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Background: One of the most interesting issues in obesity research is why certain humans are obesity-prone (OP) while others are obesity-resistant (OR) upon exposure to a high-calorie diet. However, the pathways responsible for these phenotypic differences are still largely unknown. Methods: In order to discover marker molecules determining susceptibility and/or resistance to obesity in response to high fat diet (HFD) or anti-obesity herbal medicine (TH), we conducted comparative proteomic analysis of white adipose tissue (WAT) from OR OR, as well as TH-treated mice. Results: OP mice fed HFD gained approximately 33% more body weight than OR mice, and TH significantly reduced body weight gain in HFD-fed mice by 30%. These mice were further subjected to proteomic analysis using two-dimensional electrophoresis (2-DE) combined with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS). Proteomic data revealed 59 spots that were differentially regulated from a total of 1,045 matched spots, and 57 spots of these were identified as altered WAT proteins between OP and OR mice by peptide mass finger printing. Interestingly, 45 proteins were similarly regulated in OR mice in response to TH treatment. Of these, 10 proteins have already been recognized in the context of obesity; however, other proteins involved in obesity susceptibility or resistance were identified for the first time in the present study. Conclusion: Our results suggest that TH actively contributed to body weight reduction in HFD-fed obese mice by altering protein regulation in WAT, and it was also found that TH-responsive proteins can be used as potent molecules for obesity treatment. Copyright (C) 2015 S. Karger AG, Basel

Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d−1, p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d−1. For measured RMR ≤7000 kJ·d−1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

Genetic pleiotropy may contribute to the clustering of obesity and metabolic conditions. We assessed whether genetic variants that are robustly associated with BMI and waist-to-hip ratio (WHR) also influence metabolic and cardiovascular traits, independently of obesity-related traits, in meta-analyses of up to 37,874 individuals from six European population-based studies.We examined associations of 32 BMI and 14 WHR loci, individually and combined in two genetic predisposition scores (GPSs), with glycaemic traits, blood lipids and BP, with and without adjusting for BMI and/or WHR.We observed significant associations of BMI-increasing alleles at five BMI loci with lower levels of 2 h glucose (RBJ [also known as DNAJC27], QPTCL: effect sizes -0.068 and -0.107 SD, respectively), HDL-cholesterol (SLC39A8: -0.065 SD, MTCH2: -0.039 SD), and diastolic BP (SLC39A8: -0.069 SD), and higher and lower levels of LDL- and total cholesterol (QPTCL: 0.041 and 0.042 SDs, respectively, FLJ35779 [also known as POC5]: -0.042 and -0.041 SDs, respectively) (all p These pleiotropic effects of obesity-susceptibility loci provide novel insights into mechanisms that link obesity with metabolic abnormalities.

Background Obesity causes severe healthcare problem worldwide leading to numerous diseases, such as cardiovascular diseases and diabetes mellitus. Previous Genome-Wide Association Study (GWAS) identified an association between a single nucleotide polymorphism (SNP) rs7138803, on chromosome 12q13 and obesity in European Caucasians. Since the genetic architecture governing the obesity may vary among different populations, we investigate the variant rs7138803 in Chinese population to find out whether it is associated with obesity. Methods A population-based cohort association study was carried out using the High Resolution Melt (HRM) method with 1851 participants. The association between rs7138803 genotypes and body mass index (BMI) was modeled with a general linear model, and a case–control study for the association between rs7138803 genotypes and obesity was performed using Pearson’s χ2 test. There was no indication of a deviation from Hardy-Weinberg equilibrium (HWE p value = 0.51) in our sample. Results No association was detected between SNP rs7138803 and BMI in our Chinese Han population with a P value of 0.51. SNP rs7138803 was found to be not associated with common forms of obesity after adjusting for age and sex in the Chinese population. SNP rs7138803 was not associated with other obesity related traits, including T2DM, hypertension, lipid profiles, and ischemic stroke. Conclusion Our data suggest that the rs7138803 exerts no significant effect on obesity in Chinese Han population. Larger cohorts may be more appropriate to detect an effect of this SNP on common obesity.