886 resultados para Body Mass index


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There is evidence that obesity-related disorders are increased among people with depression. Variation in the FTO (fat mass and obesity associated) gene has been shown to contribute to common forms of human obesity. This study aimed to investigate the genetic influence of polymorphisms in FTO in relation to body mass index (BMI) in two independent samples of major depressive disorder (MDD) cases and controls. We analysed 88 polymorphisms in the FTO gene in a clinically ascertained sample of 2442 MDD cases and 809 controls (Radiant Study). In all, 8 of the top 10 single-nucleotide polymorphisms (SNPs) showing the strongest associations with BMI were followed-up in a population-based cohort (PsyCoLaus Study) consisting of 1292 depression cases and 1690 controls. Linear regression analyses of the FTO variants and BMI yielded 10 SNPs significantly associated with increased BMI in the depressive group but not the control group in the Radiant sample. The same pattern was found in the PsyCoLaus sample. We found a significant interaction between genotype and affected status in relation to BMI for seven SNPs in Radiant (P<0.0057), with PsyCoLaus giving supportive evidence for five SNPs (P-values between 0.03 and 0.06), which increased in significance when the data were combined in a meta-analysis. This is the first study investigating FTO and BMI within the context of MDD, and the results indicate that having a history of depression moderates the effect of FTO on BMI. This finding suggests that FTO is involved in the mechanism underlying the association between mood disorders and obesity.

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OBJECTIVE: Although dual-energy X-ray absorptiometry (DEXA) is the preferred method to estimate adiposity, body mass index (BMI) is often used as a proxy. However, the ability of BMI to measure adiposity change among youth is poorly evidenced. This study explored which metrics of BMI change have the highest correlations with different metrics of DEXA change. METHODS: Data were from the Quebec Adipose and Lifestyle Investigation in Youth cohort, a prospective cohort of children (8-10 years at recruitment) from Québec, Canada (n=557). Height and weight were measured by trained nurses at baseline (2008) and follow-up (2010). Metrics of BMI change were raw (ΔBMIkg/m(2) ), adjusted for median BMI (ΔBMIpercentage) and age-sex-adjusted with the Centers for Disease Control and Prevention growth curves expressed as centiles (ΔBMIcentile) or z-scores (ΔBMIz-score). Metrics of DEXA change were raw (total fat mass; ΔFMkg), per cent (ΔFMpercentage), height-adjusted (fat mass index; ΔFMI) and age-sex-adjusted z-scores (ΔFMz-score). Spearman's rank correlations were derived. RESULTS: Correlations ranged from modest (0.60) to strong (0.86). ΔFMkg correlated most highly with ΔBMIkg/m(2) (r = 0.86), ΔFMI with ΔBMIkg/m(2) and ΔBMIpercentage (r = 0.83-0.84), ΔFMz-score with ΔBMIz-score (r = 0.78), and ΔFMpercentage with ΔBMIpercentage (r = 0.68). Correlations with ΔBMIcentile were consistently among the lowest. CONCLUSIONS: In 8-10-year-old children, absolute or per cent change in BMI is a good proxy for change in fat mass or FMI, and BMI z-score change is a good proxy for FM z-score change. However change in BMI centile and change in per cent fat mass perform less well and are not recommended.

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The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity.

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There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.

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Objective: to assess the agreement between different anthropometric markers in defining obesity and the effect on the prevalence of obese subjects. Methods: population-based cross-sectional study including 3213 women and 2912 men aged 35-75 years. Body fat percentage (%BF) was assessed using electric bioimpedance. Obesity was defined using established cut-points for body mass index (BMI) and waist, and three population-defined cut-points for %BF. Between-criteria agreement was assessed by the kappa statistic. Results: in men, agreement between the %BF cut-points was significantly higher (kappa values in the range 0.78 - 0.86) than with BMI or waist (0.47 - 0.62), whereas no such differences were found in women (0.41 - 0.69). In both genders, prevalence of obesity varied considerably according to the criteria used: 17% and 24% according to BMI and waist in men, and 14% and 31%, respectively, in women. For %BF, the prevalence varied between 14% and 17% in men and between 19% and 36% in women according to the cut-point used. In the older age groups, a fourfold difference in the prevalence of obesity was found when different criteria were used. Among subjects with at least one criteria for obesity (increased BMI, waist or %BF), only one third fulfilled all three criteria and one quarter two criteria. Less than half of women and 64% of men were jointly classified as obese by the three population-defined cut-points for %BF. Conclusions: the different anthropometric criteria to define obesity show a relatively poor agreement between them, leading to considerable differences in the prevalence of obesity in the general population.

