ObjectivePrevious studies that have found an increased risk for tuberculosis (TB) in people with diabetes mellitus (DM) have been conducted in segments of the population and have not adjusted for important potential confounders. We sought to determine the RR for TB in the presence of DM in a national population with data on confounding factors in order to inform the decision-making process about latent tuberculosis infection (LTBI) screening in people with diabetes.DesignWhole population historical cohort study.SettingAll Australian States and Territories with a mean TB incidence of 5.8/100 000.ParticipantsCases of TB in people with DM were identified by record linkage using the National Diabetes Services Scheme Database and TB notification databases for the years 2001–2006.Primary and secondary outcome measuresPrimary outcome was notified cases of TB. Secondary outcome was notified cases of culture-confirmed TB. RR of TB was estimated with adjustment for age, sex, TB incidence in country of birth and indigenous status.ResultsThere were 6276 cases of active TB among 19 855 283 people living in Australia between 2001 and 2006. There were 271 (188 culture positive) cases of TB among 802 087 members of the DM cohort and 130 cases of TB among 273 023 people using insulin. The crude RR of TB was 1.78 (95% CI 1.17 to 2.73) in all people with DM and 2.16 (95% CI 1.19 to 3.93) in people with DM using insulin. The adjusted RRs were 1.48 (95% CI 1.04 to 2.10) and 2.27 (95% CI 1.41 to 3.66), respectively.ConclusionsThe presence of DM alone does not justify screening for LTBI. However, when combined with other risk factors for TB, the presence of DM may be sufficient to justify screening and treatment for LTBI.

Prediabetes, the presence of impaired fasting glucose/glycaemia and/or impaired glucose tolerance, affects about 16.4% of Australian adults. People with prediabetes are at increased risk of developing diabetes, and cardiovascular and other macrovascular disease. Management includes reducing cardiovascular disease risk factors, specifically lipid and blood pressure abnormalities, and smoking‐cessation counselling. To help prevent progression to diabetes, people with prediabetes who are overweight or obese require intensive lifestyle intervention. Medication to help prevent diabetes may also be used, but only after a minimum of 6 months of lifestyle intervention. In people with prediabetes, there is no role for routinely testing: capillary blood glucose; glycated haemoglobin (HbA1c) levels; serum insulin or pancreatic C‐peptide levels; or testing for ischaemic heart disease or the microvascular complications of diabetes. Follow‐up assessment of glycaemia in prediabetes requires a formal 75 g oral glucose tolerance test, initially performed annually, with subsequent individualised testing frequency.

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ObjectiveTo compare the glycaemic control and cardiovascular risk factor profiles of younger and older patients with type 2 diabetes. Cross-sectional analysis of data from the 2015 Australian National Diabetes Audit was undertaken.MethodsData were obtained from adults with type 2 diabetes presenting to Australian secondary/tertiary diabetes centres. Logistic regression examined associations with glycated haemoglobin A1c (HbA1c) >7% (53 mmol/mol) and cardiovascular risk factors.ResultsData from 3492 patients were analysed. Mean (±SD) age was 62.9±12.5 years, mean diabetes duration 13.5±9.4 years and mean HbA1c 8.2%±1.8%. Mean HbA1c was 8.6%±2.1% and 8.0%±1.6% for the younger (<60 years) and older subgroups (≥60 years), respectively (p<0.001). The adjusted OR (aOR) of HbA1c above >7.0% was 1.5 times higher (95% CI 1.22 to 1.84) for younger patients compared with older patients after adjustment for gender, smoking, diabetes duration, renal function and body mass index. Younger patients were also more likely to have dyslipidaemia (aOR 2.02, 95% CI 1.53 to 2.68; p<0.001), be obese (aOR 1.25, 95% CI 1.05 to 1.49; p<0.001) and be current smokers (aOR 2.13 95% CI 1.64 to 2.77; p<0.001) than older patients.ConclusionsYounger age was associated with poorer glycaemic control and adverse cardiovascular risk factor profiles. It is imperative to optimise and monitor treatment in order to improve long-term outcomes.

