239 resultados para BMI(body mass index)

em Université de Lausanne, Switzerland


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Background: Leptin is produced primarily by adipocytes. Although originally associated with the central regulation of satiety and energy metabolism, increasing evidence indicates that leptin may be an important factor for congestive heart faire (CHF). In the study, we aimed to test the hypothesis that leptin may influence CHF pathophysiology via a pathway of increasing body mass index (BMI). Methods: We studied 2,389 elderly participants aged 70 and older (M; 1161, F: 1228) without CHF and with serum leptin measures at the Health Aging, and Body Composition study. We analyzed the association between serum leptin level and risk of incident CHF using Cox hazard proportional regression models. Elevated leptin level was defined as more than the highest quartile (Q4) of leptin distribution in the total sample for each gender. Adjusted-covariates included demographic, behavior, lipid and inflammation variables (partially-adjusted models), and further included BMI (fully-adjusted models). Results: In a mean 9-year follow-up, 316 participants (13.2%) developed CHF. The partially-adjusted models indicated that men and women with elevated serum leptin levels (>=9.89 ng/ml in men and >=25 ng/ml in women) had significantly higher risks of developing CHF than those with leptin level of less than Q4. The adjusted hazard ratios (95%CI) for incident CHF was 1.49 (1.04 -2.13) in men and 1.71 (1.12 -2.58) in women. However, these associations became non-significant after adjustment for including BMI for each gender. The fully-adjusted hazard ratios (95%CI) were 1.43 (0.94 -2.18) in men and 1.24 (0.77-1.99) in women. Conclusion: Subjects with elevated leptin levels have a higher risk of CHF. The study supports the hypothesis that the influence of leptin level on risk of CHF may be through a pathway related to increasing BMI.

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Odds ratios for head and neck cancer increase with greater cigarette and alcohol use and lower body mass index (BMI; weight (kg)/height(2) (m(2))). Using data from the International Head and Neck Cancer Epidemiology Consortium, the authors conducted a formal analysis of BMI as a modifier of smoking- and alcohol-related effects. Analysis of never and current smokers included 6,333 cases, while analysis of never drinkers and consumers of < or =10 drinks/day included 8,452 cases. There were 8,000 or more controls, depending on the analysis. Odds ratios for all sites increased with lower BMI, greater smoking, and greater drinking. In polytomous regression, odds ratios for BMI (P = 0.65), smoking (P = 0.52), and drinking (P = 0.73) were homogeneous for oral cavity and pharyngeal cancers. Odds ratios for BMI and drinking were greater for oral cavity/pharyngeal cancer (P < 0.01), while smoking odds ratios were greater for laryngeal cancer (P < 0.01). Lower BMI enhanced smoking- and drinking-related odds ratios for oral cavity/pharyngeal cancer (P < 0.01), while BMI did not modify smoking and drinking odds ratios for laryngeal cancer. The increased odds ratios for all sites with low BMI may suggest related carcinogenic mechanisms; however, BMI modification of smoking and drinking odds ratios for cancer of the oral cavity/pharynx but not larynx cancer suggests additional factors specific to oral cavity/pharynx cancer.

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In recent decades the percentage of energy derived from dietary fat has increased. The aim of this study was to explore the relationship between food taste preferences, BMI, age, gender and smoking habits. A computerized questionnaire using a hedonic scale (range 0 to 8) to quantify the liking for sweet and savoury, lean and fat foods, was filled by 233 adults: 171 normal weight (131 women, 40 men) and 62 overweight subjects (BMI > 25 kg/m2 42 women, 20 men). The majority of the subjects had a general preference for savoury lean food irrespective of their BMI or gender. Similarly, preference for sweet lean food was not influenced by the magnitude of the BMI. In contrast, overweight subjects had a preference for sweet fat food (p = 0.05) as well as for savoury fat food (p < 0.05). At any age or BMI, men preferred sweet fat food (p < 0.01). This was not the case for women. Overweight men over forty preferred savoury fat food, in contrast to overweight women of the same age (p < 0.01). The same difference existed between normal weight smokers and non-smokers. This study demonstrates that fat food preference plays a potential role in the development of obesity.

