 3.26 for ASF and from 3.52 to 4.48 for FM, whereas for abdominal total fat, the peak linkage (marker D15S996) decreased from 2.22 to 1.53. No evidence of linkage was found for abdominal visceral fat. Overall, for genotyped SNPs, three variants located in the putative MCTP2 gene were significantly associated with FM and the three abdominal fat phenotypes (p ≤ 0.05). The major allele and genotype of rs1424695 were associated with higher adiposity values (p < 0.004). The same trend was found for the two other polymorphisms (p < 0.05). None of the other SNPs was associated with adiposity phenotypes. The linkage for FM became non‐significant (LOD = 0.84) after adjustment for the MCTP2 polymorphisms, whereas the one for ASF remained unchanged. Discussion: These results suggest that the MCTP2 gene, located on chromosome 15q26, influences adiposity. Other studies will be needed to investigate the function of the MCTP2 gene and its role in obesity.     

Genetic effects on obesity assessed by bivariate genome scan: the Mexican-American coronary artery disease study

Objective: To identify the genetic determinants of obesity using univariate and bivariate models in a genome scan. Research Methods and Procedures: We evaluated the genetic and environmental effects and performed a genome‐wide linkage analysis of obesity‐related traits in 478 subjects from 105 Mexican‐American nuclear families ascertained through a proband with documented coronary artery disease. The available obesity traits include BMI, body surface area (BSA), waist‐to‐hip ratio (WHR), and trunk fat mass as percentage of body weight. Heritability estimates and multipoint linkage analysis were performed using a variance components procedure implemented in SOLAR software. Results: The heritability estimates were 0.62 for BMI, 0.73 for BSA, 0.40 for WHR, and 0.38 for trunk fat mass as percentage of body weight. Using a bivariate genetic model, we observed significant genetic correlations between BMI and other obesity‐related traits (all p < 0.01). Evidence for univariate linkage was observed at 252 to approximately 267 cM on chromosome 2 for three obesity‐related traits (except for WHR) and at 163 to approximately 167 cM on chromosome 5 for BMI and BSA, with the maximum logarithm of the odds ratio score of 3.12 (empirical p value, 0.002) for BSA on chromosome 2. Use of the bivariate linkage model yielded an additional peak (logarithm of the odds ratio = 3.25, empirical p value, 0.002) at 25 cM on chromosome 7 for the pair of BMI and BSA. Discussion: The evidence for linkage on chromosomes 2q36‐37 and 5q36 is supported both by univariate and bivariate analysis, and an additional linkage peak at 7p15 was identified by the bivariate model. This suggests that use of the bivariate model provides additional information to identify linkage of genes responsible for obesity‐related traits.     

Evidence for Three Novel QTLs for Adiposity on Chromosome 2 With Epistatic Interactions: The NHLBI Family Heart Study

We sought to identify quantitative trait loci (QTLs) by genome‐wide linkage analysis for BMI and waist circumference (WC) exploring various strategies to address heterogeneity including covariate adjustments and complex models based on epistatic components of variance. Because cholesterol‐lowering drugs and diabetes medications may affect adiposity and risk of coronary heart disease, we excluded subjects medicated for hypercholesterolemia and hyperglycemia. The evidence of linkage increased on 2p25 (BMI: lod = 1.59 vs. 2.43, WC: lod = 1.32 vs. 2.26). Because environmental and/or genetic components could mask the effect of a specific locus, we investigated further whether a QTL could influence adiposity independently of lipid pathway and dietary habits. Strong evidence of linkage on 2p25 (BMI: lod = 4.31; WC: lod = 4.23) was found using Willet's dietary factors and lipid profile together with age and sex in adjustment. It suggests that lipid profile and dietary habits are confounding factors for detecting a 2p25 QTL for adiposity. Because evidence of linkage has been previously detected for BMI on 7q34 and 13q14 in National Heart, Lung, and Blood Institute Family Heart Study (NHLBI FHS), and for diabetes on 15q13, we investigated epistasis between chromosome 2 and these loci. Significant epistatic interactions were found between QTLs 2p25 and 7q34, 2q37 and 7q34, 2q31 and 13q14, and 2q31–q36 and 15q13. These results suggest multiple pathways and factors involving genetic and environmental effects influencing adiposity. By taking some of these known factors into account, we clarified our linkage evidence of a QTL on 2p25 influencing BMI and WC. The 2p25, 2q24–q31, and 2q36–q37 showed evidence of epistatic interaction with 7q34, 13q14, and 15q13.     

