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Abstract Knowledge of epigenetically regulated biomarkers linked to obesity development is still scarce. Improving molecular understanding of the involved factors and pathways would improve obesity phenotype characterization and reveal potentially relevant targets for obesity intervention. The Illumina Infinium HumanMethylation450 BeadChip was used in a leucocyte epigenome-wide association study (EWAS) to quantify differential DNA methylation in 60 lean compared with 60 obese young women. Replication was done in monozygotic twins discordant for obesity. At adolescence and adulthood, the two weight groups differed significantly in obesity-related traits and metabolic risk factors. Differential hypomethylation was overrepresented in obese compared to lean women. In the adjusted model, the EWAS revealed 10 differentially methylated CpG sites linked to 8 gene loci – COX6A1P2/FGD2, SBNO2, TEX41, RPS6KA2, IGHE/IGHG1/IGHD, DMAP1, SOCS3, and SETBP1– and an enhancer locus at chromosome 2 (2p25.1). The sites linked to TEX41, IGHE/IGHG1/IGHD, DMAP1, and SETBP1 were novel findings, while COX6A1P/FGD2, SBNO2, RPS6KA2, and SOCS3 had been identified previously with concordant direction of effects. RPS6KA2, DMAP1, and SETBP1 were replicated in the BMI-discordant monozygotic twin cohort using the FDR of 5%.

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 OP, 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.

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-11, 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. [ABSTRACT FROM AUTHOR]

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.

In the present study, we investigated the effects of multi-herbal water extract mixture, Taeumjowi-tang (TH) on liver proteome alteration in mice using twodimensional electrophoresis combined with MALDI-TOFMS. Animals were fed high-fat diet with or without TH (0.3% wt/wt) supplement for 12 weeks. At the end of 5 week of experimental diet, mice fed high-fat diet only were subdivided into 2 groups, obesity-prone (OP) and obesityresistant (OR) mice based on weight gain. OR mice gained less body weight compared to OP mice despite of same food intake. TH significantly suppressed weight gain, and proteomic analysis enabled the identification of 49 liver proteins showing differential regulation between OP and OR/TH mice. Combined results of proteomic and western blot analyses revealed decreased lipogenesis via three fatty acid metabolic targets (AMPK, ACC, and FAS) in livers of OR and TH mice. Using bioinformatic classification and network analysis, most of the identified proteins were classified as hydrolases, oxidoreductases, transferases, defense/immunity proteins, and enzyme modulators based on functional analysis of the PANTHER classification system. Combined results of proteomic and bioinformatic analyses using GeneMANIA identified two proteins (LACTB2 and NIT2) in the liver that potentially interact with fatty acid metabolic proteins. Furthermore, these proteins were included in acetylation, phosphoprotein, and metabolic processes in DAVID classification. These proteins were highly expressed in OP mice; however both their transcription and protein expression were lowered by TH treatment. In conclusion, combined data from proteomic and network analyses suggest that TH exerts anti-obesity effects by modulating fatty acid metabolic proteins/genes, particularly via the AMPK pathway. Most targeted proteins/ genes were modulated toward enhancing lipid metabolism in response to TH treatment. [ABSTRACT FROM AUTHOR]

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 OP, 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. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Aims/hypothesis: 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. Methods: 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. Results: 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 < 2.4 × 10), independent of BMI. The WHR-increasing alleles at two WHR loci were significantly associated with higher proinsulin ( GRB14: 0.069 SD) and lower fasting glucose levels ( CPEB4: −0.049 SD), independent of BMI and WHR. A higher GPS-BMI was associated with lower systolic BP (−0.005 SD), diastolic BP (−0.006 SD) and 2 h glucose (−0.013 SD), while a higher GPS-WHR was associated with lower HDL-cholesterol (−0.015 SD) and higher triacylglycerol levels (0.014 SD) (all p < 2.9 × 10), independent of BMI and/or WHR. Conclusions/interpretation: These pleiotropic effects of obesity-susceptibility loci provide novel insights into mechanisms that link obesity with metabolic abnormalities. [ABSTRACT FROM AUTHOR]

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 x2 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. [ABSTRACT FROM AUTHOR]