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Obesity is a major risk factor for elevated blood pressure in children. For instance, in a school-based study of 5207 children aged 10-12 years, the prevalence of hypertension, which is sustained elevated blood pressure over several visits, was 1.5%, 3.9% and 17.5% in normal weight, overweight and obese children, respectively. High body mass index (BMI) is commonly used to define overweight and obesity. However, because BMI is merely a proxy for adiposity, there is a longstanding debate about its performance to predict elevated blood pressure (or any other health conditions associated with adiposity) and whether other adiposity indicators, such as waist circumference, waist-to-hip ratio or hip circumference, should not be preferred... In this study, 7.4% of boys and 6.4% of girls had elevated blood pressure. The adiposity indicators were highly correlated to each other, apart from weight, waist-to-hip ratio and skinfold thickness z-scores. All indicators were associated with blood pressure. The ability to identify children with elevated blood pressure, assessed by the area under the receiver operating curve (AUC) statistic, was superior for BMI, body adiposity index and waist-to-height ratio z-scores compared with other indicators. BMI z-scores had a slightly higher AUC than other indicators. The authors concluded that BMIz-scores could be a better predictor of elevated blood pressure in children than other adiposity indicators.

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Energy expenditure was measured by means of a respiratory chamber in two groups of adult rural Gambian men. The first group (n = 29) had a low body mass index (BMI; in kg/m2) < 18.5), whereas the control group (n = 29) had a higher BMI (> 22). This study shows that the energy expenditure of Gambian men with low BMI is not different from that of Gambian men with normal BMI when the results are normalized for fat-free mass or for weight by analysis of covariance. In Gambian men the nutritional status thus does not seem to affect energy metabolism notably. No differences in respiratory quotient, diet-induced thermogenesis, net work efficiency, spontaneous physical activity, heart rate, or urinary catecholamine excretion were observed between the two groups. It is, however, interesting to note that the basal metabolic rate of Gambian men, regardless of their nutritional status, is approximately 10% (range 4-12% depending on the reference value used) lower than that predicted for individuals living in industrialized countries.

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BACKGROUND: Cost effective means of assessing the levels of risk factors in the population have to be defined in order to monitor these factors over time and across populations. This study is aimed at analyzing the difference in population estimates of the mean levels of body mass index (BMI) and the prevalences of overweight, between health examination survey and telephone survey. METHODS: The study compares the results of two health surveys, one by telephone (N=820) and the other by physical examination (N=1318). The two surveys, based on independent random samples of the population, were carried out over the same period (1992-1993) in the same population (canton of Vaud, Switzerland). RESULTS: Overall participation rates were 67% and 53% for the health interview survey (HIS) and the health examination survey (HES) respectively. In the HIS, the reporting rate was over 98% for weight and height values. Self-reported weight was on average lower than measured weight, by 2.2 kg in men and 3.5 kg in women, while self-reported height was on average greater than measured height, by 1.2 cm in men and 1.9 cm in women. As a result, in comparison to HES, HIS led to substantially lower mean levels of BMI, and to a reduction of the prevalence rates of obesity (BMI>30 kg/m(2)) by more than a half. These differences are larger for women than for men. CONCLUSION: The two surveys were based on different sampling procedures. However, this difference in design is unlikely to explain the systematic bias observed between self-reported and measured values for height and weight. This bias entails the overall validity of BMI assessment from telephone surveys.

