836 resultados para BODY-MASS INDEX


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Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

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The aim of the present study was to examine body concern and satisfactions in 191 female university students and their relationships with measured body composition and circumferences of selected body parts. Body composition and circumference measurements of participants were conducted after obtaining their consent. Body concern and satisfaction were determined using the Body Shape Questionnaire (BSQ) and the Body parts and General subscales from the Body Satisfaction Scales (BSS). Increase in body composition and circumferences were associated with decrease in body concern and satisfaction. Increase in body size, including circumferences did not decrease whole body satisfaction but increased dissatisfaction at the abdominal, arm and thigh regions.

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BACKGROUND: Frequent illness and injury among workers with high body mass index (BMI) can raise the costs of employee healthcare and reduce workforce maintenance and productivity. These issues are particularly important in vocational settings such as the military, which require good physical health, regular attendance and teamwork to operate efficiently. The purpose of this study was to compare the incidence of injury and illness, absenteeism, productivity, healthcare usage and administrative outcomes among Australian Defence Force personnel with varying BMI. METHODS: Personnel were grouped into cohorts according to the following ranges for (BMI): normal (18.5-24.9 kg/m²; n = 197), overweight (25-29.9 kg/m²; n = 154) and obese (≥30 kg/m²) with restricted body fat (≤28 % for females, ≤24 % for males) (n = 148) and with no restriction on body fat (n = 180). Medical records for each individual were audited retrospectively to record the incidence of injury and illness, absenteeism, productivity, healthcare usage (i.e., consultation with medical specialists, hospital stays, medical investigations, prescriptions) and administrative outcomes (e.g., discharge from service) over one year. These data were then grouped and compared between the cohorts. RESULTS: The prevalence of injury and illness, cost of medical specialist consultations and cost of medical scans were all higher (p <0.05) in both obese cohorts compared with the normal cohort. The estimated productivity losses from restricted work days were also higher (p <0.05) in the obese cohort with no restriction on body fat compared with the normal cohort. Within the obese cohort, the prevalence of injury and illness, healthcare usage and productivity were not significantly greater in the obese cohort with no restriction on body fat compared with the cohort with restricted body fat. The number of restricted work days, the rate of re-classification of Medical Employment Classification and the rate of discharge from service were similar between all four cohorts. CONCLUSIONS: High BMI in the military increases healthcare usage, but does not disrupt workforce maintenance. The greater prevalence of injury and illness, greater healthcare usage and lower productivity in obese Australian Defence Force personnel is not related to higher levels of body fat.

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Objective: Menopause is the consequence of exhaustion of the ovarian follicular pool. AMH, an indirect hormonal marker of ovarian reserve, has been recently proposed as a predictor for age at menopause. Since BMI and smoking status are relevant independent factors associated with age at menopause we evaluated whether a model including all three of these variables could improve AMH-based prediction of age at menopause. Methods: In the present cohort study, participants were 375 eumenorrheic women aged 19–44 years and a sample of 2,635 Italian menopausal women. AMH values were obtained from the eumenorrheic women. Results: Regression analysis of the AMH data showed that a quadratic function of age provided a good description of these data plotted on a logarithmic scale, with a distribution of residual deviates that was not normal but showed significant leftskewness. Under the hypothesis that menopause can be predicted by AMH dropping below a critical threshold, a model predicting menopausal age was constructed from the AMH regression model and applied to the data on menopause. With the AMH threshold dependent on the covariates BMI and smoking status, the effects of these covariates were shown to be highly significant. Conclusions: In the present study we confirmed the good level of conformity between the distributions of observed and AMH-predicted ages at menopause, and showed that using BMI and smoking status as additional variables improves AMH-based prediction of age at menopause.

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Background It is evident from previous research that the role of dietary composition in relation to the development of childhood obesity remains inconclusive. Several studies investigating the relationship between body mass index (BMI), waist circumference (WC) and/or skin fold measurements with energy intake have suggested that the macronutrient composition of the diet (protein, carbohydrate, fat) may play an important contributing role to obesity in childhood as it does in adults. This study investigated the possible relationship between BMI and WC with energy intake and percentage energy intake from macronutrients in Australian children and adolescents. Methods Height, weight and WC measurements, along with 24 h food and drink records (FDR) intake data were collected from 2460 boys and girls aged 5-17 years living in the state of Queensland, Australia. Results Statistically significant, yet weak correlations between BMI z-score and WC with total energy intake were observed in grades 1, 5 and 10, with only 55% of subjects having a physiologically plausible 24 hr FDR. Using Pearson correlations to examine the relationship between BMI and WC with energy intake and percentage macronutrient intake, no significant correlations were observed between BMI z-score or WC and percentage energy intake from protein, carbohydrate or fat. One way ANOVAs showed that although those with a higher BMI z-score or WC consumed significantly more energy than their lean counterparts. Conclusion No evidence of an association between percentage macronutrient intake and BMI or WC was found. Evidently, more robust longitudinal studies are needed to elucidate the relationship linking obesity and dietary intake.

