994 resultados para AustLit: The Australian Literature Resource


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Context: Serum 25-hydroxyvitamin D [25(OH)D] concentration has been inversely associated with the prevalence of metabolic syndrome (MetS), but the relationship between 25(OH)D and incident MetS remains unclear.

Objective: We evaluated the prospective association between 25(OH)D, MetS, and its components in a large population-based cohort of adults aged 25 yr or older.

Design: We used baseline (1999–2000) and 5-yr follow-up data of the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab).

Participants: Of the 11,247 adults evaluated at baseline, 6,537 returned for follow-up. We studied those without MetS at baseline and with complete data (n = 4164; mean age 50 yr; 58% women; 92% Europids).

Outcome Measures: We report the associations between baseline 25(OH)D and 5-yr MetS incidence and its components, adjusted for age, sex, ethnicity, season, latitude, smoking, family history of type 2 diabetes, physical activity, education, kidney function, waist circumference (WC), and baseline MetS components.

Results: A total of 528 incident cases (12.7%) of MetS developed over 5 yr. Compared with those in the highest quintile of 25(OH)D (≥34 ng/ml), MetS risk was significantly higher in people with 25(OH)D in the first (<18 ng/ml) and second (18–23 ng/ml) quintiles; odds ratio (95% confidence interval) = 1.41 (1.02–1.95) and 1.74 (1.28–2.37), respectively. Serum 25(OH)D was inversely associated with 5-yr WC (P < 0.001), triglycerides (P < 0.01), fasting glucose (P < 0.01), and homeostasis model assessment for insulin resistance (P < 0.001) but not with 2-h plasma glucose (P = 0.29), high-density lipoprotein cholesterol (P = 0.70), or blood pressure (P = 0.46).

Conclusions: In Australian adults, lower 25(OH)D concentrations were associated with increased MetS risk and higher WC, serum triglyceride, fasting glucose, and insulin resistance at 5 yr. Vitamin D supplementation studies are required to establish whether the link between vitamin D deficiency and MetS is causal.

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Background: Lower socioeconomic status (SES) is strongly associated with a higher prevalence of major cardiovascular risk factors, but few studies have examined changes in these risk factors over time according to SES. We aimed to determine whether SES is a predictor of the change in cardiovascular risk factor levels in a contemporary Australian adult cohort

Methods: Participants in the population-based AusDiab study aged 25+ years who attended both baseline and 5-year follow-up examinations (n=5 954) were categorised according to their level of education at baseline. Cardiovascular risk factor data at both time points were ascertained through questionnaire and physical measurement. Analysis was stratified by gender.

Results: The mean levels of systolic blood pressure, total cholesterol and the prevalence of smoking decreased between the two time points across all educational categories. Increases were also seen in mean BMI and the prevalence of diabetes. For blood pressure, the smallest decrease was seen among men with lower education (age-adjusted difference from higher education 2.8 mmHg, 95% CI 1.0 to 4.6). For total cholesterol, the decrease was greatest among women with lower education (age-adjusted difference from higher education 0.11 mmol/l, 95% CI 0.19 to 0.02). Among those "not at risk" at baseline for each risk factor, women with lower education were more likely than those with higher education to progress to being "at risk" for BMI (age-adjusted odds ratio 1.60, 95% CI 1.09 to 2.35).

Conclusion: Educational gradients narrowed for total cholesterol in women, but widened for systolic blood pressure in men and remained static for other risk factors. Lower education was also associated with an earlier onset of overweight or obesity in women. Given current socioeconomic gradients in risk factors levels, these findings suggest that social inequalities in CVD will persist and may even widen in the future.

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Lower socioeconomic status (SES) is associated with a higher prevalence of major risk factors for cardiovascular disease (CVD). However, few longitudinal studies have examined the association between SES and CVD risk factors over time. We aimed to determine whether SES, using education as a proxy, is associated with the onset of CVD risk factors over 5 years in an Australian adult cohort study.

Participants in the Australian Diabetes, Obesity and Lifestyle study (AusDiab) study aged 25 years and over who attended both baseline and 5-year follow-up examinations (n=5 967) were categorised according to educational attainment. Cardiovascular risk factor data at both time points were ascertained through questionnaire and physical measurement.

Women with lower education had a greater risk of progressing from normal weight to overweight or obesity than those with higher education (age-adjusted OR 1.57, 95% CI 1.06-2.31). Both men and women with lower education were more likely to develop diabetes (age-adjusted OR from higher education 1.75, 95% CI 1.14-2.71 and 3.01, 95% CI 1.26-7.20, respectively). A lower level of education was associated with a greater number of risk factors accumulated over time in women (OR of progressing from having two or less risk factors at baseline to three or more at follow up, 2.04, 95% 1.32-3.14).

In this Australian population-based study, lower educational attainment was associated with an increased risk of developing both individual and total CVD risk factors over a 5-year period. These findings suggest that SES inequalities in CVD will persist into the future.

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This study aimed to estimate utility-based quality of life (UQoL) differences between healthy body weight and excess body weight categories. Cross-sectional analysis of 10,959 adults, participating in baseline data collection of the nationally representative Australian Diabetes, Obesity, and Lifestyle (AusDiab) Study was undertaken. Height and weight were measured by trained personnel. Body weight categories were assigned as healthy weight, overweight, and obesity subclasses I, II and III. UQoL was assessed using the SF-6D, which captures physical functioning, role limitation, social functioning, pain, mental health, and vitality on a score of 0.00–1.00 (worst-best). The relationship between body weight categories and UQoL was assessed using linear regression, adjusting for age, sex, education, and smoking. Relative to the healthy weight group (mean UQoL score 0.77), mean adjusted UQoL differences (95% confidence intervals) were 0.001 (−0.008, 0.010) for overweight, −0.012 (−0.022, −0.001) for class-I obese, −0.020 (−0.041, 0.001) for class-II obese, and −0.069 (−0.099, −0.039) for class-III obese groups. Adding metabolic syndrome markers to the covariates had little impact on these differences. Results confirmed an inverse dose–response relationship between body weight and UQoL in this study of Australian adults. This highlights the need to incorporate UQoL measures which are sensitive to the subclasses of obesity when evaluating obesity interventions.