923 resultados para Scaled Mass Index


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BACKGROUND Endocannabinoids and temperament traits have been linked to both physical activity and body mass index (BMI) however no study has explored how these factors interact in females. The aims of this cross-sectional study were to 1) examine differences among distinct BMI groups on daytime physical activity and time spent in moderate-vigorous physical activity (MVPA), temperament traits and plasma endocannabinoid concentrations; and 2) explore the association and interaction between MVPA, temperament, endocannabinoids and BMI. METHODS Physical activity was measured with the wrist-worn accelerometer Actiwatch AW7, in a sample of 189 female participants (43 morbid obese, 30 obese, and 116 healthy-weight controls). The Temperament and Character Inventory-Revised questionnaire was used to assess personality traits. BMI was calculated by bioelectrical impedance analysis via the TANITA digital scale. Blood analyses were conducted to measure levels of endocannabinoids and endocannabinoid-related compounds. Path-analysis was performed to examine the association between predictive variables and MVPA. RESULTS Obese groups showed lower MVPA and dysfunctional temperament traits compared to healthy-weight controls. Plasma concentrations of 2-arachidonoylglyceryl (2-AG) were greater in obese groups. Path-analysis identified a direct effect between greater MVPA and low BMI (b = -0.13, p = .039) and high MVPA levels were associated with elevated anandamide (AEA) levels (b = 0.16, p = .049) and N-oleylethanolamide (OEA) levels (b = 0.22, p = .004), as well as high Novelty seeking (b = 0.18, p<.001) and low Harm avoidance (b = -0.16, p<.001). CONCLUSIONS Obese individuals showed a distinct temperament profile and circulating endocannabinoids compared to controls. Temperament and endocannabinoids may act as moderators of the low MVPA in obesity.

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OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m(2)), a body fat mass index (BFMI; kg/m(2)), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI. METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated. RESULTS: FFMI values were 16.7 to 19.8 kg/m(2) for men and 14.6 to 16.8 kg/m(2) for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m(2) for men and 3.9 to 8.2 kg/m(2) for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m(2) in men and women, respectively, for obese BMI (>30 kg/m(2)). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively. CONCLUSION: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.

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FNDC5/irisin has been recently postulated as beneficial in the treatment of obesity and diabetes because it is induced in muscle by exercise, increasing energy expenditure. However, recent reports have shown that WAT also secretes irisin and that circulating irisin is elevated in obese subjects. The aim of this study was to evaluate irisin levels in conditions of extreme BMI and its correlation with basal metabolism and daily activity. The study involved 145 female patients, including 96 with extreme BMIs (30 anorexic (AN) and 66 obese (OB)) and 49 healthy normal weight (NW). The plasma irisin levels were significantly elevated in the OB patients compared with the AN and NW patients. Irisin also correlated positively with body weight, BMI, and fat mass. The OB patients exhibited the highest REE and higher daily physical activity compared with the AN patients but lower activity compared with the NW patients. The irisin levels were inversely correlated with daily physical activity and directly correlated with REE. Fat mass contributed to most of the variability of the irisin plasma levels independently of the other studied parameters. Conclusion. Irisin levels are influenced by energy expenditure independently of daily physical activity but fat mass is the main contributing factor.

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BACKGROUND Mental and body weight disorders are among the major global health challenges, and their comorbidity may play an important role in treatment and prevention of both pathologies. A growing number of studies have examined the relationship between psychiatric status and body weight, but our knowledge is still limited. OBJECTIVE The present study aims to investigate the cross-sectional relationships of psychiatric status and body mass index (BMI) in Málaga, a Mediterranean city in the South of Spain. MATERIALS AND METHODS A total of 563 participants were recruited from those who came to his primary care physician, using a systematic random sampling, non-proportional stratified by BMI categories. Structured clinical interviews were used to assess current Axes-I and II mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). BMI was calculated as weight (Kg) divided by square of height in meters (m2). Logistic regression was used to investigate the association between BMI and the presence of any mental disorder. BMI was introduced in the models using restricted cubic splines. RESULTS We found that high BMI values were directly associated with mood and adjustment disorders, and low BMI values were directly associated with avoidant and dependent personality disorders (PDs). We observed an inverse relationship between low BMI values and cluster A PDs. There were not significant relationships between anxiety or substance-related disorders and BMI. CONCLUSION Psychiatric status and BMI are related in a Mediterranean Spanish population. A multidisciplinary approach to both pathologies becomes increasingly more necessary.