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IMPORTANCE There is a high prevalence of obesity in psychiatric patients, possibly leading to metabolic complications and reducing life expectancy. The CREB-regulated transcription coactivator 1 (CRTC1) gene is involved in energy balance and obesity in animal models, but its role in human obesity is unknown. OBJECTIVE To determine whether polymorphisms within the CRTC1 gene are associated with adiposity markers in psychiatric patients and the general population. DESIGN, SETTING, AND PARTICIPANTS Retrospective and prospective data analysis and population-based samples at Lausanne and Geneva university hospitals in Switzerland and a private clinic in Lausanne, Switzerland. The effect of 3 CRTC1 polymorphisms on body mass index (BMI) and/or fat mass was investigated in a discovery cohort of psychiatric outpatients taking weight gain-inducing psychotropic drugs (sample 1, n = 152). The CRTC1 variant that was significantly associated with BMI and survived Bonferroni corrections for multiple comparison was then replicated in 2 independent psychiatric samples (sample 2, n = 174 and sample 3, n = 118) and 2 white population-based samples (sample 4, n = 5338 and sample 5, n = 123 865). INTERVENTION Noninterventional studies. MAIN OUTCOME AND MEASURE Difference in BMI and/or fat mass between CRTC1 genotype groups. RESULTS Among the CRTC1 variants tested in the first psychiatric sample, only rs3746266A>G was associated with BMI (Padjusted = .003). In the 3 psychiatric samples, carriers of the rs3746266 G allele had a lower BMI than noncarriers (AA genotype) (sample 1, P = .001; sample 2, P = .05; and sample 3, P = .0003). In the combined analysis, excluding patients taking other weight gain-inducing drugs, G allele carriers (n = 98) had a 1.81-kg/m2 lower BMI than noncarriers (n = 226; P < .0001). The strongest association was observed in women younger than 45 years, with a 3.87-kg/m2 lower BMI in G allele carriers (n = 25) compared with noncarriers (n = 48; P < .0001), explaining 9% of BMI variance. In the population-based samples, the T allele of rs6510997C>T (a proxy of the rs3746266 G allele; r2 = 0.7) was associated with lower BMI (sample 5, n = 123 865; P = .01) and fat mass (sample 4, n = 5338; P = .03). The strongest association with fat mass was observed in premenopausal women (n = 1192; P = .02). CONCLUSIONS AND RELEVANCE These findings suggest that CRTC1 contributes to the genetics of human obesity in psychiatric patients and the general population. Identification of high-risk subjects could contribute to a better individualization of the pharmacological treatment in psychiatry.

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Obese persons (those with a body mass index [BMI] ≥30 kg/m2) tend to underestimate their weight, leading to an underestimation of their true (measured) BMI and obesity prevalence.1,2 In contrast, underweight people (BMI <18.5 kg/m2) tend to report themselves heavier, resulting in a higher BMI compared with measured BMI and an underestimation of underweight prevalence.

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Several recent studies suggest that obesity may be a risk factor for fracture. The aim of this study was to investigate the association between body mass index (BMI) and future fracture risk at different skeletal sites. In prospective cohorts from more than 25 countries, baseline data on BMI were available in 398,610 women with an average age of 63 (range, 20-105) years and follow up of 2.2 million person-years during which 30,280 osteoporotic fractures (6457 hip fractures) occurred. Femoral neck BMD was measured in 108,267 of these women. Obesity (BMI ≥ 30 kg/m(2) ) was present in 22%. A majority of osteoporotic fractures (81%) and hip fractures (87%) arose in non-obese women. Compared to a BMI of 25 kg/m(2) , the hazard ratio (HR) for osteoporotic fracture at a BMI of 35 kg/m(2) was 0.87 (95% confidence interval [CI], 0.85-0.90). When adjusted for bone mineral density (BMD), however, the same comparison showed that the HR for osteoporotic fracture was increased (HR, 1.16; 95% CI, 1.09-1.23). Low BMI is a risk factor for hip and all osteoporotic fracture, but is a protective factor for lower leg fracture, whereas high BMI is a risk factor for upper arm (humerus and elbow) fracture. When adjusted for BMD, low BMI remained a risk factor for hip fracture but was protective for osteoporotic fracture, tibia and fibula fracture, distal forearm fracture, and upper arm fracture. When adjusted for BMD, high BMI remained a risk factor for upper arm fracture but was also a risk factor for all osteoporotic fractures. The association between BMI and fracture risk is complex, differs across skeletal sites, and is modified by the interaction between BMI and BMD. At a population level, high BMI remains a protective factor for most sites of fragility fracture. The contribution of increasing population rates of obesity to apparent decreases in fracture rates should be explored. © 2014 American Society for Bone and Mineral Research.