Genome-wide linkage and peak-wide association study of obesity-related quantitative traits in Caribbean Hispanics

Although obesity is more prevalent in Hispanics than non-Hispanic whites in the United States, little is known about the genetic etiology of the related traits in this population. To identify genetic loci influencing obesity in non-Mexican Hispanics, we performed a genome-wide linkage scan in 1,390 subjects from 100 Caribbean Hispanic families on six obesity-related quantitative traits: body mass index (BMI), body weight, waist circumference, waist-to-hip ratio, abdominal and average triceps skinfold thickness after adjusting for significant demographic and lifestyle factors. We then carried out an association analysis of the linkage peaks and the FTO gene in an independent community-based Hispanic subcohort (N = 652, 64% Caribbean Hispanics) from the Northern Manhattan Study. Evidence of linkage was strongest on 1q43 with multipoint LOD score of 2.45 (p = 0.0004) for body weight. Suggestive linkage evidence of LOD > 2.0 was also identified on 1q43 for BMI (LOD = 2.03), 14q32 for abdominal skinfold thickness (LOD = 2.17), 16p12 for BMI (LOD = 2.27) and weight (LOD = 2.26), and 16q23–24 for average triceps skinfold thickness (LOD = 2.32). In the association analysis of 6,440 single nucleotide polymorphisms (SNPs) under 1-LOD unit down regions of our linkage peaks on chromosome 1q43 and 16p12 as well as in the FTO gene, we found that two SNPs (rs6665519 and rs669231) on 1q43 and one FTO SNP (rs12447427) were significantly associated with BMI or body weight after adjustment for multiple testing. Our results suggest that in addition to FTO, multiple genetic loci, particularly those on 1q43 region, may contribute to the variations in obesity-related quantitative traits in Caribbean Hispanics.     

Body composition of obese subjects by air displacement plethysmography: The influence of hydration

Objective: We investigated whether air displacement plethysmography (ADP) could detect small changes in body composition of obese subjects with alterations in hydration. Research Methods and Procedures: Ten obese subjects (mean BMI, 39.3 ± 2.8 kg/m²) entered the ADP chamber without and with oil (1, 2, or 4 liters), water (1, 2, or 4 liters), or mixed (1 liter oil + 1 liter water or 2 liters oil + 2 liters water) loads. Real and measured changes in body composition were compared by regression analysis and Bland‐Altman procedures. Results: The ADP‐measured changes in volume did not differ from the real values and were strongly correlated with them (r = 0.98). In all cases, loads of differing composition and similar volume led to different values of fat, fat‐free mass, and percentage fat. Water was detected as increased fat‐free mass only with loads of ≥2 liters, most of the water being falsely detected as increased fat mass. The observed changes were correlated with the real ones for fat mass (r = 0.68; p < 0.0001), fat‐free mass (r = 0.66; p < 0.0001), and percentage fat (r = 0.61; p < 0.0001), but fat mass changes were overestimated by ～1 kg, and fat‐free mass changes were underestimated by ～1 kg. This underestimation increased with the highest water loads, as shown by the Bland‐Altman plot (r = ?0.27; p < 0.05). Percentage fat changes were overestimated by 0.8% (p < 0.001); the magnitude of the error was correlated with the weight of the water load (r = 0.62; p < 0.0001). Discussion: ADP accurately measures changes in body volume, discriminating small changes in body composition. It overestimates changes in adiposity, as most of the increased hydration is detected as an enlarged fat mass.     

