Background: The consumption of advanced glycation end products (AGEs) has increased because of modern food processing and has been linked to the development of type 2 diabetes in rodents. Objective: We determined whether changing dietary AGE intake could modulate insulin sensitivity and secretion in healthy, overweight individuals. Design: We performed a double-blind, randomized, crossover trial of diets in 20 participants [6 women and 14 men; mean 6 SD body mass index (in kg/m 2 ): 29.8 6 3.7]. Isoenergetic-and macronutrientmatched diets that were high or low in AGE content were alternately consumed for 2 wk and separated by a 4-wk washout period. At the beginning and end of each dietary period, a hyperinsulinemiceuglycemic clamp and an intravenous glucose tolerance test were performed. Dietary, plasma and urinary AGEs N V -(carboxymethyl) lysine (CML), N V -(carboxyethyl)lysin (CEL), and methylglyoxalderived hydroimadazolidine (MG-H1) were measured with the use of mass spectrometry. Results: Participants consumed less CML, CEL, and MG-H1 during the low-AGE dietary period than during the high-AGE period (all P , 0.05), which was confirmed by changes in urinary AGE excretion. There was an overall difference in insulin sensitivity of 22.1 mg $ kg 21 $ min 21 between diets (P = 0.001). Insulin sensitivity increased by 1.3 mg $ kg 21 $ min 21 after the low-AGE diet (P = 0.004), whereas it showed a tendency to decrease by 0.8 mg $ kg 21 $ min 21 after the high-AGE diet (P = 0.086). There was no difference in body weight or insulin secretion between diets (P = NS). Conclusions: A diet that is low in AGEs may reduce the risk of type 2 diabetes by increasing insulin sensitivity. Hence, a restriction in dietary AGE content may be an effective strategy to decrease diabetes and cardiovascular disease risks in overweight individuals. This trial was registered at clinicaltrials.gov as NCT00422253.

Objective: To determine the prevalence of diabetes in inpatients in Melbourne hospitals. Design: Point prevalence survey of all inpatients in each hospital on a single day between 30 November 2010 and 22 November 2012. Setting: 11 hospitals in metropolitan Melbourne including community, secondary and tertiary hospitals and one aged care and rehabilitation centre. Participants: 2308 adult inpatients in all wards apart from intensive care, emergency, obstetrics and psychiatry. Main outcome measures: Point prevalence of self-reported diabetes, details of current medication, self-reported frequency of complications. Results: Diabetes status was obtained in 2273 of 2308 inpatients (98.5%). Of these, 562 (24.7%) had diabetes (95% CI, 22.9%-26.5%). Diabetes prevalence ranged from 15.7% to 35.1% in diff erent hospitals (P < 0.001). Patients with diabetes were older, heavier and more likely to be taking lipidlowering, antihypertensive and blood-thinning medications. Of 388 patients with complete medication information, 270 (69.6%) were taking oral hypoglycaemic agents alone or in combination with insulin, 158 (40.7%) were treated with insulin (67 [17.3%] with insulin alone) and 51 (13.1%) were not taking medication for diabetes. The frequency of diabetes complications was very high: 207/290 (71.4%) for any microvascular complication, 275/527 (52.2%) for any macrovascular complication and 227/276 (82.2%) for any complication. Conclusion: The high burden of diabetes in Melbourne hospital inpatients has major implications for patient health and health care expenditure. Optimising care of these high-risk patients has the potential to decrease inpatient morbidity and length of stay as well as preventing or delaying future complications. A formal Australian national audit of inpatient diabetes would determine its true prevalence and consequences, allowing rational planning to deal with shortcomings in its management.

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Women with type 1 diabetes, even when managed in a specialist setting, still experience adverse obstetric and neonatal outcomes. Poor glycaemic control is not wholly responsible for adverse outcomes, reinforcing the importance of other risk factors, such as obesity and weight gain.