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Background: Inconsistent findings from observational studies have prolonged the controversy over the effects of dietary glycemic index (GI) and glycemic load (GL) on the risk of certain chronic diseases. Objective: The objective was to evaluate the association between GI, GL, and chronic disease risk with the use of meta-analysis techniques. Design: A systematic review of published reports identified a total of 37 prospective cohort studies of GI and GL and chronic disease risk. Studies were stratified further according to the validity of the tools used to assess dietary intake. Rate ratios (RRs) were estimated in a Cox proportional hazards model and combined by using a random-effects model. Results: From 4 to 20 y of follow-up across studies, a total of 40 129 incident cases were identified. For the comparison between the highest and lowest quantiles of GI and GL, significant positive associations were found in fully adjusted models of validated studies for type 2 diabetes (GI RR = 1.40, 95% CI: 1.23, 1.59; GL RR = 1.27, 95% CI: 1.12, 1.45), coronary heart disease (GI RR = 1.25, 95% CI: 1.00, 1.56), gallbladder disease (GI RR = 1.26, 95% CI: 1.13, 1.40; GL RR = 1.41, 95% CI: 1.25, 1.60), breast cancer (GI RR = 1.08, 95% CI: 1.02, 1.16), and all diseases combined (GI RR = 1.14, 95% CI: 1.09, 1.19; GLRR = 1.09, 95% CI: 1.04, 1.15). Conclusions: Low-GI and/or low-GL diets are independently associated with a reduced risk of certain chronic diseases. In diabetes and heart disease, the protection is comparable with that seen for whole grain and high fiber intakes. The findings support the hypothesis that higher postprandial glycemia is a universal mechanism for disease progression. KEY WORDS Glycemic index, glycemic load, dietary carbohydrates, epidemiology

Military personnel are reported to use more dietary supplements (DS) than the general population. However, a comprehensive investigation of DS use in Australian Army personnel has not been conducted. This study investigated the prevalence and types of DS used by Australian Army personnel and examined the underpinning reasons and demographic factors influencing this use.

To explore differences in craniofacial structures and obesity between Caucasian and Chinese patients with obstructive sleep apnea (OSA).

To develop models based on craniofacial photographic analysis for the prediction of obstructive sleep apnea (OSA).

Simulated data were used to assess the accuracy of determinations ofKmandVmaxof the Michaelis-Menten equation using exhaustive Monte Carlo modelling of error variances in data sets. Curves were fitted using a direct least squares fit and the unweighted double reciprocal plot (commonly but incorrectly called the Lineweaver-Burk plot). At least 10 pairs of data points are needed in an experiment forKmto have an error that is approximately normally distributed: 12 were used routinely in this study. Most published estimates ofKmare not based upon a sufficient number of data points.Evenly spaced data points were compared with substrate concentrations spaced logarithmically and inversely proportionally spaced data points. Data sets were spread below theKmaround theKmand above theKm. If the substrate concentrations are all below theKm, the unweighted double reciprocal plot method is unreliable.Kmestimates made using unweighted double reciprocal plots were consistently inferior to estimates made using the direct least squares method. Estimates ofKmusing the direct least squares fit are normally distributed under a wide range of experimental error conditions. Errors can be independent of substrate concentration, have a constant relative error, or be inversely proportional to the velocity (rate). Substrate concentrations are best evenly spaced; inversely proportionally spaced and logarithmically spaced substrates lead to skewed estimates ofKm.The majority ofKmvalues published are based on the statistically unsatisfactory unweighted double reciprocal plot. If similar data are analysed using the double reciprocal “Lineweaver-Burk” plot and the least squares methods, the least squares method can give useful results when the other does not.