Suggestive Linkages Between Markers on Human 1p32-p22 and Body Fat and Insulin Levels in the Québec Family Study

A single-gene rodent mutation (diabetes) and a quantitative trait locus (dietary obese 1) mapped to the mid portion of mouse chromosome 4 have been related to obesity and/or insulin levels. Synteny relationships place their putative human homologs on 1p31 and 1p35-p31, respectively. In 137 sibships of adult brothers and sisters from the Québec Family Study, genetic linkages between seven microsatellite markers from 1p32-p22 and various obesity- and diabetes-related quantitative phenotypes were examined using single locus sibpair linkage analysis. Suggestive linkages were observed between markers D1S476 and body mass index (p>=Q.Q5), fat mass (p=0.02), the sum of six skinfolds (p=0.02), the insulin area after an oral glucose tolerance test (p=0.02), and between the neighboring marker D1S200 and body mass index (p>=0.03), and fat mass (p=0.009). Suggestive linkages were also observed between the more telomeric markers D1S193 and body mass index (p=0.03), and between the neighboring marker DIS 197 and fasting insulin level (p=0.05). No linkage was observed with the trunk to extremity skinfolds ratio. These linkages suggest that human homologs of the mouse diabetes or dietary obese 1 and/or other genes in this interval on chromosome 1 play a role in the regulation of body mass, body composition, and insulin levels, but not of subcutaneous fat distribution.     

Meta-analysis of genome-wide linkage studies in BMI and obesity

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome‐wide logarithm of the odds (LOD) scores, non‐parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI‐defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2‐ q33.1, 12q23‐q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3‐22.3 were also observed for BMI‐defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1‐qter and 12p11.21‐q23 (p < 0.01). Conclusion: Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.     

The Relationship of Childhood Adiposity to Parent Body Mass Index and Eating Behavior

Objective: To better understand risk factors for the development of obesity in early childhood, we examined the association between children's adiposity and their parents' eating behavior and body mass index (BMI). Research Methods and Procedures: Parents of 85 white children 36 months of age (49 boys and 36 girls) completed the Three‐Factor Eating Questionnaire measuring three dimensions of parent eating behavior: disinhibited eating, cognitive restraint of eating, and susceptibility to hunger. Parent BMI (kg/m²) was calculated using self‐reported height and weight. The children's percentage body fat was assessed by dual energy X‐ray absorptiometry analysis. Results: Twenty‐six percent of parents were obese (BMI ≥ 30 kg/m²). Both maternal and paternal BMI were associated with higher scores for disinhibition (r = 0.69 and r = 0.68, p < 0.001), and maternal BMI was also associated with higher scores for hunger (r = 0.51, p < 0.001). There were no significant relationships between children's percentage body fat and parent eating scores, and the correlation between children's percentage body fat and parent BMI was significant only between mothers and daughters (r = 0.35, p = 0.04). Obese parents were no more likely to have a child who was fatter (upper quintile of percentage body fat for gender). Discussion: Among 36 month‐old white children, parent eating behavior was related to parent BMI, but not to children's adiposity. There was only a weak relationship between parent BMI and child adiposity. Despite the aggregation of adiposity within families due to shared genes and environments, children may not express differences in susceptibility to obesity by 3 years of age.     

Physical Activity as a Predictor of Body Composition in American Indian Children

Objective: To examine physical activity in second grade American Indian children as a predictor of percentage body fat 3 years later. Research Methods and Procedures: Physical activity was assessed as average vector magnitude (AVM) counts from an accelerometer in 454 second grade children as part of the Pathways study. BMI was assessed, and skinfolds and bioelectrical impedance were used to estimate fat mass, fat‐free body mass, and percentage body fat in validated prediction equations. Associations were examined using mixed models regression controlling for baseline body composition. Results: In normal‐weight children, higher AVM counts were significantly associated with decreases in percentage body fat. Among overweight children, higher AVM counts were significantly associated with increases in BMI, fat mass, and fat‐free mass but not percentage body fat. Discussion: Higher physical activity levels in second grade were associated with lower levels of percentage body fat in fifth grade in normal‐weight but not in overweight children. BMI showed no association with physical activity among normal‐weight children, and increases in BMI were associated with increasing amounts of physical activity among overweight children. These findings emphasize the importance of valid body composition measures and may indicate important differences in associations between physical activity and adiposity in normal‐weight as compared with overweight children.     

