Prevalence of obesity and the relationship between the body mass index and body fat: cross-sectional, population-based data

Background

Anthropometric measures such as the body mass index (BMI) and waist circumference are widely used as convenient indices of adiposity, yet there are limitations in their estimates of body fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the relationship between the BMI and body fat.

Methodology/Principal Findings

This population-based, cross-sectional study was conducted as part of the Geelong Osteoporosis Study. A random sample of 1,467 men and 1,076 women aged 20–96 years was assessed 2001–2008. Overweight and obesity were identified according to BMI (overweight 25.0–29.9 kg/m²; obesity ≥30.0 kg/m²) and waist circumference (overweight men 94.0–101.9 cm; women 80.0–87.9 cm; obesity men ≥102.0 cm, women ≥88.0 cm); body fat mass was assessed using dual energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4–47.9) of men and 30.2% (95%CI 27.4–33.0) of women were overweight and a further 20.2% (95%CI 18.0–22.4) of men and 28.6% (95%CI 25.8–31.3) of women were obese. Using waist circumference, 27.5% (95%CI 25.1–30.0) of men and 23.3% (95%CI 20.8–25.9) of women were overweight, and 29.3% (95%CI 26.9–31.7) of men and 44.1% (95%CI 41.2–47.1) of women, obese. Both criteria indicate that approximately 60% of the population exceeded recommended thresholds for healthy body habitus. There was no consistent pattern apparent between BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose excess weight was largely attributable to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.

Conclusions/Significance

As the BMI does not account for differences in body composition, we suggest that gender- and age-specific thresholds should be considered when the BMI is used to indicate adiposity.     

Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche

Lean body mass (LBM) and age at menarche (AAM) are two important complex traits for human health. The aim of this study was to identify pleiotropic genes for both traits using a powerful bivariate genome-wide association study (GWAS). Two studies, a discovery study and a replication study, were performed. In the discovery study, 909622 single nucleotide polymorphisms (SNPs) were genotyped in 801 unrelated female Han Chinese subjects using the Affymetrix human genome-wide SNP array 6.0 platform. Then, a bivariate GWAS was performed to identify the SNPs that may be important for LBM and AAM. In the replication study, significant findings from the discovery study were validated in 1692 unrelated Caucasian female subjects. One SNP rs3027009 that was bivariately associated with left arm lean mass and AAM in the discovery samples (P=7.26×10^?6) and in the replication samples (P=0.005) was identified. The SNP is located at the upstream of DARC (Duffy antigen receptor for chemokines) gene, suggesting that DARC may play an important role in regulating the metabolisms of both LBM and AAM.     

Adjusting body mass for measurement error with invalid validation data

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.     

A framework for testing assumptions about foraging scales,body mass,and niche separation using telemetry data

Ecological theory predicts that if animals with very similar dietary requirements inhabit the same landscape, then they should avoid niche overlap by either exploiting food resources at different times or foraging at different spatial scales. Similarly, it is often assumed that animals that fall in different body mass modes and share the same body plan will use landscapes at different spatial scales. We developed a new methodological framework for understanding the scaling of foraging (i.e. the range and distribution of scales at which animals use their landscapes) by applying a combination of three well‐established methods to satellite telemetry data to quantify foraging patch size distributions: (1) first‐passage time analysis; (2) a movement‐based kernel density estimator; and (3) statistical comparison of resulting histograms and tests for multimodality. We demonstrate our approach using two sympatric, ecologically similar species of African ducks with quite different body masses: Egyptian Geese (actually a shelduck), and Red‐billed Teal. Contrary to theoretical predictions, the two species, which are sympatric throughout the year, foraged at almost identical spatial scales. Our results show how ecologists can use GPS tracking data to explicitly quantify and compare the scales of foraging by different organisms within an animal community. Our analysis demonstrates both a novel approach to foraging data analysis and the need for caution when making assumptions about the relationships among niche separation, diet, and foraging scale.     

