3 resultados para obese-years

em University of Queensland eSpace - Australia


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Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).

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OBJECTIVE - To assess the concurrent validity of fasting indexes of insulin sensitivity and secretion in - obese prepubertal (Tanner stage 1) children and pubertal (Tanner stages 2-5) glucose tolerance test (FSIVGTT) as a criterion measure. RESEARCH DESIGN AND METHODS - Eighteen obese children and adolescents (11 girls and 7 boys, mean age 12.2 +/- 2.4 years, mean BMI 35.4 +/- 6.2 kg/m(2), mean BMI-SDS 3.5 +/- 0.5, 7 prepubertal and I I pubertal) participated in the study. All participants underwent an insulin-modified FSIVGTT on two occasions, and 15 repeated this test a third time (mean 12.9 and 12.0 weeks apart). S-i measured by the FSIVGTT was compared with homeostasis model assessment (HOMA) of insulin resistance (HOMA-IR), quantitative insulin-sensitivity check index (QUICKI), fasting glucose-to-insulin ratio (FGIR), and fasting insulin (estimates of insulin sensitivity derived from fasting samples). The acute insulin response (AIR) measured by the FSIVGTT was compared with HOMA of percent beta-cell function (HOMA-beta%), FGIR, and fasting insulin (estimates of insulin secretion derived from fasting samples). RESULTS - There was a significant negative correlation between HOMA-IR and S-i (r = -0.89, r = -0.90, and r = -0.81, P < 0.01) and a significant positive correlation between QUICKI and S-i (r = 0.89, r = 0.90, and r = 0.81, P < 0.01) at each time point. There was a significant positive correlation between FGIR and S-i (r = 0.91, r = 0.91, and r = 0.82, P < 0.01) and a significant negative correlation between fasting insulin and S-i (r = -90, r = -0.90, and r = -0.88, P < 0.01). HOMA-beta% was not as strongly correlated with AIR (r = 0.60, r = 0.54, and r = 0.61, P < 0.05). CONCLUSIONS - HOMA-IR, QUICKI, FGIR, and fasting insulin correlate strongly with S-i assessed by the FSIVGTT in obese children and adolescents. Correlations between HOMA-β% FGIR and fasting insulin, and AIR were not as strong. Indexes derived from fasting samples are a valid tool for assessing insulin sensitivity in prepubertal and pubertal obese children.

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Objective: To determine the differences in number of years lived free of cardiovascular disease (CVD) and number of years lived with CVD between men and women who were obese, pre-obese, or normal weight at 45 years of age. Research Methods and Procedures: We constructed multistate life tables for CVD, myocardial infarction, and stroke, using data from 2551 enrollees (1130 men) in the Framingham Heart Study who were 45 years of age. Results: Obesity and pre-obesity were associated with fewer number of years free of CVD, myocardial infarction, and stroke and an increase in the number of years lived with these diseases. Forty-five-year-old obese men with no CVD survived 6.0 years [95% confidence interval (CI), 4.1; 8.1] fewer than their normal weight counterparts, whereas, for women, the difference between obese and normal weight subjects was 8.4 years (95% CI: 6.2; 10.8). Obese men and women lived with CVD 2.7 (95% CI: 1.0; 4.4) and 1.4 years (95% CI: -0.3; 3.2) longer, respectively, than normal weight individuals. Discussion: In addition to reducing life expectancy, obesity before middle age is associated with a reduction in the number of years lived free of CVD and an increase in the number of years lived with CVD. Such information is paramount for preventive and therapeutic decision-making by individuals and practitioners alike.