Linkage and Association Between CA Repeat Polymorphism of the TNFR2 Gene and Obesity Phenotypes in Two Independent Caucasian Populations

Previously, our group has reported a suggestive linkage evidence of 1p36 with body mass index (BMI) (LOD = 2.09). The tumor necrosis factor receptor 2 (TNFR2) at 1p36 is an excellent positional and functional candidate gene for obesity. In this study, we have investigated the linkage and association between the TNFR2 gene and obesity phenotypes in two large independent samples, using the quantitative transmission disequilibrium tests (QTDT). The first group was made up of 1 836 individuals from 79 multi-generation pedigrees. The second group was a randomly ascertained set of 636 individuals from 157 US Caucasian nuclear families. Obesity phenotypes tested include BMI, fat mass, and percentage fat mass (PFM). A significant result (P = 0.0056) was observed for linkage with BMI in the sample of the multigenerational pedigrees. Our data support the TNFR2 gene as a quantitative trait locus (QTL) underlying BMI variation in the Caucasian populations.     

Identification of QTL genes for BMD variation using both linkage and gene-based association approaches

Low bone mineral density (BMD) is a risk factor for osteoporotic fracture with a high heritability. Previous large scale linkage study in Northern Chinese has identified four significant quantitative trait loci (QTL) for BMD variation on chromosome 2q24, 5q21, 7p21 and 13q21. We performed a replication study of these four QTL in 1,459 Southern Chinese from 306 pedigrees. Successful replication was observed on chromosome 5q21 for femoral neck BMD with a LOD score of 1.38 (nominal p value = 0.006). We have previously identified this locus in a genome scan meta-analysis of BMD variation in a white population. Subsequent QTL-wide gene-based association analysis in 800 subjects with extreme BMD identified CAST and ERAP1 as novel BMD candidate genes (empirical p value of 0.032 and 0.014, respectively). The associations were independently replicated in a Northern European population (empirical p value of 0.01 and 0.004 for CAST and ERAP1, respectively). These findings provide further evidence that 5q21 is a BMD QTL, and CAST and ERAP1 may be associated with femoral neck BMD variation.     

Genetic Contributions to Body Weight in Mice: Relationship of Exploratory Behavior to Weight

Objective: The A/J and C57BL/6J mouse strains differ markedly in their exploratory behavior and their weight gain on a high‐fat diet. We examined the genetic contributions of exploratory behavior to body weight and tested for shared, pleiotropic loci influencing energy homeostasis. Research Methods and Procedures: Segregating (A×B6)F2 intercross (n = 514) and (B6AF1×A/J)N2 backcross (N = 223) populations were studied, phenotyping for weight and exploratory behaviors. Relationships among traits were analyzed by correlations. Weight traits were dissected with a genome‐wide scan. Results: Modest correlations were found between exploratory behaviors and weight, explaining 2% to 14% of the variance. Quantitative trait loci (QTL) for body weight at 8 weeks (wgt8), 10 weeks (wgt10), and 2‐week weight gain (difference between weeks 8 and 10) on a 6% fat diet were mapped. Two QTL on chromosome 1 (peaks at 66 cM and 100 cM; Bw8q1) affected wgt8 [likelihood of the odds ratio (Lod), 3.0 and 4.4] and wgt10 (Lod, 2.2 and 3.4), respectively. In the backcross, a significant QTL on chromosome 4 (peak at 66 cM; Bw8q2) affected wgt 8 (Lod, 3.3) and wgt10 (Lod, 3.1). For 2‐week weight gain, suggestive QTL were mapped on chromosomes 4 and 6. The chromosome 6 QTL region overlaps a human 7q locus for obesity. A search for between‐strain sequence polymorphisms in the leptin and NPY genes was unrevealing. Discussion: In mice, loci influencing exploratory activity play a modest role in body‐weight regulation. Some forms of obesity may emerge from loci regulating normal body weight.     