Estimating fossil hominin body mass from cranial variables: An assessment using CT data from modern humans of known body mass

Body mass estimates are integral to a wide range of inferences in paleoanthropology. Most techniques employ postcranial elements, but predictive equations based on cranial variables have also been developed. Three studies currently provide regression equations for estimating mass from cranial variables, but none of the equations has been tested on samples of known mass. Nor have the equations been compared to each other in terms of performance. Consequently, this study assessed the performance of existing cranial equations using computed tomography scans from a large, documented sample of modern humans of known body mass. Virtual models of the skull were reconstructed and measured using computer software, and the resulting variables were entered into three sets of published regression equations. Estimated and known body masses were then compared. For most equations, prediction errors were high and few individuals were estimated within ±20% of their known mass. Only one equation satisfied the accuracy criteria. In addition, variables that had been previously argued to be good predictors of mass in hominins, including humans, did not estimate mass reliably. These results have important implications for paleoanthropology. In particular, they emphasize the need to develop new equations for estimating fossil hominin body mass from cranial variables. Am J Phys Anthropol 154:201–214, 2014. © 2014 Wiley Periodicals, Inc.     

Overweight in urban schoolchildren assessed by body mass index and body fat mass in Dalian, China

In order to assess overweight in urban schoolchildren (298 boys and 298 girls) aged 9-16 years in Dalian, China, their body height, weight, skinfolds at triceps, biceps, subscapular and suprailiac, and body circumferences of mid-upper arm, waist and hip were measured. The results showed that the prevalence of overweight, based on the United States Centers for Disease Control 2000 reference values using body mass index (BMI), was 22.9% in boys and 10.4% in girls, which was higher than that of indicated in Chinese national surveys of recent decades. More boys were overweight than girls due to excessive increase in body fat, although the prevalence of overweight in girls prevalence increased from 13 years. Discussion here focuses on the effects of behavioral patterns on overweight in Dalian schoolchildren, paying attention to gender difference.     

Biochemical systems analysis of genome-wide expression data

MOTIVATION: Modern methods of genomics have produced an unprecedented amount of raw data. The interpretation and explanation of these data constitute a major, well-recognized challenge. RESULTS: Biochemical Systems Theory (BST) is the mathematical basis of a well-established methodological framework for analyzing networks of biochemical reactions. An existing BST model of yeast glycolysis is used here to explain and interpret the glycolytic gene expression pattern of heat shocked yeast. Our analysis demonstrates that the observed gene expression profile satisfies the primary goals of increased ATP, trehalose, and NADPH production, while maintaining intermediate metabolites at reasonable levels. Based on a systematic exploration of alternative, hypothetical expression profiles, we show that the observed profile outperforms other profiles. Conclusion: BST is a useful framework for combining DNA microarray data with enzymatic process information to yield new insights into metabolic pathway regulation. AVAILABILITY: All analyses were executed with the software PLAS(Copyright), which is freely available at http://correio.cc.fc.ul.pt/~aenf/plas.html for academic use. CONTACT: VoitEO@MUSC.edu     

Similarity searches in genome-wide numerical data sets

   

We present psi-square, a program for searching the space of gene vectors. The program starts with a gene vector, i.e., the set of measurements associated with a gene, and finds similar vectors, derives a probabilistic model of these vectors, then repeats search using this model as a query, and continues to update the model and search again, until convergence. When applied to three different pathway-discovery problems, psi-square was generally more sensitive and sometimes more specific than the ad hoc methods developed for solving each of these problems before.     

A novel locus for body mass index on 5p15.2: a meta-analysis of two genome-wide association studies

Objective

Genetic factors play an important role in modulating the vulnerability to body mass index (BMI). The purpose of this study is to identify novel genetic variants for BMI using genome-wide association (GWA) meta-analysis.

Methods

PLINK software was used to perform meta-analysis of two GWA studies (the FUSION and Marshfield samples) of 5218 Caucasian individuals with BMI. A replication study was conducted using the SAGE sample with 762 individuals.