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The endocannabinoid (eCB) system can promote food intake by increasing odor detection in mice. The eCB system is over-active in human obesity. Our aim is to measure circulating eCB concentrations and olfactory capacity in a human sample that includes people with obesity and explore the possible interaction between olfaction, obesity and the eCB system. The study sample was made up of 161 females with five groups of body mass index sub-categories ranging from under-weight to morbidly obese. We assessed olfactory capacity with the "Sniffin´Sticks" test, which measures olfactory threshold-discrimination-identification (TDI) capacity. We measured plasma concentrations of the eCBs 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine or anandamide (AEA), and several eCB-related compounds, 2-acylglycerols and N-acylethanolamines. 2-AG and other 2-acylglycerols fasting plasma circulating plasma concentrations were higher in obese and morbidly obese subjects. AEA and other N-acylethanolamine circulating concentrations were lower in under-weight subjects. Olfactory TDI scores were lower in obese and morbidly obese subjects. Lower TDI scores were independently associated with higher 2-AG fasting plasma circulating concentrations, higher %body fat, and higher body mass index, after controlling for age, smoking, menstruation, and use of contraceptives. Our results show that obese subjects have a lower olfactory capacity than non-obese ones and that elevated fasting plasma circulating 2-AG concentrations in obesity are linked to a lower olfactory capacity. In agreement with previous studies we show that eCBs AEA and 2-AG, and their respective congeners have a distinct profile in relation to body mass index. The present report is the first study in humans in which olfactory capacity and circulating eCB concentrations have been measured in the same subjects.

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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.

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Marijuana use has been associated with increased appetite, high caloric diet, acute increase in blood pressure, and decreases in high-density lipoprotein cholesterol and triglycerides. Marijuana is the most commonly used illicit drug in the United States, but its long-term effects on body mass index (BMI) and cardiovascular risk factors are unknown. Using 15 years of longitudinal data from 3,617 black and white young adults participating in the Coronary Artery Risk Development in Young Adults (CARDIA) study, we assessed whether marijuana use was associated with caloric intake, BMI, and cardiovascular risk factors. Of the 3,617 participants, 1,365 (38%) reported ever using marijuana. Marijuana use was associated with male gender, tobacco smoking, and other illicit drug use. More extensive marijuana use was associated with a higher caloric intake (2,746 kcal/day in never users to 3,365 kcal/day in those who used marijuana for > or = 1,800 days over 15 years) and alcohol intake (3.6 to 10.8 drinks/week), systolic blood pressure (112.7 to 116.5 mm Hg), and triglyceride levels (84 to 100 mg/dl or 0.95 to 1.13 mmol/L, all p values for trend < 0.001), but not with higher BMI and lipid and glucose levels. In multivariate analysis, the associations between marijuana use and systolic blood pressure and triglycerides disappeared, having been mainly confounded by greater alcohol use in marijuana users. In conclusion, although marijuana use was not independently associated with cardiovascular risk factors, it was associated with other unhealthy behaviors, such as high caloric diet, tobacco smoking, and other illicit drug use, which all have long-term detrimental effects on health.

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The association between adiposity measures and dyslipidemia has seldom been assessed in a multipopulational setting. 27 populations from Europe, Australia, New Zealand and Canada (WHO MONICA project) using health surveys conducted between 1990 and 1997 in adults aged 35-64 years (n = 40,480). Dyslipidemia was defined as the total/HDL cholesterol ratio >6 (men) and >5 (women). Overall prevalence of dyslipidemia was 25% in men and 23% in women. Logistic regression showed that dyslipidemia was strongly associated with body mass index (BMI) in men and with waist circumference (WC) in women, after adjusting for region, age and smoking. Among normal-weight men and women (BMI<25 kg/m(2)), an increase in the odds for being dyslipidemic was observed between lowest and highest WC quartiles (OR = 3.6, p < 0.001). Among obese men (BMI ≥ 30), the corresponding increase was smaller (OR = 1.2, p = 0.036). A similar weakening was observed among women. Classification tree analysis was performed to assign subjects into classes of risk for dyslipidemia. BMI thresholds (25.4 and 29.2 kg/m(2)) in men and WC thresholds (81.7 and 92.6 cm) in women came out at first stages. High WC (>84.8 cm) in normal-weight men, menopause in women and regular smoking further defined subgroups at increased risk. standard categories of BMI and WC, or their combinations, do not lead to optimal risk stratification for dyslipidemia in middle-age adults. Sex-specific adaptations are necessary, in particular by taking into account abdominal obesity in normal-weight men, post-menopausal age in women and regular smoking in both sexes.

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OBJECTIVE: To explore the association between patients' body mass index (BMI) and their experiences with inpatient care. DESIGN: Cross-sectional. Mail survey. SETTING: University Hospital of Geneva. PARTICIPANTS: Questionnaires were mailed to 2385 eligible adult patients, 6 weeks after discharge (response rate = 69%). MAIN OUTCOME MEASURES: Patients' experiences with care were measured using the Picker inpatient survey questionnaire. BMI was calculated using self-reported height and weight. Main dependent variables were the global Picker patient experience (PPE-15) score and nine dimension-specific problem scores, scored from 0 (no reported problems) to 1 (all items coded as problems). We used linear regressions, adjusting for age, gender, education, subjective health, smoking and hospitalization, to assess the association between patients' BMI and their experiences with inpatient care. RESULTS: Of the patients, 4.8% were underweight, 50.8% had normal weight, 30.3% were overweight and 14.1% were obese. Adjusted analysis shows that compared with normal weight, obesity was significantly associated with fewer problematic items in the surgery-related information domain, and being underweight or overweight was associated with more problematic items in the involvement of family/friends domain. The global PPE-15 score was significantly higher (more problems) for underweight patients. CONCLUSIONS: Underweight patients, but not obese patients, reported more problems during hospitalization.

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Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.