A genomewide linkage scan for quantitative-trait loci for obesity phenotypes

Obesity is an increasingly serious health problem in the world. Body mass index (BMI), percentage fat mass, and body fat mass are important indices of obesity. For a sample of pedigrees that contains >10,000 relative pairs (including 1,249 sib pairs) that are useful for linkage analyses, we performed a whole-genome linkage scan, using 380 microsatellite markers to identify genomic regions that may contain quantitative-trait loci (QTLs) for obesity. Each pedigree was ascertained through a proband who has extremely low bone mass, which translates into a low BMI. A major QTL for BMI was identified on 2q14 near the marker D2S347 with a LOD score of 4.04 in two-point analysis and a maximum LOD score (MLS) of 4.44 in multipoint analysis. The genomic region near 2q14 also achieved an MLS >2.0 for percentage of fat mass and body fat mass. For the putative QTL on 2q14, as much as 28.2% of BMI variation (after adjustment for age and sex) may be attributable to this locus. In addition, several other genomic regions that may contain obesity-related QTLs are suggested. For example, 1p36 near the marker D1S468 may contain a QTL for BMI variation, with a LOD score of 2.75 in two-point analysis and an MLS of 2.09 in multipoint analysis. The genomic regions identified in this and earlier reports are compared for further exploration in extension studies that use larger samples and/or denser markers for confirmation and fine-mapping studies, to eventually identify major functional genes involved in obesity.     

Insulin Action,Regional Fat,and Myocyte Lipid: Altered Relationships with Increased Adiposity

Objective: Abdominal fat and myocyte triglyceride levels relate negatively to insulin sensitivity, but their interrelationships are inadequately characterized in the overweight. Using recent methods for measuring intramyocyte triglyceride, these relationships were studied in men with a broad range of adiposity. Research Methods and Procedures: Myocyte triglyceride content (¹H‐magnetic resonance spectroscopy of soleus and tibialis anterior muscles and biochemical assessment of vastus lateralis biopsies), regional fat distribution (DXA and abdominal magnetic resonance imaging), serum lipids, insulin action (euglycemic hyperinsulinemic clamp), and substrate oxidation rates (indirect calorimetry) were measured in 39 nondiabetic men (35.1 ± 7.8 years) with a broad range of adiposity (BMI 28.6 ± 4.1 kg/m², range 20.1 to 37.6 kg/m²). Results: Relationships between insulin‐stimulated glucose disposal and regional body fat depots appeared more appropriately described by nonlinear than linear models. When the group was subdivided using median total body fat as the cut‐point, insulin‐stimulated glucose disposal correlated negatively to all regional body fat measures (all p ≤ 0.004), serum triglycerides and free fatty acids (p < 0.02), and both soleus intramyocellular lipid (p = 0.003) and vastus lateralis triglyceride (p = 0.04) in the normal/less overweight group. In contrast, only visceral abdominal fat showed significant negative correlation with insulin‐stimulated glucose disposal in more overweight men (r = ?0.576, p = 0.01), some of whom surprisingly had lower than expected myocyte lipid levels. These findings persisted when the group was subdivided using different cut‐points or measures of adiposity. Discussion: Interrelationships among body fat depots, myocyte triglyceride, serum lipids, and insulin action are generally absent with increased adiposity. However, visceral abdominal fat, which corresponds less closely to total adiposity, remains an important predictor of insulin resistance in men with both normal and increased adiposity.     