Results

Through meta-analysis we identified 33 SNPs associated with BMI with p < 10^− 4. The most significant association was observed with rs2967951 (p = 1.19 × 10^− 6) at 5p15.2 within ROPN1L gene. Two additional SNPs within ROPN1L and 5 SNPs within MARCH6 (the top SNP was rs2607292 with 4.27 × 10^− 6) further supported the association with BMI on 5p15.2 (p < 1.8 × 10^− 5). Conditional analysis on 5p15.2 could not distinguish the effects of ROPN1L and MARCH6. Several SNPs within MARCH6 and ROPN1L were replicated in the SAGE sample (p < 0.05).

Conclusion

We identified a novel locus for BMI. These findings offer the potential for new insights into the pathogenesis of BMI and obesity and will serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in BMI and obesity.     

Death, Gender, and Ethnicity

Death, Gender, and Ethnicity. David Field. Jenny Hockey. and Neil Small. eds. New York: Routledge, 1997.230 pp.     

Suggestion of GLYAT gene underlying variation of bone size and body lean mass as revealed by a bivariate genome-wide association study

Bone and muscle, two major tissue types of musculoskeletal system, have strong genetic determination. Abnormality in bone and/or muscle may cause musculoskeletal diseases such as osteoporosis and sarcopenia. Bone size phenotypes (BSPs), such as hip bone size (HBS), appendicular bone size (ABS), are genetically correlated with body lean mass (mainly muscle mass). However, the specific genes shared by these phenotypes are largely unknown. In this study, we aimed to identify the specific genes with pleiotropic effects on BSPs and appendicular lean mass (ALM). We performed a bivariate genome-wide association study (GWAS) by analyzing ~690,000 SNPs in 1,627 unrelated Han Chinese adults (802 males and 825 females) followed by a replication study in 2,286 unrelated US Caucasians (558 males and 1,728 females). We identified 14 interesting single nucleotide polymorphisms (SNPs) that may contribute to variation of both BSPs and ALM, with p values <10^?6 in discovery stage. Among them, the association of three SNPs (rs2507838, rs7116722, and rs11826261) in/near GLYAT (glycine-N-acyltransferase) gene was replicated in US Caucasians, with p values ranging from 1.89 × 10^?3 to 3.71 × 10^?4 for ALM–ABS, from 5.14 × 10^?3 to 1.11 × 10^?2 for ALM–HBS, respectively. Meta-analyses yielded stronger association signals for rs2507838, rs7116722, and rs11826261, with pooled p values of 1.68 × 10^?8, 7.94 × 10^?8, 6.80 × 10^?8 for ALB–ABS and 1.22 × 10^?4, 9.85 × 10^?5, 3.96 × 10^?4 for ALM–HBS, respectively. Haplotype allele ATA based on these three SNPs was also associated with ALM–HBS and ALM–ABS in both discovery and replication samples. Interestingly, GLYAT was previously found to be essential to glucose metabolism and energy metabolism, suggesting the gene’s dual role in both bone development and muscle growth. Our findings, together with the prior biological evidence, suggest the importance of GLYAT gene in co-regulation of bone phenotypes and body lean mass.     

High frequency body mass measurement,feedback, and health behaviors

We analyze weight and fat percentage measurements of respondents in an online general population panel in the Netherlands, collected using wireless scales, with an average frequency of 1.6 measurements per week. First, we document the existence of a weekly cycle; body mass is lowest on Fridays and highest on Mondays, showing significant (p < 0.01) differences of, on average, 0.2 kilogram in weight, 0.06 in BMI value, and 0.03 in fat percentage. Second, we find that in the general population fat-based measures of obesity point at a three times larger prevalence of obesity (53%) than BMI-based measures (17%). Third, we find that feedback that includes a recommended weight range increases the temporal variation in individual body mass by almost ten percent (sd for weight increases from 1.13 to 1.22; sd for BMI increases from 0.37 to 0.41; sd for fat percentage increases from 0.55 to 0.61.     

Probabilistic protein function prediction from heterogeneous genome-wide data

Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function.     

Gravity and body mass regulation

The effects of altered gravity on body mass, food intake, energy expenditure, and body composition are examined. Metabolic adjustments are reviewed in maintenance of energy balance, neural regulation, and humoral regulation are discussed. Experiments with rats indicate that genetically obese rats respond differently to hypergravity than lean rats.