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BACKGROUND: Obesity is increasing worldwide because developing countries are adopting Western high-fat foods and sedentary lifestyles. In parallel, in many of them, hypertension is rising more rapidly, particularly with age, than in Western countries. OBJECTIVE: To assess the relationship between adiposity and blood pressure (BP) in a developing country with high average BP (The Seychelles, Indian Ocean, population mainly of African origin) in comparison to a developed country with low average BP (Switzerland, population mainly of Caucasian origin). DESIGN: Cross-sectional health examination surveys based on population random samples. SETTING: The main Seychelles island (Mahé) and two Swiss regions (Vaud-Fribourg and Ticino). SUBJECTS: Three thousand one hundred and sixteen adults (age range 35-64) untreated for hypertension. MEASUREMENTS: Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), systolic and diastolic blood pressure (SBP and DBP, mean of two measures). METHODS: Scatterplot smoothing techniques and gender-specific linear regression models. RESULTS: On average, SBP and DBP were found to increase linearly over the whole variation range of BMI, WHR and WC. A modest, but statistically significant linear association was found between each indicator of adiposity and BP levels in separate regression models controlling for age. The regression coefficients were not significantly different between the Seychelles and the two Swiss regions, but were generally higher in women than in men. For the latter, a gain of 1.7 kg/m(2) in BMI, of 4.5 cm in WC or of 3.4% in WHR corresponded to an elevation of 1 mmHg in SBP. For women, corresponding figures were 1.25 kg/m(2), 2.5 cm and 1.8% respectively. Regression coefficients for age reflected a higher effect of this variable on both SBP and DBP in the Seychelles than in Switzerland. CONCLUSION: These findings suggest a stable linear relation of adiposity with BP, independent of age and body fat distribution, across developed and developing countries. The more rapid increase of BP with age observed in the latter countries are likely to reflect higher genetic susceptibility and/or higher cumulative exposure to another risk factor than adiposity.

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BACKGROUND: The factors that contribute to increasing obesity rates in human immunodeficiency virus (HIV)-positive persons and to body mass index (BMI) increase that typically occurs after starting antiretroviral therapy (ART) are incompletely characterized. METHODS: We describe BMI trends in the entire Swiss HIV Cohort Study (SHCS) population and investigate the effects of demographics, HIV-related factors, and ART on BMI change in participants with data available before and 4 years after first starting ART. RESULTS: In the SHCS, overweight/obesity prevalence increased from 13% in 1990 (n = 1641) to 38% in 2012 (n = 8150). In the participants starting ART (n = 1601), mean BMI increase was 0.92 kg/m(2) per year (95% confidence interval, .83-1.0) during year 0-1 and 0.31 kg/m(2) per year (0.29-0.34) during years 1-4. In multivariable analyses, annualized BMI change during year 0-1 was associated with older age (0.15 [0.06-0.24] kg/m(2)) and CD4 nadir <199 cells/µL compared to nadir >350 (P < .001). Annualized BMI change during years 1-4 was associated with CD4 nadir <100 cells/µL compared to nadir >350 (P = .001) and black compared to white ethnicity (0.28 [0.16-0.37] kg/m(2)). Individual ART combinations differed little in their contribution to BMI change. CONCLUSIONS: Increasing obesity rates in the SHCS over time occurred at the same time as aging of the SHCS population, demographic changes, earlier ART start, and increasingly widespread ART coverage. Body mass index increase after ART start was typically biphasic, the BMI increase in year 0-1 being as large as the increase in years 1-4 combined. The effect of ART regimen on BMI change was limited.

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BACKGROUND: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether BMI clusters among children and how age-specific BMI clusters are related remains unknown. We aimed to identify and compare the spatial dependence of BMI in adults and children in a Swiss general population, taking into account the area's income level. METHODS: Geo-referenced data from the Bus Santé study (adults, n=6663) and Geneva School Health Service (children, n=3601) were used. We implemented global (Moran's I) and local (local indicators of spatial association (LISA)) indices of spatial autocorrelation to investigate the spatial dependence of BMI in adults (35-74 years) and children (6-7 years). Weight and height were measured using standardized procedures. Five spatial autocorrelation classes (LISA clusters) were defined including the high-high BMI class (high BMI participant's BMI value correlated with high BMI-neighbors' mean BMI values). The spatial distributions of clusters were compared between adults and children with and without adjustment for area's income level. RESULTS: In both adults and children, BMI was clearly not distributed at random across the State of Geneva. Both adults' and children's BMIs were associated with the mean BMI of their neighborhood. We found that the clusters of higher BMI in adults and children are located in close, yet different, areas of the state. Significant clusters of high versus low BMIs were clearly identified in both adults and children. Area's income level was associated with children's BMI clusters. CONCLUSIONS: BMI clusters show a specific spatial dependence in adults and children from the general population. Using a fine-scale spatial analytic approach, we identified life course-specific clusters that could guide tailored interventions.

