4 resultados para Define Overweight
Resumo:
Background. Obesity is considered a major public health issue in most developed countries nowadays. This paper provides an overview of current population data available in Spain and the approach to develop preventive strategies in the country. Methods. Review of population data available is based on individually measured weight and height as well as determinants. On this basis, the approach used in the country to develop preventive strategies is discussed. Results. According to the DORICA study, the prevalence of obesity (BMI ≥30 kg m−2) is 15.5% in Spanish adults aged 25–60 years (13.2% in men and 17.5% in women). Obesity rates are higher among women aged 45 years and older, low social class, living in semi-urban places. Population estimates for the prevalence of obesity in Spanish children and young people based on the enKid study are 13.9% for the whole group. In this study, overweight and obesity is related to absence of breastfeeding, low consumption of fruit and vegetables, high consumption of cakes, buns, softdrinks and butchery products, low physical activity levels and a positive association with time spent watching TV. In 2005, the Spanish Ministry of Health jointly with the Spanish Agency for Food Safety and Nutrition launched the multifaceted NAOS strategy for nutrition, physical activity and the prevention of obesity. The important role of the family and the school setting as well as the responsibility of the Health Administration and Pediatric Care in the prevention of obesity is highlighted in the document. The need for environmental actions is recognised. The PERSEO programme, a multicomponent school-based intervention project is part of the strategy currently in place. Conclusion. Obesity is a public health issue in Spain. A national multifaceted strategy was launched to counteract the problem. Environmental and policy actions are a priority. Young children and their families are among the main target groups.
Resumo:
OBJECTIVES To assess the relationship between life styles and eating habits with the overweight and obesity prevalence in a Spanish adult population. METHODS A population-based, cross-sectional study conducted on 2640 subjects older than 15 years, in Cádiz (Spain). Surveys were conducted in subjects' homes to obtain life styles, eating habits, and anthropometric data. Logistic regression has been used to study the association between the life style variables and overweight and obesity. RESULTS Prevalence of overweight and obesity in Cadiz is 37% and 17%, respectively; higher in males and increases with age. BMI has an inverse relationship with educational level (PR = 2.3, 1.57-2.38). The highest levels of obesity are associated with daily alcohol consumption (PR = 1.39, 1.29-1.50), greater consumption of television,and sedentary pursuit (PR 1.5, 1.07-1.24). A lower prevalence of obesity is observed among those with active physical activity (10.9% vs 21.6%), with differences between sex. Following a slimming diet is more frequent in the obese and in women but dedicate more hours than men to passive activities. In men is greater the consumption of alcohol, high energy foods and snacks. Overweight and obesity is associated with the male sex (OR = 3.35 2.75-4.07), high consumption of alcohol (OR = 1.38 1.03-1.86) and watching television (OR = 1.52 1.11-2.07), and foods likes bread and cereals (OR = 1.47 1.13-1.91). Exercise activities is a protective factor (OR = 0.76 0.63-0.98). CONCLUSIONS Life styles factors associated with overweight and obesity present different patterns in men and women and is necessary to understand them to identify areas for behavioural intervention in overweight and obesity patients.
Resumo:
By this study we seek the expectable range of waist circumference (WC) for every degree of body mass index (BMI), which will serve to studies targeting ascertaining the health risk. We studied 2,932 patients (39.6% men and 60.4% women, between 18 and 96 years ) of the same ethnic group who consecutively attended outpatient departments of our clinics between 2000 and 2004. BMI correlated linearly with the WC (cc: 0.85; p < 0.001). The men, the obese, and diabetics were older (p < 0.001). BMI was greater in women and WC in men. The women had a greater WC if they had diabetes (p < 0.01), being equal to diabetic males. The men had greater WC when they had diabetes (p < 0.001). Waist at risk was detected (men > or = 102 cm and women > or = 88 cm) in 94.3% of the obese, in 32.3% of overweight patients, in 3.8% of patients with BMI < 25, in 84.3% of diabetics, and in 72.6% of patients without diabetes. We made graphic standardisation of WC with regard to BMI, and we calculated the percentiles 10, 25, 50, 75 and 90, grouping in ranges of 2 kg/m(2) of BMI. The diabetic patients are grouped in ranges of 4 kg/m(2). As conclusion we present a standardisation of the WC measurement of patients attended to in our Endocrinology and Nutrition practices distributed in percentiles as a clinically usable tool to define the ranges of WC for every BMI value.
Resumo:
BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.