Despite a number of national initiatives to improve general practice care and specifically support better care in rural areas, cardiovascular risk management and its impact in Australian general practice patients with type 2 diabetes was still suboptimal during the study period especially among patients from rural areas. Greater effort will be required to reduce the disparity in risk factor prevention for CVD between urban and rural people with type 2 diabetes in Australia.

To study the effect of 12 weeks of high-intensity interval training (HIIT) on glycemic control in adults with type 1 diabetes and overweight or obesity. RESEARCH DESIGN AND METHODS Thirty inactive adults with type 1 diabetes who had BMI ‡25 kg/m 2 and HbA 1c ‡7.5% were randomized to 12 weeks of either HIIT exercise intervention consisting of 4 3 4-min HIIT (85-95% peak heart rate) performed thrice weekly or usual care control. In a partial crossover design, the control group subsequently performed the 12-week HIIT intervention. The primary end point was the change in HbA 1c from baseline to 12 weeks. Glycemic and cardiometabolic outcomes were measured at 0, 12, and 24 weeks. RESULTS Participants were aged 44 6 10 years with diabetes duration 19 6 11 years and BMI 30.1 6 3.1 kg/m 2. HbA 1c decreased from 8.63 6 0.66% at baseline to 8.10 6 1.04% at 12 weeks in the HIIT intervention group (P 5 0.01); however, this change was not significantly different from the control group (HIIT 20.53 6 0.61%, control 20.14 6 0.48%, P 5 0.08). In participants who undertook at least 50% of the prescribed HIIT intervention, the HbA 1c reduction was significantly greater than control (HIIT 20.64 6 0.64% [n 5 9], control 20.14 6 0.48% [n 5 15], P 5 0.04). There were no differences in insulin dose, hypoglycemia on continuous glucose monitoring, blood pressure, blood lipids, body weight, or body composition between groups. CONCLUSIONS Overall, there was no significant reduction in HbA 1c with a 12-week HIIT intervention in adults with type 1 diabetes. However, glycemic control may improve for people who undertake HIIT with greater adherence. Regular exercise is recommended for people with type 1 diabetes (1,2) and can provide multiple health benefits, including improvements in body weight, cardiorespiratory fitness, and lipid profile (3). Physical activity is associated with better glycemic control in cross-sectional studies of people with type 1 diabetes (2,4).

Classification of the severity of diabetic retinopathy {DR)and quantijkation of diabetic changes are vital for assessing the therapies and risk factors for this frequent complication of diabetes. A multilayer feedforward network has been developed for the classification of DR. One of its major strengths is that accurate feature extractions and accurate grading of DR lesions are not required. Another strength of this technique is its robustness as the network can also classify DR eflectively in noisy environments.

Two hundred Type 2 diabetic patients newly referred to the diabetes centre at a large university teaching hospital were studied over an 8-month period. Patients completed a diabetes knowledge questionnaire, and specified their educational priorities by selecting six diabetes-related topics from a list of 14. After giving 1 h of individual education and using the same list, the educators selected six topics which they considered to be most important for that particular patient to know. Choice of educational priorities differed between the patients and the corresponding educator (p less than 0.001). In only 38% of cases did the educators' first three priorities coincide with those of the patients. The major discrepancies were in the selection of 'sick day management' and 'complications', especially favoured by patients, as against 'oral hypoglycaemic agents' and other therapy-related topics, especially favoured by educators. Diabetes knowledge was a determinant of educational priority for patients (p less than 0.001) but not educators. In contrast, only the educators' overall choices were affected by duration of diabetes (p less than 0.001). Diabetes treatment type influenced both patients' and educators' selection of priorities (p less than 0.001). We conclude that an educational strategy which relies on health professionals' perceptions to determine what diabetic patients need to know may be inadequate.