Current meta-analysis suggests that the higher prevalence of hearing impairment in diabetic patients compared with nondiabetic patients was consistent regardless of age.

The aim of this meta-analysis was to compare the association of waist-to-height ratio (WHtR) with risk of incident diabetes with the associations of 3 other conventional obesity indicators (body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR)) with risk of incident diabetes. Literature searches in MEDLINE (January 1950 to April 27, 2011) and EMBASE (January 1974 to April 27, 2011) were conducted for prospective studies that made it possible to estimate the relative risk of diabetes per 1-standard deviation increase in WHtR, in addition to the RR of BMI, WC, or WHR. Strength of the estimated pooled relative risk for a 1-standard deviation increase of each indicator (expressed as RR(WHtR), RR(BMI), RR(WC), and RR(WHR)) was compared with a bivariate random-effects model. Pooled relative risks of the 15 eligible studies with 6,472 diabetes cases were 1.62 (95% CI: 1.48, 1.78) for RR(WHtR), 1.55 (95% CI: 1.43, 1.69) for RR(BMI), 1.63 (95% CI: 1.49, 1.79) for RR(WC), and 1.52 (95% CI: 1.40, 1.66) for RR(WHR). WHtR had an association stronger than that of BMI (P<0.001) or WHR (P<0.001). The present meta-analysis showed that WHtR has a modestly but statistically greater importance than BMI and WHR in prediction of diabetes. Nevertheless, measuring height in addition to WC appeared to have no additional benefit.

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Objective: Web-based treatment programs are attractive in primary care because of their ability to reach numerous individuals at low cost. Our aim of this meta-analysis is to systematically review the weight loss or maintenance effect of the Internet component in obesity treatment programs. Methods: MEDLINE and EMBASE literature searches were conducted to identify studies investigating the effect of Web-based individualized advice on lifestyle modification on weight loss. Randomized controlled trials that consisted of a Web-user experimental and non-Web user control group were included. Weight changes in the experimental group in comparison with the control group were pooled with a random-effects model. Results: A total of 23 studies comprising 8697 participants were included. Overall, using the Internet had a modest but significant additional weight-loss effect compared with non-Web user control groups (À0.68 kg, P ¼ 0.03). In comparison with the control group, stratified analysis indicated that using the Internet as an adjunct to obesity care was effective (À1.00 kg, Po0.001), but that using it as a substitute for face-to-face support was unfavorable ( þ 1.27 kg, P ¼ 0.01). An additional effect on weight control was observed when the aim of using the Internet was initial weight loss (À1.01 kg; P ¼ 0.03), but was not observed when the aim was weight maintenance ( þ 0.68 kg; P ¼ 0.26). The relative effect was diminished with longer educational periods (P-trend ¼ 0.04) and was insignificant (À0.20 kg; P ¼ 0.75) in studies with educational periods of 12 months or more. Conclusion: The current meta-analysis indicates that the Internet component in obesity treatment programs has a modest effect on weight control. However, the effect was inconsistent, largely depending on the type of usage of the Internet or the period of its use.

Aims/hypothesis The aim of this study was to examine the association between HbA 1c variability and the development of microalbuminuria as defined by an albumin/creatinine ratio ≥3.4 mg/mmol (≥30 mg/g) in at least two of three consecutive urine samples in Japanese patients with type 2 diabetes. Methods HbA 1c level was measured in 812 serially registered normoalbuminuric adults aged 21-79 years with type 2 diabetes. After registration, a 1-year period to establish baseline values for mean HbA 1c and HbA 1c variability (measured as the intrapersonal SD of serially collected HbA 1c ) was decided upon. The association between HbA 1c variability and the development of microalbuminuria was determined by Cox regression analysis after adjustment for other risk factors for microalbuminuria.