Inherited non-autosomal effects on body fat in F2 mice derived from an AKR/J × SWR/J cross

In this study we describe the contribution of matrilineal and patrilineal effects on the adiposity, body weight, and on the weights of individual fat pads in F₂ male mice derived from an SWR/J × AKR/J cross. AKR/J mice become obese after 12 weeks on a high-fat diet, whereas SWR/J mice remain relatively lean. Here we report that mice with AKR maternal and AKR paternal grandmothers have significantly larger epidydimal and retroperitoneal fat pads than those with SWR maternal and paternal grandmothers. However, grandparental strain had no effect on the overall adiposity (AI) or the weights of the inguinal, subcutaneous or mesenteric fat pads. The strain of the paternal grandparents had a small but significant effect on body weight. These effects can be attributed to in utero effects, imprinting effects, cytoplasmic and/or Y chromosome transmission of factors controlling body fat. We also describe the presence of a quantitative trait locus (QTL) on Chromosome X, close to DXMit174, which is linked to adiposity, body weight, and to the weights of the individual fat depots. However, this QTL is not responsible for the grandparental strain effects described above. Received: 3 March 1997 / Accepted: 5 May 1997     

Multiple Genes Influence BMI on Chromosome 7q31–34: The NHLBI Family Heart Study

The National Heart, Lung, and Blood Institute Family Heart Study (FHS) genome‐wide linkage scan identified a region of chromosome 7q31–34 with a lod score of 4.9 for BMI at D7S1804 (131.9 Mb). We report the results of linkage and association to BMI in this region for two independent FHS samples. The first sample includes 225 FHS pedigrees with evidence of linkage to 7q31–34, using 1,132 single‐nucleotide polymorphisms (SNPs) and 7 microsatellites. The second represents a case–control sample (318 cases; BMI >25 and 325 controls; BMI <25) derived from unrelated FHS participants who were not part of the genome scan. The latter set was genotyped for 606 SNPs, including 37 SNPs with prior evidence for association in the linked families. Although variance components linkage analysis using only SNPs generated a peak lod score that coincided with the original linkage scan at 131.9 Mb, a conditional linkage analysis showed evidence of a second quantitative trait locus (QTL) near 143 cM influencing BMI. Three SNPs (rs161339, rs12673281, and rs1993068) located near the three genes pleiotrophin (PTN), diacylglycerol (DAG) kinase iota (DGKι), and cholinergic receptor, muscarinic 2 (CHRM2) demonstrated significant association in both linked families (P = 0.0005, 0.002, and 0.03, respectively) and the case–control sample (P = 0.01, 0.0003, and 0.03, respectively), regardless of the genetic model tested. These findings suggest that several genes may be associated with BMI in the 7q31–34 region.     

Genetic Variation and Obesity in Australian Women: A Prospective Study

Objective: A number of candidate genes have been implicated in the pathogenesis of obesity in humans. This study examines associations between longitudinal changes in body mass and composition and the presence of polymorphisms in the β‐3 adrenergic receptor, tumor necrosis factor‐α, leptin, and leptin receptor (Lepr) in a cohort of Australian women. Research Methods and Procedures: Healthy white Australian women (n = 335) were randomly selected from the Barwon region of Victoria and underwent baseline anthropometry and double‐energy X‐ray absorptiometry for assessment of body mass and adiposity. These measurements were repeated again at 2‐year follow‐up. Genomic DNA was extracted and used for polymerase chain reaction‐based genotyping of all polymorphisms. Results: The Pro1019Pro Lepr polymorphism was associated with longitudinal increases in body weight (p = 0.02), fat mass (p = 0.05), and body mass index (p = 0.01) in this study, and individuals homozygous for the A allele at this locus had a greater propensity to gain body fat over time. The largest effects on body composition seemed to be in individuals already obese at baseline. Changes in body weight, fat mass, percent body fat, and body mass index over a 2‐year period were not associated with genetic variation in the β‐3 adrenergic receptor (Trp64Arg), tumor necrosis factor‐α promoter, or leptin genes in non‐obese or obese women. Discussion: These results suggest that a Lepr polymorphism is involved in the regulation of body mass and adiposity in obese Australian white women, which may have implications for the treatment of obesity in this population.     