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The aim of this study was to examine whether takeaway food consumption mediated (explained) the association between socioeconomic position and body mass index (BMI). A postal-survey was conducted among 1500 randomly selected adults aged between 25 and 64 years in Brisbane, Australia during 2009 (response rate 63.7%, N=903). BMI was calculated using self-reported weight and height. Participants reported usual takeaway food consumption, and these takeaway items were categorised into "healthy" and "less healthy" choices. Socioeconomic position was ascertained by education, household income, and occupation. The mean BMI was 27.1kg/m(2) for men and 25.7kg/m(2) for women. Among men, none of the socioeconomic measures were associated with BMI. In contrast, women with diploma/vocational education (β=2.12) and high school only (β=2.60), and those who were white-collar (β=1.55) and blue-collar employees (β=2.83) had significantly greater BMI compared with their more advantaged counterparts. However, household income was not associated with BMI. Among women, the consumption of "less healthy" takeaway food mediated BMI differences between the least and most educated, and between those employed in blue collar occupations and their higher status counterparts. Decreasing the consumption of "less healthy" takeaway options may reduce socioeconomic inequalities in overweight and obesity among women but not men.

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Objectives: To examine the association of maternal pregravid body mass index (BMI) and child offspring, all-cause hospitalisations in the first 5 years of life. Methods: Prospective birth cohort study. From 2006 to 2011, 2779 pregnant women (2807 children) were enrolled in the Environments for Healthy Living: Griffith birth cohort study in South-East Queensland, Australia. Hospital delivery record and self-report baseline survey of maternal, household and demographic factors during pregnancy were linked to the Queensland Hospital Admitted Patients Data Collection from 1 November 2006 to 30 June 2012, for child admissions. Maternal pregravid BMI was classified as underweight (<18.5 kg m−2), normal weight (18.5–24.9 kg m−2), overweight (25.0–29.9 kg m−2) or obese (30 kg m−2). Main outcomes were the total number of child hospital admissions and ICD-10-AM diagnostic groupings in the first 5 years of life. Negative binomial regression models were calculated, adjusting for follow-up duration, demographic and health factors. The cohort comprised 8397.9 person years (PYs) follow-up. Results: Children of mothers who were classified as obese had an increased risk of all-cause hospital admissions in the first 5 years of life than the children of mothers with a normal BMI (adjusted rate ratio (RR) =1.48, 95% confidence interval 1.10–1.98). Conditions of the nervous system, infections, metabolic conditions, perinatal conditions, injuries and respiratory conditions were excessive, in both absolute and relative terms, for children of obese mothers, with RRs ranging from 1.3–4.0 (PYs adjusted). Children of mothers who were underweight were 1.8 times more likely to sustain an injury or poisoning than children of normal-weight mothers (PYs adjusted). Conclusion: Results suggest that if the intergenerational impact of maternal obesity (and similarly issues related to underweight) could be addressed, a significant reduction in child health care use, costs and public health burden would be likely.

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The aim of the study was to determine the reliability of body mass index based (BMI) cutoff values in diagnosing obesity among Sri Lankan children. Height, weight, waist circumference (WC) and hip circumference (HC) in 282 children were measured. Total body water was determined by deuterium dilution and fat mass (FM) derived using age and gender specific constants. A percentage FM of 30% for girls and 25% for boys were considered as cutoff levels for obesity. Two hundred and eighty two children (M/F: 158/124) were studied and 99 (80%) girls and 72 (45.5%) boys were obese based on % body fat. Eight (6.4%) girls and nine (5.7%) boys were obese based on International Obesity Task Force (IOTF) cutoff values. Percentage FM and WC centile charts were able to diagnose a significant proportion of children as true obese children. The FM and BMI were closely associated in both girls (r = 0.82, p < 0.001) and boys (r = 0.87, p < 0.001). Percentage FM and BMI had a very low but significant association; girls (r = 0.32, p < 0.001) and boys (r = 0.68, p < 0.001). FM had a significant association with WC and HC. BMI based cutoff values had a specificity of 100% but a very low sensitivity, varying between 8% and 23.6%. BMI is a poor indicator of the percentage fat and the commonly used cutoff values were not sensitive to detect cases of childhood obesity in Sri Lankan children.

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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.