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BACKGROUND: Cigarette smoking is associated with lower body mass index (BMI), and a commonly cited reason for unwillingness to quit smoking is a concern about weight gain. Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is robustly associated with smoking quantity in smokers, but its association with BMI is unknown. We hypothesized that genotype would accurately reflect smoking exposure and that, if smoking were causally related to weight, it would be associated with BMI in smokers, but not in never smokers. METHODS: We stratified nine European study samples by smoking status and, in each stratum, analysed the association between genotype of the 15q25 SNP, rs1051730, and BMI. We meta-analysed the results (n = 24 198) and then tested for a genotype × smoking status interaction. RESULTS: There was no evidence of association between BMI and genotype in the never smokers {difference per T-allele: 0.05 kg/m(2) [95% confidence interval (95% CI): -0.05 to 0.18]; P = 0.25}. However, in ever smokers, each additional smoking-related T-allele was associated with a 0.23 kg/m(2) (95% CI: 0.13-0.31) lower BMI (P = 8 × 10(-6)). The effect size was larger in current [0.33 kg/m(2) lower BMI per T-allele (95% CI: 0.18-0.48); P = 6 × 10(-5)], than in former smokers [0.16 kg/m(2) (95% CI: 0.03-0.29); P = 0.01]. There was strong evidence of genotype × smoking interaction (P = 0.0001). CONCLUSIONS: Smoking status modifies the association between the 15q25 variant and BMI, which strengthens evidence that smoking exposure is causally associated with reduced BMI. Smoking cessation initiatives might be more successful if they include support to maintain a healthy BMI.

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BACKGROUND: Studies about the association between body mass index (BMI) and health-related quality of life (HRQOL) are often limited, because they 1) did not include a broad range of health-risk behaviors as covariates; 2) relied on clinical samples, which might lead to biased results; and 3) did not incorporate underweight individuals. Hence, this study aims to examine associations between BMI (from being underweight through obesity) and HRQOL in a population-based sample, while considering multiple health-risk behaviors (low physical activity, risky alcohol consumption, daily cigarette smoking, frequent cannabis use) as well as socio-demographic characteristics. METHODS: A total of 5 387 young Swiss men (mean age = 19.99; standard deviation = 1.24) of a cross-sectional population-based study were included. BMI was calculated (kg/m²) based on self-reported height and weight and divided into 'underweight' (<18.5), 'normal weight' (18.5-24.9), 'overweight' (25.0-29.9) and 'obese' (≥30.0). Mental and physical HRQOL was assessed via the SF-12v2. Self-reported information on physical activity, substance use (alcohol, cigarettes, and cannabis) and socio-demographic characteristics also was collected. Logistic regression analyses were conducted to study the associations between BMI categories and below average mental or physical HRQOL. Substance use variables and socio-demographic variables were used as covariates. RESULTS: Altogether, 76.3% were normal weight, whereas 3.3% were underweight, 16.5% overweight and 3.9% obese. Being overweight or obese was associated with reduced physical HRQOL (adjusted OR [95% CI] = 1.58 [1.18-2.13] and 2.45 [1.57-3.83], respectively), whereas being underweight predicted reduced mental HRQOL (adjusted OR [95% CI] = 1.49 [1.08-2.05]). Surprisingly, obesity decreased the likelihood of experiencing below average mental HRQOL (adjusted OR [95% CI] = 0.66 [0.46-0.94]). Besides BMI, expressed as a categorical variable, all health-risk behaviors and socio-demographic variables were associated with reduced physical and/or mental HRQOL. CONCLUSIONS: Deviations from normal weight are, even after controlling for important health-risk behaviors and socio-demographic characteristics, associated with compromised physical or mental HRQOL among young men. Hence, preventive programs should aim to preserve or re-establish normal weight. The self-appraised positive mental well-being of obese men noted here, which possibly reflects a response shift, might complicate such efforts.

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We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.