Associations of adiposity with measured and self-reported academic performance in early adolescence

Objective: To examine the associations of adiposity with measured and self‐reported academic performance independently of demographics and physical activity among U.S. adolescents. Research Methods and Procedures: We surveyed 666 students 11 to 14 years old from seven middle schools in Los Angeles, CA. Weight and height were measured. Actual grade point average was obtained from school records. Self‐reported school grades and physical activity time were measured by questionnaire. Adiposity measures included BMI, BMI percentile (≥85th percentile defined as at‐risk‐of‐overweight), and percentage body fat (bioimpedance). Results: After adjusting for gender, ethnicity, age, and physical activity time, overweight at‐risk status, BMI, and percentage body fat were negatively related to only self‐reported (p < 0.01) but not measured grades. Level of moderate‐to‐vigorous physical activity time was negatively related to measured and self‐reported grades, independently of adiposity (p < 0.01). Discussion: To our knowledge, this is the first study to examine both body mass and body fat in relation to measured and self‐reported school grades. Adiposity did not relate to actual academic performance in a sample of predominantly Latino and Asian‐American adolescents. The use of measured vs. self‐reported academic outcomes may represent different constructs and influence study conclusions. Cultural factors may also play a role in our findings, but this requires further study.     

Relationships between direct and indirect measures of central and total adiposity in children: What are we measuring?

Objective: The relationship between central and total fat measured by anthropometry, dual energy X‐ray absorptiometry, and magnetic resonance imaging (MRI) with each other and systolic blood pressure (SBP) was examined. Design and Methods: Participants of the Avon Longitudinal Study of Parents and Children were examined at ages 9, 11, 13, and 15 years (n = 3,796‐6,567). MRI was available on a subset of children at 11 (n = 156) and 13 (n = 95). Results: Body mass index (BMI) and waist circumference (WC) were highly correlated (r = 0.84‐0.91, across ages), and total body fat mass (TBFM) and trunk fat mass (TFM) were very strongly correlated (r ≥ 0.98). Among boys, BMI vs. WC explained a similar degree of variation in TBFM and TFM (41‐71% vs. 43‐76%, across age and overweight groups); in girls, BMI accounted for 62‐73% variance and WC 47‐69%. Adiposity measures were generally similarly correlated with SBP within age groups. Further, the relationship between intra‐abdominal adipose tissue (IAAT) volume and adiposity measures did not vary greatly at 11 (0.65‐0.67) and 13 (0.64‐0.67). Conclusions: BMI and WC contain a large amount of overlapping information as evidenced by their high correlation and similarly sized associations with fat mass, SBP, and IAAT. This suggests that WC may be an inadequate marker of central adiposity during childhood.     

Effect of Glucose Tolerance Status on PAI‐1 Plasma Levels in Overweight and Obese Subjects

Objective: The aim of our study was to examine whether plasminogen activator inhibitor‐1 (PAI‐1) plasma levels varied as a function of differences in glucose tolerance status independently of body fatness, body‐fat distribution, and insulin sensitivity. Research Methods and Procedures: Plasma PAI‐1 antigen levels, along with insulin resistance [measured by homeostatic model assessment (HOMA_IR)], central fat accumulation, body composition, blood pressure, and fasting concentrations of glucose, insulin, and lipids, were measured in 229 overweight and obese [body mass index (BMI) ≥25 kg/m²) subjects with normal glucose tolerance (NGT) and in 44 age‐ and BMI‐matched subjects with impaired glucose tolerance (IGT). Results: Plasma PAI‐1 antigen levels were significantly higher in IGT than in NGT subjects. Log PAI‐1 was positively correlated with BMI, HOMA_IR, and log insulin, and inversely associated with high‐density lipoprotein‐cholesterol both in IGT and in NGT individuals. On the other hand, log PAI‐1 was positively correlated with waist circumference, fat mass (FM), fat‐free mass, systolic and diastolic blood pressure, and log triglycerides only in the NGT group. After multivariate analyses, the strongest determinants of PAI‐1 levels were BMI, FM, waist circumference, and high‐density lipoprotein cholesterol in the NGT group and only HOMA_IR in the IGT cohort. Discussion: This study demonstrates that PAI‐1 concentrations are higher in IGT than in NGT subjects. Furthermore, we suggest that the influences of total adiposity, central fat, and insulin resistance, main determinants of PAI‐1 concentrations, are different according to the degree of glucose tolerance.