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BACKGROUND: Head and neck cancer (HNC) risk is elevated among lean people and reduced among overweight or obese people in some studies; however, it is unknown whether these associations differ for certain subgroups or are influenced by residual confounding from the effects of alcohol and tobacco use or by other sources of biases. METHODS: We pooled data from 17 case-control studies including 12 716 cases and the 17 438 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for associations between body mass index (BMI) at different ages and HNC risk, adjusted for age, sex, centre, race, education, tobacco smoking and alcohol consumption. RESULTS: Adjusted ORs (95% CIs) were elevated for people with BMI at reference (date of diagnosis for cases and date of selection for controls) 25.0-30.0 kg/m(2) (0.52, 0.44-0.60) and BMI >/=30 kg/m(2) (0.43, 0.33-0.57), compared with BMI >18.5-25.0 kg/m(2). These associations did not differ by age, sex, tumour site or control source. Although the increased risk among people with BMI 25 kg/m(2) was present only in smokers and drinkers. CONCLUSIONS: In our large pooled analysis, leanness was associated with increased HNC risk regardless of smoking and drinking status, although reverse causality cannot be excluded. The reduced risk among overweight or obese people may indicate body size is a modifier of the risk associated with smoking and drinking. Further clarification may be provided by analyses of prospective cohort and mechanistic studies.

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AIM: The resting metabolic rate (RMR) varies among pregnant women. The factors responsible for this variability are unknown. This study aimed to assess the influence of the prepregnancy body mass index (BMI) on the RMR during late pregnancy. METHODS: RMR, height, weight, and total (TEE) and activity (AEE) energy expenditures were measured in 46 healthy women aged 31 ± 5 years (mean ± SD) with low (<19.8), normal (19.8-26.0), and high (>26.0) prepregnancy BMI at 38.2 ± 1.5 weeks of gestation (t(gest)) and 40 ± 7 weeks postpartum (t(post)) (n = 27). RESULTS: The mean t(gest) RMR for the low-, normal-, and high-BMI groups was 1,373, 1,807, and 2,191 kcal/day, respectively (p = 0.001). The overall mean t(gest) RMR was 316 ± 183 kcal/day (21%), higher than the overall mean t(post) value and this difference was correlated with gestational weight gain (r = 0.78, p < 0.001). The scaled metabolic rate by allometry (RMR/kilograms⁰·⁷³) was similar in the low-, normal-, and high-BMI groups, respectively (p = 0.45). Changes in t(gest) TEE closely paralleled changes in t(gest) RMR (r = 0.84, p < 0.001). AEE was similar among the BMI groups. CONCLUSION: The RMR is significantly increased in the third trimester of pregnancy. The absolute gestational RMR is higher in women with high prepregnancy BMI due to increased body weight. The scaled metabolic rate (RMR/kilograms⁰·⁷³) is similar among the BMI groups of pregnant women.

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Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

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Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

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Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

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We assessed the association between several cardiometabolic risk factors (CRFs) (blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, uric acid, and glucose) in 390 young adults aged 19-20 years in Seychelles (Indian Ocean, Africa) and body mass index (BMI) measured either at the same time (cross-sectional analysis) or at the age of 12-15 years (longitudinal analysis). BMI tracked markedly between age of 12-15 and age of 19-20. BMI was strongly associated with all considered CRFs in both cross-sectional and longitudinal analyses, with some exceptions. Comparing overweight participants with those having a BMI below the age-specific median, the odds ratios for high blood pressure were 5.4/4.7 (male/female) cross-sectionally and 2.5/3.9 longitudinally (P < 0.05). Significant associations were also found for most other CRFs, with some exceptions. In linear regression analysis including both BMI at age of 12-15 and BMI at age of 19-20, only BMI at age of 19-20 remained significantly associated with most CRFs. We conclude that CRFs are predicted strongly by either current or past BMI levels in adolescents and young adults in this population. The observation that only current BMI remained associated with CRFs when including past and current levels together suggests that weight control at a later age may be effective in reducing CRFs in overweight children irrespective of past weight status.

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FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

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Objective: to assess the diagnostic accuracy of different anthropometric markers in defining low aerobic fitness among adolescents. Methods: cross-sectional study on 2,331 boys and 2,366 girls aged 10 - 18 years. Body mass index (BMI) was measured using standardized methods; body fat (BF) was assessed by bioelectrical impedance. Low aerobic fitness was assessed by the 20-meter shuttle run using the FITNESSGRAMR criteria. Waist was measured in a subsample of 1,933 boys and 1,897 girls. Overweight, obesity and excess fat were defined according to the International Obesity Task Force (IOTF) or FITNESSGRAMR criteria. Results: 38.5% of boys and 46.5% of girls were considered as unfit according to the FITNESSGRAMR criteria. In boys, the area under the ROC curve (AUC) and 95% confidence interval were 66.7 (64.1 - 69.3), 67.1 (64.5 - 69.6) and 64.6 (61.9 - 67.2) for BMI, BF and waist, respectively (P<0.02). In girls, the values were 68.3 (65.9 - 70.8), 63.8 (61.3 - 66.3) and 65.9 (63.4 - 68.4), respectively (P<0.001). In boys, the sensitivity and specificity to diagnose low fitness were 13% and 99% for obesity (IOTF); 38% and 86% for overweight + obesity (IOTF); 28% and 94% for obesity (FITNESSGRAMR) and 42% and 81% for excess fat (FITNESSGRAMR). For girls, the values were 9% and 99% for obesity (IOTF); 33% and 82% for overweight + obesity (IOTF); 22% and 94% for obesity (FITNESSGRAMR) and 26% and 90% for excess fat (FITNESSGRAMR). Conclusions: BMI, not body fat or waist, should be used to define low aerobic fitness. The IOTF BMI cut-points to define obesity have a very low screening capacity and should not be used.

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BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD. METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity. RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results. CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.